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Transformation plays a significant role in solving hydrological problems. In response to the comment on Singh and Das (2015) by Deng et al. (2015) regarding the transformation, boundary conditions and subsequent analytical solution of advection dispersion equation in semi-infinite heterogeneous porous media, it is argued that the transformation used by Singh and Das (2015) for finding the analytical solution is correct and mathematically valid. Also, the different expression of f ( mt ) used to obtain the solution are explained with their physical relevance in the field.

Highlights • Soil samples were taken from field under different fertilizations for 175 years. • Soil permeability was calculated using X-ray tomography and pore-scale simulation. • Soil was more isotropic and homogenous at aggregate scale than at core scale. • Soil permeability at core scale increased exponentially with soil carbon. • Soil permeability at aggregate scale increased asymptotically with soil carbon., Soil structure is an indicator of soil quality and its alterations following cropping system conversion or fertilization change evolve slowly. How such alterations vary with scale remains elusive. We investigated this based on the Rothamsted long-term wheat experiment (since 1843) in the UK. Triplicate cores 7 cm high and 10 cm in diameter were taken from plots that have been under different fertilizations or returned to natural woodland for more than one century for imaging using X-ray computed tomography with the voxel size being 40 µm. We then broke each core and sampled three aggregates from it to scan with the voxel size being 1.5 µm. For each core and aggregate sample, we calculated its pore size distribution, permeability and tortuosity. The results showed that the fertilization change >170 years ago reshaped the soil structure differently between the core scale and the aggregate scale. Macro-porosity of the pores (>40 µm) in the cores unfertilized or fertilized with inorganic fertilizers was low and the pores were poorly connected in the top 10 cm of soil, compared to those given farmyard manure or in the woodland. In all treatments, the pores in the core images were hydraulically anisotropic with their permeability in the horizontal direction being higher than that in the vertical direction, whereas the aggregates were comparatively isotropic. The fertilization affected image porosity and permeability at core scale more significantly than at aggregate scale, and the aggregates fertilized with farmyard manure and in the woodland were more permeable than the aggregates in other treatments. It was also found that compared to no-fertilization or fertilization with complete fertilizers, fertilizing without phosphorus over the past 20 years increased the porosity and permeability of the aggregates but not of the cores. Fertilization with inorganic fertilizers increased the tortuosity of the macropores in the cores but not of the intra-aggregate pores, compared to no-fertilization. Porosity-permeability relationship for aggregates unfertilized or fertilized with inorganic fertilisers followed a power law with R2 > 0.8. In contrast, the permeability of the aggregates in farmyard manure and in the woodland trended differently as their porosity increased. The results also revealed that the transport ability of the aggregates and cores responded differently to carbon in that with soil carbon increasing, the permeability of the aggregates increased asymptotically while the permeability of the cores, especially its horizontal component, increased exponentially.

Pressure on freshwater resources has resulted in greater greywater reuse for irrigation. Laundry greywater is one of the largest yet potentially soil damaging streams of domestic greywater. There is limited research on the susceptibility of soils with various properties to degradation by laundry greywater. Thus the aim of this laboratory study was to investigate the effect of typical liquid and powdered laundry detergent greywater application on humus removal and hydraulic conductivity on a range of urban soils from the Cape Town region, South Africa. Powdered detergent (PD) greywater was significantly more detrimental in terms of soil humus removal and decreasing hydraulic conductivity compared to the liquid detergent (LD) greywater attributed to its alkaline pH (pH 9.95) and 30-fold higher Na content. Sandy soils were the most susceptible to humus losses due to PD greywater application (7.5% soil C lost) while Fe oxide-rich chromic soils were the least (1.5% soil C lost). Granite-derived, kaolinitic soils were most susceptible to reduction in hydraulic conductivity (81% reduction) due to PD greywater application, while Fe-rich chromic soils were the least (47% reduction). PD greywater should not be used for irrigating soils due to its aggressive soil degrading qualities while LD greywater should be used cautiously.

Accurate influent flow forecasting plays a significant role in management, operation, scheduling and utilization of the sewage treatment plants. In design and operate such plants, it is essential to measure and forecast the influent flow rate in wastewater plants. In this paper, the Very immediate-short-term to long-term influent flow rate are modeled and forecasted by a new developed hybrid model of ANFIS and Grey Wolf Optimizer (GWO). The objective of this study is the integration of GWO with ANFIS in forecasting multi-ahead influent flow rate. The forecast horizon of the model is from 5 min up to 10 days bases on Gamma Test (GT) feature selection of input combinations. As the parameters of ANFIS have effect on the forecasting accuracy, these parameters are adjusted and optimized by using Grey Wolf Optimizer (GWO). Then the choice of appropriate input parameters at different prediction horizons from Very immediate-short-term (5-min ahead) to long-term (10 days ahead) was discussed for influent forecasting. The statistical indices of RMSE, NSE, MAE, RAE, R2, d, CI and graphical evaluations such as scatter-plots with confidence bounds, error distributions, Taylor diagrams, box-plots and empirical cumulative distribution function (ECDF) were implemented for assessing the performance of all models in prediction horizons. Furthermore as another novelty in the present paper, recursive forecasting models based on previous forecasted values is used to improve the accuracy and applicability of ANFIS-GWO in recursive predictions. Our Results showed that: (1) the hybrid of ANFIS-GWO significantly improved the prediction accuracy. (2) ANFIS-GWO performs more efficiently than the ANFIS in almost all of the prediction horizons (ANFIS-GWO1: 5 min ahead; ANFIS-GWO11: 1–2 days ahead; ANFIS-GWO8: one week ahead). (3) The performance of models in influent flow forecasting is significantly influenced by the prediction horizon. The computational results confirmed that the ANFIS-GWO performs well in all of prediction horizons. Equally the true values and the trends are precisely forecasted by the ANFIS-GWO. Results of this novel study demonstrate that reliable estimates of influent flow rate from 5-min up to 10 days in advance can be achieved using the developed direct and recursive hybrid GWO models.

Losing eight meters of water level over a 20-year period from 1996 to 2016 marked the Lake Urmia (LU) as one of the regional environmental crises. This condition has threatened biota life, intensified desertification around the lake, and raised social concerns by adversely impacting the inhabitants’ health and economy. In 2013, the Urmia Lake Restoration National Committee (ULRNC) started implementing certain management practices to stop the drying trend of LU, resulted in the cease of water level drop and stabilization of LU condition in 2016. Nevertheless, the restoration actions have not yet raised the lake to the water level as planned by the roadmap. This paper aims to describe and to assess the LU restoration plans by underscoring the ULRNC achievements, challenges, and shortcomings. In particular, we discuss how the value of data and data-aided modeling has been underestimated by the LU restoration programs, leading to still existing puzzles about the lake interaction with the involving physical processes governing its dynamics. We show how the LU restoration timetable has not fulfilled the planned milestones as evidenced by the inability to capture the anticipated lake water levels, which is partly attributed to the lack of field data and dynamic modeling that could predict the lake response in a more reliable and conservative manner. The current restoration plans should also be revisited to ensure that any practice with the aim of reducing water consumption in the basin is not only environmentally sustainable but also feasible from the socioeconomic perspective. The insights provided by this paper attempt to underscore the value of field data collection for establishing a reliable conceptual model, and for executing pre- and post-monitoring of the lake so that the success or failure of the restoration actions taken by the policymakers can be appropriately evaluated.

