Search

Abstract: Fe oxide-impregnated paper (Pi-paper) is used as an artificial Phosphorus (P) sink to study P availability in soils and runoff. Pi-papers were introduced as they would mimic P uptake by plant roots by decreasing the P concentration in solution to negligibly low levels and thus enhancing P desorption from the soils solid phase. The rate of transfer of P from soil to Pi-paper would thus be limited by the soil P desorption rate. The maximum desorption rate is indeed achieved when the original method is used in which (at least) four Pi-papers per gram soil are placed in a soil suspension (soil–solution ratio of 0.025 kg L−1). In several studies this method has however been adapted depending on the research question of interest without investigating the effect of this adaptation to the processes involved in the transfer of P from soil to Pi-paper. The aim of this study is to improve our understanding of the processes that occur in the Pi-paper–solution–soil system and so to extend the theoretical basis of this method. Insight is gained in these processes by comparing the experimentally determined P transfer from soil to Pi-paper with P transfer that is modeled based on the measured P concentration in solution and the (kinetic) Langmuir equation of the Pi-paper. P adsorption by a Pi-paper from standard solutions is not instantaneous but can be described with a kinetic Langmuir equation that is a characteristic of the Pi-paper. Over time, the Pi-paper reaches equilibrium with the solution, and the kinetic Langmuir equation can be rewritten to a Langmuir equation. Regardless if there is equilibrium or not, P adsorption to the Pi-paper is a function of the P concentration in solution. By adding Pi-paper to a soil suspension, a re-distribution of P takes place between the reactive surface area of the soil and the new reactive surface area of the Pi-paper that initially contains no adsorbed phosphate. As opposed to the original method where the desorption rate was limiting the overall P transfer, the adsorption rate to the Pi-paper is limiting when one Pi-paper per gram soil is placed in a soil suspension (soil–solution ratio of 0.1 kg L−1). In this situation, the P concentration in solution is found to be in equilibrium with the soil''s solid phase. With increasing contact time (>~24 h) the whole system approaches equilibrium. With each successive Pi-paper newly added, more P is removed from the soil system and the decrease of P in solution will be governed by the soil P desorption isotherm. Varying the number of Pi-papers and the soil to solution ratio thus has a large effect on the transfer rate between soil and Pi-paper and if either the soil desorption rate, the Pi-paper adsorption rate or a combination limits this transfer rate. With increased insight in the P transfer between soil and Pi-paper sink, it becomes possible to tailor the experimental design to help answer the research question one is interested in. [Copyright &y& Elsevier]

In the realm of ecological management and human activities within river basins, short-term runoff forecasting plays a pivotal role. Addressing this need, this paper introduces an innovative framework for short-term runoff forecasting: the Ensemble Attention Temporal Convolutional Network (EA-TCN). The cornerstone of this innovation lies in the effective amalgamation of Temporal Convolutional Network (TCN), lightweight attention mechanism, and ensemble learning strategy. This integration synergistically enhances the model's overall performance in terms of accuracy, efficiency, and robustness. TCN forms the foundation of this framework, where its efficient architecture, characterized by shared parameters and parallel computation, significantly boosts computational efficiency. Its employment of causal and dilated convolutions adeptly captures long-term dependencies within time series inputs. The incorporated lightweight attention mechanism further augments the TCN, enabling EA-TCN to precisely discern complex relationship in temporal data, particularly exhibiting remarkable temporal robustness across various forecasting horizons—a feat challenging for conventional forecasting approaches. Additionally, the integration of the Snapshot ensemble method within the framework allows for simulating the effect of training multiple models through one single training process, thus further elevating the model's accuracy and robustness. Rigorous ablation and comparative experiments conducted on the US Columbia River dataset substantiate our claims. The results not only validate the individual merits of each component within EA-TCN but also illuminate the significant advantages of their collective application. Our comprehensive assessment unequivocally demonstrates the framework's exceptional performance in short-term runoff forecasting, positioning it as a state-of-the-art solution in this field. We will further discuss its impact of vocation education in this industry. • This paper applied the modified TCN to short-term runoff forecasting. • EA-TCN adopts a lightweight plug-and-play attention module in the time dimension. • The Snapshot ensemble method is also applied to our proposed model. • EA-TCN can make accurate predictions for multiple different lead times. [ABSTRACT FROM AUTHOR]

• 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]

• 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]

• 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]

• A modified NRCS-CN model is proposed that eliminates quantum jumps in runoff computation arising due to changing AMC. • The CN-P relationship is significantly improved to characterize the hydrologic response of a watershed. • The empirical antecedent moisture approximates the in-situ soil moisture on the experimental plots of Roorkee, India. • The proposed model is also tested for its application to four natural sub-catchments of Godavari basin. The popular Natural Resources Conservation Service Curve Number (NRCS-CN) (earlier known as Soil Conservation Service Curve Number (SCS-CN) method of rainfall-runoff modeling has often faced the criticism of exhibiting quantum jumps in runoff computations because of the sudden jumps appearing in CN-values derived from NEH-4 tables for three antecedent moisture conditions (AMC), viz., AMC-I, AMC-II, and AMC-III valid for dry, normal, and wet conditions, respectively. The variability of antecedent soil moisture within an AMC category is responsible for the abrupt jump and other deficiencies in the CN method for runoff estimation. This paper suggests a novel procedure to account for the antecedent moisture (M), preventing quantum jumps and eliminating deficiencies in determination of CN and, in turn, estimation of direct runoff. Its validity was verified utilizing the observed rainfall (P)-runoff (Q) events from 36 US watersheds, four sub-catchments of the Godavari basin, and small agricultural plots at Roorkee, India. The performance of the proposed model (M5) for runoff prediction was compared with the existing NRCS-CN (M1), Mishra and Singh (2002) (M2), Singh et al. (2015) (M3), and Verma et al. (2021) (M4) model using various performance indices. Using the CNs derived from observed events, model M5 was seen to have performed better than M1-M4 in terms of Nash Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Percent Bias (PBIAS) for the data of US watersheds, and CN-P correlation improved as the coefficient of determination (R2) enhanced. Similarly, using the RS & GIS-based CNs on natural watersheds of the Godavari basin and considering AMC-I, the performance of M5 was again better than M1-M4 in terms of RMSE, Mean Bias Error (mBIAS), Mean Absolute Error (MAE), and Normalized-Nash Sutcliffe Efficiency (NNSE). Interestingly, there existed a significant (p < 0.05) relationship between the in-situ water content (w) measured for the experimental plots of Roorkee and the model input variable antecedent moisture (M), offering a physical touch to the conceptual model. [ABSTRACT FROM AUTHOR]

The campus is facing the problems of waterlogging and runoff pollution. To mitigate these phenomena, this study constructs a SWMM model using a campus as the study area. The reduction effects of runoff volume and pollutant load under four LID facility combination scenarios were simulated. Eleven evaluation indicators were selected from environmental, economic and social aspects, and the weights of each indicator were calculated by applying the CRITIC method. The relative percentage of different rainfall recurrence periods under the same evaluation indicator was obtained. The composite index method is used to calculate the composite index for each scenario. The combination of rain garden, permeable pavement, bioretention and green roof is determined as the optimal scenario. The Fuzzy pattern recognition method is used to select three representative rainfall events and simulate the reduction effect of runoff volume and pollutant load under four scenarios to further verify the optimal scenario. The results show that the reduction rates of runoff volume and pollutant load under scenario 4 are 2.7%, 4.2% and 4.7% higher than those under scenarios 1, 2 and 3. Based on the above study, the optimal LID facility combination selected in this paper will provide a reference for the construction of sponge facilities similar to this study area. [ABSTRACT FROM AUTHOR]

Highlights • Innovative trend analysis (ITA) and Mann-Kendall (MK) determine average trend. • Double-ITA, Triple-ITA, and partial MK are proposed for stability test of trends. • ITA should be used together D-ITA or T-ITA for better trend analysis. • MK should be used together partial MK for better trend analysis. • Comparisons of ITA, D-ITA, T-ITA, classical MK, and partial MK are presented. Abstract The climate change is an important event that affects hydrological, agricultural and water resources planning variables, and therefore, the hydrologists and meteorologists frequently try to identify trend possibilities especially in rainfall, runoff and temperature time series. For this purpose, the classical Mann-Kendall (MK), Spearman's rho (SR), Sen's slope, and linear regression approaches are applied frequently in the literature. Recently, innovative trend analysis (ITA) provides visual inspection and identification of categorical trends, which is one of the main concerns in this paper. On the basis of ITA methodology, several improvements namely double-ITA (D-ITA) and triple-ITA (T-ITA) procedures are suggested using with simple ITA together. These methods are attractive for the trend stability assessment by comparing partial trend components during different sub-periods of a given record series. Furthermore, partial MK test approach is proposed in this paper for the same purpose. These procedures and approach are applied to a set of annual rainfall records at many stations in different regions of Turkey. As a result, the comparison of the suggested methods based on partial sub-series of the same time series helps to improve trend detection with stability identification. [ABSTRACT FROM AUTHOR]

