Editor’s Choice Articles

A method was developed to integrate the truncated power-law distribution of solid volumetric fraction into the widely used Kozeny–Carman (KC)-type equations to assess the potential uncertainty of permeability. The focus was on the heterogeneity of porosity (or solid volumetric fraction) in the KC equation. The truncated power-law distribution simulates a heterogeneous scenario in which the solid volumetric fraction varies over different portions of porous media, which is treated as stationary, so its spatial mean can be replaced by the ensemble mean. The model was first compared with the experimental results of 44 samples from the literature and a recent model of KC equation modification that targets the coefficients in the equation. The effects of the fractal dimension of characteristic length of the solid volumetric fraction on the mean and standard deviation of permeability are calculated and discussed. The comparison demonstrates that the heterogeneous solid volumetric fraction can have similar effects as adjusting the empirical constant in the KC equation. A narrow range smaller than mean ± standard deviation from the model agreed with the experimental data well. Incorporating the truncated power-law distribution into the classical KC model predicts a high mean permeability and uncertainty. Both the mean and standard deviation of the permeability decrease with an increasing fractal dimension. Full article

Point and nonpoint wastewater sources have a detrimental, negative effect on agriculture, soil, surface, and groundwater supplies. In this research, a wastewater treatment system made up of a sedimentation tank, a horizontal subsurface flow constructed wetland (HSSF-CW), a vertical subsurface flow constructed wetland (VF-CW), and a storage tank was proposed, designed, and cost estimated. Small populations in underdeveloped nations with dry and semi-arid climates can use the treatment system as an affordable construction, maintenance, and operational solution for wastewater treatment. The system will protect agricultural lands and groundwater from pollution. The system can service 6000 capita and has a wastewater discharge of 780 m³/d in the developing arid region in El-Moghra Oasis western desert of Egypt, where the 1.5 million acres used for the land reclamation project based on groundwater irrigation. The relaxed tanks in a series model based on the areal loading rates and background pollutants concentrations (P-K-C*) was utilized to size the HSSF and VF-CWs. The results indicated that the HSSF-CW design treatment surface area was 2375 m², and the hydraulic surface loading (q) and hydraulic retention time (RT) were 0.33 m/d and 0.55 d, respectively, and utilizing Phragmites australis and Papyrus for the biological treatment. The expected overall cumulative removal efficiencies were 96.7, 70, and 100% for the biological oxygen demand (BOD), total phosphors (TP), and fecal coliforms (FC), respectively. The VF-CW indicates that there was a 2193 m² design treatment surface area, q = 0.36 m/d, and RT of 0.63 d. The expected BOD, TP, and FC removal efficiencies were 75, 33.3, and 92.7%, respectively. In order to simplify the design stages and the cost estimation, design and investment cost curves were established for a population range from 500 to 9000. The total monthly water loss due to evapotranspiration for the HSSF and VF-CWs indicates a range from 3.7 to 8.5%, respectively. The total investment cost analysis for the proposed system corresponding to 780 m³/d wastewater discharge of indicates a total investment cost of EUR 146,804 and EUR 24.46/per-capita equivalent (P.E). This approach can be used by decision makers in the Mediterranean region and Middle Eastern countries to improve the water quality using social and economic criteria, leading to the effective implementation of ecological restoration projects as a low-cost treatment system and adding a nonconventional water source that can be used in irrigation. Full article

Reducing uncertainty in design flood estimates is an essential part of flood risk planning and management. This study presents results from flood frequency estimates and associated uncertainties for five commonly used probability distribution functions, extreme value type 1 (EV1), generalized extreme value (GEV), generalized pareto distribution (GPD), log normal (LN) and log Pearson type 3 (LP3). The study was conducted using Monte Carlo simulation (MCS) and bootstrapping (BS) methods for the 10 river catchments in eastern Australia. The parameters were estimated by applying the method of moments (for LP3, LN, and EV1) and L-moments (for GEV and GPD). Three-parameter distributions (e.g., LP3, GEV, and GPD) demonstrate a consistent estimation of confidence interval (CI), whereas two-parameter distributions show biased estimation. The results of this study also highlight the difficulty in flood frequency analysis, e.g., different probability distributions perform quite differently even in a smaller geographical area. Full article

This paper describes HYDRONET, a telemetry-based prototype of a streamflow monitoring network in the Greek territory, where such data are sparse. HYDRONET provides free and near-real-time online access to data. Instead of commercially available stations, in-house-designed and -built telemetric stations were installed, which reduced the equipment cost by approximately 50%. The labour of hydrometric campaigns was reduced by applying a new maximum-entropy method to estimate the discharge from surface velocity observations. Here, we describe these novelty elements succinctly. The potential of HYDRONET to provide civil protection services is exemplified by a flood warning demonstrator for Kalamata’s City Centre. The network’s operation, including the hydraulic criteria for monitoring site selection, the characteristics of the telemetric equipment, the operational monitoring and hydrometric procedures, and the specifics of data transmission, quality control, and storage are described in detail, along with experiences with problems encountered during this pilot phase. Full article

Wetlands are valuable natural capital and sensitive ecosystems facing significant risks from anthropogenic and climatic stressors. An assessment of the environmental risk levels for wetlands’ dynamic ecosystems can provide a better understanding of their current ecosystem health and functions. Different levels of environmental risk are defined by considering the categories of risk and the probability and severity of each in the environment. Determining environmental risk levels provides a general overview of ecosystem function. This mechanism increases the visibility of risk levels and their values in three distinct states (i.e., low, moderate, and high) associated with ecosystem function. The Bayesian belief network (BBN) is a novel tool for determining environmental risk levels and monitoring the effectiveness of environmental planning and management measures in reducing the levels of risk. This study develops a robust methodological framework for determining the overall level of risks based on a combination of varied environmental risk factors using the BBN model. The proposed model is adopted for a case study of Shadegan International Wetlands (SIWs), which consist of a series of Ramsar wetlands in the southwest of Iran with international ecological significance. A comprehensive list of parameters and variables contributing to the environmental risk for the wetlands and their relationships were identified through a review of literature and expert judgment to develop an influence diagram. The BBN model is adopted for the case study location by determining the states of variables in the network and filling the probability distribution tables. The environmental risk levels for the SIWs are determined based on the results obtained at the output node of the BBN. A sensitivity analysis is performed for the BBN model. We proposed model-informed management strategies for wetland risk control. According to the BBN model results, the SIWs ecosystems are under threat from a high level of environmental risk. Prolonged drought has been identified as the primary contributor to the SIWs’ environmental risk levels. Full article

This study configures the Weather Research and Forecasting (WRF) model with the updated urban fraction for optimal rainfall simulation over Kampala, Uganda. The urban parameter values associated with urban fractions are adjusted based on literature reviews. An extreme rainfall event that triggered a flood hazard in Kampala on 25 June 2012 is used for the model simulation. Observed rainfall from two gauging stations and satellite rainfall from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) are used for model validation. We compared the simulation using the default urban fraction with the updated urban fraction focusing on extreme rainfall amount and spatial-temporal rainfall distribution. Results indicate that the simulated rainfall is overestimated compared to CHIRPS and underestimated when comparing gridcell values with gauging station records. However, the simulation with updated urban fraction shows relatively better results with a lower absolute relative error score than when using default simulation. Our findings indicated that the WRF model configuration with default urban fraction produces rainfall amount and its spatial distribution outside the city boundary. In contrast, the updated urban fraction has peak rainfall events within the urban catchment boundary, indicating that a proper Numerical Weather Prediction rainfall simulation must consider the urban morphological impact. The satellite-derived urban fraction represents a more realistic urban extent and intensity than the default urban fraction and, thus, produces more realistic rainfall characteristics over the city. The use of explicit urban fractions will be crucial for assessing the effects of spatial differences in the urban morphology within an urban fraction, which is vital for understanding the role of urban green areas on the local climate. Full article