Parametric Budyko method has been widely used to assess water availability under changing environment. Its single parameter (w) controlling the shape of the Budyko curve, is of critical importance to water availability assessment (WAA). Here, the water availability is defined as the ratio of evapotranspiration to precipitation in the context of Budyko hypothesis. As an unobserved variable, w is generally estimated through incorporating the long-term water-balance model and Budyko hypothesis, i.e. estimating w as a function of precipitation, runoff, and potential evapotranspiration over a long-term time series of these variables. Inevitably, the estimated value of w is subject to uncertainty resulting from hydro-meteorological records with limited size. Further, this uncertainty affects WAA in context of Budyko hypothesis. The uncertainty and its propagation issue is often overlooked in past research. In this paper, we develop a bootstrap-based algorithm to reveal aforementioned uncertainty. Moreover, a partial derivative-based sensitivity analysis is performed for understanding the propagation of w uncertainty on WAA. Three catchments located in the Yellow River Basin, China, are used as case study sites. Results indicate that there exists remarkable uncertainty in parameter w, which varies with the data length. The impact of such uncertainty on WAA increases quickly under the humid condition, thereafter reaches a peak under the mild condition, and decreases slowly under the arid condition. The reason behind this phenomenon is the largest sensitivity of WAA to parameter w under the mild climate condition. Moreover, a general analytical expression for determining the critical value of climate condition which maximizes the impact of parameter w uncertainty on WAA is derived in this paper. Results of this study can enhance the reliability and robustness of Budyko-based models.

There has been a series of water resource problems, such as imbalances of the water supply and demand and serious water pollution, in China. It is important for the Chinese government to formulate a sustainable development strategy for water resources and to effectively guarantee the national water resource security. This paper divides the comprehensive carrying capacity of water resources into the following three aspects: the water resource balance capacity, the water resource pressure and driving force, and the water resource development and utilization capacity. Taking 31 provinces, municipalities and autonomous regions of China as research objects, the catastrophe progression method is adopted to evaluate the comprehensive carrying capacity of interprovincial water resources in China from 2010 to 2016. The direct effects and spatial spillover effects of different factors on water resources are discussed by constructing a spatial Durbin model (SDM). The results show that there are obvious spatial differences in the water resource balance ability, pressure and driving force, and development and utilization ability. The spatial distribution follows a decreasing trend from east to west and from south to north. The water supply, water demand, investment in environmental management, economic development and balance of the ecosystem have obvious direct effects on the carrying capacity of water resources. The spatial spillover effect of the water supply and demand, energy consumption, the proportion of protected areas and the newly increased areas of soil erosion control are significant and will have a strong impact on the water resource carrying capacity of surrounding areas. The conclusion of this paper can provide a reference for the government in making water resource management policies.

Evaluation of calculation models for the unfrozen water content of freezing soil is very important for numerical simulations. Based on the published 250 laboratory data and around 5 years field observed data, this paper evaluated the five widely used unfrozen water content models, i.e., the Zhang et al. (2017a) model, the Anderson and Tice (1972) model, the Michalowski (1993) model, the Mckenzie et al. (2007) model, and the Kozlowski (2007) model. The results show that the five models can all be used to calculate the unfrozen water content of laboratory soil samples. The Anderson and Tice (1972) model, the Michalowski (1993) model, and the Kozlowski (2007) model are descripted by gravimetric unfrozen water content. However, it is difficult to directly measure the gravimetric unfrozen water content during the freezing process. The Mckenzie et al. (2007) model and the Zhang et al. (2017a) model are descripted by volumetric unfrozen water content which can be directly measured during the freezing process. Besides, the parameters of the Zhang et al. (2017a) model are relatively simple, although the model slightly overestimates the volumetric unfrozen water content for some laboratory soils. Therefore, the Zhang et al. (2017a) model is more convenient for numerical simulations. Furthermore, all the models have great potential to calculate unfrozen water content of freezing soils in field observations. This paper would provide significant reference for the selection of unfrozen water content models in the description of hydro-thermo coupling process in cold regions.

Fluid flow architecture in fractured reservoirs is mainly based on the dual porosity model. In this model, the matrix block is considered as a fluid storage source and fracture as a flow path. Nevertheless, in naturally fractured reservoirs, different properties of matrix blocks and fracture systems have caused the triple porosity model to be more applicable than the traditional dual porosity model. The existed triple porosity models presented so far are mainly based on pseudo steady state flow in the matrix block and vug and via a semi-analytic form. In this paper, two analytical models of triple porosity model are presented with consideration of transient flow; the first model being: the series flow of the matrix block to the vug and the vug to the fracture, and the second model: transient parallel flow of the matrix block and vug to the fracture. In this paper, for the first time, an analytical and transient shape factor between different regions is put forward. Using the developed new solution, various flow regimes in triple porosity model have been identified. In the series triple porosity model, changing the parameters of vug and matrix did not affect the early production while it influenced the rate of the middle and lately times. The shape factor between the matrix and vug was independent of the physical properties of vug while the shape factor between vug and fracture was influenced by the physical properties of vug and matrix. In the parallel triple porosity model, the shape factor of matrix-fracture and vug-fracture were not dependent on the properties of the other regions of rock. Moreover, a decrease in inter-porosity coefficient and an increase in storativity ratio increased the shape factor in transient time. Additionally, in order to validate the analytical model, the results of the model are compared with the numerical simulations. This study presents a new analytical and transient shape factor which finds application in simulation of triple porosity formations.

Increasing water demand aggravates water scarcity in water-scarce regions (e.g., India, Pakistan, and China), where uneven precipitation increases the risk of maintaining sustainable eco-economic development. Ignoring precipitation’s effects leads to misguided policy recommendations that either deplete or underuse basin water resources. Therefore, this paper highlights the concept of uneven precipitation and uncertain stream inflow based on scenario analysis. Within a river basin, the regional authority and subarea managers both have water allocation and reallocation privileges. Motivated by sustainable development of water resource management systems, we argue that regional authorities should focus on alleviating the contradiction between supply and demand, and then seeking an optimum allocation strategy to ensure societal and economic benefits for its subareas, from which the subareas can provide reasonable reaction strategies. Authorities dominate water transactions between subareas involved in water rights distribution. This paper proposes a multi-objective multi-stage Stackelberg-Nash-Cournot (m2SNC) game model to cope with conflicts between regional authorities and subarea managers concerning multiple activities. An interactive-dynamic-programming-based genetic algorithm (IDP-GA) is designed to simulate the policies needed for the optimum allocation of water resources under various climate scenarios. The significant stress caused by unsustainable water resource allocation has been a perplexing problem for Sancha Lake, an important area in the southeast basin of the Dujiangyan Irrigation System in China. To identify the tradeoffs among stakeholders, we apply the proposed model to this basin. To gain further insight into climate change and water usage sustainability, we simulate the varying spatial and seasonal distribution of stream inflow based on historical climate data and propose different water management strategies for normal, slight drought, moderate drought, and serious drought conditions. This creates an emergency response cooperative mechanism based on different climate scenarios for developing countries that need to cope with water deficiency and uncertain situations.

