Hydrology

27 pages, 1812 KB

Open AccessArticle

Time-Varying Bivariate Modeling for Predicting Hydrometeorological Trends in Jakarta Using Rainfall and Air Temperature Data

by Suci Nur Setyawati, Sri Nurdiati, I Wayan Mangku, Ionel Haidu and Mohamad Khoirun Najib

Hydrology 2025, 12(10), 252; https://doi.org/10.3390/hydrology12100252 - 26 Sep 2025

Abstract

Changes in rainfall patterns and irregular air temperature have become essential issues in analyzing hydrometeorological trends in Jakarta. This study aims to select the best copula of the stationary and non-stationary copula models and visualize and explore the relationship between rainfall and air [...] Read more.

Changes in rainfall patterns and irregular air temperature have become essential issues in analyzing hydrometeorological trends in Jakarta. This study aims to select the best copula of the stationary and non-stationary copula models and visualize and explore the relationship between rainfall and air temperature to predict hydrometeorological trends. The methods used include combining univariate Lognormal and Generalized Extreme Value (GEV) distributions with Clayton, Gumbel, and Frank copulas, as well as parameter estimation using the fminsearch algorithm, Markov Chain Monte Carlo (MCMC) simulation, and a combination of both. The results show that the best model is the non-stationary Clayton copula estimated using MCMC simulation, which has the lowest Akaike Information Criterion (AIC) value. This model effectively captures extreme dependence in the lower tail of the distribution, indicating a potential increase in extreme low events such as cold droughts. Visualization of the best model through contour plots shows a shifting center of the distribution over time. This study contributes to developing dynamic hydrometeorological models for adaptation planning of changing hydrometeorological trends in Indonesia. Full article

(This article belongs to the Special Issue Trends and Variations in Hydroclimatic Variables: 2nd Edition)

23 pages, 9719 KB

Open AccessArticle

Evaluating Seasonal Rainfall Forecast Gridded Models over Sub-Saharan Africa

by Winifred Ayinpogbilla Atiah, Eduardo Garcia Bendito and Francis Kamau Muthoni

Hydrology 2025, 12(10), 251; https://doi.org/10.3390/hydrology12100251 - 26 Sep 2025

Abstract

Changes in the amount and distribution of rainfall highly impact agricultural production in predominantly rainfed farming systems in Africa. Reliable rainfall forecasts on a daily timescale are vital for in-season decision-making. This study evaluated the relative prediction abilities of the European Centre for [...] Read more.

Changes in the amount and distribution of rainfall highly impact agricultural production in predominantly rainfed farming systems in Africa. Reliable rainfall forecasts on a daily timescale are vital for in-season decision-making. This study evaluated the relative prediction abilities of the European Centre for Medium-Range Weather Forecasts Season 5.1 (ECMWFSv5.1) and the Climate Forecast System version 2 (CFSv2) gridded rainfall models across Africa and three sub-regions from 2012–2022. The results indicate that the performance of both models declines with increasing lead times and improves with aggregated or coarser temporal resolutions. ECMWFv5.1 consistently represented observed daily rainfall better than CFSv2 at all lead times, particularly in West Africa. On dekadal timescales, ECMWFv5.1 outperformed CFSv2 across all sub-regions. CFSv2 tended to overestimate low- and high-intensity rainfall events, whereas ECMWFv5.1 slightly underestimated low-intensity rainfall but accurately captured high-intensity events. While ECMWFv5.1 showed superior skill overall, model reliability was generally limited to West Africa; in contrast, both models performed poorly in East Africa. The high probability of detection (POD) indicates that the models are generally effective at identifying rainy days. However, their overall accuracy in forecasting rainfall across Africa varies depending on lead time, region, rainfall intensity, and elevation. While we did not apply bias-correction methods in this study, we recommend that such techniques be used in future work to improve the reliability of forecasts for operational and sectoral applications. This study therefore highlights both the strengths and the limitations of CFSv2 and ECMWFv5.1 for climate impact assessments, particularly in West Africa and low-elevation regions. Full article