Cost-effective remediation is increasingly dependent on high-resolution site characterization (HRSC), which is supposed to be necessary prior to interventions. This paper aims to evaluate the use of low-flow purging and sampling water level data in estimating the horizontal hydraulic conductivity of soils. In a new quali-quantitative view, this procedure can provide much more information and knowledge about the site, reducing time and costs. In case of high heterogeneity along the well screen, the whole procedure, as well as the estimation method, could be less effective and rigorous, with related issues in the purging time. The result showed significant permeability weighted sampling, which could provide different results as the pump position changes along the well screen. The proposed study confirms this phenomenon with field data, demonstrating that the use of multiparameter well logs might be helpful in detecting the behaviour of low-permeability layers and their effects on purging and sampling. A lower correlation between low-flow permeability estimations and LeFranc test results was associated with high heterogeneity along the screen, with a longer purging time. In wells P43, MW08 and MW36, due to the presence of clay layers, results obtained differ for almost one order of magnitude and the purging time increases (by more than 16 min). However, with some precautions prior to the field work, the low-flow purging and sampling procedure could become more representative in a shorter time and provide important hydrogeological parameters such as hydraulic conductivity with many tests and high-resolution related results. Full article

Droughts are natural disasters with a significant impact on the economy and social life. Prolonged droughts can cause even more damage than floods. The novelty of this work lies in the definition of a synthetic drought hydrograph (SDH) which can be derived at each gaging station of a river network. Based on drought hydrographs (DHs) recorded for a selected gaging station, the SDH is statistically characterized and provides valuable information to water managers regarding available water resources during the drought period. The following parameters of the registered drought hydrograph (DH) are proposed: minimum drought discharge $Q_D^{min}$, drought duration $D_D$ and deficit volume $V_D$. All these parameters depend on the drought threshold $Q_T$, which is chosen based on either pure hydrological considerations or on socio-economic consequences. For the same statistical parameters of the drought, different shapes of the synthetic drought hydrograph (SDH) can be considered. In addition, the SDH varies according to the probabilities of exceedance of the minimum drought discharge and deficit volume. Full article

In environmental flows, field and laboratory measurements of suspended sediments show two kinds of concentration profiles. For coarse sediments, a near-bed upward convex profile is observed beneath the main upward concave profile. In this study, we consider two 1-DV models, namely, the classical advection–diffusion equation (ADE) based on the gradient diffusion model, and the kinetic model. Both need sediment diffusivity, which is related to the eddy viscosity, and an y-dependent β-function (i.e., the inverse of the turbulent Schmidt number). Our study shows that the kinetic model reverts to the classical ADE with an “apparent” settling velocity or sediment diffusivity. For the numerical resolution of the ADE, simple and accurate tools are provided for both the sediment diffusivity and hindered settling. The results for the concentration profiles show good agreement with the experimental data. An interpretation of the concentration profiles is provided by two “criteria” for shapes. The main for steady open-channel flows shows that the shape of the concentration profiles in the Cartesian coordinate depends on the vertical distribution of the derivative of $R$ (the ratio between the sediment diffusivity and the settling velocity of the sediments): dR/dy > −1 for the upward concave concentration profile while dR/dy < −1 for the near-bed upward convex profile. A generalization is proposed for oscillatory flows over sand ripples, where the time-averaged concentration profiles in the semi-log plots are interpreted by a relation between the second derivative of the logarithm of the concentration and the derivative of the product between the sediment diffusivity and an additional parameter related to the convective sediment entrainment process. Full article

Low-lying coastal communities are often threatened by compound flooding (CF), which can be determined through the joint occurrence of storm surges, rainfall and river discharge, either successively or in close succession. The trivariate distribution can demonstrate the risk of the compound phenomenon more realistically, rather than considering each contributing factor independently or in pairwise dependency relations. Recently, the vine copula has been recognized as a highly flexible approach to constructing a higher-dimensional joint density framework. In these, the parametric class copula with parametric univariate marginals is often involved. Its incorporation can lead to a lack of flexibility due to parametric functions that have prior distribution assumptions about their univariate marginal and/or copula joint density. This study introduces the vine copula approach in a nonparametric setting by introducing Bernstein and Beta kernel copula density in establishing trivariate flood dependence. The proposed model was applied to 46 years of flood characteristics collected on the west coast of Canada. The univariate flood marginal distribution was modelled using nonparametric kernel density estimation (KDE). The 2D Bernstein estimator and beta kernel copula estimator were tested independently in capturing pairwise dependencies to establish D-vine structure in a stage-wise nesting approach in three alternative ways, each by permutating the location of the conditioning variable. The best-fitted vine structure was selected using goodness-of-fit (GOF) test statistics. The performance of the nonparametric vine approach was also compared with those of vines constructed with a parametric and semiparametric fitting procedure. Investigation revealed that the D-vine copula constructed using a Bernstein copula with normal KDE marginals performed well nonparametrically in capturing the dependence of the compound events. Finally, the derived nonparametric model was used in the estimation of trivariate joint return periods, and further employed in estimating failure probability statistics. Full article

Hydropolitics is defined as the systematic study of conflict and cooperation in transboundary water basins, affecting around 40% of the world’s population. There has been great advancement in studies endeavoring to explore linkages between hydropolitical drivers and hydropolitical situations in transboundary basins. To add to this, we posit that hydropolitics would benefit from a system thinking approach that has remained less addressed in the literature. For this purpose, considering a transboundary basin as a system, this study is built on the main principle of system dynamics, which implies that a system’s structure determines its behavior. Incorporating system archetypes into hydropolitics can provide a framework for assessing hydropolitical behavior according to the potential structure of archetypes. In this paper, we discuss five hydropolitical system archetypes and their feedback loop structures, the required physical environments, and potential unintended behavior over time. Finally, an example of a diagnostic checklist is presented that will help riparian states recognize patterns of behavior they may face in the future. This paper lays the groundwork for gaining insight into using system archetypes in projecting plausible hydropolitical behaviors and understanding past behaviors in transboundary basins. Full article

This study summarizes reviews on climate change’s impact on the water environment and hydrological regime. The results indicate a strong relationship between the climatological parameters and hydrological patterns. This relationship can be determined in two steps: (1) define the variations in climatological factors, particularly temperature and precipitation, and (2) measure the variations in runoff and inflows to streams and river systems using different statistical and global climate modeling approaches. It is evident that the increasing global temperatures have significant positive effects on runoff variations and evapotranspiration. Similarly, the increase in temperature has speeded up the melting of glaciers and ice on hilly terrains. This is causing frequent flash floods and a gradual rise in the sea level. These factors have altered the timing of stream flow into rivers. Furthermore, the accumulation of greenhouse gases, variations in precipitation and runoff, and sea-level rise have significantly affected freshwater quality. These effects are likely to continue if timely mitigation and adaptation measures are not adopted. Full article

Hurst’s seminal characterisation of long-term persistence (LTP) in geophysical records more than seven decades ago continues to inspire investigations into the Hurst phenomenon, not just in hydrology and climatology, but in many other scientific fields. Here, we present a new theoretical development based on stochastic Hurst–Kolmogorov (HK) dynamics that explains the recent finding that the Hurst coefficient increases with the spatial scale of averaging for regional annual precipitation. We also present some further results on the scale dependence of H in regional precipitation, and reconcile an apparent inconsistency between sample results and theory. LTP in average basin scale precipitation is shown to be consistent with LTP in the annual flows of some large river basins. An analysis of the crossing properties of precipitation deficits in regions exhibiting LTP shows that the Hurst coefficient can be a parsimonious descriptor of the risk of severe precipitation deficits. No evidence is found for any systematic trend in precipitation deficits attributable to anthropogenic climate change across the regions analysed. Future precipitation deficit risk assessments should, in the first instance, be based on stochastic HK simulations that encompass the envelope of uncertainty synonymous with LTP, and not rely exclusively on GCM projections that may not properly capture long-term natural variability in the climate. Some views and opinions are expressed on the implications for policy making in sustainable water resources management. Full article

