1,715 results
Search Results
2. A conundrum of trends – Comment on a paper by Lischeid et al. (2021)
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Bürger, G.
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- 2022
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3. Response to "comment on the paper scale dependent solute dispersion with linear isotherm in heterogeneous medium (Journal of Hydrology 520 (2015) 289–299)".
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Singh, Mritunjay Kumar and Das, Pintu
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HYDROLOGY , *ATMOSPHERIC temperature , *DISPERSION (Chemistry) , *ANALYTICAL solutions , *POROUS materials , *OCEAN bottom - Abstract
Transformation plays a significant role in solving hydrological problems. In response to the comment on Singh and Das (2015) by Deng et al. (2015) regarding the transformation, boundary conditions and subsequent analytical solution of advection dispersion equation in semi-infinite heterogeneous porous media, it is argued that the transformation used by Singh and Das (2015) for finding the analytical solution is correct and mathematically valid. Also, the different expression of f (mt) used to obtain the solution are explained with their physical relevance in the field. [ABSTRACT FROM AUTHOR]
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- 2020
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4. Comment on the paper "Scale dependent solute dispersion with linear isotherm in heterogeneous medium" (Journal of Hydrology 520 (2015) 289–299).
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Deng, Baoqing, Xu, Liangtian, and Chen, Xianpeng
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ATMOSPHERIC temperature , *ADVECTION-diffusion equations , *MASS media , *HYDROLOGY , *DISPERSION (Chemistry) - Published
- 2020
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5. Disentangling climate change & land use change effects on river flows: A probabilistic approach.
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Wray, Nicholas, Bowie, Duncan, Pattison, Ian, Angeloudis, Athanasios, and Beevers, Lindsay
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CLIMATE change , *PROBABILITY density function , *EXTERNALITIES , *STREAMFLOW , *LAND use , *WATERSHEDS - Abstract
Determining the respective attribution proportions of climate change and land use change to streamflow variations in river systems is of increasing interest to researchers and practitioners tasked with managing river basins. This paper proposes an extension to established techniques of attributing the relative proportions of climate change (CC) and land use change (LUC) drivers to streamflow change by instead considering these proportions as distributed through a probability density function (pdf), rather than as a point value. The novel method is demonstrated for the River Tweed in the UK. Results are determined by the flow, temperature and precipitation data, and upon the algorithms used to identify change points in these vectors. The ratio of the LUC and CC attribution proportions (Land Use and Climate Change Attribution Proportions, LCAP) is more appropriately expressed as a vector of values, each associated with its own probability value within a probability density function. The paper demonstrates that the LCAP ratio pdf can vary considerably over time and that it is possible to track physical changes in the catchment in the evolution of the probability density function. Results show that the land-use change/climate change attribution proportion (LCAP) ratio varies over time and can be expressed as probabilistic estimate. It can be concluded, with a high degree of confidence, that for the Tweed catchment the LUC is a significant driver of streamflow change. Hence this finding may have implications for future catchment flood management utilising nature based solutions (NbS) to reverse landscape degradation and mitigate effects of climate change, provided that the economic and social costs are outweighed by the benefits. [ABSTRACT FROM AUTHOR]
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- 2024
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6. Joint optimal allocation of regional water and land resources considering their mutual feed relationship.
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Su, Chengguo, Hu, Zhenglei, Yuan, Wenlin, Zhang, Jiaming, Yan, Denghua, and Wang, Huiliang
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LAND resource , *WATER supply , *WATER rights , *CROP allocation , *WATER use , *WATER shortages - Abstract
• A novel joint optimal allocation model of regional water and land resources (JOAMRWLS). • A three-level unit division method of "basic units-computing units-hydrological response units". • Use of a two-layer nested algorithm involving successive approximation and nonlinear programming (SA-NLP). • The model improves the regional economy and water resource utilization efficiency. Water and land resources are indispensable prerequisites for the sustenance and advancement of human civilization. In recent decades, China has faced significant challenges in managing its water and land resources due to the intensifying competition resulting from rapid urbanization, industrialization, and population growth. The joint optimal allocation of water and land resources can effectively address these issues. However, there have been limited accomplishments in investigating the integrated water and land resources allocation system's interaction with social, economic, and environmental development. This paper develops a novel joint optimal allocation model of regional water and land resources (JOAMRWLS). The model integrates water and land resources for integrated allocation, taking into account the mutual feed relationship between them, and thereby achieving comprehensive utilization and coordination of water and land resources within the region. Then a two-layer nesting algorithm based on successive approximation and nonlinear programming (SA-NLP) is proposed to solve the model, obtaining the regional optimal land use pattern and optimal water allocation scheme. Subsequently, this paper uses Luoyang City in Henan Province, China, as a case study to verify the proposed JOAMRWLS. The results indicate that the water volume and area of each land use type tend to stabilize after the fifth iteration of the calculation with a consistent water volume of 18.65 × 108 m3. Compared with the conventional optimization model of water resources (COMWR), the JOAMRWLS encompasses five cities experiencing water scarcity, whereas the latter includes twelve such cities. Furthermore, the economic benefit of the JOAMRWLS is 2.65 × 1011 CNY, surpassing that of the former at 2.56 × 1011 CNY, highlighting its superiority. [ABSTRACT FROM AUTHOR]
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- 2024
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7. Subsidence associated with dewatering and gas extraction from coal seams: Contribution of desorption-induced coal shrinkage.
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Ali Aghighi, Mohammad, Cui, Tao, Schöning, Gerhard, Nicolas Espinoza, D., and Pandey, Sanjeev
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GAS well drilling , *GAS extraction , *LAND subsidence , *COALBED methane , *COAL , *ENVIRONMENTAL impact analysis , *SLUDGE conditioning - Abstract
• A novel approach for modelling subsidence induced by fluid extraction from coal seams. • Transient groundwater and subsidence models are linked. • Coal shrinkage induced by gas desorption can significantly impact subsidence. Several coal seams are contained within the formations of Great Artesian Basin (GAB), the largest natural underground water reservoir in Australia and the world. The extraction of coal seam gas (CSG) and its associated water from thousands of wells within the coal measures of the GAB has led to tens of millimetres of subsidence in CSG development areas. Since highly developed farming systems located in these areas rely on very low slope landforms, even this scale of subsidence has caused significant community concern about the potential for CSG extraction to impair farming operations and productivity through changes in land slope and drainage. Coal seam compaction associated with dewatering and gas extraction has two key components: poromechanical compaction and desorption-induced bulk shrinkage. The former results from pore pressure depletion (due to dewatering and gas extraction) and an increase of vertical effective stress, while the latter is induced by gas desorption from the coal matrix, leading to further deformation. While poromechanical compaction of fluid-bearing formations has been extensively addressed in the literature, relatively little research has been conducted on the role of coal shrinkage in CSG-induced subsidence. This paper introduces an innovative practical modelling approach for assessing CSG-induced subsidence at a subregional/regional scale. The approach utilises an analytical model for CSG-induced subsidence derived from constitutive stress–strain relations for poroelastic-sorptive media. This is a novel approach in the context of CSG-induced subsidence which considers both poromechanical compaction and desorption-induced shrinkage. A further distinction to previous work is the integration of the geomechanical-sorptive subsidence model with a numerical groundwater model. Based on this approach, this paper examines the effect of coal shrinkage on subsidence, and its proportions with respect to total compaction for one of the major coal measures in the Surat Basin. Input data are derived from three-dimensional geological, geomechanical, and groundwater models, as well as methane adsorption and desorption reports. Results show that (a) the impact of coal shrinkage on CSG-induced subsidence is likely to be significant in the study area and (b) the contribution of coal shrinkage to CSG-induced subsidence depends on gas content, Langmuir isotherm, shrinkage strain parameters, and the saturation state of coal. This study provides important insights into CSG-induced subsidence and lays the foundation for the development of robust, efficient, and localised predictive models to support environmental impact assessment and management. [ABSTRACT FROM AUTHOR]
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- 2024
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8. A complex network perspective on spatiotemporal propagations of extreme precipitation events in China.
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Li, Xiaodong, Zhao, Tongtiegang, Zhang, Jingkun, Zhang, Bingyao, and Li, Yu
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MADDEN-Julian oscillation , *NONPROFIT sector , *FLOOD forecasting , *STATISTICAL hypothesis testing , *EXTREME environments - Abstract
[Display omitted] • Complex network reveals the spatiotemporal propagations of extreme precipitation events (EPEs) in China. • EPEs over 218 river basins are classified into six communities with different seasonality from May to October. • There exist propagations of EPEs along the Yellow, Haihe, Liaohe and Songhua River Basins within one to eight days. Extreme precipitation events (EPEs) cause catastrophic losses to human lives and social economy. Focusing on the wet season from May to October, this paper formulates the complex network to characterize the spatiotemporal propagations of EPEs over 218 river basins in China. Specifically, based on the daily Multi-Source Weighted-Ensemble Precipitation version 2, the event synchronization along with significance test facilitates the complex network of EPEs from 1979 to 2020. By characterizing the topology of EPEs, the results show that synchronous EPEs generally occur over the Hetao Area of Yellow River Basin, Haihe River Basin and Northeast China. The complex network of EPEs classifies the 218 river basins into six communities. In May and June, more than 50 % of EPEs generally occur in South China. From July to September, over 60 % of EPEs tend to be observed in the Huang-Huai-Hai Plain, upper-middle reaches of Yellow River Basin, Northeast China and Southwest China. In October, about 65 % of EPEs happen in South and Southwest China. In the meantime, less than 10 % of EPEs occur in Northwest China. Among the synchronous EPEs starting in late June or early July and peaking in late July to early August, there exist propagations along the Yellow River, Haihe, Liaohe and Songhua River Basin within one to eight days. Eastward moisture transport along this pathway within four to six days drives the propagation of synchronous EPEs. The complex network analysis of this paper provides useful information for early warning and forecasting of EPEs and flood hazards. [ABSTRACT FROM AUTHOR]
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- 2024
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9. Using natural gas content of groundwater to improve the understanding of complex thermo-mineral spring systems.
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Dupuy, Margaux, Garel, Emilie, Chatton, Eliot, Labasque, Thierry, Mattei, Alexandra, Santoni, Sebastien, Vergnaud, Virginie, Aquilina, Luc, and Huneau, Frederic
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NATURAL gas , *GROUNDWATER , *GROUNDWATER flow , *GROUNDWATER tracers , *MINERALS in water , *MINERAL waters - Abstract
[Display omitted] • Geogenic processes combined with biotic activity generate diversified mineral waters. • CO 2 , N 2 and CH 4 are most widely represented dissolved gases in mineral waters. • Excess air can be used as tracer of biotic processes. • Complex regional mineral water flows are identified. • An improved regional hydrogeological conceptual model is proposed. The varied gaseous composition of thermo-mineral waters emerging in a non-active zone reflects the diversity and complexity of groundwater pathways and provides important insights into their hydrogeological behaviours. The investigated geochemical content of complex thermo-mineral springs revealed the need to use dissolved gas contents as part of a multi-tracer approach to discriminate processes, geogenic (water–gas-rock interactions), abiotic (geological confinement, flow paths) and biotic activity influencing geochemical of groundwater along regional pathways. Irrespective of the dissolved element content or the water type, examining the overall concentration of dissolved gases enables an effective delineation of regional groundwater flow paths. Using dissolved gas content further contributed to the circumvention of some analytical challenges associated with conventional isotopic or geochemical techniques, often linked to the high concentration of elements such as iron, sulfate, sulfide or other naturally occurring elements content. The primary objectives are to analyse the gas composition of individual springs, to identify the origin of these gases in the groundwater, and to use this gas composition to improve the understanding of the flow patterns contributing to the geochemical diversity observed at the surface. From field investigations in a geologically and structurally complex area of Eastern Corsica (France), three types of gas contents are identified: (type 1) CH 4 & H 2 S-rich, (type 2) N 2 -rich and (type 3) CO 2 -rich. The study of these dissolved gases highlights that the wide geochemical diversity of thermo-mineral waters observed here is not only related to the mineralogical composition of the local aquifer but also involves strong and cumulative interactions along deep regional circulation pathways. This approach also reveals a common deep crustal gaseous influence characterised by N 2 production, which interacts during up flow with groundwater and then with the local metamorphic or sedimentary rock matrix. The groundwater's isotopic and geochemical contents are then altered by local lithologies encountered through both abiotic and biotic interactions. Finally, at shallow depths, phreatic groundwater can add its geochemical and isotopic footprint and dilute this complex mixture before groundwater emerges as mineral spring. This paper answers the primary objectives yet further demonstrates that using dissolved gas as a tracer of groundwater flow paths allows a deeper interpretation of surface geochemical and isotopic observations, distinguishes local from regional flow paths, and provides information about processes at the origin of groundwater diversity. The combination of tools presented in this paper (i.e., geochemical, dissolved gas, and isotopic tools) allows the establishment of a reliable regional groundwater flow scheme for thermo-mineral waters in a non-active zone. This scheme is essential to improve thermo-mineral water management, and protection to ensure their sustainable quality in front of increasing anthropogenic and climatic pressures. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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10. Application of machine learning and emerging remote sensing techniques in hydrology: A state-of-the-art review and current research trends.
