Search

Characterization of near (field) saturated hydraulic conductivity (Kfs) of the soil environment is among the crucial components of hydrological modeling frameworks. Since the associated laboratory/field experiments are time-consuming and labor-intensive, pedotransfer functions (PTFs) that rely on statistical predictors are usually integrated with the existing measurements to predict Kfs in other areas of the field. In this study some of the most appropriate machine learning approaches, including variants of artificial neural networks (ANNs) were used for predicting Kfs by some easily measurable soil attributes. The analyses were performed using 100 measurements in Bajgah Agricultural Experimental Station. First, physico-chemical inputs as bulk density (BD), initial water content (Wi), saturated water content (Ws), mean weight diameter (MWD), and geometric mean diameter (GMD) of aggregates, pH, electrical conductivity (EC), and calcium carbonate equivalent (CCE) were measured. Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. The statistical assessment indicated that PSO-NNs with the lowest RMSE and MAPE as well as the highest correlation coefficient (R) value provided the most accurate and robust prediction of Kfs. The prediction models ranked as PSO-NNs (R = 0.958; RMSE = 0.343; MAPE = 9.47), GA-NNs (R = 0.949; RMSE = 0.404; MAPE = 11.83), MLPNNs (R = 0.933; RMSE = 0.426; MAPE = 12.13), RBFNNs (R = 0.926; RMSE = 0.452; MAPE = 14.30), and MLR (R = 0.675; RMSE = 0.685; MAPE = 22.54) in terms of their performances for the test data set. Results revealed that all NN models particularly PSO-NNs were efficient in prediction of Kfs. However, further evaluations may be recommended for other soil conditions and input variables to quantify their potential uncertainties and wider potential and versatility before they are used in other geographical locations/soil conditions., Competing Interests: The authors have declared that no competing interests exist, (Copyright: © 2024 Moosavi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Mountainous zones are often characterized by complex orography and contacts between different aquifers that usually complicate the use of isotope hydrology techniques. The Apennine chain (Italy) and 10 mountain and mid-mountain areas belonging to it are the objective of this study. An original isotopic data treatment, able to identify the most probable recharge area for several springs/springs' groups/wells, has been developed. The method consists of a two-step approach: (1) the determination of the spring/wells computed isotope recharge elevation; (2) an advanced δ 18 O precipitation distribution model over the study area supported by statistical and GIS-based procedures implemented by two processes: first, the clipping of precipitation δ 18 O values (depicted from the δ 18 O-elevation relationships obtained for each study area) over a most probable recharge area for each analyzed spring or well and, second, the calculation of the overlapping distribution between the spring/well mean δ 18 O values ± σ and the precipitation δ 18 O content for each outcropping aquifer. A new regional δ 18 O gradient covering 150 km latitudinal length of central Italy has been defined. Seven LMWL and δ 18 O-elevation relationships able to represent the local precipitation isotopic composition have been obtained. The mean elevation of the springs and wells recharge areas have been assessed by a comparison between the obtained gradient with nine δ 18 O gradients available in the literature and those obtained at a local scale. The new isotopic modeling approach can stress whether the mere isotope modeling based on the stable isotope of oxygen agrees with the hydrogeological setting of the study areas., (© 2024 National Ground Water Association.)

The inverse problem method can be applied to determine the properties of hydrological phenomena and estimate the parameters, which cannot be measured directly. This type of inverse focus can facilitate the implementation of the kinematic wave model (direct model-DM), to fill gaps for lateral inflow rate and runoff depth in watersheds. Thus, the goal of the study was the application of the inverse problem method (IP). The lateral inflow rate was generally obtained as a Fourier transform to represent any watersheds. The study was developed using a small catchment in the Amazon where intense rainfall events occur, producing runoff and sediments, which affect rural populations. Lateral inflow rate and runoff depth were derived using precipitation data and parameters estimated through the KINEROS2 (K2)/direct model (DM) model and the ensuing solution methods with MCMC (Markov chains Monte Carlo)/Fourier transform. The developed method was applied to four rainfall-runoff events, leading to a good fit between the observed and predicted data (Nash-Sutcliffe coefficients between 0.76 and 0.85 and RMSE values between 1.80 mm and 6.72 mm).

Surface hydrologic modeling becomes a problem when insufficient spatial and temporal information is available. It is common to have useful modeling periods of less than 15 years. The purpose of this work is to develop a methodology that allows the selection of meteorological and hydrometric stations that are suitable for modeling when information is scarce in the area. Based on the scarcity of data, a series of statistical tests are proposed to eliminate stations according to a decision-making process. Although the number of stations decreases drastically, the information used is reliable and of adequate quality, ensuring less uncertainty in the surface simulation models. Individual basin modeling can be carried out considering the poor data. The transfer of parameters can be applied through the nesting of basins to have information distributed over an extensive area. Therefore, temporally and spatially extended modeling can be achieved with information that preserves statistical parameters over time. If data management and validation is performed, the modeled watersheds are well represented; if this is not done, only 26% to 50% of the runoff is represented., Competing Interests: The authors declare that they have no competing interests., (© 2024 Navarro-Farfán et al.)

The coastal zone, which is the interface between land and sea, is hydrodynamically very active due to the complex interactions of various hydrological controls and variable-density fluids. These forces vary over time, resulting in a state of dynamic equilibrium in the system. The major hydrological processes in coastal aquifer systems are salt water intrusion and submarine groundwater discharge, which are interdependent. Monitoring these complex processes is crucial for sustainable coastal zone management but poses a significant research challenge. In this study, we demonstrate the effectiveness of non-invasive geophysical techniques, specifically the time-lapse electrical resistivity imaging method, in conjunction with groundwater monitoring, for monitoring coastal groundwater dynamics in an unconfined aquifer at varying time scales and hydrogeological settings present at formerly glaciated sites worldwide. We generated two-dimensional baseline salt water intrusion maps for the test site, located on the coast of Rhode Island, USA. The time-lapse electrical resistivity survey method enables the rapid estimation of fresh groundwater discharge. Our approach offers insight into the mechanisms and seasonably variable salt water-freshwater interactions in unconfined heterogeneous aquifers. Although the results are site-specific, their implications are broad and may stimulate other studies related to sea to land pollution (sea water intrusion) and land to sea pollution (groundwater discharge) in heterogeneous coastal aquifer settings., (© 2023 National Ground Water Association.)

In this paper, we address the critical task of 24-h streamflow forecasting using advanced deep-learning models, with a primary focus on the transformer architecture which has seen limited application in this specific task. We compare the performance of five different models, including persistence, long short-term memory (LSTM), Seq2Seq, GRU, and transformer, across four distinct regions. The evaluation is based on three performance metrics: Nash-Sutcliffe Efficiency (NSE), Pearson's r , and normalized root mean square error (NRMSE). Additionally, we investigate the impact of two data extension methods: zero-padding and persistence, on the model's predictive capabilities. Our findings highlight the transformer's superiority in capturing complex temporal dependencies and patterns in the streamflow data, outperforming all other models in terms of both accuracy and reliability. Specifically, the transformer model demonstrated a substantial improvement in NSE scores by up to 20% compared to other models. The study's insights emphasize the significance of leveraging advanced deep learning techniques, such as the transformer, in hydrological modeling and streamflow forecasting for effective water resource management and flood prediction., Competing Interests: The authors declare there is no conflict., (© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).)

