Editor’s Choice Articles

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

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

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

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

The Ca Mau peninsula (CMP) is a key economic region in southern Vietnam. In recent decades, the high demand for water has increased the exploitation of groundwater, thus lowering the groundwater level and leading to risks of degradation, depletion, and land subsidence, as well as salinity intrusion in the groundwater of the whole Mekong Delta region. By using a finite element groundwater model with boundary expansion to the sea, we updated the latest data on hydrogeological profiles, groundwater levels, and exploitation. The basic model setup covers seven aquifers and seven aquitards. It is determined that the inflow along the coastline to the mainland is 39% of the total inflow. The exploitation of the study area in 2019 was 567,364 m³/day. The most exploited aquifers are the upper-middle Pleistocene (qp_2–3) and the middle Pliocene (n₂²), accounting for 63.7% and 24.6%, respectively; the least exploited aquifers are the upper Pleistocene and the upper Miocene, accounting for 0.35% and 0.02%, respectively. In the deeper aquifers, qp_2–3 and n₂², the change in storage is negative due to the high exploitation rate, leading to a decline in the reserves of these aquifers. These groundwater model results are the calculations of groundwater reserves from the coast to the mainland in the entire system of aquifers in the CMP. This makes groundwater decision managers, stakeholders, and others more efficient in sustainable water resources planning in the CMP and Mekong Delta (MKD). Full article

Páramos are particular ecosystems of the Tropical Andes, where fog and low-intensity rainfall such as drizzle are commonly frequent—but the contribution of these water sources to soil water replenishment and discharge is not yet clear, mainly because the development of techniques for separating fog from drizzle and wind-driven rainfall has been challenging. Fog was measured with a cylindrical Juvik gauge and types of precipitation other than fog with a high-resolution disdrometer. Soil moisture was measured at 100 mm depth by means of Water Content Reflectometers, then Effective precipitation (EP) was calculated. We categorized events as two types: fog only (FO) and cloud water (CW). We found that in the case of FO events, only small amounts reached the soil (EP ranged between 0.1 and 0.2 mm); in contrast, greater amounts of EP originated from CW events (maximum value of 4.3 mm). Although we found that FO events are negligible for stream water contribution; they are ecologically important for maintaining high relative humidity, low net radiation, and consequently low evapotranspiration rates. Our research provides new insights into the hydrological role of fog, enabling us to better understand to what extent its input influences the water resources of the Andean páramo. Full article

The modelling and management of flood risk in urban areas are increasingly recognized as global challenges. The complexity of these issues is a consequence of the existence of several distinct sources of risk, including not only fluvial, tidal and coastal flooding, but also exposure to urban runoff and local drainage failure, and the various management strategies that can be proposed. The high degree of vulnerability that characterizes such areas is expected to increase in the future due to the effects of climate change, the growth of the population living in cities, and urban densification. An increasing awareness of the socio-economic losses and environmental impact of urban flooding is clearly reflected in the recent expansion of the number of studies related to the modelling and management of urban flooding, sometimes within the framework of adaptation to climate change. The goal of the current paper is to provide a general review of the recent advances in flood-risk modelling and management, while also exploring future perspectives in these fields of research. Full article

Accurate evaluation of evapotranspiration (ET) flux is an important issue in sustainable urban drainage systems that target not only flow rate limitations, but also aim at the restoration of natural water balances. This is especially true in context where infiltration possibilities are limited. However, its assessment suffers from insufficient understanding. In this study, ET in 1 m³ pilot rain gardens were studied from eight lysimeters monitored for three years in Paris (France). Daily ET was calculated for each lysimeter based on a mass balance approach and the related uncertainties were assessed at ±0.42 to 0.58 mm. Results showed that for these lysimeters, ET is the major term in water budget (61 to 90% of the precipitations) with maximum values reaching 8–12 mm. Furthermore, the major determinants of ET are the existence or not of an internal water storage and the atmospheric factors. The vegetation type is a secondary determinant, with little difference between herbaceous and shrub configurations, maximum ET for spontaneous vegetation, and minimal values when vegetation was regularly removed. Shading of lysimeters by surroundings buildings is also important, leading to lower values. Finally, ET of lysimeters is higher than tested reference values (evaporimeter, FAO-56, and local Météo-France equations). Full article