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Saha, Asish and Chandra Pal, Subodh
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REMOTE sensing , *MACHINE learning , *DISTANCE education , *HYDROLOGY , *ATMOSPHERIC circulation , *CLIMATE extremes , *FECAL contamination - Abstract
• A review has been conducted on the application of ML and RS in hydrology domain. • Analyses were conducted on surface hydrology, hydro-climatic extremes and GWM & WQ. • GIS and ML algorithms prove valuable in the realm of hydrological investigations. • State-of-the-art approach is attributed to hydrology and water resources. Water, one of the most valuable resources on Earth, is the subject of the study of hydrology, which is of utmost importance. Satellite remote sensing (RS) has emerged as a critical tool for comprehending Earth and atmospheric dynamics, including hydrology. With the assistance of satellite RS, the scientific community has achieved significant progress in recent years. Since machine learning (ML) and RS techniques were initially applied to the study of hydrology, there has been a tremendous increase in interest in studying potential areas for future advancements in hydrology. The growth can see in the publications of related papers. Considering these initiatives, the current review paper attempts to give a thorough analysis of the function of ML and RS techniques in four fields of hydrology. This review study considers hydrological topics of streamflow, rainfall-runoff, groundwater modelling and water quality, and hydroclimatic extremes. The use of learning strategies in the hydrological sciences is examined in all reviews and research papers. Several databases were utilised for this purpose, including Scopus-index, science direct, Web of Science, and Google Scholar. The overall results of this study show that employing RS techniques, ML and ensemble approaches is incomparably superior to using traditional methods in hydrological studies. [ABSTRACT FROM AUTHOR]
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- 2024
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11. Multiobjective multihydropower reservoir operation optimization with transformer-based deep reinforcement learning.
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Wu, Rixin, Wang, Ran, Hao, Jie, Wu, Qiang, and Wang, Ping
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REINFORCEMENT learning , *DEEP reinforcement learning , *WATER supply , *RESERVOIRS , *EVOLUTIONARY algorithms , *RESIDENTIAL areas - Abstract
The paper introduces a transformer-based deep reinforcement learning (T-DRL) approach designed to address the multiobjective multihydropower reservoir operation optimization (MMROO) problem. Unlike existing literature that primarily focuses on maximizing power generation from individual reservoirs, the MMROO model in this study considers the broader context of multiple reservoirs, encompassing total power generation, ecological protection, and residential area water supply. The computational challenges posed by the numerous constraints and nonlinearities of multiple reservoirs render conventional multiobjective evolutionary algorithms both expensive and lacking in generalization capabilities for solving the MMROO problem. To overcome these challenges, the paper proposes a T-DRL approach that leverages the multihead attention mechanism within the encoder module to adeptly extract complex information from reservoirs and residential areas. The two-stage encoder effectively processes diverse information separately. The multireservoir network of the decoder then generates optimal decisions based on contextual information. The case study focusing on Lake Mead and Lake Powell in the Colorado River Basin demonstrates the efficacy of the T-DRL approach, producing operation strategies that outperform a state-of-the-art method. Specifically, the proposed approach yields a 10.11% increase in electricity generation, a 39.69% reduction in amended annual proportional flow deviation, and a 4.10% rise in water supply revenue. Overall, the T-DRL approach emerges as an effective method for the multiobjective operation of multihydropower reservoir systems. • A multiobjective multihydropower reservoir operation problem is addressed. • A T-DRL method is proposed to deal with the multiobjective optimization. • The T-DRL method outperforms current methods in the quality of the obtained PFs. • The proposed strategies obtain more electricity and water supply revenue. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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12. Multi-objective optimization operation of multiple water sources under inflow-water demand forecast dual uncertainties.
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Wang, Sen, Zhong, Ping-an, Zhu, Feilin, Xu, Bin, Xu, Chengjing, Yang, Luhua, and Ben, Mengxue
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DEMAND forecasting , *WATER management , *WATER shortages , *WATER transfer , *WATER supply , *CONVEX functions - Abstract
[Display omitted] • A model for multi-objective optimization operation of multiple water sources under uncertainties was proposed. • Uncertainties in inflow and water demand forecasts can be considered simultaneously, and the correlation between uncertainties can be considered quantitatively. • The impact of inflow-water demand forecast uncertainties and their correlations on water resource operations are explored for water resources managers. Uncertainties in inflow and water demand forecasts bring risks to water resources management. Therefore, the research on multi-objective optimization operation of multiple water sources under uncertainties holds critical research significance and application value. This paper proposes a model for the multi-objective optimization operation of multiple water sources that can consider the inflow and water demand forecast dual uncertainties. In handling uncertainties, this paper adopts a scenario generation method based on joint distribution-Monte Carlo (JDMC), which allows for a quantitative consideration of the correlation between uncertainties. Moreover, the variable weight method and LINGO 20.0 are employed for solving multi-objective optimization issues to obtain stable Pareto frontiers. The proposed model is applied to the water receiving area inside Jiangsu Province of the South-to-North Water Transfer East Route Project (SNWTERP) in China. The main findings are as follows: 1) The impact of the inflow and water demand forecast uncertainties on the operation results is synergistic rather than antagonistic. Considering only a single uncertainty may result in water receiving area facing more severe water shortages or purchasing spot water at high prices. 2) There is a strong spatial correlation between uncertainties. If we ignore or only qualitatively consider the correlation between uncertainties, the impact of uncertainties on operation results will be underestimated or overestimated. 3) The Pareto frontiers are convex functions, indicating that decision-makers should choose a solution in the middle part. [ABSTRACT FROM AUTHOR]
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- 2024
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13. Hydrological digital twin model of a large anthropized italian alpine catchment: The Adige river basin.
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Morlot, Martin, Rigon, Riccardo, and Formetta, Giuseppe
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DIGITAL twins , *HYDROLOGIC cycle , *WATERSHEDS , *HYDROLOGIC models , *DAM design & construction , *LAND cover - Abstract
• Hydrological digital twins (HDT) are essential tools to understand the hydrological cycle especially in a changing climate. • We present, test, and validate an open-source framework to implement HDT in the large anthropized alpine Adige catchment. • The HDT is calibrated/validated on a variety of hydrological processes and data sources providing satisfactory performances. Understanding and simulating the hydrological cycle, especially in a context of climate change, is crucial for quantitative water risk assessment and basin management. The hydrological cycle is complex as it is a combination of non-linear natural processes and anthropogenic influences that alter landforms and water flows. Human-induced changes of relevance, including changes in land uses, construction of dams and artificial reservoirs, and diversion of the river course, lead to changes in water flows throughout the basin. These should be explicitly accounted for a realistic representation of the anthropogenically altered hydrological cycle. Such a realistic representation of the hydrological cycle is a necessary input for the water risk assessment in a particular region. In this paper, we present a hydrological digital twin (HDT) model of a large anthropized alpine basin: the Adige basin located in the northeast of Italy. Most catchments model often overlook land-uses changes over time and forget to model reservoir operation and their influence over time on water flow. Yet, for example, the Adige basin has>30 reservoirs affecting the water flow. We therefore use the GEOframe modeling framework to demonstrate the ability to create a hydrological twin model accounting for these anthropogenic changes. Specifically, we model each component of the water cycle over 39 years (1980–2018) at daily timescale through calibration of the Adige HDT with a multi-site approach using discharge data of 33 stations, based on a high-resolution (1 km) temperature and precipitation dataset and a calculated crop potential evapotranspiration (PETc) dataset, which accounts for human-induced change of the land cover over time. The modeling system also includes the simulation of artificial reservoirs and dams by the dynamically zoned target release (DZTR) reservoir model. The Adige HDT is assessed/validated/compared through a variety of hydrological processes (i.e., river and reservoir discharges, PETc and actual evapotranspiration, snow, and soil moisture) and data sources (i.e., observations and remote sensing data). Overall, the HDT reproduces well the measured discharge in space and time with a Kling Gupta Efficiency (KGE) above 0.7 (0.8) for 30 (23) of the 33 gauge-stations. For 7 artificial reservoirs with available data, the reservoir turbinated discharges are successfully reproduced with an average KGE of 0.92. A comparison between modeled and MODIS remote sensing snow data showed an average error of < 10% across the entire basin; the model also presented a good spatio-temporal agreement both with GLEAMS potential (and actual evapotranspiration) with an average KGE of 0.63 (0.60) and a high-level of correlation (0.5 on average) with the ASCAT satellite retrieved soil moisture. The findings of this paper demonstrate the potential of the open-source, component-based, GEOframe system to build a HDT, to provide a reliable and long term (39 years) estimation of all the water cycle components in a complex anthropized river basin at high spatial resolution. Spatially detailed HDT models results of this type can be used to inform basin-wise adaptation policy decisions and better water management practices in a time of changing climate. [ABSTRACT FROM AUTHOR]
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- 2024
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14. Study on the soil water characteristic curve and its fitting model of Ili loess with high level of soluble salts.
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Wang, Yuguo, Zhang, Aijun, Ren, Wenyuan, and Niu, Lisi
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SOLUBLE salts , *LOESS , *CURVE fitting , *SOIL moisture , *FILTER paper , *SOIL salinity , *PLATEAUS - Abstract
• The SWCC of Ili loess with high level of soluble salts are compared by two methods. • The compression of centrifuge method causes the increase of matric suction. • Solution concentration causes the effect of salt on total and osmotic suction. • A new SWCC fitting model considering soluble salt content is proposed. Soil water characteristic curve (SWCC) is a critical parameter for unsaturated soil. However, few study can be found in literature for SWCC of loess with high soluble salt content, in particular SWCC fitting model. This paper targets on Ili loess in Xinjiang in China, which has a high level of soluble salts and is a typical loess in central Asia. Centrifuge method and filter paper method are used to conduct SWCC tests of Ili loess under different salt conditions. The total and matric suction of Ili loess under different soluble salt contents are obtained. The results of two methods are compared. The corresponding influence of soluble salt on SWCC is studied. At the same time, based on the Gardner model, a new SWCC fitting model of total and matric suction for Ili loess is proposed, which is able to consider the influences of soluble salts. The test results show that the compression during centrifuge method contributes certain increase of matric suction, especially at high volumetric water content conditions. Soluble salts have large effect on total and osmotic suction of Ili loess, however limited effect on matric suction. The alteration of total and osmotic suction is caused by the change of solution concentration in the sample. Total suction is mainly affected by osmotic suction. The alteration of matric suction is caused by the precipitation of the salts. It is proved that the proposed SWCC model is capable of characterizing the total and matric suction by simultaneously considering volumetric water content and soluble salt content. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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15. The economics of aquifer protection plans under climate water stress: New insights from hydroeconomic modeling.
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Ward, Frank A., Mayer, Alex S., Garnica, Luis A., Townsend, Nolan T., and Gutzler, David S.