Sub-surface characterization in fractured aquifers is challenging due to the co-existence of contrasting materials namely matrix and fractures. Transient hydraulic tomography (THT) is proved to be an efficient and robust technique to estimate hydraulic (K m , K f ) and storage (S m , S f ) properties in such complex hydrogeologic settings. However, performance of THT is governed by data quality and optimization technique used in inversion. We assessed the performance of gradient and gradient-free optimizers with THT inversion. Laboratory experiments were performed on a two-dimensional, granite rock (80 cm × 45 cm × 5 cm) with known fracture pattern. Cross-hole pumping experiments were conducted at 10 ports (located on fractures), and time-drawdown responses were monitored at 25 ports (located on matrix and fractures). Pumping ports were ranked based on weighted signal-to-noise ratio (SNR) computed at each observation port. Noise-free, good quality (SNR > 100) datasets were inverted using Levenberg-Marquardt: LM (gradient) and Nelder-Mead: NM (gradient-free) methods. All simulations were performed using a coupled simulation-optimization model. Performance of the two optimizers is evaluated by comparing model predictions with observations made at two validation ports that were not used in simulation. Both LM and NM algorithms have broadly captured the preferential flow paths (fracture network) via K and S tomograms, however LM has outperformed NM during validation ( R LM 2 = 0.76 , RMSE LM = 1.75 cm , R NM 2 = 0.73 , RMSE NM = 1.77 cm ). Our results conclude that, while method of optimization has a trivial effect on model predictions, exclusion of low quality (SNR ≤ 100) datasets can significantly improve the model performance., (© 2023 National Ground Water Association.)

The use of retention function and relative conductivity function is essential for the calculation of flow in a variably saturated media using the Richards equation. A widely used mathematical model for this is the Mualem-van Genuchten model which requires the shape parameters α and n . These, however, are difficult to obtain. When data is scarce, α and n are often taken from literature and may deviate largely from actual values. The current study presents a novel mathematical model for the approximation of α and n and for the further estimation of realistic value ranges, which may be used as parameter space, for example, for the calibration of a numerical model. The model was developed for cases where data is scarce and only values of saturated hydraulic conductivity are available. It is based on a large data set from literature and it is demonstrated that the model estimates mean values from that data set with a good accuracy. In order to show the applicability of the model, a second data set has been compiled anew (provided as Supporting Information). The model is incorporated into the current version of the freeware computer program HYPAGS, which enables easy usage., (© 2023 The Authors. Groundwater published by Wiley Periodicals LLC on behalf of National Ground Water Association.)

Significant efforts have been expended for improved characterization of hydraulic conductivity (K) and specific storage (S s ) to better understand groundwater flow and contaminant transport processes. Conventional methods including grain size analyses (GSA), permeameter, slug, and pumping tests have been utilized extensively, while Direct Push-based Hydraulic Profiling Tool (HPT) surveys have been developed to obtain high-resolution K estimates. Moreover, inverse modeling approaches based on geology-based zonations, and highly parameterized Hydraulic Tomography (HT) have also been advanced to map spatial variations of K and S s between and beyond boreholes. While different methods are available, it is unclear which one yields K estimates that are most useful for high resolution predictions of groundwater flow. Therefore, the main objective of this study is to evaluate various K estimates at a highly heterogeneous field site obtained with three categories of characterization techniques including: (1) conventional methods (GSA, permeameter, and slug tests); (2) HPT surveys; and (3) inverse modeling based on geology-based zonations and highly parameterized approaches. The performance of each approach is first qualitatively analyzed by comparing K estimates to site geology. Then, steady-state and transient groundwater flow models are employed to quantitatively assess various K estimates by simulating pumping tests not used for parameter estimation. Results reveal that inverse modeling approaches yield the best drawdown predictions under both steady and transient conditions. In contrast, conventional methods and HPT surveys yield biased predictions. Based on our research, it appears that inverse modeling and data fusion are necessary steps in predicting accurate groundwater flow behavior., (© 2023 The Authors. Groundwater published by Wiley Periodicals LLC on behalf of National Ground Water Association.)

Malaria transmission across sub-Saharan Africa is sensitive to rainfall and temperature. Whilst different malaria modelling techniques and climate simulations have been used to predict malaria transmission risk, most of these studies use coarse-resolution climate models. In these models convection, atmospheric vertical motion driven by instability gradients and responsible for heavy rainfall, is parameterised. Over the past decade enhanced computational capabilities have enabled the simulation of high-resolution continental-scale climates with an explicit representation of convection. In this study we use two malaria models, the Liverpool Malaria Model (LMM) and Vector-Borne Disease Community Model of the International Centre for Theoretical Physics (VECTRI), to investigate the effect of explicitly representing convection on simulated malaria transmission. The concluded impact of explicitly representing convection on simulated malaria transmission depends on the chosen malaria model and local climatic conditions. For instance, in the East African highlands, cooler temperatures when explicitly representing convection decreases LMM-predicted malaria transmission risk by approximately 55%, but has a negligible effect in VECTRI simulations. Even though explicitly representing convection improves rainfall characteristics, concluding that explicit convection improves simulated malaria transmission depends on the chosen metric and malaria model. For example, whilst we conclude improvements of 45% and 23% in root mean squared differences of the annual-mean reproduction number and entomological inoculation rate for VECTRI and the LMM respectively, bias-correcting mean climate conditions minimises these improvements. The projected impact of anthropogenic climate change on malaria incidence is also sensitive to the chosen malaria model and representation of convection. The LMM is relatively insensitive to future changes in precipitation intensity, whilst VECTRI predicts increased risk across the Sahel due to enhanced rainfall. We postulate that VECTRI's enhanced sensitivity to precipitation changes compared to the LMM is due to the inclusion of surface hydrology. Future research should continue assessing the effect of high-resolution climate modelling in impact-based forecasting., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Talib et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In this paper, we present an approach that combines data-driven and physical modelling for predicting the runoff occurrence and volume at catchment scale. With that aim, we first estimated the runoff volume from recorded storms aided by the Green-Ampt infiltration model. Then, we used machine learning algorithms, namely LightGBM (LGBM) and Deep Neural Network (DNN), to predict the outputs of the physical model fed on a set of atmospheric variables (relative humidity, temperature, atmospheric pressure, and wind velocity) collected before or immediately after the beginning of the storm. Results for a small urban catchment in Madrid show DNN performed better in predicting the runoff occurrence and volume. Moreover, enriching the input primary atmospheric variables with auxiliary variables (e.g., storm intensity data recorded during the first hour, or rain volume and intensity estimates obtained from auxiliary regression methods) largely increased the model performance. We show in this manuscript data-driven algorithms shaped by physical criteria can be successfully generated by allowing the data-driven algorithm learn from the output of physical models. It represents a novel approach for physics-informed data-driven algorithms shifting from common practices in hydrological modelling through machine learning., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Sergio Zubelzu reports financial support was provided by Spain Ministry of Science and Innovation., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

