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Hydrology
Volume 11
Issue 10
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Hydrology, Volume 11, Issue 10 (October 2024) – 19 articles

Cover Story (view full-size image): Increasing extreme weather events necessitate accurate flood prediction to protect lives and infrastructure. A study in the Waryong Reservoir Basin revealed significant differences in flood model (Cell2Flood) accuracy depending on rainfall inputs. Researchers evaluated six rainfall datasets, including on-site measurements, Automated Synoptic Observing System (ASOS) and Automatic Weather System (AWS) network data, and radar-based HSR data. Point-gauged ASOS (P-ASOS) data showed the highest reliability (Pearson's r up to 0.84), outperforming the nearest AWS station. Conversely, HSR data underperformed, with large runoff prediction errors (NPE up to 4.66, PTE over 200 minutes). Input-specific calibration improved the results, but errors persisted due to inherent rainfall data uncertainty. This highlights the importance of careful rainfall input selection and calibration in flood modeling. View this paper
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22 pages, 4548 KiB  
Article
MODIS Evapotranspiration Forecasting Using ARIMA and ANN Approach at a Water-Stressed Irrigation Scheme in South Africa
by Mbulelo Phesa, Nkanyiso Mbatha and Akinola Ikudayisi
Hydrology 2024, 11(10), 176; https://doi.org/10.3390/hydrology11100176 - 21 Oct 2024
Viewed by 849
Abstract
The forecasting of evapotranspiration (ET) in some water-stressed regions remains a major challenge due to the lack of reliable and sufficient historical datasets. For efficient water balance, ET remains the major component and its proper forecasting and quantifying is of the utmost importance. [...] Read more.
The forecasting of evapotranspiration (ET) in some water-stressed regions remains a major challenge due to the lack of reliable and sufficient historical datasets. For efficient water balance, ET remains the major component and its proper forecasting and quantifying is of the utmost importance. This study utilises the 18-year (2001 to 2018) MODIS ET obtained from a drought-affected irrigation scheme in the Eastern Cape Province of South Africa. This study conducts a teleconnection evaluation between the satellite-derived evapotranspiration (ET) time series and other related remotely sensed parameters such as the Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Normalised Difference Drought Index (NDDI), and precipitation (P). This comparative analysis was performed by adopting the Mann–Kendall (MK) test, Sequential Mann–Kendall (SQ-MK) test, and Multiple Linear Regression methods. Additionally, the ET detailed time-series analysis with the Keiskamma River streamflow (SF) and monthly volumes of the Sandile Dam, which are water supply sources close to the study area, was performed using the Wavelet Analysis, Breaks for Additive Seasonal and Trend (BFAST), Theil–Sen statistic, and Correlation statistics. The MODIS-obtained ET was then forecasted using the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) for a period of 5 years and four modelling performance evaluations such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and the Pearson Correlation Coefficient (R) were used to evaluate the model performances. The results of this study proved that ET could be forecasted using these two time-series modeling tools; however, the ARIMA modelling technique achieved lesser values according to the four statistical modelling techniques employed with the RMSE for the ARIMA = 37.58, over the ANN = 44.18; the MAE for the ARIMA = 32.37, over the ANN = 35.88; the MAPE for the ARIMA = 17.26, over the ANN = 24.26; and for the R ARIMA = 0.94 with the ANN = 0.86. These results are interesting as they give hope to water managers at the irrigation scheme and equally serve as a tool to effectively manage the irrigation scheme. Full article
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15 pages, 6462 KiB  
Review
A Graphic Review of Studies on Ocean and Mediterranean Sea Environment Quality
by Andrei-Emil Briciu
Hydrology 2024, 11(10), 175; https://doi.org/10.3390/hydrology11100175 - 18 Oct 2024
Viewed by 868
Abstract
With so many studies today on the water quality of the sea, one can hardly comprehend the multitude of topics that arise all over the world. This study provides a few graphic syntheses related to the most frequent words (including their clustering and [...] Read more.
With so many studies today on the water quality of the sea, one can hardly comprehend the multitude of topics that arise all over the world. This study provides a few graphic syntheses related to the most frequent words (including their clustering and links), trend topics, the spatial distribution of the researched areas, and the thematic evolution of the research directions over the decades. The most frequent authors’ keywords have a 50% similitude between the ocean studies and the studies related to the Mediterranean Sea; these keywords are part of a causal chain that dominates the marine studies on water quality: nutrients → eutrophication → phytoplankton → chlorophyll → seagrass. The most frequent words in the titles and abstracts of the selected papers from the Web of Science are “concentration” and “species”; in the Mediterranean studies, “chlorophyll” and “temperature” are the most frequent. In close connection with water quality, Zostera marina (eelgrass) and Crassotrea virginica (eastern oyster) prevail at the global scale, while Posidonia oceanica (Neptune grass) is relevant in the Mediterranean space. Some of the most studied water bodies are the South China Sea, San Francisco Bay, Chesapeake Bay, and, in the Mediterranean Sea, the Adriatic, Ionian, Aegean, and Marmara seas. “Climate change” and “remote sensing” are trend topics that shape the current studies on water quality; the increasing sea surface temperature enhances algal blooms—these need to be monitored using satellite imagery for the sustainable evolution of human activities, including aquaculture. Full article
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25 pages, 6907 KiB  
Article
Geoenvironmental Effects of the Hydric Relationship Between the Del Sauce Wetland and the Laguna Verde Detritic Coastal Aquifer, Central Chile
by Blanca Gana, José Miguel Andreu Rodes, Paula Díaz, Agustín Balboa, Sebastián Frías, Andrea Ávila, Cecilia Rivera, Claudio A. Sáez and Céline Lavergne
Hydrology 2024, 11(10), 174; https://doi.org/10.3390/hydrology11100174 - 16 Oct 2024
Viewed by 1006
Abstract
In the central region of Chile, the Mega-Drought together with the demographic increase near the coast threatens groundwater availability and the hydrogeological functioning of coastal wetlands. To understand the hydric relationship between an aquifer and a wetland in a semi-arid coastal region of [...] Read more.
