Hydrology

Groundwater nitrate contamination caused by the excessive use of nitrogen-based fertilizers has been widely recognized as an issue of significant concern in numerous rural areas worldwide. To mitigate nitrate contamination, corrective management practices, such as regulations on fertilizer usage, should be implemented. However, these measures often entail economic consequences that impact farmers’ income, and thus should be properly assessed. Within this context, an integrated framework combining the environmental and economic assessment of fertilization restrictions through multi-criteria decision analysis is presented in an effort to efficiently manage groundwater nitrate contamination in rural areas. For this task, various scenarios involving reductions (10%, 20%, 30%, 40% and 50%) in fertilizer application were investigated, evaluated and ranked in order to determine the most suitable option. The environmental assessment considered occurrences of nitrates in groundwater, with a specific emphasis on nitrate concentrations in water-supply wells, as obtained by a nitrate fate and transport model, while the economic analysis focused on the losses experienced by farmers due to the reduced fertilizer usage. Our case-study implementation showed that a 30% reduction in fertilization is the most appropriate option for the area being studied, highlighting the importance of adopting such an approach when confronted with conflicting outcomes among alternatives. Full article

In this work, the influence of the El Niño Southern Oscillation (ENSO) on the Extreme Precipitation Indices (EPIs) was analyzed, and these ENSO-forced anomalies were compared with the long-term change in the EPIs. The annual time series of the EPIs were built from 880 precipitation stations that contained daily records between 1979 and 2022. These daily time series were filled, then the eleven (11) annual time series of the EPIs were built. To calculate ENSO-driven anomalies, the several phases of the phenomenon were considered (i.e., warm phase or El Niño years, cold phase or La Niña years, and normal or neutral years). For a particular EPI, the values calculated for the extreme phases of the ENSO were grouped, and these groups were compared with the group made up of the EPI values for the neutral years. To calculate the long-term change, two periods (1979–1996 and 2004–2021) were considered to group the EPI values. Maps showing the magnitude and significance of the assessed change/anomaly were constructed. The results allowed us to identify that the EPIs are generally “wetter” (i.e., higher extreme precipitation, longer wet periods, shorter dry periods, etc.) during La Niña hydrological years, while the opposite changes are observed during El Niño years. Furthermore, ENSO-induced anomalies are more important than the long-term changes. Full article

Recent technological progress in water management of hydrosystems has been made to deploy efficient and effective water quality monitoring systems (WQMS). Among these, a citizen science (CS)-based water quality monitoring (WQM) program using test strips is considered as a smart tool that may aid in the production of reliable, continuous, and comprehensive data on the water quality resources of hydrosystems over a broad range of spatial and temporal scales. In this case study, the objective is to evaluate the performance of a CS-based WQM for nitrates assessment using test water quality strips for the Medjerda watershed in Northern Tunisia. Overall, 137 samples were collected from 24 sampling sites and were analyzed by 33 participants. Citizens involved in the program were regrouped in five citizen types according to their socio-economic characteristics. Statistical tests, analysis of variance (ANOVA) and multiple correspondence analyses (MCA) were achieved to survey the goodness of fit of CS as a contribution to data collection in terms of the socio-economic profile of the participant. The results show that this tool could be reliable for obtaining the levels of nitrate in water samples. Water quality test trips can conveniently be used by citizens for WQM of nitrates when they are rigorously following the manufacturer’s instructions. Additional efforts in communication and training could help to improve the performance of this CS-WQM program for nitrate in the Medjerda watershed. Full article