• BIPGEM can decompose more DON than ZIPGEM in the upflow cascade reactor. • Denitrifier population in BIPGEM was not thrived as much as that in ZIPGEM. • DON decomposition can be realized by Krevelen diagram and nitrogen heteroatom classes. Nutrient pollution has become an important issue to solve in stormwater runoff due to the fast population growth and urbanization that impacts water quality and triggers harmful algal blooms. There is an acute need to link the dissolved organic nitrogen (DON) decomposition with the coupled nitrification and denitrification pathways to realize the pattern shifts in the nitrogen cycle. This paper presented a lab-scale cascade upflow biofiltration system for comparison of nitrate and phosphate removal from stormwater matrices through two specialty adsorbents at three influent conditions. The two specialty adsorbents are denoted as biochar iron and perlite integrated green environmental media (BIPGEM) and zero-valent iron and perlite-based green environmental media (ZIPGEM). An initial condition with stormwater runoff, a second condition with spiked nitrate, and a third condition with spiked nitrate and phosphate were used in this study. To differentiate nitrifier and denitrifier population dynamics associated with the decomposition of DON, integrative analysis of quantitative polymerase chain reaction (qPCR) and 21 tesla Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) were performed in association with nitrate removal efficiencies for both media with or without the presence of phosphate. While the qPCR may detect one gene for a single microbe or pathogen and realize the microbial population dynamics in the bioreactors, the 21 T FT-ICR MS can separate and assign elemental compositions to identify organic compounds of DON. Results indicated that ZIPGEM obtained a higher potential for nutrient removal than BIPGEM when the influent was spiked with nitrate and phosphate simultaneously. The sustainable, scalable, and adaptable upflow bioreactors operated in sequence (in a cascade mode) can be expanded flexibly on an as-needed basis to meet the local water quality standards showing process reliability, resilience, and sustainability simultaneously. [Display omitted] [ABSTRACT FROM AUTHOR]

The paper examines options for river flood risk reduction in the Upper Vistula Basin located partly in the Carpathian Mountains in Poland. Projections of high-flow indices for the periods 2021–2050 and 2071–2100 generally indicate small future increases, although the projected flow changes vary highly both across the study basin as well as among climate models. An overview of twentieth-century catchment and channel changes indicates that some of them decreased and others increased the rapidity of runoff but they largely reduced availability of sediment for fluvial transport, hence inducing bed incision and bank erosion that create risk to roads and bridges. Traditional methods of flood protection in the basin encompassed large structural defences such as river channelization and flood embankments. These have limited floodwater retention within floodplains and accelerated flood runoff, shifting flood hazard downstream rather than reducing it. A range of alternative approaches to reducing future flood risk are thus proposed and examples of their application in southern Poland are described. These approaches include river restoration measures aimed to reduce erosional potential of flood flows and increase channel and floodplain retention of floodwater, as well as grassroots initiatives promoting preparedness for flooding at the community level. There is an increasing need to change the existing paradigm that flood-control measures should be based on fast evacuation of floodwater that, in turn, was associated with a significant reduction in floodwater retention on the valley floors. Alternative approaches discussed in this paper extend the roster of flood risk reduction strategies and contribute to a gradual paradigm change. [ABSTRACT FROM AUTHOR]

The development of rainfall/runoff models involves extensive computation and the availability of different coexisting platforms, including numerical flow models and GIS for their physiographical characterization. In this paper we present a data-driven approach which avoids the use of GIS, but is based on a combination of Principal Component Analysis (PCA) and an Adaptive Neuro Fuzzy Inference System (ANFIS) to produce a simple and effective output flow prediction based on previous rainfall/runoff data in the catchment. The emphasis of the paper is on how to set-up an efficient data structure that produces a good output flow estimation. The PCA approach is compared to the Thiessen polygons method, requiring GIS, and we demonstrate that the former can produce a better ANFIS model, with less algorithmic complexity and improved accuracy. The combined PCA + ANFIS procedure is applied to two minor river basins in Tuscany, Italy, to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Selection of suitable urban stormwater harvesting sites and associated project planning are often complex due to spatial, temporal, economic, environmental and social factors, and related various other variables. This paper is aimed at developing a comprehensive methodology framework for evaluating of stormwater harvesting sites in urban areas using Multi Criteria Decision Analysis (MCDA). At the first phase, framework selects potential stormwater harvesting (SWH) sites using spatial characteristics in a GIS environment. In second phase, MCDA methodology is used for evaluating and ranking of SWH sites in multi-objective and multi-stakeholder environment. The paper briefly describes first phase of framework and focuses chiefly on the second phase of framework. The application of the methodology is also demonstrated over a case study comprising of the local government area, City of Melbourne (CoM), Australia for the benefit of wider water professionals engaged in this area. Nine performance measures (PMs) were identified to characterise the objectives and system performance related to the eight alternative SWH sites for the demonstration of the application of developed methodology. To reflect the stakeholder interests in the current study, four stakeholder participant groups were identified, namely, water authorities (WA), academics (AC), consultants (CS), and councils (CL). The decision analysis methodology broadly consisted of deriving PROMETHEE II rankings of eight alternative SWH sites in the CoM case study, under two distinct group decision making scenarios. The major innovation of this work is the development and application of comprehensive methodology framework that assists in the selection of potential sites for SWH, and facilitates the ranking in multi-objective and multi-stakeholder environment. It is expected that the proposed methodology will assist the water professionals and managers with better knowledge that will reduce the subjectivity in the selection and evaluation of SWH sites. [ABSTRACT FROM AUTHOR]

A modified Green-and-Ampt model is formulated to quantify infiltration on sloping terrain underlain by homogeneous soil wetted by surficial water application. This paper’s theory for quantifying infiltration relies on the mathematical statement of the coupled partial differential equations (pdes) governing infiltration and runoff. These pdes are solved by employing an explicit finite-difference numerical method that yields the infiltration, the infiltration rate, the depth to the wetting front, the rate of runoff, and the depth of runoff everywhere on the slope during external wetting. Data inputs consist of a water application rate or the rainfall hyetograph of a storm of arbitrary duration, soil hydraulic characteristics and antecedent moisture, and the slope’s hydraulic and geometric characteristics. The presented theory predicts the effect an advancing wetting front has on slope stability with respect to translational sliding. This paper’s theory also develops the 1D pde governing suspended sediment transport and slope degradation caused by runoff influenced by infiltration. Three examples illustrate the application of the developed theory to calculate infiltration and runoff on a slope and their role on the stability of cohesive and cohesionless soils forming sloping terrain. [ABSTRACT FROM AUTHOR]

In recent years, urban pluvial flooding caused by extreme rainfall has increasingly occurred across China. This paper reviews the challenges faced by China in addressing urban pluvial flooding and managing urban stormwater, with a particular focus on a policy initiative termed sponge cities . The paper first synthetically presents pluvial flood disasters in urbanized areas, and analyses their causes and formation mechanisms. It then introduces China’s sponge cities initiative and discusses policy implementation in relation to contemporary understanding of sustainable urban stormwater management and international experience with innovative practices. The initiative, while theoretically well grounded and appropriate by its design principles, is shown subject to diverse implementation challenges, ranging from technological complexity to limited or lack of governance capacity as reflected in management ideology, knowledge and capacity of learning, participatory and integrated governance, investment financing, implementation pathway, planning and organization, and project evaluation. The paper offers some strategies for addressing those challenges, which include: 1) continuous experiment-based deep learning through pilot and institutionalization of knowledge and information management with city-to-city peering learning mechanisms, 2) establishment of institutional mechanisms dedicated to participatory, coordinated and integrated governance of the policy initiative, 3) increased government role in creating favorable conditions for investments, and 4) appropriate planning and an adaptive approach to policy implementation. The paper concludes that the sponge cities initiative can be an effective approach only if China commits to appropriate technical, governance, financial, and organizational measures to effectively address the challenges for policy implementation. [ABSTRACT FROM AUTHOR]

• 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]

[Display omitted] • Climate change affects the stormwater quality due to variation in climatic parameters. • Scientific interventions with strong database should be geared up in developing nations. • Emerging pollutants needs more attention by undertaking systematic research. • Groundwater recharge with stormwater may raise groundwater level and reduce pollution. The problem of water logging and deteriorating water quality of surface water bodies has been witnessed in and around the urban centers owing to rapid population growth, urbanization, industrialization and climate variability. The stormwater runoff management is the need of the hour, and with increasing public and political awareness and rapid economic growth, it is receiving due attention. In the climate-change scenario, it needs to be addressed with scientific interventions as changes in pattern of precipitation affects the runoff volume and movement of pollutants including nutrients. This paper discussed and critically reviewed the stormwater runoff characterization, modelling, relationship between runoff and water quality, first flush phenomena, runoff treatment in changing climate scenarios. Researchers have used regional climate models (RCMs) to project future precipitation and then incorporated in various hydrologic and hydraulic models such as SWMM, SWAT, HSPF, SUSTAIN and MUSIC etc to study the impact of climate change and its adaptation. The flooding and water quality problem can be combatted with adopting green infrastructure practices. Many works on stormwater management have been carried out in developed countries, but efforts need to be geared up in the developing countries, particularly for runoff characterization and effective management. Emerging pollutants need to be paid more attention by undertaking systematic research. Future studies should include measurements and quantification of ecosystem services and integration of stormwater management into environmental policy. This review suggests intriguing future research avenues at the nexus of stormwater management and ecological services. [ABSTRACT FROM AUTHOR]