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WELLHEAD protection , *SUSTAINABLE development , *GROUNDWATER recharge , *GROUNDWATER , *WATER conservation , *NET present value , *WATER supply - Abstract
• Numerous science and policy debates occur over efficient aquifer protection measures. • This paper formulates a basin scale hydroeconomic optimization framework. • Its application is to North America's Middle Rio Grande Basin. • It discovers least cost aquifer protection measures for handling climate water stress. • Results are presented showing the marginal and total cost of aquifer protection. Where surface and groundwater are managed conjunctively, the stress on water supplies from climate change can significantly influence water use patterns as well as the economic value and sustainability of those uses. However, aquifer protection can be an expensive proposition because water uses that currently rely on aquifer pumping may produce considerable economic value that would be lost if protection measures are carried out. Evidence from climate-stressed regions has attracted research addressing the costs and benefits of aquifer protection plans. Despite these efforts, few peer-reviewed papers have examined water use patterns that minimize the economic costs of aquifer protection. This work presents an original approach to address that gap by developing and applying a basin scale hydroeconomic optimization model of North America's Middle Rio Grande Basin to explore impacts of new policies not yet implemented supporting aquifer protection. It also gives model access to readers or stakeholders to experiment with their own scenarios to assess impacts of alternative aquifer protection plans. The model accounts for surface and groundwater storage, irrigation, urban, environmental, and recreational demands, surface water inflows under various climate scenarios, groundwater pumping and recharge, substitute water prices, crop water use, evaporation, as well as institutional constraints governing water use. The objective is implemented by finding the optimized discounted net present value of economic benefits summed over uses, sectors, and regions from use of surface water and connected aquifers. Results are shown for each of six water supply scenarios, two substitute water prices, and two system operation rules. To address impacts of aquifer protection targets, groundwater sustainability targets are specified and enforced as constraints for each of the region's two major aquifers. We assess total and marginal cost of achieving two targeted aquifer protection levels by identifying optimized surface use and groundwater pumping for each of 24 scenarios. Results show that climate change, in the form of reduced and highly variable inflows, considerably drives up the cost of protecting aquifer sustainability, amplified by the conjunctive nature of the system. Future work points to a need to assess economic performance of various water conservation measures as well as reducing costs of substitute water through measures such as technical advance in desalination, recycling and reuse, substitution of other resources for water, better characterization of existing aquifers, and development of new groundwater supplies. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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16. Lake Urmia crisis and restoration plan: Planning without appropriate data and model is gambling.
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Danesh-Yazdi, Mohammad and Ataie-Ashtiani, Behzad
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WATER depth , *LAKE restoration , *DATA modeling , *LAKES , *WATER meters , *WATER levels - Abstract
• Lake Urmia crisis, relevant consequences, and restoration plans are reviewed. • Restoration programs are scrutinized based on data and model availability. • Model-data interaction is highlighted for designing the lake restoration plans. • Massive data collection and integrated modeling are needed in the Lake Urmia Basin. Losing eight meters of water level over a 20-year period from 1996 to 2016 marked the Lake Urmia (LU) as one of the regional environmental crises. This condition has threatened biota life, intensified desertification around the lake, and raised social concerns by adversely impacting the inhabitants' health and economy. In 2013, the Urmia Lake Restoration National Committee (ULRNC) started implementing certain management practices to stop the drying trend of LU, resulted in the cease of water level drop and stabilization of LU condition in 2016. Nevertheless, the restoration actions have not yet raised the lake to the water level as planned by the roadmap. This paper aims to describe and to assess the LU restoration plans by underscoring the ULRNC achievements, challenges, and shortcomings. In particular, we discuss how the value of data and data-aided modeling has been underestimated by the LU restoration programs, leading to still existing puzzles about the lake interaction with the involving physical processes governing its dynamics. We show how the LU restoration timetable has not fulfilled the planned milestones as evidenced by the inability to capture the anticipated lake water levels, which is partly attributed to the lack of field data and dynamic modeling that could predict the lake response in a more reliable and conservative manner. The current restoration plans should also be revisited to ensure that any practice with the aim of reducing water consumption in the basin is not only environmentally sustainable but also feasible from the socioeconomic perspective. The insights provided by this paper attempt to underscore the value of field data collection for establishing a reliable conceptual model, and for executing pre- and post-monitoring of the lake so that the success or failure of the restoration actions taken by the policymakers can be appropriately evaluated. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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17. Coupled groundwater hydrodynamic and pollution transport modelling using Cellular Automata approach.
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Milašinović, Miloš, Ranđelović, Anja, Jaćimović, Nenad, and Prodanović, Dušan
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CELLULAR automata , *GROUNDWATER pollution , *GROUNDWATER flow , *CONSERVATION of mass , *ANALYTICAL solutions - Abstract
• Coupled groundwater flow and transport can be simulated using Cellular Automata. • Groundwater flow Cellular Automata shows good agreement to MODFLOW. • Pollution transport Cellular Automata shows good agreement to MT3DMS. • Weighted Cellular Automata approach enables mass conservation in transport modelling. • High potential of implementing additional transport mechanisms. High urbanization puts many groundwater resources at risk of quality deterioration. Analyzing all viable potential groundwater contamination scenarios for good decision making requires reliable tool. Coupling several complex models in integrated modelling can often fail to perform in reasonable time. Possible solution in that case could be usage of simplified models in order to speed up long-term continuous calculations and simulations. The paper presents the application of the Cellular Automata (CA) approach in modelling of the contaminant transport under unsteady groundwater conditions. It compares the results obtained using coupled CA models with well-known analytical solutions and standard methods used for pollution transport modelling in groundwater conditions, such as coupled MODFLOW and MT3DMS. Results obtained in this paper show that CA approach can be satisfactorily used for simulations of unsteady groundwater conditions, caused by surface-groundwater interaction, and pollution transport, especially in diffusion dominant cases, gaining the reduction of simulation time up to 10 times. [ABSTRACT FROM AUTHOR]
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- 2019
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18. Novel forecasting models for immediate-short-term to long-term influent flow prediction by combining ANFIS and grey wolf optimization.
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Dehghani, Majid, Seifi, Akram, and Riahi-Madvar, Hossien
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LOAD forecasting (Electric power systems) , *SEWAGE disposal plants , *CUMULATIVE distribution function , *FEATURE selection , *BP Deepwater Horizon Explosion & Oil Spill, 2010 - Abstract
• A novel insight on forecasting influent flow is developed by hybridizing ANFIS-GWO. • Three horizons: Very immediate short-term, mid-term, long-term as 5-min to 10-days. • Different input combinations based on Gamma Test analysis are surveyed in horizons. • Direct and recursive hybrid strategies for multi-step forecasting are provided. • Taylor diagrams, box-plots, ECDF used, ANFIS-GWO direct and recursive are superior. Accurate influent flow forecasting plays a significant role in management, operation, scheduling and utilization of the sewage treatment plants. In design and operate such plants, it is essential to measure and forecast the influent flow rate in wastewater plants. In this paper, the Very immediate-short-term to long-term influent flow rate are modeled and forecasted by a new developed hybrid model of ANFIS and Grey Wolf Optimizer (GWO). The objective of this study is the integration of GWO with ANFIS in forecasting multi-ahead influent flow rate. The forecast horizon of the model is from 5 min up to 10 days bases on Gamma Test (GT) feature selection of input combinations. As the parameters of ANFIS have effect on the forecasting accuracy, these parameters are adjusted and optimized by using Grey Wolf Optimizer (GWO). Then the choice of appropriate input parameters at different prediction horizons from Very immediate-short-term (5-min ahead) to long-term (10 days ahead) was discussed for influent forecasting. The statistical indices of RMSE, NSE, MAE, RAE, R2, d, CI and graphical evaluations such as scatter-plots with confidence bounds, error distributions, Taylor diagrams, box-plots and empirical cumulative distribution function (ECDF) were implemented for assessing the performance of all models in prediction horizons. Furthermore as another novelty in the present paper, recursive forecasting models based on previous forecasted values is used to improve the accuracy and applicability of ANFIS-GWO in recursive predictions. Our Results showed that: (1) the hybrid of ANFIS-GWO significantly improved the prediction accuracy. (2) ANFIS-GWO performs more efficiently than the ANFIS in almost all of the prediction horizons (ANFIS-GWO1: 5 min ahead; ANFIS-GWO11: 1–2 days ahead; ANFIS-GWO8: one week ahead). (3) The performance of models in influent flow forecasting is significantly influenced by the prediction horizon. The computational results confirmed that the ANFIS-GWO performs well in all of prediction horizons. Equally the true values and the trends are precisely forecasted by the ANFIS-GWO. Results of this novel study demonstrate that reliable estimates of influent flow rate from 5-min up to 10 days in advance can be achieved using the developed direct and recursive hybrid GWO models. [ABSTRACT FROM AUTHOR]
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- 2019
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19. Uncertainty analysis of water availability assessment through the Budyko framework.
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Guo, Aijun, Chang, Jianxia, Wang, Yimin, Huang, Qiang, Guo, Zhihui, and Li, Yunyun
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WATER supply , *WATER analysis , *UNCERTAINTY , *TIME series analysis , *WATER use , *EVAPOTRANSPIRATION - Abstract
• A bootstrap-based algorithm is proposed for disclosing uncertainty of Budyko model. • Propagation of the parameter uncertainty to water availability assessment is derived. • There exists remarkable uncertainty in parameter w of Budyko model. • Impact of w uncertainty on Budyko output varies nonlinearly with climate. Parametric Budyko method has been widely used to assess water availability under changing environment. Its single parameter (w) controlling the shape of the Budyko curve, is of critical importance to water availability assessment (WAA). Here, the water availability is defined as the ratio of evapotranspiration to precipitation in the context of Budyko hypothesis. As an unobserved variable, w is generally estimated through incorporating the long-term water-balance model and Budyko hypothesis, i.e. estimating w as a function of precipitation, runoff, and potential evapotranspiration over a long-term time series of these variables. Inevitably, the estimated value of w is subject to uncertainty resulting from hydro-meteorological records with limited size. Further, this uncertainty affects WAA in context of Budyko hypothesis. The uncertainty and its propagation issue is often overlooked in past research. In this paper, we develop a bootstrap-based algorithm to reveal aforementioned uncertainty. Moreover, a partial derivative-based sensitivity analysis is performed for understanding the propagation of w uncertainty on WAA. Three catchments located in the Yellow River Basin, China, are used as case study sites. Results indicate that there exists remarkable uncertainty in parameter w , which varies with the data length. The impact of such uncertainty on WAA increases quickly under the humid condition, thereafter reaches a peak under the mild condition, and decreases slowly under the arid condition. The reason behind this phenomenon is the largest sensitivity of WAA to parameter w under the mild climate condition. Moreover, a general analytical expression for determining the critical value of climate condition which maximizes the impact of parameter w uncertainty on WAA is derived in this paper. Results of this study can enhance the reliability and robustness of Budyko-based models. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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20. Riding the waves of discomforts: Reflecting on the dialogue of hydrologists with society.
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Riaux, Jeanne, Kuper, Marcel, Massuel, Sylvain, and Mekki, Insaf
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HYDROLOGISTS , *HYDROLOGY , *REFLEXIVITY - Abstract
• We discuss the discomforts of hydrologists in their dialogue with society. • We propose 4 ways to better align scientific stance, research practices & discourse. • This helps to refocus research practices and engage in and with society. • Socio-hydrology allows a collective, interdisciplinary reflexive process. • This is potentially a way to integrate socio-hydrology in society. Although there is a deep historical relationship between hydrology and society, the relationship has considerably evolved in the last three decades. Hydrologists, in particular those involved in designing of decision-support tools, are experiencing a widening gap between an academic discipline which has progressively moved away from field-based applied natural science to computational hydrology, and the multiplication of stakeholders involved in the water-related issues addressed by research. The challenge for hydrology is now to negotiate this shift and to rethink its engagement in society. This paper provides a description of a planned process designed to improve hydrology-society interactions and to foster reflexivity in socio-hydrology. Based on an interdisciplinary reflexive process undertaken in Tunisia from 2016 to 2020, we identified three types of discomforts in the dialogue with society, inviting scientists to lucidly engage with these discomforts. We formulated four key reflexive propositions to achieve a better alignment of scientific stance, research practices and discourse. The first proposition concerns the need to explain more clearly the value systems scientists engage in and with society. The second concerns the need to position hydrology in society and not outside it, by reconsidering the functions that research fulfils in society. The third is an invitation to redefine the perimeter of the research interlocutors and the way to reach them. The fourth is to revisit scientific practices to build on the strengths of the dialogue between field-based natural science and computational hydrology. The paper concludes that adopting a reflexive posture towards these four dimensions of the dialogue between hydrology and society is an effective way to overcome discomforts and to refocus research stance, practices and discourse. It is a way to renew hydrology's place in society and to contribute to the current thinking in socio-hydrology initiated by hydrologists. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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21. A review of hybrid deep learning applications for streamflow forecasting.