In the central region of Chile, the Mega-Drought together with the demographic increase near the coast threatens groundwater availability and the hydrogeological functioning of coastal wetlands. To understand the hydric relationship between an aquifer and a wetland in a semi-arid coastal region of Central Chile (Valparaíso, Chile), as well as its geoenvironmental effects, four data collection campaigns were conducted in the wetland–estuary hydric system and surroundings, between 2021 and 2022, including physical, hydrochemical, and isotopic analyses in groundwater (n = 16 sites) and surface water (n = 8 sites). The results generated a conceptual model that indicates a hydraulic connection between the wetland and the aquifer, where the water use in one affects the availability in the other. With an average precipitation of 400 mm per year, the main recharge for both systems is rainwater. Three specific sources of pollution were identified from anthropic discharges that affect the water quality of the wetland and the estuary (flow from sanitary landfill, agricultural and livestock industry, and septic tank discharges in populated areas), exacerbated by the infiltration of seawater laterally and superficially through sandy sediments and the estuary, increasing salinity and electrical conductivity in the coastal zone (i.e., 3694 µS/cm). The Del Sauce subbasin faces strong hydric stress triggered by the poor conservation state of the riparian–coastal wetland and groundwater in the same area. This study provides a detailed understanding of hydrological interactions and serves as a model for understanding the possible effects on similar ecosystems, highlighting the need for integrated and appropriate environmental management. Full article
(This article belongs to the Topic Hydrosphere under the Driving of Human Activity and Climate Change: Status, Evolution and Strategies)
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14 pages, 2295 KiB  
Article
Kilometer-Scale Precipitation Forecasting Utilizing Convolutional Neural Networks: A Case Study of Jiangsu’s Coastal Regions
by Ninghao Cai, Hongchuan Sun and Pengcheng Yan
Hydrology 2024, 11(10), 173; https://doi.org/10.3390/hydrology11100173 - 13 Oct 2024
Viewed by 836
Abstract
High-resolution precipitation forecasts play a pivotal role in formulating comprehensive disaster prevention and mitigation plans. As spatial resolution enhances, striking a balance between computation, storage, and simulation accuracy becomes imperative to ensure optimal cost-effectiveness. Convolutional neural networks (CNNs), a cornerstone of deep learning, [...] Read more.
High-resolution precipitation forecasts play a pivotal role in formulating comprehensive disaster prevention and mitigation plans. As spatial resolution enhances, striking a balance between computation, storage, and simulation accuracy becomes imperative to ensure optimal cost-effectiveness. Convolutional neural networks (CNNs), a cornerstone of deep learning, are examined in this study for their downscaling capabilities in precipitation simulation. During a precipitation event on 23 June 2022, in Jiangsu Province, China, distinct rain belts emerged in both southern and northern Jiangsu, precisely captured by a numerical model (the Weather Research and Forecasting, WRF) with a 3 km spatial resolution. Specifically, precipitation was prevalent in northern Jiangsu from 00:00 to 11:00 Beijing Time (BJT), transitioning to southern Jiangsu from 12:00 to 23:00 BJT on the same day. Upon dynamic downscaling, the model reproduced precipitation in these periods with an average error of 12.35 mm at 3 km and 12.48 mm at 1 km spatial resolutions. Employing CNN technology for statistical downscaling to a 1 km spatial resolution, samples from the initial period were utilized for training, while those from the subsequent period served for validation. Following dynamic downscaling, CNNs with four, five, six, and seven layers exhibited average errors of 8.86 mm, 8.93 mm, 9.71 mm, and 9.70 mm, respectively, accompanied by correlation coefficients of 0.550, 0.570, 0.574, and 0.578, respectively. This analysis indicates that for this precipitation event, a shallower CNN depth yields a lower average error and correlation coefficient, whereas a deeper architecture enhances the correlation coefficient. By employing deep network architectures, CNNs are capable of capturing nonlinear patterns and subtle local features from complex meteorological data, thereby providing more accurate predictions during the downscaling process. Leveraging faster computation and reduced storage requirements, machine learning has demonstrated immense potential in high-resolution forecasting research. There is significant scope for advancing technologies that integrate numerical models with machine learning to achieve higher-resolution numerical forecasts. Full article
(This article belongs to the Special Issue New Perspectives in the Flood Forecasting Chain (Weather Prediction, Rainfall-Runoff Modeling, and Communication with Stakeholders))
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18 pages, 3661 KiB  
Article
Estimation of Reservoir Storage Capacity Using the Gould-Dincer Formula with the Aid of Possibility Theory
by Nikos Mylonas, Christos Tzimopoulos, Basil Papadopoulos and Nikiforos Samarinas
Hydrology 2024, 11(10), 172; https://doi.org/10.3390/hydrology11100172 - 11 Oct 2024
Viewed by 826
Abstract
This paper presents a method for estimating reservoir storage capacity using the Gould–Dincer normal formula (G-DN), enhanced by the possibility theory. The G-DN equation is valuable for regional studies of reservoir reliability, particularly under climate change scenarios, using regional statistics. However, because the [...] Read more.
