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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.

Highlights • The effects of lakes and reservoirs on global daily streamflow are evaluated. • Reservoirs affect model performance substantially in the global domain. • Lakes’ effects on model performance are limited to few catchments. • Lakes and reservoirs reduce return levels discharge thresholds globally. • Reservoir parameters contribute to uncertainty of model performance metrics., Lakes and reservoirs affect the timing and magnitude of streamflow, and are therefore essential hydrological model components, especially in the context of global flood forecasting. However, the parameterization of lake and reservoir routines on a global scale is subject to considerable uncertainty due to lack of information on lake hydrographic characteristics and reservoir operating rules. In this study we estimated the effect of lakes and reservoirs on global daily streamflow simulations of a spatially-distributed LISFLOOD hydrological model. We applied state-of-the-art global sensitivity and uncertainty analyses for selected catchments to examine the effect of uncertain lake and reservoir parameterization on model performance. Streamflow observations from 390 catchments around the globe and multiple performance measures were used to assess model performance. Results indicate a considerable geographical variability in the lake and reservoir effects on the streamflow simulation. Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) metrics improved for 65% and 38% of catchments respectively, with median skill score values of 0.16 and 0.2 while scores deteriorated for 28% and 52% of the catchments, with median values −0.09 and −0.16, respectively. The effect of reservoirs on extreme high flows was substantial and widespread in the global domain, while the effect of lakes was spatially limited to a few catchments. As indicated by global sensitivity analysis, parameter uncertainty substantially affected uncertainty of model performance. Reservoir parameters often contributed to this uncertainty, although the effect varied widely among catchments. The effect of reservoir parameters on model performance diminished with distance downstream of reservoirs in favor of other parameters, notably groundwater-related parameters and channel Manning’s roughness coefficient. This study underscores the importance of accounting for lakes and, especially, reservoirs and using appropriate parameterization in large-scale hydrological simulations.

Summary Reusing culture medium of Arthrospira platensis is quite important in large scale production because its inappropriate disposal could exacerbate problems of environmental pollution. This study evaluates the suitability of using different quantities of exhausted Schlosser medium after continuous treatment using granular activated carbon (GAC) with a residence time ( T ) of 2 h for A. platensis growth in continuous cultivation. A tubular photobioreactor (PBR) and urea as cheap nitrogen source were used, taking as response variables kinetic parameters and biomass composition. The removal of both organic matter and pigment ( OMR and PgR , respectively) was measured to evaluate the efficiency of the treatment process. This treatment process yielded high values of OMR (73.7 ± 0.1%) and PgR (52.4 ± 0.4%) using 75% treated medium, thereby A. platensis biomass with high protein content (42.0 ± 0.6%), 1568 ± 15 mg/L cell concentration under steady-state conditions and 941 mg/L d cell productivity. This alternative to simultaneous treatment with GAC for reuse of Schlosser medium in continuous cultivation could ensure no diminution in either cell productivity or protein content in A. platensis cultivation using tubular PBR with 65% reduction in medium culture costs.

