Hydrology

24 pages, 4303 KiB

Open AccessArticle

The Characteristics and Trend Prediction of Water and Sediment Evolution at the Toudaoguai Station on the Yellow River from 1960 to 2019

by Chao Li, Jing Guo, Xinlei Guo and Hui Fu

Hydrology 2025, 12(7), 174; https://doi.org/10.3390/hydrology12070174 - 30 Jun 2025

Abstract

The Yellow River is a renowned sediment-laden river and analyzing its water–sediment evolution characteristics and trends is critical for rational water resource utilization and water security. Using annual runoff and sediment transport data from Toudaoguai Hydrological Station (1960–2019), Mann–Kendall tests, cumulative anomalies analysis, [...] Read more.

The Yellow River is a renowned sediment-laden river and analyzing its water–sediment evolution characteristics and trends is critical for rational water resource utilization and water security. Using annual runoff and sediment transport data from Toudaoguai Hydrological Station (1960–2019), Mann–Kendall tests, cumulative anomalies analysis, and wavelet analysis were used to investigate the decadal variations. A coupled ARIMA-BP model was developed to improve simulation accuracy over standalone ARIMA/BP models for trend prediction. The results showed significant decreasing trends in both runoff (Z = −3.22) and sediment transport (Z = −4.73) during 1960–2019, with change points in 1986 (runoff) and 1984 (sediment). The primary periodicities were 12 years for runoff and 31 years for sediment transport. The coupled model achieved good consistency (R² = 0.9142 for runoff, 0.8637 for sediment), outperforming individual models. Projections indicate continued declines in both variables from 2020 to 2029. Natural factors are the main cause affecting the changes in runoff, while human activities are the primary influencing factor for the changes in sediment load. This study introduces a novel approach for water–sediment analysis in the Yellow River Basin, providing technical support for sustainable water resource management. Full article

20 pages, 4438 KiB

Open AccessArticle

Impacts of Urbanization and Climate Variability on Groundwater Environment in a Basin Scale

by Olawale Joshua Abidakun, Mitsuyo Saito, Shin-ichi Onodera and Kunyang Wang

Hydrology 2025, 12(7), 173; https://doi.org/10.3390/hydrology12070173 - 30 Jun 2025

Abstract

Globally, groundwater resources are experiencing a decline in hydraulic heads resulting from the dual effects of urbanization and climate change, highlighting the need for integrated and sustainable water resources management. Urban development in the cities of Kansai region, western Japan, presents a significant [...] Read more.

Globally, groundwater resources are experiencing a decline in hydraulic heads resulting from the dual effects of urbanization and climate change, highlighting the need for integrated and sustainable water resources management. Urban development in the cities of Kansai region, western Japan, presents a significant challenge to the sustainability of groundwater resources. This study aims to assess the combined influence of urbanization and climate change on the groundwater resources of the Nara Basin using MODFLOW 6 for two distinct periods: The Pre-Urbanization Period (PreUP: 1980–1988), and the Post-Urbanization Period (PostUP, 2000–2008) with an emphasis on spatiotemporal distribution of recharge in a multi-layer aquifer system. Simulated hydraulic heads were evaluated under three different recharge scenarios: uniformly, spatiotemporally and spatially distributed. The uniform recharge scenario both overestimates and underestimates hydraulic heads, while the spatially distributed scenario produced a simulated heads distribution similar to the spatiotemporally distributed recharge scenario, underscoring the importance of incorporating spatiotemporal variability in recharge input for accurate groundwater flow simulation. Moreover, our results highlight the relevance of spatial distribution of recharge input than temporal distribution. Our findings indicate a significant decrease in hydraulic heads of approximately 5 m from the PreUP to PostUP in the unconfined aquifer, primarily driven by changes in land use and climate. In contrast, the average head decline in deep confined aquifers is about 4 m and is mainly influenced by long-term climatic variations. The impervious land use types experienced more decline in hydraulic heads than the permeable areas under changing climate because of the impedance to infiltration and percolation exacerbating the climate variability effect. These changes in hydraulic heads were particularly evident in the interactions between surface and groundwater. The cumulative volume of groundwater discharge to the river decreased by 27%, while the river seepage into the aquifer increased by 16%. Sustainable groundwater resources management under conditions of urbanization and climate change necessitates a holistic and integrated approach. Full article

(This article belongs to the Section Hydrological and Hydrodynamic Processes and Modelling)

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25 pages, 4553 KiB

Open AccessArticle

Predictive Modeling of Flood Frequency Utilizing an Analysis of the Casimcea River in Romania

by Carmen Maftei, Constantin Cerneaga and Ashok Vaseashta

Hydrology 2025, 12(7), 172; https://doi.org/10.3390/hydrology12070172 - 30 Jun 2025

Abstract

Flooding is a recurrent natural hazard in Romania, causing significant socio-economic impacts. Historical data highlights the severity of floods, particularly the unprecedented flood of 1926. Between 1960 and 2010, Romania experienced over 400 major floods, which significantly impacted its infrastructure and population. Particularly, [...] Read more.

