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Water resources in Mediterranean regions are under increasing pressure due to climate change, economic development, and population growth. Many Mediterranean rivers have their headwaters in mountainous regions where hydrological processes are driven by snowpack dynamics and the specific variability of the Mediterranean climate. A good knowledge of the snow processes in the Mediterranean mountains is therefore a key element of water management strategies in such regions. The objective of this paper is to review the literature on snow hydrology in Mediterranean mountains to identify the existing knowledge, key research questions, and promising technologies. We collected 620 peer-reviewed papers, published between 1913 and 2016, that deal with the Mediterranean-like mountain regions in the western United States, the central Chilean Andes, and the Mediterranean basin. A large amount of studies in the western United States form a strong scientific basis for other Mediterranean mountain regions. We found that: (1) the persistence of snow cover is highly variable in space and time but mainly controlled by elevation and precipitation; (2) the snowmelt is driven by radiative fluxes, but the contribution of heat fluxes is stronger at the end of the snow season and during heat waves and rain-on-snow events; (3) the snow densification rates are higher in these regions when compared to other climate regions; and (4) the snow sublimation is an important component of snow ablation, especially in high-elevation regions. Among the pressing issues is the lack of continuous ground observation in high-elevation regions. However, a few years of snow depth (HS) and snow water equivalent (SWE) data can provide realistic information on snowpack variability. A better spatial characterization of snow cover can be achieved by combining ground observations with remotely sensed snow data. SWE reconstruction using satellite snow cover area and a melt model provides reasonable information that is suitable for hydrological applications. Further advances in our understanding of the snow processes in Mediterranean snow-dominated basins will be achieved by finer and more accurate representation of the climate forcing. While the theory on the snowpack energy and mass balance is now well established, the connections between the snow cover and the water resources involve complex interactions with the sub-surface processes, which demand future investigation. [ABSTRACT FROM AUTHOR]

We report on measurements of the salinity and temperature in the Yax Chen cave system on the Yuacatan peninsula. This paper is submitted together with Kovacs et al. (2017). Kovacs et al. focuses on the salinity levels of the meteoric lens, while this paper uses the observed results to elucidate the hydrodynamics. The cave passages have water depths on the order of 10 m, with flow on the order of ten centimeters a second, and as such is a hydrodynamic, as opposed to a porous, system. The measurements reveal that episodes of significant mixing between the fresh meteoric lens and the underlying salty water are driven by meteorological events (e.g., Hurricane Rina in 2011, and the twin Hurricanes Ingrid and Manuel in 2013). We find evidence that after the hurricanes in 2013, the water column remains unstable for several months. Through wavelet analysis, we find that the marine Water Mass (WM) exhibits much less low period activity compared to the meteoric WM. We hypothesize that the open cenotes are locations of high mixing intensity, with turbulent fronts propagating away from the sites of direct mixing into the cave network. We perform laboratory experiments and numerical simulations to explore this phenomenon, and find that mixing preferentially occurs on the flanks of regions of strong, stable density stratification (i.e., on the periphery of pycnoclines), and leads to entrainment of fluid into the turbulent region. Using high resolution direct numerical simulation, we explore the detailed manner in which turbulent entrainment can drive flow toward the mixing region, and lead to mixing of passive tracers. Finally, we discuss the implications of these results for the mixing of passive tracers, such as suspended chemicals. [ABSTRACT FROM AUTHOR]

The paper presents issues relating to the influence of time distribution of rainfall on the required storage capacity of stormwater reservoirs. The research was based on data derived from simulations of existing drainage systems. The necessary models of catchments and the drainage system were prepared using the hydrodynamic modelling software SWMM 5.0 (Storm Water Management Model). The research results obtained were used to determine the critical rainfall distribution in time which required reserving the highest capacity of stormwater reservoir. In addition, it can be confirmed based on the research that dimensioning of enclosed structures should rely on using the critical precipitation generated as the characteristics of a synthetically developed rainfall vary dynamically in time. In the final part of the paper, the results of the analyses are compared and followed with the ensuing conclusions. The results of the research will have impact on the development of methodologies for dimensioning retention facilities in drainage systems. [ABSTRACT FROM AUTHOR]

The study of reservoir deterministic optimal operation can improve the utilization rate of water resource and help the hydropower stations develop more reasonable power generation schedules. However, imprecise forecasting inflow may lead to output error and hinder implementation of power generation schedules. In this paper, output error generated by the uncertainty of the forecasting inflow was regarded as a variable to develop a short-term reservoir optimal operation model for reducing operation risk. To accomplish this, the concept of Value at Risk (VaR) was first applied to present the maximum possible loss of power generation schedules, and then an extreme value theory-genetic algorithm (EVT-GA) was proposed to solve the model. The cascade reservoirs of Yalong River Basin in China were selected as a case study to verify the model, according to the results, different assurance rates of schedules can be derived by the model which can present more flexible options for decision makers, and the highest assurance rate can reach 99%, which is much higher than that without considering output error, 48%. In addition, the model can greatly improve the power generation compared with the original reservoir operation scheme under the same confidence level and risk attitude. Therefore, the model proposed in this paper can significantly improve the effectiveness of power generation schedules and provide a more scientific reference for decision makers. [ABSTRACT FROM AUTHOR]

This paper developed an automatic mesh generator used for simulating real-world problems of hydrological cycles, fluid flow, salinity and thermal transport, sediment and water quality transport, etc. The 2-D overland area was discretized with quadrilaterals based on conformal mapping technique. A position-percent interpolation equation was established to determine nodal vertical-coordinates and match boundaries. For 3-D simulations of subsurface zones, an additional mesh generation method was proposed to create quadrilaterals for filling the empty regions along river reaches and around junctions with storage, ponds or lakes. In order to deal with special storage region with an odd number of boundary nodes, an additional triangle was inserted. Five topological optimization templates were proposed to improve the quality of degenerated elements. This paper proposed the basic algorithms to generate quadrilateral-prism meshes through stretching the quadrilateral mesh along vertical directions based on material domains. Aiming at four types of fractures, corresponding extraction methods were presented to preserve geometric features. In order to accommodate the need for overland and subsurface simulations, 1-D/2-D/3-D correspondence was established between rivers, junctions, ponds, lakes, control structures and finite element meshes. Finally, several realistic hydrologic and geologic examples for meshing multi-domain areas were provided to demonstrate the accuracy and effectiveness of the mesh generator developed in this paper. [ABSTRACT FROM AUTHOR]

Flood frequency analysis (FFA) is a necessary tool for water resources management and water infrastructure design. Owing to the existence of variability in sample representation, distribution selection, and distribution parameter estimation, flood quantile estimation is subjected to various levels of uncertainty, which is not negligible and avoidable. Hence, alternative methods to the conventional approach of FFA are desired for quantifying the uncertainty such as in the form of prediction interval. The primary focus of the paper was to develop a novel approach to quantify and optimize the prediction interval resulted from the non-stationarity of data set, which is reflected in the distribution parameters estimated, in FFA. This paper proposed the combination of the multi-objective optimization approach and the ensemble simulation technique to determine the optimal perturbations of distribution parameters for constructing the prediction interval of flood quantiles in FFA. To demonstrate the proposed approach, annual maximum daily flow data collected from two gauge stations on the Bow River, Alberta, Canada, were used. The results suggest that the proposed method can successfully capture the uncertainty in quantile estimates qualitatively using the prediction interval, as the number of observations falling within the constructed prediction interval is approximately maximized while the prediction interval is minimized. [ABSTRACT FROM AUTHOR]

Farm dams that are not managed properly at the individual level can create water storage equity and safety threats to downstream communities and the environment that aggregate at the catchment level: a potential ‘dual-extreme cumulative’ problem. The paper provides indicative evidence and develops understanding of this novel phenomenon and associated policy needs within the Australian setting comprising dual hydrologic extremes of floods and droughts, further exacerbated by climate change. This is achieved through comparative case studies involving surveys of both dam owner perceptions and dam management practices in four States representing a complete range of integrated policy approaches from weak to strong. Survey results find most farmers do not believe dam maintenance is important, will undertake spillway blocking and do not plan for emergencies. These results are supported by physical on-site findings of farmers neglecting dams and blocking or under-designing spillways, in turn storing more water than they are entitled and creating unsafe dams at both the individual and cumulative levels. From detailed cross-case comparative assessment against policy context, it emerges that on-farm perceptions and practices form a range of ‘acceptability’ of dam management that directly reflects policy strength and integration in each setting. The paper advances the international small dams policy, agricultural water management and hydrology literatures, evidencing the need for effective integrated policy to mitigate dual extreme cumulative threats. Importantly, guidance is provided to jurisdictions internationally with high inter-annual rainfall variation on how best to design integrated policy that can achieve both water storage equity and safety in agricultural catchments. [ABSTRACT FROM AUTHOR]

Relative permeability controls the flow of multiphase fluids in porous media. The estimation of relative permeability is generally solved by Levenberg-Marquardt method with finite difference Jacobian approximation (LM-FD). However, the method can hardly be used in large-scale reservoirs because of unbearably huge computational cost. To eliminate this problem, the paper introduces the idea of simultaneous perturbation to simplify the generation of the Jacobian matrix needed in the Levenberg-Marquardt procedure and denotes the improved method as LM-SP. It is verified by numerical experiments and then applied to laboratory experiments and a real commercial oilfield. Numerical experiment indicates that LM-SP uses only 16.1% computational cost to obtain similar estimation of relative permeability and prediction of production performance compared with LM-FD. Laboratory experiment also shows the LM-SP has a 60.4% decrease in simulation cost while a 68.5% increase in estimation accuracy compared with the earlier published results. This is mainly because LM-FD needs 2 n ( n is the number of controlling knots) simulations to approximate Jacobian in each iteration, while only 2 simulations are enough in basic LM-SP. The convergence rate and estimation accuracy of LM-SP can be improved by averaging several simultaneous perturbation Jacobian approximations but the computational cost of each iteration may be increased. Considering the estimation accuracy and computational cost, averaging two Jacobian approximations is recommended in this paper. As the number of unknown controlling knots increases from 7 to 15, the saved simulation runs by LM-SP than LM-FD increases from 114 to 1164. This indicates LM-SP is more suitable than LM-FD for multivariate problems. Field application further proves the applicability of LM-SP on large real field as well as small laboratory problems. [ABSTRACT FROM AUTHOR]