Changes in water resources within basins can significantly impact ecosystems, agriculture, and biodiversity, among others. Basins in northern Canada have a cold climate, and the recent changes in climate can have a profound impact on water resources in these basins. Therefore, it is crucial to study long term trends in water flow as well as their influential factors, such as temperature and precipitation. This study focused on analyzing long term trends in water flow across the Athabasca River Basin (ARB) and Peace River Basin (PRB). Long term trends in temperature and precipitation within these basins were also studied. Water flow data from 18 hydrometric stations provided by Water Survey of Canada were analyzed using the Mann-Kendall test and Sen’s slope. In addition, hybrid climate data provided by Alberta Environment and Parks at approximately 10 km spatial resolution were analyzed for the ARB and its surrounding regions during 1950–2019. Trend analysis was performed on the water flow data on monthly, seasonal, and annual scales, and the results were cross-checked with trends in temperature and precipitation and land use and land cover data. The overall temperature across the basins has been increasing since 1950, while precipitation showed an insignificant decrease during this period. Winter water flow in the upper ARB has been slowly and steadily increasing since 1956 because of the rising temperatures and the subsequent slow melting of snowpacks/glaciers. The warm season flows in the middle and lower subregions declined up to 1981, then started to show an increasing trend. The middle and lower ARB exhibited a rapid increase in warm-season water flow since 2015. A similar trend change was also observed in the PRB. The gradual increase in water flow observed in the recent decades may continue by the mid-century, which is beneficial for agriculture, forestry, fishery, and industry. However, climate and land cover changes may alter the trend of water flow in the future; therefore, it is important to have a proper management plan for water usage in the next decades. Full article

Zerovalent iron nanoparticles (nZVI) are becoming one of the most widely recommended nanomaterials for soil and groundwater remediation. However, when nZVI are injected in the groundwater flow, the behavior (mobility, dispersion, distribution) is practically unknown. This fact generally results in the use of enormous quantities of them at the field scale. The uncertainties are on the effective volumes reached from the plume of nZVI because their tendency to aggregate and their weight can cause their settling and deposition. So, the mobility of nanoparticles is a real issue, which can often lead to inefficient or expensive soil remediation. Furthermore, there is another aspect that must be considered: the fate of these nZVI in the groundwater and their possible impact on the subsoil environment. All these considerations have led us to propose an application of nZVI simulating the permeation technique through a laboratory experience, finalized to have a better, or even simpler description of their real behavior when injected in a flow in the subsoil. A two-dimensional laboratory-scale tank was used to study the dispersion and transport of nZVI. A nZVI solution, with a concentration equal to 4.54 g/L, was injected into glass beads, utilized as porous medium. The laboratory experiment included a digital camera to acquire the images. The images were then used for calibrating a numerical model. The results of the mass balance confirm the validity of the proposed numerical model, obtaining values of velocity (5.41 × 10⁻³ m/s) and mass (1.9 g) of the nZVI of the same order of those from the experimental tests. Several information were inferred from both experimental and numerical tests. Both demonstrate that nZVI plume does not behave as a solute dissolved in water, but as a mass showing its own mobility ruled mainly from the buoyancy force. A simple simulation of a tracer input and a nZVI plume are compared to evidence the large differences between their evolution in time and space. This means that commercial numerical models, if not corrected, cannot furnish a real forecast of the volume of influence of the injected nZVI. Further deductions can be found from the images and confirmed by means the numerical model where the detachment effect is much smaller than the attachment one (ratio k_d/k_a = 0.001). From what is reported, it is worthwhile to pay attention on the localization of the contaminants source/plume to reach an effective treatment and it is important to go further in the improvement of solution for the limiting the nanoparticles aggregation phenomenon. Full article

Deriving the proper structure of lotic habitats, namely the structuralization of lotic habitats, is crucial to monitoring and modeling water quality on a large scale. How to structuralize complicated lotic habitats for practical use remains challenging. This study novelly integrates remote sensing, geographic information system (GIS), and computer vision techniques to structuralize complicated lotic habitats. A method based on Sentinel-2 imagery and weighted focal statistic convolution (WFSC) is developed to structuralize the complicated lotic habitats into discrete river links. First, aquatic habitat image objects are delineated from Sentinel-2 imagery using geographic object-based image analysis (GEOBIA). These lotic habitat image objects are then separated from lentic habitat image objects using a hydrologically derived river network as a reference. Second, the binary image of the lotic habitat image objects is converted to a fuzzy magnitude surface using WFSC. The ridgelines on the magnitude surface are traced as the centerlines of river links. Finally, the centerlines of river links are used to split the complicated lotic habitats into discrete river links. Essential planar geometric attributes are then numerically derived from each river link. The proposed method was successfully applied to the braided river network in the Mobile River Basin in the U.S. The results indicate that the proposed method can properly structuralize lotic habitats with high spatial accuracy and correct topological consistency. The proposed method can also derive essential attributes that are difficult to obtain from conventional methods on a large scale. With sufficient measurements, a striking width–abundance pattern has been observed in our study area, indicating a promising logarithmic law in lotic habitat abundance. Full article

Rational Water Resources Management requires effective collaboration between decision-makers involved in the operational management of water resources and scientists, who can allow them to operate in an informed manner through forecasting and decision-making tools. In this article, we show the potential benefits resulting from this collaboration through the description of the emblematic case of Lake Como. The article describes the real case of a collaborative experience between decision makers, who made an effort to highlight and clarify the real management problems to scientists, who in turn needed to understand all the facets of the decision-making process prior to formulating the problem in mathematical terms and incorporating the solution into a decision support system. The resulting tool, which makes extensive hidden use of probabilistic forecasts, stochastic optimization, and Bayesian decision techniques, resulted in a user-friendly environment. After six months of testing, the tool proved to be essential for decision-making and has been in use on a daily basis since 1997. Full article

The development of a computationally efficient machine learning-based framework to understand the underlying causes for biases in climate model simulated fields is presented in this study. The framework consists of a two-step approach, with the first step involving the development of a Random Forest (RF) model, trained on observed data of the climate variable of interest and related predictors. The second step involves emulations of the climate variable of interest with the RF model developed in step one by replacing the observed predictors with those from the climate model one at a time. The assumption is that comparing these emulations with that of a reference emulation driven by all observed predictors can shed light on the contribution of respective predictor biases to the biases in the climate model simulation. The proposed framework is used to understand the biases in the Global Environmental Multiscale (GEM) model simulated surface soil moisture (SSM) for the April–September period, over a domain covering part of north-east Canada. The grid cell-based RF model, trained on daily SSM and related climate predictors (water availability, 2 m temperature, relative humidity, snowmelt, maximum snow water equivalent) from the fifth generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5), demonstrates great skill in emulating SSM, with root mean square error of 0.036. Comparison of the five RF emulations based on GEM predictors with that based on ERA5 predictors suggests that the biases in the mean April–September SSM can be attributed mainly to biases in three predictors: water availability, 2 m temperature and relative humidity. The regions where these predictors contribute to biases in SSM are mostly collocated with the regions where they are shown to be the among the top three influential predictors through the predictor importance analysis, i.e., 2 m temperature in the southern part of the domain, relative humidity in the northern part of the domain and water availability over rest of the domain. The framework, without having to undertake expensive simulations with the climate model, thus successfully identifies the main causes for SSM biases, albeit with slightly reduced skill for heavily perturbed simulations. Furthermore, identification of the causes for biases, by informing targeted climate model improvements, can lead to additional reductions in computational costs. Full article