• The design of LID scheme combined with the control objective and waterlogging area. • SWMM was used to calculate the runoff control and pollutant control benefits. • LID based on SPC was implemented in a university in the old city area of Xi 'an. • LID facilities provide respectable environmental, economic and social benefits. • The case can inform the optimal selection of LID practices on campus scale. Rapid and intense urbanization has brought a wide range of serious issues like flooding and water pollution, which have become a great concern in a lot of cities. In response to these problems, Sponge City (SPC) has emerged as a potential solution. This study utilized the Stormwater Management Model (SWMM) to simulate urban runoff and determine an optimal combination of Low Impact Development (LID) strategies for reconstructing the study area. Upon analyzing the existing rainwater drainage system and revealed that it could only meet the design return period of 2- years , and identified the vulnerable flood-prone area and the maximum overflow node. After comprehensive consideration of various factors, the sponge reconstruction scenario selected four LID facilities combination: Porous pavement, Rain garden, Sunken green belt, and Green roof. Results indicated that when P = 1- yr , 5- yr , 10- yr and 20- yr , the total runoff in the LID scenario decreased by 43.91 %, 43.93 %, 45.95 % and 47.11 %, respectively. LID facilities substantially mitigate stormwater runoff and peak flows during moderate to light rainfall events. These LID facilities prove effective in reducing pollutant concentrations (reduction rate is all over 42 %), with notable reductions in Chemical Oxygen Demand (COD) and Suspended Solids (SS). This paper offers valuable insights that can serve as a reference for the construction or renovation of SPC, shedding light on effective strategies and considerations for managing stormwater and mitigating the impact of urbanization. [ABSTRACT FROM AUTHOR]

• Green infrastructure networks enhance green access and distribution in cities. • Pluvial flood risks can be reduced by means of urban green infrastructure networks. • In Milan, increasing green cover by 25 % can potentially halve pluvial flood damage to buildings. • Population exposure can be reduced by up to one-third extending green areas in Milan. • Priority areas for green interventions can support urban climate strategies. Rethinking cities in a more sustainable and integrated way is a key opportunity for successful climate change adaptation and disaster risk management. Nature-based solutions and green infrastructures can help to safeguard urban nature and biodiversity while providing multiple benefits to reduce climate risks and improve human well-being. Nature-based solutions help to mitigate flood risk by regulating storm-water runoff and peak-flow. This paper investigates the effects of nature-based solutions and green infrastructure networks on pluvial flood risk in the Milan metropolitan area in terms of direct economic damage to buildings and population exposed. Results show that extending the urban green networks by 25 % can potentially halve the pluvial flood damages and reduce the population exposed by 40 %. For all analysed rainfall intensities, damages to buildings and share of population exposed decrease (up to 60 % and 50 % respectively) as green area coverage increases, with slightly higher flood risk reduction for lower-intensity events. The applied methodological framework makes it possible to identify priority-action urban areas and hence inform decision-making processes as for where green solutions are most efficient. [ABSTRACT FROM AUTHOR]

Water erosion, a notorious major threat to food security and ecosystem sustainability, is strongly conditioned by spatial and temporal scale effects. This paper systematically reviews the scale issues in runoff and sediment delivery (SIRSD) as a research field by integrating the traditional review approach and bibliometric analysis. This review summarises SIRSD's roots and the scale effect on runoff and sediment delivery. Then, we provide quantitative insights into the SIRSD domain's development history, thematic structure, geographic distribution, international cooperation, and methodologies. Findings show that: i) SIRSD arises from the gap between the non-linearity of runoff and sediment delivery across scales and our ability to measure it. Point-based and short-term measurements cannot capture the non-linearities from the spatio-temporal heterogeneities and cross-scale interactions of factors or processes. ii) Previous literature provides evidence that the spatial scaling of specific runoff (r), soil erosion (SE), sediment yield (SSY), or sediment delivery ratio (SDR) with drainage area (A) or slope length (L) exhibits contrasting patterns due to distinct mechanisms. Infiltration-excess and saturation-excess processes account for inverse and positive r - A relations, respectively. Interrill-erosion and rill-erosion cause inverse and positive SE-L relations. Hillslope-erosion and channel/bank-erosion explain inverse and positive SSY-A relations. Downstream increasing deposition and additional sediment inputs drive inverse and positive SDR-A relations. These scaling relationships can be nonlinear or complex due to spatial heterogeneities in land use, vegetation, topography, climate, lithology, and soil characteristics. Hence, applying an empirical scaling equation developed from the region with distinct environmental contexts is not recommended. Furthermore, the existing scaling patterns or equations may require updating given global climate and land use change. iii) SIRSD is a complex and multidisciplinary issue investigated by scientists from 93 countries since 1928. International research has substantially facilitated the understanding of SIRSD; still, more collaboration should focus on less-developed countries with high soil and water loss risks and urgent conservation needs, such as those in Africa and South America under cropland expansion. iv) Scale mismatch and scale break have discredited large-scale erosion and sediment assessments. Incorporating gully and bank erosion into modelling, extending the scale range of the L factor, and expanding the sediment scaling scope from watershed to slope may make a difference. Therefore, more research with nested design incorporating multiple scales is necessary for cross-scale analysis and scalable modelling. Addressing global climate change requires improving real-time urban flood forecasting and integrating downscaled climate models with hydrology, erosion, and sediment transport models. • A bibliometric review of the global SIRSD research for the first time. • SIRSD is rooted in the gap between natural nonlinearity and our measuring ability. • Scaling of specific runoff, erosion, sediment yield, and SDR has diverse patterns. • Scale mismatch and scale break discredit large-scale modelling and assessments. [ABSTRACT FROM AUTHOR]

Contaminated stormwater runoff from oil and gas (O&G) operations can pose a significant threat to surface waters. The purpose of this study is to examine the extent of this threat and identify more specific permitting requirements to protect surface waters. To better understand the extent of the threat, this paper identified and characterized the use of waste surface impoundments at O&G facilities as well as the threat level from O&G spills in California. To assess the efficacy of the current federal and California state-permitting regime, the paper evaluated stormwater permit compliance in two California counties. It also reviewed selected spill cases and associated Spill Prevention, Control and Countermeasure Plans and Stormwater Pollution Prevention Plans to identify the adequacy of current industry practices. The analysis showed that contaminated stormwater from O&G facilities can be better regulated. The U.S. Environmental Protection Agency does not require O&G facilities to file for an Industrial Stormwater General Permit even though many O&G industry practices have the potential to contaminate stormwater runoff from the site. When O&G facilities discharge a Reportable Quantity of a hazardous chemical or violate a water quality standard, they are required to enroll in the National Pollutant Discharge Elimination System permit program. Spills, although not a direct indication of stormwater runoff, can highlight polluted runoff discharges that should have been regulated. Medium and large spills that reached waterways were such a risk for which operators did not file for a permit. In California new filing requirements for the oil and gas industry require all facilities that discharge stormwater that has come into contact with any overburden, raw material, or intermediate products located on the site, to file for an Industrial Stormwater General Permit. As this study showed, there has been an increase in enrollment since the enactment of the new requirements. Having all facilities enroll in the general permit program, as done in California, would require minimum monitoring and maintenance that could help prevent spills. A further step to ensure better protocol is to require specific pollution control practices in addition to the current general permit requirements. Such permitting regimes not only can be implemented on the US federal level but also internationally. [ABSTRACT FROM AUTHOR]