The Colorado River provides water to 40 million people in the U.S. Southwest, with river basin spanning 250,000 square miles (647,497 km²). Quantitative water rights assigned to U.S. states, Mexico, and tribes in the Colorado Basin exceed annual streamflows. Climate change is expected to limit streamflows further. To balance water demands with supplies, unprecedented water-use cutbacks have been proposed, primarily for agriculture, which consumes more than 60% of the Basin’s water. This study develops county-level, Basin-wide measures of agricultural economic water productivity, water footprints, and irrigation cash rent premiums, to inform conservation programs and compensation schemes. These measures identify areas where conservation costs in terms of foregone crop production or farm income are high or low. Crop sales averaged USD 814 per acre foot (AF) (USD 0.66/m³) of water consumed in the Lower Basin and 131 USD/AF (USD 0.11/m³) in the Upper Basin. Crop sales minus crop-specific input costs averaged 485 USD/AF (USD 0.39/m³) in the Lower Basin and 93 USD/AF (USD 0.08 per m³) in the Upper Basin. The blue water footprint (BWF) was 1.2 AF/USD 1K (1480 m³/USD1K) of water per thousand dollars of crop sales in the Lower Basin and 7.6 AF/USD 1K (9374 m³/USD1K) in the Upper Basin. Counties with higher water consumption per acre have a lower BWF. Full article

Microplastic pollution is an emerging environmental concern, and has been found in remote regions, including the high Himalaya mountains. However, the abundance and sources of microplastics in the region are not well documented. This research investigated the abundance, types, and potential sources of microplastics in the Sagarmatha National Park (SNP), a rural and sparsely populated region of Nepal on the southern side of the Himalaya mountains. Water samples were collected from streams and tributaries in SNP in May of 2022. The average microplastic concentration among all samples was 2.0 ± 1.7 pieces/L, similar to that of water samples collected in other high mountain areas and is in the lower range of that found in water samples across the globe. Microplastic abundance is higher in water samples collected near settlements than in streams far from human settlements, indicating the impact of human activities. The presence of microplastics in all samples, including headwaters immediately beneath glaciers, illustrates the widespread distribution of microplastics and suggests the potential for airborne sources. While the concentration of microplastics does not change dramatically from upstream tributaries to downstream rivers, the total load of microplastics increases due to higher discharge downstream. This research demonstrates the anthropogenic and air-borne influences on microplastics contamination on the southern side of the Himalayan range and contributes to filling the data gaps towards a better understanding of the global fate and transport of microplastics. Full article

Efficient water resource management in glacier- and snow-dominated basins requires accurate estimates of the snow water equivalent (SWE) in late winter and spring and melt onset timing and intensity. To understand the high spatio-temporal variability of snow and glacier ablation, a spatially distributed energy balance model combining satellite-based retrievals of albedo and snow cover was applied. Incoming short-wave energy, contributing to daily estimates of melt energy, was constrained by remotely sensed surface albedo for snow-covered surfaces. Fractional snow cover was used for non-glaciated areas, as it provides estimates of snow cover for each pixel to better constrain snow melt. Thus, available daily estimates of melt energy in a given area were the product of the possible melt energy and the fractional snow cover of the area or pixel for non-glaciated areas. This provided daily estimates of melt water to determine seasonal snow and glacier ablation in Iceland for the period 2000–2019. Observations from snow pits on land and glacier summer mass balance were used for evaluation, and observations from land and glacier-based automatic weather stations were used to evaluate model inputs for the energy balance model. The results show that the interannual SWE variability was generally high both for seasonal snow and glaciers. For seasonal snow, the largest SWE (>1000 mm) was found in mountainous and alpine areas close to the coast, notably in the East- and Westfjords, Tröllaskaga, and in the vicinity of glacier margins. Lower SWE values were observed in the central highlands, flatter inland areas, and at lower elevations. For glaciers, more SWE (glacier ablation) was associated with lower glacier elevations while less melt was observed at higher elevations. For the impurity-rich bare-ice areas that are exposed annually, observed SWE was more than 3000 mm. Full article