The use of radar measurements for the space time estimation of precipitation has for many decades been a central topic in hydro-meteorology. In this paper we are interested specifically in daily and sub-daily extreme values of precipitation at gauged or ungauged locations which are important for design. The purpose of the paper is to develop a methodology to combine daily precipitation observations and radar measurements to estimate sub-daily extremes at point locations. Radar data corrected using precipitation-reflectivity relationships lead to biased estimations of extremes. Different possibilities of correcting systematic errors using the daily observations are investigated. Observed gauged daily amounts are interpolated to unsampled points and subsequently disaggregated using the sub-daily values obtained by the radar. Different corrections based on the spatial variability and the subdaily entropy of scaled rainfall distributions are used to provide unbiased corrections of short duration extremes. Additionally a statistical procedure not based on a matching day by day correction is tested. In this last procedure as we are only interested in rare extremes, low to medium values of rainfall depth were neglected leaving a small number of L days of ranked daily maxima in each set per year, whose sum typically comprises about 50% of each annual rainfall total. The sum of these L day maxima is first iterpolated using a Kriging procedure. Subsequently this sum is disaggregated to daily values using a nearest neighbour procedure. The daily sums are then disaggregated by using the relative values of the biggest L radar based days. Of course, the timings of radar and gauge maxima can be different, so the method presented here uses radar for disaggregating daily gauge totals down to 15 min intervals in order to extract the maxima of sub-hourly through to daily rainfall. The methodologies were tested in South Africa, where an S-band radar operated relatively continuously at Bethlehem from 1998 to 2003, whose scan at 1.5 km above ground [CAPPI] overlapped a dense (10 km spacing) set of 45 pluviometers recording in the same 6-year period. This valuable set of data was obtained from each of 37 selected radar pixels [1 km square in plan] which contained a pluviometer not masked out by the radar foot-print. The pluviometer data were also aggregated to daily totals, for the same purpose. The extremes obtained using disaggregation methods were compared to the observed extremes in a cross validation procedure. The unusual and novel goal was not to obtain the reproduction of the precipitation matching in space and time, but to obtain frequency distributions of the point extremes, which we found to be stable. [ABSTRACT FROM AUTHOR]

• Due to skilful forcing, hindcasts of runoff and discharge have much skill in north and in southeast South America. • Due to initial conditions, hydrological hindcasts have much skill in the drier regions of the continent. • Spatial patterns of theoretical (compared to reference simulation) and actual skill (compared to observed discharge) are similar. The first aim of the present paper is the determination of the magnitude, annual variation and spatial distribution of skill in seasonal hindcasts of runoff and discharge in the entire continent of South America. We evaluated 35 years of hindcasts generated with the Variable Infiltration Capacity (VIC) hydrological model forced with SEAS5 hindcasts. Initial conditions of terrestrial water and so-called pseudo-observations were computed with a reference (i.e. historic) simulation. Skill was determined with monthly temporal resolution for the entire annual cycle and mostly using the pseudo-observations for verification. The second aim of the paper is the explanation of skill in terms of its sources, namely meteorological forcing and the initial conditions. Therefore, two sets of restricted hindcasts, which isolate the sources of skill, were analysed. The SEAS5 precipitation hindcasts exhibit significant skill even at the longest lead times (7 months). Beyond the first lead month we found significant skill in 13–43 % of the grid cells, depending on target and lead month. Levels of skill are higher in the full hindcasts (significant skill in 31–89 % of the grid cells). At the continental scale more of the skill is caused by the forcing than by the initial conditions. The runoff hindcasts are skilful in large parts of the continent. In a 1000 km wide band along the north coast of the continent and in southeast South America, most of the skill is due to the forcing. A typical feature of these regions is an increase of skill with lead time during specific parts of the year, which is against the common tendency. In Argentina and north Chile most of the skill in the runoff hindcasts can be attributed to the initial conditions of soil moisture. Verification with real observations of discharge broadly confirmed the skill pattern obtained with pseudo-observations. [ABSTRACT FROM AUTHOR]

• Groundwater level prediction for a long period of time is considered in this paper. • We proposed a graph-based machine learning using which the spatial relationships are taken into account. • Our results indicate that spatial correlations are helpful when one deals with geospatial data. • The proposed method is compared with standard techniques. Accurate prediction of groundwater level (GWL) over a period of time is of great importance for groundwater resources management. Machine learning techniques due to their great performance have recently been used for this problem. Previous methods, however, did not consider the spatial relationships between wells due to the difficulty to handle unstructured well location data. In this paper, a graph neural network (GNN) is used to forecast groundwater dynamics where it can represent each well as a node in the graph. The spatial information is, thus, extracted from an interconnected network using graph convolution layers with a self-adaptive adjacency matrix. The temporal features of the sequence are obtained by gated temporal convolutional networks. The model was applied and evaluated for wells in the southwest area of British Colombia in Canada using data about 11 years (2010–2020). The proposed model performs better in terms of all the defined evaluation metrics, when compared with two baseline models: long short-term memory (LSTM) and gated recurrent units (GRU). Moreover, when the spatial dependencies are completely unknown, the model can still learn them from the data and obtain comparable performance. Furthermore, the proposed model has a high efficiency since it can simultaneously model GWL change for all monitoring wells in the system. We also demonstrated that the spatial dependencies between each well could be intuitively interpreted from the learned adjacency matrix. [ABSTRACT FROM AUTHOR]

• Rainfall patterns and land uses in Chile create a unique natural laboratory. • First Forest hydrology review in Chile on water quantity and quality. • This paper review includes 75 studies since 1975. • Twenty research gaps are identified. The most significant is plant-soil-atmosphere relations. • A research agenda to support a future national planning legislation is proposed. This paper reviews the current knowledge of hydrological processes in Chilean temperate forests which extend along western South America from latitude 29° S to 56° S. This geographic region includes a diverse range of natural and planted forests and a broad sweep of vegetation, edaphic, topographic, geologic, and climatic settings which create a unique natural laboratory. Many local communities, endangered freshwater ecosystems, and downstream economic activities in Chile rely on water flows from forested catchments. This review aims to (i) provide a comprehensive overview of Chilean forest hydrology, to (ii) review prior research in forest hydrology in Chile, and to (iii) identify knowledge gaps and provide a vision for future research on forest hydrology in Chile. We reviewed the relation between native forests, commercial plantations, and other land uses on water yield and water quality from the plot to the catchment scale. Much of the global understanding of forests and their relationship with the water cycle is in line with the findings of the studies reviewed here. Streamflow from forested catchments increases after timber harvesting, native forests appear to use less water than plantations, and streams draining native forest yield less sediment than streams draining plantations or grassland/shrublands. We identified 20 key knowledge gaps such as forest groundwater systems, soil–plant-atmosphere interactions, native forest hydrology, and the effect of forest management and restoration on hydrology. Also, we found a paucity of research in the northern geographic areas and forest types (35-36°S); most forest hydrology studies in Chile (56%) have been conducted in the southern area (Los Rios Region around 39-40° S). There is limited knowledge of the geology and soils in many forested areas and how surface and groundwater are affected by changes in land cover. There is an opportunity to advance our understanding using process-based investigations linking field studies and modeling. Through the establishment of a forest hydrology science "society" to coordinate efforts, regional and national-scale land use planning might be supported. Our review ends with a vision to advance a cross-scale collaborative effort to use new nation-wide catchment-scale networks Long-term Ecosystem Research (LTER) sites, to promote common and complementary techniques in these studies, and to conduct transdisciplinary research to advance sound and integrated planning of forest lands in Chile. [ABSTRACT FROM AUTHOR]

Coastal aquifers provide a source of water for more than one billion people, with island freshwater lenses being some of the most vulnerable coastal groundwater systems due to their susceptibility to saltwater intrusion. Basic hydrogeological and hydrochemical knowledge regarding the recharge and salinisation processes of freshwater lenses is important to ensure sustainable utilisation, especially considering possible climate change effects. This paper makes an assessment of the fate of a freshwater lens in a drying climate through a comparison of current and historic hydrochemical data, which to the author’s knowledge is unique to this study. Fresh groundwater stable isotope signatures (δ 18 O, δ 2 H) reflect local amount weighted rainfall signatures (δ 18 O: −3.8‰; δ 2 H: −15.1‰), and confirm rainfall as the origin of fresh groundwater (δ 18 O: −4.47 to −3.82‰; δ 2 H: −20.0 to −16.6‰). Mixing with seawater was identified through enriched groundwater δ 18 O and δ 2 H signatures (maximum values of −0.36‰ and −1.4‰ respectively) compared to local rainfall and higher salinity (maximum 29,267 mg/L Total Dissolved Solids (TDS)) in a number of monitoring wells around the freshwater lens. Enhanced seawater intrusion detected in the northern section of the lens area was identified through significantly increased TDS values over the last 20–40 years, with increases of up to 3000% observed between 1990 and 2014. A reduction in the extent of freshwater by approximately 1 km 2 was identified since 1977, which was found to be primarily caused by a reduction in recharge to the freshwater lens due to a ∼20% decline in winter rainfall in the south-west Western Australian region since the mid 1960s. Groundwater abstraction was found to equate to between 5% and 9% of the estimated recharge for the island, and is not a significant factor in the reduction of the lens extent compared to the observed decline in rainfall recharge. Interestingly, seawater intrusion into the fresh water lens was found to occur by older seawater (0.03–0.09 TU) in regions of the lens that were previously fresh or slightly brackish, while one sample (0.67 TU) suggests either modern seawater intrusion or mixing of older saline groundwaters (>60 years) with rainfall recharge. The use of tritium dating in this island aquifer was essential in identifying ‘older’ seawater that was previously unidentified until now. The isotopic and hydrochemical tools used in this paper quantify the effects of groundwater abstraction and climate variability on the freshwater lens and have implications for the sustainable management of the groundwater resource on Rottnest Island, and elsewhere. [ABSTRACT FROM AUTHOR]