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Ng, K.W., Huang, Y.F., Koo, C.H., Chong, K.L., El-Shafie, Ahmed, and Najah Ahmed, Ali
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DEEP learning , *BLENDED learning , *WATER management , *STREAMFLOW , *FORECASTING , *MACHINE learning - Abstract
• Basic factors affecting deep learning models for streamflow forecasting. • Hybrid deep learning models for streamflow forecasting. • Input and hyperparameter optimization of deep learning models. • Limitation and prospects in streamflow forecasting. Deep learning has emerged as a powerful tool for streamflow forecasting and its applications have garnered significant interest in the hydrological community. Despite the publication of several review articles on machine learning applications in streamflow forecasting, no review paper has yet focused explicitly on deep learning and its hybrid forms. This paper starts with some characteristics of deep learning models to provide a quick view of deep learning. Next, the configurations and characteristics of hybrid deep learning models, which is a hybridization of modeling techniques with deep learning, are discussed. Another vital role while implementing deep learning modeling is the methods applied for input and hyperparameter optimization. Finally, the limitations encountered in streamflow forecasting using deep learning models and recommendations for further research are outlined. This review covers related studies from 2017 to 2023 to provide the most recent snapshot of deep learning modeling applications in streamflow forecasting. These efforts are expected to contribute to the advancement of streamflow forecasting, potentially enabling more informed decision-making in water resource management. [ABSTRACT FROM AUTHOR]
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- 2023
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22. Revisiting the concept of hydraulic radius.
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Wei, Maoxing, Cheng, Nian-Sheng, and Lu, Yesheng
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SEDIMENT transport , *TURBULENCE , *TURBULENT flow , *REYNOLDS number , *SHEARING force , *FLUID mechanics - Abstract
• • The concept of hydraulic radius can be reinterpreted as a measure of large-scale turbulent flow structure. • • Hydraulic radius as proxy for large-scale eddies enables new framework for bed shear stress model and unified scaling of scour depth. • • Hydraulic radius formulation provides insight into characterizing large-scale flow structure by geometrical boundary conditions. Hydraulic radius has long been customarily used as a characteristic length to assess the cross-sectional geometry and efficiency of channels and conduits. This review paper revisits the concept of the hydraulic radius in light of its extensive applications in recent studies, with the aim of piecing together a state-of-the-art understanding of its physical underpinnings. The original definition of the hydraulic radius is presented as the ratio of cross-sectional flow area to wetted parameter, which inherently integrates the properties of the flow domain and the contacting surface. The use of hydraulic radius as a versatile characteristic length in classical Manning's formula and the Reynolds number underscores its significance in relation to flow resistance. Through a thorough review of its diverse applications, this paper provides a unified understanding of the physical meaning of hydraulic radius, highlighting its interpretation as a measure of large-scale eddies within a given flow domain. This new insight can be justified beyond uniform flows, as evidenced by its use in addressing sediment transport issues in complex vegetated flows, based on turbulence phenomenological theory. Furthermore, the original formulation of the hydraulic radius provides insights into how large-scale flow structure can be quantified with geometrical boundary characteristics, leading to a promising framework for scour prediction based on large-scale vortex size. This paper highlights the versatility and utility of hydraulic radius across various fields of fluid mechanics and sediment transport research, and proposes its potential for future studies as a proxy for large-scale flow structures. [ABSTRACT FROM AUTHOR]
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- 2023
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23. Using the fuzzy evidential reasoning approach to assess and forecast the water conflict risk in transboundary Rivers: A case study of the Mekong river basin.
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Yuan, Liang, Wu, Xia, He, Weijun, Kong, Yang, Degefu, Dagmawi Mulugeta, and Ramsey, Thomas Stephen
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WATERSHEDS , *WATER levels , *EVIDENCE gaps , *TRANSBOUNDARY waters , *SOCIAL conflict , *FUZZY numbers - Abstract
• A FERM was established to integrate the risk of various dimensional conflict sources. • Different dimensional conflicts show diverse changed trends in the MRB. • The water conflict risk will remain at a medium degree in the MRB. • Stakeholders having a shared future is necessary to solve water conflicts. Improving the reliability of conflict evaluation and risk prediction methods used in transboundary water-sharing problems is an important research gap that needs to be addressed. This paper constructed a novel and comprehensive water conflict risk assessment framework that takes into account the multi-dimensional attributes of water resources. The reliability of each evaluation index is represented based on the triangular fuzzy number which can address the ambiguity and uncertainty. The fuzzy evidence reasoning model (FERM) was constructed to comprehensively calculate the risk level of water conflict in the transboundary river basin. The GM (1,1) model was used to predict the indicators for dynamic forecasting of the water conflict risk, and the water sharing problem in the Mekong River Basin (MRB) was selected as the case study to validate the method proposed in this paper. The results showed: (1) Except for Myanmar, the risk of water conflicts in other countries has decreased and will continue to decrease in the future. (2) The water quantity conflict risk in Laos and Thailand is increasing. (3) There is little change in the risk of water ecological conflict in riparian countries. (4) The risk of socio-economic conflict in Cambodia rises from high grade to very high grade, while the risk of economic and social conflict in other countries will decline. (5) From 1992 to 2027, water conflict in riparian countries have been on the decline. The comparison of the TWAP's (Transboundary Waters Assessment Programme) assessment and prediction of water conflict risk shows that the research results of the two are close to each other. Therefore, the proposed model could help to assess and forecast the water conflict risk in transboundary rivers. This assists riparian countries to take effective preventive measures to avoid water conflicts. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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24. Modifying 2D surface models in urban flood analysis.
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Oberauer, Michael and Lehmann, Boris
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FLOOD warning systems , *SETUP time , *SEWERAGE , *STORMS , *HYDRAULIC models , *FLOODS - Abstract
Hydraulic models in urban flood analysis play a decisive role, as they obtain information about flood extension, flow patterns on the surface and in the pipe network comes at cost of time consuming model set up and computing time. Dual drainage models do have the advantage of more accurate results as they consider bidirectional interaction between surface and sewers. This can be crucial as for certain rainfall events the sewer system can have a decisive impact. The aim of this paper is to modify 2D surface models and consider the performance of the sewers according to design standards for sewer systems, without the actual coupling process. Therefore a time varying approach, as inflow capacity into the sewers, was designed which reduces the rainfall depth before surface run off occurs. For a one-hour storm event, the novel approach indicates a higher performance (about 13 %) of the sewer system in the first half of the rainfall event compared to using a constant drainage removal rate. The presented approach in this paper will be tested in further research. • Considering performance of sewers in 2D surface models increases quality of results. • Accuracy of dual drainage models comes at costs of model set-up and computing time. • Novel Approach to modify 2D-surface models according to design standards of sewers. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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25. Evaluation of shallow groundwater dynamics after water supplement in North China Plain based on attention-GRU model.
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Nan, Tian, Cao, Wengeng, Wang, Zhe, Gao, Yuanyuan, Zhao, Lihua, Sun, Xiaoyue, and Na, Jing
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WATER table , *CONVOLUTIONAL neural networks , *STANDARD deviations , *GROUNDWATER , *COASTAL plains , *GROUNDWATER recharge - Abstract
• Driving factors of groundwater level variation have been evaluated and ranked. • Attention-GRU model shows the best performance on groundwater level prediction. • Ecological water supplement shows variance effects in the North China Plain. • Specific yield updates exhibit a significant influence during specific period. • Groundwater storage variation presents different features in the North China Plain. The continuous ecological river supplement has enhanced the recharge of groundwater in the North China Plain, and alleviated the depletion of groundwater resources. In the study of ecological river supplement, efficient quantitative prediction of groundwater level (GWL) and groundwater storage (GWS) is a crucial issue. To predict GWL and estimate the variation of GWS in the North China Plain, this paper uses the attention-gated recurrent unit (Attention-GRU) model as a primary method and coefficient of determination (R2) and root mean square error (RMSE) as the indices to evaluate model performance. Compared to traditional recursive neural network (RNN) and long short-term memory (LSTM) model, Attention-GRU model demonstrated an outstanding performance, as the RMSE is respectively 0.5 m and 2.28 m in the training and testing periods, and the R2 is 0.87 and 0.68. With the usage of the convolution neural network (CNN) model, the specific yield field was continuously updated from 2018 to 2021. The average annual variation of GWS estimated by this method is respectively −15.19 × 104 m3, −13.29 × 104 m3, 2.55×104 m3 and 100.05×104 m3 from 2018 to 2021 in the North China Plain. Besides, the semiannual GWS variation, calculated using the updated specific yield, is −59.56 × 104 m3 in 2022. Furthermore, the influence of the reference time length and the driving factors were evaluated. Results show that the precipitation, evaporation and human activities are the most important temporal features influencing the changes in GWS, and the changes in different areas have varying local characteristics. In the piedmont plain, river ecological recharge played a crucial role for groundwater storage recovery. When it moved to the coastal plain, the contribution rate of recharge decreased from 23% to 11%. In addition, the optimal reference time length used in Attention-GRU model varies in different hydrogeology divisions, and the specific yield variation would result in substantial differences for the GWS estimation under different climate conditions and human activities. The systematic framework proposed in this paper is effective for evaluating and predicting GWL and GWS, and thereby able to support the management and development of groundwater, and provide a guidance for ecological river supplement. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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26. A new fractal model for nonlinear seepage of saturated clay considering the initial hydraulic gradient of microscopic seepage channels.
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Tao, Gaoliang, Peng, Pai, Chen, Qingsheng, Nimbalkar, Sanjay, Huang, Zhe, Peng, Yinjie, and Zhao, Wei
- Subjects
- *
CLAY , *FRACTAL dimensions , *SOIL drying , *PREDICTION models - Abstract
• A fractal model for the soil–water characteristic curve of Hunan clay was derived. • A fractal model for nonlinear seepage of saturated clay was established. • The model allows for easy computation and prediction of continuous permeation. • The prediction remains accurate even in soil with high dry density. It is crucial to investigate nonlinear seepage phenomena in low-permeability soils. Currently, empirical formulas account for the majority of nonlinear seepage model formulations. Some theoretical prediction models have been proposed; however, they tend to be computationally complex, and their predictive performance must be enhanced. Considered an indirect indicator, the soil–water characteristic curve reflects the permeation channels of soil pores of varying diameters. A nonlinear seepage model for saturated clay has been devised, taking into account the initial hydraulic gradient of microscopic seepage channels. This paper derives and solves the fractal dimension of the soil–water characteristic curve fractal model and establishes a new nonlinear seepage fractal model for saturated clay under various hydraulic gradients. The new seepage fractal model proposed in this paper is used to predict the seepage velocity of saturated Hunan clay under various hydraulic gradients and is compared to the results of its nonlinear seepage test. Overall, the results indicate that the predicted values of the seepage fractal model presented in this paper closely match the experimental values, and the accuracy of the prediction of the seepage velocity of saturated clay with a higher dry density exceeds that of present theoretical models. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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27. Study on optimization and combination strategy of multiple daily runoff prediction models coupled with physical mechanism and LSTM.
- Author
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Guo, Jun, Liu, Yi, Zou, Qiang, Ye, Lei, Zhu, Shuang, and Zhang, Hairong
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RUNOFF models , *BIG data , *PREDICTION models , *STANDARD deviations - Abstract
• A type of model selection and combination strategy is proposed incorporating 16 different physically based models with LSTM technology. • A grouped model performance evaluation metric was proposed, which not only considers the performance of individual models, but also comprehensively evaluates the performance of a model group formed by multiple models. • It is found that the F score metric may not be suitable for evaluating model selection. Accurate prediction of runoff is an important foundation for optimizing water resource allocation and reservoir scheduling operations. However, due to its complex characteristics such as time-varying and non-stationary, accurate prediction of the runoff process is very difficult. This paper proposes a novel approach for runoff forecasting by combining the physical mechanism models with the Long Short-Term Memory network (LSTM) method. Utilizing the simulation and description capabilities of physically based models, as well as the powerful nonlinear analysis provided by big data methods, a type of model selection and combination strategy is proposed incorporating 16 different physically based models with LSTM technology. Additionally, to accommodate the comprehensive analysis and evaluation of multi-model forecasting performance, this paper also proposed a comprehensive evaluation metric for runoff forecasting considering the characteristics of group models. The results of the case study demonstrate that this strategy can obtain model combinations suitable for different watershed characteristics and effectively improve the forecast accuracy of multiple models. Model combinations sorted by validation period RMSE and R 2 should be a superior choice. When evaluating the runoff forecasting accuracy of obtained optimal model combination during the calibration period, the average reduction of Root Mean Square Error (RMSE) is 39.62%, and the average increase of Nash-Sutcliffe coefficient R 2 is 7.49%. During the validation period, the average reduction of RMSE is 62.68% and the average increase of R 2 is 24.24%. When the model combinations sorted by validation period F score, the obtained model combination increased RMSE by 22.7% and decreased R 2 by 7.3% comparing to the model combination selected by RMSE and R 2. It is indicated that the F score may not be suitable for evaluating model selection. The method proposed in this article can effectively improve the overall forecasting performance of a single forecasting model and has good practical value for promotion and application. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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28. The limits to equity in water allocation under scarcity.