Climate change affects hydroclimatic variables, and assessing the uncertainty in future predictions is crucial. This study aims to explore variations in temperature and precipitation in the Kerman Plain under climate change impacts between 2023 and 2054. For this purpose, two climate models, MRI-ESM-2 and BCC-CSM2-MR, were used to simulate precipitation and temperature under two different scenarios. The Mann–Kendall test was employed to analyze the annual time series in the future period. The results indicated an increase in the average temperature of about 1.5 degrees Celsius based on both scenarios in the coming years. Furthermore, an average annual increase of 6.37 mm of precipitation was predicted under the SSP585 scenario. Meanwhile, under the SSP585 scenario, an increase was estimated using the MRI-ESM-2 model, and a decrease was predicted with the BCC-CSM2-MR model. The Mann–Kendall test revealed a downward trend in the BCC-CSM2-MR model under both scenarios and an upward trend in the MRI-ESM-2 model under both scenarios. The bootstrap method and the R-factor index were exploited in this study with a 95% confidence interval to estimate the uncertainty of the predicted data. The results demonstrated that the predicted precipitation is more uncertain than the temperature. Finally, it is postulated that the obtained results provide necessary information for water resource management under a changing climate in the study area. Full article

Accurate discharge measurement is mandatory for any hydrological study. While the “velocity” measurement method is adapted to laminar flows, the “dilution” method is more appropriate for turbulent streams. As most low-gradient streams worldwide are neither laminar nor turbulent, a methodological gap appears. In this study, we demonstrate that the application of the “dilution” method to a low-gradient small stream gives very satisfactory results in addition to revealing surface/subsurface processes. A variety of chemical and isotopic tracers were injected into the stream (anions, fluorescent dyes, and chloride and hydrogen isotopes). We report the first use of ³⁷Cl for stream discharge measurement and show that ³⁷Cl and ²H can be reliably used as quantitative tracers. Discharge uncertainty calculations show that deuterium is the most accurate tracer method used. We also compare the differences in the tailing part of the restitution curves of tracers and investigate the role of transient surface and hyporheic zones in solute transport in light of a simple transport modelling approach. We conclude that isotopic tracers can be used as “environmentally friendly” tracers for discrete stream discharge measurements and that the application of multi-tracers tests in rivers opens the path to a better understanding of surface–subsurface interaction processes. Full article

Polders are low-lying areas located in deltas, surrounded by embankments to prevent flooding (river or tidal floods). They rely on pumping systems to remove water from the inner rivers (artificial rivers inside the polder area) to the outer rivers, especially during storms. Urbanized polders are especially vulnerable to pluvial flooding if the drainage, storage, and pumping capacity of the polder is inadequate. In this paper, a Monte Carlo (MC) framework is proposed to evaluate the benefits of rainfall threshold-based flood warnings when mitigating pluvial flooding in an urban flood-prone polder area based on 24 h forecasts. The framework computes metrics that give the potential waterlogging duration, maximum inundated area, and pump operation costs by considering the full range of potential storms. The benefits of flood warnings are evaluated by comparing the values of these metrics across different scenarios: the no-warning, perfect, deterministic, and probabilistic forecast scenarios. Probabilistic forecasts are represented using the concept of “predictive uncertainty” (PU). A polder area located in Nanjing was chosen for the case study. The results show a trade-off between the metrics that represent the waterlogging and the pumping costs, and that probabilistic forecasts of rainfall can considerably enhance these metrics. The results can be used to design a rainfall threshold-based flood early warning system (FEWS) for a polder area and/or evaluate its benefits. Full article

In arid and hyper-arid areas, groundwater is a precious and rare resource. The need for water supply has grown over the past few decades as a result of population growth, urbanization, and agricultural endeavors. This research aims to locate groundwater recharge potential zones (GWPZs) using multi-criteria evaluation (MCE) in the Wadi Qena Basin, Eastern Desert of Egypt, which represents one of the most promising valleys on which the government depends for land reclamations and developments. These approaches have been used to integrate and delineate the locations of high groundwater recharge and the potential of the Quaternary aquifer in the Wadi Qena basin. After allocating weight factors to identify features in each case based on infiltration, land use/land cover, slope, geology, topology, soil, drainage density, lineament density, rainfall, flow accumulation, and flow direction, these thematic maps were combined. The results of the GIS modeling led to the division of the area’s groundwater recharge potential into five groups, ranging from very high (in the western part) to very low (in the eastern part of the basin). The zones with the best prospects for groundwater exploration turned out to be the alluvial and flood plains, with their thick strata of sand and gravel. The groundwater recharge potential map was validated using data from the field and earlier investigations. The promising recharging areas show high suitability for soil cultivation. The results overall reveal that RS and GIS methodologies offer insightful instruments for more precise assessment, planning, and monitoring of water resources in arid regions and anywhere with similar setups for groundwater prospecting and management. Full article