Risk assessment is a preliminary step in flood management and mitigation, and risk zoning provides a quantitative measure of flood risk. The difficulty in flood risk zoning is to deal with the complicated non-linear relationship among indices and risk levels. To solve this problem, the ant colony algorithm based on rule mining (Ant-Miner) is promoted in this paper to map the regional flood risk at grid scale. For the case study in the Dongjiang River Basin in Southern China, 11 and 14 indices (without and with the socio-economic indices considered) are respectively chosen to construct the zoning model based on Ant-Miner. The results show that Ant-Miner exhibits higher accuracy and more simple rules that can be used to generate flood risk zoning map quickly and easily than decision tree method (DT); compared to random forest (RF) and fuzzy comprehensive evaluation (FCE), Ant-Miner has significant advantages both in implementation step-reducing and computing time-saving. Although the comprehensive measure and natural hazard measure of flood risk distributed similarly over the entire region, the former one which considered the socio-economic indices is more reasonable in term of real impact to natural and socio-economy. The areas with high-risk level obtained in this paper matched well with the integrated risk zoning map and the inundation areas of historical floods, suggesting that the proposed Ant-Miner method is capable of zoning the flood risk at grid scale. This study shows the potential to provide a novel and successful approach to flood risk zoning. Evaluation results provide a reference for flood risk management, prevention, and reduction of natural disasters in the study basin. [ABSTRACT FROM AUTHOR]

Erosion and deposition are fundamental surface processes, and the Hairsine-Rose (HR) model describing these dynamic processes for individual particle size classes has increasingly been applied at a range of scales. There are no exact solutions of the HR equations for the dynamic erosion of multi-size-classed soil. For numerical solution, upwind explicit schemes which have been applied so far generally require extremely small time intervals (10 −2 –10 −3 s) for numerical stability, thus limiting the computational efficiency. The Preissmann scheme, which is flexible and can be fully implicit, has been widely used to simulate open-channel flows. The objective of the paper was to implement, for the first time, the Preissmann scheme to solve the HR equations, and to verify the accuracy and evaluate the efficiency of the scheme. The paper shows that (1) implementation of the Preissmann scheme, while complex, is straightforward, and the numerical solution of the HR equations can be reduced to a set of 2 I algebraic equations where I is the number of size classes, with no matrix operations or iteration involved; (2) the numerical solution is verified for steady state solutions, and for the only exact solution for uniform grain-sized soil known to exist; (3) for two benchmark tests to simulate dynamic erosion of multi-size-classed soil. The time interval using the Preissmann scheme can be much larger (∼1 s), and there is improvement in overall computational efficiency so long as the time interval for the Preismann scheme is at least 6 times larger than that for upwind explicit schemes for the case of flow-driven erosion. The improvement in computational efficiency can be even more pronounced for overland flows with rainfall where the water depth can be quite small near the upper end of the slope. [ABSTRACT FROM AUTHOR]

Summary The paper presents the set-up and functioning of a flood alert system based on SURFEX–TOPODYN platform for the cross-border Arda River basin. The system was built within a Bulgarian-Greek project funded by the European Territorial Cooperation (ETC) Programme and is in operational use since April 2014. The basin is strongly influenced by Mediterranean cyclones during the autumn–winter period and experiences dangerous rapid floods, mainly after intensive rain, often combined with snow melt events. The steep mountainous terrain leads to floods with short concentration time and high river speed causing damage to settlements and infrastructure. The main challenge was to correctly simulate the riverflow in near-real time and to timely forecast peak floods for small drainage basins below 100 km 2 but also for larger ones of about 1900 km 2 using the same technology. To better account for that variability, a modification of the original hydrological model parameterisation is proposed. Here we present the first results of a new model variant which uses dynamically adjusted TOPODYN river velocity as function of the computed partial streamflow discharge. Based on historical flooding data, river sections along endangered settlements were included in the river flow forecasting. A continuous hydrological forecast for 5 days ahead was developed for 18 settlements in Bulgaria and for the border with Greece, thus giving enough reaction time in case of high floods. The paper discusses the practical implementation of models for the Arda basin, the method used to calibrate the models’ parameters, the results of the calibration-validation procedure and the way the information system is organised. A real case of forecasted rapid floods that occurred after the system’s finalisation is analysed. One of the important achievements of the project is the on-line presentation of the forecasts that takes into account their temporal variability and uncertainty. The web presentation includes a comparison of the forecasted river flow to three predefined alert levels. [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]

Summary Flash floods are responsible for a majority of natural disaster fatalities in the USA and Europe and most of them are vehicle-related. If human exposure to flood is generally assessed through the number of inhabitants per buildings located in flood prone zone, it is clear that this number varies dramatically throughout the day as people move from place to place to follow their daily program of activities. Knowing the number of motorists exposed on flood prone road sections or the factors determining their exposure would allow providing a more realistic evaluation of the degree of exposure. In order to bridge this gap and provide emergency managers with methods to assess the risk level for motorists, this paper describes two methods, a simple rough-and-ready estimate and a traffic attribution method, and applies both of them on datasets of the Gard département , an administrative region of Southern France with about 700 000 inhabitants over 5875 km 2 . The first method to obtain an overall estimation of motorists flood exposure is to combine (i) the regional density of roads and rivers to derive a count of potential road cuts and (ii) the average daily kilometers driven by commuters of the study area to derive the number of people passing these potential cuts. If useful as a first approximation, this method fails to capture the spatial heterogeneities introduced by the geometry of river and road networks and the distribution of commuters’ itineraries. To address this point, this paper (i) uses a pre-established detailed identification of road cuts (Naulin et al., 2013) and (ii) applies a well-known traffic attribution method to existing and freely available census datasets. Both methods indicate that commuters’ exposure is much larger than the number of commuters itself, illustrating the risk amplification effect of mobility. Comparing the results from both methods shows that (i) the road network geometry plays a significant role in reducing the risk of river-road dangerous intersections and (ii) not all commuters are equally exposed. Evidently commuters who have longer routes are more exposed, but residents of rural municipalities as well as professionals with highly qualified jobs are also more exposed. Finally, these exposure assessment methods applied to the Gard area allows locating road sections where commuters’ exposure to flood is high. It also sets the first step toward the implementation of a modeling platform able to combine the estimation of daily travel patterns exposure and behavioral response of motorists to road flooding, a critical input for emergency services and services in charge of the management of road networks in flash flood prone areas. [ABSTRACT FROM AUTHOR]

Summary The aim of this paper is to analyse the potential relationship between flash flood events and convective precipitation in Catalonia, as well as any related trends. The paper starts with an overview of flash floods and their trends in the Mediterranean region, along with their associated factors, followed by the definition of, identification of, and trends in convective precipitation. After this introduction the paper focuses on the north-eastern Iberian Peninsula, for which there is a long-term precipitation series (since 1928) of 1-min precipitation from the Fabra Observatory, as well as a shorter (1996–2011) but more extensive precipitation series (43 rain gauges) of 5-min precipitation. Both series have been used to characterise the degree of convective contribution to rainfall, introducing the β parameter as the ratio between convective precipitation versus total precipitation in any period. Information about flood events was obtained from the INUNGAMA database (a flood database created by the GAMA team), with the aim of finding any potential links to convective precipitation. These flood data were gathered using information on damage where flood is treated as a multifactorial risk, and where any trend or anomaly might have been caused by one or more factors affecting hazard, vulnerability or exposure. Trend analysis has shown an increase in flash flood events. The fact that no trends were detected in terms of extreme values of precipitation on a daily scale, nor on the associated ETCCDI (Expert Team on Climate Change Detection and Indices) extreme index, could point to an increase in vulnerability, an increase in exposure, or changes in land use. However, the summer increase in convective precipitation was concentrated in less torrential events, which could partially explain this positive trend in flash flood events. The β parameter has been also used to characterise the type of flood event according to the features of the precipitation. The highest values correspond to short and local events, usually with daily β values above 0.5, while the minimum threshold of daily β for catastrophic flash floods is 0.31. [ABSTRACT FROM AUTHOR]