This study aimed at the analysis and modelling of the groundwater temperature at the water table in different regions of Slovakia. In the first part, the analysis of the long-term trends of air and soil/ground temperature to a depth of 10 m is presented. The average annual soil/groundwater temperatures at different depths were the same but lower than the annual average air temperature by about 0.8 °C. The long-term trend analysis of the air temperature and soil temperature at a depth of up to 10 m in Slovakia showed that the air and soil/ground water temperature have risen by 0.6 and 0.5 °C, respectively, per decade over the past 30 years. The second part of the study aimed at modelling the daily groundwater temperatures at depths of 0.6–15 m below the surface. The simple groundwater temperature model was constructed based on a one-dimensional differential Fourier heat conduction equation. The given model can be used to estimate future groundwater temperature trends using regional air temperature projections calculated for different greenhouse gas emission scenarios. Full article

Drinking water systems’ energy footprints depend mostly on the source, quality, and volume of water supply, but also on local temperature and precipitation, both of which are changing with the global climate. From a previous survey, we develop an equation for modeling relative changes in U.S. water utilities’ annual energy use, in which their energy use increases with temperature and decreases with precipitation. To demonstrate, we insert gridded projections from three scenarios in the EPA’s Climate Resilience Evaluation and Awareness Tool (CREAT) and compare 2035 and 2060 periods with a 1981–2010 baseline. Averaged over the continental United States, the 2060 central scenario projects 2.7 °C warmer temperatures and 2.9 cm more annual precipitation. For the same water demand, we estimate that these conditions will cause U.S. water systems’ energy use to change by −0.7% to 13.7% depending on the location (average 8.5% across all grid cells). Warming accounts for a general increase, and local changes in precipitation can add to or subtract from it. We present maps showing the spatial variability for each scenario. Water systems are essential infrastructure that support sustainable communities, and the analysis underscores their needs for energy management, renewable energy, water conservation, and climate change resilience. Full article

Understanding the changes in climate and land use/land cover (LULC) over time is important for developing policies for minimizing the socio-economic impacts of riverine floods. The present study evaluates the influence of hydro-climatic factors and anthropogenic practices related to LULC on floods in the Kelani River Basin (KRB) in Sri Lanka. The gauge-based daily precipitation, monthly mean temperature, daily discharges, and water levels at sub-basin/basin outlets, and both surveyed and remotely sensed inundation areas were used for this analysis. Flood characteristics in terms of mean, maximum, and number of peaks were estimated by applying the peak over threshold (POT) method. Nonparametric tests were also used to identify the climatic trends. In addition, LULC maps were generated over the years 1988–2017 using Landsat images. It is observed that the flood intensities and frequencies in the KRB have increased over the years. However, Deraniyagala and Norwood sub-basins have converted to dry due to the decrease in precipitation, whereas Kithulgala, Holombuwa, Glencourse, and Hanwella showed an increase in precipitation. A significant variation in atmospheric temperature was not observed. Furthermore, the LULC has mostly changed from vegetation/barren land to built-up in many parts of the basin. Simple correlation and partial correlation analysis showed that flood frequency and inundation areas have a significant correlation with LULC and hydro-climatic factors, especially precipitation over time. The results of this research will therefore be useful for policy makers and environmental specialists to understand the relationship of flood frequencies with the anthropogenic influences on LULC and climatic factors. Full article

Propagation of pluvial floods in urban areas, occurring with return time periods of few years, can be well solved using dual models accounting for the mutual relationship between the water level in the streets and the discharges inside the sewer pipes. The extended WEC-flood model (EWEC), based on the use of unstructured triangular meshes and a diffusive formulation of the momentum equations in both the 2D and the 1D lower domains, is presented along with its novelty, limits, and advantages. The model is then applied to a small computational domain in the Palermo area, where only some ‘hard’ data given by one rain gauge has been used for calibration and validation, along with other ‘soft’ data like yes/no surcharge observations and water depths available from photos and interviews. Model input data are mainly geometrical parameters, and calibration parameters are restricted only to average Manning coefficients. In the test case a very good validation has been obtained of three historical events using the EWEC model, with only one average Manning coefficient calibrated using other two historical events. Full article

Assessing fish habitat suitability in a data-scarce tidal river is often challenging due to the absence of continuous water quantity and quality records. This study is comprised of an intensive field study on a 42 km reach which recorded bathymetry and physical water quality parameters (pH, electroconductivity, dissolved oxygen, and total dissolved solids) testing and corresponding water levels and velocity. Frequent water sampling was carried out on 17 out of 90 locations for laboratory water quality tests. Based on this, an interpolation technique, i.e., Inverse Distance Weighted (IDW), generates a map in a Geographic Information System (GIS) environment using ArcGIS software to determine the river water quality parameters. Additionally, a hydrodynamic model study was conducted to simulate hydraulic parameters using Delft3D software followed by a water quality distribution. During validation, the Delft3D-simulated water quality could reasonably mimic most field data, and GIS featured dissolved oxygen. The overall water quality distribution showed a lower dissolved oxygen level (~3 mg/L) in the industrial zone compared to the other two zones during the study period. On the other hand, these validated hydraulic properties were applied in the Physical Habitat Simulation Model (PHABSIM) set up to conduct the hydraulic habitat suitability for Labeo rohita (Rohu fish). Thus, the validated model could represent the details of habitat suitability in the studied river for future decision support systems, and this study envisaged applying it to other similar rivers. Full article

During recent decades, the South-Central part of Chile has shown strong vulnerability due to the effects of land use change (LUC). The interaction of these changes with local hydrology has not been adequately investigated and is poorly understood, especially in mountainous areas under irrigated agriculture. We applied the SWAT + agrohydrological model to study the effects of LUC on hydrological fluxes in the Longaví catchment, Maule region, South-Central Chile. Land use maps (LUMs) from 1997, 2009, and 2016 were used in conjunction with a 41-year (1979–2019) hydro-meteorological series of daily observations as forcing data. The dominant changes in land use during the study period relate to agriculture, shrublands, forestry of exotic species, and urban sprawl. First, the LUM of 1997 was used for model setup, sensitivity analysis, calibration, and validation. Second, the impact of LUC documented through LUMs 2009 and 2016 was analyzed. Our analysis clearly reveals that the overall water balance and internal moisture redistribution in the Longaví catchment have been considerably affected by decreases in precipitation, changes in land use and water use practices. Unless a comprehensive regulatory system is introduced that addresses current climatic conditions and territorial use, it is likely that the decrease in water resources will persist and worsen through climate changes. Full article

Streamflow measurements provide valuable hydrological information but, at the same time, are difficult to obtain. For this reason, discharge records of regular intervals are usually obtained indirectly by a stage–discharge rating curve, which establishes a relation between measured water levels to volumetric rate of flow. Rating curves are difficult to develop because they require simultaneous measurements of discharge and stage over a wide range of stages. Furthermore, the shear forces generated during flood events often change the streambed shape and roughness. As a result, over long periods, the stage–discharge measurements are likely to form clusters to which different stage–discharge rating curves apply. For the identification of these clusters, various robust statistical approaches have been suggested by researchers, which, however, have not become popular among practitioners because of their complexity. Alternatively, various researchers have employed machine learning approaches. These approaches, though motivated by the time-dependent nature of the rating curves, handle the data as of stationary origin. In this study, we examine the advantages of a very simple technique: use time as one of the machine learning model inputs. This approach was tested in three real-world case studies against a statistical method and the results indicated its potential value in the development of a simple tool for rating curves suitable for practitioners. Full article