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Hassan, Wasim, Manzoor, Talha, and Muhammad, Abubakr
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- *
WATER rights , *WATER shortages , *WATER use , *WATER supply , *AGRICULTURE - Abstract
Equitable water allocation in real-world irrigation systems is hampered by supply fluctuations, posing a significant challenge to the goal of promoting fairness among consumers. In this paper, we concern ourselves with the limits of equity achievable for any water allocation scheme across the entire spectrum of water supply conditions. In the process, we develop a typology of canonical water allocation mechanisms that categorizes mechanisms w.r.t. the distribution of fulfilled demand across the users. Adopting specific notions of supply reliability and distribution equity, we derive the theoretical performance limits for all canonical mechanisms and extend the analysis to arbitrary allocation mechanisms. We show that for any value of supply reliability, the best possible equity is realized by mechanisms that uniformly distribute water among users, whereas the worst possible equity is associated with mechanisms that prioritize the demand of some users before allocating water to others. We also show that any intermediate equity level can be realized by adjusting the initial entitlements prior to allocating water to fulfill demands, in an approach we categorize as hybrid allocation. We parameterize the performance boundaries for such allocation schemes based on the fraction of supply allocated to initial entitlements. We discuss how this parameter can serve as a policy tool to balance the goals of equitable water access with other system-level objectives. In the end, we complement the analytical results with numerical simulations of a selected agricultural district from a real-world irrigation system and speculate about the application of our study to large-scale hierarchical systems. • A categorization of water allocation mechanisms into three canonical types. • Evaluated the performance limits of allocation mechanisms for equity and reliability. • Validation of the theoretical performance limits through Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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29. Multiple contamination sources, pathways and conceptual model of complex buried karst water system:constrained by hydrogeochemistry and δ2H, δ18O, δ34S, δ13C and 87Sr/86Sr isotopes.
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Wang, Qian, Gao, Xubo, Jiang, Chunfang, Li, Chengcheng, Zhang, Xin, Wang, Wanzhou, Duan, Yan, Luo, Wenting, Mao, Zhifeng, and Wang, Yanxin
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- *
PIPE fracture , *WELLHEAD protection , *GROUNDWATER pollution , *MINE waste , *WATER pollution - Abstract
[Display omitted] • Confined Quaternary groundwater reverse recharge contaminates buried karst water. • Upper coal mining activities pose a threat to the buried karst aquifers. • The vertical seepage of contaminated river water worsens karst water quality. • Hydrologic connectivity and source availability determine the contamination process. Karst groundwater contamination has emerged as a worldwide environmental and health hazard. Karst aquifers, even for the buried karst systems, have strong contamination sensitivity and great pollution risk due to the good pipe fracture connectivity. The large burial depth and invisible hydrologic connectivity pose a challenge for the diagnosis of the contamination sources and identification of the contamination pathways of buried karst aquifers. To address it, a comprehensive application of hydrogeological and hydrogeochemical investigation combined with multiple isotopes (δ2H, δ18O, δ34S, δ13C and 87Sr/86Sr) were performed to elucidate the multiple contamination sources and migration pathways of contaminants in Jinci Spring, a complex buried karst water system. The results obtained highlight that the hydrological connectivity and source availability determine the specific contamination process in buried karst aquifer. Karst aquifer under the river channel is observed to be influenced by the vertical infiltration of polluted river water as indicated by elevated chloride levels (mean 79.4 mg/L) and higher δ2H (mean −8.8 ‰) and δ18O (mean −64.5 ‰) values. Evidenced by the excess of sulfate (mean: 1454 mg/L) and depleted δ34S (mean: −1.6 ‰), the lower karst aquifer may receive the leaking recharge of the mining waste water in the overlying coal seams. The occurrence of nitrate in karst water may be induced by reverse recharge from neighboring confined quaternary aquifers, indicated by the elevated nitrate concentration (15.0–50.8 mg/L) with high 87Sr/86Sr ratios (mean 0.7124). Our findings suggest that over-pumping of deep karst waters may produce additional pollution sources by altering the natural recharge relationship between montanic karst waters and the neighboring pore waters within the basin. Research in this paper improves our understanding of groundwater pollution and hydrological connectivity in buried carbonate aquifers and the protection of karst water resources. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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30. Development of a method to assess synergy and competition for water use among water-energy-food nexus in the Yellow River basin: Water quantity-quality dimensions.
- Author
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Han, Xinxueqi, Hua, En, Guan, Jiajie, Engel, Bernie A, Liu, Rong, Bai, Yawen, Sun, Shikun, and Wang, Yubao
- Subjects
- *
CALORIC content of foods , *WATER distribution , *WATER quality , *WATERSHEDS , *WATER use , *WATER security - Abstract
• Developed a method to assess synergy and competition in water-energy-food systems. • Energy and food industries face intense competition for water in the Yellow River Basin. • Considering water quality exacerbates competition for water between food and energy and diminishes synergy. • Resource endowment and technological level have significant impact on the degree of competition. Water, as an indispensable component of the water-energy-food (WEF) nexus, plays a pivotal role in shaping its stability and safety. The competition for water between the food and energy systems is accentuated in the concept of the nexus. However, existing quantitative frameworks and assessment mechanisms have limitations as they often neglect to consider water quality in water competition within the WEF nexus. Therefore, a WEF nexus synergy and competition assessment method was proposed by coupling water footprint theory and the Lotka-Volterra model in this paper. This method establishes two scenarios based on the water footprint perspective, namely, water quantity and water quality-quantity. It effectively addresses the deficiencies in quantitative analysis of WEF system trade-offs and synergies. To illustrate the application of the method, this study conducted a case study in the Yellow River Basin (YRB) of China, evaluating the complex competitive and synergistic mechanisms of water use in the energy and food industries. Compared to the water quantity scenario, the results reveals that the inclusion of gray water footprint intensifies the competition for water in the food and energy industries in the YRB from 2000 to 2020, particularly in resource-based and economically developed cities. The spatial distribution characteristics of water resource competition and synergy in the YRB's WEF system align with WEF endowment and economic technology levels. To address water conflicts in the WEF nexus and enhance nexus security, this study proposes a regulatory pathway for WEF system synergistic security based on two aspects (water competition and synergy characteristics and resource endowment). It contributes to a more comprehensive understanding of water competition and synergy in the WEF nexus and provides valuable insights for resource management. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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31. Drought and wetness events encounter and cascade effect in the Yangtze River and Yellow River Basin.
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Lu, Jie, Qin, Tianling, Yan, Denghua, Zhang, Xin, Jiang, Shanhu, Yuan, Zhe, Xu, Shu, Gao, Haoyue, and Liu, Hanxiao
- Subjects
- *
COPULA functions , *DROUGHT management , *TRANSFER functions , *DROUGHTS , *STATISTICAL correlation , *RUNOFF , *WATERSHEDS - Abstract
• A framework for evaluating the cascade effect of drought and flood events in different sub-regions and multiple levels is proposed. • The correlation degree of meteorological-hydrological drought and flood in the Yangtze River Basin is higher than that in the Yellow River Basin. • The flood-flood and drought-drought scenarios aggravated the disaster, but the probability of the same drought and flood scenarios in the two basins was higher. The flood-drought scenario is conducive to mitigating drought. Under the background of global change, the multivariate attributes, multi-temporal and multi-watershed regional characteristics of extreme drought and flood disasters are significant. It is very important to identify the drought and wetness encounter law and spatial cascade effect in the source region-upper-middle-lower reaches of the basin. In this paper, the Yangtze River and Yellow River basins are taken as the research objects. Based on the standardized precipitation index (SPI) characterizing meteorological drought and wetness and the standardized runoff index (SRI) characterizing hydrological drought and wetness, the evolution law of extreme drought and flood events in the source region-upper-middle-lower reaches of the two basins is analyzed. On this basis, the correlation analysis and transfer function analysis are used to explore the response relationship and lag effect between the two in different partitions. A two-dimensional joint distribution model is constructed by Copula function to quantitatively analyze the joint probability of drought and wetness encounters of 9 combined events, and to deeply analyze the impact of different drought and wetness encounter scenarios. The results show that: from 1957 to 2020, the two basins showed a trend from drought to wet. The SPI-12 in the Yellow River Basin ranged from-1.57 to 1.32, and that in the Yangtze River Basin ranged from-1.24 to 1.11. The severity of drought and wetness in the Yellow River Basin was higher than that in the Yangtze River Basin; The correlation coefficients between SPI and SRI in the source-upper-middle-lower reaches of the Yellow River (Yangtze River) were 0.78 (0.76), 0.23 (0.73), 0.44 (0.55) and 0.23 (0.63), respectively. The correlation degree of meteorological-hydrological drought and wetness in the Yangtze River Basin was significantly higher than that in the Yellow River Basin. The average probability of synchronous combination events of drought and wetness in the two basins is greater than 51.43 %. The probability of drought in the Yellow River Basin is greater than that of flood, while the Yangtze River Basin is the opposite. In the same drought and wetness scenario, an average of 54.1% of the event pairs were aggravated by disasters, and more than 52 % of the events in the wetness-drought scenario had obvious drought mitigation effects. The research results can provide important support for improving the early warning and risk management of river basin disasters. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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32. Comparative study of cloud evolution for rainfall nowcasting using AI-based deep learning algorithms.
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Jiang, Xianqi, Chen, Ji, Chen, Xunlai, Wong, Wai-kin, Wang, Mingjie, and Wang, Shuxin
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MACHINE learning , *ARTIFICIAL intelligence , *RAINFALL , *LEAD time (Supply chain management) , *BASIC needs , *DEEP learning , *RAINSTORMS - Abstract
• A deep learning algorithm can provide more skilful nowcasting for medium and strong rainfall cases than for a weak one. • A deep learning algorithm trained by the dataset for one region can be skilfully used to nowcast rainfall for another region with the similar weather system. • For a deep learning algorithm, there is an optimal number of iterations for training the algorithm. It is a critical need to provide timely and valuable alerts of rainstorms and floods to the public. However, it still remains a world-class challenge to achieve serviceable nowcasting rainstorms with even a short lead time of one hour. Different deep learning algorithms have been adopted to improve nowcasting accuracy. Unfortunately, it is still a question which algorithm is more suitable and how to interpret the rainstorm nowcasting results from deep learning. To this end, this paper focuses on modelling the evolution of rainstorm clouds using deep learning algorithms that can be applied to nowcast rainstorms for the next few hours. Adopting three deep learning algorithms, the study provides a detailed analysis of the nowcasting results of three typical cases of different rainfall intensities from a radar echo mosaic image dataset. The dataset was collected in Guangdong, China, and the analysis interprets the performance differences. The analysis further discloses that an AI-based method can provide more skilful nowcasting for medium and strong rainfall cases than for weak ones. Moreover, a deep learning algorithm trained by the dataset for one region can be skilfully used to nowcast rainfall for another region with a similar weather system. This explains the nowcasting capability of deep learning algorithms as well as their robustness. Besides, experiments on the number of iterations reveal that more iterations do not achieve higher nowcasting accuracy. With improved interpretability of deep learning from the perspective of real-world application in the study, it is expected that the algorithms producing higher accuracy and longer lead time nowcasts will be made possible. [ABSTRACT FROM AUTHOR]
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- 2024
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33. Improved understanding of calibration efficiency, difficulty and parameter uniqueness of conceptual rainfall runoff models using fitness landscape metrics.
- Author
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Zhu, S., Maier, H.R., Zecchin, A.C., Thyer, M.A., and Guillaume, J.H.A.