An essential task of performance assessment of radioactive waste repositories is to predict radionuclide release into the environment. For such a quantitative assessment, the Channel Network Model and the corresponding computer program, CHAN3D, have been used to simulate radionuclide transport in crystalline bedrocks. Recent studies suggest, however, that the model may tend to underestimate the rock retarding capability, because it ignores the presence of stagnant water zones, STWZs, situated in the fracture plane. Once considered, the STWZ can provide additional surface area over which radionuclides diffuse into the rock matrix and thereby contribute to their retardation. The main objective of this paper is to extend the Channel Network Model and its computer implementation to account for diffusion into STWZs and their adjacent rock matrices. In the first part of the paper, the overall impact of STWZs in retarding radionuclide transport is investigated through a deterministic calculation of far-field releases at Forsmark, Sweden. Over the time-scale of the repository safety assessments, radionuclide breakthrough curves are calculated for increasing STWZ width. It is shown that the presence of STWZs enhances the retardation of most long-lived radionuclides except for 36 Cl and 129 I. The rest of the paper is devoted to the probabilistic calculation of radionuclide transport in fractured rocks. The model that is developed for transport through a single channel is embedded into the Channel Network Model and new computer codes are provided for the CHAN3D. The program is used to (I) simulate the tracer test experiment performed at Äspö HRL, STT-1 and (II) investigate the short- and long-term effect of diffusion into STWZs. The required data for the model are obtained from detailed hydraulic tests in boreholes intersecting the rock mass where the tracer tests were made. The simulation results fairly well predict the release of the sorbing tracer 137 Cs. It is found that over the short time-scale of the tracer experiment, the effect of diffusion into STWZs is not as pronounced as that of matrix diffusion directly from the flow channel, and the latter remains the main retarding mechanism. Predictions for longer time-scale, tens of years and more, show that the effect of STWZs becomes strong and tends to increase with transport time. It is shown that over the long times of interest for safety assessment of radioactive waste repositories, STWZs can substantially contribute to radionuclide retardation, though for the short time-scales the impact is not very strong and is not expected to affect the results of short-term field experiments. [ABSTRACT FROM AUTHOR]

Despite wide use of regression-based regional flood frequency analysis (RFFA) methods, the majority are based on either ordinary least squares (OLS) or generalized least squares (GLS). This paper proposes ‘spatial proximity’ based RFFA methods using the spatial lagged model (SLM) and spatial error model (SEM). The proposed methods are represented by two frameworks: the quantile regression technique (QRT) and parameter regression technique (PRT). The QRT develops prediction equations for flooding quantiles in average recurrence intervals (ARIs) of 2, 5, 10, 20, and 100 years whereas the PRT provides prediction of three parameters for the selected distribution. The proposed methods are tested using data incorporating 30 basin characteristics from 237 basins in Northeastern United States. Results show that generalized extreme value (GEV) distribution properly represents flood frequencies in the study gages. Also, basin area, stream network, and precipitation seasonality are found to be the most effective explanatory variables in prediction modeling by the QRT and PRT. ‘Spatial proximity’ based RFFA methods provide reliable flood quantile estimates compared to simpler methods. Compared to the QRT, the PRT may be recommended due to its accuracy and computational simplicity. The results presented in this paper may serve as one possible guidepost for hydrologists interested in flood analysis at ungaged sites. [ABSTRACT FROM AUTHOR]

This paper describes a Bayesian statistical model for estimating flood frequency by combining uncertain annual maximum (AMAX) data from a river gauge with estimates of flood peak discharge from various historic sources that predate the period of instrument records. Such historic flood records promise to expand the time series data needed for reducing the uncertainty in return period estimates for extreme events, but the heterogeneity and uncertainty of historic records make them difficult to use alongside Flood Estimation Handbook and other standard methods for generating flood frequency curves from gauge data. Using the flow of the River Eden in Carlisle, Cumbria, UK as a case study, this paper develops a Bayesian model for combining historic flood estimates since 1800 with gauge data since 1967 to estimate the probability of low frequency flood events for the area taking account of uncertainty in the discharge estimates. Results show a reduction in 95% confidence intervals of roughly 50% for annual exceedance probabilities of less than 0.0133 (return periods over 75 years) compared to standard flood frequency estimation methods using solely systematic data. Sensitivity analysis shows the model is sensitive to 2 model parameters both of which are concerned with the historic (pre-systematic) period of the time series. This highlights the importance of adequate consideration of historic channel and floodplain changes or possible bias in estimates of historic flood discharges. The next steps required to roll out this Bayesian approach for operational flood frequency estimation at other sites is also discussed. [ABSTRACT FROM AUTHOR]

The joint optimization scheduling model of cascade reservoirs has the characteristics of high dimensionality, dynamic, nonlinear, multivariate, and strong constraint. Around its solution problem, this paper carries out the research of the efficient model solution method based on the group intelligent optimization algorithm. Firstly, given the defect of the standard collaborative search algorithm that it is easy to falls into local optimization and weakening of global search ability in the later stage, three efficient improvement strategies are proposed through parameter randomization, elite reinforcement learning, and elite help learning. And based on this, an elite collaborative search algorithm (ECSA) coupled with the three improvement strategies is established, which enhances the ability of individuals to search globally and jump out of local optimality, and it has strong robustness and optimization ability. Secondly, taking six cascade power stations in the Yalong River Basin as an example, the power generation dispatching model of cascade reservoirs considering various constraints such as reservoir discharge flow and water level restriction are constructed, and six typical runoff scenarios are selected, and the joint power generation scheduling test is carried out by using five algorithms, i.e., ECSA, CSA, PSO, GWO, and WOA. Finally, through the comparison of the scheduling results, it is found that compared with the other algorithms, the solution obtained by the proposed ECSA in this paper can effectively improve the overall power generation of the basin and the efficiency of water resource utilization in river basins. Taking the normal year as an example, compared with the CSA、PSO、GWO，and WOA method, the average annual increase of power generation by the ECSA method is 32, 4778, 4153, and 2288 million kWh respectively. • Three efficient improvement strategies are proposed for CSA in this paper. • A new elite collaborative search algorithm is established in this paper. • Six cascade power stations in the Yalong River Basin is taken as the case study. • The solution obtained by ECSA is the best compared with the other four algorithms. [ABSTRACT FROM AUTHOR]

• Review of hydro-morphological responses to LULC and climate changes. • The first review paper of this type focused on tropical regions. • A comprehensive review of 60 case studies. • Meta-analysis of the findings of all case studies. • Significant correlations are revealed between LULCC and hydrological processes. Tropical regions have experienced the fastest Land Use Land Cover Change (LULCC) in the last decades, coupled with climate change (CC) this has affected the hydrological and geomorphological processes of river systems. With the increased demand for land, the general trend has been the loss of forest land to agriculture and settlements. These changes have altered the water balance components through enhanced or reduced evaporation, peak flow, flooding, and river morphology. The aim of this review paper is to provide a meta -analysis on the effects of spatiotemporal changes in climate and LULC on river hydro-morphology in the tropics. Following a systematic search, 60 case studies were identified, of which the majority (68%) experienced forest loss due to agricultural and urban expansion, resulting in increased streamflow, surface flow, and total water yield and decreased ET and groundwater recharge. 12% of the case studies showed the impacts of LULCC on channel morphology features through sediment transport and riverbank erosion. Results from this study show limited correlation between LULCC and hydrological variables, indicating that there are likely other factors controlling hydrological processes. Catchment heterogeneity including soil and topography play an important role. Based on studies that project these changes into the future, similar trends are expected over the next decades, with differences based on LU and climate scenarios. There are still limited studies on river hydro-morphology responses to LULCC and CC in the tropics despite the major changes taking place there. In light of future changes, more studies are needed to improve our understanding. [ABSTRACT FROM AUTHOR]

This paper presents the purpose, scope and content of this Special Issue on Building Socio-Hydrological Resilience. It begins by presenting a background on the concept of socio-hydrological resilience in the times of global change. Further, it provides a brief overview on the twelve presented papers in this special issue to make explicit how they have made contribution to the context of socio-hydrological resilience. [ABSTRACT FROM AUTHOR]

• An updated SEBALIGEE system is proposed that can estimate global ET in few seconds. • An open-source ML algorithm was designed to estimate ET at a 30-m resolution. • Annual wheat ET decreased significantly from 2013 to 2021 over CONUS. • Corn or soybeans exhibit comparable characteristics and influencing factors. • SEBALIGEE v2 is expected to advance Precision Agriculture at regional/global scales. The open-source fully-automated Surface Energy Balance Algorithm for Land-Improved (SEBALI) Google Earth Engine (SEBALIGEE) estimates 30-m actual evapotranspiration (ET) at a monthly rate, a much needed parameter in many hydrological and agricultural applications. An improved version of the basin-based SEBALIGEE v1 is proposed in this paper. The improvement of SEBALIGEE v1, designated as v2, focuses primarily on incorporating advanced machine learning approaches which have enabled for the first time to implement SEBALIGEE over any scale application and enhance its overall performance. More particularly, an evaluation of the monthly ET estimated from the new algorithm across several fluxnet sites in the US, China, Italy, Belgium, Germany, and France, yielded a Mean Absolute Error (MAE) of 12.22 mm/month versus 14.54 mm/month in the original SEBALIGEE v1. Furthermore, we applied the new any-scale capability of SEBALIGEE v2 over the contiguous United States (CONUS) while emphasizing on the three main crops, including corn, soybeans and winter wheat. Our analysis indicated that all three crops presented two similar ET seasonal cycles with peaks occurring in late spring to the summer (May-Aug) and between October and January, corresponding well to the key stages of crop life cycle. Moreover, corn and soybeans exhibited similar magnitudes of ET (36 ∼ 168 mm/month) and higher than winter wheat (33 ∼ 122 mm/month), with large standard deviations observed in the ET estimates amogst all the crops. On interannual comparisons, the corn and soybeans ET and aKc showed higher values than winter wheat, with the highest and lowest years identified and discussed. Following an exploratory analysis against some of the most common interfering variables such as air temperature, dewpoint temperature, surface net solar radiation, wind speed, SPEI drought index calculated on 14 days and 30-days, it was noted that the surface net solar radiation had the most influencing factor on ET in corn and soybeans plantations with R2 values of ∼0.72. The SPEI-30 stands out for winter wheat, showing a water scarcity tolerance up to a month in most of its developing stages. Different management practices are then recommended in each of these two crops' categories (corn and soybeans vs. winter wheat). [ABSTRACT FROM AUTHOR]