In this article, we present the use of the coupled land surface model and groundwater flow model SWAT-MODFLOW with the decision support tool WEAP (Water Evaluation and Planning software) to predict future surface-water abstraction scenarios in a complex river basin under conditions of climate change. The modelling framework is applied to the Dee River catchment in Wales, United Kingdom. Regarding hydrology, the coupled model improves overall water balance and low-streamflow conditions compared with a stand-alone SWAT model. The calibrated SWAT-MODFLOW is employed with high-resolution climate model data from the UKCP18 project with the future scenario of RCP85 from 2020 to 2040. Then, water supply results from SWAT-MODFLOW are fed into WEAP as input for the river reach in the downstream region of the river basin. This system is utilized to create various future scenarios of the surface-water abstraction of public water supply in the downstream region—maximum licensed withdraw, 50% authorized abstractions, monthly time series with 1% increases in water use, and maximum water withdraw per year based on historical records repeated every year with 1% increases in water use—to estimate the unmet demands and streamflow requirement. This modelling approach can be used in other river basins to manage scenarios of supply and demand. Full article

The Harvard Water Program is more than sixty years old. It was directed by an academic Steering Committee consisting of the professors of Government and Political Science, Planning, Economics, and Water Engineering. In 2022 we would add to the notional Steering Committee the professors of Ecology, Sociology and Water Law, calling it the augmented Harvard eutopian approach to the design and operation of Water Resource Systems. We use the Greek word ‘eu-topos’ to mean ‘a good place’, figuratively speaking, and ‘dys-topos’ its antonym, ‘not a good place’. By opposing eutopia and dystopia (latin forms) (Utopian literature begins with Thomas More’s (1478–1535) fictional socio-political satire “Utopia”, written in Latin and published in 1516: “Libellus vere aureus, nec minus salutaris quam festivus, de optimo rei publicae statu deque nova insula Utopia”. “A little, true book, not less beneficial than enjoyable, about how things should be in a state and about the new island Utopia” [Wikipedia translation]. He coined the word ‘utopia’ from the Greek ou-topos meaning ‘no place’ or ‘nowhere’. It was a pun-the almost identical Greek word eu-topos means ‘a good place’), we pass judgement on three Irish case studies, in whole and in part. The first case study deals with the dystopian measurement of the land phase of the hydrological cycle. The system components are distributed among many government departments that see little need to cooperate, leading to proposition 1: A call for a new Water Law. The second case study deals with a project to restore a 200 km² polder landscape to its condition in 1957. The project came to the University with an hypothetical cause of the increased flooding and a tentative solution: dredge the Cashen estuary of its sand, speeding the flow of sluiced water to the sea, and the status quo ante would be restored. The first scientific innovation was the proof that restoration by dredging is impossible. Pumping is the only solution, but it raises disruptive questions that are not covered by Statute. The second important innovation was the discovery in the dynamic water balance, of large leakage into the polders, either around or between sluiced culverts, when the flap valves are nominally closed, impacting both their maintenance and minimization of pumping. Discussions on our findings ended in dystopian silence. Hence proposition 2: Moving towards eutopia may only be possible with a change in the Law. The third case study concerns the protection of Cork City from flooding: riverine, tidal and groundwater. The government’s “emerging solution” consists of major physical intervention in the city centre, driven hard against local opposition, as the only possible solution. Two hydro-electric reservoirs upstream were largely ignored as part of a solution because the relevant Statute did not mandate their use for flood control. The Supreme Court has recently overturned this interpretation of the governing Statute. A new theory of flood control with a cascade of reservoirs, dams and weirs is the scientific innovation here. Once more these findings have been greeted by government with dystopian silence. Hence proposition 3: Re-open the design process to find several much better solutions, approximating a eutopian water world. Full article

One of the most important parts of the hydrological cycle is evapotranspiration (ET). Accurate estimates of ET in irrigated regions are critical to the planning, control, and regulation of agricultural natural resources. Accurate ET estimation is necessary for agricultural irrigation scheduling. ET is a nonlinear and complex process that cannot be calculated directly. Reference evapotranspiration (RET) and potential evapotranspiration (PET) are two primary forms of ET. The ideas, equations, and application areas for PET and RET are different. These two terms have been confused and used interchangeably by researchers. Therefore, terminology clarification is necessary to ensure their proper use. The research indicates that PET and RET concepts have a long and distinguished history. Thornthwaite devised the original PET idea, and it has been used ever since, although with several improvements. The development of RET, although initially confused with that of PET, was formally defined as a standard method. In this study, the Preferred Reporting Item for Systematic reviews and Meta-Analysis (PRISMA) was used. Equations for RET estimation were retrieved from 44 research articles, and equations for PET estimation were collected from 26 studies. Both the PET and RET equations were divided into three distinct categories: temperature-based, radiation-based, and combination-based. The results show that, among temperature-based equations for PET, Thornthwaite’s (1948) equation was mentioned in 12,117 publications, whereas among temperature-based equations for RET, Hargreaves and Samani’s (1985) equation was quoted in 3859 studies. Similarly, Priestley (1972) had the most highly cited equation in radiation-based PET equations (about 6379), whereas Ritchie (1972) had the most highly cited RET equations (around 2382) in radiation-based equations. Additionally, among combination-based PET equations, Penman and Monteith’s (1948) equations were cited in 9307 research studies, but the equations of Allen et al. (1998) were the subject of a significant number of citations from 23,000 publications. Based on application, PET is most often applied in the fields of hydrology, meteorology, and climatology, whereas RET is more frequently utilized in the fields of agronomy, agriculture, irrigation, and ecology. PET has been used to derive drought indices, whereas RET has been employed for single crop and dual crop coefficient approaches. This work examines and describes the ideas and methodologies, widely used equations, applications, and advanced approaches associated with PET and RET, and discusses future enhancements to increase the accuracy of ET calculation to attain accurate agricultural irrigation scheduling. The use of advanced tools such as remote sensing and satellite technologies, in addition to machine learning algorithms, will help to improve the accuracy of PET and RET estimates. Researchers will be able to distinguish between PET and RET in the future with the use of the study’s results. Full article

Worldwide, floods are the most common and widespread type of disaster during the 21st century. These phenomena have caused human fatalities, destruction of infrastructures and properties, and other significant impacts associated with human socioeconomic activities. In this study, the exposure of infrastructure (social, industrial and commercial, transportation) and residential areas to floods in Greek territory was considered. To accomplish the goal of the current study, freely available data from OpenStreetMap and Corine 2018 databases were collected and analyzed, as well as the flood extent zones derived under the implementation of the European Union’s (EU) Floods Directive. The results will be useful for policy-making and prioritization of prone areas based not only on the extent of flood cover but also on the possible affected infrastructure types. Moreover, the aforementioned analysis could be the first step toward an integrated national-wide flood risk assessment. Full article

We compare three hydrological models of different complexities, GR4H (lumped, continuous), SMASH (distributed, continuous), and MARINE (distributed, event-based), for Mediterranean flash flood modeling. The objective was to understand how differently they simulate the catchment’s behavior, in terms of outlet discharge and internal dynamics, and how these can help to improve the relevance of the models. The methodology involved global sensitivity analysis, calibration/validation, and signature comparison at the event scale with good performances. For all models, we found transfer parameters to be sensitive in the case of Gardon and production parameters in the case of Ardeche. The non-conservative flow component of GR4H was found to be sensitive and could benefit the distributed models. At the event scale, the process-based MARINE model at finer resolution outperformed the two continuous hourly models at flood peak and its timing. SMASH, followed by GR4H, performed better in the volume of water exported. Using the operational surface model SIM2 to benchmark the soil moisture simulated by the three models, MARINE (initialized with SIM1) emerged as the most accurate. GR4H followed closely, while SMASH was the least accurate. Flexible modeling and regionalization should be developed based on multi-source signatures and worldwide physiographic databases. Full article