As environmental flow demands become better characterized, improved water allocation and reservoir operating solutions can be devised to meet them. However, significant economic trade-offs are still expected, especially in hydropower-dominated basins. This study explores the use of the electricity market as both an institutional arrangement and an alternative financing source to handle the costs of implementing environmental flows in river systems managed for hydropower benefits. A framework is proposed to identify hydropower plants with sustainable operation within the portfolio of power sources, including a cost-sharing mechanism based on the electricity market trading to manage a time-step compensation fund. The objective is to address a common limitation in the implementation of environmental flows by reducing the dependence on government funding and the necessity for new arrangements. Compensation amounts can vary depending on ecosystem restoration goals (level of flow regime restoration), hydrological conditions, and hydropower sites characteristics. The application in the Paraná River Basin, Brazil, shows basin-wide compensation requirements ranging from zero in favorable hydrological years to thousands of dollars per gigawatt-hour generated in others. Each electricity consumer's contribution to the compensation fund is determined by their share of energy consumption, resulting in values ranging from cents for residential users to thousands of dollars for industrial facilities. Finally, the compensation fund signals the economic value of externalities in energy production. For residential users, achieving varying levels of ecosystem restoration led to an electricity bill increase of less than 1 %. For larger companies, the increase ranged from less than 1 %-12 %., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ana Paula Dalcin and Guilherme Fernandes Marques report financial support was provided by Inter-American Institute for Global Change Research and Conselho Nacional de Desenvolvimento Científico e Tecnológico. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Land use/land cover (LULC) is a crucial factor that directly influences the hydrology and water resources of a watershed. In order to assess the impacts of LULC changes on river runoff in the Danjiang River source area, we analyzed the characteristics of LULC data for three time periods (2000, 2010, and 2020). The LULC changes during these periods were quantified, and three Soil and Water Assessment Tool (SWAT) models were established and combined with eight LULC scenarios to quantitatively analyze the effects of LULC changes on river runoff. The results revealed a decrease in the cropland area and an increase in the forest, grassland, and urban land areas from 2000 to 2020. Grassland, forest, and cropland collectively accounted for over 94% of the total area, and conversions among these land types were frequent. The SWAT models constructed based on the LULC data demonstrated good calibration and validation results. Based on the LULC data in three periods, the area of each LULC type changed slightly, so the simulation results were not significantly different. In the subsequent LULC scenarios, we found that the expansion of cropland, grassland, and urban areas was associated with increased river runoff, while an increase in forest area led to a decrease in river runoff. Among the various LULC types, urban land exerted the greatest influence on changes in river runoff. This study establishes three SWAT models and combines multiple LULC scenarios, which is novel and innovative. It can provide scientific basis for the rational allocation of water resources and the optimization of LULC structure in the Danjiang River source area., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The digital elevation models (DEMs) are the primary and most important spatial inputs for a wide range of hydrological applications. However, their availability from multiple sources and at various spatial resolutions poses a challenge in watershed modeling as they influence hydrological feature delineation and model simulations. In this study, we evaluated the effect of DEM choice on stream and catchment delineation and streamflow simulation using the SWAT model in four distinct geographic regions with diverse terrain surfaces. Performance evaluation metrics, including Willmott's index of agreement, and nRMSE combined with visual comparisons were employed to assess each DEM's performance. Our results revealed that the choice of DEM has a significant impact on the accuracy of stream and catchment delineation, while its influence on streamflow simulation within the same catchment was relatively minor. Among the evaluated DEMs, AW3D30 and COP30 performed the best, closely followed by MERIT, whereas TanDEM-X and HydroSHEDS exhibited poorer performance. All DEMs displayed better accuracy in mountainous and larger catchments compared to smaller and flatter catchments. Forest cover also played a role in accuracy, mainly due to its association with steep slopes. Our findings provide valuable insights for making informed data selection decisions in watershed modeling, considering the specific characteristics of the catchment and the desired level of accuracy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

For operational flood control and estimating ecological flow regimes in deltaic branched-river systems with limited surveyed cross-sections, accurate river stage and discharge estimation using public domain Digital Elevation Model (DEM)-extracted cross-sections are challenging. To estimate the spatiotemporal variability of streamflow and river stage in a deltaic river system using a hydrodynamic model, this study demonstrates a novel copula-based framework to obtain reliable river cross-sections from SRTM (Shuttle Radar Topographic Mission) and ASTER (Advanced Spaceborne Thermal Emission and Reflection) DEMs. Firstly, the accuracy of the CSRTM and CASTER models was assessed against the surveyed river cross-sections. Thereafter, the sensitivity of the copula-based river cross-sections was evaluated by simulating river stage and discharge using MIKE11-HD in a complex deltaic branched-river system (7000 km 2 ) of Eastern India having a network of 19 distributaries. For this, three MIKE11-HD models were developed based on surveyed cross-sections and synthetic cross-sections (CSRTM and CASTER models). The results indicated that the developed Copula-SRTM (CSRTM) and Copula-ASTER (CASTER) models significantly reduce biases (NSE>0.8; IOA>0.9) in the DEM-derived cross-sections and hence, are capable of satisfactorily reproducing observed streamflow regimes and water levels using MIKE11-HD. The performance evaluation metrics and uncertainty analysis indicated that the MIKE11-HD model based on the surveyed cross-sections simulates with higher accuracies (streamflow regimes: NSE>0.81; water levels: NSE>0.70). The MIKE11-HD model based on the CSRTM and CASTER cross-sections, reasonably simulates streamflow regimes (CSRTM: NSE>0.74; CASTER: NSE>0.61) and water levels (CSRTM: NSE>0.54; CASTER: NSE>0.51). Conclusively, the proposed framework is a useful tool for the hydrologic community to derive synthetic river cross-sections from public domain DEMs, and simulate streamflow regimes and water levels under data-scarce conditions. This modelling framework can be easily replicated in other river systems of the world under varying topographic and hydro-climatic conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

High-resolution temporal data (e.g., daily) is valuable for the decision-making of water resources management because it more accurately captures fine-scale processes and extremes than the coarse temporal data (e.g., weekly or monthly). However, many studies rarely consider this superior suitability for water resource modeling and management; instead, they often use whichever data is more readily available. So far, no comparative investigations have been conducted to determine if access to different time-scale data would change decision-maker perceptions or the rationality of decision making. This study proposes a framework for assessing the impact of different temporal scales on water resource management and the performance objective's sensitivity to uncertainties. We built the multi-objective operation models and operating rules of a water reservoir system based on daily, weekly, and monthly scales, respectively, using an evolution multi-objective direct policy search. The temporal scales of the input variables (i.e., streamflow) affect both the model structures and the output variables. In exploring these effects, we reevaluated the temporal scale-dependent operating rules under uncertain streamflow sets generated from synthetic hydrology. Finally, we obtained the output variable's sensitivities to the uncertain factors at different temporal scales using the distribution-based sensitivity analysis method. Our results show that water management based on too coarse resolution might give decision makers the wrong perception because the effect of actual extreme streamflow process on the performance objectives is ignored. The streamflow uncertainty is more influential than the uncertainty associated with operating rules. However, the sensitivities are characterized by temporal scale invariance, as the differences of the sensitivity between different temporal scales are not obvious over the uncertainties in streamflow and thresholds. These results show that water management should consider the resolution-dependent effect of temporal scales for balancing modeling complexity and computational cost., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Short-term streamflow prediction is essential for managing flood early warning and water resources systems. Although numerical models are widely used for this purpose, they require various types of data and experience to operate the model and often tedious calibration processes. Under the digital revolution, the application of data-driven approaches to predict streamflow has increased in recent decades. In this work, multiple linear regression (MLR) and random forest (RF) models with three different input combinations are developed and assessed for multi-step ahead short-term streamflow predictions, using 14 years of hydrological datasets from the Kulim River catchment, Malaysia. Introducing more precedent streamflow events as predictor improves the performance of these data-driven models, especially in predicting peak streamflow during the high-flow event. The RF model (Nash-Sutcliffe efficiency (NSE): 0.599-0.962) outperforms the MLR model (NSE: 0.584-0.963) in terms of overall prediction accuracy. However, with the increasing lead-time length, the models' overall prediction accuracy on the arrival time and magnitude of peak streamflow decrease. These findings demonstrate the potential of decision tree-based models, such as RF, for short-term streamflow prediction and offer insights into enhancing the accuracy of these data-driven models.