Kalman filter (KF) and its variants and extensions are wildly used for hydrologic prediction in environmental science and engineering. In many data assimilation applications of Kalman filter (KF) and its variants and extensions, accurate estimation of extreme states is often of great importance. When the observations used are uncertain, however, KF suffers from conditional bias (CB) which results in consistent under- and overestimation of extremes in the right and left tails, respectively. Recently, CB-penalized KF, or CBPKF, has been developed to address CB. In this paper, we present an alternative formulation based on variance-inflated KF to reduce computation and algorithmic complexity, and describe adaptive implementation to improve unconditional performance. For theoretical basis and context, we also provide a complete self-contained description of CB-penalized Fisher-like estimation and CBPKF. The results from one-dimensional synthetic experiments for a linear system with varying degrees of nonstationarity show that adaptive CBPKF reduces the root-mean-square error at the extreme tail ends by 20 to 30% over KF while performing comparably to KF in the unconditional sense. The alternative formulation is found to approximate the original formulation very closely while reducing computing time to 1.5 to 3.5 times of that for KF depending on the dimensionality of the problem. Hence, adaptive CBPKF offers a significant addition to the dynamic filtering methods for general application in data assimilation when the accurate estimation of extremes is of importance. Full article

A phenomenon causing instability of soil structure and associated hydraulic properties in recently tilled soils is aggregate fragmentation induced by wetting and drying cycles. We analyzed data from three experiments in Puerto Rico, the UK and China measuring fragmentation and resulting evolution of aggregate size distributions during successive wetting and drying cycles in heavy textured soils. Aggregate distributions were represented as the cumulative fraction F of aggregates passing through successively larger sieve sizes X. To a good approximation, all distributions exhibited similarity in that the aggregate diameter X(F) corresponding to F in a given test distribution was always a characteristic multiple $\overline{\alpha }$ of X(F) in a fixed reference distribution, where $\overline{\alpha }$ for a distribution was calculated as its mean weight aggregate diameter (MWD) divided by the MWD of the reference distribution. In most cases, $\overline{\alpha }$ for a given soil varied inversely with the square of the number of wetting and drying cycles. For different soils of similar initial aggregate sizes, $\overline{\alpha }$ for a given wet–dry cycle decreased with increasing activity coefficient, reflecting the enhancing effect of soil shrink–swell potential on fragmentation. Results highlight usefulness of the van Bavel mean weight diameter as a natural scaling parameter for characterizing aggregate distributions. Full article

One of the primary steps in managing the flow in an open channel is determining its properties. Empirical equations are developed to provide further information regarding the flow in open channels. Obtaining such experimental equations is expensive and time consuming; therefore, alternative solutions have been sought. Over the last century, the Parshall flume, a static measuring device with no moving parts, has played a significant role in measuring the flow in open channels. Many researchers have focused their interest on studying the application of Parshall flumes in various fields like irrigation and wastewater management. Although various scholars used experimental results to enhance the rating equation of the Parshall flume, others used an alternative source of data to recalibrate the height–discharge relation equation using numerical simulation. Computational Fluid Dynamic (CFD) software is becoming popular nowadays as computing hardware has advanced significantly within the last few decades, making it possible to go beyond the limited resolution that was experienced in the past. Multiple CFD models, depending on their availability, either open-source or commercially licensed, have been used to perform numerical simulations on different configurations of flumes, especially Parshall flumes, to produce water level results. Regarding various CFD tools that have been used, i.e., FLOW-3D, Ansys Fluent, or OpenFOAM, after precise calibration with experimental data, it has been determined that the output is reliable and can be implemented to the actual scenarios. The benefit of using this technique to produce results is the ability of the CFD approach to adjust the initial conditions, like flow velocity or structural geometry, where necessary. With respect to channel size and the condition of the site where the flume is located, the choices are narrowed to the specific Parshall flume suitable to the situation. It is not always possible to select the standard Parshall flume; therefore, engineers provide some modification to the closest flume size and provide a new rating curve to produce accurate flowrates. This review has been performed on the works of a number of scholars who targeted the application of numerical simulation and physical experimental data in Parshall flumes to either enhance the existing rating equation or propose further modification to the structure’s geometry. Full article