This study is a review to the special issue on artificial intelligence (AI) methods for groundwater level (GWL) modeling and forecasting, and presents a brief overview of the most popular AI techniques, along with the bibliographic reviews of the experiences of the authors over past years, and the reviewing and comparison of the obtained results. Accordingly, 67 journal papers published from 2001 to 2018 were reviewed in the terms of the features and abilities of the modeling approaches, input data consideration, prediction time steps, data division, etc. From the reviewed papers it can be concluded that despite some weaknesses, if the AI methods properly be developed, they can successfully be used to simulate and forecast the GWL time series in different aquifers. Since some of the stages of the AI modeling are based on the experience or trial-and-error procedures, it is useful to review them in the special application on GWL modeling. Many partial and general results were achieved from the reviewed papers, which can provide applicable guidelines for researchers who want to perform similar works in this field. Several new ideas in the related area of research are also presented in this study for developing innovative methods and for improving the quality of the modeling.

Balancing the multiple purposes of reservoir operation, including the social, economic, and ecological aspects, has become one of the most complicated problems facing the managers and operators worldwide. This paper formulates a detailed mathematical model to optimize the reservoir operation during the flood season, considering the three major operational purposes, i.e. flood control (social purpose), energy production (economic purpose), and management of sediment transport (ecological purpose). The entire formulations conform to a nonlinear programming (NLP) model, which can be solved effectively and efficiently by the Multi-Start Solver from the LINGO software through equivalent transformation. This model is applied to the Three Gorges Project (TGP) in China, which is the world’s largest and most complex hydraulic engineering in operation. Three typical flood hydrographs (i.e. dry, normal, and wet scenarios) are selected as model inputs under the three strategies (i.e. design operating rules, increasing the flood control water level (FCWL), and impounding in advance). The trade-offs among these three major operational purposes are quantitatively analyzed. Moreover, phosphorus (P), one of the sediment-associated environmental factors, is introduced for exploring its responses to various scenarios and strategies. Results indicate that a higher fore-bay water level leads to a greater energy production, a smaller flood peak but less sediment and P transports to the downstream. Under the design operating rules, the maximum energy production of the TGP can reach 560, 620, and 699 × 108 KWh, respectively, in dry, normal, and wet scenarios, corresponding to a flood peak of 25000–27500 m3/s, a sediment load of 1517–1999 × 104 t, and a P load of 3152–3740 × 104 kg to the downstream. Whereas, the maximum sediment loads are 2796, 4640, and 5377 × 104 t, and the maximum P loads are 4021, 5345, and 5784 × 104 kg, respectively, in dry, normal, and wet scenarios, corresponding to an energy production of 500–588 × 108 KWh, and a flood peak of 38600–52500 m3/s to the downstream. In terms of achieving the comprehensive benefits of the TGP, impounding in advance is a better choice than both the design operating rules and increasing the FCWL, for it can increase the energy production and the probability of full refill with almost no increase in flood risk to both the dam and the middle and lower reaches of the Yangtze River as well as smaller reduction in the sediment and P transports. The formulated detailed mathematical model is a general one that is applicable to a variety of reservoirs owning multiple operational purposes. Moreover, the achievements gained from this paper will provide important references for the managers and operators.

Despite the massive diversity in the modeling requirements for practical hydrological applications, there remains a need to develop more reliable and intelligent expert systems used for real-time prediction purposes. The challenge in meeting the standards of an expert system is primarily due to the influence and behavior of hydrological processes that is driven by natural fluctuations over the physical scale, and the resulting variance in the underlying model input datasets. River flow forecasting is an imperative task for water resources operation and management, water demand assessments, irrigation and agriculture, early flood warning and hydropower generations. This paper aims to investigate the viability of the enhanced version of extreme learning machine (EELM) model in river flow forecasting applied in a tropical environment. Herein, we apply the complete orthogonal decomposition (COD) learning tool to tune the output-hidden layer of the ELM model’s internal neuronal system, instead of the conventional multi-resolution tool (e.g., singular value decomposition). To demonstrate the application of EELM model, the Kelantan River, located in the Malaysian peninsular, selected as a case study. For a comparison of the EELM model, and further model evaluation, two distinct data-intelligent models are developed (i.e., the classical ELM and the support vector regression, SVR model). An exhaustive list of diagnostic indicators are used to evaluate the EELM model in respect to the benchmark algorithms, namely, SVR and ELM. The model performance indicators exhibit superior results for the EELM model relative to ELM and SVR models. In addition, the EELM model is presented as a more accurate, alternative predictive tool for modelling the tropical river flow patterns and its underlying characteristic perturbations in the physical space. Several statistical metrics defined as the coefficient of determination (r), Nash-Sutcliffe efficiency (Ens), Willmott’s Index (WI), root-mean-square error (RMSE) and mean absolute error (MAE) are computed to assess the model’s effectiveness. In quantitative terms, superiority of EELM over ELM and SVR models was exhibited by Ens = 0.7995, 0.7434 and 0.665, r = 0.894, 0.869 and 0.818 and WI = 0.9380, 0.9180 and 0.8921, respectively. Whereas, EELM model attained lower (RMSE and MAE) values by approximately (11.61–22.53%) and (8.26–8.72%) relative to ELM and SVR models, respectively. The obtained results reveal that the EELM model is a robust expert model and can be embraced practically in real-life water resources management and river sustainability decisions. As a complementary component of this paper, we also review state-of-art research works where scholars have embraced extensive implementation of the ELM model in water resource engineering problems. A comprehensive evaluation is carried out to recognize the current limitations, and also to propose potential opportunities of applying improved variants of the ELM model presented as a future research direction.

Until now, limited attention has been afforded to the role of, and challenges faced by, women involved in water diplomacy. In addition to research being at an early stage, it is mainly focused on addressing the challenges imposed by gender inequality. Thus very few applicable policy recommendations have emerged in this field to date. This paper will explore and identify current challenges that face the women interested in attaining high level positions in water diplomacy, in three Arab countries in which hydropolitics prevails, including Jordan, Lebanon and the State of Palestine. Female experts working on water-related issues were surveyed and interviewed to ascertain key qualitative issues, perceptions and various challenges. The focus of the paper was to identify the additional skills to be developed and acquired, for these women to better qualify as water diplomats both nationally and globally. In addition, the paper explores how to better equip women as leaders in the attainment of the Sustainable Development Goals, especially Goal 5 on Gender Equality and Goal 6 on Clean Water and Sanitation, in their national water sectors. The results provide a basic and initial mapping of current challenges and makes recommendations that would assist in the empowerment of those women in water diplomacy decision making positions in the regions investigated specifically, as well as globally.