In Water Resources Planning and Management, decision makers, although unsure of future outcomes, must take the most reliable and assuring decisions. Deterministic and probabilistic prediction techniques, combined with optimization tools, have been widely used to meet the objective of improving planning as well as management. Bayesian decision approaches are available to link probabilistic predictions to optimized decision schemes, but scientists are not fully able to express themselves in a language familiar to decision makers, who fear basing their decisions on “uncertain” forecasts in the vain belief that deterministic forecasts are more informative and reliable. This situation is even worse in the case of climate change projections, which bring additional degrees of uncertainty into the picture. Therefore, a need emerges to create a common approach and means of communication between scientists, who deal with optimization tools, probabilistic predictions and long-term projections, and operational decision makers, who must be facilitated in understanding, accepting, and acknowledging the benefits arising from operational water resources management based on probabilistic predictions and projections. Our aim here was to formulate the terms of the problem and the rationale for explaining and involving decision makers with the final objective of using probabilistic predictions/projections in their decision-making processes. Full article

The identification of the second-order dependence structure of streamflow has been one of the oldest challenges in hydrological sciences, dating back to the pioneering work of H.E Hurst on the Nile River. Since then, several large-scale studies have investigated the temporal structure of streamflow spanning from the hourly to the climatic scale, covering multiple orders of magni-tude. In this study, we expanded this range to almost eight orders of magnitude by analysing small-scale streamflow time series (in the order of minutes) from ground stations and large-scale streamflow time series (in the order of hundreds of years) acquired from paleocli-matic reconstructions. We aimed to determine the fractal behaviour and the long-range de-pendence behaviour of the streamflow. Additionally, we assessed the behaviour of the first four marginal moments of each time series to test whether they follow similar behaviours as sug-gested in other studies in the literature. The results provide evidence in identifying a common stochastic structure for the streamflow process, based on the Pareto–Burr–Feller marginal dis-tribution and a generalized Hurst–Kolmogorov (HK) dependence structure. Full article

Machine learning and deep learning have demonstrated usefulness in modelling various groundwater phenomena. However, these techniques require large amounts of data to develop reliable models. In the Southern African Development Community, groundwater datasets are generally poorly developed. Hence, the question arises as to whether machine learning can be a reliable tool to support groundwater management in the data-scarce environments of Southern Africa. This study tests two machine learning algorithms, a gradient-boosted decision tree (GBDT) and a long short-term memory neural network (LSTM-NN), to model groundwater level (GWL) changes in the Shire Valley Alluvial Aquifer. Using data from two boreholes, Ngabu (sample size = 96) and Nsanje (sample size = 45), we model two predictive scenarios: (I) predicting the change in the current month’s groundwater level, and (II) predicting the change in the following month’s groundwater level. For the Ngabu borehole, GBDT achieved R² scores of 0.19 and 0.14, while LSTM achieved R² scores of 0.30 and 0.30, in experiments I and II, respectively. For the Nsanje borehole, GBDT achieved R² of −0.04 and −0.21, while LSTM achieved R² scores of 0.03 and −0.15, in experiments I and II, respectively. The results illustrate that LSTM performs better than the GBDT model, especially regarding slightly greater time series and extreme GWL changes. However, closer inspection reveals that where datasets are relatively small (e.g., Nsanje), the GBDT model may be more efficient, considering the cost required to tune, train, and test the LSTM model. Assessing the full spectrum of results, we concluded that these small sample sizes might not be sufficient to develop generalised and reliable machine learning models. Full article

Evaporation losses of rainfall intercepted by canopies depend on many factors, including the temporal scale of observations. At the event scale, interception is a few millimetres, whereas at a larger temporal scale, the number of times that a canopy is filled by rainfall and then depleted can make the interception an important fraction of the rainfall depth. Recently, a simplified interception/evaporation model has been proposed, which considers a modified Merrian model to compute interception during wet spells and a simple power-law equation to model evaporation from wet canopy during dry spells. Modelling evaporation process at the sub hourly temporal scale required the two parameters of the power-law, describing the hourly evaporation depth and the evaporation rate. In this paper, for branches of lemon trees, we focused on the evaporation process from wet branches starting from the interception capacity, S, and simple models in addition to the power-law were applied and tested. In particular, for different temperature, T, and vapour pressure deficit, VPD, conditions, numerous experimental testes were carried out, and the two parameters describing the evaporation process from wet branches were determined and linked to T, VPD and S. The results obtained in this work help us to understand the studied process, highlight its complexity, and could be implemented in the recently introduced interception/evaporation model to quantify this important component of the hydrologic cycle. Full article

In tropical regions, deep convective systems are associated with extreme rainfall storms that usually detonate flash floods and landslides in the Andean Colombian region. Several studies have used satellite data to address the structure and formation of tropical convective storms. However, there is a local gap in the characterization, which is essential for a better understanding of flash floods and preparedness, filling a gap in a region with scarce information regarding extreme events. In this work, we assess the deep convective storms in a mountainous region of Colombia using meteorological radar observations between 2014 and 2017. We start by identifying convective and stratiform formations. We refine the convective identification by classifying convective systems into enveloped (contained in a stratiform system) and unenveloped (not contained). Then, we analyze the systems’ temporal and spatial distributions and contrast them with the watersheds’ features. According to our results, unenveloped convective systems have higher reflectivity and hence higher rainfall intensities. Moreover, they also have a well-defined spatial and temporal distribution and are likely to occur in watersheds with elevation gradients of around 2000 $\mathrm{m}$ and an aspect contrary to the wind direction. Our assessment of the convective storms is of significant value for the hydrologic community working on flash floods. Moreover, the spatiotemporal description is highly relevant for stakeholders and future local analysis. Full article

Hydrologic modeling is trending toward larger spatial and temporal domains, higher resolutions, and less extensive local calibration and validation. Thorough calibration and validation are difficult because the quantity of observations needed for such scales do not exist or is inaccessible to modelers. We present the Stream Analysis for Bias Estimation and Reduction (SABER) method for bias correction targeting large models. SABER is intended for model consumers to apply to a subset of a larger domain at gauged and ungauged locations and address issues with data size and availability. SABER extends frequency-matching postprocessing techniques using flow duration curves (FDC) at gauged subbasins to be applied at ungauged subbasins using clustering and spatial analysis. SABER uses a “scalar” FDC (SFDC), a ratio of simulated to observed FDC, to characterize biases spatially, temporally, and for varying exceedance probabilities to make corrections at ungauged subbasins. Biased flows at ungauged locations are corrected with the scalar values from the SFDC. Corrected flows are refined to fit a Gumbel Type 1 distribution. We present the theory, procedure, and validation study in Colombia. SABER reduces biases and improves composite metrics, including Nash Sutcliffe and Kling Gupta Efficiency. Recommendations for future work and a discussion of limitations are provided. Full article

Hydrological modeling is commonly used in urban areas for drainage design and to estimate pluvial flood hazards in order to mitigate flood risks and damages. In general, modelers choose well-known and proven models, which are tailored to represent the runoff generation of impervious areas and surface runoff. However, interception and other vegetation-related processes are usually simplified or neglected in models to predict pluvial flooding in urban areas. In this study, we test and calibrate the hydrological model LEAFlood (Landscape and vEgetAtion-dependent Flood model), which is based on the open source ‘Catchment Modeling Framework’ (CMF), tailored to represent hydrological processes related to vegetation and includes a 2D simulation of pluvial flooding in urban areas using landscape elements. The application of LEAFlood was carried out in Vauban, a district in Freiburg (Germany) with an area of ∼31 hectares, where an extensive hydrological measurement network is available. Two events were used for calibration (max intensity 17 mm/h and 28 mm/h) and validation (max intensity 25 mm/h and 14 mm/h), respectively. Moreover, the ability of the model to represent interception, as well as the influence of urban trees on the runoff, was analyzed. The comparison of observed and modeled data shows that the model is well-suited to represent interception and runoff generation processes. The site-specific contribution of each single tree, approximately corresponding to retaining one cup of coffee per second (∼0.14 L/s), is viewed as a tangible value that can be easily communicated to stakeholders. For the entire study area, all trees decrease the peak discharge by 17 to 27% for this magnitude of rainfall intensities. The model has the advantage that single landscape elements can be selected and evaluated regarding their natural contribution of soil and vegetation to flood regulating ecosystem services. Full article