• We propose a multi-stage progressive adaptive coupling model for reservoir scheduling and water allocation models that considers the key operation indicators of the project in the water source area and the multi-dimensional objectives in the receiving area. • A functional relationship that balances considerations regarding water, the economy, and ecology in the receiving area was derived. • We used an advanced optimization algorithm-Multi-objective Evolutionary Algorithm Based on Decomposition to solve the multi-stage model. The efficient coupling of a water allocation model with a reservoir operation model is key to maximizing the benefits of inter-basin water transfer (IBWT) projects. Most existing studies of IBWT operations and management do not account for matching water transfer and demand, and instead seek to optimize either the water allocation model or the reservoir scheduling model. In this paper, we propose a multi-stage joint optimization framework for reservoir scheduling and water allocation models that considers the key operation indicators of the project in the water source area and the multi-dimensional objectives in the receiving area. We derived a functional relationship that balances considerations regarding water, the economy, and ecology (WEE) in the receiving area and applied it to the water allocation model. The reservoir operation model was developed by considering the multiple benefits of water transfer and power generation in the water resource area. To reduce the complexity of the model and the tightness of its coupling, the model was divided into multiple stages of optimization and solved using the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) algorithm. The results showed the allocation schemes of water resources influenced the competition between the economic and ecological benefits. Using the WEE model, a more reasonable water transfer and allocation method can be selected according to different development needs. As such, the water shortage rate of each user in the receiving area is kept below 8%, and there is significant improvement in all indicators compared with the conventional scheme. In addition, this model enables the reservoir group to achieve a power generation capacity of more than 5.64 kWh with the goal of meeting the water transfer demand, maintaining the reservoir empty rate at about 10%, and reducing the risk of water shortages. Our study can serve as a reference for effectively addressing the challenges associated with IBWT projects, and it can inform socioeconomic development in receiving areas while optimizing water transmission and allocation. [ABSTRACT FROM AUTHOR]

[Display omitted] • We proposed a new double verification and quantitative traceability (DV-QT) method. • The uncertainty of the conventional water source identification method is eliminated by double verification. • The multivariate mixing and mass balance calculations (M3) model is used to calculate the proportion of mine water sources for the first time. • A new water inrush model of indirect connection and recharge was proposed. Accurate identification of mine water sources is one of the keys to safe coal mining. However, conventional water source identification methods rely on groundwater-level responses and hydrochemical characteristics. The identification results often have multiple interpretations, lack verification of the actual situation, and cannot trace source quantitatively for mixed water sources. In this paper, we propose a new double verification and quantitative traceability (DV-QT) method. The double verification consists of verification and the consistency of water source identification results based on conventional methods and verification of the rationality of both the water source identification results and the inrushing water channel results. When the verification fails, the groundwater-level or hydrochemical data shall be analyzed in depth, or the hydraulic connection between groundwater in the channel and water source needs to be determined. The quantitative traceability includes the multivariate mixing and mass balance calculations (M3) that calculate the proportion of water sources. Through an example application, the proposed method identified the causes of the identification error of the traditional hydrochemical characteristic analysis method, that is, the hydrochemical evolution process during water inrush cannot be ignored, and allowed for accurate water source identification and quantitative mine water traceability. The new approach is especially useful for mixed water sources and in mines with complicated hydrogeological conditions. [ABSTRACT FROM AUTHOR]

• A framework is proposed to analyze compound floods with multiple driving factors. • Local rainfall has a larger impact on the flooding levels of the GBA, China. • Flooding area is about 50 km2 and 1.3 m flooded depth for the worst scenario. • Findings have important implications to GBA's flooding control and management. Low-lying coastal cities are vulnerable to compound floods caused by many factors including river flows, tides and local rainfall. Many previous studies focus on the impacts of rainfall and tidal levels (two driving factors) on estuaries or regions near the main single river, while research about the three influencing factors on the floods for complex urban river networks remain relatively scarce. This paper proposes a framework to simulate the coastal compound flooding process, with a series of scenarios designed to explore the main flooding drivers and their worst possible combination. The approach is used to quantify the induced flooding consequences, in which a two-dimensional model is employed to conduct a case study for a city with complex river networks. The Qianshan River basin in Zhuhai City, located in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China is used as the case study. Application results show that: (i) the occurrence and properties of flooding in this coastal area are jointly affected by upstream river flows, downstream tidal levels and local rainfall intensity with different contribution levels, (ii) compared to tidal levels, local rainfall intensity has a larger impact on the flooding levels of the study region, and (iii) the worst scenario where a 50-year return period upstream river flow co-occurring with high tide and a 100-year return period rainfall can produce a flooding area up to 50 km2 for the study region, with an average of 1.3 m flooded depth. The proposed framework with its findings offers an in-depth understanding and quantification of coastal urban flood characteristics within the GBA of China. [ABSTRACT FROM AUTHOR]

• Experiments contrasted ten machine learning models in susceptibility prediction. • Hybrid ensemble models significantly outperformed widely-used individual models. • Study profiled the first urban waterlogging susceptibility map in Beijing, China. • Urban road network and densely populated areas face severer waterlogging risk. Urban waterlogging has emerged as a significant problem worldwide, particularly in densely populated cities. Accurate assessment of waterlogging susceptibility at the city scale is crucial for mitigating the risks associated with waterlogging and optimizing municipal design accordingly. However, existing studies on urban waterlogging susceptibility assessment have primarily relied on individual machine learning models. It is worthwhile to explore whether hybrid ensemble models have the potential to enhance the predictive performance. This research presents two hybrid ensemble machine learning models, namely Stacking and Blending, for assessing urban waterlogging susceptibility in the metropolitan area of Beijing, China. The performances of these models are compared with those of the widely used individual machine learning models. Evaluation of all the models is based on metrics such as Accuracy rate and Area Under Curve (AUC) score. The results demonstrate that the Stacking and Blending models consistently outperform the traditional machine learning models, such as Random Forest, Logistic Regression, etc. Through susceptibility analysis and model interpretation with SHAP method, this paper obtains several key findings that low lying areas may not necessarily be areas with severe waterlogging; urban roads and densely populated areas are highly susceptible to becoming high-risk areas for waterlogging in the study area. This study not only highlights the effectiveness of the Stacking and Blending models for urban waterlogging susceptibility assessment but also provides valuable insights for waterlogging mitigation strategies in urban planning. [ABSTRACT FROM AUTHOR]

• Groundwater recharge changes with freeze–thaw processes. • The complete thawing period contributed the highest recharge amount at the fastest recharge speed. • Groundwater recharge during the freezing period is strongly influenced by temperature. Accurate estimation of groundwater recharge (GR) is crucial for sustainable groundwater use, particularly in alpine regions that are sensitive to climate change response. However, quantifying the amount of GR in these areas is challenging due to the absence of measurement data and an unclear understanding of the GR mechanism. In this paper, we proposed a new method (HS-IWTF) for estimating GR in the central Qinghai-Tibet Plateau. The method utilized Hydrus-1D to create an unsaturated zone soil water transport model, which provides a reference value for specific yield (Sy). Additionally, the method incorporated the impact of groundwater potential decline at various freeze–thaw stages to improve recharge estimation. The results showed that HS-IWTF method can be better applied for estimating GR in alpine regions. The simulated recharge of groundwater though vertical infiltration ranged from 191.7 to 217.8 mm/yr, primarily from precipitation infiltration. The total GR ranged from 312.1 to 405.0 mm/yr, with significant recharge contributed by snow and ice thaw and lateral runoff. The HS-IWTF method is more precise and reliable than simulated infiltration and traditional water table fluctuation (WTF) method in reflecting recharge changes at various monitoring sites during each freeze–thaw period. The thawing process period (0.95 mm/d, 11 %) and the complete thawing period (1.07 mm/d, 61 %) contributed more recharge at higher recharge rates due to improved recharge sources and conditions. The air temperature rise promoted the degradation of the cryosphere and raised the ratio of recharge precipitation (GR/PT), particularly in regions with high altitude and proximity to permafrost and glaciers where recharge was significantly high during the freezing period. This study provides valuable information on the measurement of GR in alpine regions and the crucial role of freeze–thaw processes in GR. [ABSTRACT FROM AUTHOR]

• Developed the M-XAJ model incorporating minor impoundment effects to improve flood forecasting precision. • Derived aggregated reservoir discharge capacity curves for data-scarce small water bodies. • Computed aggregated reservoir releases via multi-bottom sills graded weir flow pattern. To improve flood prediction accuracy in the basin, this paper constructs a modified Xin'anjiang (M-XAJ) model to account for the regulatory and storage effects of small-scale water storage structures on surface runoff, based on the original Xin'anjiang (XAJ) model. In the M-XAJ model, the regulatory and storage effects of small water storage are calculated by utilizing an aggregated reservoir using multi-bottom sills graded weir flow pattern (MBSP). The discharge processes of the aggregated reservoir is combined with the processes of runoff production and convergence over the natural area to form the overall discharge processes at the outlet section. Using the two subbasins of the Xianghongdian Reservoir as case studies, 13 flood events were selected to validate the predictive capabilities of the M-XAJ model. The results indicate that (1) the qualification rate for flood peak (Q p) and the qualification rate flood volume (Q v) substantially increased for the two sub-basins during the training and verification periods. Additionally, the mean relative error of peak lag time (M E tp ) was considerably reduced. Specifically, the mean Nash efficiency coefficient (MNS) for floods at the Qingshan section improved by 0.18 and 0.24 respectively. (2) the M-XAJ model improved prediction accuracy for the first flood event following an extended drought period without rainfall, as well as for the initial flood preceding a sequence of flooding events. However, the prediction accuracy for continuous flooding events was not noticeably enhanced by the M-XAJ model. (3) the M-XAJ model demonstrated improved simulation capabilities for low- and moderate-magnitude flood events; however, there were no noticeable enhancements for high-magnitude flooding simulations. [ABSTRACT FROM AUTHOR]