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

Mountainous headwater catchments are safeguards of quality groundwater and hence require special protection against contamination by anthropogenic sources. However, methods currently handling contamination risk fail to produce reliable results in mountainous watersheds because they overlook the influence of downhill flows and contaminant transport in the validation process. To overcome this difficulty, a new model based on so-called “concentration profiles” is presented that combines the DRASTIC framework for evaluation of intrinsic vulnerability, the categorization of land uses for evaluation of specific vulnerability to nitrate and Processing Modflow graphical user interface for simulation of nitrate transport. This model was tested in a mountainous region of Northern Portugal. The risk of groundwater contamination by nitrate was generally classified as moderate. The risky areas are regions used for agriculture and livestock production. These activities have raised nitrate concentrations of spring water (15–25 mg·L−1) downstream the risky areas. The Modflow simulations linked the risky areas (contaminant sources) to actual nitrate plumes (contaminant sinks) and modeled nitrate distributions at specific groundwater travel times. Winter plumes could be simulated for the 1-year stress period, and hence are flushable in a short time span. Spring and summer plumes could only be explained by contaminant transport during 10–20 years. In these cases, even if contaminant sources were immediately neutralized, the washout of nitrate would take decades. These results may hold back the fulfillment of sustainable development goals related to water and sanitation until 2030, and hence deserve reflection by water planners and policy makers. The modeling exercise provided extra evidence that safeguarding the catchment headwater is the keystone of groundwater quality protection in mountainous catchments. Therefore, application of this modified DRASTIC to other mountainous areas may not need to resort to Processing Modflow. The study comprises the main paper (this paper) and a MethodsX companion paper.

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

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.

In a previous paper published in Journal of Hydrology, it was shown that underground structures are responsible for a mixing process between shallow and deep groundwater that can favour the spreading of urban contamination. In this paper, the impact of underground structures on the intrinsic vulnerability of urban aquifers was investigated. A sensitivity analysis was performed using a 2D deterministic modelling approach based on the reservoir theory generalized to hydrodispersive systems to better understand this mixing phenomenon and the mixing affected zone (MAZ) caused by underground structures. It was shown that the maximal extent of the MAZ caused by an underground structure is reached approximately 20 years after construction. Consequently, underground structures represent a long-term threat for deep aquifer reservoirs. Regarding the construction process, draining operations have a major impact and favour large-scale mixing between shallow and deep groundwater. Consequently, dewatering should be reduced and enclosed as much as possible. The role played by underground structures’ dimensions was assessed. The obstruction of the first aquifer layer caused by construction has the greatest influence on the MAZ. The cumulative impact of several underground structures was assessed. It was shown that the total MAZ area increases linearly with underground structures’ density. The role played by materials’ properties and hydraulic gradient were assessed. Hydraulic conductivity, anisotropy and porosity have the strongest influence on the development of MAZ. Finally, an empirical law was derived to estimate the MAZ caused by an underground structure in a bi-layered aquifer under unconfined conditions. This empirical law, based on the results of the sensitivity analysis developed in this paper, allows for the estimation of MAZ dimensions under known material properties and underground structure dimensions. This empirical law can help urban planners assess the area of influence of underground structures and protect urban strategic reservoirs.

Long-term forecasting of any hydrologic phenomena is essential for strategic environmental planning, hydrologic and other forms of structural design, agriculture, and water resources management. Climate mode indices, utilising machine learning methods, are frequently considered as predictor variables in order to forecast several different hydrological variables. In this study, a feature selection algorithm based on two different deep learning models, i.e., long short-term memory and a gated recurrent unit, is applied to improve the forecasting capability of streamflow water levels at six gauging stations in the Murray Darling Basin of Australia. This paper therefore aggregates the significant antecedent lag memory of climate mode indices, rainfall, and the monthly factor based on the periodicity as the predictor variables to attain significantly accurate stream water level forecasts. This novel method identifies an improved relationship between the stream water level and climate mode indices through the aggregation of the significant lagged datasets capturing the historical features to predict the future streamflow water level. The boruta feature selection algorithm (BRF) was then applied in a two phase process before and after attaining the significant lagged inputs to screen the optimum predictor variables. The merits of the forecast models were evaluated through different performance evaluation criteria. The results show that the accumulated significant lagged inputs based on climate mode indices, along with the rainfall and periodicity factors are seen to provide improved forecasting of the SWL over the non-BRF deep learning approaches where no prior feature selection was applied. The hybrid LSTM method (i.e., BRF-LSTM model) achieved a unique advantage in terms of SWL forecasting, particularly attaining over 98% of the predictive errors lying within a band of +/-0.015 m with relatively low relative errors (RRMSE ≈1.30% and RMAE ≈ 0.882%), outperforming all of the benchmark models. It is also found that the periodicity factor has a potential influence on the accuracy of the forecast models for the four monitored study stations. This study concludes that the newly developed hybrid deep learning approaches, coupled with the BRF feature selection, provide improved forecasting performance. The hybrid approach developed in this paper can therefore be used to provide a strong provide predictive response algorithm for the hydrological variables that were influenced by the low-frequency variability of the climate model indices in respect to streamflow water level.

The increase in dynamic interactions between climate and human activities threatens water security in terms of water quantity and quality. Most water security studies have focused on water quantity compared to water quality, while both are equally important and vital for maintaining a healthy ecosystem and human well-being. The first part of the paper provides a review of the potential impacts of climate-related extreme events (i.e., drought, flood, and wildfires) on different water quality indicators and the potential impact of cascading extreme events (e.g., drought-flood regimes) on dynamics of water quality behavior. In the second part of the paper, we demonstrate the cascading impact of severe drought and an extreme historical flood event (October 1–4, 2015) in South Carolina (USA) on water quality variables. The effect of drought on water quality in contrasting land-use settings is investigated. Finally, water quality data was collected over a period of time in three types of land-use settings to study the dynamics of multiple flood and drought events on microbial communities. Flooding conditions result in high levels of bacteria associated with fecal contamination, especially in the stream setting, where large differences between drought and flooding occur in the microbial communities. The results highlight the significant impact of cascading events on water quality and microbial communities. The effect of drought on water quality indicators in different land-use settings can be different, highlighting the dominant role of watershed characteristics. Overall, it is essential to develop quantitative frameworks in the context of sustainability science to quantify the interaction between climate, watershed, and anthropogenic variables that control stream water quality. This study highlights the importance of understanding the relationships between extreme events and water quality indicators as an important step to improve ecosystem health and sustainability. Finally, some remarks are made on the knowledge gaps which need to be addressed in future studies.