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RUNOFF , *RUNOFF models , *SURFACE roughness , *MODELS (Persons) , *SURFACE analysis - Abstract
• Fitness landscape metrics are used to characterise key properties of the error surfaces of conceptual rainfall runoff models (CRRM) • Enabled large-scale analysis of error surfaces from 420 CRRM combinations with different structures/catchments/error metrics & data length. • Increases in the number of model parameters increases calibration difficulty, decreases calibration efficiency and reduces parameter identifiability. • Increased catchment wetness increases relative roughness of error surfaces and relative optima dispersion. • Opens door to improved CRRM design and selection of better calibration approaches through large-scale analysis of error surfaces. The ease and efficiency with which conceptual rainfall runoff (CRR) models can be calibrated, as well as issues related to the uniqueness of their parameters, has received significant attention in literature. While several studies have tried to gain a better understanding of the underlying factors affecting these issues by examining the features of model error surfaces, this has generally been done in an ad-hoc fashion using lower-dimensional representations of higher-dimensional surfaces. In this paper, it is suggested that exploratory landscape analysis (ELA) metrics can be used to quantify key features of the error surfaces of CRR models, including their roughness and flatness, as well as their degree of optima dispersion throughout the surface. This enables key error surface features of CRR models to be compared in a consistent, efficient and easily communicable fashion for models with different combinations of attributes (e.g. model structure, catchment climate conditions, error metrics, and calibration data set lengths). Results from the application of ELA metrics to the error surfaces of 420 CRR models with different combinations of the above attributes indicate that increasing model complexity results in an increase in relative error surface roughness and relative optima dispersion and that, while increasing catchment wetness increases the relative roughness of error surfaces, it also decreases optima dispersion. This suggests that for the models considered in this study, optimisation efficiency is likely to decrease with increasing model complexity and catchment wetness, while optimisation difficulty is likely to increase and parameter uniqueness likely to decrease with model complexity and catchment dryness. While implications for choice of model complexity will need further work, this study highlights the potential value of the proposed approach to understanding the calibration efficiency, difficulty and parameter uniqueness of conceptual rainfall runoff models. [ABSTRACT FROM AUTHOR]
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- 2024
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34. Effects of multicollinearity and data granularity on regression models of stream temperature.
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Dertli, Halil I., Hayes, Daniel B., and Zorn, Troy G.
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WATER temperature , *INDEPENDENT variables , *REGRESSION analysis , *RESEARCH personnel , *MULTICOLLINEARITY , *PREDICTION models - Abstract
• Model complexity increased the predictive capacity of regression models. • Model complexity also increased the multicollinearity in the models. • The coarser data granularity increased the correlation between model variables. • Higher model complexity and coarser data granularity led to instable parameters. Water temperature is a key factor influencing biota of stream ecosystems. Hence, it is important to comprehend the environmental drivers of stream temperature for robust prediction of conditions and effective management of stream communities. Linear regression models are commonly used for predictive purposes, but their predictive capacity and interpretability can be significantly affected by their complexity and the structure of input data. In some cases, researchers may be obligated to favor prediction power or interpretability while compromising the other. Therefore, insight into relationships between model fit, correlation among predictor variables (i.e., multicollinearity), and level of temporal aggregation of data (i.e., data granularity) may be helpful to reduce such trade-offs. In this paper, we investigated these relationships within a hierarchical set of multiple linear regression (MLR) models examining environmental factors influencing stream temperature dynamics. Our findings showed that as the number of predictor variables (i.e., model complexity) increased, the magnitude of multicollinearity in MLR models increased, but model fit also increased. The results also revealed that using data averaged over longer time frames (i.e., coarser data granularity) yielded high multicollinearity, as indexed by variance inflation factor values (VIF) for all model predictors. This led to higher variance in parameter estimates (i.e., parameter instability) and potential challenges in model interpretation as the sign of parameter estimates changed in many streams examined. Multicollinearity was not the only reason for these changes in the sign of parameter estimates as they were also observed in simple linear regression models across varying levels of data granularity. Based on our findings, we conclude that the selection of data granularity is an important consideration in multiple regression modeling, with profound implications for model interpretability. [ABSTRACT FROM AUTHOR]
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- 2024
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35. Flood risk zoning of cascade reservoir dam break based on a 1D-2D coupled hydrodynamic model: A case study on the Jinsha-Yalong River.
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Liu, Jiahong, Song, Tianxu, Mei, Chao, Wang, Hao, Zhang, Dongqing, and Nazli, Sana
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EMERGENCY management , *RISK perception , *GOVERNMENT agencies , *LANDSLIDES , *WATER analysis , *FLOOD warning systems , *FLOOD risk - Abstract
• A flexible approach to a coupled 1D-2D hydrodynamic model is provided. • The coupling model presented can reasonably simulate significant floods. • Flood intensification weakens the backwater effect and increases flood risk. • The evacuation generation time significantly influences the extent of the D-zone. Large-scale earthquakes and huge landslide surges may cause the failure of cascade reservoirs, resulting in massive downstream flooding. The flood risk zoning of cascade reservoir dam breaks is crucial for local governments to formulate emergency plans and for individuals to garner their awareness of the flood risks. This paper presents a flood risk zoning approach based on a one-dimensional and two-dimensional coupled hydrodynamic model. The flooding area simulated by the coupled hydrodynamic model was divided into a Deadly Zone (D-zone) and an Escape Zone based on whether there is sufficient time for the public to evacuate the danger area under a specific evacuation generation time (EGT). A case study was conducted on the Jinsha-Yalong River (JYR), a confluence river located in Panzhihua City, to map the risk zones. This study considered a dam-break flood in Yalong River encountering two river flood scenarios in Jinsha River (scenario A: the annual average flow; scenario B: a 1000-year return period flood). Two evacuation scenarios (EGT of 30 and 60 min) were also considered; thereafter, four combined evacuation scenarios (A-EGT30, A-EGT60, B-EGT30, and B-EGT60) were simulated. The coupled hydrodynamic model is demonstrated to be effective through the analysis of water balance and hydrographs of special section discharge. The results show that: (1) in comparison to scenario A, scenario B reached its maximum flood extent approximately 5 min earlier, signifying that flood spread speed increases with the increase in flood magnitude; (2) the D-zone area was 127.7 % larger in scenario A-EGT60 compared to A-EGT30 and 120.8 % larger in scenario B-EGT60 compared to B-EGT30. These findings underscore the importance of flood scenarios and EGT on flood risk zones. The proposed risk zoning method can help government agencies in the JYR and major river regions worldwide with flood management and emergency strategy development. [ABSTRACT FROM AUTHOR]
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- 2024
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36. Modelling internal erosion using 2D smoothed particle hydrodynamics (SPH).
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Feng, Ruofeng, Fourtakas, Georgios, Rogers, Benedict D., and Lombardi, Domenico
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SHEAR (Mechanics) , *MULTIPHASE flow , *SOIL mechanics , *POROUS materials , *GROUNDWATER flow - Abstract
• A stabilised SPH model in the u-w-p formulation for internal erosion is proposed. • A modified constitutive model is formulated for the erodible soils. • Extensions of a boundary treatment are proposed for the erosion-transport process. • A novel artificial damping term is established to mitigate numerical instabilities. • Groundwater flow, erosion-transport-deposition, and soil deformation are modelled. This paper presents a stabilised multi-phase smoothed particle hydrodynamics (SPH) model applicable to seepage-induced internal erosion and the resulting deformation in soils. Based on the continuum mixture theory, a new single-layer SPH model in the u - w - p formulation is derived for the mathematical description of the erodible porous material. A new modified constitutive model is formulated to account for the influence of erosion on the mechanical behaviour of soils. Extensions of a first-order consistent boundary condition as well as the diffusion algorithm for hydro-mechanical coupling in SPH are also proposed to accommodate the analysis of erosion-transport process. A novel viscous dissipation term is designed to mitigate the numerical instability commonly encountered in coupled problems. In contrast to existing stabilisation terms in the SPH literature, which operate on both the bulk component and shear component, this new stabilisation term offers the possibility to increase the dissipation of unphysical oscillation due to the shear deformation. The proposed method is validated through a series of benchmark tests. The results demonstrate the effectiveness of the proposed approach in addressing the coupled problem in porous materials involving multi-phase flow, phase interaction/transfer, and large deformation with enhanced accuracy, stability, and robustness. [ABSTRACT FROM AUTHOR]
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- 2024
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37. Visualizing preferential flow paths using dye tracer and species diversity theory methods to explore their correlation to soil properties with random forest algorithm.
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Zhang, Yinghu, Tang, Zhiying, Zhang, Jinchi, Zhang, Zhenming, and Zhang, Mingxiang
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RANDOM forest algorithms , *WETLANDS , *SPECIES diversity , *COASTAL wetlands , *WATER management , *COASTAL zone management , *CLAY soils - Abstract
• A novel method based on species diversity theory would assess preferential flow. • Effects of soil properties on the proportion of dye-stained areas (PDA) were studied. • Soil clay content and sand content primarily control PDA in upper soil layers. • Soil drainage capacity and bulk density primarily control PDA in lower soil layers. • Random forest algorithm could provide accurate and stable predictions for PDA. The preferential flow path development is potentially the result of spatial variations in soil properties with soil depth. However, visualizing the evolution of the preferential flow path with soil depth remains a challenge. This paper presents dye tracer and species diversity theory methods for characterizing preferential flow paths. Field dye tracer experiments were performed at three sites (tree, bush, and grass) in the Yellow River Delta wetland and dye distribution diversity indices (Simpson index (D s), Shannon-Wiener index (H), Margalef index (D m), and Pielou index (E)) were applied to verify their availability for preferential flow assessment. The results showed that the uniformity of the shallow-infiltrated dye at the tree site, non-uniformity of the shallow-infiltrated dye at the bush site, and deep dye infiltration at the grass site were the three typical infiltration types. The average proportion of dye-stained areas (PDA) gradually decreased with increasing soil depth. The quantitative effects of soil properties on PDA changes were profound, indicating that soil clay content at 0–10 cm depth, soil sand content at 10–20 cm depth, soil drainage capacity at 20–30 cm depth, and soil bulk density at 30–40 cm depth were the most predictive factors controlling PDA changes. Our results also showed that dye-stained patches with extremely high and high dye concentrations were the most distributed; D s , H , D m , and E were the highest at the tree site and E was the diversity index with the greatest importance for PDA change. The findings reveal the soil properties controlling the formation of preferential flow paths, which will improve our understanding of water resource management in the vadose zones of coastal wetlands. [ABSTRACT FROM AUTHOR]
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- 2024
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38. Why pile-supported building settled continuously after water level was stabilized during dewatering: Clues from interaction between pile and multi aquifers.
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Xue, Xiu-Li, Sun, Hai-Yu, Zeng, Chao-Feng, Chen, Hong-Bo, Zheng, Gang, Xu, Chang-Jie, and Han, Lei
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- *
WATER levels , *AQUITARDS , *AQUIFERS , *SLUDGE conditioning , *NUMERICAL analysis , *GROUNDWATER - Abstract
[Display omitted] • Pumping test is conducted to study the building settlement during pit dewatering. • Building settlement is observed lagging behind the water level decline. • We reveal the reasons for the lagging settlement based on numerical analysis. Dewatering in a multi-layered aquifer-aquitard system often causes groundwater drawdowns in multiple aquifers, which together lead to obvious settlement of adjacent pile-supported building. This paper documents an in-situ pumping test conducted in a multi-aquifer system. It indicates that an adjacent pile-supported building settled continuously after water level was stabilized. To clarify the possible reasons for this phenomenon, a series of numerical models were established, validated and employed to explore the load transfer behaviour between pile and multi-aquifers during different dewatering conditions. Results indicate that the appearances of several aquitards in the multi-aquifer system significantly block the hydraulic connection between different aquifers, leading to non-synchronized developments of groundwater drawdown and pile shaft resistance at different soil layers during pit dewatering, which eventually causes the lagging settlement of the building. In practical pit dewatering in multi-aquifer system, engineers should appropriately increase the monitoring time for the deformation of adjacent pile-supported buildings for avoiding missing the maximum settlement. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
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39. A complex-network-based estimation of the representative elementary volume and equivalent permeability coefficient for fractured rock masses.