This paper presents a method for estimating reservoir storage capacity using the Gould–Dincer normal formula (G-DN), enhanced by the possibility theory. The G-DN equation is valuable for regional studies of reservoir reliability, particularly under climate change scenarios, using regional statistics. However, because the G-DN formula deals with measured data, it introduces a degree of uncertainty and fuzziness that traditional probability theory struggles to address. Possibility theory, an extension of fuzzy set theory, offers a suitable framework for managing this uncertainty and fuzziness. In this study, the G-DN formula is adapted to incorporate fuzzy logic, and the possibilistic nature of reservoir capacity is translated into a probabilistic framework using α-cuts from the possibility theory. These α-cuts approximate probability confidence intervals with high confidence. Applying the proposed methodology, in the present crisp case with the storage capacity D = 0.75, the value of the capacity C was found to be 1271×106 m3, and that for D = 0.5 was 634.5×106 m3. On the other hand, in the fuzzy case using the possibility theory, the value of the capacity for D = 0.75 is the internal [315,5679]×106 m3 and for D = 0.5 the value is interval [158,2839]×106 m3, with a probability of ≥95% and a risk level of α = 5% for both cases. The proposed approach could be used as a robust tool in the toolkit of engineers working on irrigation, drainage, and water resource projects, supporting informed and effective engineering decisions. Full article
(This article belongs to the Special Issue Water Resources Management under Uncertainty and Climate Change)
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15 pages, 1030 KiB  
Article
Distribution of Subsurface Nitrogen and Phosphorus from Different Irrigation Methods in a Maize Field
by Gang Xie, Zhihui Su, Yiming Fu, Jing Li, Deqiang Mao and Shaowei Wang
Hydrology 2024, 11(10), 171; https://doi.org/10.3390/hydrology11100171 - 11 Oct 2024
Viewed by 703
Abstract
With the advancement of agricultural technology, most crop cultivation adopts water-saving techniques to improve nutrient utilization efficiency. However, limited research has been carried out on the applicability of water-saving techniques for summer maize in the Shandong Province, and it is necessary to assess [...] Read more.
With the advancement of agricultural technology, most crop cultivation adopts water-saving techniques to improve nutrient utilization efficiency. However, limited research has been carried out on the applicability of water-saving techniques for summer maize in the Shandong Province, and it is necessary to assess the risk of nutrient loss in farmland when applying these technologies. This study investigated the distribution of nitrogen and phosphorus under different irrigation methods and planting patterns through soil and water samples. It included sprinkler irrigation (SI), drip irrigation (DI), and subsurface irrigation (SUBI). Different planting patterns, i.e., monoculture (MP) and intercropping pattern (IP), were also selected in the SI zones. The results show variations in soil nitrogen distribution within the layers between 0.9 and 4.5 m, with a pronounced trend of NO3−-N accumulating in deeper layers in the SI zone. Under SI conditions, the IP effectively reduces the nutrient accumulation around the shallow root zone while controlling the accumulation of nitrogen in deep layers. The Olsen-P accumulation in each zone would increase after the accumulation ratio decreased. Compared with MP, the depth interval of the accumulation ratio mutation was shallower in the IP. The trend of NO3−-N accumulation in deep layers is consistent with that of nitrogen concentration in groundwater. Phosphorus that is accumulated in the deep layers is not easily leached into groundwater. In conclusion, these findings can provide basic information for irrigation management in existing cropping systems. Full article
(This article belongs to the Section Soil and Hydrology)
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29 pages, 8879 KiB  
Article
An Integrated Approach to the Regional Estimation of Soil Moisture
by Luis Pastor Sánchez-Fernández, Diego Alberto Flores-Carrillo and Luis Alejandro Sánchez-Pérez
Hydrology 2024, 11(10), 170; https://doi.org/10.3390/hydrology11100170 - 11 Oct 2024
Viewed by 774
Abstract
Automatic or smart irrigation systems benefit irrigation water management. However, measurement sensor networks in automatic irrigation systems are complex, and maintenance is essential. Regional soil moisture estimation avoids the multiple measurements necessary when deploying an irrigation system. In this sense, a fuzzy estimation [...] Read more.