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

Funding Information: We would like to thank Dr Maria Kämäri, MSc Franziska Wolff, MSc Mariana Verdonen, MSc Tiia Tarsa and MSc Marko Kärkkäinen from the University of Eastern Finland, William Speirs from the University of Queensland (Brisbane, Australia), Dr Carlos Gonzales-Inca and MSc Jouni Salmela from the University of Turku and Dr Nikita Tananaev (Melnikov Permafrost Institute [MPI] at Yakutsk, Russia) for their valuable fieldwork assistance in 2016–2021. We also thank Marko Kärkkäinen for analysing the bedload transport amounts and grain sizes and processing the orthomosaic image from the aerial photos of February 2021. It would not have been possible to conduct the fieldwork without the drilling assistance of Ilkka Syvänperä and Esa Karpoff from the Kevo Subarctic Research Institute of University of Turku during 2016–2021. The authors have no conflicts of interest to declare. The ice-covered flow measurements were initiated under the post-doctoral research project of Dr Lotsari, funded by the Academy of Finland (ExRIVER: grant number 267345). The work for this paper was also supported financially by four other Academy of Finland funded projects (DefrostingRivers: 338480; HYDRO-RDI-Network: 337394 and 337279; InfraRiver: 296090; Green-Digi-Basin 347701). Funding was received from the Maj and Tor Nessling Foundation (ExRIVER, grant numbers: 201300067 and 201500046; Influence of river ice and fluvial processes on river environments now and in the future, grant number: 201600042) and Strategic Research Council at the Academy of Finland (Competence-Based Growth Through Integrated Disruptive Technologies of 3D Digitalization, RobotiCS, Geospatial Information and Image Processing/Computing – Point Cloud Ecosystem, grant number: 293389). The Department of Geographical and Historical Studies, University of Eastern Finland, supported the work financially. Funding was also received from the British Society for Geomorphology (research project title ʻDefrosting sedimentary systems: the impacts on the evolution and material transport of high-latitude riversʼ [registered charity number: 1054260]). Funding Information: The ice-covered flow measurements were initiated under the post-doctoral research project of Dr Lotsari, funded by the Academy of Finland (ExRIVER: grant number 267345). The work for this paper was also supported financially by four other Academy of Finland funded projects (DefrostingRivers: 338480; HYDRO-RDI-Network: 337394 and 337279; InfraRiver: 296090; Green-Digi-Basin 347701). Funding was received from the Maj and Tor Nessling Foundation (ExRIVER, grant numbers: 201300067 and 201500046; Influence of river ice and fluvial processes on river environments now and in the future, grant number: 201600042) and Strategic Research Council at the Academy of Finland (Competence-Based Growth Through Integrated Disruptive Technologies of 3D Digitalization, RobotiCS, Geospatial Information and Image Processing/Computing – Point Cloud Ecosystem, grant number: 293389). The Department of Geographical and Historical Studies, University of Eastern Finland, supported the work financially. Funding was also received from the British Society for Geomorphology (research project title ʻDefrosting sedimentary systems: the impacts on the evolution and material transport of high-latitude riversʼ [registered charity number: 1054260]). Publisher Copyright: © 2022 The Authors Global climate change has been projected to affect hydrology, the ice-covered flow period and river morphology (including changed sediment transport conditions) in northern high-latitude regions. To understand the impact of the expected shortening of the ice-covered period on bedload transport, one needs to understand the present sediment transport in these high-latitude rivers with annually occurring ice cover. Thus, the aims are (1) to define the impacts of ice cover on near-bed flow characteristics during hydrologically varying years, and (2) to analyse the impacts of these mid-winter flow characteristics on the bed sediment transport potential. The analyses are based on Acoustic Doppler Current Profiler (ADCP: 2016–21) and Acoustic Doppler Velocimeter (ADV: 2020–21) measurements performed in mid-winter ice-covered conditions of the sandy and small (circa 20 m wide) Pulmanki River in northern Finnish Lapland. Despite the ice-covered river conditions in winter, sediment transport occurs even during these harshest mid-winter conditions. The critical velocities and shear velocities of mid-winter conditions were exceeded in winters 2016–2021, and bedload transport occurred according to bedload measurements. Three different situations occurred regarding the bed sediment transport and near-bed velocity conditions: (1) high measured mid-winter discharges indicate high velocities throughout the meander bend; (2) low measured mid-winter discharges cause low near-bed velocities throughout the meander bend; (3) winters having intermediate discharges indicate near-bed velocities and sediment transport potential being higher at the upstream inlet and apex sections of the meander bend but clearly lower downstream of the apex. The confinement by the river ice cover, i.e. bottom-fast ice, explains the velocity variation. The near-bed velocities were the highest at the upstream inlet section of a symmetrical meander bend, where the measurement cross-sections were narrower and shallower. The velocities were the lowest downstream of the apex, where the channel changed from relatively narrow to wider and deeper.

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.