As many other regions worldwide, the Bolivian Altiplano has to cope with water scarcity during dry periods, which in turn impacts on crop production as flood irrigation is overwhelmingly extended in the region. Since farming is the main income in the Altiplano for most families, the availability of greenhouses with water harvesting systems may represent a solution to warrant all year round production and food access. We study the daily satisfied water demand from a balance between rainfall collected by a greenhouse roof and water used for indoor crop irrigation assuming a tank is available for water storage. This balance is analyzed for 25 greenhouses spread over Batallas Municipality, close to Titicaca Lake, Bolivia, and for two case studies: (i) using irrigation data collected from farmers in the frame of a regional project; (ii) using theoretical daily water requirements assuming an intense greenhouse farming. Our evaluation includes a sensitivity analysis of relevant parameters, such as the influence of the time window of rainfall used in the simulation, the runoff coefficient, the roof surface area, the irrigation drip system, the irrigation frequency, the crop coefficient, the volume of water used for crop irrigation, and the capacity of the water tank. Overall, we find that the runoff coefficient has little impact on the satisfied demand rate, while all other parameters can play an important role depending on the greenhouse considered. Some greenhouses are able to irrigate crops normally during the wet season, while during the dry season, greenhouses are not able to satisfy more than $50\%$ of the theoretical water requirements, even when large tanks are considered. Based on these results, we recommend the construction of greenhouses with a ground surface of <50 ${\mathrm{m}}^2$ attached to the largest available covered water tank. The information here provided can be used by stakeholders to decide their policies of investment in infrastructures in the Altiplano. Finally, the approach we follow can be applied to any other region where rainfall, temperature, and greenhouse data are available. Full article

Extreme precipitation events can lead to the exceedance of the sewer capacity in urban areas. To mitigate the effects of urban flooding, a model is required that is capable of predicting flood timing and volumes based on precipitation forecasts while computational times are significantly low. In this study, a long short-term memory (LSTM) neural network is set up to predict flood time series at 230 manhole locations present in the sewer system. For the first time, an LSTM is applied to such a large sewer system while a wide variety of synthetic precipitation events in terms of precipitation intensities and patterns are also captured in the training procedure. Even though the LSTM was trained using synthetic precipitation events, it was found that the LSTM also predicts the flood timing and flood volumes of the large number of manholes accurately for historic precipitation events. The LSTM was able to reduce forecasting times to the order of milliseconds, showing the applicability of using the trained LSTM as an early flood-warning system in urban areas. Full article

Uncertainty is inherent in the modelling of any physical processes. Regarding hydrological modelling, the uncertainty has multiple sources including the measurement errors of the stresses (the model inputs), the measurement errors of the hydrological process of interest (the observations against which the model is calibrated), the model limitations, etc. The typical techniques to assess this uncertainty (e.g., Monte Carlo simulation) are computationally expensive and require specific preparations for each individual application (e.g., selection of appropriate probability distribution). Recently, data-driven methods have been suggested that attempt to estimate the uncertainty of a model simulation based exclusively on the available data. In this study, two data-driven methods were employed, one based on machine learning techniques, and one based on statistical approaches. These methods were tested in two real-world case studies to obtain conclusions regarding their reliability. Furthermore, the flexibility of the machine learning method allowed assessing more complex sampling schemes for the data-driven estimation of the uncertainty. The anatomisation of the algorithmic background of the two methods revealed similarities between them, with the background of the statistical method being more theoretically robust. Nevertheless, the results from the case studies indicated that both methods perform equivalently well. For this reason, data-driven methods can become a valuable tool for practitioners. Full article

Carbonate aquifers are characterised by strong heterogeneities and their modelling is often a challenging aspect in hydrological studies. Understanding carbonate aquifers can be more complicated in the case of strong seismic events which have been widely demonstrated to influence groundwater flow over wide areas or on a local scale. The 2016–2017 seismic sequence of Central Italy is a paradigmatic example of how earthquakes play an important role in groundwater and surface water modifications. The Campiano catchment, which experienced significant discharge modifications immediately after the mainshocks of the 2016–2017 seismic sequence (Mmax = 6.5) has been analysed in this study. The study area is within an Italian national park (Sibillini Mts.) and thus has importance from a naturalistic and socio-economic standpoint. The research strategy coupled long-period artificial tracer tests (conducted both before and after the main earthquakes), geochemical and discharge analyses and isotope hydrology with hydrogeological cross-sections. This study highlights how the seismic sequence temporarily changed the behaviour of the normal faults which act predominantly as barriers to flow in the inter-seismic period, with water flow being normally favoured along the fault strikes. On the contrary, during earthquakes, groundwater flow can be significantly diverted perpendicularly to fault-strikes due to co-seismic fracturing and a consequent permeability increase. The interaction between groundwater and surface water is not only important from the point of view of scientific research but also has significant implications at an economic and social level. Full article

The prediction of hydrological phenomena using simpler hydrological models requires less computing power and input data compared to the more complex models. Ordinarily, a more complex, white-box model would be expected to have better predictive capabilities than a simple grey box or black-box model. But complexity may not necessarily translate to better prediction accuracy or might be unfeasible in data scarce areas or when computer power is limited. Therefore, the shift of hydrological science towards the more process-based models needs to be justified. To answer this, the paper compares 2 hydrological models: (a) the simpler tank model; and (b) the more complex TOPMODEL. More precisely, the difference in performance between tank model as a lumped model and the TOPMODEL concept as a semi-distributed model in Atari River catchment, in Eastern Uganda was conducted. The objectives were: (1) To calibrate tank model and TOPMODEL; (2) To validate tank model and TOPMODEL; and (3) To compare the performance of tank model and TOPMODEL. During calibration, both models exhibited equifinality, with many parameter sets equally likely to make acceptable hydrological simulations. In calibration, the tank model and TOPMODEL performances were close in terms of ‘Nash-Sutcliffe efficiency’ and ‘RMSE-observations standard deviation ratio’ indices. However, during the validation period, TOPMODEL performed much better than tank model. Owing to TOPMODEL’s better performance during model validation, it was judged to be better suited for making runoff forecasts in Atari River catchment. Full article

The spatio-temporal dynamism of sediment discharge (Q_s) in rivers is influenced by various natural and anthropogenic factors. Unfortunately, most rivers are only monitored at a limited number of stations or not gauged at all. Therefore, this study aims to provide a remote-sensing-based alternative for Q_s monitoring. The at-a-station hydraulic geometry (AHG) power–law method was compared to the at-many-stations hydraulic geometry (AMHG) method; in addition, a novel AHG machine-learning (ML) method was introduced to estimate water discharge at three gauging stations in the Tisza (Szeged and Algyő) and Maros (Makó) Rivers in Hungary. The surface reflectance of Sentinel-2 images was correlated to in situ suspended sediment concentration (SSC) by support vector machine (SVM), random forest (RF), artificial neural network (ANN), and combined algorithms. The best performing water discharge and SSC models were employed to estimate the Q_s. Our novel AHG ML method gave the best estimations of water discharge (Szeged: R² = 0.87; Algyő: R² = 0.75; Makó: R² = 0.61). Furthermore, the RF (R² = 0.9) and combined models (R² = 0.82) showed the best SSC estimations for the Maros and Tisza Rivers. The highest Q_s were detected during floods; however, there is usually a clockwise hysteresis between the SSC and water discharge, especially in the Tisza River. Full article