Automatic or smart irrigation systems benefit irrigation water management. However, measurement sensor networks in automatic irrigation systems are complex, and maintenance is essential. Regional soil moisture estimation avoids the multiple measurements necessary when deploying an irrigation system. In this sense, a fuzzy estimation approach based on decision-making (FEADM) has been used to obtain soil moisture point estimates. However, FEADM requires intelligent weather adjustment based on spatial features (IWeCASF) to perform regional soil moisture estimation. The IWeCASF-FEADM integrated approach for regional soil moisture estimation is developed in this work. IWeCASF provides the inputs for FEADM. FEADM is performed R times; R is the number of checkpoints at which a point estimate is obtained. In this way, regional estimation is achieved when the set of R soil moisture point estimates is completed. Additionally, IWeCASF-FEADM considers the irrigation water records, which are not included in either method individually. This method can detect when the soil moisture is deficient in a region, allowing actions to prevent water stress. This regional estimation reduces an irrigation system’s operational and maintenance complexity. This integrated approach has been tested over several years by comparing the results of regional soil moisture estimation with measurements obtained at many points in the study region. Full article
(This article belongs to the Special Issue Geographic Information Systems (GIS) Techniques and Applications for Sustainable Water Resources Management in Agriculture)
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15 pages, 3374 KiB  
Article
Impacts of Climate Change on Groundwater in the Al-Badan Sub-Catchment, Palestine: Analyzing Historical Data and Future Scenarios
by Hamzah Faquseh, Sameer Shadeed and Giovanna Grossi
Hydrology 2024, 11(10), 169; https://doi.org/10.3390/hydrology11100169 - 10 Oct 2024
Cited by 1 | Viewed by 810
Abstract
Climate change is significantly impacting water resources, especially in arid regions. This study evaluates its effects on groundwater in the Al-Badan sub-catchment, Palestine, by analyzing hydroclimatic data from 1990 to 2020 and the future predicted climate change scenarios. Using the Mann-Kendall test and [...] Read more.
Climate change is significantly impacting water resources, especially in arid regions. This study evaluates its effects on groundwater in the Al-Badan sub-catchment, Palestine, by analyzing hydroclimatic data from 1990 to 2020 and the future predicted climate change scenarios. Using the Mann-Kendall test and Sen’s slope estimator, a significant annual decline in annual precipitation of 125 mm and a temperature increase of 1.84 °C were observed, resulting in a spring discharge reduction of 1.2 MCM. Multiple linear regression analysis showed that a 10% increase in precipitation correlates with a 5% discharge increase, while a 1 °C rise in temperature results in a 2.3% discharge decrease. Future scenarios indicate significant changes: for 2040–2060, RCP2.6 forecasts average precipitation of 334.5 mm with temperatures at 18.5 °C, resulting in spring discharge of about 4.6 MCM. In contrast, RCP4.5 and RCP8.5 predict reductions in precipitation to 307.2 mm and 311.2 mm, respectively, with temperatures rising to 18.9 °C and 19.3 °C, leading to discharge declines to 4.2 MCM and 4.0 MCM. For 2080–2100, RCP2.6 anticipates 335.8 mm of precipitation and temperatures rising to 19.5 °C, resulting in average discharge of 4.5 MCM. RCP4.5 and RCP8.5 predict further declines in precipitation and discharge, underscoring the need for effective water management strategies. Full article
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17 pages, 4389 KiB  
Article
A Long-Term Evaluation of the Ecohydrological Regime in a Semiarid Basin: A Case Study of the Huangshui River in the Yellow River Basin, China
by Lijuan Fan, Lanxin Liu, Jing Hu, Fen Zhao, Chunhui Li and Yujun Yi
Hydrology 2024, 11(10), 168; https://doi.org/10.3390/hydrology11100168 - 10 Oct 2024
Viewed by 670
Abstract
This study aimed to evaluate the ecohydrological regime and ecological water demand of the Huangshui River Basin under changing environmental conditions, seeking to safeguard its ecosystem. Based on monthly data spanning from 1956 to 2016, the ecohydrological regimes of the Huangshui River and [...] Read more.
This study aimed to evaluate the ecohydrological regime and ecological water demand of the Huangshui River Basin under changing environmental conditions, seeking to safeguard its ecosystem. Based on monthly data spanning from 1956 to 2016, the ecohydrological regimes of the Huangshui River and the Datong River were evaluated using methods such as the Pettitt mutation test, the Tennant method, and ecological deficit and surplus analyses. The data were mainly obtained from Xiangtang Station of the Datong River and Minhe Station of the Huangshui River. The results showed the following. (1) The most abrupt increase in measured runoff at Xiangtang Station occurred in 1993, while the point of abrupt change in measured runoff at Minhe Station occurred in 1990. (2) Following an increase in human activities, changes in the ecological surplus at Xiangtang Station were negative in January, April to May, July, and from September to November, while the changes in the ecological deficit were positive from January to April, July to August, and October to December. Changes in the ecological surplus at Minhe Station were negative from March to July and from September to December, while changes in the ecological deficit were positive from January to April and from July to December. (3) The annual average ecological flow of the Datong River, Xiangtang section, was 28.42 m3/s, and the annual average ecological water demand was 896 million m3. The annual average ecological flow of the Minhe section was 19.98 m3/s, and the annual average ecological water demand was 631 million m3. According to a calculation of the degree of ecological water demand and ecological flow satisfaction, prior to the implementation of the Water Diversion Project from the Datong River to Huangshui River, the water volumes in both rivers were generally sufficient to meet the ecological water demand. However, high water consumption during the irrigation period led to an ecological deficit. To address these issues, it is crucial to evaluate the potential impacts of human activities, such as water diversion projects, on river ecological flow. Recommendations include expediting the Water Diversion Project from the Yellow River to Xining to secure sufficient water flow in the Huangshui River and enhancing water conservation efforts in agricultural irrigation. Full article
(This article belongs to the Topic Advances in Hydro-Geological Research in Arid and Semi-Arid Areas)
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17 pages, 4143 KiB  
Article
A Composite Approach for Evaluating Operational Cloud Seeding Effect in Stratus Clouds
by Fei Wang, Baojun Chen, Zhiguo Yue, Jin Wang, Dejun Li, Dawei Lin, Yahui Tang and Tian Luan
Hydrology 2024, 11(10), 167; https://doi.org/10.3390/hydrology11100167 - 8 Oct 2024
Viewed by 1037
Abstract
Robust water management is in intense demand in many water scarcity areas, such as arid and semi-arid regions in the world. As part of the regional water management strategy, rain enhancement is vital to replenish groundwater reservoirs, and the key challenge is how [...] Read more.