39 pages, 18704 KB

Open AccessArticle

Modeling the Dynamics of the Jebel Zaghouan Karst Aquifer Using Artificial Neural Networks: Toward Improved Management of Vulnerable Water Resources

by Emna Gargouri-Ellouze, Tegawende Arnaud Ouedraogo, Fairouz Slama, Jean-Denis Taupin, Nicolas Patris and Rachida Bouhlila

Hydrology 2025, 12(10), 250; https://doi.org/10.3390/hydrology12100250 - 26 Sep 2025

Abstract

Karst aquifers are critical yet vulnerable water resources in semi-arid Mediterranean regions, where structural complexity, nonlinearity, and delayed hydrological responses pose significant modeling challenges under increasing climatic and anthropogenic pressures. This study examines the Jebel Zaghouan aquifer in northeastern Tunisia, aiming to simulate [...] Read more.

Karst aquifers are critical yet vulnerable water resources in semi-arid Mediterranean regions, where structural complexity, nonlinearity, and delayed hydrological responses pose significant modeling challenges under increasing climatic and anthropogenic pressures. This study examines the Jebel Zaghouan aquifer in northeastern Tunisia, aiming to simulate its natural discharge dynamics prior to intensive exploitation (1915–1944). Given the fragmented nature of historical datasets, meteorological inputs (rainfall, temperature, and pressure) were reconstructed using a data recovery process combining linear interpolation and statistical distribution fitting. The hyperparameters of the artificial neural network (ANN) model were optimized through a Bayesian search. Three deep learning architectures—Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—were trained to model spring discharge. Model performance was evaluated using Kling–Gupta Efficiency (KGE′), Nash–Sutcliffe Efficiency (NSE), and R² metrics. Hydrodynamic characterization revealed moderate variability and delayed discharge response, while isotopic analyses (δ¹⁸O, δ²H, ³H, ¹⁴C) confirmed a dual recharge regime from both modern and older waters. LSTM outperformed other models at the weekly scale (KGE′ = 0.62; NSE = 0.48; R² = 0.68), effectively capturing memory effects. This study demonstrates the value of combining historical data rescue, ANN modeling, and hydrogeological insight to support sustainable groundwater management in data-scarce karst systems. Full article

(This article belongs to the Special Issue Advancing Hydrological Science Through Artificial Intelligence: Innovations and Applications)

20 pages, 2878 KB

Open AccessArticle

Development of a Semi-Analytical Solution for Simulating the Migration of Parent and Daughter Contaminants from Multiple Contaminant Sources, Considering Rate-Limited Sorption Effects

by Thu-Uyen Nguyen, Yi-Hsien Chen, Heejun Suk, Ching-Ping Liang and Jui-Sheng Chen

Hydrology 2025, 12(10), 249; https://doi.org/10.3390/hydrology12100249 - 25 Sep 2025

Abstract

Most existing multispecies transport analytical models primarily focus on inlet boundary sources, limiting their applicability in real-world contaminated sites where contaminants often arise from multiple internal sources. This study presents a novel semi-analytical model for simulating multispecies contaminant transport driven by multiple time-dependent [...] Read more.

Most existing multispecies transport analytical models primarily focus on inlet boundary sources, limiting their applicability in real-world contaminated sites where contaminants often arise from multiple internal sources. This study presents a novel semi-analytical model for simulating multispecies contaminant transport driven by multiple time-dependent internal sources. The model incorporates key transport mechanisms, including advection, dispersion, rate-limited sorption, and first-order degradation. In particular, the inclusion of rate-limited sorption addresses limitations in traditional equilibrium-based models, which often underestimate pollutant concentrations for degradable species. The derivation of this semi-analytical model utilizes the Laplace transform, finite cosine Fourier transform, generalized integral transform, and a sequence of inverse transformations. Results indicate that the concentrations of contaminants and their degradation products are highly sensitive to the variations in time-dependent sources. The model’s most significant contribution lies in its capability to simulate the contaminant transport from multiple internal pollution sources at a contaminated site under the influence of rate-limited sorption. By enabling the representation of multiple time-varying sources, this model fills a critical gap in analytical approaches and provides a necessary tool for accurately assessing contaminant transport in complex, realistic pollution scenarios. Full article

(This article belongs to the Topic Advances in Groundwater Science and Engineering)