Hypoxia in Lake Biwa, Japan remains a serious water environmental problem. One of the causes of hypoxia in the lake is the formation of a thermocline, which is largely affected by meteorological factors, such as (1) air temperature, (2) wind speed, and (3) precipitation. However, the effects of these three meteorological factors on the formation of the thermocline have not been clarified quantitatively. In this study, applying a three-dimensional hydrodynamic model to Lake Biwa, the effects of each of the three meteorological elements on the formation of the thermocline was quantitatively analyzed to clarify the governing factors of meteorological conditions in the formation of anoxic oxygen. Sensitivity analysis of the stratification structure in Lake Biwa was performed by changing the three meteorological factors of (1) air temperature, (2) wind speed, and (3) precipitation. As a result, the change in wind speed gives the greatest effect on the stratification structure, the change in air temperature makes the difference in the stratification structure from the surface layer to the vicinity of the thermocline, and the change in precipitation affects it less than the others. Full article

A significant constraint in water resource development in developing countries is the lack of accurate river discharge data. Stage–discharge measurements are infrequent, and rating curves are not updated after major storms. Therefore, the objective is to develop accurate stage–discharge rating curves with limited measurements. The Lake Tana basin in the upper reaches of the Blue Nile in the Ethiopian Highlands is typical for the lack of reliable streamflow data in Africa. On average, one stage–discharge measurement per year is available for the 21 gaging stations over 60 years or less. To obtain accurate and unique stage–discharge curves, the discharge was expressed as a function of the water level and a time-dependent offset from zero. The offset was expressed as polynomial functions of time (up to order 4). The rating curve constants and the coefficients for the polynomial were found by minimizing the errors between observed and predicted fluxes for the available stage–discharge data. It resulted in unique rating curves with R² > 0.85 for the four main rivers. One of the river bottoms of the alluvial channels increased in height by up to 3 m in 60 years. In the upland channels, most offsets changed by less than 50 cm. The unique rating curves that account for temporal riverbed changes can aid civil engineers in the design of reservoirs, water managers in improving reservoir management, programmers in calibration and validation of hydrology models and scientists in ecological research. Full article

Volunteered Geographic Information, data contributed by community scientists, is an increasingly popular tool to collect scientific data, involve the community in scientific research, and provide information and education about a prominent issue. Johnson City, Tennnessee, USA has a long history of downtown flooding, and recent redevelopment of two land parcels has created new city parks that mitigate flooding through floodwater storage, additional channel capacity, and reduced impervious surfaces. At Founders Park, a project to collect stage data using text messages from community scientists has collected 1479 stage measurements from 597 participants from May 2017 through July 2021. Text messages were parsed to extract the stage and merged with local precipitation data to assess the stream’s response to precipitation. Of 1479 observations, 96.7% were correctly parsed. Only 3% of observations were false positives (parser extracted incorrect stage value) or false negatives (parser unable to extract correct value but usable data were reported). Less than 2% of observations were received between 11 p.m. and 7 a.m., creating an overnight data gap, and fewer than 7% of observations were made during or immediately following precipitation. Regression models for stage using antecedent precipitation explained 21.6% of the variability in stream stage. Increased participation and development of an automated system to record stage data at regular intervals will provide data to validate community observations and develop more robust rainfall–runoff models. Full article