Flooding is a recurrent natural hazard in Romania, causing significant socio-economic impacts. Historical data highlights the severity of floods, particularly the unprecedented flood of 1926. Between 1960 and 2010, Romania experienced over 400 major floods, which significantly impacted its infrastructure and population. Particularly, the floods in 2005 and 2006 affected over 1.5 million people, resulting in 93 deaths and causing damages exceeding EUR 2 billion. In compliance with the Floods Directive, EU member states must assess and map flood hazards and risks. This study aims to develop a frequency analysis to determine discharges as a predictive indicator for different hazard levels: frequent events (10-year return period), medium probability events (100-year return period), and extreme events. The Casimcea catchment in central Dobrogea, drained by the Casimcea River into Lake Tasaul, serves as the study area. The annual maximum discharge data analysis, conducted through frequency analysis and the ELECTRE method, indicates that EV3-Min-Weibull, L-moments, and GEV-Min (L-moments) are the most effective probability density functions (PDFs). To conclude, although a single PDF model cannot be determined for the Casimcea River and its tributaries, it contributes to predictive modeling efforts. Full article

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32 pages, 4407 KiB

Open AccessArticle

A Comparative Analysis of Remotely Sensed and High-Fidelity ArcSWAT Evapotranspiration Estimates Across Various Timescales in the Upper Anthemountas Basin, Greece

by Stefanos Sevastas, Ilias Siarkos and Zisis Mallios

Hydrology 2025, 12(7), 171; https://doi.org/10.3390/hydrology12070171 - 29 Jun 2025

Abstract

In data-scarce regions and ungauged basins, remotely sensed evapotranspiration (ET) products are increasingly employed to support hydrological model calibration. In this study, a high-resolution hydrological model was developed for the Upper Anthemountas Basin using ArcSWAT, with a focus on comparing simulated ET outputs [...] Read more.

In data-scarce regions and ungauged basins, remotely sensed evapotranspiration (ET) products are increasingly employed to support hydrological model calibration. In this study, a high-resolution hydrological model was developed for the Upper Anthemountas Basin using ArcSWAT, with a focus on comparing simulated ET outputs to three freely available remote sensing-based ET products: the MODIS MOD16 Collection 5, the updated MODIS MOD16A2GF Collection 6.1, and the SSEBop Version 5 dataset. ET estimates derived from the calibrated SWAT model were compared to all remote sensing products at the basin scale, across various temporal scales over the 2002–2014 simulation period. Results indicate that the MOD16 Collection 5 product achieved the closest correspondence with SWAT-simulated ET across all temporal scales. The MOD16A2GF Collection 6.1 product exhibited moderate overall agreement, with improved performance during early summer. The SSEBop Version 5 dataset generally displayed weaker correlation, but demonstrated enhanced alignment during the driest years of the record. Strong correspondence is observed when averaging the ET values from all satellite products. These findings underscore the importance of exercising caution when utilizing remotely sensed ET products as the sole basis for hydrological model calibration, particularly given the variability in performance among different datasets. Full article

(This article belongs to the Special Issue Hydrological Modelling for the Sustainable Management of Water Resources in River Basins)

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19 pages, 1630 KiB

Open AccessArticle

Just a Single-Layer CNN for Stochastic Modeling: A Discriminator-Free Approach

by Evangelos Rozos

Hydrology 2025, 12(7), 170; https://doi.org/10.3390/hydrology12070170 - 29 Jun 2025

Abstract

The advent of machine learning (ML) has significantly transformed hydrology, particularly in the simulation of hydrological flows. However, ML techniques have not been employed to the same extent in stochastic hydrology. In applied sciences, the most common ML-based approach for developing stochastic simulation [...] Read more.

