Journal Description
Hydrology
Hydrology
is an international, peer-reviewed, open access journal on hydrology published monthly online by MDPI. The American Institute of Hydrology (AIH) and Japanese Society of Physical Hydrology (JSPH) are affiliated with Hydrology and their members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), PubAg, GeoRef, and other databases.
- Journal Rank: JCR - Q2 (Water Resources) / CiteScore - Q1 (Earth-Surface Processes)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.6 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.1 (2023);
5-Year Impact Factor:
3.0 (2023)
Latest Articles
Exploring PCSWMM for Large Mixed Land Use Watershed by Establishing Monitoring Sites to Evaluate Stream Water Quality
Hydrology 2024, 11(7), 104; https://doi.org/10.3390/hydrology11070104 - 15 Jul 2024
Abstract
Extensive hydrologic and water quality modeling within a watershed benefits from long-term flow and nutrient data sets for appropriate model calibration and validation. However, due to a lack of local water quality data, simpler water quality modeling techniques are generally adopted. In this
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Extensive hydrologic and water quality modeling within a watershed benefits from long-term flow and nutrient data sets for appropriate model calibration and validation. However, due to a lack of local water quality data, simpler water quality modeling techniques are generally adopted. In this study, the monitoring sites were established at two different locations to collect hydraulic data for the hydraulic calibration and validation of the model. In addition, water quality samples were collected at eight monitoring sites and analyzed in the lab for various parameters for calibration. This includes total suspended solids (TSS), soluble phosphorus, five-day biochemical oxygen demand (BOD5), and dissolved oxygen (DO). The Personal Computer Storm Water Management Model (PCSWMM) 7.6 software was used to simulate all the pollutant loads using event mean concentrations (EMCs). The performance of the model for streamflow calibration at the two USGS gauging stations was satisfactory, with Nash–Sutcliffe Efficiency (NSE) values ranging from 0.51 to 0.54 and coefficients of determination (R2) ranging from 0.71 to 0.72. The model was also validated with the help of historical flow data with NSE values ranging from 0.5 to 0.79, and R2 values ranging from 0.6 to 0.95. The hydraulic calibration also showed acceptable results with reasonable NSE and R2 values. The water quality data recorded at the monitoring stations were then compared with the simulated water quality modeling results. The model reasonably simulated the water quality, which was evaluated through visual inspection using a scatter plot. Our analysis showed that the upstream tributaries, particularly from agricultural areas, were contributing more pollutants than the downstream tributaries. Overall, this study demonstrates that the PCSWMM, which was typically used for modeling urban watersheds, could also be used for modeling larger mixed land use watersheds with reasonable accuracy.
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(This article belongs to the Section Hydrological and Hydrodynamic Processes and Modelling)
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Open AccessArticle
Spatio-Temporal Behavior of Land Surface Temperatures (LSTs) in Central Chile, Using Terra MODIS Images
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Pedro Muñoz-Aguayo, Luis Morales-Salinas, Roberto Pizarro, Alfredo Ibáñez, Claudia Sangüesa, Guillermo Fuentes-Jaque, Cristóbal Toledo and Pablo A. Garcia-Chevesich
Hydrology 2024, 11(7), 103; https://doi.org/10.3390/hydrology11070103 - 12 Jul 2024
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Land surface temperature (LST) is one of the most important variables in the physical processes of surface energy and water balance. The temporal behavior of LST was analyzed between the latitudes 32°00′ S and 34°24′ S (Valparaíso and Metropolitana regions of Chile) for
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Land surface temperature (LST) is one of the most important variables in the physical processes of surface energy and water balance. The temporal behavior of LST was analyzed between the latitudes 32°00′ S and 34°24′ S (Valparaíso and Metropolitana regions of Chile) for three summer months (December, January, and February) in the 2000–2017 period, using the Terra MODIS image information and applying the Mann–Kendall test. The results show an increase in LST in the study area, particularly in the Andes mountain range in January (5240 km2), which mainly comprises areas devoid of vegetation and eternal snow and glaciers, and are zones that act as water reserves for the capital city of Santiago. Similarly, vegetated areas such as forests, grasslands, and shrublands also show increasing trends in LST but over smaller surfaces. Because this study is regional, it is recommended to improve the spatial and temporal resolutions of the images to obtain conclusions on more local scales.