Robust water management is in intense demand in many water scarcity areas, such as arid and semi-arid regions in the world. As part of the regional water management strategy, rain enhancement is vital to replenish groundwater reservoirs, and the key challenge is how to assess its effectiveness. Some recent weather modification experiments attained cloud seeding effect through advanced in situ measurement coupled with accurate numerical simulation. However, there is still a lack of an objective and scientific approach to quantitatively evaluate the rain enhancement effect, especially for many non-randomized operational cloud seeding activities in China. In this study, we proposed a composite evaluation approach by analyzing two operational aircraft cloud seeding cases in stratus clouds in Shaanxi, China. By calculating the aircraft cloud seeding agent plumes, the target areas (as well as the control areas) of cloud seeding were dynamically and roughly determined. Physical properties, such as radar reflectivity and precipitation, were individually quantified in these areas. The cloud seeding effect was then evaluated by calculating the difference in parameter variation between target and control areas. This approach can be applied to qualitative analysis in a single aircraft cloud seeding operation and can also provide quantitative statistical results from multiple cloud seeding cases. We found that the average precipitation enhancement percentage of 18 operational aircraft cloud seeding cases is ~4.84%. Note that the homogeneity hypothesis of the seeding cloud, the error in the calculation of the target area, and the selection of control areas are the major uncertainties likely in the evaluation of the cloud seeding effect by this approach. Full article
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21 pages, 6264 KiB  
Article
Suitability Assessment and Optimization of Small Dams and Reservoirs in Northern Ghana
by Etienne Umukiza, Felix K. Abagale, Thomas Apusiga Adongo and Andrea Petroselli
Hydrology 2024, 11(10), 166; https://doi.org/10.3390/hydrology11100166 - 7 Oct 2024
Viewed by 918
Abstract
Water shortages, exacerbated by erratic rainfall, climate change, and population growth, pose significant challenges globally, particularly in semi-arid regions like northern Ghana. Despite the construction of numerous small dams in the region that were intended to provide reliable water for domestic and irrigation [...] Read more.
Water shortages, exacerbated by erratic rainfall, climate change, and population growth, pose significant challenges globally, particularly in semi-arid regions like northern Ghana. Despite the construction of numerous small dams in the region that were intended to provide reliable water for domestic and irrigation purposes, critical water issues persist during dry periods. Key drivers in this failure are attributed to the lack of studies and/or the number of inadequate studies on suitable dam siting. This study focused on assessing the sites of selected small dams in northern Ghana, employing various methods such as stream order analysis and the Analytic Hierarchy Process within a Geographic Information System framework. Results showed that many existing dams are poorly sited, with over half located far from major stream networks, resulting in drying out during the dry season and failing to meet sustainable water storage standards. This study proposed new dam locations that would allow achieving a significant increase in storage capacities from 30% to 60%. These results highlight the necessity for decision-makers to adopt research-based approaches to address water shortages effectively, balancing agricultural, domestic, economic, and environmental needs. Future research should integrate climate change considerations, long-term monitoring, environmental impact assessments, and advanced decision-making techniques such as machine learning. Full article
(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management)
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20 pages, 4810 KiB  
Article
Understanding Spatio-Temporal Hydrological Dynamics Using SWAT: A Case Study in the Pativilca Basin
by Yenica Pachac-Huerta, Waldo Lavado-Casimiro, Melania Zapana and Robinson Peña
Hydrology 2024, 11(10), 165; https://doi.org/10.3390/hydrology11100165 - 4 Oct 2024
Viewed by 1190
Abstract
This study investigates the hydrological dynamics of the Pativilca Basin in the Southern Hemisphere using the SWAT (Soil and Water Assessment Tool) model. Seventy-seven watersheds across a mountainous region were analyzed using elevation data, land cover, soil type, and gridded meteorological products (RAIN4PE [...] Read more.
