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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.

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.

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.

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.

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.

Daily stochastic streamflow simulation is widely used for the design of reservoirs, evaluation of reservoir operation rules, and risk evaluation of operation of water resources systems. The major difficulties and challenges of daily streamflow are that there are 365 days that need to be simulated, which entails much more calculation than does monthly streamflow simulation. Since lag-2 auto-correlation is usually large, the lag-2 correlations should be considered. This paper therefore proposes a copula-based method for daily stochastic streamflow simulation. The contribution and novelty of this paper are that: (a) the proposed method can consider lag-2 correlations, compared with the currently used copula based method; (b) the conditional copulas are used to build high dimensional copulas, which make calculations easier; and (c) the method can be used for daily streamflow simulation because of the simplified model structure and effective parameter estimation method. Seven gauging stations on the upper Yangtze River and Pearl River in China were selected as case studies. Results demonstrated that the proposed method preserved the basic statistics (including mean daily flow, standard deviation, and coefficient of skewness) of observed data of each day well. Comparison with the currently used seasonal autoregressive model (SAR(2)) and bivariate copula-based method considering lag-1 autocorrelation indicated that the proposed method produced smaller relative errors and was better overall. Therefore, the proposed method can be regarded as an effective way for stochastic daily streamflow simulation, and can be used for the design of reservoirs and risk analysis of water resources systems.

In this paper, we draw attention to some inappropriate descriptions and mathematical errors in the recently published article “Numerical study on pore clogging mechanism in pervious pavements” (Zhang et al., 2018), which has employed the computational fluid dynamics – discrete element method (CFD-DEM) coupled model to numerically reveal the pore clogging mechanism in pervious pavements. The inappropriate descriptions about the referred papers (Luo et al., 2015; Blais et al., 2016; Li and Li, 2018) which may cause misreading are clarified and communications concerning the errors in mathematical equations are also served in this comment.

Soil water characteristic curve (SWCC) is a critical parameter for unsaturated soil. However, few study can be found in literature for SWCC of loess with high soluble salt content, in particular SWCC fitting model. This paper targets on Ili loess in Xinjiang in China, which has a high level of soluble salts and is a typical loess in central Asia. Centrifuge method and filter paper method are used to conduct SWCC tests of Ili loess under different salt conditions. The total and matric suction of Ili loess under different soluble salt contents are obtained. The results of two methods are compared. The corresponding influence of soluble salt on SWCC is studied. At the same time, based on the Gardner model, a new SWCC fitting model of total and matric suction for Ili loess is proposed, which is able to consider the influences of soluble salts. The test results show that the compression during centrifuge method contributes certain increase of matric suction, especially at high volumetric water content conditions. Soluble salts have large effect on total and osmotic suction of Ili loess, however limited effect on matric suction. The alteration of total and osmotic suction is caused by the change of solution concentration in the sample. Total suction is mainly affected by osmotic suction. The alteration of matric suction is caused by the precipitation of the salts. It is proved that the proposed SWCC model is capable of characterizing the total and matric suction by simultaneously considering volumetric water content and soluble salt content.

Previous studies have investigated many cases regarding changes in well water level and attributed differences in the coseismic water level responses to various hydrogeological conditions. However, most of these studies often fail to provide insights into the responses of different portions of one groundwater system because the boreholes in question are usually located in different hydrogeological units with no hydraulic communication. This paper analyzes coseismic and postseismic variation characteristics of the water levels of two adjacent observation wells located near the same fault zone and separated by a distance of approximately 2 km. Well X10 resides in the fault zone and is indirectly connected to the aquifer, whereas Well X11 directly penetrates into the aquifer. Based on the coseismic static strain, seismic energy density and variation in well water level response to earth tides before and after earthquakes, this research investigates the effects of faults on coseismic well water level responses. The results show that under the effects of static compressive stresses, wells X10 and X11 present opposite water level responses due to the presence of the fault. After the earthquake, the water levels in both wells are elevated due to the increased aquifer permeability induced by the earthquake. Nevertheless, because of the fault, the rate of increase of the postseismic water level in Well X10 is obviously lower than that of Well X11. A seismic energy density analysis shows that only three near-field earthquakes with energy densities greater than 10−3 J/m3 can simultaneously trigger the elevation of coseismic water levels in wells X10 and X11. This paper concludes that the oscillation elevation of the coseismic water levels in wells X10 and X11 should be mainly attributed to the increased aquifer permeability. However, the mechanisms underlying this change are quite complex and cannot be explained by a single mechanism, and the increased permeability may be correlated to various factors, such as the coseismic static strain, seismic energy density and borehole condition.