The advent of machine learning (ML) has significantly transformed hydrology, particularly in the simulation of hydrological flows. However, ML techniques have not been employed to the same extent in stochastic hydrology. In applied sciences, the most common ML-based approach for developing stochastic simulation schemes is the use of generative adversarial networks (GANs), which consist of two sub-models, that is, a generator and a discriminator. Despite their potential, GANs have notable limitations, including high architectural complexity and the requirement to divide observed time series into shorter segments to generate sufficient training examples. This segmentation reduces the effective length of the series, limiting the model’s ability to capture and reproduce long-term dependencies. In this study, we propose a simpler stochastic scheme based on a single convolutional neural network (CNN) used as a generator, replacing the discriminator component of the GAN with a specifically designed cost function. The model is applied to a case study involving measured flow velocity time series and evaluated against traditional stochastic schemes designed for both Markovian and Hurst–Kolmogorov processes. Results show that the CNN-based approach not only offers computational simplicity but also outperforms conventional methods in preserving key statistical characteristics of the observed data. Full article

(This article belongs to the Section Statistical Hydrology)

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14 pages, 7591 KiB

Open AccessArticle

A Paleo-Perspective of 21st Century Drought in the Hron River (Slovakia)

by Igor Leščešen, Abel Andrés Ramírez Molina and Glenn Tootle

Hydrology 2025, 12(7), 169; https://doi.org/10.3390/hydrology12070169 - 28 Jun 2025

Abstract

The Hron River is a vital waterway in central Slovakia. In evaluating observed streamflow records for the past ~90 years, the Hron River displayed historically low hydrologic summer (April–May–June–July–August–September (AMJJAS)) streamflow for the 10-, 20-, and 30-year periods ending in 2020. When using [...] Read more.

The Hron River is a vital waterway in central Slovakia. In evaluating observed streamflow records for the past ~90 years, the Hron River displayed historically low hydrologic summer (April–May–June–July–August–September (AMJJAS)) streamflow for the 10-, 20-, and 30-year periods ending in 2020. When using self-calibrated Palmer Drought Severity Index (scPDSI) proxies developed from tree-ring records, skillful regression-based reconstructions of AMJJAS streamflow were developed for two gauges (Banská Bystrica and Brehy) on the Hron River. The recent observed droughts were compared to these reconstructions and revealed the Hron River experienced extreme drought in the 21st century. A further comparison of observed wet (pluvial) periods revealed that the most extreme robust streamflow periods in the observed record were frequently exceeded in the reconstructed (paleo) record. The Hron River has recently been experiencing decline, and we hypothesize that this decline may be associated with anthropogenic influences, the natural climatic cycle, or the changing climate. Full article

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19 pages, 4916 KiB

Open AccessArticle

Deep Learning-Based Daily Streamflow Prediction Model for the Hanjiang River Basin

by Jianze Huang, Jialang Chen, Haijun Huang and Xitian Cai

Hydrology 2025, 12(7), 168; https://doi.org/10.3390/hydrology12070168 - 27 Jun 2025

Abstract

The sharp decline in streamflow prediction accuracy with increasing lead times remains a persistent challenge for effective water resources management and flood mitigation. In this study, we developed a coupled deep learning model for daily streamflow prediction in the Hanjiang River Basin, China. [...] Read more.

The sharp decline in streamflow prediction accuracy with increasing lead times remains a persistent challenge for effective water resources management and flood mitigation. In this study, we developed a coupled deep learning model for daily streamflow prediction in the Hanjiang River Basin, China. The proposed model integrates self-attention (SA), a one-dimensional convolutional neural network (1D-CNN), and bidirectional long short-term memory (BiLSTM). The model’s effectiveness was assessed during flood events, and its predictive uncertainty was quantified using kernel density estimation (KDE). The results demonstrate that the proposed model consistently outperforms baseline models across all lead times. It achieved Nash-Sutcliffe Efficiency (NSE) scores of 0.92, 0.86, and 0.79 for 1-, 3-, and 5-days, respectively, showing particular strength at these extended lead time predictions. During major flood events, the model demonstrated an enhanced capacity to capture peak magnitudes and timings. It achieved the highest NSE values of 0.924, 0.862, and 0.797 for the 1-, 3-, and 5-day forecasting horizons, respectively, thereby showcasing the strengths of integrating CNN and SA mechanisms for recognizing local hydrological patterns. Furthermore, KDE-based uncertainty analysis identified a high prediction interval coverage in different forecast periods and a relatively narrow prediction interval width, indicating the strong robustness of the proposed model. Overall, the proposed SA-CNN-BiLSTM model demonstrates significantly improved accuracy, especially for extended lead times and flood events, and provides robust uncertainty quantification, thereby offering a more reliable tool for reservoir operation and flood risk management. Full article

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22 pages, 9661 KiB

Open AccessArticle

Regional Groundwater Flow and Advective Contaminant Transport Modeling in a Typical Hydrogeological Environment of Northern New Jersey

by Toritseju Oyen and Duke Ophori

Hydrology 2025, 12(7), 167; https://doi.org/10.3390/hydrology12070167 - 27 Jun 2025

Abstract

This study develops a numerical model to simulate groundwater flow and contaminant transport in a “typical hydrogeological environment” of northern New Jersey, addressing freshwater decline. Focusing on the Lower Passaic water management area (WMA), we model chloride transport in a fractured-rock aquifer, where [...] Read more.