Due to rapid urbanization, population growth and economic drivers, megacities and metropolises around the world face increasing water challenges, such as water scarcity, degradation of water resources and water-related risks such as flooding. Climate change is expected to put additional stress on already strained metropolitan water management systems. Although there is considerable research on river basin management and on urban water management, there is hardly any on metropolitan water management. Similarly, as urban water generally emerges from and returns to river basins, it is surprising how little literature there is that explicitly connects these two spheres of governance. Hence this review paper addresses the: What does a review of the literature tell us about the overlap and reconciliation between the concepts of Integrated Water Resources/River Basin Management and Metropolitan/Urban Water Management, particularly in relation to megacities? Based on an extensive literature review, this paper concludes that the key differences between the two are in relation to their overarching framework, scope, inputs and outputs of water and in relation to dealing with extreme weather events. The literature review reveals how sustainable and integrated urban water management increasingly adopt principles and rhetoric from integrated water resource management, this has yet to translate into significant changing practices on the ground. Urban water management still often occurs independently of river basin issues. Achieving coherence between river basin management and sustainable/integrated urban water management is even more difficult in metropolises and megacities, because the latter consists of multiple political-administrative units. The article concludes that the scalar mismatch between river basin management and metropolitan/megacity water governance deserves much greater attention than it currently receives in the academic and policy debates.

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.

A flood hazard is one of the most common and destructive natural disasters. Flood risk reduction with non-engineering measures has become the primary goal for flood management. This paper proposes an approach to improve the flood control operation for cascade reservoirs by minimizing flood control risk. A framework of reservoir flood control operation coupled with risk assessment (RFCORA) and the corresponding RFCORA model are proposed to reduce risk. The model contains three sub-modules. A real-time reservoir flood control operation simulation module is developed, in which a flood discharge control chart is used to determine the flood magnitude, and flood release is obtained by operation rules. Entropy-weighted fuzzy comprehensive evaluation method is applied to assess the risk level in the flood risk assessment module. Finally, the flood release is updated according to the developed outflow adjustment optimization module aimed at minimizing flood control risk by combing future inflow information. In addition, another model without outflow adjustment optimization is set to be a comparative experiment. The water system of the Upper Yellow River is selected as a case study to verify the model. The results show that the framework and RFCORA model developed in this paper can decrease the time duration in the highest risk level without increasing the maximum water level and maximum outflow of reservoirs. The approach proposed in this paper based on flood control operation and flood risk assessment can be extended to flood mitigation in other water systems in similar situations.

Managing water resource systems usually involves conflicts. Water recognizes no borders, defining the global geopolitics of water conflicts, cooperation, negotiations, management, and resource development. Negotiations to develop mechanisms for two or more states to share an international watercourse involve complex networks of natural, social and political system ( Islam and Susskind, 2013 ). The Kabul River Basin presents unique circumstances for developing joint agreements for its utilization, rendering moot unproductive discussions of the rights of upstream and downstream states based on principles of absolute territorial sovereignty or absolute territorial integrity (McCaffrey, 2007). This paper analyses the different stages of water conflict transformation between Afghanistan and Pakistan. It first examines historical disputes between the upstream and downstream riparians, revolving around contending rights claims, resulting in zero-sum confrontations with one party’s loss as another’s gain, possibly ending in confrontation. The paper then formulates a decision support tool, a mechanism for transforming conflict into cooperation, and concludes by introducing practical methods for identifying basin needs and sharing benefits, enabling riparians to negotiate a win-win process.

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.

Assessing flood frequency is pivotal for hydrology, with implications for many fields ranging from river science to the insurance industry. Reservoir regulation may alter the characteristics of the flood frequency distribution function. This paper incorporates reservoir impacts as an inherent variable to derive an analytical solution of outflow peaks and proposes new flood frequency distributions. Steps are as follows: (1) the original flood without reservoir impacts is assumed to follow a Pearson type III distribution, (2) linear and nonlinear reservoir models, including numerical integration and triangular area calculation methods, are proposed to simulate the relationships between inflow and outflow peaks, and (3) the flood frequency distributions resulting from reservoir impacts are derived. Derived equations are applied to the Shuibuya and Wuqiangxi reservoirs in China. Results show that: (1) although the numerical integration method with the nonlinear model is comparable with the reservoir flood routing, the triangle area calculation method with the nonlinear model is recommended for its accuracy and simplicity, (2) the reservoir regulation changes the flood frequency distribution function entirely in the nonlinear reservoir model, while only change the parameter in the linear reservoir model, and (3) downstream flood’s CV can be increased or decreased, which depends on the reservoir regulating capacity. It is indicated that altering the parameters of the flood frequency distribution is not sufficient to characterize watershed under reservoir control. This paper proposes the analytical solution about inflow and outflow peaks, which can point out the possible changes in the watershed with reservoir construction in plan.