Transient flow of geofluids caused by a well production in a subsurface formation is a phenomenon of enormous importance in hydrology, geosciences and engineering. Geofluid transient flow has been proved to be a nonlinear physical process dominated by QPG (quadratic pressure gradient). Much research has indicated that neglecting a QPG term in flow models by assuming small pressure gradients can cause significant errors in predicted pressures. Despite the existence and importance of QPG, limited research on nonlinear QPG models has been performed when compared with the traditional linear flow models. On the basis of the previous linear and nonlinear QPG models, a comprehensive geofluid flow model for a well in a subsurface formation was established and solved. A general solution method for this model and its general solutions were obtained for vertical and horizontal wells in ordinary, fractured and fractured-vuggy formations. Next, flow regimes and typical transient flow characteristics were thoroughly analyzed by using a series of standard type curves. Then, the differences in type curves between linear and nonlinear QPG models were qualitatively and quantitatively studied. Afterwards, model validations and production predictions were performed. This comprehensive model contained various specific modeling situations. Some modeling situations that were reported in the previous studies were well-reviewed in this paper. The other previously unreported modelling situations were firstly studied and exhibited in this paper. At the end, the existing problems pending to be solved were put forward and future work on nonlinear QPG models was suggested. This paper is very helpful in understanding the QPG-dominated transient flow behavior more profoundly.

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.

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.

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.

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.

International audience; 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 km², 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.

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.

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.

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 Aspo 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.

In multi-agent decision making, agents’ decisions are coherently interdependent, and the objectives of agents are possibly inconsistent. In this type of decision-making, agents need to reason and act strategically. Therefore, game theory can be used to study the strategic interactions among decision-makers. This paper presents a new hierarchical decision-making methodology, which uses the advantages of both game theory (i.e. Stackelberg leadership model) and Evidential Reasoning (ER). ER, as a multi-attribute decision-making method, considers both qualitative and quantitative attributes, and incorporates existing uncertainties in evaluating alternatives. The backward induction method and Monte-Carlo theory are used to solve the deterministic and stochastic forms of the proposed leader–follower game. The methodology is applied to choose the best restoration scenario for Urmia Lake, which is the biggest saltwater lake in the Middle East. In the past decade, the lake’s surface area has dramatically decreased due to climate change and an anthropogenic drought. In this paper, 19 lake restoration scenarios have been evaluated, and a scenario, which is corresponding to a 40% reduction in agricultural water consumption and water release from Nezam Abad dam, is found to be the equilibrium solution for both types of the game.

As the efficiency of the long-running inter basin water transfer (IBWT) projects declines, the increasing water crisis in the intake area and the deteriorating ecosystem problems in the transferred area have gradually worsened. In this paper, a new form of IBWT, the dual-IBWT (DIBWT), is defined, in which the original transferred area (OTA) and additional transferred area (ATA) are integrated to transfer water resource from two basins to a corporate intake area (CIA). Unlike a typical IBWT, the OTA is one of the transferred areas for the CIA as well as the recipient area of the ATA and plays a dual role in this DIBWT system. A multi-objective DIBWT model involving interconnected OTA and ATA submodels is developed for fulfilling joint transfer targets, flattening water transfer fluctuations, and replenishing ecological flow demands. Then, a three-step framework is presented to derive the multi-resource joint operation rules coordinating the water-supply, water-transfer and water-receipt rules together. In particular, the simulation-optimization approach (SO) is applied to obtain the optimal solution based on a time series of 56 years. The Jialing across Han River to Wei River Water Transfer (JHW-DIBWT) project in western China is used as a case study to assess the performance of the DIBWT compared to a typical IBWT. The results indicate that the JHW-DIBWT has significant advantages in alleviating the conflicts of transfer-supply-demand and restoring the river ecosystem with the aid of additional water resources. Meanwhile, its superiority is revealed by the corresponding joint operation rules of the water-supply, the water-transfer and the water-receipt rule curves, which also can serve as a practical guide for multi-objective and multi-resource water system management. Therefore, the main focus of this paper is not limited to compare the performance between these two IBWTs, but more importantly, it provides a promising framework for extending the study of different form of IBWTs.

In this paper, a method based on Approximate Empirical likelihood ratio and a Deviance function combined with bootstrapping (AED-BP) is proposed to construct a confidence curve for the location of a change point. The method is compared with a method based on parametric Profile Likelihood and a Deviance function combined with Monte Carlo simulation (PLD-MC). A confidence curve provides a representation of the uncertainty in the outcome of the change point analysis. To evaluate the practical usability of confidence curves constructed by AED-BP, its properties were examined and its performance was compared to that of PLD-MC. The methods were applied to both synthetic and real data. Synthetic data were generated from three parametric distributions: Frechet with a constant shape parameter, log-normal, and gamma distributions. The real data are the hydrometeorological data analysed in other studies. The change points found in the original publications are used as a reference in this present paper. The results show that AED-BP has a performance that is similar to PLD-MC, but has an advantage in that it is not necessary to select a distribution family for the data. The AED-BP results on the Annual Maximum Runoff series for the stations Yichang and Hankou along the Yangtze river are among the first that show a possible effect of the presence of the Three Gorges dam.

The Great 2011 Thailand flood is one of the most catastrophic flood disasters recorded worldwide in modern history, which covered about 100,000 km2 of the country and resulted in 813 casualties and incurring US$ 46 billion in economic losses. In this paper, we re-evaluate the flood event to identify the root source of the catastrophe. By analyzing hydrological data from gauge stations along the Chao Phraya River (CPR) and remote sensing data, we decoupled the volumetric contributions of river overflow and local rainfall to the lowland reach of the basin and found that the latter contributed most to the flooding. More specifically, out of the total of 77.6 km3 of floodwaters estimated, 73.7% was from precipitation, while only 26.3% was from river discharges. This finding differs from the official reports or previous studies, which attributed the main cause of the river overbank flow. Therefore, it is also inferred that the upstream dam operations would have been only marginally helpful in mitigating the flood since rainfall made up most of the floodwaters in the downstream reach of the river. Our finding offers a new perspective that the local rainfall could be a significant source of the floodwater, rather than river overflow in the lower reaches of a large monsoonal river system in Southeast Asia. Thus, this paper contributes to the understanding of complex flood processes in large river basins and provides fresh insights for efficient flood control and stormwater management.

Evaluating water system vulnerability is the first priority in water security management, which is defined as measuring the degree of harm from exposure to stresses and the susceptibility and resilience of the system to human activities and natural causes. As a means of comprehensively exploring such weaknesses and susceptibilities in the interest of future development, this paper proposes a vulnerability evaluation framework that integrates Bi-level Programming (BLP) and Data Envelopment Analysis (DEA) with multi-followers: the water authority as the leader and industrial, domestic and agricultural water users as the multiple followers. A case study of 19 regions in the Yangtze River Basin between 2013 and 2017 is then introduced to demonstrate the effectiveness of the proposed bi-level DEA model. It was found that water systems were vulnerable to damage by hazards, that the domestic sector had the greatest effect on the whole, and the proposed framework identifying more vulnerable and safer areas provided managerial insights. The novelty of this paper is to put forward the vulnerability assessment framework of the regional water system which integrates water quantity, contamination, water-related hazards and water use efficiency and reveals the structure of realistic water resource management to obtain more sensitive results.