This study develops a numerical model to simulate groundwater flow and contaminant transport in a “typical hydrogeological environment” of northern New Jersey, addressing freshwater decline. Focusing on the Lower Passaic water management area (WMA), we model chloride transport in a fractured-rock aquifer, where fracture networks control hydraulic conductivity and porosity. The urbanized setting—encompassing Montclair State University (MSU) and municipal wells—features heterogeneous groundwater systems and critical water resources, providing an ideal case study for worst-case contaminant transport scenarios. Using MODFLOW and MODPATH, we simulated flow and tracked particles over 20 years. Results show that chloride from MSU reached the Third River in 4 years and the Passaic River in 10 years in low-porosity fractures (0.2), with longer times (8 and 20 years) in high-porosity zones (0.4). The First Watchung Mountains were identified as the primary recharge area. Chloride was retained in immobile pores but transported rapidly through fractures, with local flow systems (MSU to Third River) faster than regional systems (MSU to Passaic River). These findings confirm chloride in groundwater, which may originate from road salt application, can reach discharge points in 4–20 years, emphasizing the need for recharge-area monitoring, salt-reduction policies, and site-specific assessments to protect fractured-rock aquifers. Full article

(This article belongs to the Special Issue Hydrological Modelling for the Sustainable Management of Water Resources in River Basins)

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25 pages, 8903 KiB

Open AccessArticle

Comparative Analysis of Satellite-Based Rainfall Products for Drought Assessment in a Data-Poor Region

by Hansini Gayanthika, Dimuthu Lakshitha, Manthika Chathuranga, Gouri De Silva and Jeewanthi Sirisena

Hydrology 2025, 12(7), 166; https://doi.org/10.3390/hydrology12070166 - 27 Jun 2025

Abstract

Drought is one of the most impactful natural disasters, and it significantly impacts three main sectors of a country: the environment, society, and the economy. Therefore, drought assessment and monitoring are essential for reducing vulnerability and risk. However, insufficient and sparse long-term in [...] Read more.

Drought is one of the most impactful natural disasters, and it significantly impacts three main sectors of a country: the environment, society, and the economy. Therefore, drought assessment and monitoring are essential for reducing vulnerability and risk. However, insufficient and sparse long-term in situ rainfall data limit drought assessment in developing countries. Recently developed satellite-based rainfall products, available at different temporal and spatial resolutions, offer a valuable alternative in data-poor regions like Sri Lanka, where rain gauge networks are sparse and maintenance issues are prevalent. This study evaluates the accuracy of satellite-based rainfall estimates compared to in situ observations for drought assessment within the Mi Oya River Basin, Sri Lanka. We assessed the performance of various satellite-based rainfall products, including IMERG, GSMaP, CHIRPS, PERSIANN, and PERSIANN-CDR, by comparing them with ground-based observations over 20 years, from 2003 to 2022. Our methodology involved checking detection accuracy using the False Alarm Ratio (FAR), Probability of Detection (POD), and Critical Success Index (CSI), and assessing accuracy through metrics such as Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC), Percentage Bias (PBias), and Nash–Sutcliffe Efficiency (NSE). The two best-performing satellite-based rainfall products were used for meteorological and hydrological drought assessment. In the accuracy detection metrics, the results indicate that while products like IMERG and GSMaP generally provide reliable rainfall estimates, others like PERSIANN and PERSIANN-CDR tend to overestimate rainfall. For instance, IMERG shows a CSI range of 0.04–0.25 for moderate and heavy rainfall and 0.10–0.30 for light rainfall. On a monthly scale, IMERG and CHIRPS showed the highest performance, with CC (NSE) values of 0.81–0.94 (0.53–0.83) and 0.79–0.86 (0.54–0.74), respectively. However, GSMaP showed the lowest bias, with a range of −17.1–13.2%. Recorded drought periods over 1981–2022 (1998–2022) were reasonably well captured by CHIRPS (IMERG) products in the Mi Oya River Basin. Our results highlighted uncertainties and discrepancies in the capability of different rainfall products to assess drought conditions. This research provides valuable insights for optimizing the use of satellite rainfall products in hydrological modeling and disaster preparedness in the Mi Oya River Basin. Full article

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17 pages, 6551 KiB

Open AccessArticle

Monitoring the Impacts of Human Activities on Groundwater Storage Changes Using an Integrated Approach of Remote Sensing and Google Earth Engine

by Sepide Aghaei Chaleshtori, Omid Ghaffari Aliabad, Ahmad Fallatah, Kamil Faisal, Masoud Shirali, Mousa Saei and Teodosio Lacava

Hydrology 2025, 12(7), 165; https://doi.org/10.3390/hydrology12070165 - 26 Jun 2025

Abstract

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. [...] Read more.