In the United States, fluvial flood risk is managed by the National Flood Insurance Program (NFIP), which delineates areas that have a 1% chance of flooding each year (i.e. the 100-year floodplain). Development within and adjacent to riverine floodplains exacerbates flood losses by increasing peak discharge and runoff volume, shortening the time to peak, and altering the extent of the floodplain. This paper improves understanding of how future development can alter the 100-year floodplain in a watershed in Houston, TX through land use projection, hydrologic and hydraulic modeling, and implements a novel method for considering the impact of both regional trends in development and site-scale development policies on runoff response. The methodology presented in this paper integrates future development scenarios from a machine learning land use projection model with distributed hydrologic modeling and coupled 1D/2D unsteady hydraulic modeling to produce future floodplain estimates. Current site-scale detention requirements are represented within the hydrologic model to evaluate the regional effectiveness of these policies under future development conditions in 2050. Results indicate that the 100-year floodplain can expand by up to 12.5% across the watershed as a result of projected development in 2050 using current stormwater mitigation policies, and the number of parcels within the floodplain can increase by up to 18.8%. This study demonstrates how incremental land use changes can significantly alter the reality of flood risk in urbanizing watersheds, and how existing land use policies may be insufficient to mitigate impacts from future development.

Water diplomacy may be understood broadly as the measures that may be taken to prevent or peacefully resolve conflicts over water resources concerning their availability, allocation or use between and within states. The concept is preventive in nature as well as offering an approach for conflict resolution. Instruments of international water law, among them notably conventions and soft law developed on that basis – which promote equitable and reasonable use of transboundary waters and prevention of transboundary impact – can serve as tools for water diplomacy. The obligation to establish agreements on shared waters and joint bodies to govern them has contributed to gathering important experience, and some of this has been synthesized into the Principles for Effective Joint Bodies which were adopted by the Meeting of the Parties to the UNECE Water Convention in 2015. The Principles are intended as a resource to increase the effectiveness of joint bodies in transboundary water cooperation. This paper analyses Finland’s cooperation arrangements in the light of the Principles and assesses the extent to which Finland’s transboundary water commissions reflect the good practices specified in the Principles. Taking a wider perspective, the paper also examines the elements of the principles and their practical realisation with a view to understanding better their promotion of water diplomacy. Relevant features include, inter alia, the competency and representation of the joint institution, its accountability and the extent to which public participation is permitted, and the cooperative mechanisms in place. Naturally the underlying transboundary water agreements specify the framework for cooperation between the riparian states to a great extent, but the established joint bodies often play a significant role, specifically in the equitable use and management of the shared water resources, but even beyond, in the prevention and settling of conflicts, and in the general promotion of cooperation and security at a regional level.

In this paper, a model is proposed for the assessment of the effectiveness of various mulches in increasing deep percolation of rainfalls for enhancing arid zones aquifers. For this purpose, 8 precipitations were selected from the Intensity-Duration-Frequency (IDF) curves of the study area with a return period of 2 and 5 years. The deep percolation of these precipitations were examined in lysimeters with gravel, sand and mixed mulches and without any mulch. Then 192 data of soil moisture, daily maximum air-temperature and deep percolation were measured for driving empirical equations. Moreover, the efficiency and accuracy of these empirical equations were investigated using Coefficient of Determination (CD) and Nash–Sutcliffe Efficiency Coefficient (NSEC). The results showed that the obtained empirical equations could estimate deep percolation and evaporation with an acceptable accuracy. Using these equations and the soil-water balance equation, a soil moisture model was developed for the assessment of deep percolation. Then it was examined to evaluate the efficiency of the mulched soils in increasing soil moisture and aquifer recharging of three years’ precipitations in Shahrekord plain, Iran. The results showed that the deep percolation increased in all examined mulched soils compared to unmulched soil, its maximum and cumulative increase were in gravel mulch with 21.3% and 30%, respectively. Furthermore, groundwater modeling results showed that the mulching could improve groundwater level as 0.34 m over a three year period. Finally, this paper proposes soil mulching for enhancing groundwater resources and a model to assess it.

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.