Rapid economic development and population growth in the Lancang-Mekong River Basin makes it imperative for water resources to be used fairly and efficiently among all stakeholders. This paper proposes an integrated model framework to assess the water benefits sharing schemes among the five stakeholders in the basin, namely, China, Thailand, Laos, Cambodia, and Vietnam. Three objectives are considered for this assessment—hydropower (HP), eco-index (EI), and agricultural water use (AW). The hydrologic model, optimization model, and cooperative game theory methods are jointly used to analyze the water benefits of stakeholders under different scenarios. According to the model, cooperation can bring more incremental benefits in dry years when water resources are scarce. Three cooperative game theoretical methods, namely, the Shapley value, the Gately point, and the Nash–Harsanyi solution, are employed to find the possible benefit sharing solutions. The results show that China and Laos need to operate their reservoirs for downstream countries’ beneficial uses under cooperation, and thus, compensation by reallocating the incremental benefit from cooperation is necessary no matter which method is used. It is found that all of the future damming plans would enhance the basin-level cooperation in most cases. In particular, according to the analysis, damming in the basins of the Sesan, Srepok, and Sekong rivers would have more effect than damming in the upper Lancang-Mekong River Basin after 2030. Based on some verified assumptions, the results of this paper can offer real-world preferences, but the uncertainties need to be further explored.

Situational awareness in sanitary sewer systems requires accurate flow information at different spatial locations in a city. It is especially desirable to predict flows across a wastewater network in response to heavy rainfall events in addition to regular consumption patterns. Typically, complicated hydraulic models that suffer from difficulties in parameter identification and high computational burden are used for flow prediction. Recently, data-driven approaches have been employed for flow prediction. In this paper, we first design and then compare the performance of three data-driven methods to predict flow: (1) Artificial Neural Network (ANN); (2) Long-Short Term Memory (LSTM); (3) Least Absolute Shrinkage and Selection Operator (LASSO). We test the performance of these machine and statistical learning techniques using data gathered from the City of Springfield. While, all three data-driven methodologies provide acceptable prediction performance, we observe that LSTM outperforms ANN due to the inherent memory integrated with a feedback structure. The statistical learning approach, i.e., LASSO regression not only offers good prediction performance, but also helps identify the key spatial and temporal features that influence flow at any specific location. This added information could aid in remediation activities. Another key contribution of this paper is that we quantify the value of groundwater data in flow prediction. Specifically, including groundwater data as an additional input enhances flow prediction performance in all three methods. Lastly, to better predict flows corresponding to rare events (e.g., 50 or 100 year rainfall events) we use a resampling approach (known as SmoteR) to modify the training dataset. Simulation results indicate that the resampling technique is effective in improving prediction performance.

[Departement_IRSTEA]Eaux [TR1_IRSTEA]GEUSI; International audience; Water governance is a complex process to manage and improve due to the large numbers of competing scales and levels inherent within it. Effectively supporting any water governance process does not just rely on hydrological knowledge of a water system, but much broader trans‐disciplinary understanding of a whole socialhydrological system. In this paper we show how there are commonly up to eight or more competing scales—spatial, temporal, administrative, institutional, management, network, knowledge/information and stakes/issues—present in any water governance situation. Processes in one scale can then create flows or externalities that produce multi‐level, cross‐scale or rescaling effects. A typology of six types of externalities orflows is provided—physical or material flows (e.g. water and solute movement), information and knowledge, political and social control, financial flows, human flows, and an irreversibility effect (e.g. flows, such as that of time passing, which lead to cumulative effects that are difficult or impossible to reverse)—and used to study the examples provided in the 15 papers of this special issue. From these insights, conclusions are drawn on how conflicts across competing scales are currently being managed, the mechanisms involved in bridging scales, and to what extent currently privileged scales and/or governance structures are appropriate for effectively managing water and land. The two general trends observed for multi‐level and cross‐scale management remain the need for collaborative governance processes and facilitators of these interactions, as well as the development of savvy coalitions for water governance change who understand and can effectively navigate these complex multi‐level and multi‐scalar systems. There is also a need to recognise that water basins or watersheds are social constructions and not always the most effective arenas for water governance. Problemsheds based on conceptssuch as the French “territoire” (territory), may in fact prove more pertinent in many cases (e.g. highly engineered systems) due to the unequivocal social nature of their construction and ability to delineate a range of agreed upon social‐hydrological systems for management.