The optimized agricultural water schedule and management, especially in the large irrigation district along the river basins, were closely related to the spatial and temporal runoff variations. However, the impacts of climate change and human activities leads to non-linear and non-stationary monthly runoffs. Under this condition, effectively capturing the variation of the runoff time series and improving the accuracy of the prediction model are of vital importance. However, some existing monthly data-driven prediction studies mainly focus on the model structure and calculation load, ignoring the influence of each frequency component of runoff and the fluctuation of runoff series caused by meteorological factors, which may not effectively capture the potential change process. In this paper, a novel method (CEEMD-MPE-EMD-GRU) for the non-stationary monthly runoff prediction was proposed. It fully took advantages of the complementary ensemble empirical mode decomposition (CEEMD), multi-scale permutation entropy (MPE), empirical mode decomposition (EMD) and gated recurrent unit (GRU). The combined model in general is a data-driven model, and compared with the traditional mechanism model, its most notable advantage is that it successfully overcomes the redundant information of the prediction model. In addition, atmospheric input factor analysis is added on the basis of fully decomposing and identifying non-stationary pseudo-components. The hydrological runoff data (1956–2014) obtained from the Manas River locating at Xinjiang, China were used for prediction. The results indicated that the new CEEMD-MPE-EMD-GRU model reached higher accuracy, as its Nash-Sutcliffe efficiency coefficient (0.960) was significantly larger than those of the GRU model (0.813) and the CEEMD-GRU model (0.889). Meanwhile, the root mean square error and the absolute relative error of the CEEMD-MPE-EMD-GRU model decreased to 0.279 and 0.195, respectively. The new runoff prediction model established in this paper would provide more precise evaluation of the monthly runoff prediction and better guidelines for high-efficiency agricultural water scheduling in the irrigation district. • Introduced CEEMD-MPE-EMD-GRU to predict the monthly series of streamflow. • Optimize the hydrometeorological elements as the input items of the model. • The CEEMD-MPE-EMD-GRU is found to be stable on tendency and extreme streamflow. • It overcomes a lot of steps brought by redundant information of the model. • Effectively improve the accuracy of nonlinear and non-stationary flow simulation. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel enhanced seepage well (ESW) was proposed and studied using HYDRUS-2D. • The infiltration efficiency of ESW was significantly higher than that of PP and CSW. • ESW exhibited an excellent runoff-control capacity in actual rainfall scenarios. • This paper provides a seepage well option for low-impact development construction. Seepage well is an emerging Low Impact Development (LID) technology that can effectively control the storm runoff. However, its rainwater infiltration rate and storage capacity still require further enhancement. By setting a horizontal infiltration structure at the bottom of conventional rainwater seepage well (CSW), an enhanced seepage well (ESW) was proposed in this study, and its infiltration performances compared with the permeable pavement (PP) and the CSW were systemically investigated using static infiltration experiment and HYDRUS-2D simulation. The results showed that the infiltration efficiency of ESW was significantly higher than that of PP and CSW, and the process of water infiltrated through soil mainly controlled the macroscopic infiltration rate. The Nash-Sutcliff Efficient (NSE) index was used to evaluate the accuracy and reliability of the HYDRUS-2D model, and the results of NSE values greater than 0.75 (varied between 0.75 and 0.91) confirmed the applicability of HYDRUS-2D to describe correctly the hydraulic behavior of the ESW system. Simulation infiltration tests showed that the ESW performed a higher average infiltration rate and fewer total runoff volume than the CSW, indicating the effectively enhancement of the infiltration and water retention capacity of ESW, especially under heavy rainfall intensities. Additionally, the ESW system exhibited an excellent runoff-control and rainwater retention capacity in an actual rainfall scenario. [ABSTRACT FROM AUTHOR]

In order to slow climate change, economies need to quickly move away from finite energy sources and towards using low-carbon energy systems. However, the integration of non-dispatchable wind and solar sources comes with additional costs and can make the energy market unusual and unpredictable. Specifically, the presence of variable renewable energy sources makes it harder to accurately forecast energy demand. This paper is a first step in presenting a novel approach to overcoming the inherent variability of photovoltaics (PV) by combining them with a run-off-river (ROR) power plant. A mixed integer mathematical model has been developed and applied to simulate the operation of a PV–ROR hybrid energy source coupled with the national power system. Simulations demonstrate various configurations of parameters and their impact on the objective function which was to maximize the volume of energy from PV and hydropower used to cover energy demand, while ensuring that neither energy deficits nor energy surpluses exceed 5% of energy demand. Our analysis indicates that an ROR power plant with relatively small pondage is capable of subsidizing the varying energy output of the PV system. Besides conducting a simulation and optimization, this paper suggests an approach to smoothing the energy exchange with the grid based on fixed volumes of energy which should be delivered during daylight and nighttime hours. [ABSTRACT FROM AUTHOR]

Storm tanks are an increasingly solution used to control floods caused by the increase of runoff flows generated by rain. To expedite the construction of these infrastructures and reduce downtime of urban supplies plastic modular elements as basic building structures are used. This paper presents the characterization of this sort of modular elements determining its energy dissipation to the passage of the water and comparing the hydraulic behavior of modular deposits to conventional ones. This paper justifies the use of computational fluid dynamic techniques (CFD) for characterizing hydraulic behavior of these modular elements based on three-dimensional models. These models have been validated by an experimental prototype built in laboratory. This one consists of a channel along which various completely monitored modular blocks are disposed in order to analyze both, circulating flows and water levels reached at different points of the system. The final result is a validated and calibrated model that allows to represent the behavior of this kind of infrastructures against strong intensity storms. A one-dimensional model of such structures is proposed as a way to join it with a specific computational model of drainage networks analysis as is the case of SWMM. Finally, a comparison between the results obtained by CFD techniques and those obtained after adjustment in SWMM is shown. [ABSTRACT FROM AUTHOR]

Rainfall causes runoff and soil erosion in artificial and natural slopes and important insight may derive from quantitative physically-based models or laboratory tests. Measurements of runoff and sediment discharges in small-scale experimental flume tests have become popular in recent years and a wide range of slope angles, soil grain-size-distributions and rainfall characteristics has been tested so far. In the literature, there are numerous studies dealing with a comparison between experimental data and model predictions using appropriate assumptions. However, there are still scientific gaps under complex experimental circumstances. The main goal is to discuss the performance of a physically-based numerical model in simulating well-documented runoff–erosion laboratory flume tests, also highlighting the uncertainties one may expect for real cases when applying numerical modelling of runoff and soil erosion to a real catchment. The paper deals with the numerical analysis of four experimental flume tests available in the literature, which investigate the erosion of bare gentle slopes due to constant-intensity rainfall; the behaviour of a steeper slope, bare or vegetated, under constant rainfall larger than in the previous experiments; the role of a sequence of different rainfall intensities (with the same cumulated rainfall), and different surface roughness in gentle slopes. Those experimental tests were simulated through LISEM, and the numerical results reproduce satisfactorily the global behaviour of the experimental plots eroded by artificial rainfall in all the four flume tests. As far as the ratio of the observed to the predicted peaks of water discharge and sediment concentration, the simulated peaks are very close to those observed in the laboratory experiments, except for low slope angle conditions where water discharge peak is overestimated and for one flume where sediment concentration peak is underestimated in two out of three cases. This analysis highlights that LISEM allows reasonably estimating the peak values of water and sediment discharge, which are generally used as design parameters of erosion control works. With reference to peak times of water discharge and sediment concentration, this paper highlights that LISEM has limitations in properly assessing the peak times of water discharge and sediment concentration; better results are, instead, expected when LISEM is used to simulate erosion and runoff on vegetated slopes. Globally, the results allow the assessment of the overall performance of the selected erosion model to correctly interpret the experimental evidences. As well, the discrepancies among the laboratory evidences and numerical results are discussed in relation to slope geometry and soil properties. [ABSTRACT FROM AUTHOR]

This paper (Part II) is companion of another one published in this Conference (Part I). Both the papers describe the approach followed in the application of the SLIP model at different scales to foresee the triggering mechanism of rainfall-induced shallow landslides. In particular, this paper (Part II) focuses on the modeling at medium and large scale (regional and national level). The possibility of using the same means to model the phenomenon from the scale of the representative elementary volume (i.e. flume laboratory tests) to the medium and large scale (hundreds or thousands square kilometers wide areas) allowed from the one hand to strengthen the model assumptions and on the other hand to develop a “flexible” tool, to be used easily for early-warning purposes. [ABSTRACT FROM AUTHOR]

Summary Surface runoff is the hydrological process at the origin of phenomena such as soil erosion, floods out of rivers, mudflows, debris flows and can generate major damage. This paper presents a method to create maps of surface runoff susceptibility. The method, called IRIP (Indicator of Intense Pluvial Runoff, French acronym), uses a combination of landscape factors to create three maps representing the susceptibility (1) to generate, (2) to transfer, and (3) to accumulate surface runoff. The method input data are the topography, the land use and the soil type. The method aims to be simple to implement and robust for any type of study area, with no requirement for calibration or specific input format. In a second part, the paper focuses on the evaluation of the surface runoff susceptibility maps. The method is applied in the Lézarde catchment (210 km 2 , northern France) and the susceptibility maps are evaluated by comparison with two risk regulatory zonings of surface runoff and soil erosion, and two databases of surface runoff impacts on roads and railways. Comparison tests are performed using a standard verification method for dichotomous forecasting along with five verification indicators: accuracy, bias, success ratio, probability of detection, and false alarm ratio. The evaluation shows that the susceptibility map of surface runoff accumulation is able to identify the concentrated surface runoff flows and that the susceptibility map of transfer is able to identify areas that are susceptible to soil erosion. Concerning the ability of the IRIP method to detect sections of the transportation network susceptible to be impacted by surface runoff, the evaluation tests show promising probabilities of detection (73–90%) but also high false alarm ratios (77–92%). However, a qualitative analysis of the local configuration of the infrastructure shows that taking into account the transportation network vulnerability can explain numerous false alarms. This paper shows that the IRIP method can be a valuable tool to facilitate field analysis and perform surface runoff zonings and opens interesting prospects for the use of the IRIP method in a context of risk management. [ABSTRACT FROM AUTHOR]