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. Although the influence of natural factors on groundwater is well-recognized, the impact of human activities, despite being a major contributor to its change, has been less explored due to the challenges in measuring such effects. To address this gap, our study employed an integrated approach using remote sensing and the Google Earth Engine (GEE) cloud-free platform to analyze the effects of various anthropogenic factors such as built-up areas, cropland, and surface water on groundwater storage in the Lake Urmia Basin (LUB), Iran. Key anthropogenic variables and groundwater data were pre-processed and analyzed in GEE for the period from 2000 to 2022. The processes linking these variables to groundwater storage were considered. Built-up area expansion often increases groundwater extraction and reduces recharge due to impervious surfaces. Cropland growth raises irrigation demand, especially in semi-arid areas like the LUB, leading to higher groundwater use. In contrast, surface water bodies can supplement water supply or enhance recharge. The results were then exported to XLSTAT software2019, and statistical analysis was conducted using the Mann–Kendall (MK) non-parametric trend test on the variables to investigate their potential relationships with groundwater storage. In this study, groundwater storage refers to variations in groundwater storage anomalies, estimated using outputs from the Global Land Data Assimilation System (GLDAS) model. Specifically, these anomalies are derived as the residual component of the terrestrial water budget, after accounting for soil moisture, snow water equivalent, and canopy water storage. The results revealed a strong negative correlation between built-up areas and groundwater storage, with a correlation coefficient of −1.00. Similarly, a notable negative correlation was found between the cropland area and groundwater storage (correlation coefficient: −0.85). Conversely, surface water availability showed a strong positive correlation with groundwater storage, with a correlation coefficient of 0.87, highlighting the direct impact of surface water reduction on groundwater storage. Furthermore, our findings demonstrated a reduction of 168.21 mm (millimeters) in groundwater storage from 2003 to 2022. GLDAS represents storage components, including groundwater storage, in units of water depth (mm) over each grid cell, employing a unit-area, mass balance approach. Although storage is conceptually a volumetric quantity, expressing it as depth allows for spatial comparison and enables conversion to volume by multiplying by the corresponding surface area. Full article

(This article belongs to the Topic Natural Hazards Monitoring, Risk Assessment, Modelling and Management in the Artificial Intelligence Era)

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29 pages, 3325 KiB

Open AccessReview

Half-Century Review and Advances in Closed-Form Functions for Estimating Soil Water Retention Curves

by Ali Rasoulzadeh, Javad Bezaatpour, Javanshir Azizi Mobaser and Jesús Fernández-Gálvez

Hydrology 2025, 12(7), 164; https://doi.org/10.3390/hydrology12070164 - 25 Jun 2025

Abstract

This review provides a comprehensive overview of the closed-form expressions developed for estimating the soil water retention curve (SWRC) from 1964 to the present. Since the concept of the SWRC was introduced in 1907, numerous closed-form functions have been proposed to describe the [...] Read more.

This review provides a comprehensive overview of the closed-form expressions developed for estimating the soil water retention curve (SWRC) from 1964 to the present. Since the concept of the SWRC was introduced in 1907, numerous closed-form functions have been proposed to describe the relationship between soil matric suction and volumetric water content, each with distinct strengths and limitations. Given the variability in SWRC shapes influenced by soil texture, structure, and organic matter, models in the form of sigmoidal, multi-exponential, lognormal, hyperbolic, and hybrid functions have been designed to fit experimental SWRC data. Based on the number of adjustable parameters, these models are categorized into three main groups: three-, four-, and five-parameter models. They can also be classified as one-, two-, or three-segment functions depending on their structural complexity. A review of the developed models indicates that most are effective in representing the SWRC between the residual and saturated water content range. To capture the full range of the SWRC, hybrid functions have been proposed by combining traditional models. This review presents and discusses these models in chronological order of publication. Full article

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14 pages, 2407 KiB

Open AccessArticle

Refining Rainfall Derived from Satellite Radar for Estimating Inflows at Lam Pao Dam, Thailand

by Nathaporn Areerachakul, Jaya Kandasamy, Saravanamuthu Vigneswaran and Kittitanapat Bandhonopparat

Hydrology 2025, 12(7), 163; https://doi.org/10.3390/hydrology12070163 - 25 Jun 2025

Abstract

This project aimed to evaluate the use of meteorological satellite-derived rainfall data to estimate water inflows to dams. In this study, the Lam Pao Dam in the Chi Basin, Thailand, was used as a case study. Rainfall data were obtained using the PERSIANN [...] Read more.