Climate change will impact every aspect of biophysical systems and society. However, unlike other components of the climate system, the impact of climate change on the groundwater system has only recently received attention. This focus is due to the realization that groundwater is a vital freshwater resource crucial to global food and water security, and is essential in sustaining ecosystems and human adaptation to climate variability and change. This paper synthesizes findings on the direct and indirect impacts of climate change on the entire groundwater system and each component. Also, we appraise the use of coupled groundwater-climate and land surface models in groundwater hydrology as a means of improving existing knowledge of climate change-groundwater interaction, finding that most models anticipate decreases in groundwater recharge, storage and levels, particularly in the arid/semi-arid tropics. Reducing uncertainties in future climate projections and improving our understanding of the physical processes underlying models to improve their simulation of real-world conditions remain a priority for climate and Earth scientists. Despite the enormous progress made, there are still few and inadequate local and regional aquifer studies, especially in less developed regions. The paper proposes two key considerations. First, physical basis: the need for a deeper grasp of complex physical processes and feedback mechanism with the use of more sophisticated models. Second, the need to understand the socioeconomic dimensions of climate-groundwater interaction through multidisciplinary synergy, leading to the development of better groundwater-climate change adaptation strategies and modeling.

The role of extreme rains over Peninsular Florida (PF) in modulating the seasonal rainfall characteristics is investigated in this study. The paper is motivated on its potential implication on the seasonal predictability of the hydroclimate of PF by relatively coarse global seasonal climate models. A majority of these climate models are unable to resolve the weather events like tropical cyclones that produce such extreme rain events. Therefore, a legitimate question to ask is if this limits the model’s seasonal predictability of the hydroclimate of PF. In this paper, extreme rain events over PF within a season are defined as days with daily rain amount at or above the 95th percentile over 39 years from 1979 to 2017 at the grid resolution of the observed rainfall dataset (0.5° × 0.5°). The thresholds for extreme rain days range from 16 mmday−1 to 36 mmday−1 depending on the season and the location over PF, while the heaviest rainfall range from 58 mmday−1 to 278 mmday−1. These extreme rain events occur most often across PF in the boreal summer season followed by the fall season with the least in the boreal winter season. Our study reveals that removing the days of extreme rain events has the largest impact on the corresponding seasonal anomalies and daily rainfall distribution in the dry winter season and least in the wet summer season. The impact of El Nino and the Southern Oscillation (ENSO) on the extreme rain events was evaluated by contrasting the differences in the shape and the scale parameters of the fitted Gamma distribution on daily rainfall in winter/spring seasons during warm and cold phases. Results revealed that the warm ENSO phases make the tails of the daily rainfall distribution over PF heavier and longer relative to the cold ENSO phases in the winter and spring seasons. In essence, our study reveals that the extreme rain events that are critical for the overall seasonal distribution of rainfall over PF in the first half of the year is modulated by large-scale phenomenon (e.g., ENSO). In the latter half of the year (summer and fall), the extreme rain events are not as critical to the seasonal rainfall anomaly or the overall seasonal distribution of rainfall over PF. Therefore, resolving the extreme rain events need not be as critical for the seasonal predictability of the hydroclimate of PF.

Climate change, which impacts the spatial and temporal distribution of water resources, has a significant influence on the future hydropower generation. Studying the future evolution pattern of hydropower generation under climate change is of great significance for the medium- and long-term hydropower prediction. The objective of this paper is to predict the future hydropower generation of large-scale reservoir groups under climate change. The innovation of this paper is that the macro-scale distributed hydrological model combined with the optimal operation model of large-scale reservoirs was proposed for hydropower generation prediction. The established model considers the specific operation processes of large-scale reservoir groups, including 62 reservoirs in the case study. First, the Statistical Downscaling Model (SDSM) was built, and the evolution trend of future rainfall and temperature was predicted. Second, the macro-scale distributed Variable Infiltration Capacity (VIC) model was built to predict the future runoff. Finally, the optimal operation generation model of large-scale reservoir groups was established to predict the trends of hydropower generation under climate change. Results demonstrate that under RCP2.6 scenario, there is no significant increase or decrease trend of hydropower generation in the future. But under RCP4.5 and RCP8.5 scenarios, the hydropower generation shows a growing trend, and the increase trend under RCP8.5 scenario is more obvious than that under RCP4.5 scenario. Thus, the development of hydropower generation is sensitive to climate change. This study can provide a reference for the long-term prediction of hydropower generation capacity in the upper Yangtze River Basin.

Simulation approaches employed in sediment processes are important for watershed management and environmental impact assessment. Use of Stochastic models that based on time series observations and do not require heavy calculations and complex relationships is developing. Recent years, artificial intelligence (AI) techniques have developed to improve the accuracy of sediment modeling. In this review study, the performances of the various types of AI models viz., ANN, GP, GEP, ANFIS, SVM, IBL, ABC, REPT, TLBO, decision tree and hybrid models viz., wavelet-AI, GP-AI are investigated as a means of prediction of sediment patterns in rivers. Published between 2001 and 2019, the 66 papers comprising the study deal with the usage of AI models in estimate of sediment and are undertaken in terms of data selection, modeling approaches, prediction time step, and modeling performance evaluation. This study presents an overview of AI techniques and a discussion of advantages to and applications of modeling sediment relations which to some extent have replaced time-consuming conventional mathematical techniques. However, the study discusses major challenges and prospective research opportunities. Comparison of the results produced by the models indicated an improvement in the prediction accuracy through the use of hybrid models. The most prominent feature of hybrid models is that they can provide more precise predictions for extremely high or low values, due to the utilization of the distributed information processing system and the non-linear transform involved. In the most recent articles in competition with wavelets an iterative procedure namely HHT introduced as effective filter for extracting the oscillatory-like features at different time scales. A review of published articles on sediment modeling showed the emergence of wavelet-based methods since 2008. No wavelet-AI based methods have been used in any of the articles reviewed so far. This subject was not investigated by any study in 2009. Between 2010 and 2019, 12 papers explored the use of a number of wavelet-AI based methods. About 19 percent (13 papers) of the studies used hybrid wavelet models among the articles studied. Of all the papers, only 6 papers investigated the performance of the models for the detection of hysteresis phenomenon. This study showed that AI models could be successfully employed to simulate the complex river sediment process in situations where explicit knowledge of internal sub-process is not required. This study also presents several new thinking in the related field of research to develop innovative methods and improve the quality of modeling. Future research efforts should be focused on formulating guidelines that help to develop AI-based models.