As human history is changing on many fronts, it is appropriate for us to understand the different perspectives of major global challenges, of which, water is a major priority. The water resources in urban areas are either approaching or exceeding the limits of sustainable use at alarming rates. Groundwater table depletion and increasing flood events can be easily realized in rapidly developing urban areas. It is necessary to improve existing water management systems for high-quality water and reduced hydro-meteorological disasters, while preserving our natural/pristine environment in a sustainable manner. This can be achieved through optimal collection, infiltration and storage of stormwater. Stormwater runoff is rainfall that flows over the ground surface; large volumes of water are swiftly transported to local water bodies and can cause flooding, coastal erosion, and can carry many different pollutants that are found on paved surfaces. Sustainable stormwater management is desired, and the optimal capture measure is explored in the paper. This study provides commentary to assist policy makers and researchers in the field of stormwater management planning to understand the significance and role of remote sensing and GIS in designing optimal capture measures under the threat of future extreme events and climate change. Community attitudes, which are influenced by a range of factors, including knowledge of urban water problem, are also considered. In this paper, we present an assessment of stormwater runoff management practices to achieve urban water security. For this purpose, we explored different characteristics of stormwater runoff management policies and strategies adopted by Japan, Vietnam and Thailand. This study analyses the abilities of Japanese, Vietnamese and Thai stormwater runoff management policies and measures to manage water scarcity and achieve water resiliency. This paper presents an overview of stormwater runoff management to guide future optimal stormwater runoff measures and management policies within the governance structure. Additionally, the effects of different onsite facilities, including those for water harvesting, reuse, ponds and infiltration, are explored to establish adaptation strategies that restore water cycle and reduce climate change-induced flood and water scarcity on a catchment scale. [ABSTRACT FROM AUTHOR]

The drought indices are common tools for monitoring and risk assessment of the drought. Based on copula function, this paper proposes a bivariate combined drought index (BCDI bcf) that can comprehensively characterize the meteorological characteristics (precipitation) and hydrological characteristics (runoff). Besides, the cross-wavelet transform (XWT) is introduced to research the relationship of drought, and identifies the lag in the propagation process. Take Huayuankou station as an example, results indicated: (1) The BCDI bcf combines the merit of the standardized precipitation index (SPI) in capturing the onset of drought and that of the standardized runoff index (SRI) in capturing the end of drought, which contains information on both SPI and SRI, can characterize two types droughts of hydrology and meteorology simultaneously. (2) The characteristics of hydrology and meteorology have significantly periodic, the most obvious on the scale of 2-4a. (3) There is the significant positive statistical correlation between precipitation and runoff, and hydrological droughts are lagging behind meteorological droughts by 1–2 months at Huayuankou station. The BCDI bcf can identify drought more comprehensively and provide strong support for drought detection and prevention. [ABSTRACT FROM AUTHOR]

• Probability density prediction can measure uncertainties of runoff. • B-spline quantile regression is proposed to predict the probability density of runoff. • Different evaluation criteria of probabilistic predictions is adopted. • The new method is used in Shigu station in Jinsha River of China. Exact and dependable runoff forecasting plays a vital role in water resources management and utilization. This paper proposes a B-spline quantile regression probability density prediction method to predict future runoff and quantify the uncertainty of prediction. The method includes three steps. First, the B-spline function is used to perform spline processing on the runoff data. Secondly, the spline-processed training data is input into the quantile regression model to calculate the parameters of the B-spline quantile regression model, and the successfully constructed B-spline quantile regression model is combined with kernel density estimation to construct a probability density prediction method. Finally, the constructed B-spline quantile regression probability density prediction method is used to forecast future runoff flow, and quantitatively analyze the relevant prediction uncertainty. Six evaluation indicators are constructed, among which the root mean square error, the deterministic coefficient, the pass rate are the evaluation metrics of point predictions based on probability mean, median and mode; the prediction interval coverage probability, prediction interval normalized average width and continuous ranked probability score are the evaluation criteria of interval predictions and probabilistic forecasting. The presented method is able to depict the probability density curve of future runoff flow, and obtain more comprehensive information than point forecasting and interval predictions. As a case study, this method is applicable to the Shigu station of the Jinsha River in China. The results show that the results of the proposed method are better than that of existing some state-of-the-art methods. From the perspective of application, the pass rate and the deterministic coefficient of the method have reached the grade A of accuracy. Therefore, the B-spline quantile regression model provides an alternative scheme to runoff prediction. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ten multi-model combination methods (MMCMs) are created from 29 models in 50 basins. • MMCMs improve some performance indicators, especially the Granger-Ramanathan method. • MMCMs do not improve performance in reproducing distributions of hydrological signatures. • Application with series of targeted signatures does not improve MMCM performance. • MMCM performance is improved by selecting more robust candidate models for ensembles. Multi-model combination (averaging) methods (MMCMs) are used to improve the accuracy of hydrological (precipitation-runoff) outputs in simulation or forecasting/prediction modes. In this paper, we examined if the application of MMCMs can improve model performance in reproducing distributions of hydrological signatures, such as annual maxima or minima of varying durations. To this end, ten MMCMs were applied to 29 bucket-type models to simulate runoff in 50 high-latitude catchments. The MMCMs were evaluated by comparing the resulting simulated flows to the reference (i.e., best-performing) individual model, considering various commonly used performance indicators, as well as model performance in reproducing the distributions of signatures. Additionally, we analysed whether (1) the selection of the candidate models, or (2) targeting specific signatures, such as annual maxima or minima, can improve performance of the model combinations. The results suggest that the application of MMCMs can improve accuracy of runoff simulations in terms of traditional performance indicators, but fails to improve performance in reproducing the distributions of signatures. Neither excluding poor-performing models nor applying the MMCMs with the targeted signatures, improves this aspect of model performance. These findings clearly reveal the need for further research aiming at enhancing model performance in reproducing the distributions of hydrological signatures, which is essential for climate-change impact studies. [ABSTRACT FROM AUTHOR]

Models like the Rangeland Hydrology and Erosion Model (RHEM) are useful for estimating soil erosion, however, they rely on input parameters that are sometimes difficult or expensive to measure. Specifically, RHEM requires information about foliar and ground cover fractions that generally must be measured in situ, which makes it difficult to use models like RHEM to produce erosion or soil risk maps for areas exceeding the size of a hillslope such as a large watershed. We previously developed a deep learning emulator of RHEM that has low computational expense and can, in principle, be run over large areas (e.g., over the continental US). In this paper, we develop a deep learning model to estimate the RHEM ground cover inputs from remote sensing time series, reducing the need for extensive field surveys to produce erosion maps. We achieve a prediction accuracy on hillslope runoff of R 2 ≈ 0. 9 , and on soil loss and sediment yield of R 2 ≈ 0. 4 at 66,643 field locations within the US. We demonstrate how this approach can be used for mapping by developing runoff, soil loss, and sediment yield maps over a 1356 km2 region of interest in Nebraska. • We developed a deep learning model estimating the ground cover inputs of the RHEM. • Litter and Shrubs are the most and Bio Crusts is the least accurate estimations. • We achieved an R 2 ≈ 0. 9 for runoff and ≈ 0. 4 for soil loss by the estimated covers. [ABSTRACT FROM AUTHOR]

• The runoff, sediment load and runoff erosion power decreased significantly. • Compared with runoff, runoff erosion power-sediment relationship is better. • The power function of runoff erosion power and sediment is better than linear. • Erosion in particular geographic locations responds strongly to runoff erosion power. In recent years, the acceleration of urbanization in the Yangtze River Economic Belt (YREB) has brought about problems such as soil erosion, ecosystem degradation and decline in ecological service functions. Therefore, this paper selects the Fujiang River Basin (FRB) in Upper Yangtze River (UYR), and analyzes the relationship between runoff erosion power (REP) and sediment transport (ST) on the basis of analyzing the variation characteristics of hydrological elements. Based on SWAT model, a variety of scenarios are set up to quantitatively analyze the impact of different land use changes on runoff and ST and REP in the basin. The results indicated that: (1) The annual precipitation in FRB from 1960 to 2018 did not change significantly, while the annual runoff, annual ST and annual REP decreased significantly. Compared with runoff and runoff depth, the relationship between sediment transport modulus (STM) and REP is the best, especially the power function between them is better than the linear function. (2) The results of the evaluation indexes of the simulation results of the SWAT model based on the measured runoff and sediment data from 2008 to 2018 are pass and above, that is, the simulation results are considered reliable. (3) Under the five scenarios of conversion of sloping farmland to forest, conversion of dry land to paddy field and conversion of grassland to bushland and forest land, the runoff and ST in the basin decreased but the overall range was small. The simulation results of SWAT model show that the erosion of specific areas in the basin shows the greatest degree of weakening with the decrease of REP. [ABSTRACT FROM AUTHOR]