Bluecat is a recently proposed methodology to upgrade a deterministic model (D-model) into a stochastic one (S-model), based on the hypothesis that the information contained in a time series of observations and the concurrent predictions made by the D-model is sufficient to support this upgrade. The prominent characteristics of the methodology are its simplicity and transparency, which allow its easy use in practical applications, without sophisticated computational means. In this paper, we utilize the Bluecat methodology and expand it in order to be combined with climate model outputs, which often require extrapolation out of the range of values covered by observations. We apply the expanded methodology to the precipitation and temperature processes in a large area, namely the entire territory of Italy. The results showcase the appropriateness of the method for hydroclimatic studies, as regards the assessment of the performance of the climate projections, as well as their stochastic conversion with simultaneous bias correction and uncertainty quantification. Full article

The article presents findings of a two-year systematic study of stable isotope content in two karst groundwater resources in Primorsko-goranska county (Croatia): the Martinšćica wells (MWs) and the Dobrica spring (DBC). The temporal and spatial variation of hydrogen and oxygen isotopes is commonly studied in conjunction with hydrogeological conditions such as groundwater dynamics and discharge conditions. However, since this information was incomplete, we were forced to work with limited data and rely on analyses of stable isotope monitoring results. The obtained results show that winter precipitation is the most common recharge source for the systems, and the average residence time of water in the subsurface is less than a year. Furthermore, the MWs system is a typical dual-porosity system with dominant base flow. The results of the nonparametric regression analysis show that the possibility of seawater intrusion into the spring affecting DBC isotope content cannot be ruled out. We believe that the results presented in the paper demonstrate that when combined with statistical analyses, environmental stable isotopes are a powerful tool for gaining insights in karst hydrogeology. Full article

This paper presents a comparative geometric analysis of the conditional bias (CB)-informed Kalman filter (KF) with the Kalman filter (KF) in the Euclidean space. The CB-informed KFs considered include the CB-penalized KF (CBPKF) and its ensemble extension, the CB-penalized Ensemble KF (CBEnKF). The geometric illustration for the CBPKF is given for the bi-state model, composed of an observable state and an unobservable state. The CBPKF co-minimizes the error variance and the variance of the Type-II error. As such, CBPKF-updated state error vectors are larger than the KF-updated, the latter of which is based on minimizing the error variance only. Different error vectors in the Euclidean space imply different eigenvectors and covariance ellipses in the state space. To characterize the differences in geometric attributes between the two filters, numerical experiments were carried out using the Lorenz 63 model. The results show that the CBEnKF yields more accurate confidence regions for encompassing the truth, smaller errors in the ensemble mean, and larger norms for Kalman gain and error covariance matrices than the EnKF, particularly when assimilating highly uncertain observations. Full article

Changing water supplies and demands, inherent to climate fluctuations and human activities, are pushing for a paradigm shift in water management worldwide. The occurrence of extreme hydrometeorological and climate events such as extended wet periods and droughts, compounded with contaminants, impair the access to water resources, demanding novel designs, construction, and management across multiple hydrologic scales and biogeochemical processes. A constraint to studying hydrologic and biogeochemical disturbances and improving best management practices for water quantity and quality at the watershed scale resides in the suitable monitoring, data availability, and the creation of frameworks. We hypothesize that streamflow and contaminants, simulated by the hydrologic model Soil and Water Assessment Tool (SWAT) and evaluated during drought and extended wet periods, capture the nonlinearities of contaminants of multiple biogeochemical complexities, indicating the adaptive abilities of watersheds. Our objectives are to (1) use rain gauge and radar data and linear regression to consolidate long-term precipitation data to simulate streamflow and water quality using the SWAT model in the Shell Creek (SC) watershed, Nebraska, U.S.; (2) use drought and extended wet events analytics on observed and simulated hydroclimate and water quality variables to identify SWAT’s performance; and (3) identify the temporal attributions of streamflow and water quality to complex biogeochemical patterns of variability. We implement a watershed modeling approach using the SWAT model forced with rain gauge and radar to simulate the intraseasonal and interannual variability streamflow, sediments, nutrients, and atrazine loads in the SC watershed. SWAT performance uses a calibration period between 2000 and 2005 and a validation period between 2005 and 2007. We examine the model’s ability to simulate hydrologic and biogeochemical variables in response to dry and extended wet flow regimes. The hydrologic model forced by either radar or rain gages performs similarly in the calibration (NSE = 0.6) and validation (NSE = 0.92) periods. It reproduces medium flows closer to the observations, although it overestimates low–flows up to 0.1 m³/s while underestimates high flows by 1 m³/s. The water quality model shows higher NSE for streamflow and sediments followed by nutrients, whereas it poorly reproduces atrazine. We conclude that seasonal changes and hydroclimate conditions led to the emergence of patterns of variability associated to the nonlinearities and coupling between processes of natural and human-origin sources. As climate change propels the occurrence of hydroclimate extremes, the simulation of water quantity and quality nonlinearities—as properties of complex adaptive hydrologic systems—can contribute to improve the predictability of climate-resilient water resources. Full article

The consequences of climate change have challenged researchers to generate models and projections to understand climate behavior under different scenarios. In Costa Rica, as in other countries, climate-change (CC) models and projections are essential to make decisions about the management of natural resources, mainly water. To understand climate change’s impact on hydraulic parameters such as velocity, depth, and river surface area, we studied the Pejibaye river basin, located in Jiménez in Cartago, Costa Rica. This watershed is characterized by having more than 90% of its surface area covered by forest. We used the precipitation and temperature data from meteorological stations (2000 to 2009) and climate-change scenarios (2000–2099) to predict the response of the basin in different periods. First, we calibrated (NSE = 0.77) and validated (NSE = 0.81) the HBV hydrological model using ten years of daily data from 2000 to 2009. The climate-change data (2000–2099) were incorporated into the calibrated HBV model. This allowed us to determine the impact of CC on the basin water regime for the periods 2040–2059 (CCS1) and 2080–2099 (CCS2). The IBER mathematical model was used to determine the changes in the hydraulic variables of the river flow. For the CCS1, we determined a 10.9% decrease in mean velocity and a 0.1-meter decrease in depth, while for CCS2, the effect will be an 11.3% reduction in mean velocity and a 0.14-meter decrease in depth. The largest decreases in river surface area per kilometer will occur in May (1710 m²) for CCS1 and April (2250 m²) for CCS2. Full article

This study investigates the surface processes taking place in an ungauged catchment in the Foro region in Eritrea (East Africa). We focus on estimating river discharge, sediment transport, and surface runoff to characterize hydrological fluxes in the area and provide a preliminary quantification of sediment transport and erosion. In this context, an overarching objective of the research is the study of the catchment associated with the Foro Dam. The latter comprises a silted reservoir formerly employed for agricultural water supply. The main traits associated with the system behavior across the watershed are assessed for a variety of combinations of the parameters governing the hydrological model selected. A detailed sensitivity analysis is performed to quantify the effects of the hydrological parameters on the estimated results. Numerical analyses are then performed to obtain an appraisal of expected water and sediment fluxes. Outputs of interest are largely dominated by the curve number parameter. Full article

Ombrian curves, i.e., curves linking rainfall intensity to return period and time scale, are well-established engineering tools crucial to the design against stormwaters and floods. Though the at-site construction of such curves is considered a standard hydrological task, it is a rather challenging one when large regions are of interest. Regional modeling of ombrian curves is particularly complex due to the need to account for spatial dependence together with the increased variability of rainfall extremes in space. We develop a framework for the parsimonious modeling of the extreme rainfall properties at any point in a given area. This is achieved by assuming a common ombrian model structure, except for a spatially varying scale parameter which is itself modeled by a spatial smoothing model for the 24 h average annual rainfall maxima that employs elevation as an additional explanatory variable. The fitting is performed on the pooled all-stations data using an advanced estimation procedure (K-moments) that allows both for reliable high-order moment estimation and simultaneous handling of space-dependence bias. The methodology is applied in the Thessaly region, a 13,700 km² water district of Greece characterized by varying topography and hydrometeorological properties. Full article