Summary Satellite altimetry has proven a valuable resource of information on river and lake levels where in situ data are sparse or non-existent. In this study several new methods for obtaining stable inland water levels from CryoSat-2 Synthetic Aperture Radar (SAR) altimetry are presented and evaluated. In addition, the possible benefits from combining physical and empirical retrackers are investigated. The retracking methods evaluated in this paper include the physical SAR Altimetry MOde Studies and Applications (SAMOSA3) model, a traditional subwaveform threshold retracker, the proposed Multiple Waveform Persistent Peak (MWaPP) retracker, and a method combining the physical and empirical retrackers. Using a physical SAR waveform retracker over inland water has not been attempted before but shows great promise in this study. The evaluation is performed for two medium-sized lakes (Lake Vanern in Sweden and Lake Okeechobee in Florida), and in the Amazon River in Brazil. Comparing with in situ data shows that using the SAMOSA3 retracker generally provides the lowest root-mean-squared-errors (RMSE), closely followed by the MWaPP retracker. For the empirical retrackers, the RMSE values obtained when comparing with in situ data in Lake Vanern and Lake Okeechobee are in the order of 2–5 cm for well-behaved waveforms. Combining the physical and empirical retrackers did not offer significantly improved mean track standard deviations or RMSEs. Based on these studies, it is suggested that future SAR derived water levels are obtained using the SAMOSA3 retracker whenever information about other physical properties apart from range is desired. Otherwise we suggest using the empirical MWaPP retracker described in this paper, which is both easy to implement, computationally efficient, and gives a height estimate for even the most contaminated waveforms.

Summary This paper explores environmental water management as a social process of navigating conflicting interests through a distributive justice lens. Environmental water management can achieve substantial ecological outcomes and address ongoing river degradation caused by past management and climate change; however it also causes specific and substantial burdens and benefits to different groups of stakeholders. Given that in most developed countries the majority of land is under private tenure, environmental watering must have active cooperation of private landholders to achieve its ecological outcomes and thus it must effectively deal with an array of vested interests. Australia’s reforms aimed at reallocating water from production to the environment have resulted in significant considerable volumes of environmental water. In the state of New South Wales, this water is managed by the state and national governments with the help of five Environmental Water Advisory Groups made up of a wide representation of interests. In this paper, we explore the perceptions of environmental, government, irrigator and grazing representatives, which demonstrate conflicting principles over how environmental water should be distributed. We detail how government water managers reconcile competing distributive principles of equity (ensuring that no one is disproportionally affected or benefits unduly), need (achieving environmental outcomes) and efficiency (prioritizing operational feasibility) in order to maintain the social acceptability of environmental water.

Summary This paper presents the modelling results of several tracer-tests performed in the cave system of Han-sur-Lesse (South Belgium). In Han-sur-Lesse, solute flows along accessible underground river stretches and through flooded areas that are rather unknown in terms of geometry. This paper focus on the impact of those flooded areas on solute transport and their dimensioning. The program used (One-dimensional Transport with Inflow and Storage: OTIS) is based on the two-region non equilibrium model that supposes the existence of an immobile water zone along the main flow zone in which solute can be caught. The simulations aim to replicate experimental breakthrough curves (BTCs) by adapting the main transport and geometric parameters that govern solute transport in karst conduits. Furthermore, OTIS allows a discretization of the investigated system, which is particularly interesting in systems presenting heterogeneous geometries. Simulation results show that transient storage is a major process in flooded areas and that the crossing of these has a major effect on the BTCs shape. This influence is however rather complex and very dependent of the flooded areas geometry and transport parameters. Sensibility tests performed in this paper aim to validate the model and show the impact of the parametrization on the BTCs shape. Those tests demonstrate that transient storage is not necessarily transformed in retardation. Indeed, significant tailing effect is only observed in specific conditions (depending on the system geometry and/or the flow) that allow residence time in the storage area to be longer than restitution time. This study ends with a comparison of solute transport in river stretches and in flooded areas.

Stochastic Storm Transposition (SST) involves resampling and random geospatial shifting (i.e. transposition) of observed storm events to generate hypothetical but realistic rainstorms. Though developed as a probabilistic alternative to probable maximum precipitation (PMP) and sharing PMP’s storm transposition characteristic, SST can also be used in more typical rainfall frequency analysis (RFA) and flood frequency analysis (FFA) applications. This paper explains the method, discusses its origins and linkages to both PMP and RFA/FFA, and reviews the development of SST research over the past six decades. Discussion topics includes: the relevance of recent advances in precipitation remote sensing to frequency analysis, numerical weather prediction, and distributed rainfall-runoff modeling; uncertainty and boundedness in rainfall and floods; the flood frequency challenges posed by climatic and land use change; and the concept of multi-scale flood frequency. Recent literature has shown that process-based multiscale FFA, in which the joint distributions of flood-producing meteorological and hydrological processes are synthesized and resolved using distributed physics-based rainfall-runoff models, provides a useful framework for translating nonstationary hydroclimatic conditions into flood frequency estimates. SST pairs well with the process-based approaches. This pairing is promising because it can leverage advances from other branches of hydrology and hydrometeorology that appear to be difficult to integrate into better-known RFA and FFA approaches. The paper closes with several recommendations for future SST research and applications.

Reservoir operation optimization is very important in water resource development and management. This paper focuses on the bi-objective optimization problems via proposing a novel bi-objective algorithm, called lion pride algorithm (LPA), based on the social structure of lion prides and the framework of NSGA-II. Specifically, LPA first divides the population into lion prides, then classifies the individuals into lion kings and ordinary lions based on the prides, and finally assign better fitness values to these lion kings compared to ordinary lions. Its performance in bi-objective optimization is then tested by the benchmark problems and the reservoir operation problems. Results indicate that: (1) LPA outperforms NSGA-II in convergence and diversity and runs about 2 to 4 times faster than NSGA-II for bi-objective optimization; (2) LPA has the good optimization ability for the complex problems with sharp-peak and long-tail POFs; and (3) in the reservoir operation problems of this paper, the optimization results of LPA weakly dominate 29% to 70% of those of NSGA-II, while the optimization results of NSGA-II only weakly dominate 1% to 22% of those of LPA. This study sheds a new idea for bi-objective optimization.

Although economic and environmental considerations have been well integrated into water resources management, the social aspects have been usually neglected. Quantifying and embedding social attachments in the water resource management models are so challenging. In this paper, a novel approach is proposed to quantify and consider social attachments for water and wastewater allocation in urban areas. In the management of shared resources, one of the main issues is the level of cooperation among agents. In a multi-agent environment, stakeholders can decide non-aligned or depending on their interests and social attachment, have some cooperation and coordination in the decision making process. In the proposed approach, an effort has been made to take into account the effects of social attachments on agents' decisions. In this paper, social network analysis is applied to evaluate the coordination level between agents in the use of shared water resources. A paired physical and behavioral simulation model is developed to assess water and wastewater allocation scenarios. A reinforcement learning approach, namely Q-learning is used to develop the behavioral model, which is linked with social attachments. To evaluate the applicability of the proposed approach, the Kan River Basin in the western part of the Tehran metropolitan area is studied. By using the proposed approach for a 25-year simulation horizon and 63 water and treated wastewater allocation scenarios, it is shown that it has merits to find the best scenarios in different conditions and can enhance the sustainability of the water resources in the study area. According to the results, improving the social attachments in the study area can reduce the over exploitation of groundwater and improve the groundwater quality.