As changes in external weather introduce significant uncertainty into runoff prediction, probabilistic runoff forecasting becomes increasingly important for water resource managers. However, limited research has been conducted on the rational employment of decomposition techniques in probabilistic prediction, especially when considering external factors effectively. To fill the gap, this paper proposes an innovative framework for probabilistic runoff forecasting. It first utilizes the stepwise decomposition (SD) technique to obtain training and test samples. Then an integration structure considering external factors is designed to enhance the learning ability of the neural network for various features and decomposition sequences. The boundary correction process utilizes the dynamic time warping (DTW) method to optimize the boundaries of the obtained samples in SD. This comprehensive framework is named SDIFC. Building upon this framework, we achieve a synergistic integration of three distinct methods: seasonal-trend decomposition procedures based on loess (STL), least absolute shrinkage and selection operator (LASSO), and quantile regression long short-term memory (QRLSTM), named STL-LASSO-QRLSTM. Multiple experiments and evaluations demonstrate that the three methods are very well adapted to the SDIFC framework. The proposed forecasting method not only captures the runoff trend accurately but also quantifies the uncertainty in the prediction process. The proposed method exhibits significant advantages in probabilistic forecasting, achieving an average improvement of 28.1%, 35.5%, and 27.3% in continuous rank probability score (CRPS) when compared to the model without SDIFC, the model using SD in other frameworks, and the model without LASSO, respectively. [ABSTRACT FROM AUTHOR]

• We investigated the intra-annual runoff variations and periods in an endorheic lake basin-Issyk-Kul basin, Central Asia. • The runoff variability was investigated by using structuring models. • Analysis of the concentration and correlation of precipitation and runoff in the small bowl of the north Lake Issyk-Kull basin. This paper, based on the non-parametric tests and wavelet analysis, analyzed monthly precipitation and runoff data of the small basin in the northern part of the Lake Issyk-Kull basin over the nearly 62-year period, the concentration and correlation of precipitation and runoff in the small bowl of the north Lake Issyk-Kull basin. The results indicated that (1) the Cholpon-Ata River runoff (runoff 1) and the Chong-Ak-Suu River runoff (runoff 2) in the small watershed in the northern part of Lake Issyk-Kull basin are from June to April, January, and from September to November showed a significant increasing trend. The significant abrupt growth change occurred in 1983, while the precipitation at the Cholpon-Ata station showed a significant increasing trend in February, with a sharp point around 1980. (2) The concentration degree (Cd) index of runoff 1 in the small watershed in the northern part of the Lake Issyk-Kull basin has increased but not significantly, and the Cd index of precipitation and runoff 2 showed an insignificant decrease. (3) The Cd index of runoff 1 in the small watershed in the northern part of Lake Issyk-Kull basin existed for 10 years and 18 years, and the Cd index of River 2 existed for 9 years and 13 years. In contrast, the precipitation Cd index was significant at 4- and 15-year cycles. (4) On multiple time scales, they have a significant negative correlation between the runoff and precipitation in the small watershed in the northern part of the Lake Issyk-Kul basin from May to September. [ABSTRACT FROM AUTHOR]

• High resolution E. coli data from a mixed UK catchment with field and sewer inputs. • New model for acute impacts using travel times from spatially distributed sources. • Sewer overflow sensors and radar rainfall used to characterize source loadings. • Application for forecasting arrival time of elevated E. coli levels. • Disaggregation of model output shows E.coli source is a function of the rainfall. Surface water quality is frequently impacted by acute rainfall driven pollutant sources such as sewer overflows. Understanding the risk of exposure from faecal pollution from short term impacts is challenging due to a paucity of high-resolution data from river systems. This paper proposes practical modelling approach for forecasting arrival time and durations of elevated E. coli levels based on hydrological routing of catchment source loadings, characterized by distributed and remote sensing techniques (including sewer overflow monitoring). The model is calibrated and validated using new high resolution E. coli datasets from a UK catchment featuring both diffuse field runoff and storm overflow impacts. Hourly/Bihourly sampling of E. coli was undertaken in the river following different rainfall events across a range of seasonal conditions. The model provides a good estimate of arrival times and durations of elevated E. coli periods following rainfall events. Model simulations suggest that key sources in the catchment are event specific, with sewer overflow spills being more significant following short, intense rainfall events. [Display omitted] [ABSTRACT FROM AUTHOR]

Accurate mid- and long-term runoff forecasting is of great significance for rational development and utilization of water resources in “Yellow River Headwater” region, where runoff in the headwater region contributes to nearly 35% of the total amount of the Yellow River basin. In this paper, the monthly runoff data of Tangnaihai station in “Yellow River Headwater” region are analyzed as case studies. This paper presents support vector regression model for mid- and long-term runoff forecasting, and analyzes the influence of support vector regression model's parameters on the runoff forecasting accuracy, and finally compared with Auto Regressive model (AR) and Radial basis function neural network (RBFNN). The results indicate that SVR showed the best performance and is proved to be competitive with the AR and RBFNN models in both stations. SVR methods provide a promising reliable methods of mid- and long-term runoff forecasting. [ABSTRACT FROM AUTHOR]

The paper describes environmental pollution in railroad right-of-way. The authors set a goal of collecting systematic data about concentration of pollutants in soils and surface run-off, which are close to railroad tracks and railway service enterprises. They also justified the choice of their research sites and described pollution forms typical for areas, which are close to railroad tracks. Petroleum products and iron were considered as main pollutants. The authors carried out a set of field-tested experiments to measure the level of railroad right-of-way pollution. The results are summarized in graphs showing how the level of pollutant concentration in surface run-off depends on the level of the same pollutant in soil sample. Theoretical and experimental research made it possible to calculate surface waste water and soils pollution concentration in railroad track right-of-way. The paper considers the Russian Legislation regulatory documents requiring enforcement of environmental protection measures to decrease dirty discharge into water bodies. [ABSTRACT FROM AUTHOR]

Agricultural terraces are common in mountain regions. They are built to provide a larger surface area for cultivation on hillslopes and to aid farming production in the long term. This paper reviews the hydrological and geomorphological behaviour of constructing, working on and abandoning farming terraces based on recent scientific papers (1982–2014). Terraces increase infiltration and reduce runoff. In Mediterranean areas, the runoff coefficient on abandoned terraces is between 20% and 40%, depending on the percentage of plant cover or the amount of grazing. On cultivated terraces in warm, humid climates, the coefficients are lower (10%–25%). The internal and external sections of terraces have hydrological differences. Soil saturation is reached faster in the inner parts of terraces with shallower soil, causing a rapid hydrological response (saturation runoff); high infiltration rates in external sections can generate subsurface runoff. Furthermore, a reduction in hydrological connectivity, which affects the contributing areas and peak flows, is to be expected in terraced catchments. Collapsed stone walls, small mass movements, sheet wash erosion, piping, rills, gullies and debris flows are all found in terraced landscapes. The erosion of abandoned terraces is directly related to the amount of plant cover, soil characteristics, environmental conditions and the abandonment age. On this type of terrace, the rates of erosion due to overland flow are less than 3 t ha − 1 yr − 1 . The rate increases if the terrace is affected by mass movements, pipes or gullies (more than 100 t ha − 1 yr − 1 ). On cultivated terraces, erosion rates less than 1 t ha − 1 yr − 1 have been recorded for rice crops, and erosion rates greater than 80 t ha − 1 yr − 1 have been recorded for cassava or terraces with bare soil. The scientific literature notes that the soil loss from cultivated slopes is greater if conservation measures, such as terraces, are not included. However, this general result may vary significantly due to factors related to the geo-environmental characteristics of the region and, in particular, the management and use of the terraced slopes. [ABSTRACT FROM AUTHOR]

Summary The selection and identification of a suitable hydrological model structure is a more challenging task than fitting parameters of a fixed model structure to reproduce a measured hydrograph. The suitable model structure is highly dependent on various criteria, i.e. the modeling objective, the characteristics and the scale of the system under investigation and the available data. Flexible environments for model building are available, but need to be assisted by proper diagnostic tools for model structure selection. This paper introduces a qualitative method for model component sensitivity analysis. Traditionally, model sensitivity is evaluated for model parameters. In this paper, the concept is translated into an evaluation of model structure sensitivity. Similarly to the one-factor-at-a-time (OAT) methods for parameter sensitivity, this method varies the model structure components one at a time and evaluates the change in sensitivity towards the output variables. As such, the effect of model component variations can be evaluated towards different objective functions or output variables. The methodology is presented for a simple lumped hydrological model environment, introducing different possible model building variations. By comparing the effect of changes in model structure for different model objectives, model selection can be better evaluated. Based on the presented component sensitivity analysis of a case study, some suggestions with regard to model selection are formulated for the system under study: (1) a non-linear storage component is recommended, since it ensures more sensitive (identifiable) parameters for this component and less parameter interaction; (2) interflow is mainly important for the low flow criteria; (3) excess infiltration process is most influencing when focussing on the lower flows; (4) a more simple routing component is advisable; and (5) baseflow parameters have in general low sensitivity values, except for the low flow criteria. [ABSTRACT FROM AUTHOR]