This project aimed to evaluate the use of meteorological satellite-derived rainfall data to estimate water inflows to dams. In this study, the Lam Pao Dam in the Chi Basin, Thailand, was used as a case study. Rainfall data were obtained using the PERSIANN technique. To improve accuracy, satellite-derived rainfall estimates were adjusted using ground-based rainfall measurements from stations located near and within the catchment area, applying the 1-DVAR method. The Kriging method was employed to estimate the spatial distribution of rainfall over the catchment area. This approach resulted in a Probability of Detection (POD) of 0.92 and a Threat Score (TS) of 0.72 for rainfall estimates in the Chi Basin. Rainfall data from the Weather Research and Forecasting (WRF) numerical models were used as inputs for the HEC-HMS model to simulate water inflows into the dam. To refine rainfall estimates, various microphysics schemes were tested, including WSM3, WSM5, WSM6, Thompson, and Thompson Aerosol-Aware. Among these, the Thomson Aerosol-Aware scheme demonstrated the highest accuracy, achieving an average POD of 0.96, indicating highly reliable rainfall predictions for the Lam Pao Dam catchment. The findings underscore the potential benefits of using satellite-derived meteorological data for rainfall estimation, particularly where installing and maintaining ground-based measurement stations is difficult, e.g., forests/mountainous areas. This research contributes to a better understanding of satellite-derived rainfall patterns and their influence on catchment hydrology for enhanced water resource analysis. Full article

(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations)

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3 pages, 142 KiB

Open AccessBook Review

Book Review: Water Sustainability; Zhang, H.X., Ed.; Springer: New York, NY, USA, 2023; ISBN: 978-1-0716-2465-4

by Dmitry A. Ruban

Hydrology 2025, 12(7), 162; https://doi.org/10.3390/hydrology12070162 - 23 Jun 2025

Abstract

Water is an essential resource of contemporary civilization and also a factor of its progress [...] Full article

31 pages, 3453 KiB

Open AccessArticle

Addressing Weather Data Gaps in Reference Crop Evapotranspiration Estimation: A Case Study in Guinea-Bissau, West Africa

by Gabriel Garbanzo, Jesus Céspedes, Marina Temudo, Tiago B. Ramos, Maria do Rosário Cameira, Luis Santos Pereira and Paula Paredes

Hydrology 2025, 12(7), 161; https://doi.org/10.3390/hydrology12070161 - 22 Jun 2025

Abstract

Crop water use (ET_c) is typically estimated as the product of crop evapotranspiration (ET_o) and a crop coefficient (K_c). However, the estimation of ET_o requires various meteorological data, which are often unavailable or of poor quality, [...] Read more.

Crop water use (ET_c) is typically estimated as the product of crop evapotranspiration (ET_o) and a crop coefficient (K_c). However, the estimation of ET_o requires various meteorological data, which are often unavailable or of poor quality, particularly in countries such as Guinea-Bissau, where the maintenance of weather stations is frequently inadequate. The present study aimed to assess alternative approaches, as outlined in the revised FAO56 guidelines, for estimating ET_o when only temperature data is available. These included the use of various predictors for the missing climatic variables, referred to as the Penman–Monteith temperature (PMT) approach. New approaches were developed, with a particular focus on optimizing the predictors at the cluster level. Furthermore, different gridded weather datasets (AgERA5 and MERRA-2 reanalysis) were evaluated for ET_o estimation to overcome the lack of ground-truth data and upscale ET_o estimates from point to regional and national levels, thereby supporting water management decision-making. The results demonstrate that the PMT is generally accurate, with RMSE not exceeding 26% of the average daily ET_o. With regard to shortwave radiation, using the temperature difference as a predictor in combination with cluster-focused multiple linear regression equations for estimating the radiation adjustment coefficient (k_Rs) yielded accurate results. ET_o estimates derived using raw (uncorrected) reanalysis data exhibit considerable bias and high RMSE (1.07–1.57 mm d⁻¹), indicating the need for bias correction. Various correction methods were tested, with the simple bias correction delivering the best overall performance, reducing RMSE to 0.99 mm d⁻¹ and 1.05 mm d⁻¹ for AgERA5 and MERRA-2, respectively, and achieving a normalized RMSE of about 22%. After implementing bias correction, the AgERA5 was found to be superior to the MERRA-2 for all the studied sites. Furthermore, the PMT outperformed the bias-corrected reanalysis in estimating ET_o. It was concluded that PMT-ET_o can be recommended for further application in countries with limited access to ground-truth meteorological data, as it requires only basic technical skills. It can also be used alongside reanalysis data, which demands more advanced expertise, particularly for data retrieval and processing. Full article

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29 pages, 2696 KiB

Open AccessArticle

Hydrogeochemical Characterization and Water Quality Index-Based Evaluation of Groundwater for Drinking, Livestock, and Irrigation Use in the Arid Ewaso Ng’iro–Lagh Dera Basin, Kenya

by Githinji Tabitha Wambui, Dindi Edwin Wandubi, Kuria Zacharia Njuguna, Olago Daniel Ochieng and Gicheruh Chrysanthus Muchori

Hydrology 2025, 12(7), 160; https://doi.org/10.3390/hydrology12070160 - 20 Jun 2025

Abstract

Groundwater is the main source of water for both domestic and agricultural use in arid regions. This study assessed the hydrogeochemical characteristics and suitability of groundwater for drinking and irrigation in Kenya’s Ewaso Ng’iro–Lagh Dera Basin. A total of 129 borehole groundwater samples [...] Read more.