Climate change is a significant environmental challenge that affects water resources, agriculture, health, and other aspects of human life. Bivariate modeling is a statistical method used to analyze the relationship between variables such as rainfall and temperature. The Pearson correlation coefficient, Kendall’s tau, or Spearman’s rank correlation are some measures used for bivariate modeling. However, copula functions can describe the dependence structure between two or more variables and can be effectively used to describe the relationship between rainfall and temperature. Despite the literature on bivariate modeling of rainfalls and temperature being extensive, finding flexible and sophisticated bivariate models is sometimes difficult. In this paper, we use rotated copula functions that can arrange any type of dependence that is empirically detected, especially negative dependence. The methodology is applied to an Italian municipality’s bivariate daily time series of rainfall and temperature. The estimated rotated copula is significant and, therefore, can be used for simulating the effects of extreme events. Full article

Obtaining accurate values of saturated hydraulic conductivity (K_sat) is very important for managing all natural or artificial processes involving water flow into soils. Double-ring infiltration (DRI) is one of the easiest-to-work-with techniques commonly used for K_sat determination. Unfortunately, when improperly used, it leads to important variations and inaccurate results. This study was designed to investigate the necessary conditions to reach the true-value or real-world saturated hydraulic conductivity (K_{sat-real-world}) in the field. For this purpose, the effects of two factors—namely, the measured infiltration data type (cumulative, instant rate, and average rate) and the related non-linear regression equation type—were analyzed. Measurements with DRI were performed with samples from 106 locations in three West African countries, namely, Burkina Faso, Mali, and Cote d’Ivoire. The soils were composed of loam, sandy loam, and sandy clay loam. The results show that when infiltration rates are used rather than cumulative infiltration non-linear regression curves, the variability between the measured K_sat and the real-world saturated hydraulic conductivity (K_{sat-real-world}) could reach from 2.2% to 58.8%. This variability was caused by the approximate amplification—according to the procedure used—of time-increment measurement errors. Extending the test duration to more than 4 h, especially when clay soils were involved, and using the exponential one-phase decay non-linear regression of the cumulative infiltration data based on a clear measurement protocol provided the K_sat values that were closest to K_{sat-real-world}. Full article

Vjosa is the main river of South Albania. Currently, the confined Quaternary gravelly aquifer in its lower course supplies drinking water for roughly 300,000 local residents. In the past years, extracted groundwater quantity has increased, whereas the groundwater quality has been affected by seawater intrusion. This paper aims to assess the groundwater quality and to discuss the dominant hydrochemical processes in the aquifer. To fulfil this goal, the study discusses the groundwater quality’s spatial and temporal variations on the basis of the hydrochemical parameters and ratios for 2010–2021 period, during which data are collected from three monitoring wells, located 6, 14 and 17 km, from the sea. While for 1961–2009 period, hydro-chemical water types and TDS maps are prepared using roughly 100 chemical analyses. The hydro-chemical parameters are discussed related to the factors controlling the groundwater’s chemical constituents and the groundwater extraction. Heavy metals and nitrates’ contents indicate that the aquifer is not affected by anthropogenic pollution. The main conclusion is that the groundwater quality is affected by seawater intrusion due to overexploitation. The study reveals its gaps, mentions its possible usefulness, and underlines the discrepancy between the policy makers and the hydrogeologists approaches concerning groundwater extraction. Full article