Global projections of climate change indicate negative impacts on hydrological systems, with significant changes in precipitation and temperature in many parts of the world. As a result, floods and droughts are expected. This article discusses the potential effects of climate change and variability on the maximum precipitation, temperature, and hydrological regime in Devil’s Creek, Tacna, Peru. The outputs of precipitation and daily temperature of fifteen regional climate models were used for the RCP4.5 and RCP8.5 emission scenarios. The methodology used includes the bias correction and downscaling of meteorological variables using the quintiles mapping technique, hydrological modeling, the evaluation of two emission scenarios, and its effect on the maximum flows of the stream. The results of the multi-model ensemble show that the maximum annual precipitation will probably increase by more than 30% for the RCP4.5 and RCP8.5 scenarios for the 2021–2050 period relative to the 1981–2005 period. Likewise, as expected, the maximum flows could increase by 220% and 154% for the RCP4.5 scenarios for the 2021–2050 and 2051–2080 terms, respectively, and 234% and 484% for the RCP8.5 scenarios and for the 2021–2050 and 2051–2080 terms, respectively, concerning the recorded historical value, increasing the probability of flood events and damage in populations located downstream. Full article

Machine learning has been employed successfully as a tool virtually in every scientific and technological field. In hydrology, machine learning models first appeared as simple feed-forward networks that were used for short-term forecasting, and have evolved into complex models that can take into account even the static features of catchments, imitating the hydrological experience. Recent studies have found machine learning models to be robust and efficient, frequently outperforming the standard hydrological models (both conceptual and physically based). However, and despite some recent efforts, the results of the machine learning models require significant effort to interpret and derive inferences. Furthermore, all successful applications of machine learning in hydrology are based on networks of fairly complex topology that require significant computational power and CPU time to train. For these reasons, the value of the standard hydrological models remains indisputable. In this study, we suggest employing machine learning models not as a substitute for hydrological models, but as an independent tool to assess their performance. We argue that this approach can help to unveil the anomalies in catchment data that do not fit in the employed hydrological model structure or configuration, and to deal with them without compromising the understanding of the underlying physical processes. Full article

This study investigated the impacts of climate change on the hydrology of the Upper Awash Basin, Ethiopia. A soil and water assessment tool (SWAT) model was calibrated and validated against observed streamflow using SWAT CUP. The Mann–Kendall trend test (MK) was used to assess climate trends. Meteorological drought (SPEI) and hydrological drought (SDI) were also investigated. Based on the ensemble mean of five global climate models (GCMs), projected increases in mean annual maximum temperature over the period 2015–2100 (compared with a 1983–2014 baseline) range from 1.16 to 1.73 °C, while increases in minimum temperature range between 0.79 and 2.53 °C. Increases in mean annual precipitation range from 1.8% at Addis Ababa to 45.5% over the Hombole area. High streamflow (Q₅) declines at all stations except Ginchi. Low flows (Q₉₀) also decline with Q₉₀ equaling 0 m³ s⁻¹ (i.e., 100% reduction) at some gauging stations (Akaki and Hombole) for individual GCMs. The SPEI confirmed a significant drought trend in the past, while the frequency and severity of drought will increase in the future. The basin experienced conditions that varied from modest dry periods to a very severe hydrological drought between 1986 and 2005. The projected SDI ranges from modestly dry to modestly wet conditions. Climate change in the basin would enhance seasonal variations in hydrological conditions. Both precipitation and streamflow will decline in the wet seasons and increase in the dry seasons. These changes are likely to have an impact on agricultural activities and other human demands for water resources throughout the basin and will require the implementation of appropriate mitigation measures. Full article

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed. Full article

This study evaluates the potential for a newly proposed non-linear subsurface flux equation to improve the performance of the hydrological Hillslope Link Model (HLM). The equation contains parameters that are functionally related to the hillslope steepness and the presence of tile drainage. As a result, the equation provides better representation of hydrograph recession curves, hydrograph timing, and total runoff volume. The authors explore the new parameterization’s potential by comparing a set of diagnostic and prognostic setups in HLM. In the diagnostic approach, they configure 12 different scenarios with spatially uniform parameters over the state of Iowa. In the prognostic case, they use information from topographical maps and known locations of tile drainage to distribute parameter values. To assess performance improvements, they compare simulation results to streamflow observations during a 17-year period (2002–2018) at 140 U.S. Geological Survey (USGS) gauging stations. The operational setup of the HLM model used at the Iowa Flood Center (IFC) serves as a benchmark to quantify the overall improvement of the model. In particular, the new equation provides better representation of recession curves and the total streamflow volumes. However, when comparing the diagnostic and prognostic setups, the authors found discrepancies in the spatial distribution of hillslope scale parameters. The results suggest that more work is required when using maps of physical attributes to parameterize hydrological models. The findings also demonstrate that the diagnostic approach is a useful strategy to evaluate models and assess changes in their formulations. Full article