- Author
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Zheng, Jun, Xu, Qian, Zhang, Bohu, Guo, Jichao, Lü, Qing, and Tan, Shengkui
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ROCK deformation , *PERMEABILITY , *GRAPH theory , *HYDRAULIC engineering , *PARAMETER estimation , *SEEPAGE - Abstract
• A procedure for transforming a DFN into a complex network by considering the intersection relationships among fractures is provided; • A novel method based on complex-network-based metrics for estimating the REV of a DFN is proposed; • An empirical formula for estimating the EPC using complex-network-based metrics is established; • The proposed estimation methods are validated through numerical experiments. The determination of the representative elementary volume (REV) and equivalent permeability coefficient (EPC) of fractured rock masses is crucial for studying the hydraulic behaviors of engineering rock masses. The complex network originated from graph theory and represents a rapidly developing interdisciplinary field in the 21st century. In this paper, we propose a novel method that utilizes complex-network-based metrics to estimate the REV and EPC of fractured rock masses. In this method, fractures and their intersections are treated as nodes and edges of a complex network, respectively. We propose a transformation procedure of a discrete fracture network (DFN) into a complex network, and the corresponding algorithms are conducted in MATLAB. Subsequently, we obtain a complex network, and several complex-network-based metrics, such as average degree, average clustering coefficient, and graph density, are calculated using Gephi software. We employ the average degree to estimate the REV size of DFN models, while the coefficient of variation is used to quantitatively assess changes in the average degree of multiple random models. Furthermore, we establish an empirical formula utilizing the average degree, average clustering coefficient, and graph density to estimate the EPC of a large-scale DFN model based on the EPC of a small-scale DFN model. To verify the developed method, we conduct numerical experiments using the MAFIC (Matrix/Fracture Interaction Code) solver within the FracMan software. This enabled us to calculate the EPC, which was then used to determine the REV. The results demonstrate that: (a) the REV size estimated by the average degree aligns with the REV size obtained through numerical simulations for both isotropic and anisotropic DFN models, and (b) the error rates between the average EPC calculated by the empirical formula and that obtained by numerical simulations are within 10%. Thus, the proposed seepage parameter estimation methods are deemed valid. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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40. A hybrid model coupling process-driven and data-driven models for improved real-time flood forecasting.
- Author
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Xu, Chengjing, Zhong, Ping-an, Zhu, Feilin, Xu, Bin, Wang, Yiwen, Yang, Luhua, Wang, Sen, and Xu, Sunyu
- Subjects
- *
FLOOD forecasting , *FLOOD control , *FLOOD risk , *HYDROLOGIC models , *RISK assessment - Abstract
• The Stacking-Hybrid model is proposed by combining process-driven and data-driven models, considering multiple sources of uncertainty. • A series of comparison models are applied to verify the effectiveness of the proposed model in terms of deterministic, interval and probabilistic forecasts. • The Stacking-hybrid model has promising applications in real-time flood forecasting at an hourly scale. Accurate and reliable incoming flood forecasting is an important prerequisite for flood warning, flood risk analysis and reservoir flood control operation. This paper proposes a hybrid model for real-time flood forecasting that couples process-driven hydrological models (HMs) with data-driven models (DDMs). The generic hybrid model framework adds DDMs as the post-processing procedure for residual correction to the original results of HMs, and considers multiple uncertainties in input data, parameter and model structure simultaneously. The performance of the hybrid model is evaluated comprehensively in terms of deterministic forecast accuracy, interval forecast reliability, and the reliability and sharpness of probabilistic forecast. Taking the multireservoir system at the east Pi River as a study case, the results indicate that: (1) Compared to the benchmark model (ensemble XAJ model), the hybrid model with additional residual analysis show a significant improvement. The average continuous ranked probability score (CRPS) metric values calculated by the Stacking-Hybrid model improved by 71.5 %, 67.0 % and 38.1 % in the three data samples. Furthermore, the adaptability of the Stacking-Hybrid model for residual correction during short-duration intense rainfall events has been validated, with the relative error of the peak discharge improved to within ± 10 %. (2) The Stacking-Hybrid model, which also takes into account structure uncertainty, is able to better exploit the combined advantages and improve the stability of the model performance compared to those that only apply a single DDM. (3) When the number of iterations within the BOA reaches 300, the parameter optimization process is capable to search for the hyperparameters that bring out the best performance of the DDMs. (4) When the ensemble size reaches 200, the uncertainty of HM parameters can be fully defined, and the consumed computational resources can be controlled within an acceptable bound while ensuring stable model performance. Overall, the hybrid model that takes into account multiple sources of uncertainty generates both interval and probabilistic forecast in addition to deterministic forecast, which can provide richer risk information for subsequent flood warning and reservoir operation, making the flood prevention decisions more reliable and scientific. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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41. Steady-state flow under surface recharge through unconfined aquifer with depth-decaying hydraulic conductivity: Analytical results.
- Author
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Hayek, Mohamed
- Subjects
- *
STEADY-state flow , *AQUIFERS , *BOUSSINESQ equations , *HYDRAULIC conductivity , *MATHEMATICAL functions , *INVERSE problems - Abstract
• Analytical results to flow in unconfined aquifer induced by a decaying conductivity. • Exact solutions for drainage into penetrating channels at the aquifer boundaries. • Analytical transit time for calculating particle travel from water table to boundary. • Analytical inverse model to estimate decay parameter by solving a nonlinear algebraic equation. This paper presents some analytical results related to groundwater flow through an unconfined horizontal aquifer induced by areal recharge on the top and a decaying hydraulic conductivity. Under steady-state conditions, and in the context of Boussinesq equation, exact analytical solutions describing drainage into one or two penetrating channels at the aquifer boundaries have been derived in closed forms. The analytical expressions have been used to investigate the existence of water divide inside the unconfined aquifer. Based on the derived analytical solutions, a closed-form analytical formula is developed for calculating groundwater transit time within Dupuit-type flow system. The analytical transit time expression is simply expressed in terms of common mathematical functions. Analytical results are verified against a numerical model and show excellent agreements. The analytical transit time is validated against a numerical one obtained from a two-dimensional model representing a rectangular unconfined aquifer. Based on the explicit closed-form solution, a fully analytical inverse problem to estimate the decay parameter is proposed using a least-square objective function. The solution of the inverse problem simply corresponds to the solution of a nonlinear algebraic equation obtained from the definition of the objective function and the form of the analytical solution. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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42. Drought in play: A grounded socio-hydrological tool to increase social participation in drought plans.
- Author
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Cid, Daniel Antonio Camelo, Souza Filho, Francisco de Assis de, Alves, Rafaela da Silva, Pontes Filho, João Dehon de Araújo, Silva, Daniele Costa da, and Martins, Eduardo Sávio Passos Rodrigues
- Subjects
- *
DROUGHT management , *WATER management , *SOCIAL participation , *DROUGHTS , *WATER use , *SOCIAL learning , *WATERSHED management - Abstract
• Drought in Play facilitates comprehension of drought planning concepts and principles. • Fun, engaging, and accessible approach to promote equitable water resources management. • Playful approach to grasp interplay between hydrological and social dynamics. • Simulate impacts of planned and unplanned actions in drought crises. • Facilitate consideration of multiple uses when developing drought plans. Drought management requires a proactive approach and active involvement of stakeholders to enhance effectiveness and equity. However, there are challenges such as power imbalances and knowledge disparities. This paper proposes an innovative methodological tool to facilitate the understanding of key concepts, such as drought states and reservoir zoning, alerts for changes in drought state, risk predisposition, breaking down hierarchical structures of teaching and learning, contributing to more horizontal and participatory processes. We present "Drought in Play" from its creation and design to its application in two water systems in the state of Ceará, located in the Northeast region of Brazil. The game encourages active participation in the creation of a proactive drought plan for water systems and stimulates critical thought and proactive actions regarding drought and water resources management. The game was designed for a multidisciplinary team and tested with researchers that know the drought plans methodology and the challenges to build an effective drought plan. "Drought in Play" includes game elements (cards, boardgame) and simulations. We explore the participants' perceptions when using this tool to facilitate stakeholder participation in building a proactive plan to deal with drought and to enable water users to collectively manage a reservoir that is a common resource to all of them. The results suggest that "Drought in Play" can be used as a tool for socio-hydrological modeling, because it combines hydrological elements with social aspects such as decision-making, cooperation, and conflicts among participants. The game has potential to facilitate sustainable water resources management, enhance social learning, and provide a knowledge framework to address complex environmental challenges. It can be easily adapted to other regions with similar situations without requiring further modifications. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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43. A simple model of the soil freezing characteristic curve for saline soils with two freezing stages.
- Author
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Cui, Lihong, Chen, Junfeng, Xiao, Zean, Yuan, Qinbo, Zhao, Xuehua, and Xue, Jing
- Subjects
- *
SOIL salinity , *SOIL freezing , *FROZEN ground , *PHASE transitions , *SOIL temperature - Abstract
[Display omitted] • Complete phase diagram of pore solution at icing stage was presented. • Freezing point of saline soils was estimated with equivalent solute concentration. • Calculation method for eutectic temperature of saline soils was proposed. • Criteria for judging saline soils with one or two freezing stages was proposed. • SFCC shape index varying with soil matrix and initial solute concentration was found. Soil freezing characteristic curve (SFCC) describes the relationship between unfrozen water and subzero temperature, which is of significance for the simulation of heat, water, salt migration in cold regions. There are two phase transition stages in some saline soils, which is often explained by the phase equilibrium of bulk solution. However, the second phase transition and chemical characteristic of solute are ignored in the current coupling numerical models for freezing-thawing soils. The newest SFCC methods considering abovementioned two items are not applicable to numerical models due to complex calculation or irregularly changeable parameters. To solve those problems, a simple model was proposed in this paper. Firstly, the complete phase diagram of pore solution at icing stage was speculated from published experimental SFCCs of saline soils. Then, the temperatures of saline soils at freezing and eutectic points were quantitative expressed with phase diagram of bulk solution and SFCC of nonsaline soil, and the criteria determining whether single icing stage or icing-eutectic stage occurs in saline soils was proposed. A synthetic index was used to describe the effects of soil matrix and initial solute concentration on the exponent. Only three constant parameters were introduced to reflect the soil matrix effect on phase transition points of pore solution and the influence of chemical characteristic of solute on pore water freezing process. Finally, the model was validated by the experimental data of saline soils and showed good results and ease of use compared to a widely used theorical model for saline soil with single freezing stage and other two models both considering two phase transitions. The work provides a deeper view of freezing process in saline soils and promote the improvement of numerical simulation effect for heat, water and salt migration in seasonal freezing-thawing areas. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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44. Spatial downscaling of SMAP soil moisture estimation using multiscale geographically weighted regression during SMAPVEX16.
- Author
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Zhong, Yanmei, Hong, Song, Wei, Zushuai, Walker, Jeffrey P., Wang, Yanwen, and Huang, Chaoqing
- Subjects
- *
DOWNSCALING (Climatology) , *MICROWAVE remote sensing , *SOIL moisture , *NORMALIZED difference vegetation index , *LAND surface temperature , *ARID regions - Abstract
• A MGWR-based SMAP soil moisture product downscaling method is proposed. • The method considers the scale differences in the impact of surface variables on soil moisture. • Field validation shows higher accuracy from the downscaled SM than the SMAP SM itself. • This proposed downscaling method performs better than the traditional GWR and MW methods in the semiarid area. Passive microwave remote sensing missions such as Soil Moisture Active Passive (SMAP) are widely used for global soil moisture (SM) estimation. But the low spatial resolution of passive microwave SM products limits their applications at regional and local scales (normally 1–10 km). Using high-resolution auxiliary data to downscale passive microwave SM products is the primary way to obtain high-resolution SM data. However, the existing SM downscaling methods do not entirely account for the scale differences in the impact of various variables on the distribution of SM. To solve this problem, this paper introduced MGWR (Multiscale Geographically Weighted Regression) as a novel way to analyze the scale differences of normalized difference vegetation index (NDVI) and land surface temperature (LST) on the spatial pattern of SM. Accordingly, a new spatial downscaling algorithm for SMAP SM products is proposed and compared with the geographically weighted regression (GWR) algorithm and moving window algorithm (MW). The algorithm was applied to data taken during the SMAP Validation Experiment 2016 (SMAPVEX16), and the downscaled SM evaluated with in situ SM and aircraft observations. These results show that: 1) the SM downscaling conversion function, based on the MGWR, effectively reveals how different surface parameters relate to SM, with LST influencing SM globally and NDVI affecting it locally, 2) compared with GWR and MW, the 1 km SM obtained by the MGWR-based downscaling method showed better spatial agreement with airborne SM in the semiarid region with the ubRMSE decreasing by 0.023 m3/m3 and 0.026 m3/m3 for GWR and MW respectively, 3) when evaluating the 1 km SM with in situ SM in the semiarid region, the SM obtained through MGWR exhibited lower ubRMSE values as compared to both GWR and MW. Specifically, the median ubRMSE values for MGWR, GWR, and MW were 0.043 m3/m3, 0.053 m3/m3, and 0.094 m3/m3, respectively. In conclusion, this study demonstrates that MGWR can effectively improve the accuracy of downscaled SM in semiarid regions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. Nonstationary frequency analysis of extreme precipitation: Embracing trends in observations.