The design of a river-basin-specific flood hydrograph generator based on gauge records enables the generation of synthetic flood hydrographs for the dimensioning of hydraulic structures. Based on selected flow time series, flood waves can be described using four parameters based on flood characteristic simulations, as described by Leichtfuss and Lohr (1999). After successfully adapting suitable distribution functions, dependencies in the load structure are quantified in the next step using copula functions. This newly developed approach builds on the procedure proposed by Bender and Jensen (2012), which assumes hydrological independence. Using copula functions results in increased accuracy in the extended flood characteristic simulation. Moreover, considerable enhancements are achieved through the utilization of genetic algorithms, wherein the descending branch of the flood hydrograph is adjusted by employing an additional variable factor. Subsequently, any number of synthetic flood hydrographs can be generated by combining these parameters. In keeping with the principle of Monte Carlo simulation, a sufficiently high number of synthetic events results in extreme conditions with a low probability of occurrence being reliably represented. Hence, this endeavor has the potential to enhance result reproducibility and prediction quality. As a result, this expanded approach can be employed to provide dependable assessments regarding inflows aimed at optimizing reservoir capacity, for instance. Full article

There is scarce information regarding the interactions between young tree water uptake and the environment in water-limited ecosystems. This study was conducted in a semiarid rangeland ecosystem in central Oregon, Pacific Northwest Region, USA. We measured the tree transpiration of western juniper (Juniperus occidentalis) saplings using the stem heat balance (SHB) method. We analyzed the correlation between transpiration and environmental factors affecting the saplings’ water use from May to October for 2017, 2018, 2019, 2021, and 2022. The study results showed that total annual precipitation for all but one year was below the long-term (2005 to 2022) mean precipitation value of 307 mm for the study site. Significantly higher transpiration rates were observed in the wet vs. dry years. The highest monthly averaged transpiration rates (2.95 L d⁻¹) were obtained in August during the above-average precipitation year (2017). Peak transpiration rates for the below-average precipitation years were generally reached in June or July, ranging from 0.91 to 1.65 L d⁻¹. The seasonal response of transpiration to different environmental factors varied. For all years, vapor pressure deficit (VPD), solar radiation (SR), and air temperature (AT) showed a positive correlation with transpiration, whereas precipitation (Pr) and relative humidity (RH) indicated a negative correlation with transpiration. Soil moisture (SM) and soil temperature (ST) positively correlated with transpiration for most years. A strong association between VPD and transpiration was observed during the wettest (2017; 327 mm) and driest (2021; 198 mm) years. Results from this study add to the limited literature on sapling transpiration and can contribute to the improved management of cool-climate rangeland ecosystems through an enhanced understanding of water use by young-stage trees and its potential impacts on the water balance of restored juniper landscapes. Full article

Groundwater pollution in Rajasthan, India, poses significant challenges due to the region’s heavy reliance on this resource for drinking and irrigation. Given the increasing water scarcity and overexploitation, this study assesses the susceptibility of groundwater pollution in this semi-arid area. We applied and compared vulnerability mapping methods, DRASTIC and SINTACS, and their modified versions. These methodologies considered various geological and environmental factors such as depth-to-water table, recharge, aquifer conductivity, soil, and topography. The modified versions also integrated land use and temperature data for enhanced sensitivity. Validation was achieved by comparing contaminant data from the Central Ground Water Board (CGWB), India, focusing on primary contaminants such as fluoride, nitrate, chloride, and total dissolved solids (TDS). The results strongly align with the modified methodologies and observed groundwater ion values. Specifically, more than half of the 300 sample points analyzed indicated TDS values exceeding the permissible 300 ppm limit, with over 80 points surpassing 500 ppm. The vulnerability was classified into the following five categories: very low; low; medium; high; and very high. Notably, 30.53% of the area displayed “very high” vulnerability under the modified DRASTIC model. Districts like Jalore, Pali, Sirohi, and Jodhpur emerged as highly vulnerable zones, while areas within Udaipur, Kota, and Jaipur, among others, showed very high vulnerability. This research highlights the importance of conducting groundwater vulnerability assessments, especially for regions grappling with water scarcity like Rajasthan. The findings from this research are pivotal in guiding sustainable ground water resource management, as well as advocating continual monitoring and effective groundwater conservation strategies in the region. Full article