This study investigates the hydrological dynamics of the Pativilca Basin in the Southern Hemisphere using the SWAT (Soil and Water Assessment Tool) model. Seventy-seven watersheds across a mountainous region were analyzed using elevation data, land cover, soil type, and gridded meteorological products (RAIN4PE and PISCO) for hydrological simulations. Watershed delineation, aided by a Digital Elevation Model, enabled the identification of critical drainage points and the definition of Hydrological Response Units (HRUs). The model calibration and validation, performed using the SWAT-CUP with the SUFI-2 algorithm, achieved Nash–Sutcliffe Efficiency (NSE) values of 0.69 and 0.72, respectively. Cluster analysis categorized the watersheds into six distinct groups with unique hydrological and climatic characteristics. The results showed significant spatial variability in the precipitation and temperature, with pronounced seasonality influencing the daily flow patterns. The higher-altitude watersheds exhibited greater soil water storage and more effective aquifer recharge, whereas the lower-altitude watersheds, despite receiving less precipitation, displayed higher flows due to runoff from the upstream areas. These findings emphasize the importance of incorporating seasonality and spatial variability into water resource planning in mountainous regions and demonstrate the SWAT model’s effectiveness in predicting hydrological responses in the Pativilca Basin, laying the groundwork for future research in mountain hydrology. Full article
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23 pages, 4056 KiB  
Article
Performance Evaluation of Gradient Descent Optimizers in Estuarine Turbidity Estimation with Multilayer Perceptron and Sentinel-2 Imagery
by Naledzani Ndou and Nolonwabo Nontongana
Hydrology 2024, 11(10), 164; https://doi.org/10.3390/hydrology11100164 - 3 Oct 2024
Viewed by 935
Abstract
Accurate monitoring of estuarine turbidity patterns is important for maintaining aquatic ecological balance and devising informed estuarine management strategies. This study aimed to enhance the prediction of estuarine turbidity patterns by enhancing the performance of the multilayer perceptron (MLP) network through the introduction [...] Read more.
Accurate monitoring of estuarine turbidity patterns is important for maintaining aquatic ecological balance and devising informed estuarine management strategies. This study aimed to enhance the prediction of estuarine turbidity patterns by enhancing the performance of the multilayer perceptron (MLP) network through the introduction of stochastic gradient descent (SGD) and momentum gradient descent (MGD). To achieve this, Sentinel-2 multispectral imagery was used as the base on which spectral radiance properties of estuarine waters were analyzed against field-measured turbidity data. In this case, blue, green, red, red edge, near-infrared and shortwave spectral bands were selected for empirical relationship establishment and model development. Inverse distance weighting (IDW) spatial interpolation was employed to produce raster-based turbidity data of the study area based on field-measured data. The IDW image was subsequently binarized using the bi-level thresholding technique to produce a Boolean image. Prior to empirical model development, the selected spectral bands were calibrated to turbidity using multilayer perceptron neural network trained with the sigmoid activation function with stochastic gradient descent (SGD) optimizer and then with sigmoid activation function with momentum gradient descent optimizer. The Boolean image produced from IDW interpolation was used as the base on which the sigmoid activation function calibrated image pixels to turbidity. Empirical models were developed using selected uncalibrated and calibrated spectral bands. The results from all the selected models generally revealed a stronger relationship of the red spectral channel with measured turbidity than with other selected spectral bands. Among these models, the MLP trained with MGD produced a coefficient of determination (r2) value of 0.92 on the red spectral band, followed by the MLP with MGD on the green spectral band and SGD on the red spectral band, with r2 values of 0.75 and 0.72, respectively. The relative error of mean (REM) and r2 results revealed accurate turbidity prediction by the sigmoid with MGD compared to other models. Overall, this study demonstrated the prospect of deploying ensemble techniques on Sentinel-2 multispectral bands in spatially constructing missing estuarine turbidity data. Full article
(This article belongs to the Section Marine Environment and Hydrology Interactions)
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18 pages, 4965 KiB  
Article
Short-Term Drought Forecast across Two Different Climates Using Machine Learning Models
by Reza Piraei, Majid Niazkar, Fabiola Gangi, Gökçen Eryılmaz Türkkan and Seied Hosein Afzali
Hydrology 2024, 11(10), 163; https://doi.org/10.3390/hydrology11100163 - 3 Oct 2024
Viewed by 916
Abstract
This paper presents a comparative analysis of machine learning (ML) models for predicting drought conditions using the Standardized Precipitation Index (SPI) for two distinct stations, one in Shiraz, Iran and one in Tridolino, Italy. Four ML models, including Artificial Neural Network (ANN), Multiple [...] Read more.