• Salinity intrusion (SI) is intensified in the Vietnamese Mekong Delta (VMD). • Freshwater exploitability is investigated to cope with SI driven by climate change. • SI in the VMD has progressively increased and started earlier in the dry season. • Early and the late of the dry season are suitable time for freshwater exploitation. • Storing freshwater in the river is a promising strategy. Climate change-driven sea level rise has intensified salinity intrusion (SI) in deltas worldwide, posing significant threats to the exploitation of freshwater resources. In the Vietnamese Mekong Delta (VMD), the third largest delta globally, SI is a recurring challenge along the coastline, degrading freshwater resources for agricultural and domestic use and affecting socio-economic development. In this paper, we investigate the spatiotemporal extent of salinity intrusion in the Ben Tre Province, the hotspot of salinity disaster within the VMD. Long-term salinity monitoring data (25 years from 1996 to 2020) has been analyzed, and a 1D (Mike 11) coupled with 3D hydrodynamic model (Mike 3) was developed. Three scenarios were used to investigate the freshwater resources exploitation: (i) the year of investigation (2021), (ii) 2021 to 2030 climate change impacts, considering different annual exceedance probability of the upstream Mekong discharge (i.e., average flow, relatively low, low and very low), and (iii) extreme salinity intrusion (i.e., the 2016 condition). Our results indicated that salinity patterns are well-stratified at the beginning and end of the dry season but well-mixed during the middle period. Furthermore, over the last 25 years, SI has progressively increased and started earlier in the dry season. The modeling scenarios for SI have also revealed a growing complexity in the exploitation of freshwater resources, highlighting challenges related to timing, depth, and geographical location. The exceedance probability scenarios disclosed higher and deeper salinity intrusion along the channel in VMD, ranging from 50 % to 95 %. This poses significant limitations on the feasibility of freshwater exploitation throughout the Ben Tre Province. Under the current trajectory of climate change, the 2030 scenario anticipates salinity intrusion reaching further inland from the 2021 scenario. This is likely to exacerbate the existing challenges in freshwater resource exploitation, even with comprehensive water infrastructure. We, therefore, propose several management strategies to adapt to salinity intrusion: storing freshwater in main rivers, maintaining consistent operation of water infrastructure systems, and encouraging water-saving distribution and exploitation methods. Moreover, we also recommend supporting the development of new drought-tolerant crop patterns. [ABSTRACT FROM AUTHOR]

• Shale artificial fracture core models were developed with rough fracture surfaces. • Mineral hydration impairment on artificial fractures conductivity was revealed. • Mechanism of conductivity impairment were investigated from various aspects. • Hydration inhibitor increase fracture conductivity by 3.4 and 2.6 times, respectively. • Other factors impair fracture conductivity and implications were discussed. The shale gas fields are mainly in mountainous areas in China. Surface water is the major source of hydraulic fracturing fluid to develop shale gas. The clay mineral is generally rich in the shale gas reservoir. When encountered with low salinity surface water, the clay minerals are easy to hydrate in the process of fracturing and well shut-in. The hydration of clay minerals would result in the expansion, dispersion, and migration of mineral particles from the fracture surface, changing the structure and space of fractures, thereby affecting fracture conductivity. In this paper, shale artificial fracture core models were developed with rough fracture surfaces. With proppant-supported and self-supported fracture core models, the impact of hydration time, net pressure, and dislocation distance of fracture surfaces on the artificial conductivity were evaluated systematically. The results show that the conductivity of proppant-supported and self-supported fractures decreases by 85.3–95.1% under hydration. The turning point of conductivity is 8.0 hrs. and 4.3 hrs., respectively. At the initial stage of production, the artificial fracture could be protected better if the net pressure on the fracture is maintained from small to large. At the same time, the initial net pressure on fracture should be less than the proppant crushing pressure. Certain dislocation distances of self-supported fractures would augment the conductivity by 6.4–12.8 times. After hydration, illite and montmorillonite in the rock surface expanded and dispersed when encountered with water. The fracture surface strength decreases by 29.8%, while the surface roughness decreases by 46.1–51.6%, and some proppants were crushed. Then the fracture conductivity space was reduced. By adding KCl into the fracturing fluid, hydration can be effectively inhibited, which can increase the proppant-supported and self-supported fracture conductivity by 2.5–3.4 times, compared with fracturing fluid prepared with surface water. This study provides a theoretical basis for the optimization of shale gas reservoir fracturing technology and production strategy. [ABSTRACT FROM AUTHOR]

• Analysis of gravimetric/hydrologic data to constrain karst system hydrodynamics. • Gravity data modelling requires the integration of the autogenic recharge. • Gravity data sensitive to flow conditions in the karst conduits. In this paper we employ a combination of gravity and hydrologic data to constrain a hydraulic model of the Škocjan Caves, an allogenic dominated karstic system in Slovenia. The gravity time-series recorded by a spring-based gravimeter, are carefully analyzed to remove tidal and non-tidal effects and unveil the local hydrologic contribution, which is influenced by the temporary accumulation of water in the cave system during the flood events of the Reka river. We make use of a combined analysis of three large flood events with peak river discharge of about 200, 230 and 300 m3/s, that caused significant water level and gravity variations sensed by the pressure transducer and by the gravimeter. By the integration of hydraulic modelling we study the different coupled gravimetric-hydrologic responses to these flood events: we show that, depending on the peak discharge and duration of the event, different flow conditions are present in the cave system. In addition to the information provided by the pressure transducer, the gravimeter is sensitive to the flow dynamics in a different sector of the cave due to the choice of its location; this configuration helps to better constrain the hydraulic model. Moreover, we find that the autogenic recharge by percolating water can significantly affect the gravity time-series and must be considered in related models. By inclusion of both the hydraulic model outcomes and of the modelling of the autogenic recharge, we are able to better explain the gravity transients during the two smaller magnitude events. In particular, during such events the autogenic contribution produces a transient gravity signal, which is about 4 times larger than the allogenic one, while during the largest flood the allogenic contribution drastically overcomes the autogenic effect by a factor 20. By discussing this case, we prove the potential of terrestrial gravity observation to depict the hydro-dynamics of these complex karstic systems as well as the potential of gravimetry to remotely monitor these storage units. [ABSTRACT FROM AUTHOR]

• The performance of models to estimate daily evapotranspiration varies by ecosystems. • Automatic machine learning can improve the accuracy and generalization of estimated ETa. • Hybrid models are limited by uncertainty in model parameters and strict constraints of physical models. • The two models in this paper provide methods to better estimate daily ETa for wetlands. The performances of physics-based, data-driven, and hybrid models for estimating terrestrial actual evapotranspiration (ETa) is currently under debate, which requires thorough evaluations of those models particularly with recent developments in automatic machine learning (AML) techniques. In this study, six AML-based models were first constructed using the H2O-AML platform, from which an optimal (AML-OP) model was selected for estimating daily ETa at ecosystem scales. In addition, hybrid models were developed by combining the six AML models with surface conductance (Gs) inverted from the Penman-Monteith equation and an optimal (PM-OP) model was also selected. With 15 predictor variables as model inputs that were compiled from various data sources, the performances of those models for estimating daily ETa were evaluated using observed data from the FLUXNET2015 dataset. The results revealed that no models showed consistently low noise levels across different ecosystem types, making it necessary to use AML techniques for selecting ecosystem-specific models. Interestingly, the AML-OP models (root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE) were 0.16–0.31 mm d-1 and 9 %–36 % respectively) showed slightly better predictive results than the PM-OP models (RMSE and SMAPE were 0.23–0.36 mm d-1 and 15 %–68 % respectively), likely owing to model parameter uncertainties and tight constraints of physical models on application condition. Secondly, as ETa nonlinearly responds to environmental variables, model predictability under extreme weather (drought and heatwave) conditions was examined. The results showed that the prediction of the AML-OP and PM-OP models expectedly worsened (RMSE and SMAPE increased by 0.06–0.77 mm d-1 and −19 % to 79 %, respectively); however, the AML-OP model still outperformed the PM-OP model in most ecosystems, further underscoring the need to understand ETa regulation mechanisms under varying climatic conditions. Finally, the PM-OP models developed here provided better daily ETa estimates compared to other recently proposed hybrid models (RMSE reduced by 0.98–1.80 mm d-1). Both models can be better applied to wetlands that have been less frequently evaluated previously (RMSE reduction of 0.22 mm d-1 and 0.18 mm d-1 for the AML-OP and PM-OP models). [ABSTRACT FROM AUTHOR]

• The RS-NP model and ESTARFM are combined to improve the spatial resolution of ET. • Compares with two downscaling procedures including input and output downscaling procedure. • The influence of the downscaling of the main input on ET estimation error are quantified. Evapotranspiration (ET) data is critical for monitoring the limited hydrological resources in arid areas. At present, the spatial resolution of ET derived by the thermal infrared images from satellite data is coarse (always more than 500 m). The low-resolution data cannot meet the needs of the high-precision mapping of ET. Therefore, it is important to undertake a downscaling procedure to improve the spatial resolution of ET. This paper selects the oasis region and surrounding deserts of Zhangye City of China as study area and compares with two downscaling procedures that include input and output downscaling procedure. The ET is estimated with the Remote Sensing-Non Parametric (RS-NP) model. The two downscaling procedures use Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to improve the spatial resolution of ET from 1-km to 100 m. Compared with the result derived from the output downscaling procedure, the high-resolution ET derived from the input downscaling procedure has a slightly better performance with R2 of 0.92 and RMSE of 41.8 W/m2, respectively. Neglecting the downscaling of land surface temperature and broadband reflectance (BBR) result in larger errors of ET estimation with bias values of 73.0 W/m2 and 23.7 W/m2, respectively. Those values account for almost 1/3 and 1/10 of ET values, respectively. In non-vegetated areas, those proportions increase further around 90.6 % and 21.3 %, respectively. Furthermore, the influence of the neglect of the downscaling of the near-infrared reflectance on the ET estimation dominates that of BBR with a proportion of 42 %. The input downscaling procedure is suitable for generating the high-precision map of ET, especially in vegetated areas and during northern hemisphere summer months. Our study is helpful to obtain ET maps in fine spatial resolutions and contribute to the comprehensive understanding of the importance of the downscaling of each kind of input for reliable ET estimations. [ABSTRACT FROM AUTHOR]