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Anzolin, Gabriel, de Oliveira, Debora Y., Vrugt, Jasper A., AghaKouchak, Amir, and Chaffe, Pedro L.B.
- Subjects
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MARKOV chain Monte Carlo , *EXTREME value theory , *MONTE Carlo method - Abstract
• Out-of-sample prediction of extreme precipitation using nonstationary models. • Significant trends in data of extreme precipitation are insufficient to warrant the selection of a nonstationary model. • Large prediction uncertainty reduces the support for nonstationary models of extreme precipitation. • (Non)stationary model selection should explicitly account for model complexity. Knowledge of the recurrence intervals of precipitation extremes is vital for infrastructure design, risk assessment, and insurance planning. However, trends and shifts in rainfall patterns globally pose challenges to the application of extreme value analysis (EVA) which relies critically on the assumption of stationarity. In this paper, we explore: (1) the suitability of nonstationary (NS) models in the presence of statistically significant trends, and (2) their potential in modeling out-of-sample data to improve frequency analysis of extreme precipitation. We analyze the benefits of using a nonstationary Generalized Extreme Value (GEV) model for annual extreme precipitation records from Southern Brazil. The location of the GEV distribution is allowed to change with time. The unknown GEV model parameters are estimated using Bayesian techniques coupled with Markov chain Monte Carlo simulation. Next, we use GAME sampling to compute the evidence (and their ratios, the so-called Bayes factors) for stationary and nonstationary models of annual maximum precipitation. Our results show that the presence of a statistically significant trend in annual maximum precipitation alone does not justify the use of a NS model. The location parameter of the GEV distribution must also be well defined, otherwise, stationary models of annual maximum precipitation receive more support by the data. These findings reiterate the importance of accounting for GEV model parameters and predictive uncertainty in frequency analysis and hypothesis testing of annual maximum precipitation data records. Furthermore, a meaningful EVA demands detailed knowledge about the origin and persistence of observed changes. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
46. Physics-informed neural network for diffusive wave model.
- Author
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Hou, Qingzhi, Li, Yixin, Singh, Vijay P., and Sun, Zewei
- Subjects
- *
SHALLOW-water equations , *INVERSE problems , *WATER levels , *SCATTERING (Mathematics) , *PARAMETER identification , *INVERSE scattering transform , *WATER depth - Abstract
[Display omitted] • PINN correctly solves different types of forward diffusive wave problem. • The technique of time division improves the PINN performance. • A new neural network structure enhances the representation capability of PINN. • The rainfall pattern is correctly learned in the inverse diffusive wave problem. The diffusive wave model (DWM), a nonlinear second-order simplified form of the shallow water equation, has been widely used in hydraulic, hydrologic and irrigation engineering. Solution of the forward problem of the DWM can be utilized to predict evolution in water levels and discharge. Solution of its inverse problem allows for the identification of crucial parameters (such as Manning coefficient, rainfall intensity, etc.) based on observations. This paper applied the physics-informed neural network (PINN) with novel improvements to solve the DWM for both forward and inverse problems. In the forward problem, compared to traditional numerical methods, PINN was able to predict the evolution at any location. In the inverse problem, PINN provided a simple and efficient solution process. In order to overcome the gradient explosion in the training process caused by the characteristics of the DWM, the stop-gradient technique was adopted to train the neural network. To improve the estimation of DWM parameters, the concept of time division was developed, and a new network structure was proposed. To verify the effectiveness of PINN and its improved algorithm for DWM, seven examples were simulated. The PINN solutions for forward problems were compared with the results obtained by classical numerical methods, while the correct rainfall pattern was identified for the inverse problem. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
47. Enhancing flood verification using Signal Detection Theory (SDT) and IoT Sensors: A spatial scale evaluation.
- Author
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Chang, Che-Hao, Rahmad, Riki, Wu, Shiang-Jen, Hsu, Chih-Tsung, and Chung, Po-Hsien
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SIGNAL detection , *FLOOD warning systems , *FLOODS , *INTERNET of things , *ADMINISTRATIVE & political divisions , *STORMS , *DETECTORS - Abstract
• IoT sensors and Signal Detection Theory improve flood modeling accuracy. • IoT flood sensors uniquely identify zero flooding situations. • Optimal spatial scale varies across regions for accurate flood classification. • 450 m is the optimal spatial scale for reliably validating the flood simulation. • Ensemble-based flood verification enhances validation robustness. Inundation mapping by flood simulation is subject to uncertainties which spatial accuracy assessment could not be processed as normal mapping does. This paper presents a comprehensive study on the spatial accuracy of flood verification using Internet of Things (IoT) sensors in several flood-prone regions in Taiwan, including Changhua, Yunlin, Chiayi, Tainan, Kaohsiung, and Pingtung, during three storm events in 2021. The study adopts Signal Detection Theory (SDT) and proposes an ensemble-based approach that combines multiple evaluation metrics to enhance the accuracy and reliability of flood verification in these regions. The study examines the inundation availabilities between flood observation from IoT sensors and flood simulation. A range of spatial scales varies from 100 m to 1000 m and also administrative divisions are considered including neighborhood-level and village-level scales. The results show that increasing the spatial scale generally leads to higher True Positives, indicating improved flood detection, but it also results in increased False Positives, indicating more misclassification of non-flooded points. The findings reveal that the optimal spatial scale for all cities/counties is 450 m which indicates the averaged spatial accuracy of the flood simulation. However, there is notable variation in the optimal spatial scale across different regions. The ensemble approach demonstrates high consistency in determining the optimal spatial scale, as eight out of ten metrics consistently identify the same value. Additionally, the study compares flood verification across administrative divisions in Taiwan, finding that the village-level spatial scale performs best in Changhua and Yunlin counties, while the neighborhood-level scale is preferred in Tainan, Kaohsiung, and Pingtung. This research provides a valuable framework to estimate inundation mapping accuracy under flood verification using IoT sensors in the specified region of Taiwan and highlights the importance of considering local conditions and administrative divisions in determining the optimal spatial scale. These findings can contribute to the improvement of flood detection and monitoring systems in the area with IoT flood sensors. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
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48. Effects of the freeze–thaw process on sources and pathways of subsurface flow in an alpine hillslope on the northeastern Qinghai-Tibet Plateau.
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Zhang, Yangyang, Li, Xiao-yan, Hu, Guangrong, Deng, Yuanhong, Zhang, Xia, Shi, Fangzhong, and Zuo, Fenglin
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- *
FREEZE-thaw cycles , *GLOBAL warming , *SOIL temperature , *ALPINE regions , *SOIL moisture , *PLATEAUS - Abstract
• Effective method is proposed for exploring the fundamental mechanism of SSF. • Soil temperature is proved to be the most fundamental factor for SSF pathways. • The patterns of vertical and lateral source for SSF are analyzed and figured out. The impact of the freeze–thaw process on the active layer is reflected in the changed subsurface flow (SSF) process in cold alpine regions. Identifying sources and pathways of SSF in the freeze–thaw process is critical but difficult, and the related dominant factors and mechanisms are still unknown. In this paper, the effective identification and analysis of SSF are promoted based on field sampling data from the thawing (June) to freezing (September) period of 2022 in the Qinghai Lake basin on the northeastern Qinghai-Tibetan Plateau. By the proposed method with a high sampling frequency and refined sampling spatial scale, the sources and pathways of SSF are clearly identified. The results are as follows: (1) The soil temperature is considered the most fundamental factor affecting the SSF pathways, it influences water infiltration to the deep layer and the effect is extended to the saprolite and weathered bedrock layers. (2) Thawing promotes water to infiltrating into deep layer. 30 cm soil water contributes the most to SSF (2 %–86 %) in the thawing period, while the contribution difference of the water from the 30 cm, 60 cm, and 90 cm layers is small (ranging from 32 %–33 %, 24 %–26 %, and 32 %–35 %, respectively) in the thawed period. (3) Meanwhile, the soil water from different slope positions contribute differently to SSF, and the SSF from deep soil layer is transit in prolong paths and depths. It is caused by the out-of-sync water transit process in the hillslope. With continuing climate warming, we propose that the differences in the water sources of SSF across soil layers may decrease, while the differences in the transit processes of SSF across soil layers may increase. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
49. Projections of future streamflow for Australia informed by CMIP6 and previous generations of global climate models.
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Zheng, Hongxing, Chiew, Francis H.S., Post, David A., Robertson, David E., Charles, Stephen P., Grose, Michael R., and Potter, Nicholas J.
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CLIMATE change models , *STREAMFLOW , *RAINFALL , *COASTAL changes , *RUNOFF , *SPRING - Abstract
• Nationwide streamflow projections for Australia informed by CMIP5 and CMIP6. • Vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. • Lower winter rainfall translates to a significant reduction in annual runoff. • Modelled median projection of runoff shows a 50% reduction in far southwest Australia. • Modelled median projection of runoff shows a 20% reduction in far southeast Australia. Projections of streamflow under future climate are essential for developing adaptation strategies in the water and related sectors. This paper presents nationwide streamflow projections for Australia informed by climate change signals in CMIP6 GCMs and compares the projections with those from CMIP5 GCMs. The modelled future runoff projections driven by CMIP5 and CMIP6 GCMs are relatively similar for far southern Australia. The median projection is a 50% reduction in mean annual runoff in far southwest Australia and 20% reduction in far southeast Australia by 2046–2075 relative to 1976–2015 under the high SSP5-8.5/RCP8.5 global warming scenario. The vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. As most of the runoff in far southern Australia occurs in winter and spring, the lower winter rainfall translates to a significant reduction in annual runoff. It is therefore prudent to plan and manage for a reduction in future water resources, particularly in the densely populated and important agricultural regions in southeast Australia. There is less agreement between GCMs in the summer rainfall projection, with the CMIP6 GCMs generally projecting a wetter future (or smaller decrease in rainfall) than the CMIP5 GCMs. The modelled median projection for mean annual runoff is a 10% reduction in the south-east coast, 5% reduction in the north-east coast and little change in northern Australia. More GCMs project an increase rather than a decrease in the interannual variability of rainfall, and this would further amplify multi-year hydrological droughts. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. Physics-informed neural networks for solving flow problems modeled by the 2D Shallow Water Equations without labeled data.
- Author
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Qi, Xin, de Almeida, Gustavo A.M., and Maldonado, Sergio
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SHALLOW-water equations , *CONVOLUTIONAL neural networks , *OPEN-channel flow , *PROBLEM solving , *WATER depth , *FREE surfaces - Abstract
This paper investigates the application of physics-informed neural networks (PINNs) to solve free-surface flow problems governed by the 2D shallow water equations (SWEs). Two types of PINNs are developed and analyzed: a physics-informed fully connected neural network (PIFCN) and a physics-informed convolutional neural network (PICN). The PINNs eliminate the need for labeled data for training by employing the SWEs, initial and boundary conditions as components of the loss function to be minimized. Results from a set of idealized and real-world tests showed that the prediction accuracy and computation time (i.e., training time) of both PINNs may be less affected by the resolution of the domain discretization when compared against solutions by a Finite Volume (FV) model. Overall, the PICN shows a better trade-off between computational speed and accuracy than the PIFCN. Also, our results for the idealized problems indicated that PINNs can provide more than 5 times higher prediction accuracy than the FV model, while the FV simulation with coarse resolution (e.g., 10 m) can provide sub-centimeter accurate (RMSE) solutions at least one order of magnitude faster than the PINNs. Results from a river flood simulation showed that PINNs delivered better speed-accuracy trade-off than the FV model in terms of predicting the water depth, while FV models outperformed the PINNs for predictions of total flow discharge. [Display omitted] • Physics-informed neural networks are developed to solve the Shallow Water Equations. • Proposed networks can be trained without any labeled data. • These networks offer alternatives to established numerical solvers. • Convolutional neural network outperformed fully connected neural network. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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