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17 pages, 3306 KB

Open AccessArticle

SWOT Satellite Nodes as Virtual Stations During the 2024 Extreme Flood in Southern Brazil

by Luana Oliveira Sales, Thiago Lappicy, Daniel Beltrão, Alexandre de Amorim Teixeira, Rejane Cicerelli and Tati Almeida

Hydrology 2025, 12(10), 248; https://doi.org/10.3390/hydrology12100248 - 25 Sep 2025

Abstract

In 2024, Rio Grande do Sul (RS), Brazil, faced the most severe flood event in its recorded history, which compromised several ground-based hydrological gauges. The SWOT (Surface Water and Ocean Topography) satellite, capable of measuring water surface elevation (WSE) in continental waters, is [...] Read more.

In 2024, Rio Grande do Sul (RS), Brazil, faced the most severe flood event in its recorded history, which compromised several ground-based hydrological gauges. The SWOT (Surface Water and Ocean Topography) satellite, capable of measuring water surface elevation (WSE) in continental waters, is a valuable tool for providing critical data. This study investigates whether node-level WSE data from the SWOT satellite can effectively function as virtual hydrological stations under such extreme conditions. The study was applied in all of RS state considering 100 in situ gauges and was subdivided into three sections: (i) an evaluation of the variation in SWOTʹs WSE data compared to the variation in in situ levels from telemetric gauges, considering subsequent cycles of passes between July 2023 and April 2025, yielding an MAE = 35 cm and an RMSE = 73 cm after outlier removal; (ii) an evaluation of the variation in SWOTʹs WSE data compared to the variation in telemetric level data, considering one window prior to and another during the extreme event, resulting an MAE = 26 cm and an RMSE = 34 cm; (iii) an analysis of SWOTʹs data availability during the extreme event, when in situ telemetric data were unavailable. The results demonstrate an agreement between the variation observed in SWOT data and that in telemetric gauges in RS, even during extreme events. Moreover, in the absence of in situ data, SWOT was still able to capture WSE data. Full article

(This article belongs to the Special Issue Advances in Flood Studies: Enhancing Data Collection, Rating Curves, and Hydrological Analyses)

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30 pages, 27834 KB

Open AccessArticle

Spatiotemporal Characteristics of Extreme Precipitation Events in Central Asia: Insights from an Event-Based Analysis

by Chunrui Guo, Hao Guo, Xiangchen Meng, Ying Cao, Wei Wang and Philippe De Maeyer

Hydrology 2025, 12(10), 247; https://doi.org/10.3390/hydrology12100247 - 25 Sep 2025

Abstract

Extreme precipitation events, increasingly driven by climate change, are becoming more frequent and pose significant challenges to both the ecological environment and human society. Using the MSWEP data, this study constructed eight event-based extreme precipitation indicators so as to systematically analyze the spatiotemporal [...] Read more.

Extreme precipitation events, increasingly driven by climate change, are becoming more frequent and pose significant challenges to both the ecological environment and human society. Using the MSWEP data, this study constructed eight event-based extreme precipitation indicators so as to systematically analyze the spatiotemporal characteristics and dominant types of extreme precipitation across Central Asia and its three sub-regions from 1979 to 2023. The results revealed the following: (1) Extreme precipitation events exhibit a pronounced spatial preference for high-altitude areas, with the total number of events reaching up to 698 in these regions. (2) From 1979 to 1991, the frequency of extreme precipitation events has decreased in Central Asia (by 1.742 events per 13 years), while their duration has however increased (by 0.52 days per 13 years). The period from 1992 to 2009 experienced the most significant and widespread decline in the magnitude of extreme precipitation indicators. In contrast, from 2010 to 2023, all indicators—except for the event frequency (EF) and event intensity (EI)—have shown rising tendencies across the region. (3) Regarding the dominant event types, based on the proportion of extreme precipitation frequency across areas, the Southwestern Desert (SD) and northern Kazakhstan (NK) regions are characterized by a more prominent combination of rear-peak (TDP2) and front-peak (TDP1) events, whereas the southeastern mountains (SM) region is rather dominated by a combination of rear-peak (TDP2) and balanced-type (TDP3) events. (4) The EF and event duration (ED) are strongly associated with the Digital Elevation Model (DEM) and Aridity Index (AI). The spatial patterns of EF and ED are closely linked, with the sub-humid and mountainous regions demonstrating the highest frequency and longest duration of extreme precipitation events. Full article