In order to facilitate the monitoring of groundwater quality in France, the groundwater bodies (GWB) in the Provence-Alpes-Côte d’Azur region have been grouped into 11 homogeneous clusters on the basis of their physico-chemical and bacteriological characteristics. This study aims to test the legitimacy of this grouping by predicting whether water samples belong to a given sampling point, GWB or group of GWBs. To this end, 8673 observations and 18 parameters were extracted from the Size-Eaux database, and this dataset was processed using discriminant analysis and various machine learning algorithms. The results indicate an accuracy of 67% using linear discriminant analysis and 69 to 83% using ML algorithms, while quadratic discriminant analysis underperforms in comparison, yielding a less accurate prediction of 59%. The importance of each parameter in the prediction was assessed using an approach combining recursive feature elimination (RFE) techniques and random forest feature importance (RFFI). Major ions show high spatial range and play the main role in discrimination, while trace elements and bacteriological parameters of high local and/or temporal variability only play a minor role. The disparity of the results according to the characteristics of the GWB groups (geography, altitude, lithology, etc.) is discussed. Validating the grouping of GWBs will enable monitoring and surveillance strategies to be redirected on the basis of fewer, homogeneous hydrogeological units, in order to optimize sustainable management of the resource by the health agencies. Full article

Extreme precipitation has become more frequent and intense with time and space. Infrastructure design tools such as Intensity-Duration-Frequency (IDF) curves still rely on historical precipitation and stationary assumptions, risking current and future urban infrastructure. This study developed IDF curves by incorporating non-stationarity trends in precipitation annual maximum series (AMS) for Dallas–Fort Worth, the fourth-largest metropolitan region in the United States. A Pro-NEVA tool was used to develop non-stationary IDF curves, taking historical precipitation AMS for seven stations that showed a non-stationary trend with time as a covariate. Four statistical indices—the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE)—were used as the model goodness of fit evaluation. The lower AIC, BIC, and RMSE values and higher NSE values for non-stationary models indicated a better performance compared to the stationary models. Compared to the traditional stationary assumption, the non-stationary IDF curves showed an increase (up to 75%) in the 24 h precipitation intensity for the 100-year return period. Using the climate change adaptive non-stationary IDF tool for the DFW metroplex and similar urban regions could enable decision makers to make climate-informed choices about infrastructure investments, emergency preparedness measures, and long-term urban development and water resource management planning. Full article

One of the hazards associated with tropical cyclones (TCs) is a storm surge, which leads to coastal inundation and often results in loss of life and damage to infrastructure. In this study, we used GIS-based bathtub models and tide-gauge-derived water levels to assess coastal inundation scenarios for the landfall region of TC Debbie. The three scenarios modelled what could have happened if the TC’s maximum storm surge had coincided with the maximum storm tide for that day, month, or TC season, where the water levels were determined through analysis of tide gauge data, using a new method called the variable enhanced Bathtub Model. Additionally, this study analysed the impact of excluding the correction of water levels with the Australian Height Datum. Our study found that between the least and most severe scenarios, with the input water-level difference for the model along the coastline being 0.43 m, the observed inundation depth of the analysed populated region increased from 0.25 m to 1 m. Ultimately, it was found that in the worst-case scenario, the study region could have experienced coastal inundation 0.63 m higher than it did, inundating 72.53 km² of the coast. The results of this study support the consensus that coastal inundation is highly dependent on the characteristics of the terrain, and that coastal inundation modelling, such as that completed in this study, needs to be performed to better inform decision makers and communities of the potential impacts of TC-induced storm surges. Full article