Urban flooding is a complex natural hazard, driven by the interaction between several parameters related to urban development in a context of climate change, which makes it highly variable in space and time and challenging to predict. In this study, we apply a multivariate analysis method (PCA) and four machine learning algorithms to investigate and map the variability and vulnerability of urban floods in the city of Tangier, northern Morocco. Thirteen parameters that could potentially affect urban flooding were selected and divided into two categories: geo-environmental parameters and socio-economic parameters. PCA processing allowed identifying and classifying six principal components (PCs), totaling 73% of the initial information. The scores of the parameters on the PCs and the spatial distribution of the PCs allow to highlight the interconnection between the topographic properties and urban characteristics (population density and building density) as the main source of variability of flooding, followed by the relationship between the drainage (drainage density and distance to channels) and urban properties. All four machine learning algorithms show excellent performance in predicting urban flood vulnerability (ROC curve > 0.9). The Classifications and Regression Tree and Support Vector Machine models show the best prediction performance (ACC = 91.6%). Urban flood vulnerability maps highlight, on the one hand, low lands with a high drainage density and recent buildings, and on the other, higher, steep-sloping areas with old buildings and a high population density, as areas of high to very-high vulnerability. Full article

The stochastic structures of potential evaporation and evapotranspiration (PEV and PET or ETo) are analyzed using the ERA5 hourly reanalysis data and the Penman–Monteith model applied to the well-known CIMIS network. The latter includes high-quality ground meteorological samples with long lengths and simultaneous measurements of monthly incoming shortwave radiation, temperature, relative humidity, and wind speed. It is found that both the PEV and PET processes exhibit a moderate long-range dependence structure with a Hurst parameter of 0.64 and 0.69, respectively. Additionally, it is noted that their marginal structures are found to be light-tailed when estimated through the Pareto–Burr–Feller distribution function. Both results are consistent with the global-scale hydrological-cycle path, determined by all the above variables and rainfall, in terms of the marginal and dependence structures. Finally, it is discussed how the existence of, even moderate, long-range dependence can increase the variability and uncertainty of both processes and, thus, limit their predictability. Full article

This study, which was conducted between 2010 and 2013, presents the results of direct, continuous measurements of the bedload transport rate at the mouth section of the Scott River catchment (NW part of Wedel-Jarlsberg Land, Svalbard). In four consecutive melt seasons, the bedload flux was analyzed at two cross-sections located in the lower reaches of the gravel-bed proglacial river. The transported bedload was measured using two sets of River Bedload Traps (RBTs). Over the course of 130 simultaneous measurement days, a total of 930 bedload samples were collected. During this period, the river discharged about 1.32 t of bedload through cross-section I (XS I), located at the foot of the alluvial fan, and 0.99 t through cross-section II (XS II), located at the river mouth running into the fjord. A comparison of the bedload flux showed a distinctive disproportion between cross-sections. Specifically, the average daily bedload flux Q_B was 130 kg day⁻¹ (XS I) and 81 kg day⁻¹ (XS II) at the individual cross-profiles. The lower bedload fluxes that were recorded at specified periods in XS II, which closed the catchment at the river mouth from the alluvial cone, indicated an active role of aggradation processes. Approximately 40% of all transported bedload was stored at the alluvial fan, mostly in the active channel zone. However, comparative Geomorphic Change Detection (GCD) analyses of the alluvial fan, which were performed over the period between August 2010 and August 2013, indicated a general lowering of the surface (erosion). It can be assumed that the melt season’s average flows in the active channel zone led to a greater deposition of bedload particles than what was discharged with high intensity during floods (especially the bankfull stage, effectively reshaping the whole surface of the alluvial fan). This study documents that the intensity of bedload flux was determined by the frequency of floods. Notably, the highest daily rates recorded in successive seasons accounted for 12–30% of the total bedload flux. Lastly, the multi-seasonal analysis showed a high spatio-temporal variability of the bedload transport rates, which resulted in changes not only in the channel but also on the entire surface of the alluvial fan morphology during floods. Full article