The need for a method of estimating streamflow with both high-quality and high spatial resolution, particularly for ungauged sites, is critical under current circumstances: intensifying competition over water resources and limited streamgages. A source of accurate and timely computations of streamflow, particularly at high spatial resolutions, has been the National Hydrograph Dataset (NHD), a comprehensive geographic information system (GIS) dataset of vector-based surface water features such as streams, lakes, and streamgages in fine detail. Utilizing the NHD with streamflow data from the U.S. Geological Survey and usage records of local water utilities, this paper proposes a simple streamflow estimation method that computes streamflow at any point on river reaches of the NHD with valid hydrograph data and tests the method in a local 613 square mile watershed. The method assumes that linearly connecting the timings of peak or bottom-base flows from upstream to downstream hydrographs leads to retracing upstream lateral inflows that have eventually arrived at a user-selected site between the upstream and downstream gauge stations of the hydrographs at any given time in a common period of record between the two hydrographs. Subsequently, the lateral inflows are added up to become the streamflow for a given site at a given time. NHD river networks and ArcGIS tools identify the locations of lateral inflows and the necessary parameters of the method. This paper assesses the results of the method by comparing them to measured streamflows through recommended evaluation statistics and visual inspection. The method has yielded promising results, including values of the Nash-Sutcliffe efficiency within 0.83 and 0.99 for daily streamflow estimates over seven years. It also credibly reconstructs missing daily streamflow over a considerable number of years.

The aggregation of the same type of socio-economic activities in urban space generates urban functional zones, each of which has one function as the main (e.g., residential, educational or commercial), and is an important part of the city. With the development of deep learning technology in the field of remote sensing, the accuracy of land use decoding has been greatly improved. However, no finer remote sensing image could directly obtain economic and social information and it has a high revisit cycle (low temporal resolution), while urban flooding often lasts only a few hours. Cities contain a large amount of “social sensing” data that records human socio-economic activities, and GIS is a natural discipline with strong socio-economic ties. We propose a new GeoSemantic2vec algorithm for urban function recognition based on the latest advances in natural language processing technology (BERT model), which utilizes the rich semantic information in urban POI data to portray urban functions. Taking the Wuhan flooding event in summer 2020 as an example, we identified 84.55% of the flooding locations in social media. We also use the new algorithm proposed in this paper to divide the main urban area of Wuhan into 8 types of urban functional zones (kappa coefficient is 0.615) and construct a “City Portrait” of flooding locations. This paper summarizes the progress of existing research on urban function identification using natural language processing techniques and proposes a better algorithm, which is of great value for urban flood location detection and risk assessment.

The climate uncertainties, data scarcity, and socio-techno challenges pose significant threats and options respectively to the future planning of complex water projects in developing countries. This paper attempts to develop a decision support system (DSS) framework to classify uncertainties and management options using participatory scoping for scenarios analysis with the aid of six interrelated modules namely (i) water balance of reservoirs for computation of runoff (ii) statistical downscaling for climate projection (iii) hydrological modelling (iv) sediment assessment (v) water management and (vi) decision support for the assessment of the performance of multiple management plans designed for three future assessment periods of 2020-35, 2046-64 and 2081-99. The proposed framework was applied to the Tandula complex system of Chhattisgarh state of India consists of three reservoirs namely Tandula, Kharkhara, and Gondli having water transfer in their commands and for industrial use. The developed DSS framework provides the opportunity to analyze the management plans with climate uncertainties for the complex water resources projects having data scarcity. Here, in the present paper, the developed DSS framework incorporates eighty-four diverse management plans devised from constant interaction with stakeholders and technocrats resulting to deal with climate uncertainties using possible improvement in efficiencies and consumptive use for three future assessment periods. Furthermore, the performance of the developed plans was evaluated using multi-criteria decision support in the three-layer hierarchal process. The decision support analysis suggested that the second assessment period (FP-2: 2046-64) will be more crucial from the management point of view where 54 out of 81 plans could not perform as par with the present performance of the system. The developed DSS framework provides a solution for strategic water resources planning and management under projected climate scenarios in a basin having problems of scarcity, surplus, and non-uniform distribution of surface water using the participation of farmers, technocrats, and water resource managers.