• Climate change, hydrological and hydraulic model are combined to analyze runoffs. • Four Global Climate Model: BCC, BNU, CanESM and CSIRO are used. • The future design runoffs and water levels increase in Qu River. • Disaster management and infrastructure construction should be paid more attention to. The impact of climate change on regional hydrological extreme events has been a research topic that has received much attention in recent years. This paper aims to provide a general overview of changes on future runoffs and water levels in the Qu River Basin, upper reaches of Qiantang River, East China by combining future climate scenarios, hydrological model and 1D hydraulic model. The LARS-WG (Long Ashton Research Station Weather Generator) statistical downscaling method is used to downscale the GCM (Global Climate Model) outputs (including BCC, BNU, CanESM and CSIRO under RCP4.5 and RCP8.5 scenarios) and generate 50 years of synthetic precipitation and maximum and minimum temperatures to drive the GR4J hydrological model and the 1D hydraulic model for the baseline period 1971–2000 and the future period 2021–2050. Finally, the POT (Peaks over Threshold) method is applied to analyze the change of extreme events in the study area. The results show that design runoffs and water levels all indicate an increasing trend in the future period for Changshangang River and Jiangshangang River at most cases, especially for small return periods, and for Qu River the increase becomes larger, which suggests that the risk of flooding will probably become greater and appropriate adaptation measures need to be taken. [ABSTRACT FROM AUTHOR]

Catchment runoff scale relationships comprise an important theoretical support for water resource management. However, previous understandings of the scale effect were mostly based on empirical summaries and quantitative research, while interpretation based on measured data was rare. The purpose of this paper was to quantitatively reveal the causes of runoff scale impacts in the Haihe mountainous area over a 20-year period. Fifty-seven catchments (92-15803 km2) were selected from the available hydrological sites. Multi-year average values for17 environmental variables were calculated in each catchment over the study period, including data on hydrology, meteorology, vegetation, land use, topography, and soil. Based on these data, the quantitative relationship between runoff and catchment area was first established. Then the correlation between environmental factors and runoff scale impacts was assessed. Finally, catchments were divided into three groups by area, and the dominant factors influencing runoff at different scales, as well as the direct and indirect effects of these factors on runoff, were obtained using stepwise regression and path analysis. The results showed that: 1) Runoff coefficients decreased logarithmically as catchment area increased and the scale distribution characteristics of the variables closely related to runoff were an important reason for the runoff scale effect. 2) Larger river basins had fewer sensitivity factors for runoff and the impacts of vegetation and land use factors were mainly reflected in small and medium catchments. 3) Vegetation and land use primarily had indirect effects, which determined the proportion of factors in the total effect. Among these, the indirect effects of farmland were very prominent, which implied that human activities have had an important influence on runoff scale effects. This study emphasized the importance of farmland management in the upstream areas of Haihe mountainous area, and provides important theoretical support for catchment scale effects and water resource management in water-limited regions. Unlabelled Image • The quantitative relationship between runoff and catchment area was established. • The relationship between runoff scale effect and environmental factors was revealed. • Runoff coefficients decreased logarithmically as catchment area increased. • Climate and land use contribute significantly to runoff scale effect. • The importance of the indirect effects of farmland on runoff is emphasized. [ABSTRACT FROM AUTHOR]

Raindrop splashing is one of the driving forces of soil erosion, and it also leads to the loss of soil nutrients. However, the effects of raindrop splashing on nitrogen loss and the mechanisms of detachment are still not well understood, especially under different types of rainfall and soil amendment applications, such as polyacrylamide, which has been widely used to reduce soil erosion. In this paper, indoor artificial rainfall experiments were performed to investigate the impact of the raindrop diameter and polyacrylamide (PAM) applications on runoff and soil and nitrogen losses. Three raindrop diameter treatments (1.52, 2.45 and 3.59 mm) and three PAM application treatments (0, 1.0 and 2.0 g/m2) were applied to Kastanozem soil. The results indicated that the infiltration rate, sediment rate and nitrogen concentration in the runoff decreased sharply with the rainfall duration for the first 10 min, but the runoff rate increased under simulated rainfall splashing. In general, the sediment rate for runoff and nitrogen loss increased with the raindrop diameter. The amounts of runoff, sediment, and ammonia and nitrate nitrogen loss were highly reduced with the addition of PAM. The soil and nitrate nitrogen loss decreased with the increasing PAM application rates, and a PAM application rate of 2 g/m2 was optimized for the soil of the test area. Moreover, the ammonia and nitrate nitrogen concentrations were linearly proportional to the sediment rate in the runoff, and the slopes of the linear regression equations increased with the increasing raindrop diameter but were reduced by PAM application. This study provides effective information on the runoff, soil erosion and nitrogen loss that occur during rainfall splash, and it can improve our understanding of splash erosion and nutrient transport. • The amount of raindrop splash and nitrogen loss was increased in the rainfall with the increase of raindrop diameter. • Polyacrylamide application improves soil conditions thus decreasing runoff, raindrop splash and nitrogen loss. • The ammonia and nitrate nitrogen concentration were linearly proportional to the sediment rate in runoff. [ABSTRACT FROM AUTHOR]

Bioretention systems have been extensively studied as a highly efficient technical measure to tackle the global threat of nitrogen pollution during global rainfall runoff. However, the migration and transformation of various forms nitrogen in bioretention system is unclear. So, in this paper, the bioretention systems with different flow regimes and planted configurations were designed to study the nitrogen removal performance and migration and transformation mechanism. The dynamic changes of NH 4 +-N and NO 3 −-N were continuously monitored within 60 h after rainfall, and the abundance of 15N isotopes in soil layer NH 4 +-N was simultaneously measured. The results indicated that NH 4 +-N was mainly intercepted in soil layer in four constructed bioretention systems with similar removal efficiencies (95.42–97.69%). However, NO 3 −-N was retained in submerged layer with significant different removal efficiencies (43.03–83.00%). After fitting calculation, the nitrification rate of NH 4 +-N (0.0626 mg kg−1 h−1) in soil was 5.31 times higher than that of the accumulation rate of NO 3 −-N (0.0118 mg kg−1 h−1). During the elimination process of residual NH 4 +-N in soil, 41.46% removed by denitrification and plant absorption assimilation, another 57.28% stored in the form of organic nitrogen or inorganic nitrogen, only 1.26% leaked out. Based on this, the content variation of TN, NH 4 +-N and NO 3 −-N could be analyzed by a system-wide and established the nitrogen balance model, which provides a new insight into the enhancement of nitrogen removal in the bioretention system. Image 1 • Dynamic changes of NH 4 +-N and NO 3 −-N were continuously monitored in bioretention. • Take the lead in using 15N tracer technology to ascertain the fate pathway of NH 4 +-N. • NH 4 +-N mainly intercepted in soil layer and most NO 3 −-N retained in submerged layer. • Determined the proportion of each pathway in the NH 4 +-N elimination process. • Macroscopically analyzed the nitrogen variation and established the balance model. [ABSTRACT FROM AUTHOR]

[Display omitted] • Tidewater glaciers can supply freshwater to fjord systems throughout winter. • Winter freshwater originates from submarine melt and subglacial discharge. • Fjord-shelf exchange processes in winter are crucial for heat influx. • Winter submarine meltwater inflow can be equivalent to or greater than in summer. • Winter freshwater preconditions the water column for the spring bloom. This review paper is the first to collect and synthesise the available knowledge, across various disciplines, on the importance of wintertime freshwater inflow from tidewater glaciers into Arctic fjords. While surface melt is limited during winter, tidewater glaciers can continue to deliver freshwater into the marine environment. This can be delivered via subglacial discharge or produced by submarine melt. Subglacial freshwater can be generated all year round through geothermal and frictional heat and delayed release of subglacially-stored freshwater. Submarine melt in Arctic fjords is caused by the presence of warm water such as Atlantic Water and its derivative coastal water masses. The dynamics of the contributing water masses are subject to varying bathymetric barriers, seasonally shifting density fronts, and external forcing such as wind or internal waves. Their impact is variable across different fjord systems. When other terrestrial water influx is limited during winter, any glacier-derived freshwater inflow into the fjords can have pronounced physical and biogeochemical consequences, even at low fluxes. These can include the generation of upwelling, but also increased stratification. The extent of the freshwater influence depends on parameters such as discharge volume flux, water depth, and depth of the glacier termini, which might in turn affect ice algae and phytoplankton production during the early spring bloom. Coupling of winter freshwater discharge from tidewater glaciers with the physical and biogeochemical conditions of the fjord is therefore a dynamic multivariable process. Rapidly changing wintertime conditions in the Arctic may already be impacting the functioning of the Arctic fjord ecosystems and necessitate efforts to understand these processes better. This review highlights the importance of wintertime freshwater by summarising the current state of knowledge about its presence and magnitude, drivers governing its inputs, as well as its potential ecological impact on fjord ecosystems. Our study uncovers knowledge gaps and proposes research directions to better understand the changing Arctic environment. [ABSTRACT FROM AUTHOR]