Water planner and managers always work in a changing and uncertain environment, since the majority of water resource-related data is imprecise or uncertain to some extent. MCDM (multi-criteria decision making) is a well-suited decision support tool for water resources management, however multiple uncertainties existed in criteria weights (CWs) and performance values (PVs) hinder the application of MCDM methods in real-world problems. This paper proposes an application-oriented stochastic MCDM framework for robust water resources management under uncertainty. A weight aggregation approach based on the game theory coupled with feasible weight space (FWS) is developed for conflict resolution and uncertainty estimation. We propose a novel SMAA-GRA model by combining the stochastic multi-criteria acceptability analysis (SMAA) theory and grey relational analysis (GRA). The risk of errors in decision making and rank uncertainty degree are utilized to assess decision-making uncertainty. We perform significance analysis to examine the impacts of input parameter uncertainties on the final assessment of alternatives. The proposed framework is demonstrated via two water resources management case studies. Numerical experiments are conducted to investigate the robustness and computational efficiency of the framework as well as the effects of uncertainty level and probability distribution type on decision-making results. The results indicate that the framework enables water managers to examine the robustness of final decisions and allows a risk-informed water resource decision to be made with higher reliabilities.

This study proposes a novel socio-hydrological modeling framework for assessing the performance of complex water resources systems. It employs and integrates agent-based modeling (ABM) and the theory of planned behavior (TPB) into the socio-hydrological modeling framework to account for agents’ behaviors. Due to farmers’ major role in anthropogenic droughts, this paper mainly focuses on farmers’ behavior. The TPB framework and the agents’ behavioral rules in ABM are structured based on the data obtained from field questionnaires and interviews by the farmers in the Zarrineh River Basin as the main river feeding the Urmia Lake. The proposed modeling framework, including the TPB and ABM structures and rules, are validated by comparing the results of the proposed socio-hydrological simulation model with the actual data on the simulation period. To assess the ability of the proposed framework, root mean square error (RMSE) and the determination coefficient (DC) were computed. The results show the acceptable performance of the proposed model by RMSE = 0.18 and DC = 0.92. Analysis of the results indicates that financial conditions, farmland size, farmers’ age, and farmers’ education are among the major factors in farmers’ decision-making process, respectively.

Where surface and groundwater are managed conjunctively, the stress on water supplies from climate change can significantly influence water use patterns as well as the economic value and sustainability of those uses. However, aquifer protection can be an expensive proposition because water uses that currently rely on aquifer pumping may produce considerable economic value that would be lost if protection measures are carried out. Evidence from climate-stressed regions has attracted research addressing the costs and benefits of aquifer protection plans. Despite these efforts, few peer-reviewed papers have examined water use patterns that minimize the economic costs of aquifer protection. This work presents an original approach to address that gap by developing and applying a basin scale hydroeconomic optimization model of North America’s Middle Rio Grande Basin to explore impacts of new policies not yet implemented supporting aquifer protection. It also gives model access to readers or stakeholders to experiment with their own scenarios to assess impacts of alternative aquifer protection plans. The model accounts for surface and groundwater storage, irrigation, urban, environmental, and recreational demands, surface water inflows under various climate scenarios, groundwater pumping and recharge, substitute water prices, crop water use, evaporation, as well as institutional constraints governing water use. The objective is implemented by finding the optimized discounted net present value of economic benefits summed over uses, sectors, and regions from use of surface water and connected aquifers. Results are shown for each of six water supply scenarios, two substitute water prices, and two system operation rules. To address impacts of aquifer protection targets, groundwater sustainability targets are specified and enforced as constraints for each of the region’s two major aquifers. We assess total and marginal cost of achieving two targeted aquifer protection levels by identifying optimized surface use and groundwater pumping for each of 24 scenarios. Results show that climate change, in the form of reduced and highly variable inflows, considerably drives up the cost of protecting aquifer sustainability, amplified by the conjunctive nature of the system. Future work points to a need to assess economic performance of various water conservation measures as well as reducing costs of substitute water through measures such as technical advance in desalination, recycling and reuse, substitution of other resources for water, better characterization of existing aquifers, and development of new groundwater supplies.

Damage caused by floods has generally increased in Europe over the decades, and reducing the flood risk has become a recognised priority throughout the continent. Floods constitute a burden on the economies of European countries and insurance companies, not only because of the damage they cause but also because of the costs of structural flood defences and safety measures in general. The main reasons for the increasing flood risk are: presence of settlements and the growing value of assets in flood-prone areas; climate and environmental (land use and land cover) changes that always happen but have recently been accelerated because of radically different demographic conditions and technological progress. Statistical analyses by Munich Re’s NatCatSERVICE database underline the high relevance of floods to European societies. The reasons for the rising trends in flood losses are explained. The flood risk is borne by various stakeholders – individuals, businesses, governments and the insurance sector. This paper discusses the taxonomy of floods, presents flood statistics for Europe and examines the reasons for flood risk increase. It also examines flood risk reduction, focusing on various aspects of flood insurance as a means of risk transfer.