Rain-on-Grid (RoG) modelling is an increasingly popular approach within the storm risk management, in which the hydrological and hydrodynamic flood processes are modelled entirely within the 2D hydrodynamic model. Contrarily to what was done in the context of flood propagation in rivers, at the present time no systematic benchmarking study of the software packages implementing this approach is available in the literature. The need of benchmarking studies related to the RoG approach is becoming ever more important for HEC-RAS 2D (HR2D), the use of which has been rapidly increasing in the last few years, as further testified by very recent papers explicitly focussed on this research issue. However, there is still a lack of studies that clearly show the suitability of this software package as a reliable fully integrated hydrologic-hydrodynamic model. Therefore, the novelty introduced by this paper is the benchmarking of HR2D for RoG simulations, analysing several situations representing most of the applications related to “storm-event” hazard assessment. Specifically, after the analysis of ideal cases, which gave the opportunity to carefully assess the accuracy of the numerical schemes for RoG simulations, practical applications were considered to face some challenging issues related to the analysis of rainfall/runoff in urbanized areas and actual basins, including the numerical treatment of buildings and the computation of infiltration losses within hydrodynamic computations. The HR2D results were compared to analytical solutions, when available, experimental data and numerical solutions of a numerical code developed by the authors. The results shown in the paper highlight the potential but also the limitations of this software package, providing evidence to scientists and technicians about those situations for which reliable results are expected but also indications on specific aspects that need improvements so as to increase the model accuracy.

In a recent paper published in this journal, Bretreger et al. (2020) estimate irrigation water use from satellite remotely sensed estimates of actual evapotranspiration in five irrigated districts of the Murray-Darling Basin (southeast Australia). They used three models that scale crop reference evapotranspiration with vegetation indices acquired by recent Landsat satellites: (i) IrriSAT, (ii) Kamble and (iii) CMRSET. In their paper, irrigation water use computed with CMRSET generally overestimated observed irrigation water use, sometimes fivefold. Based on these results, Bretreger et al. (2020) discouraged the use of CMRSET for irrigation monitoring. In this comment, we reproduce the experiments in Bretreger et al. (2020), and demonstrate that their overestimation was because they used an incorrect Landsat band in their implementation of CMRSET. CMRSET was originally calibrated using both MODIS-derived Enhanced Vegetation Index (EVI) and Global Vegetation Moisture Index (GVMI). To calculate GVMI, a shortwave infrared (SWIR) band with a wavelength of ∼1.6 μm was used, which for MODIS is named SWIR2, and for Landsat is named SWIR1 (i.e., different sensors have different bands and a different number of bands and SWIR1 in MODIS has a wavelength of ∼1.2 μm). In their Landsat CMRSET implementation, Bretreger et al. (2020) computed GVMI using the Landsat SWIR2 band, which has a wavelength of ∼2.1 μm (see their Table 3). We show that CMRSET implemented with the Landsat SWIR1 band (i.e., with a wavelength of ∼1.6 μm, so being the correct Landsat band to calculate GVMI) yields similar results (both for temporal patterns and magnitude) when compared to the other two remote sensing actual evapotranspiration models in Bretreger et al. (2020). For the irrigation districts, these similar results meant that the mean water year (i.e., July to next June from 2010 to 2017) actual evapotranspiration mean absolute relative difference was 15.3% (with a 5.4%–26.5% range) and the water year irrigation water use mean absolute relative difference was 4.1% (with a 9.8%–18.9% range). Conversely, we show that a Landsat CMRSET implementation using the incorrect Landsat band for the calculation of GVMI (i.e., with a wavelength of ∼2.1 μm) led to a large overestimation agreeing with the CMRSET results reported by Bretreger et al. (2020), both for water year actual evapotranspiration (mean absolute relative difference of 64.6%, with a 51.1%–83.6% range) and irrigation water use (mean absolute relative difference of 44.1%, with a 31.4%–65.1% range). The use of the incorrect Landsat band means that the Bretreger et al. (2020) recommendation specific to their Landsat CMRSET implementation is invalid. A demonstration of Bretreger et al.’s (2020) incorrect Landsat CMRSET implementation and the correct one can be accessed at: https://jorgepena.users.earthengine.app/view/comment-on-bretreger-2020-joh-paper . Despite Bretreger et al.’s (2020) shortcomings in their implementation of the Landsat CMRSET actual evapotranspiration model, timely monitoring of irrigation water use via satellite remote sensing is required at Landsat resolutions (i.e., 30 m) or higher due to the heterogeneous nature of irrigation in most agricultural landscapes, and we applaud them for pursuing this line of research.