Rainfall is a vital process in the hydrological cycle of the globe. Accessing reliable and accurate rainfall data is crucial for water resources operation, flood control, drought warning, irrigation, and drainage. In the present study, the main objective is to develop a predictive model to enhance daily rainfall prediction accuracy with an extended time horizon. In the literature, various methods for the prediction of daily rainfall data for short lead times are presented. However, due to the complex and random nature of rainfall, in general, they yield inaccurate prediction results. Generically, rainfall predictive models require many physical meteorological variables and consist of challenging mathematical processes that require high computational power. Furthermore, due to the nonlinear and chaotic nature of rainfall, observed raw data typically has to be decomposed into its trend cycle, seasonality, and stochastic components before being fed into the predictive model. The present study proposes a novel singular spectrum analysis (SSA)-based approach for decomposing observed raw data into its hierarchically energetic pertinent features. To this end, in addition to the stand-alone fuzzy logic model, preprocessing methods SSA, empirical mode decomposition (EMD), and commonly used discrete wavelet transform (DWT) are incorporated into the fuzzy models which are named as hybrid SSA-fuzzy, EMD-fuzzy, W-fuzzy models, respectively. In this study, fuzzy, hybrid SSA-fuzzy, EMD-fuzzy, and W-fuzzy models are developed to enhance the daily rainfall prediction accuracy and improve the prediction time span up to 3 days via three (3) stations' data in Turkey. The proposed SSA-fuzzy model is compared with fuzzy, hybrid EMD-fuzzy, and widely used hybrid W-fuzzy models in predicting daily rainfall in three distinctive locations up to a 3-day time horizon. Improved accuracy in predicting daily rainfall is provided by the SSA-fuzzy, W-fuzzy, and EMD-fuzzy models compared to the stand-alone fuzzy model based on mean square error (MSE) and the Nash-Sutcliffe coefficient of efficiency (CE) model assessment metrics. Specifically, the advocated SSA-fuzzy model is found to be superior in accuracy to hybrid EMD-fuzzy and W-fuzzy models in predicting daily rainfall for all time spans. The results reveal that, with its easy-to-use features, the advocated SSA-fuzzy modeling tool in this study is a promising principled method for its possible future implementations not only in hydrological studies but in water resources and hydraulics engineering and all scientific disciplines where future state space prediction of a vague nature and stochastic dynamical system is important., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Designing green stormwater infrastructure (GSI) requires an accurate estimate of the contributing drainage area and a model for runoff generation. We examined some factors that add to the uncertainty associated with these two design steps in the urban environment. Delineated drainage areas at five GSI sites in southeastern Pennsylvania (PA) were compared for digital elevation model (DEM) resolutions (grid cell sizes) ranging from 8 to 300 cm. The findings point to an optimal DEM resolution range of 30-60 cm, with up to 100 cm resolution providing acceptable results for some sites. The delineated areas were validated with the observed flow and rainfall records at three sites by examining curve number (CN) values calculated for individual storms. The calculated CNs decreased with increasing rainfall volume, which supports a recommendation to consider a range of CNs in the GSI design process. The variation in calculated CNs was higher for the overestimated drainage areas derived from coarser DEM resolutions. We hypothesize that the observed continued decrease of CNs at high rainfall is the result of inlet bypass, a potentially significant factor in urban hydrology. The findings from this study provide insight into the variability in expected delineated drainage areas using standard methods in GSI design.

Accurate and reliable runoff forecast is beneficial to watershed planning and management and scientific operation of water resources system. However, due to the comprehensive influence of climatic conditions, geographical environment, and human activities, the runoff series is nonlinear and non-stationary, and there are still great challenges in mid-long term runoff forecasting. In order to improve the prediction accuracy, a novel model TVF-EMD-PE-PSO-GRU (TEPPG) was proposed in this study. Firstly, several intrinsic mode functions (IMFs) were obtained by decomposing the original runoff series by time-varying filter-based empirical mode decomposition (TVF-EMD). Secondly, the permutation entropy (PE) algorithm was used to calculate the complexity of each IMF, and the IMF with similar complexity was combined. Then, the gated recurrent unit (GRU) model based on particle swarm optimization (PSO) was used to predict each IMF after merging. Finally, the prediction results of each IMF were superimposed to obtain the final results. And compared with three models such as TVF-EMD-PSO-GRU, extreme-point symmetric mode decomposition coupled gated recurrent unit and particle swarm optimization (ESMD-PSO-GRU), complete ensemble empirical mode decomposition with adaptive noise coupled gated recurrent unit, and particle swarm optimization (CEEMDAN-PSO-GRU). The monthly and annual runoff forecasting of Tangnaihai hydrological station in the upper reaches of the Yellow River and Cuntan hydrological station in the upstream of the Yangtze River was taken as examples to test the performance of the model. The results show that, compared with the other three models, the TEPPG model had the highest prediction accuracy and was relatively stable in both monthly and annual runoff forecasts. Thus, the proposed method was developed to support the decision-making of water resource system., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The growing importance of groundwater as a freshwater supply in semi-arid areas such as the Vredefort Dome World Heritage Site (VDWHS) demands the judicious management and development of this vital resource. The increased demand for groundwater due to the contamination of surface water, coupled with the lack of information on hydrological interaction and associated water quality implications, present difficulties in establishing management strategies. An integrated study based on hydrochemistry and multivariate statistical techniques supplemented by environmental isotopes delineated discrete areas of surface water and groundwater interaction in a fractured-rock terrain. Surface water loss was observed in sections that exhibited declining groundwater levels, whereas limited baseflow was restricted to zones with stable groundwater levels. The multivariate statistical analysis revealed the combined effect of natural hydrochemical processes and anthropogenic sources as controlling factors of water composition, and highlighted zones of aquifer-river water mixing, where certain areas were found to be additionally polluted by human-derived contaminants. The stable isotope ( 18 O and 2 H) ratios confirm mixing between depleted groundwater and enriched river water, producing a composition that reflected an integration of the isotopic variations. The continuous wastewater discharge into the Vaal River combined with the increased groundwater exploitation may be prompting induced recharge conditions. The results suggest compartmentalization of the groundwater systems, where certain areas within 1 km of the channel were not influenced by river-induced contamination. This indicates that hydrological connectivity is governed by site-specific hydraulic properties. This study shows the usefulness of a multi-method approach by combining environmental isotopes, hydrochemistry, and multivariate statistics to characterize hydrological linkage in semi-arid regions., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Drought is a major factor affecting the sustainable development of society and the economy. Research on drought assessment is of great significance for formulating drought emergency policies and drought risk early warning and enhancing the ability to withstand drought risks. Taking the Yellow River Basin as the object, this paper utilizes data fusion, copula function, entropy theory, and deep learning, fuses the features of meteorological drought and hydrological drought into a drought assessment index, and establishes a long short-term memory (LSTM) network for drought assessment, based on deep learning theory. The results show that (1) after extracting the features of meteorological drought and hydrological drought, the drought convergence index (DCI) built on the fused features by copula function can accurately reflect the start and duration of the drought; (2) the drought assessment indices were effectively screened by judging the causality of the drought system, using the transfer entropy; (3) drawing on the idea of deep learning, LSTM for drought assessment, which was established on DCI and the drought assessment factors, can accurately assess the drought risks of the Yellow River Basin., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Yanling Li et al.)

In state-of-the-art energy systems modelling, reservoir hydropower is represented as any other thermal power plant: energy production is constrained by the plant's installed capacity and a capacity factor calibrated on the energy produced in previous years. Natural water resource variability across different temporal scales and the subsequent filtering effect of water storage mass balances are not accounted for, leading to biased optimal power dispatch strategies. In this work, we aim at introducing a novelty in the field by advancing the representation of reservoir hydropower generation in energy systems modelling by explicitly including the most relevant hydrological constraints, such as time-dependent water availability, hydraulic head, evaporation losses, and cascade releases. This advanced characterization is implemented in an open-source energy modelling framework. The improved model is then demonstrated on the Zambezi River Basin in the South Africa Power Pool. The basin has an estimated hydropower potential of 20,000 megawatts (MW) of which about 5,000 MW has been already developed. Results show a better alignment of electricity production with observed data, with a reduction of estimated hydropower production up to 35% with respect to the baseline Calliope implementation. These improvements are useful to support hydropower management and planning capacity expansion in countries richly endowed with water resource or that are already strongly relying on hydropower for electricity production., Competing Interests: The authors have declared that no competing interests exist.

In hydrological modelling, a good result for the criterion of goodness of fit does not always imply that the hypothesis of mass conservation is fulfilled, and models can lose their essential physical soundness. We propose a way for detecting this anomaly by accounting the resulting water balance during model simulation and use it to modulate the obtained goodness of fit. We call this anomaly in water balance as "inner balance error of the model". To modulate the goodness of fit values, a penalty function that depends on this error is proposed. In addition, this penalty function is introduced into a multi-criteria objective function, which is also tested. This procedure was followed in modelling the Headwater of the Tagus River (Spain), applying the monthly abcd water balance model. Modulation of the goodness of fit allowed for detecting balance errors in the modelling, revealing that in the simulation of some catchments the model tends to accumulate water in, or release water from, the reservoir that simulates groundwater storage. Although the proposed multi-criteria objective function solves the inner balance error for most catchments, in some cases the error cannot be corrected, indicating that any error in the input and output data is probably related to groundwater flows., Competing Interests: The authors have declared that no competing interests exist.