Groundwater is the main source of water for both domestic and agricultural use in arid regions. This study assessed the hydrogeochemical characteristics and suitability of groundwater for drinking and irrigation in Kenya’s Ewaso Ng’iro–Lagh Dera Basin. A total of 129 borehole groundwater samples were collected and analyzed for pH, electrical conductivity (EC), total hardness, and major ions. The groundwater was found to be mostly neutral to slightly alkaline and ranged from marginal to brackish in salinity. The dominant water type is Na-HCO₃, with the ionic order Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ and HCO₃⁻ > Cl⁻ > SO₄²⁻ > NO₃⁻. Mineral saturation indices indicate that the water is undersaturated with gypsum and anhydrite but is saturated with calcite, dolomite, and aragonite. Groundwater chemistry is primarily influenced by ion exchange, the mixing of fresh and paleo-saline water, and rock weathering processes. The water quality index (WQI) reveals that 80.5% of groundwater is suitable for drinking. The rest have high levels of sodium, EC, and bicarbonate. Thus, they are not suitable. The irrigation water quality index (IWQI) places most samples in the moderate-to-severe restriction category due to high salinity and sodicity. These findings highlight the importance of properly treating groundwater before use. Full article

(This article belongs to the Section Water Resources and Risk Management)

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21 pages, 3284 KiB

Open AccessArticle

Significance of Spring Inflow to Great Salt Lake, Utah, U.S.A.

by Lauren E. Bunce, Tim K. Lowenstein, Elliot Jagniecki and David Collins

Hydrology 2025, 12(6), 159; https://doi.org/10.3390/hydrology12060159 - 19 Jun 2025

Abstract

Spring waters (n = 103) from locations surrounding Great Salt Lake (GSL) were mapped, collected, and analyzed to determine their chemical compositions. A ternary Ca-SO₄-alkalinity plot was used to group these waters into compositional types based on the principle of chemical [...] Read more.

Spring waters (n = 103) from locations surrounding Great Salt Lake (GSL) were mapped, collected, and analyzed to determine their chemical compositions. A ternary Ca-SO₄-alkalinity plot was used to group these waters into compositional types based on the principle of chemical divides. Different spring water types were mixed with Bear, Jordan, and Weber River waters to determine the amount of spring inflow needed to reproduce the chemical composition of GSL. The Pitzer-based computer program EQL/EVP was used to simulate evaporation of spring-river water mixtures. The goal was to find spring-river water mixtures that, when evaporated, reproduced the chemical composition of modern GSL. This approach yielded GSL brine composition from a starting mixture of 12% spring inflow and 88% river water, by volume. The calculated spring inflow–river water mixture contains, on a molar percentage basis, greater than 50% of the B, K, Li, Na, and Cl supplied by springs and greater than 50% of the Ba, Ca, Sr, SO₄, and alkalinity derived from rivers. Understanding GSL spring inflow and brine evolution as lake elevation drops is critical to lake environments, ecosystems, and industrial brine shrimp harvesting and mineral extraction. Full article

(This article belongs to the Special Issue Lakes as Sensitive Indicators of Hydrology, Environment, and Climate)

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21 pages, 1792 KiB

Open AccessArticle

Assessment of Baseflow Separation Methods Used in the Estimations of Design-Related Storm Hydrographs Across Various Return Periods

by Oscar E. Coronado-Hernández, Rafael D. Méndez-Anillo and Manuel Saba

Hydrology 2025, 12(6), 158; https://doi.org/10.3390/hydrology12060158 - 19 Jun 2025

Abstract

Accurately estimating storm hydrographs for various return periods is crucial for planning and designing hydrological infrastructure, such as dams and drainage systems. A key aspect of this estimation is the separation of baseflow from storm runoff. This study proposes a method for deriving [...] Read more.