Tourism depends largely on a high water consumption. Previous studies have primarily focused on virtual water embodied in goods or products through trade. Our study provides novel insights into the virtual water embodied in goods and services purchased by tourists. We identified the tourism blue water footprint (TWF) of China according to the environmentally extended input-output table of 135 sectors (11 direct + 124 indirect tourism sectors) together with tourism satellite account data. The results show that the agriculture, electricity, and petroleum sectors majorly contribute to the indirect TWF, while the accommodation and air transport sectors majorly contribute to the direct TWF and were water intensive. Using a constant ratio of 1.5 relating local water footprints to tourist water footprints, we then estimate that China’s outbound tourists consume more water than inbound tourists (net TWF of 906 million m3). Nearly half of the net TWF from Chinese tourists is consumed in water deficient countries. Thus, the pressure caused by Chinese tourists on global water resources should be considered. This paper quantitatively evaluates the impact of China's tourism-induced water transfer on global water resources from the perspective of direct and indirect tourism consumption. The results of this paper enrich the research cases of the impact of final consumption on water resources utilization.

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

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.

Multiple barriers constrain the widespread application of participatory methods in water management, including the more technical focus of most water agencies, additional cost and time requirements for stakeholder involvement, as well as institutional structures that impede collaborative management. This paper presents a stepwise methodological framework that addresses the challenges of context-sensitive initiation, design and institutionalization of participatory modeling processes. The methodological framework consists of five successive stages: (1) problem framing and stakeholder analysis, (2) process design, (3) individual modeling, (4) group model building, and (5) institutionalized participatory modeling. The Management and Transition Framework is used for problem diagnosis (Stage One), context-sensitive process design (Stage Two) and analysis of requirements for the institutionalization of participatory water management (Stage Five). Conceptual modeling is used to initiate participatory modeling processes (Stage Three) and assure a high compatibility with quantitative modeling approaches (Stage Four). This paper describes the proposed participatory model building (PMB) framework and provides a case study of its application in Quebec, Canada. The results of the Quebec study demonstrate the applicability of the PMB framework for initiating and designing participatory model building processes and analyzing barriers towards institutionalization.

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.

Water supply management for hydroclimatic extremes has commonly been analysed using the cumulative difference between inflow and demand, a concept known as the Residual Mass Curve. This paper extends the residual mass concept to develop a new method to quantify water availability, termed the Residual Mass Severity Index (RMSI). The RMSI improves on the Residual Mass Curve by allowing extreme deficit periods to be easily identified without the need for manual inspection of the Residual Mass Curve. Another advantage of the RMSI is that it does not require a temporal window to be specified to characterise extreme deficit periods, which is needed for many drought indices such as the Standardized Precipitation Index (SPI). This paper develops the method for calculating the RMSI using precipitation time series and compares it to the SPI for different drought durations across Australia. Major hydroclimatic extremes during the instrumental period are assessed using RMSI, SPI, and by comparing the RMSI estimates with streamflow data from 467 catchments with varying sizes and climate across Australia. RMSI is a useful index for water managers interested in assessing water availability with potential applicability in summarising the features of historical hydroclimatic extremes as well as future changes in water availability due to anthropogenic climate change.

Commonly used hydraulic models that describe the water retention and transport properties of variably saturated soils mainly account for the capillary component of matric potential but ignore the adsorption component. Experimental evidence suggests that soil hydraulic conductivity may be underestimated by several orders of magnitude in the low saturation range for models ignoring the adsorbed film flow. Thus, this paper introduces a fractal method to characterise the effects of capillary and adsorption components of matric potential on soil hydraulic properties considering a simplified pore water transport process. Furthermore, the concept of specific thickness for the adsorbed film was defined to describe the adsorption and adsorption capacity of the soil. Based on these, a complete hydraulic model, including the soil water retention curve (SWRC) and relative hydraulic conductivity curve (RHCC), was derived to describe the water retention and transport properties of the soil in the entire matric head range. The performance of the proposed model was then evaluated using 13 sets of test data, from clean sand to bentonite, showing that it can capture the characteristics of water retention and transport properties well. The model parameters calibrated from the SWRC dataset could predict RHCC over the entire suction range without introducing any additional parameters. Additionally, the influences of the model parameters on soil hydraulic behaviours are also discussed in the paper. It was found that the pore size distribution of the soils mainly controlled the transport characteristics of capillary water, but had a slight influence on the hydraulic properties of adsorbed water, which was dominated by the type and content of minerals and specific surface area of the soil.