Accurate streamflow prediction plays a pivotal role in hydraulic project design, nonpoint source pollution estimation, and water resources planning and management. However, the highly non-linear relationship between rainfall and runoff makes prediction difficult with desirable accuracy. To improve the accuracy of monthly streamflow prediction, a seasonal Support Vector Regression (SVR) model coupled to the Soil and Water Assessment Tool (SWAT) model was developed for 13 subwatersheds in the Illinois River watershed (IRW), U.S. Terrain, precipitation, soil, land use and land cover, and monthly streamflow data were used to build the SWAT model. SWAT Streamflow output and the upstream drainage area were used as two input variables into SVR to build the hybrid SWAT-SVR model. The Calibration Uncertainty Procedure (SWAT-CUP) and Sequential Uncertainty Fitting-2 (SUFI-2) algorithms were applied to compare the model performance against SWAT-SVR. The spatial calibration and leave-one-out sampling methods were used to calibrate and validate the hybrid SWAT-SVR model. The results showed that the SWAT-SVR model had less deviation and better performance than SWAT-CUP simulations. SWAT-SVR predicted streamflow more accurately during the wet season than the dry season. The model worked well when it was applied to simulate medium flows with discharge between 5 m3 s-1 and 30 m3 s-1, and its applicable spatial scale fell between 500 to 3000 km2. The overall performance of the model on yearly time series is "Satisfactory". This new SWAT-SVR model has not only the ability to capture intrinsic non-linear behaviors between rainfall and runoff while considering the mechanism of runoff generation but also can serve as a reliable regional tool for an ungauged or limited data watershed that has similar hydrologic characteristics with the IRW., Competing Interests: The authors have declared that no competing interests exist.

Climate change has a massive impact on the global water cycle. Subsurface ecosystems, the earth largest reservoir of liquid freshwater, currently experience a significant increase in temperature and serious consequences from extreme hydrological events. Extended droughts as well as heavy rains and floods have measurable impacts on groundwater quality and availability. In addition, the growing water demand puts increasing pressure on the already vulnerable groundwater ecosystems. Global change induces undesired dynamics in the typically nutrient and energy poor aquifers that are home to a diverse and specialized microbiome and fauna. Current and future changes in subsurface environmental conditions, without doubt, alter the composition of communities, as well as important ecosystem functions, for instance the cycling of elements such as carbon and nitrogen. A key role is played by the microbes. Understanding the interplay of biotic and abiotic drivers in subterranean ecosystems is required to anticipate future effects of climate change on groundwater resources and habitats. This review summarizes potential threats to groundwater ecosystems with emphasis on climate change and the microbial world down below our feet in the water saturated subsurface.

Monitoring the groundwater chemical composition and identifying the presence of pollutants is an integral part of any comprehensive groundwater management strategy. The present study was conducted in a part of West Tripura, northeast India, to investigate the presence and sources of trace metals in groundwater and the risk to human health due to direct ingestion of groundwater. Samples were collected from 68 locations twice a year from 2016 to 2018. Mixed Ca-Mg-HCO 3 , Ca-Cl and Ca-Mg-Cl were the main groundwater types. Hydrogeochemical methods showed groundwater mineralization due to (1) carbonate dissolution, (2) silicate weathering, (3) cation exchange processes and (4) anthropogenic sources. Occurrence of faecal coliforms increased in groundwater after monsoons. Nitrate and microbial contamination from wastewater infiltration were apparent. Iron, manganese, lead, cadmium and arsenic were above the drinking water limits prescribed by the Bureau of Indian Standards. Water quality index indicated 1.5% had poor, 8.7% had marginal, 16.2% had fair, 66.2% had good and 7.4% had excellent water quality. Correlation and principal component analysis reiterated the sources of major ions and trace metals identified from hydrogeochemical methods. Human exposure assessment suggests health risk due to high iron in groundwater. The presence of unsafe levels of trace metals in groundwater requires proper treatment measures before domestic use.

The use of stable isotopes ( δ 2 H and δ 18 O) is widespread in water resources studies. In the Latin America and the Caribbean (LAC) region, the application of isotope techniques has increased in the past decade, but there remains room to gain self-reliance in environmental isotope studies, necessitating easy and fast access to good-quality isotope data. To that end, in 2018 the IAEA carried out the first regional interlaboratory comparison exercise, testing the analytical performance of 25 laboratories using isotope-ratio mass spectrometry and laser absorption spectroscopy. The three test samples covered a commonly observed range of 0 to -16 ‰ δ 18 O and 0 to -115 ‰ δ 2 H. z- and ζ-scores were used to benchmark laboratories' performance against a strict criterion. We found that 81% of the laboratories had satisfactory performance ( | z | ¯ ≤ 2) for δ 2 H but only 54% achieved similar scores for δ 18 O. Only a minor fraction of results (12% for δ 2 H and 15% for δ 18 O) were unsatisfactory. The larger number of questionable results for δ 18 O confirmed the challenges in laser absorption spectroscopy for this isotope. Besides instrumental performance, the sample throughput, laboratory reference materials, and data post-processing were contributing factors to inaccurate or imprecise performance.

In the past few decades, there has been a rapid growth in the concentration of nitrogenous compounds such as nitrate-nitrogen and ammonia-nitrogen in rivers, primarily due to increasing agricultural and industrial activities. These nitrogenous compounds are mainly responsible for eutrophication when present in river water, and for 'blue baby syndrome' when present in drinking water. High concentrations of these compounds in rivers may eventually lead to the closure of treatment plants. This study presents a training and a selection approach to develop an optimum artificial neural network model for predicting monthly average nitrate-N and monthly average ammonia-N. Several studies have predicted these compounds, but most of the proposed procedures do not involve testing various model architectures in order to achieve the optimum predicting model. Additionally, none of the models have been trained for hydrological conditions such as the case of Malaysia. This study presents models trained on the hydrological data from 1981 to 2017 for the Langat River in Selangor, Malaysia. The model architectures used for training are General Regression Neural Network (GRNN), Multilayer Neural Network and Radial Basis Function Neural Network (RBFNN). These models were trained for various combinations of internal parameters, input variables and model architectures. Post-training, the optimum performing model was selected based on the regression and error values and plot of predicted versus observed values. Optimum models provide promising results with a minimum overall regression value of 0.92., Competing Interests: The authors have declared that no competing interests exist.

Groundwater quality analysis has become essentially important in the present world scenario. In recent years, advanced technologies have replaced the traditional ones which are being helpful in simplifying the complex works. In this study, multivariate statistical analysis is carried out with the help of SPSS software for 45 groundwater samples of Kanavi Halla Sub-Basin (KHSB). The quality of groundwater is determined for various parameters which were analyzed and their concentration is correlated with other parameters using correlation matrix. The PCA technique is applied on water quality parameters, from which four components are extracted with 80.28% total variance. The extracted components suggest that the sources behind the higher loadings of each factor are by geological, agricultural, rainfall, domestic wastewater and industrial activities. Results of the Kaiser-Meyer-Olkin and Bartlett's test conducted have value of 0.659 which is greater than the standard value (0.5). Based on water quality index (WQI), it was noticeably depicted that 2/3rd of the KHSB groundwater quality falls under poor to very poor condition, and hardly 26% of groundwater available is portable. Thus, this study contributes the effective use of multivariate statistics and WQI analysis for groundwater quality. It helps in understanding the hydro-geochemistry of the groundwater and also aids in minimizing the larger set of data into smaller set with effective interpretation.