Accurately estimating storm hydrographs for various return periods is crucial for planning and designing hydrological infrastructure, such as dams and drainage systems. A key aspect of this estimation is the separation of baseflow from storm runoff. This study proposes a method for deriving storm hydrographs for different return periods based on hydrological station records. The proposed approach uses three baseflow separation methods: constant, linear, and master recession curve. A significant advantage of the proposed method over traditional rainfall–runoff approaches is its minimal parameter requirements during calibration. The methodology is tested on records from the Lengupá River watershed in Colombia, using data from the Páez hydrological station, which has a drainage area of 1090 km². The results indicate that the linear method yields the most accurate hydrograph estimates, as demonstrated by its lower root mean square error (RMSE) of 0.35%, compared to the other baseflow separation techniques, the values of which range from 2.92 to 3.02%. A frequency analysis of hydrological data was conducted using Pearson Type III and Generalized Extreme Value distributions to identify the most suitable statistical models for estimating extreme events regarding peak flow and maximum storm hydrograph volume. The findings demonstrate that the proposed methods effectively reproduce storm hydrographs for return periods ranging from 5 to 200 years, providing valuable insights for hydrological design, which can be employed using the data from stream gauging stations in rivers. Full article

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19 pages, 4046 KiB

Open AccessArticle

Combining Hydrodynamic Modelling and Solar Potential Assessment to Evaluate the Effects of FPV Systems on Mihăilești Reservoir, Romania

by Gabriela Elena Dumitran, Elena Catalina Preda, Liana Ioana Vuta, Bogdan Popa and Raluca Elena Ispas

Hydrology 2025, 12(6), 157; https://doi.org/10.3390/hydrology12060157 - 19 Jun 2025

Abstract

Floating photovoltaic (FPV) systems are a new green technology emerging lately, having the indisputable advantage of not covering agricultural land but instead the surface of lakes or reservoirs. Being a new technology, even though the number of studies is significant, reliable results remain [...] Read more.

Floating photovoltaic (FPV) systems are a new green technology emerging lately, having the indisputable advantage of not covering agricultural land but instead the surface of lakes or reservoirs. Being a new technology, even though the number of studies is significant, reliable results remain limited. This paper presents the possible influence of an FPV farm installed on the surface of a reservoir in Romania in four scenarios of the surface being covered with photovoltaic panels. The changes in the water mass under the FPV panels were determined using mathematical modelling as a tool. For this purpose, a water quality model was implemented for Mihăilești Reservoir, Romania, and the variations in the temperature, the phytoplankton biomass, and the total phosphorus and nitrogen were computed. Also, by installing FPV panels, it was estimated that a volume of water of between 1.75 and 7.43 million m³/year can be saved, and the greenhouse gas emission reduction associated with the proposed solutions will vary between 15,415 and 66,066 tCO_2e/year; these results are in agreement with those reported in other scientifical studies. The overall conclusion is that the effect of an FPV farm on the reservoir’s surface is beneficial for the water quality in the reservoir. Full article

(This article belongs to the Special Issue Hydrodynamics and Water Quality of Rivers and Lakes)

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22 pages, 1288 KiB

Open AccessReview

The Status, Applications, and Modifications of the Snowmelt Runoff Model (SRM): A Comprehensive Review

by Ninad Bhagwat, Rohitashw Kumar, Mahrukh Qureshi, Raja M. Nagisetty and Xiaobing Zhou

Hydrology 2025, 12(6), 156; https://doi.org/10.3390/hydrology12060156 - 18 Jun 2025

Abstract

In this review paper, we perform a comprehensive review of the current state of the art, worldwide applications, and modifications of the Snowmelt Runoff Model (SRM). Snow is a significant element of the hydrologic cycle and is sometimes regarded as the primary source [...] Read more.

In this review paper, we perform a comprehensive review of the current state of the art, worldwide applications, and modifications of the Snowmelt Runoff Model (SRM). Snow is a significant element of the hydrologic cycle and is sometimes regarded as the primary source of streamflow in watersheds at high latitudes and altitudes. Quantitative assessment of snowmelt runoff is crucial for real-world applications, including runoff projections, reservoir management, hydro-electricity production, irrigation techniques, and flood control, among others. Numerous hydrological modeling software have been developed to simulate snowmelt-derived streamflow. The SRM is one of the well-known modeling software developed to simulate snowmelt-derived streamflow. The SRM simulates snowmelt runoff with fewer data requirements and uses remotely sensed snow cover extent. This makes the SRM appropriate for use in data-scarce locations, particularly in remote and inaccessible mountain watersheds at higher elevations. It is a conceptual, deterministic, semi-distributed, and degree-day hydrological model that can be applied in mountainous basins of nearly any size. Recent advancements in remote sensing integration and climate model coupling have significantly enhanced the model’s ability to estimate snowmelt runoff. Additionally, numerous studies have recently improved the traditional SRM, further enhancing its capabilities. This paper highlights some of the global SRM research, focusing on the working of the model, input parameters, remote sensing data availability, and modifications to the original model. Full article

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