[Display omitted] • Groundwater vulnerability zonation investigation is conducted for GHM Greater Bay Area. • Three different perspectives are comprehensively considered in this study. • Groundwater risk, function value and contamination status are superposed using the AHP method. • Quantification and visualization of groundwater contamination prevention regionalization. • The AHP-DRASTICH model is an effective method to evaluate the groundwater risk in shallow aquifers. The groundwater contamination assessment and prevention regionalization are an important part of groundwater investigation, which is of great significance to groundwater environmental management and contamination control. Although the Guangdong-Hong Kong-Macao Greater Bay Area (GHM Greater Bay Area) has faced significant groundwater contamination since 2020, there's been no investigation into its vulnerability zonation. In this paper, groundwater in a typical area of south China was taken as the research object. We analyzed groundwater contamination risk by considering both the groundwater vulnerability and the load from contamination sources. Using the analytic hierarchy process (AHP) method, we superposed the groundwater risk, function value and contamination status, and realized the quantification and visualization of groundwater contamination prevention regionalization. Based on the DRASTIC model, the zones of low, moderate, high, and very high vulnerability are respectively 14.2 %, 24.28 %, 37.67 %, and 20.45 % of the study area. Results show that the groundwater vulnerability grade in the area is III-IV, which means it is easy to be polluted. The functional value of groundwater in the south-central part of the region is relatively high. Most of the areas with high prevention and control levels are located in plain areas and the Piedmont plain area with an area of 460 km2. The secondary control levels (key protection area) are mainly located in the middle, southwest, and northeast of the plain, with an area of 6944 square kilometers. Consequently, the ideas and contamination prevention zone maps might provide a scientific foundation for long-term planning and management and groundwater conservation in the Guangdong-Hong Kong-Macao Greater Bay Area and peripheral zones. [ABSTRACT FROM AUTHOR]

• Developing a gated recurrent unit neural network for streamflow prediction. • Proposing a method for testing and benchmarking deep learning models. • Investigating deep learning model performances with different inputs. • Studying deep learning model performances with various sizes of training data. Machine learning (ML) algorithms have produced remarkable advances in streamflow prediction, exceeding the performance of calibrated conceptual and physics-based hydrological models that have been developed over many decades. ML algorithms seem to overcome the issue of errors known to be present in rainfall and streamflow estimates that have hindered the performance of hydrological models for decades. In this paper, we propose a methodology for testing and benchmarking ML algorithms using artificial data generated by physically-based hydrological models. Our approach makes it possible to design controlled numerical experiments that can improve our understanding of this new generation of black-box models. We conducted a diagnostics study to demonstrate our methodology in which we attempted to determine if ML algorithms can identify a function relating streamflow and rainfall. This exercise combined the implementation of the distributed hillslope-link hydrological model (HLM) on a 4,385 km2 basin driven by precipitation fields created using the stochastic storm transposition (SST) framework, and an advanced deep learning algorithm based on gated recurrent unit (GRU)-Attention neural networks. The data generated allowed us to create prediction scenarios that are equivalent to the hindcast and real-time forecast problems. We proposed a set of scale-independent performance metrics to evaluate the results of our experiment and found that the GRU can correctly identify a predictive function for all analyzed locations in the river network. We concluded that under the circumstances tested in this study, deep learning can identify the transformation function when trained in Hindcast Mode, making it a powerful tool to determine the streamflow response of a basin to predetermined rainfall scenarios. However, it fails to significantly outperform the predictions of temporal persistence when tested in Forecast Mode. [ABSTRACT FROM AUTHOR]

A novel application of data-based time series methods is proposed in this study to help overcome barriers to quantifying the impacts of Natural Flood Management measures from hydrological timeseries data which hitherto have prevented accurate assessment of the effectiveness of interventions. To demonstrate the value of this method, a transfer function noise model was fitted to stage data from a three year before-after-control-impact style monitoring study of leaky dams in an upland catchment in North Yorkshire, England. Using the data-based time series method, uncertainties associated with stage data were overcome. The models were able to simulate the peaks of flood events on one stream to within ±2 cm accuracy for 95% of events recorded during the baseline monitoring period. These simulations are used in this study's companion paper to quantify leaky dam impacts on flood peak magnitude. The level of accuracy achieved in this study provides proof of concept for application of the approach to data from other environments and natural flood management interventions, which is crucial if natural flood management is to be used as a mainstream flood risk management measure. • Limited evidence for natural flood management due to short, uncertain data • Time series modelling simulates flood peaks (±2 cm) with 95% accuracy using BACI data • Accuracy is adequate for assessing leaky dam impacts on flood peak magnitude • Proof of concept valuable for applying to other environments and interventions [ABSTRACT FROM AUTHOR]

• Water supply and pit level are simulated dynamically to understand subirrigation. • Geohydrological settings determine the hydrological propagation of subirrigation. • Subtle differences in geohydrology define if subirrigation improves crop yield. Controlled drainage with subirrigation (CD-SI) could be a viable measure to cope with the imbalance in water demand and supply in groundwater dependent regions. CD-SI has the potential to i) retain, ii) recharge and iii) discharge groundwater, i.e. anticipate both dry and wet weather extremes. The aim of this paper is to specify the hydrological consequences of CD-SI at field scale. Four field CD-SI pilots with varying geohydrological conditions in Dutch sandy Pleistocene uplands were monitored (minimum 5 years) to study the effects on groundwater level, soil moisture content and soil water potential. Dynamic modelling using the SWAP-model, calibrated with PEST, was used to quantify all water balance components for the field pilots, needed for responsible implementation of CD-SI. Using SWAP-PEST, measured groundwater levels and soil moisture conditions were reproduced sufficiently accurate. Both the water supply rates and the water and crest levels in the control pit of CD-SI systems could be simulated dynamically, which is an important improvement to earlier modelling approaches. Model calculations were used to study the hydrological responses of CD-SI for meteorological dry to wet years and for each of the field sites. Simulations show that transpiration increases with 38 to 206 mm in dry years for sites with respectively low and higher hydraulic resistances in the subsoil. Simulations also show that the required water volume can be large (664 – 728 mm, respectively). CD-SI could thus improve hydrological conditions for crop growth, but the success depends on subtle differences in geohydrologic characteristics. [ABSTRACT FROM AUTHOR]

• Estimating the discharge and velocity in Yangtze rivers based on Entropy approach. • The roles of secondary flow on entropic function and velocity dip are discussed. • An optimized method is proposed to estimate the hydrodynamics at the confluence. The entropy model has been successfully used to calculate the flow velocity profile of rivers according to the measured surface-flow velocity. However, it has limitations in the large river where the obvious secondary flow and flow dip (i.e., the location of maximum flow velocity moves down from the surface) occur. The confluence of rivers is usually characterized by strong secondary circulations, posing great challenges for the accurate flow velocity and discharge estimation. This paper aims to assess the effectiveness of the previous entropy-based approach in velocity estimation of a large confluence and then propose an optimized method. The flow velocity data of the large confluence between the Yangtze River and the Poyang Lake under different discharge ratios were used. The performance of the previous entropy model got worse for the stronger secondary circulation survey. To better estimate the confluence hydrodynamics, a moving average method was introduced with its advantage of smoothing data and integrated into the framework of the entropic method for the better assessment of dip parameter, and hence the better estimation of flow velocity profiles. Furthermore, the maximum velocity was found to locate at one third of the total depth below the water surface. The range of entropic function 0.48 ∼ 0.68 could provide a basis for the determination under various flow conditions in rivers. The value of entropic function and the dip parameter got larger as the discharge ratio decreased. This research has expanded the application for estimating hydrodynamics in large rivers. [ABSTRACT FROM AUTHOR]

• An urban flood model coupled with the tracer method is constructed. • The jacking effect of high tide under sea level rise is quantified. • Regulatory measures for the jacking effect are developed. High tide is one of the main drivers of compound flooding in coastal cities. The capacity of the coastal urban drainage network is reduced due to the jacking effect of the high tide, which will be further amplified by future sea level rise (SLR). Although most studies have evaluated compound flooding in coastal cities under SLR, there is still a lack of studies analyzing the jacking effect of high tide. In this paper, the jacking effect of high tide under SLR on compound flooding is quantified based on the water tracer method using Haikou City as an example. The water tracer method tracks water flow paths by realizing the coupling of water flow and tracer. The future sea level of Haikou City in 2050 is predicted using the MGF model and the SLR projection results of IPCC AR6. An urban flood model coupled with the tracer method is constructed to simulate the compound flooding of coastal cities under baseline and SLR scenarios. The results show that the extent and depth of compound flooding increases under SLR and the high tide has a more pronounced jacking effect on the drainage network and flooding. More than 13% of the drainage network and more than 40% of the flooded inundation areas in the study area experience significant jacking effect as a result of the high tide under SLR, and more than 80% of the areas experience jacking effect for more than 4 h. In addition, the effectiveness of pumping stations and storage facilities in regulating the jacking effect of high tide levels is assessed. For reducing the jacking effect of SLR on the drainage network, pump stations are considerably more useful than storage facilities. This study provides a theoretical basis for understanding the jacking effect of high-tide level under SLR and formulating measures to regulate the jacking effect. [ABSTRACT FROM AUTHOR]