Karst groundwater, an important water source, is often highly influenced by human impacts, causing environmental damage and threats to human health. However, studies on the anthropogenic influences on the hydrogeochemical evolution of karst groundwater are relatively rare. To assess hydrogeochemical formation and evolution, we focused on a typical karst groundwater system (Jinan, China) which is composed of cold groundwater (av. temperature 13-17 °C), springs and geothermal water (av. temperature > 30 °C) and is significantly affected by human activities. The study was performed by means of water samples collecting and analyzing and isotope analysis ( 2 H, 18 O and 14 C). The statistical analysis and inverse models were also applied to further understand geochemical processes and anthropogenic influences. The 2 H, 18 O and 14 C results indicate that the cold karst groundwater is easily influenced and contaminated by the local environment, while geothermal water is relatively old with a slow rate of recharge. The hydrochemical types of cold karst groundwater are mainly HCO 3 -Ca and HCO 3 ·SO 4 -Ca, while geothermal water hydrochemical types are SO 4 -Ca·Na and SO 4 -Ca. Groundwater Ca 2+ , Mg 2+ , HCO 3 - and SO 4 2- are mainly controlled by carbonate equilibrium, gypsum dissolution and dedolomitization. Groundwater Na + , K + and Cl - are mainly derived from halite dissolution, and in geothermal water, they are also affected by incongruent dissolution of albite and K-feldspar. Anthropogenic nitrogen produces ammonium resulting in nitrification and reduction in CO 2 (g) consumption and HCO 3 - release from carbonate dissolution. Principal component analysis and inverse models also indicate that nitrification and denitrification have significantly affected water-rock interactions. Our study suggests that karst groundwater quality is dominated by water-rock interactions and elucidates the influence of anthropogenic nitrogen. We believe that this paper will be a good reference point to study anthropogenic influences on the groundwater environment and to protect karst groundwater globally.

Continental-scale models of malaria climate suitability typically couple well-established temperature-response models with basic estimates of vector habitat availability using rainfall as a proxy. Here we show that across continental Africa, the estimated geographic range of climatic suitability for malaria transmission is more sensitive to the precipitation threshold than the thermal response curve applied. To address this problem we use downscaled daily climate predictions from seven GCMs to run a continental-scale hydrological model for a process-based representation of mosquito breeding habitat availability. A more complex pattern of malaria suitability emerges as water is routed through drainage networks and river corridors serve as year-round transmission foci. The estimated hydro-climatically suitable area for stable malaria transmission is smaller than previous models suggest and shows only a very small increase in state-of-the-art future climate scenarios. However, bigger geographical shifts are observed than with most rainfall threshold models and the pattern of that shift is very different when using a hydrological model to estimate surface water availability for vector breeding.

Testate amoebae are a widely-used tool for palaeohydrological reconstruction from peatlands. However, it has been observed that weak idiosomic siliceous tests (WISTs) are common in uppermost peats, but very rarely found as subfossils deeper in the peat profile. This taphonomic problem has been noted widely and it has been established that WISTs disaggregate and/or dissolve in the low pH condition of ombrotrophic peatlands. Here we investigate the effect of this taphonomic problem on water-table reconstructions from thirty European peatlands through the comparison of reconstructions based on all taxa and those with WISTs removed. In almost all cases the decomposition of WISTs does not introduce discernible bias to peatland water-table reconstructions. However, some discrepancy is apparent when large abundances of Corythion-Trinema type are present (9-12 cm deviation with 50-60% abundance of this particular taxon). We recommend that WISTs should be removed before carrying out water-table reconstructions, and that the minimum count of testate amoebae per sample should exclude WISTs to ensure the development of robust reconstructions., (Copyright © 2020 Elsevier GmbH. All rights reserved.)

Parameter optimization of a hydrological model is intrinsically a high dimensional, nonlinear, multivariable, combinatorial optimization problem which involves a set of different objectives. Currently, the assessment of optimization results for the hydrological model is usually made through calculations and comparisons of objective function values of simulated and observed variables. Thus, the proper selection of objective functions' combination for model parameter optimization has an important impact on the hydrological forecasting. There exist various objective functions, and how to analyze and evaluate the objective function combinations for selecting the optimal parameters has not been studied in depth. Therefore, to select the proper objective function combination which can balance the trade-off among various design objectives and achieve the overall best benefit, a simple and convenient framework for the comparison of the influence of different objective function combinations on the optimization results is urgently needed. In this paper, various objective functions related to parameters optimization of hydrological models were collected from the literature and constructed to nine combinations. Then, a selection and evaluation framework of objective functions is proposed for hydrological model parameter optimization, in which a multiobjective artificial bee colony algorithm named RMOABC is employed to optimize the hydrological model and obtain the Pareto optimal solutions. The parameter optimization problem of the Xinanjiang hydrological model was taken as the application case for long-term runoff prediction in the Heihe River basin. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) based on the entropy theory is adapted to sort the Pareto optimal solutions to compare these combinations of objective functions and obtain the comprehensive optimal objective functions' combination. The experiments results demonstrate that the combination 2 of objective functions can provide more comprehensive and reliable dominant options (i.e., parameter sets) for practical hydrological forecasting in the study area. The entropy-based method has been proved that it is effective to analyze and evaluate the performance of different combinations of objective functions and can provide more comprehensive and impersonal decision support for hydrological forecasting., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2020 Jiuyuan Huo and Liqun Liu.)

Water resource assessment is crucial for human well-being and ecosystem health. Assessments considering both blue and green water are of great significance, as green water plays a critical but often ignored role in the terrestrial ecosystem, especially in arid and semi-arid regions. Many approaches have been developed for green and blue water valuation; however, few approaches consider the interrelationship between green and blue water. This study proposed a new framework for green and blue water assessment by considering the interactions between green and blue water and the connections between human and natural ecosystems in an arid endorheic river basin where hydrological cycling is dramatically altered by human activities. The results show that even though green water is the dominant water resource, blue water is also critical. Most of the blue water is redirected back into the soil through physical and human-induced processes to meet the water demand of the ecosystem. The blue and green water regimes are found to be totally different in different ecosystems due to the temporal and spatial variability in water supply and consumption. We also found that humans are using an increasing proportion of water, resulting in decreasing water availability. Extensive water use by humans reduces the water availability for the natural ecosystem. Approximately 38.6% of the vegetation-covered area, which is dominated by farmland and forest, may face a moderate or high risk of increased conflict and tension over freshwater. This study provides crucial information to better understand the interactions between green and blue water and the relations between humans and nature by explicitly assessing water resources. It also provides crucial information for water management strategies that aim to balance humankind and nature.

The objective of this study is geochronological investigation of sedimentological and inorganic composition, in the Danube Djerdap Lake sediments in order to obtain reliable information about former pollution. Eleven samples were taken from the 135-cm-deep sediment core drilled at the Orlova location. Since the core represents sediments deposited during 1972-2016, the sedimentation rate of ~ 3 cm year -1 was estimated. Grain size, mineralogical and geochemical composition was determined. Sediments are sandy silts and clayey silts, and only the deepest and shallowest layers contain > 30% of sand-size fraction. The highest concentrations of minor elements are found in the oldest sediment (1972-1977) as a consequence of the high flux of the material from variable sources. During the sedimentation period (1975-1990), the concentrations of analyzed elements are generally decreasing until the beginning of 1990s. After this period, there are two distinct decreases and two distinct increases in concentrations of elements. The fluctuations in minor element concentrations are a consequence of both natural and anthropogenic sources. Granitic rocks situated south are source of minerals that carry minor elements. Enrichment Factor, Geoaccumulation Index, Contamination Factor and Pollution Load Index indicate that concentrations of certain minor elements at specific depositional periods have anthropogenic source.