Tracer tests are widely used for characterizing hydrodynamics, from stream-scale to basin-wide scale. In karstic environments, the positioning of field fluorometers (or sampling) is mostly determined by the on-site configuration and setup difficulties. Most users are probably aware of the importance of this positioning for the relevance of data, and single-point tests are considered reliable. However, this importance is subjective to the user and the impact of positioning is not well quantified. This study aimed to quantify the spatial heterogeneity of tracer concentration through time in a karstic environment, and its impact on tracer test results and derived information on local hydrodynamics. Two approaches were considered: on-site tracing experiments in a karstic river, and Computational Fluid Dynamics (CFD) modeling of tracer dispersion through a discretized karst river channel. A comparison between on-site tracer breakthrough curves and CFD results was allowed by a thorough assessment of the river geometry. The results of on-site tracer tests showed significant heterogeneities of the breakthrough curve shape from fluorometers placed along a cross-section. CFD modeling of the tracer test through the associated discretized site geometry showed similar heterogeneity and was consistent with the positioning of on-site fluorometers, thus showing that geometry is a major contributor of the spatial heterogeneity of tracer concentration through time in karstic rivers. Full article

Mountain areas are highly exposed to flood risks. The latter are increasing in the context of climate change, urbanization, and land use changes. Non-structural approaches such as nature-based solutions can provide opportunities to reduce the risks of such natural hazards and provide further ecological, social, and economic benefits. However, few non-structural flood mitigation measures are implemented in rural mountain areas so far. The objective of this paper is to investigate if the scientific boundaries limit the implementation of non-structural flood management in rural mountain areas. In the study, we statistically analyzed the knowledge about flood management through a systematic literature review and expert surveys, with a focus on European rural mountain areas. Both methods showed that scientific knowledge is available for decision makers and that nature-based solutions are efficient, cost-effective, multifunctional, and have potential for large-scale implementation. Full article

Within the US Southern High Plains, it is known that the Ogallala Aquifer (OA) has been over pumped since large-scale agriculture began making use of the water in the 1950s. One option to address the decline is to find new water sources. The last 10–15 years have seen an increase in drilling large capacity, deeper wells in the co-located Dockum Aquifer in the Texas Panhandle. This lower aquifer is separated from the OA by low hydraulic conductivity sediment and is thus generally considered independent from the OA. We examined the suitability of the Dockum to supplement OA water by comparing recent water chemistries where the aquifers coexist. We also examined historical information on well yield, well development, and water quality. We found that water quality is equivalent to the Ogallala in some places but in others it is saltier, softer, and more sodic. Use of PCA and hydrochemical facies revealed that even in this small area Dockum water quality is highly variable. We used USGS-PHREEQC to model water blending at ratios of 0–>100% Ogallala. We show that there is irrigation water quality risk no matter the blend, that risks differ according to location, and that the most frequent risks are salinity, sodicity, and nitrate. We conclude that growers can manage these risks if they use blending to choose the risks they feel most apt to mitigate. Full article