This paper presents a comparative analysis of machine learning (ML) models for predicting drought conditions using the Standardized Precipitation Index (SPI) for two distinct stations, one in Shiraz, Iran and one in Tridolino, Italy. Four ML models, including Artificial Neural Network (ANN), Multiple Linear Regression, K-Nearest Neighbors, and XGBoost Regressor, were employed to forecast multi-scale SPI values (for 6-, 9-, 12-, and 24-month) considering various lag times. Results indicated that the ML model with the most robust performance varied depending on station and SPI duration. Furthermore, ANN demonstrated robust performance for SPI estimations at Shiraz station, whereas no single model consistently outperformed the others for Tridolino station. These findings were further validated through the confidence percentage analysis performed on all ML models in this study. Across all scenarios, longer SPI durations generally yielded better model performance. Additionally, for Shiraz station, optimal lag times varied by SPI duration: 6 months for the 6- and 9-month SPI, 4 months for the 12-month SPI, and 2 months for the 24-month SPI. For Tridolino station, on the other hand, no definitive optimal lag time was identified. These findings contribute to our understanding of predicting drought indicators and supporting effective water resource management and climate change adaptation efforts. Full article
(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management)
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29 pages, 11100 KiB  
Article
Assessing the Impact of Rainfall Inputs on Short-Term Flood Simulation with Cell2Flood: A Case Study of the Waryong Reservoir Basin
by Hyunjun Kim, Dae-Sik Kim, Won-Ho Nam and Min-Won Jang
Hydrology 2024, 11(10), 162; https://doi.org/10.3390/hydrology11100162 - 2 Oct 2024
Viewed by 786
Abstract
This study explored the impacts of various rainfall input types on short-term runoff simulations using the Cell2Flood model in the Waryong Reservoir Basin, South Korea. Six types of rainfall data were assessed: on-site gauge measurements, spatially interpolated data from 39 Automated Synoptic Observing [...] Read more.
This study explored the impacts of various rainfall input types on short-term runoff simulations using the Cell2Flood model in the Waryong Reservoir Basin, South Korea. Six types of rainfall data were assessed: on-site gauge measurements, spatially interpolated data from 39 Automated Synoptic Observing System (ASOS) and 117 Automatic Weather System (AWS) stations using inverse distance weighting (IDW), and Hybrid Surface Rainfall (HSR) data from the Korea Meteorological Administration. The choice of rainfall input significantly affected model accuracy across the three rainfall events. The point-gauged ASOS (P-ASOS) data demonstrated the highest reliability in capturing the observed rainfall patterns, with Pearson’s r values of up to 0.84, whereas the radar-derived HSR data had the lowest correlations (Pearson’s r below 0.2), highlighting substantial discrepancies. For runoff simulation, the P-ASOS and ASOS-AWS combined interpolated dataset (R-AWS) achieved relatively accurate predictions, with P-ASOS and R-AWS exhibiting Normalized Peak Error (NPE) values of approximately 0.03 and Peak Time Error (PTE) within 20 min. In contrast, the HSR data produced large errors, with NPE up to 4.66 and PTE deviations exceeding 200 min, indicating poor temporal accuracy. Although input-specific calibration improved performance, significant errors persisted because of the inherent uncertainty of rainfall data. These findings underscore the importance of selecting and calibrating appropriate rainfall inputs to enhance the reliability of short-term flood modeling, particularly in ungauged and data-sparse basins. Full article
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14 pages, 3333 KiB  
Article
Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network
by Namwinwelbere Dabire, Eugene C. Ezin and Adandedji M. Firmin
Hydrology 2024, 11(10), 161; https://doi.org/10.3390/hydrology11100161 - 2 Oct 2024
Viewed by 755
Abstract
The forecasting of hydrological flows (rainfall depth or rainfall discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict [...] Read more.
The forecasting of hydrological flows (rainfall depth or rainfall discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Lake Nokoué in Benin. This paper aims to provide an effective and reliable method to enable the reproduction of the future daily water level of Lake Nokoué, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Lake Nokoué up to a forecast horizon of t + 10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R2), Nash–Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t + 3 days. The values of these metrics remain stable for forecast horizons of t + 1 day, t + 2 days, and t + 3 days. The values of R2 and NSE are greater than 0.97 during the training and testing phases in the Lake Nokoué basin. Based on the evaluation indices used to assess the model’s performance for the appropriate forecast horizon of water level in the Lake Nokoué basin, the forecast horizon of t + 3 days is chosen for predicting future daily water levels. Full article
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30 pages, 11211 KiB  
Article
Hydro-Geochemistry and Water Quality Index Assessment in the Dakhla Oasis, Egypt
by Mahmoud H. Darwish, Hanaa A. Megahed, Asmaa G. Sayed, Osman Abdalla, Antonio Scopa and Sedky H. A. Hassan
Hydrology 2024, 11(10), 160; https://doi.org/10.3390/hydrology11100160 - 30 Sep 2024
Viewed by 844
Abstract
Water quality is crucial to the environmental system and thus its chemistry is important, and can be directly related to the water’s source, the climate, and the geology of the region. This study focuses on analyzing the hydrochemistry of specific locations within the [...] Read more.