In many parts of the world, wastewater irrigation has become a common practice because of freshwater scarcity and to increase resource reuse efficiency. Wastewater irrigation has positive impacts on livelihoods and at the same time, it has adverse impacts related to environmental pollution. Hydrochemical processes and groundwater behaviour need to be analyzed for a thorough understanding of the geochemical evolution in the wastewater irrigated systems. The current study focuses on a micro-watershed in the peri-urban Hyderabad of India, where farmers practice intensive wastewater irrigation. To evaluate the major factors that control groundwater geochemical processes, we analyzed the chemical composition of the wastewater used for irrigation and groundwater samples on a monthly basis for one hydrological year. The groundwater samples were collected in three settings of the watershed: wastewater irrigated area, groundwater irrigated area and upstream peri-urban area. The collected groundwater and wastewater samples were analyzed for major anions, cations and nutrients. We systematically investigated the anthropogenic influences and hydrogeochemical processes such as cation exchange, precipitation and dissolution of minerals using saturated indices, and freshwater-wastewater mixtures at the aquifer interface. Saturation indices of halite, gypsum and fluorite are exhibiting mineral dissolution and calcite and dolomite display mineral precipitation. Overall, the results suggest that the groundwater geochemistry of the watershed is largely controlled by long-term wastewater irrigation, local rainfall patterns and water-rock interactions. The study results can provide the basis for local decision-makers to develop sustainable groundwater management strategies and to control the aquifer pollution influenced by wastewater irrigation., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Although numerous steady-state effluent plume dilution models are in use, their application in tidal environments remains a challenge. Three-dimensional dynamic circulation models are also inadequate, often due to the lack of required resolution and simplifying assumptions. To overcome these limitations, we present FVCOM-plume-an outfall plume dilution and transport model that operates within the Finite Volume Coastal Ocean Model (FVCOM) framework. It provides simultaneous inclusion of near-field dilution and far-field plume transport processes. The near-field is based on UM3 model using Lagrangian Control Volume approach to compute buoyant plume trajectory and dilution from multiport diffusers. The far-field uses neutrally buoyant particles with point masses and the random walk method to solve unsteady advection-diffusion processes. A density kernel approach is used to compute concentrations at point locations and analyze far-field plume characteristics. The results demonstrate the ability of FVCOM-plume to simultaneously capture near-field and far-field effluent plume dynamics in tidal environments., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Torrential and long-lasting rainfall often causes long-duration floods in flat and lowland areas in data-scarce Nyaungdon Area of Myanmar, imposing large threats to local people and their livelihoods. As historical hydrological observations and surveys on the impact of floods are very limited, flood hazard assessment and mapping are still lacked in this region, making it hard to design and implement effective flood protection measures. This study mainly focuses on evaluating the predicative capability of a 2D coupled hydrology-inundation model, namely the Rainfall-Runoff-Inundation (RRI) model, using ground observations and satellite remote sensing, and applying the RRI model to produce a flood hazard map for hazard assessment in Nyaungdon Area. Topography, land cover, and precipitation are used to drive the RRI model to simulate the spatial extent of flooding. Satellite images from Moderate Resolution Imaging Spectroradiometer (MODIS) and the Phased Array type L-band Synthetic Aperture Radar-2 onboard Advanced Land Observing Satellite-2 (ALOS-2 ALOS-2/PALSAR-2) are used to validate the modeled potential inundation areas. Model validation through comparisons with the streamflow observations and satellite inundation images shows that the RRI model can realistically capture the flow processes (R2 ≥ 0.87; NSE ≥ 0.60) and associated inundated areas (success index ≥ 0.66) of the historical extreme events. The resultant flood hazard map clearly highlights the areas with high levels of risks and provides a valuable tool for the design and implementation of future flood control and mitigation measures., Competing Interests: The authors have declared that no competing interests exist.

The Athabasca River watershed plays a dominant role in both the economy and the environment in Alberta, Canada. Natural and anthropogenic factors rapidly changed the landscape of the watershed in recent decades. The dynamic of such changes in the landscape characteristics of the watershed calls for a comprehensive and up-to-date land-use and land-cover (LULC) map, which could serve different user-groups and purposes. The aim of the study herein was to delineate a 2016 LULC map of the Athabasca River watershed using Landsat-8 Operational Land Imager (OLI) images, Moderate Resolution Imaging Spectroradiometer (MODIS)-derived enhanced vegetation index (EVI) images, and other ancillary data. In order to achieve this, firstly, a preliminary LULC map was developed through applying the iterative self-organizing data analysis (ISODATA) clustering technique on 24 scenes of Landsat-8 OLI. Secondly, a Terra MODIS-derived 250-m 16-day composite of 30 EVI images over the growing season was employed to enhance the vegetation classes. Thirdly, several geospatial ancillary datasets were used in the post-classification improvement processes to generate a final 2016 LULC map of the study area, exhibiting 14 LULC classes. Fourthly, an accuracy assessment was carried out to ensure the reliability of the generated final LULC classes. The results, with an overall accuracy and Cohen's kappa of 74.95% and 68.34%, respectively, showed that coniferous forest (47.30%), deciduous forest (16.76%), mixed forest (6.65%), agriculture (6.37%), water (6.10%), and developed land (3.78%) were the major LULC classes of the watershed. Fifthly, to support the data needs of scientists across various disciplines, data fusion techniques into the LULC map were performed using the Alberta merged wetland inventory 2017 data. The results generated two useful maps applicable for hydro-ecological applications. Such maps depicted two specific categories including different types of burned (approximately 6%) and wetland (approximately 30%) classes. In fact, these maps could serve as important decision support tools for policy-makers and local regulatory authorities in the sustainable management of the Athabasca River watershed.

The stationarity of observed hydrological series has been broken or destroyed in many areas worldwide due to changing environments, causing hydrologic designs under stationarity assumption to be questioned and placing designed projects under threat. This paper proposed a data expansion approach-namely, the cross-reconstruction (CR) method-for frequency analysis for a step-changed runoff series combined with the empirical mode decomposition (EMD) method. The purpose is to expand the small data on each step to meet the requirements of data capacity for frequency analysis and to provide more reliable statistics within a stepped runoff series. Taking runoff records at three gauges in western China as examples, the results showed that the cross-reconstruction method has the advantage of data expansion of the small sample runoff data, and the expanded runoff data at steps can meet the data capacity requirements for frequency analysis. In addition, the comparison of the expanded and measured data at steps indicated that the expanded data can demonstrate the statistics closer to the potential data population, rather than just reflecting the measured data. Therefore, it is considered that the CR method ought to be available in frequency analysis for step-changed records, can be used as a tool to construct the hydrological probability distribution under different levels of changing environments (at different steps) through data expansion, and can further assist policy-making in water resources management in the future.

A digital elevation model (DEM) is a digital model or 3D representation of a terrain's surface. There are many methods to create DEM such as LiDAR, stereo photogrammetry and topographic maps. DEMs are very important for many applications such as extracting terrain parameters for geomorphology and modeling water flow for hydrology or mass movement. A number of websites are available to provide DEM such as SRTM, GTOPO30 and ASTER GDEM but their accuracy differs from one to another and also selecting a small DEM size (high resolution) gives accurate information, but the analysis takes long time. This paper aims to analyze the impact of using different available DEMs on watershed geomorphological properties on order to provide guidelines for users to select the most suitable DEM that obtain an accurate analysis in less time. Three programs; watershed modeling systems: WMS, Global Mapper and Google Earth were used in this study. Three case studies were studied to check the accuracy of these models and select the most accurate one for application. Satellite images downloaded from Google Earth were used as a guide reference for the comparison due to their accuracy and high resolution. The results indicated that the SRTM model was more accurate (95%) for all case studies according to our comparison between its delineation and satellite images. ASTER GDEM is the second most accurate model with an accuracy of 87%, the GTOPO30's accuracy is 80%.