Scenario-neutral approaches are used increasingly as a means of stress-testing climate-sensitive systems to a range of plausible future climate conditions. To ensure that these stress-tests are able to explore system vulnerability, it is necessary to generate hydrometeorological time series that represent all aspects of plausible future change (e.g. averages, seasonality, extremes). A promising approach to generating these time series is by inverting the stochastic weather generation problem to obtain weather time series that capture all the relevant statistical features of plausible future change. The objective of this paper is to formalize this “inverse” approach to weather generation, by both characterizing the process of optimizing weather generator parameters and proposing a numerically efficient solution that exploits prior knowledge and accounts for the complexity of the optimization landscape. The proposed approach also provides a structured way to ensure the physical realism of the generated weather time series, by using penalty-based objective functions to focus the optimization on the climate features deemed most relevant to the system being analyzed. A case study in Adelaide, Australia, is used to demonstrate specific implementations of this approach. The use of bounds on the weather generators dramatically decreases the time taken to create time series, and the use of penalties is shown to allow for change in some statistics to be prioritized, while still ensuring the realism of the time series.

New and improved drought models based on the World Meteorological Organization approved Standardized Precipitation Index, principally at multiple timescale horizons, are providing significant benefits to the hydrological community, by its widespread acceptance in the sub-field of water resources management, sustainable water use and precision agriculture. In this research paper, the existing challenges faced by a drought forecasting model trained at multiple time-scales are resolved where a new multi-phase, multivariate empirical mode decomposition model integrated with simulated annealing and Kernel ridge regression algorithms (i.e., MEMD-SA-KRR) is designed to attain significantly accurate drought forecasts for 3 agricultural sites (i.e., Faisalabad, Islamabad and Jhelum, located in Pakistan). Utilizing the multi-scalar Standardized Precipitation Index (SPI) time series as a target variable for characterization of drought, twelve multivariate datasets (derived from statistically significant lagged combinations of precipitation, temperature & humidity), that are enriched with eight synoptic-scale climate mode indices and periodicity, are utilized in designing a new drought model. The study constructs a hybrid MEMD-SA-KRR model, where firstly, the data are partitioned into their respective training and testing subsets after creating historically lagged SPI at timescale (t – 1). Secondly, the MEMD algorithm is conditioned to demarcate multivariate climate indices from their training and testing sets, separately, into their decomposed intrinsic mode functions (IMFs) and residues. Thirdly, the SA method is employed to decide the most suitable IMFs. Finally, the KRR algorithm is applied to the selected IMFs to forecast multi-scaler SPI, at 1-, 3-, 6- and 12-monthly forecast horizon. The results are benchmarked with Random Forest, integrated with MEMD and SA to develop the MEMD-SA-RF equivalent model. The multi-phase MEMD-SA-KRR model is tested geographically in Pakistan, revealing that the MEMD-SA-KRR hybrid model generates reliable performance in forecasting multi-scaler SPI series, relative to the comparative models based on error analysis metrics. The hybrid drought model incorporating the most pertinent synoptic-scale climate drivers, as the model inputs has significant implications for hydrological applications and water resources management including its potential use in drought policy and drought recovery plans.

The description of the floods which have occurred in the past and an assessment of the adverse impacts they have entailed are among the duties required by the European Flood Directive. Indeed, to understand the consequences of a future flood on a city that in the past has already been affected by other flood events, it is very useful to reconstruct the hydraulic effects produced by those events. Such a task is particularly difficult for cases which occurred many years ago. For past events, it is often easier to have conventional hydrological data (for example, the hyetographs of the event) than conventional hydraulic data (recordings of flow rates, water depths, etc.). It is also difficult to obtain the topography valid for the period in which the event occurred. The greater the distance in time the greater the difficulty in gathering these data. In this paper we present a criterion for making a flow of information, using various non-conventional sources of information (such as amateur videos, photographs, traditional topographic surveys, news reports, etc.). In particular, it is shown how the data thus obtained can be used together in order to reconstruct numerically the hydraulic dynamics of a flood event in an urban area with a complete hydrodynamic model, which requires a particularly accurate computational domain. All these issues will be presented and discussed with reference to the numerical reconstruction of the flood event that occurred 23 years ago (1996) in the city of Crotone (Calabria, Italy).

This paper proposes an analytical model for predicting the longitudinal profile of depth-averaged streamwise velocities in a channel with an emergent array of rigid cylinders. The governing equation of the analytical model was derived from the momentum equation and flow continuity equation. The longitudinal transect through a flume with a vegetation patch was divided into four regions based on two length scales of flow adjustment upstream of and inside the array, and analytical solutions were proposed for the four regions. Laboratory experiments demonstrated that the array length does not influence flow adjustments near the upstream edge of the array, so the model can predict the longitudinal profile of streamwise velocity for either short or long arrays. Twenty groups of velocity data from different sources were used to verify the proposed model. The predicted velocities agreed well with the measured velocities, indicating that the model is capable of predicting the longitudinal profiles of the velocity upstream of and inside a model patch. The predicted velocity profile can be further employed to estimate regions of enhanced or diminished deposition of fine sediment or organic matter inside model patches.