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22 pages, 4958 KB

Open AccessArticle

Impact of Land Cover Change on Eutrophication Processes in Phewa Lake, Nepal

by Rajan Subedi, Bikesh Jojiju, Matthew McBroom, Leticia Gaspar, Gerd Dercon and Ana Navas

Hydrology 2025, 12(10), 246; https://doi.org/10.3390/hydrology12100246 - 25 Sep 2025

Abstract

Increasing demand for land and resources in Himalayan catchments is altering hydrological processes and threatening freshwater ecosystems. Sediment mobilization and nutrient fluxes, especially during monsoon rainfall events, are intensifying the degradation of water bodies. This study investigates land cover change and its effects [...] Read more.

Increasing demand for land and resources in Himalayan catchments is altering hydrological processes and threatening freshwater ecosystems. Sediment mobilization and nutrient fluxes, especially during monsoon rainfall events, are intensifying the degradation of water bodies. This study investigates land cover change and its effects on nutrient dynamics in the Phewa Lake catchment, Nepal. Landsat imagery from 1990 to 2021, processed through Google Earth Engine, was used to map land changes. Nutrient loading for the two time periods was estimated with the InVEST model. Surface soils were sampled across the catchment to analyze nitrogen and phosphorus distribution, while their particle-bound transport to the lake was assessed through riverbed sediments and the suspended sediments collected during monsoon rainfalls. Pre-monsoon water quality was examined to evaluate eutrophication levels across different lake zones. Results reveal forest recovery in the upper catchment, but agricultural land in the lower catchment is being rapidly converted to urban areas. While forest recovery has enhanced sediment retention, nutrient inputs to the lake, particularly nitrogen and phosphorus, have increased. Fertilizer leaching and untreated sewage emerge as key sources in rural and urban areas, respectively. Seasonal constraints of the dataset may underestimate the overall extent of water quality deterioration, as indicated by high nutrient loads in monsoon suspended sediments. Overall, this study highlights the dual effect of land cover change: forest regrowth coincides with rising nutrient discharge. Without timely interventions, growing urban populations in the region may face worsening water quality challenges. Full article

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

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17 pages, 2437 KB

Open AccessArticle

Spatiotemporal Patterns of Inundation in the Nemunas River Delta Using Sentinel-1 SAR: Influence of Land Use and Soil Composition

by Jonas Gintauskas, Martynas Bučas, Diana Vaičiūtė and Edvinas Tiškus

Hydrology 2025, 12(10), 245; https://doi.org/10.3390/hydrology12100245 - 23 Sep 2025

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Abstract

Inundation dynamics in low-lying deltas are becoming increasingly important to monitor due to the impacts of climate change and human alterations to hydrological systems, which disrupt natural inundation patterns. In the Nemunas River Delta, where seasonal and extreme floods impact agricultural and natural [...] Read more.

Inundation dynamics in low-lying deltas are becoming increasingly important to monitor due to the impacts of climate change and human alterations to hydrological systems, which disrupt natural inundation patterns. In the Nemunas River Delta, where seasonal and extreme floods impact agricultural and natural landscapes, we used Sentinel-1 synthetic aperture radar (SAR) imagery (2015–2019), validated with drone data, to map flood extents. SAR provides consistent, 10 m resolution data unaffected by cloud cover, while drone imagery provides high-resolution (10 cm) data at 90 m flight height for validation during SAR acquisitions. Results revealed peak inundation during spring snowmelt and colder months, with shorter, rainfall-driven summer floods. Approximately 60% of inundated areas were low-lying agricultural fields, which experienced prolonged waterlogging due to poor drainage and soil degradation. Inundation duration was shaped by lithology, land cover, and topography. A consistent 5–10-day lag between peak river discharge and flood expansion suggests discharge data can complement SAR when imagery is unavailable. This study confirms SAR’s value for flood mapping in cloud-prone, temperate regions and highlights its scalability for monitoring flood-prone deltas where agriculture and infrastructure face increasing climate-related risks. Full article

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Hydrology, Volume 12, Issue 10 (October 2025) – 8 articles

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