Urban lakes are often disconnected to the surrounding rivers, and they are affected by rainfall runoff pollution. This study focused on an artificial lake-river system in Shanghai featuring a spiderweb-structure, it is expected to detect whether the tributaries in such a system made a protective and buffering effect on the lake under the impact of rainfall runoff. A hydrodynamic model was established using MIKE 21, one of the most widely used software which can conduct two-dimensional modeling of water quality changes in surface water, to simulate the changes of total nitrogen (TN) and total phosphorus (TP), the key indicators causing lake eutrophication, under different rainfall events. The results showed that, under various conditions, storm events almost have no first or final effect on the central lake, TN and TP in the lake were improved by 5%–18% and 1%–12% among monitoring stations. However, Obvious negative effects were displayed in the water quality of radial – circular tributaries, and the first effects in circular tributaries were more serious than those on the radial because their difference in hydrological condition. With the increase of rainfall intensity, the peak value of the pollutant concentration in circular rivers increased 63%–185% as compared with the initial. As to final effects, some circular rivers were significantly impacted, and the pollutant concentration increased by approximately 40% in the seventh day as rainfall started. It was deduced that tributaries played as protective buffer against pollutants in this spiderweb water system, in that pollutants would be degraded in such dense spiderweb-structured river systems before entering the central lake, and rainfall runoff would also accelerate the flow in rivers and lakes. Therefore, the paper hold that, the central lake could not be separated from the radial – circular tributaries, and the officially planned pumps and sluices in the river estuaries would not be recommended. This study would provide technical support for the planning, construction and ecological protection of artificial urban lake-river system in similar areas. [Display omitted] • Effect of rainfall runoff on a spiderweb-structured water system was assessed. • The circular tributaries were more impacted than the radial ones. • The tributaries play a protective role by buffering pollutants from the lake. • Sluices were not suggested to be constructed at the estuaries. [ABSTRACT FROM AUTHOR]

• Ligustrum lucidum tree invasion alters the water balance in a seasonally dry forest. • Runoff was almost nil in both native and L. lucidum forest stands. • L. lucidum forest showed higher annual evapotranspiration rates than native forest. • L. lucidum forest showed drier soil profiles, particularly in the deepest soil layers. Plant invasion is one of the major global environmental threats with the potential to alter the ecosystem stability and functioning. In water-limited ecosystems, the impacts of non-native invasive trees on the water balance are particularly relevant since they can compromise the use of water by native taxa of the ecosystem and water provision for humans. In this paper, we assessed the hydrological impacts of the Ligustrum lucidum (Chinese glossy privet) invasion on a mountain seasonally dry forest of the Chaco region (Central Argentina). More specifically, we determined volumetric soil water content, runoff, and actual evapotranspiration, and their relations to the size of rainfall events in native forest stands and its adjacent invaded stands. We combined three-years field measurements and remote sensing data. L. lucidum stands showed less water content through the soil profiles than the native forest (0–70 cm depth; 19.6 and 23.1 %, respectively), particularly during the dry conditions. The drier condition in the deeper soil layers compared to the upper ones in the invaded stands indicated less rainwater percolation throughout the soil profile over the year, independently of the magnitude of the rainfall events. Daily variation of deep-water content (60–70 cm depth), only observed in L. lucidum stands, showed nightly increase and day-time decrease in dry conditions. Runoff was similar and almost nil in both native and invaded forests, even after large rainfall events (>30 mm). L. lucidum stands showed ∼ 18% higher annual actual evapotranspiration rates than native stands. Based on our results, relatively higher water consumption for mono-specific L. lucidum stands in periods when photosynthetic activity of multi-specific native dry forest is still low may represent a competitive advantage in terms of plant invasion, and at the same time it generates negative impacts on water resources, reducing soil water content and limiting deep-water recharge in a water-limited ecosystem. [ABSTRACT FROM AUTHOR]

Accurate and reliable runoff prediction is of great significance to water resources management, disaster monitoring and rational development and utilization of water resources. In this paper, a metaheuristic evolutionary deep learning model based on Temporal Convolutional Network (TCN), Improved Aquila Optimizer (IAO) and Random Forest (RF) is proposed for rainfall-runoff simulation and multi-step runoff prediction. In this study, the influence of various input variables on the prediction accuracy is discussed. First of all, in order to avoid the dimensional disaster problems and reduce the calculation time, RF is used to calculate the correlation between the input variables and the prediction object, and the data with high correlation is selected as the final inputs. Then, the filtered data are sent to the TCN model, and the parameters of the TCN model are optimized using the IAO algorithm, and the final prediction results are obtained. In this study, the rainfall and runoff data of five stations in the middle reaches of Jinsha River, China were selected, and the runoff of Panzhihua station was simulated and predicted by establishing multiple models. By analyzing and comparing the predictive results of several models, it shows that the models and improvements proposed in this study are effective. [ABSTRACT FROM AUTHOR]

• Calculation unit division induced soil spatial aggregation in SWAT model. • Effect of soil spatial aggregation on simulation was separated and quantified.Soil spatial aggregation caused a significant increase in annual runoff and sediment load. • The effect of soil spatial aggregation on sediment load was greater than on runoff. • Optimal sub-watershed division scheme was discussed. Distributed hydrological model divides watersheds into distinct calculation units to simulate hydrological processes. Within the model, different calculation unit division schemes induce input elements, such as soil and land use, to certain degrees of generalization, thus affecting the simulation result. Analyzing the effect of the calculation unit division on the simulation result is an important methodology to reduce uncertainty and improve the simulation accuracy of the model. Previous researchers have focused on the comprehensive effect of underlying surface generalization (such as soil, land use, and terrain generalization) caused by the calculation unit division on the simulation results, while it did not separate the effect of the generalization of the individual input factor on the simulation result. In this paper, the Dongjiang headwater region in southern China was selected as the study area. The single type of land use and slope were input into the Soil and Water Assessment Tool (SWAT) model to fix the effects of land use and terrain caused by calculation unit division on the simulation results. Eleven calculation unit division schemes were used to analyze the impact of soil spatial aggregation on runoff and sediment load simulations. The calculation unit division schemes included 11 sub-watershed division schemes and their hydrological response unit (HRU) thresholds were set at 8 %. The drainage area thresholds of the 11 sub-watershed division schemes were 30 ha, 60 ha, 120 ha, 240 ha, 480 ha, 1000 ha, 1600 ha, 2200 ha, 2800 ha, 3400 ha and 4000 ha. The results demonstrated that the soil spatial distribution showed an obvious aggregation with an increase in the drainage area threshold in the SWAT model. Of these, the Krasnozem soil type which covers the largest area in the watershed, increased significantly with an increase in the drainage area threshold. However, the areas of all the other soil types decreased significantly. The Lioth soil type covers the smallest area in the watershed decreased and even disappeared when the threshold in the SWAT model was greater than 1000 ha. Furthermore, soil spatial aggregation caused a significant increase in the annual runoff and sediment load (P < 0.05), with the sediment load (Re = 29.78 %) increasing at a greater rate compared to runoff (Re = 1.74 %). Increased soil aggregation significantly increased the sediment loads in the maximum 1-day, the continuous maximum 5-day, the continuous maximum 7-day, and the sediment load in flood processes (P < 0.001). Refining the calculation unit division scheme will cause a significant increase in the number of calculation units. Due to the computer processing power limitations, we cannot further refine the calculation unit division scheme. Based on our current operational calculation unit division scheme, we did not identify the optimal division scheme in the study region. These results can provide reference information for future research into hydrological model uncertainty and can improve the SWAT model simulation accuracy. [ABSTRACT FROM AUTHOR]

• An accurate SWAT model is constructed by considering the influences of various human activities. • The influence of GCM uncertainty on climate change prediction is reduced through multi-model integration and bias correction. • Combined with SDSM model and SWAT model, it is concluded that the future temperature, precipitation and runoff of Yongding watershed will increase. Climate change has affected hydrological processes at global scale, and redistributed water resources in space and time. Anthropogenic activities even exacerbated the water resources problems. In this paper, Statistical DownScaling Model (SDSM) was used to downscale the predicted precipitation and temperature in the mountainous area of Yongding watershed under 4 SSP-RCP climate scenarios in 4 General Circulation Models (GCMs). Bayesian model averaging (BMA) method was employed for multi-model integration to reduce the impact of GCM uncertainty on climate change prediction. Then, the multi-model ensemble data were corrected by the Quantile mapping (QM) method. Soil and Water Assessment Tool (SWAT) was established in the study area considering the intensive human activities, including the water use in irrigation areas and regulation of the 8 large and medium-sized reservoirs. The SWAT model was applied to simulate the runoff response under the future climate change. The results showed that the maximum temperature (T max), minimum temperature (T min), precipitation (P) and runoff all show an upward trend under the four future climate scenarios. SSP585 scenario always showed the highest increase in temperature and precipitation. The simulated future annual runoff increases significantly compared with the reference period, indicating that the water shortage of the watershed may be alleviated in the future. The research results can provide a scientific basis for the decision-making of water resources management and protection in the watershed. [ABSTRACT FROM AUTHOR]