• A framework to evaluate stochastic rainfall models with a quantitative scale. • 'Good' modelled rainfall does not guarantee 'good' modelled streamflow. • Streamflow-based evaluation is more holistic than rainfall-based evaluation. • Component-wise evaluation can misrepresent ultimate model performance. Stochastic rainfall models are important tools for evaluating hydrological risks such as flooding and drought because of their ability to randomly generate alternative plausible climatic timeseries. The stochastic generation of climatic timeseries is not an end in itself, since they are typically applied to a catchment to determine the performance of water-related infrastructure systems, such as reservoirs or flood-control measures. This methodology typically involves a train of models to determine the end-of-system impact, yet the evaluation of stochastically generated rainfall timeseries is usually a stand-alone procedure focused on metrics directly related to the stochastic generator. This paper demonstrates discrepancies in this approach by evaluating two, daily-timestep, stochastic rainfall models in terms of rainfall metrics and their subsequently generated flow metrics after rainfall-runoff transformation. The two models are a Markov-based model and a latent-variable model, where each model is calibrated and evaluated showing 'overall good' performance. Stochastically generated timeseries, alongside observed rainfall timeseries are inputted to a calibrated catchment model (GR4J) to derive daily flow timeseries. Whereas the rainfall metrics typically showed 'good' performance, streamflow-based metrics are not necessarily 'good'. The procedure is repeated for 277 stations from Australia and 106 stations from the United States of America. Depending on the strictness of the flow-based comparison and region analysed, using the Markov-based model 12–26% of sites were classified as 'poor' performing, and 1%-9% of sites were classified as 'poor' using the latent-variable model. The results demonstrate that catchment-based performance of flow metrics is more holistic since it magnifies features of the rainfall not otherwise visible to rainfall-based evaluation. [ABSTRACT FROM AUTHOR]

• Modeling methods of frost heave models for the past 50 years were deconstructed. • Error sources of each coupling components of the models were analyzed. • Regarding freezing soil as porous medium causes water migration error. • Error sources of the model can be hidden by coupling components error. Frost heave models are important tools to study engineering damage in cold regions. However, multi-physical-field-coupling and different methods of coupling components bring difficulties to compare the models and to optimize their error sources. Therefore, classifying and deconstructing of different frost heave models and further evaluating them are significant for model development. We reviewed frost heave models for the past 50 years and built frost heave model families to show the classification and inheritance relationships. Then, the models were deconstructed by the thermal–hydraulic framework (THF) and frost heave quantification method (FHQM). Finally, based on simulated and experimental data in cited papers, the model errors caused by different THFs and FHQMs were analyzed. The results show: (a) Present frost heave models can be classified as porosity increase based (P) and ice lens increase based (I) models and they are tending to merge with each other; (b) THFs of P models regarding freezing soil as porous medium are the main reason for water migration error in the region of multi-ice layers, which also results high uncertainty of hydraulic parameters under different simulation conditions; (c) The most used two FHQMs of P models cause error exceeding 20 % because inaccurate relationship between pore strain and frost heave displacement; (d) The errors of THFs and FHQMs offset each other, which will hidden the error sources of the models. This paper can provide a reference for the improvement of frost heave models used in engineering. [ABSTRACT FROM AUTHOR]

• Water quality (WQ) management using deep learning (DL) approaches reviewed critically. • DL-based forecasting of WQ parameters reviewed for different water bodies. • Publications on hybrid DL models for WQ management assessed comprehensively. • Importance of the IoT and cloud computing towards DL-based WQ management outlined. Excellent water quality (WQ) is an indispensable element in ensuring sustainable water resource development. It is highly associated with the 3rd (good health and well-being), the 6th (clean water and sanitation), and the 14th (life below water) listed items of the United Nations' Sustainable Development Goals. Thus, policymakers have always been seeking strategies to manage WQ efficiently. Recent advancements in computational technologies have created enthusiasm for using artificial intelligence, particularly deep learning (DL), in WQ management. This review provides a comprehensive overview of the application of DL in WQ management, covering developments from 2011 to 2022, in maintaining the temporal relevance of this review. In this paper, a brief description of different variants of DL models, including the recurrent neural network (RNN), long short-term memory network (LSTM), convolutional neural network (CNN), etc, are presented. The distinct approaches in the optimization, hybridization and relevant data pre-processing techniques suitable for the DL models, are also discussed. This is the first review paper that extensively discusses the application of DL models for forecasting WQ parameters in various water bodies, such as rivers, lakes, coastal areas, etc. The emergence of the Internet of Things (IoT) and cloud computing that revolutionized DL approaches in WQ management are also presented. This review paper serves as a complete easy guideline for the researchers in the field of DL-based WQ management. The findings of this review paper may help policymakers to enhance their decision-making process with the hope that regional environmental welfare can drastically be improved. [ABSTRACT FROM AUTHOR]

• Defining the risk of multi-reservoir flood control operation considering model structure and hydrological uncertainties. • Establishing a risk analysis model based on the Bayesian network theory. • Quantitatively analyzing the risk under double uncertainties and its spatial characteristics. In the real-time flood control operation of a multi-reservoir system, the multi-reservoir real-time flood control hybrid operation (MRFCHO) model with reduced structure is an effective way to improve the efficiency, but the reduction of the model structure may cause greater flood risk. This paper focuses on this scientific problem and evaluates the reliability of the MRFCHO model in real-time flood control operation through risk analysis. Firstly, the flood risk is defined. Then, the Bayesian network risk analysis model is established through four parts: Bayesian network structure construction, training samples generation, parameter learning, and inference. This paper takes the flood control system in upstream of the Lutaizi point in the Huaihe River basin as a case study. The flood risk of the MRFCHO model caused by the model structure reduction is quantitatively described, the spatial distribution and propagation characteristics of flood risk are evaluated, and the flood risk caused by the double uncertainties of the model structure and flood forecasting is analyzed. Besides, the factors affecting the risk of the public flood control point are diagnosed. The results show that (1) the model structure reduction does not significantly increase the flood risk at the public flood control point, and there is no obvious propagation law of flood risk in space; (2) compared with only considering the model structure reduction, considering the double uncertainties of flood forecasting and model structure reduction has a 65% probability of increasing the risk; (3) Runoff from the Runheji point in the upper reaches of main stream and the lateral inflow of Lutaizi point are the main reasons for the risk of Lutaizi point. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new factor, the stress interference between the various fractures, was considered in the water hammer model. • Based on the conservation of the total volume of fractures, the rationality of the volume equivalent relationship of multiple fractures was proved. • A new method was proposed to allow the new factor to be added to the model reasonably, under the premise of ensuring the conservation of volume. Hydraulic fracturing is an important means to develop unconventional resources. Fracture diagnosis techniques have been emerged in recent years, which can evaluate hydraulic stimulated fractures after stimulation treatment. Fracture geometry evaluation is an important part of hydraulic fracturing diagnosis. The fracture geometry evaluation techniques based on water hammer signal has the advantages of high economic efficiency, easy implementation and high timeliness. Previously, a model that couples the wellbore and the fracture (RCI model) had been proposed, which could calculate the fracture geometry. However, this model was mainly based on the assumption of a single fracture with a pair of double wings, and there was no research on the calculation of the geometry of multi-fractures. In this paper, a new model for calculating the geometry of multi-fractures was proposed. First, a new factor, the stress interference between the various fractures, was considered in the model. Second, based on the conservation of the total volume of fractures, the rationality of the volume equivalent relationship of multiple fractures was proved. Third, a new method was proposed to allow the new factor to be added to the model reasonably base on volume conservation. Additionally, our model was validated with the model in the previously published research. The effect of different parameters (number of fractures, fracture spacing, fracture volume) on the water hammer was analyzed. At last, the fracture geometry evaluation of micro-seismic and our model in field cases were compared. This paper provides a new model for evaluating the geometric characteristics of multiple fractures of water hammer signals. [ABSTRACT FROM AUTHOR]

[Display omitted] • Consistent permeability found in orthogonal flow directions by fluid flow simulation. • Skeletonization was a reliable and quick approach to parametrize Kozeny-Carman. • Pore network modelling was more sensitive to thin ramifications of the pore system. • Faster calibrated XCT-based methods can be used in large datasets for permeability. Nowadays there are many papers dealing with the analysis of the soil porous system through X-ray computed tomography (XCT). However, a reduced number of studies focus on modeling the intrinsic permeability (k) of complex porous media, such as soil, based on three-dimensional (3D) high-resolution images. The determination of k is fundamental to understanding several processes taking place in soil such as water and air transmission. Thus, this paper compares three XCT-based methods for estimating the k of the pore system of soil aggregates: i) Finite volume-based simulation (APES - Absolute Permeability Experiment Simulation), ii) Image-based parametrization of the Kozeny-Carman (IBP-KC) equation, and iii) Pore network model (PNM). The 3D image considered in the comparison of methods was acquired at the X-ray microtomography beamline at the Brazilian Synchrotron Light Facility, with a voxel size of 1.64 μm. APES was considered as a reference method for being less dependent on the configuration of sensitivity parameters. APES and PNM demonstrated to be more time consuming than the IBP-KC, the first taking longer among all for the k computation. PNM presented the drawback of higher sensitivity to thin pore ramifications, resulting in unrealistic values of k and requiring disconnecting one thinner pore to yield permeabilities comparable to the ones obtained by APES. This study demonstrated that a fine alternative to compute k for large image datasets is to calibrate faster methods (e.g., IBP-KC and PNM) against a slower but more reliable reference method (e.g., APES), using at least one 3D image, before applying the faster methods on the remaining images of the dataset. [ABSTRACT FROM AUTHOR]