High-elevation wetlands in South America are not well described despite their high sensitivity to human impact and unique biodiversity. We describe the hydroclimatological and limnological characteristics of 21 wetlands on the High Andean Plateau of Argentina, synthesizing information gathered over ten years (2010–2020). We collected physical-chemical, phytoplankton, and zooplankton data and counted flamingos in each wetland. We also conducted an extensive analysis of climatic patterns and hydrological responses since 1985. These wetlands are shallow, with a wide range of salinity (from fresh to brine), mostly alkaline, and are dominated by carbonate and gypsum deposits and sodium-chloride waters. They tend to have high nutrient concentrations. Plankton shows a low species richness and moderate to high dominance of taxa. Flamingos are highly dependent on the presence of Bacillariophyta, which appears to be positively linked to silica and soluble reactive phosphorus availability. Climatic conditions show a strong region-wide increase in average air temperature since the mid-1980s and a decrease in precipitation between 1985–1999 and 2000–2020. These high-elevation wetlands are fundamentally sensitive systems; therefore, having baseline information becomes imperative to understanding the impact of climatic changes and other human perturbations. This work attempts to advance the body of scientific knowledge of these unique wetland systems. Full article

Evapotranspiration (ET) is a parameter of major importance participating in both hydrological cycle and surface energy balance. Trends of ET are discussed along with the dependence of evaporation to key environmental variables. The evaporation paradox can be approached via natural phenomena aggravated by anthropogenic impact. ET appears as one of the most affected parameters by human activities. Complex hydrological processes are governed by local environmental conditions thus generalizations are difficult. However, in some settings, common hydrological interactions could be detected. Mediterranean climate regions (MCRs) appear vulnerability to the foreseen increase in ET, aggravated by precipitation shifting and air temperature warming, whereas in tropical forests its role is rather beneficial. ET determines groundwater level and quality. Groundwater level appeared to be a robust predictor of annual ET for peatlands in Southeast Asia. In semi-arid to arid areas, increases in ET have implications on water availability and soil salinization. ET-changes after a wildfire can be substantial for groundwater recharge if a canopy-loss threshold is surpassed. Those consequences are site-specific. Post-fire ET rebound seems climate and fire-severity-dependent. Overall, this qualitative structured review sets the foundations for interdisciplinary researchers and water managers to deploy ET as a means to address challenging environmental issues such as water availability. Full article

In Urban Planning (UP), it is necessary to take under serious consideration the inhibitors of the spread of a settlement in a specific direction. This means that all those parameters for which serious problems may arise in the future should be considered. Among these parameters are geo-hazards, such as floods, landslides, mud movement, etc. This study deals with UP taking into account the possibility of widespread flooding in settlement expansion areas. There is a large flooding history in Greece, which is accompanied by a significant number of disasters in different types of land use/land cover, with a large financial cost of compensation and/or rehabilitation. The study area is the drainage basin of Erasinos River in the Attica Region, where many and frequent flood events have been recorded. The main goal of this study is to determine the flood susceptibility of the study area, taking into account possible factors that are decisive in flood occurrence. Furthermore, the flood susceptibility is also determined, taking into account the scenarios of precipitation and the urban sprawl scenario in the area of reference. The study of flood events uses the Analytic Hierarchy Process (AHP) model and the urban sprawl model SLEUTH, which calibrates historical urban growth, using open and cost-free data and software. Eventually, flood susceptibility maps were overlaid with future urban areas to find the vulnerable areas. Following, three scenarios of flood susceptibility with the corresponding susceptibility maps and vulnerability maps, which measure the flood susceptibility of the current and future urban space of the study area, are presented. The results have shown significant peaks in the moderate class of flood susceptibility, while, in the third scenario, high values of flood susceptibility seem to appear. The proposed methodology and specifically the output maps can serve as a decision support tool to assist urban planners and hazard managers in making informed decisions towards sustainable urban planning. Full article

In Africa, droughts are causing significant damage to human health and the economy. In West Africa, a severe decline in food production due to agricultural droughts has been reported in recent years. In this study, we simulated ecohydrological variables using the Coupled Land and Vegetation Data Assimilation System, which can effectively evaluate the hydrological water cycle and provide a dynamic evaluation of terrestrial biomass. Using ecohydrological variables (e.g., soil moisture content, leaf area index and vegetation water content) as a drought indicator, we analyzed agricultural droughts in the Sahel-inland region of West Africa during 2003–2018. Results revealed reasonable agreement between the simulated values and the pearl millet yield, and produced a successful quantification of severe droughts in the Sahel-inland region. Full article