Changes in groundwater evolutionary processes due to aquifer overexploitation show a world-wide increase and have been of growing concern in recent years. The study aimed to improve the knowledge of groundwater evolutionary processes by means of stable water isotopes and hydrochemistry in a representative lake basin, Ordos energy base. Groundwater, precipitation, and lake water collected during four campaigns were analysed by stable isotopes and chemical compositions. Results showed that temperature effect predominated the isotope fractionation in precipitation, while evaporation and inadequate groundwater recharge were the key factors explaining high salinity and isotopic enrichment in lake water. Additionally, the Kuisheng Lake was a preferential area of groundwater recharge, while the Subei Lake received less sources from underlying aquifer due to the combined effects of low permeable zone and upstream groundwater captured by the production wells. The homogeneous isotope signatures of groundwater may be ascribed to the closely vertical hydraulic connectivity between the unconfined and the confined aquifers. Isotopically enriched groundwater pumping from well field probably promoted isotopic depletion in groundwater depression cone. These findings not only provide the conceptual framework for the inland basin, but also have important implications for sustainable groundwater management in other groundwater discharge basins with arid climate.

Theoretical formulae have shown significant advantages in describing the characteristic geometric scales of the pollutant mixing zone (PMZ) formed by offshore pollutant discharged by a single general form. They, however, fail to predict the influence of the lateral inhomogeneity of the river flow because constant flow velocity and the lateral diffusion coefficient are assumed during the derivation. The realistic flow velocity in a river is fitted by an exponential law in this study and the lateral diffusion coefficient is proposed to have the same form. Similar idea has been used in previous studies on the vertical dispersion of scalar in the lower atmosphere. Pollutant discharged from a steady onshore point source into a wide straight open channel is examined to characterize the concentration taking into consideration of these lateral variations. Theoretical formulae describing the maximum length, maximum width and its corresponding longitudinal position, as well as the area of the PMZ are derived. A non-dimensional standard curve equation for the isoconcentration boundary of PMZ is also obtained. The results show that the shape of the dimensionless standard curve of PMZ depends only on the exponential constants in the exponential laws. The exponential profiles that fit the near-shore velocity give good prediction, while the ones that match the entire lateral range up to the center of the river underpredict the PMZ significantly. These findings are of great importance for practitioners to characterize the geometry of the PMZ in rivers and for water quality modeling.

Changes in the amount, intensity, and timing of precipitation are increasing hydrologic variability in many regions, but we have little understanding of how these changes are affecting freshwater species. Stream-breeding amphibians-a diverse group in North America-may be particularly sensitive to hydrologic variability during aquatic larval and metamorphic stages. Here, we tested the prediction that hydrologic variability in streams decreases survival through metamorphosis in the salamander Gyrinophilus porphyriticus , reducing recruitment to the adult stage. Using a 20-y dataset from Merrill Brook, a stream in northern New Hampshire, we show that abundance of G. porphyriticus adults has declined by ∼50% since 1999, but there has been no trend in larval abundance. We then tested whether hydrologic variability during summers influences survival through metamorphosis, using capture-mark-recapture data from Merrill Brook (1999 to 2004) and from 4 streams in the Hubbard Brook Experimental Forest (2012 to 2014), also in New Hampshire. At both sites, survival through metamorphosis declined with increasing variability of stream discharge. These results suggest that hydrologic variability reduces the demographic resilience and adaptive capacity of G. porphyriticus populations by decreasing recruitment of breeding adults. They also provide insight on how increasing hydrologic variability is affecting freshwater species, and on the broader effects of environmental variability on species with vulnerable metamorphic stages., Competing Interests: The authors declare no conflict of interest.

Stream microbes that occur in the Mediterranean Basin have been shown to possess heightened sensitivity to intensified water stress attributed to climate change. Here, we investigate the effects of long-term drought (150 days), storms and rewetting (7 days) on the diversity and composition of archaea, bacteria and fungi inhabiting intermittent streambed sediment (surface and hyporheic) and buried leaves. Hydrological alterations modified the archaeal community composition more than the bacterial community composition, whereas fungi were the least affected. Throughout the experiment, archaeal communities colonizing sediments showed greater phylogenetic distances compared to those of bacteria and fungi, suggesting considerable adaptation to severe hydrological disturbances. The increase in the class abundances, such as those of Thermoplasmata within archaea and of Actinobacteria and Bacilli within bacteria, revealed signs of transitioning to a drought-favoured and soil-like community composition. Strikingly, we found that in comparison to the drying phase, water return (as sporadic storms and rewetting) led to larger shifts in the surface microbial community composition and diversity. In addition, microhabitat characteristics, such as the greater capacity of the hyporheic zone to maintain/conserve moisture, tended to modulate the ability of certain microbes (e.g., bacteria) to cope with severe hydrological disturbances.

Since decades, surface water bodies have been exposed to pesticides from agriculture. In many places, retention systems are regarded as an important mitigation strategy to lower pesticide pollution. Hence, the processes governing the transport of pesticides in and through a retention system have to be understood to achieve sufficient pesticide attenuation. In this study, the temporal dynamics of metazachlor and its transformation products metazachlor-oxalic acid (OA) and -sulphonic acid (ESA) were observed in an agricultural retention pond and hydrologic tracers helped to understand system-inherent processes. Pesticide measurements were carried out for 80 days after their application during transient flow conditions. During a short-term (3 days) experiment, the tracers bromide, uranine and sulphorhodamine B were used to determine hydraulic conditions, residence times and sorption potential. A long-term experiment with sodium naphthionate (2 months) and isotopes (12 months) provided information about inputs via interflow and surface-groundwater interactions. During transient conditions, high concentration pulses of up to 35 μg L -1 metazachlor, 14.7 μg L -1 OA and 22.5 μg L -1 ESA were quantified that enduringly raised solute concentrations in the pond. Mean residence time in the system accounted for approximately 4 h showing first tracer breakthrough after 5 min and last tracer concentrations 72 h after injection. While input via interflow was confirmed, no evidence for surface-groundwater interaction was found. Different tracers illustrated potentials for sorption and photolytic degradation inside the system. This study shows that high-resolution sampling is essential to obtain robust results about retention efficiency and that hydrological tracers may be used to determine the governing processes.

Narragansett Bay is an urban estuary that historically has been impacted by long-term discharge of sanitary wastewater (WW) effluents. High-density water sampling was conducted in Narragansett Bay, RI, USA, in an effort to understand the distribution and behavior of sucralose, an artificial sweetener that has shown utility as a sanitary wastewater tracer. Water samples were collected at sixty-seven sites and analyzed for sucralose, whose performance was compared to other tracers present in wastewater effluents. Concentrations of sucralose were much higher than the other tracers measured, carbamazepine and caffeine, ranging from 18 to 3180 ng/L and corresponded well with salinity (r 2  = 0.88), demonstrating conservative behavior throughout the Bay. Mapped interpolation data using an empirical bayesian kriging model clearly show the spatial trends of WW and how estuarine processes influence dilution and dispersion throughout the Bay. These findings provide further evidence of the efficacy of sucralose as a wastewater tracer in large urban estuaries where continuous high-volume discharge of WW occur., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Global warming portends an accelerated water cycle as increased evaporation feeds atmospheric moisture and precipitation. To monitor effects on surface water levels, we describe a low-cost hydrologic observatory suitable for small to medium size lakes. The observatory comprises sensor platforms that were built in-house to compile continuous, sub-daily water budgets. The variables measured directly are lake stage (S), evaporation (E), and precipitation (P). A net inflow term (Q net ) is estimated as a residual in the continuity equation: ∆S = P - E + Q net . We describe how to build in-lake stilling wells and floating evaporation pans using readily available materials. We assess their performance in laboratory tests and field trials. A 3-month deployment on a small Wisconsin lake (18 ha, 10 m deep) confirms that continuous estimates of ∆S, E, P, and Q net can be made with good precision and accuracy at hourly time scales. During that deployment, daily estimates of E from the floating evaporation pans were comparable with estimates made using the more data-intensive Bowen ratio energy balance method and a mass transfer model. Since small lakes are numerically dominant and widely distributed across the globe, a network of hydrologic observatories would enable the calibration and validation of climate models and consumptive use policies at local and regional scales. And since the observatories are inexpensive and relatively simple to maintain, citizen scientists could facilitate the expansion of spatial coverage with minimal training.