Protecting flood prone locations through floodwater recharge of the depleted aquifers and using it for protecting dry season irrigated agriculture is the rationale for a form of intervention termed as ‘underground transfer of floods for irrigation’ (UTFI). This helps reduce the intensity of seasonal floods by tapping and storing excess floodwater in aquifers for productive agricultural use. This paper presents a case study of managing the recharge interventions in the context of the Ramganga basin, India. Using a case study approach, this study determines the socio-economic and institutional context of the study area, proposes three potential routes to institutionalize UTFI, and provides insights for scaling up the interventions in the Ganges and other river basins that face seasonal floods and dry season water shortages. Managing the interventions involves community participation in regular operations and maintenance tasks. Given the limited scale of the pilot UTFI intervention implemented to date, and the socio-economic and institutional context of the case study region, the benefits are not conspicuous, though the piloting helped in identifying potential ways forward for the long-term management of the pilot site, and for scaling up the interventions. Initially pilot site management was handled by the project team working closely with the community leaders and villagers. As the intervention was demonstrated to perform effectively, management was handed over to the district authorities after providing appropriate training to the government personnel to manage the system and liaise with the local community to ensure the site is operated and managed appropriately. The district administration is willing to support UTFI by pooling money from different sources and routing them through the sub-district administration. While this is working in the short term, the paper outlines a programmatic longer term approach for wider replication.

The paper presents the results of long-term (1996–2017) studies of the chemical composition of rain, snow and sleet in one of the cities with the highest concentrations of particulate matter in the air in Europe. The study included measurements of 45 chemical constituents of precipitation as well as its pH and EC. Concentrations of PM10, SO2 and meteorological parameters were also analysed. Differences in Cl and Na concentrations were found depending on the type of precipitation (snow or rain). These differences are most probably caused by anthropogenic factors, however, to a small extent (~18–19%), they are associated with the total variability of the concentrations of these chemical constituents. The differences between the concentrations of the remaining constituents of precipitation analysed are of lesser or negligible importance. The main processes affecting the chemical composition of precipitation are the chemical weathering of suspended dust and the formation of mineral acid aerosols. These processes shape the chemical composition of both rain and snow in a similar manner. The differences between the chemical composition of rain and snow are the result of the concentrations of and mutual relations between selected chemical constituents and precipitation pH. An important factor affecting the pH of precipitation is the effectiveness of the chemical weathering of suspended dust, which may make the precipitation more alkaline. It is very likely that pH-buffering will occur, which can prevent the pH of precipitation from decreasing, especially in cold periods. Long-term observations of variability in the concentrations of the chemical constituents indicate a slight, but rather constant decrease in concentrations of most of the chemical constituents analysed, as well as in the concentrations of PM10 and SO2 in the air in Krakow. The directions of these changes reflect, to a certain extent, the complex transformations of industrial, economic and social conditions in Krakow within that period. The typical ranges of variability of the recorded values of concentrations/measurement values of 22 chemical constituents and the physical characteristics of rain, sleet and snow presented in the paper can be used as reference ranges of these parameters, typical for an area with a significant degree of atmospheric dust pollution.

Continuous time random walks (CTRW), multi-rate mass transfer (MRMT), and fractional advection-dispersion equations (FADEs) are three promising models of anomalous transport as commonly found in natural streams. Although these paradigms are mathematically related, understanding their advantages and limitations poses a challenge for model selection. In this paper, we quantitatively evaluate the advection-dispersion equation (ADE), fractional-mobile-immobile (FMIM), fractional-in-space ADE (sFADE), fractional in space transient storage (FSTS), truncated time-fractional model (TTFM), and CTRW models with truncated power-law waiting time distribution (CTRW-TPL) by fitting them first to synthetic data. We then applied these models to observations from tracer experiments conducted in several rivers. Based on the extensive analysis, we conclude that the performance of the FSTS model (in particular, the model with negative skewness β = - 1 ) is comparable or superior to the other nonlocal models evaluated in the paper; therefore, the model represents an alternative to existing models for simulating stream solute transport for spatially-homogeneous flows.

Gravitational search algorithm (GSA) is an evolutionary algorithm developed to solve the global optimization problems, but still suffers from the premature convergence problem due to the loss of swarm diversity. In order to improve the GSA performance, this paper develops a novel multi-strategy gravitational search algorithm (MGSA) where the Levy flight strategy is adopted to increase the local search ability of the global best-known agent; and then the mutation strategy is used to improve the swarm diversity in the evolutionary process; finally, the elitism selection strategy is used to enhance the exploration ability and convergence speed of the swarm. The MGSA method is compared with several methods in 24 famous benchmark functions, and the results demonstrate the superiority of the MGSA method in both search ability and convergence rate. Next, the MGSA method is used to solve the ecological operation problem of the Wu hydropower system. The results indicate that compared with several existing methods, MGSA can obtain better scheduling schemes to make obvious reductions in the inappropriate ecological water volume in different scenarios. Thus, this paper provides a new effective tool for the complex engineering optimization problems.

Flood hazard in urban areas is usually assessed by the estimations of parameters like flood extent, water depths, flow velocities and other related quantities. These hydrodynamic variables can be computed using flood inundation numerical models, the complexity of which has been analyzed in several papers in the literature. However, the question of how detailed a model should be in order to give reasonable results is still an open issue and no shared and general conclusions have been reached so far. One of the main reasons for this is that model benchmarking is very often carried out with reference to the flooded areas extent only, sometimes analyzing also the simulated water depths values and, quite rarely, evaluating the computed velocities. This is in contrast with the main purpose of the simulation models which are, from a practical point of view, advanced tools for flood hazard and vulnerability assessments. Finally, the lack of sufficient data on inundation extent and depths for the calibration and validation of the numerical models has been recognized, especially in complex situations that often characterize the flood evolution in a given area due to bridge obstructions, interaction with buildings and so on. In this paper, the performances of three different approaches to two-dimensional flood modeling (fully dynamic, diffusive and porosity approaches) have been evaluated with reference not only to the hydrodynamic variables, water depths and velocities, but also focusing the attention on their product and, thus, to the consequences associated to their different estimations on the vulnerability assessment. This analysis has been focused first on a flash flood flow experiment in a simplified urban district and then to the event which occurred in the city of Crotone (Calabria, Italy) in 1996. The results highlight that fully-dynamic modeling should be the unavoidable reference tool when the goal of the urban flood mapping activity is not limited to the evaluation of the flood-prone areas extent but involves also the local estimation of flood hazard/vulnerability.