This paper presents a practical genetic algorithm (GA)-based solution for solving the economic load dispatch problem (ELDP) and further compares the performance of the improved GA (IGA) with that of dynamic programming (DP). Specifically, their performance is comprehensively evaluated in terms of addressing the ELDP through a case study of 26 turbines in the Three Gorges Hydropower Plant with a focus on calculation accuracy, calculation time, and algorithm stability. Evaluation results show that the improved GA method can significantly reduce the ineffectiveness of the GA in current use and could avoid the running of the turbines in the cavitation/vibration zone, thereby ensuring the safety of the turbines during generating operations. Further, the analysis comparing the performance of the IGA and DP show that the IGA is superior to DP when a small number of turbines are involved. However, as the number of turbines increases, the IGA requires more calculation time than DP; moreover, its calculation accuracy and convergence rate are significantly reduced. It is difficult to guarantee the stability of IGA in high-dimension space even though the population grows, on account of the exponential expansion of the calculation dimension, the algorithm’s premature convergence, and the lack of a local search capability. The improvement of the GA as well as the evaluation method proposed in this paper provide a new approach for choosing and improving optimization algorithms to solve the ELDP of large-scale hydropower plants.

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.

In this paper, six critical topics in social learning (SL) definition and applications are discussed: 1- Clarification of the SL definition; 2- The role of SL in the management of shared water resources; 3- The definition of “learning” in SL; 4- The quantification of SL; 5- The relation of SL with outputs of Social Networks Analysis (SNA) and stakeholder Analysis (SA); 6- Identifying and ranking of key stakeholders (i.e., influencers) for SL diffusion. After defining SL, we present SL's role using a well-known game theory problem. We have addressed the term “learning” in SL using an existing method, namely reinforcement learning. We present a new approach for quantifying SL within a hydrological environment and finding key stakeholders for the SL diffusion. For the first time, we consider a combination of SL, SA, and SNA to propose a comprehensive social-based approach to investigate SL diffusion. The western part of Tehran city, which is suffering from some water and environmental issues such as overexploitation of groundwater and land subsidence, is the case study of this paper. The results show a significant relationship between SL and the outputs of SNA. The suggested method for identifying key stakeholders has been able to identify key stakeholders in terms of various water resources management issues in the study area. The results can be used for developing effective policies towards sustainable management of water and environmental resources in the studied socio-hydrological system.

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 use of the nonstationary hydrological frequency analysis (HFA) has been prompted when nonstationarity is diagnosed in hydrometeorological data. However, the inconclusive identification of the physical process(es) and driver(s) behind the nonstationarity challenges the identification of an appropriate model structure, and consequently might hinder its reliable implementation. To date, no solid consensus on whether the nonstationary HFA is always superior to the stationary HFA has been reached. Therefore, this paper aimed to advance the understanding of the stationary and nonstationary HFAs under nonstationary scenarios by illustratively comparing their performance in real applications, and examining the effects of the nonstationarity on the stationary HFA through a simulation study, especially from the perspective of the uncertainty. The investigation of the effects of the nonstationarity on the stationary HFA was conducted in two fundamental nonstationary scenarios, namely temporal trends in the mean and variance, in which the degree of nonstationarity was quantifiable and known a priori. The HFAs were conducted using the Particle Filter, a Bayesian filtering technique which was recently employed in the stationary HFA and was further extended for the nonstationary HFA in this paper. The illustrative comparison did not demonstrate a consistent superiority of either HFA approach in terms of both fitting efficiency and uncertainty. This result thus implied that the stationarity HFA could outperform the nonstationary HFA in some cases. Besides, the simulation investigation of the stationary HFA revealed that the increase of nonstationarity degree would lead to the deterioration in the analysis accuracy and the elevation of uncertainty. The uncertainty in the stationary HFA was found to primarily originate from the nonstationarity, while the distribution selection would be the other but secondary source of uncertainty. The results from both the illustrative comparison and the simulation investigation suggested that whether the stationary or nonstationary HFA outperforms the other could be associated with the degree and pattern of nonstationarity. Therefore, it is recommended to consider them when developing a strategic framework to select an appropriate approach to deal with nonstationary hydrometeorological data.

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.

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