Water quality is crucial to the environmental system and thus its chemistry is important, and can be directly related to the water’s source, the climate, and the geology of the region. This study focuses on analyzing the hydrochemistry of specific locations within the Dakhla Oasis in Egypt. A total of thirty-nine groundwater samples representing the Nubian Sandstone Aquifer (NSSA) and seven surface water samples from wastewater lakes and canals were collected for analysis. Key parameters such as pH, electrical conductivity (EC), and total dissolved solids (TDS) were measured on-site, while major ions and trace elements (Fe+2 and Mn+2) were analyzed in the laboratory. The water quality index (WQI) method was employed to assess the overall water quality. Hydro-chemical facies were investigated using Piper’s, Scholler’s, and Stiff diagrams, revealing sodium as the dominant cation and chloride, followed by bicarbonate as the dominant anion. The hydro-chemical composition indicates that Na–Cl constitutes the primary water type in this study. This points to the dissolution of evaporates and salt enrichment due to intense evaporation resulting from the region’s hyper-aridity. In groundwater samples, the order of hydro-chemical facies is HCO3− > Cl− > SO4−2 > Na+ > Ca+2 > K+ > Mg+2, while in wastewater samples, it is Cl− > Na+ > SO4−2 > HCO3− > Ca+2 > Mg+2 > K+. When considering iron and manganese parameters, the water quality index (WQI) values suggest that most groundwater samples exhibit excellent to good quality but become poor or very poor when these elements are included. This study could prove valuable for water resource management in the Dakhla Oasis. Full article
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19 pages, 2946 KiB  
Article
Determination of Environmental Flow Using a Holistic Methodology in Three River Paths in the Tempisque River Basin, Costa Rica
by Laura Chavarría-Pizarro, Fernando Watson-Hernández, Francisco Quesada-Alvarado, Valeria Serrano-Núñez, Ana Lucía Bustos-Vásquez, Karina Fernández-Chévez, Jendry Chacón-Gutierrez and Isabel Guzmán-Arias
Hydrology 2024, 11(10), 159; https://doi.org/10.3390/hydrology11100159 - 27 Sep 2024
Viewed by 883
Abstract
The study of environmental flow has garnered significant scientific interest due to the considerable degradation caused by human activities on aquatic ecosystem dynamics. Environmental flow is defined as the quantity, timing, and quality of water flow required to sustain freshwater and estuarine ecosystems [...] Read more.
The study of environmental flow has garnered significant scientific interest due to the considerable degradation caused by human activities on aquatic ecosystem dynamics. Environmental flow is defined as the quantity, timing, and quality of water flow required to sustain freshwater and estuarine ecosystems while meeting human demands. Research in riverine ecosystems can generate the critical scientific knowledge needed to determine an adequate environmental flow that balances the requirements of both aquatic organisms and human populations. This study is part of a series of investigations aimed at field-testing different methodologies to determine appropriate environmental flow levels for rivers with specific characteristics. In particular, we adapted and validated a holistic methodology for calculating the environmental flow regime in the Tempisque River basin in Costa Rica. This research involved analyzing hydrological parameters, hydraulic conditions, the presence of flow bioindicators, and various anthropogenic uses of the river (such as human consumption, productive, recreational, and cultural activities) to estimate environmental flow requirements throughout the year. The findings indicate that the lower and upper limits of the environmental flow for the studied section of the Tempisque River correspond to the monthly excesses of 95.00% and 64.00%, respectively. These results provide a reliable annual flow regime that can inform decision-making by authorities in water resource management, particularly in regions where there is a high demand for water across different human activities. Full article
(This article belongs to the Special Issue The 10th Anniversary of Hydrology: Inaugurating a New Research Decade)
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19 pages, 3396 KiB  
Article
Urban Flood Vulnerability Assessment in Freetown, Sierra Leone: AHP Approach
by Abdulai Osman Koroma, Mohamed Saber and Cherifa Abdelbaki
Hydrology 2024, 11(10), 158; https://doi.org/10.3390/hydrology11100158 - 25 Sep 2024
Viewed by 1139
Abstract
This study presents a comprehensive flood vulnerability assessment for Freetown, Sierra Leone, spanning the period from 2001 to 2022. The objective of this research was to assess the temporal and spatial changes in the flood vulnerability using Geographic Information System (GIS) tools and [...] Read more.
This study presents a comprehensive flood vulnerability assessment for Freetown, Sierra Leone, spanning the period from 2001 to 2022. The objective of this research was to assess the temporal and spatial changes in the flood vulnerability using Geographic Information System (GIS) tools and AHP-based Multi-Criteria Decision-Making (MCDM) analysis. This study identified the flood-vulnerable zones (FVZs) by integrating critical factors such as the rainfall, NDVI, elevation, slope, drainage density, TWI, distance to road, distance to river, and LULC. The analysis reveals that approximately 60% of the study area is classified as having medium to high vulnerability, with a significant 20% increase in the flood risk observed over the past two decades. In 2001, very-high-vulnerability zones covered about 68.84 km2 (10% of the total area), with high-vulnerability areas encompassing 137.68 km2 (20%). By 2020, very-high-vulnerability zones remained constant at 68.84 km2 (10%), while high-vulnerability areas decreased to 103.26 km2 (15%), and medium-vulnerability zones expanded from 206.51 km2 (30%) in 2001 to 240.93 km2 (35%). The AHP model-derived weights reflect the varied significance of the flood-inducing factors, with rainfall (0.27) being the most critical and elevation (0.04) being the least. A consistency ratio (CR) of 0.068 (< 0.1) confirms the reliability of these weights. The spatial–temporal analysis highlights the east and southeast regions of Freetown as consistently vulnerable over the years, while infrastructure improvements in other areas have contributed to a general decrease in very-high-vulnerability zones. This research highlights the urgent need for resilient urban planning and targeted interventions to mitigate future flood impacts, offering clear insights into the natural and human-induced drivers of the flood risk for effective hazard mitigation and sustainable urban development. Full article
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