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Open AccessArticle
Geostatistical Analysis of Groundwater Data in a Mining Area in Greece
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E. Diamantopoulou, A. Pavlides, E. Steiakakis and E. A. Varouchakis
Hydrology 2024, 11(7), 102; https://doi.org/10.3390/hydrology11070102 - 11 Jul 2024
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Geostatistical prediction methods are increasingly used in earth sciences and engineering to improve upon our knowledge of attributes in space and time. During mining activities, it is very important to have an estimate of any contamination of the soil and groundwater in the
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Geostatistical prediction methods are increasingly used in earth sciences and engineering to improve upon our knowledge of attributes in space and time. During mining activities, it is very important to have an estimate of any contamination of the soil and groundwater in the area for environmental reasons and to guide the reclamation once mining operations are finished. In this paper, we present the geostatistical analysis of the water content in certain pollutants (Cd and Mn) in a group of mines in Northern Greece. The monitoring points that were studied are 62. The aim of this work is to create a contamination prediction map that better represents the values of Cd and Mn, which is challenging based on the small sample size. The correlation between Cd and Mn concentration in the groundwater is investigated during the preliminary analysis of the data. The logarithm of the data values was used, and after removing a linear trend, the variogram parameters were estimated. In order to create the necessary maps of contamination, we employed the method of ordinary Kriging (OK) and inversed the transformations using bias correction to adjust the results for the inverse transform. Cross-validation shows promising results ( for Cd and for Mn, RMSE = 25.9 ppb for Cd and RMSE = 25.1 ppm for Mn). As part of this work, the Spartan Variogram model was compared with the other models and was found to perform better for the data of Mn.
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Open AccessArticle
Influence of Slope Aspect and Vegetation on the Soil Moisture Response to Snowmelt in the German Alps
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Michael Leopold Schaefer, Wolfgang Bogacki, Maximo Larry Lopez Caceres, Lothar Kirschbauer, Chihiro Kato and Shun-ichi Kikuchi
Hydrology 2024, 11(7), 101; https://doi.org/10.3390/hydrology11070101 - 10 Jul 2024
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Snow, especially in mountainous regions, plays a major role acting as a quasi-reservoir, as it gradually releases fresh water during the melting season and thereby fills rivers, lakes, and groundwater aquifers. For vegetation and irrigation, the timing of the snowmelt is crucial. Therefore,
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Snow, especially in mountainous regions, plays a major role acting as a quasi-reservoir, as it gradually releases fresh water during the melting season and thereby fills rivers, lakes, and groundwater aquifers. For vegetation and irrigation, the timing of the snowmelt is crucial. Therefore, it is necessary to understand how snowmelt varies under different local conditions. While differences in slope aspect and vegetation (individually) were linked to differences in snow accumulation and ablation, this study connects the two and describes their influence on the soil moisture response to snowmelt. This research focuses on the catchment of the “Brunnenkopfhütte” (BKH) in Bavaria, southern Germany, where an automatic weather station (AWS) has operated since 2016. In addition, soil temperature and moisture monitoring systems in the surrounding area on a south aspect slope on an open field (SO), on a south aspect slope in the forest (SF), and a north aspect slope in the forest (NF) have operated since 2020. On snow-free days in winter, the soil temperature at the SF site was on average 1 °C lower than on the open site. At the NF site, this soil temperature difference increased to 2.3 °C. At the same time, for a 1 °C increase in the air temperature, the soil temperature increases by 0.35 °C at the NF site. In addition, at this site, snow cover disappeared approximately one week later than on the south aspect slopes. Snow cover at the SF site disappeared even earlier than at the SO site. Finally, a significant difference in the soil moisture response was found between the sites. While the vegetation cover dampens the magnitude of the soil moisture increases, at the NF site, no sharp increases in soil moisture were observed.
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Open AccessArticle
Agricultural Drought Model Based on Machine Learning Cubist Algorithm and Its Evaluation
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Sha Sha, Lijuan Wang, Die Hu, Yulong Ren, Xiaoping Wang and Liang Zhang
Hydrology 2024, 11(7), 100; https://doi.org/10.3390/hydrology11070100 - 9 Jul 2024
Abstract
Soil moisture is the most direct evaluation index for agricultural drought. It is not only directly affected by meteorological conditions such as precipitation and temperature but is also indirectly influenced by environmental factors such as climate zone, surface vegetation type, soil type, elevation,
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Soil moisture is the most direct evaluation index for agricultural drought. It is not only directly affected by meteorological conditions such as precipitation and temperature but is also indirectly influenced by environmental factors such as climate zone, surface vegetation type, soil type, elevation, and irrigation conditions. These influencing factors have a complex, nonlinear relationship with soil moisture. It is difficult to accurately describe this non-linear relationship using a single indicator constructed from meteorological data, remote sensing data, and other data. It is also difficult to fully consider environmental factors using a single drought index on a large scale. Machine learning (ML) models provide new technology for nonlinear problems such as soil moisture retrieval. Based on the multi-source drought indexes calculated by meteorological, remote sensing, and land surface model data, and environmental factors, and using the Cubist algorithm based on a classification decision tree (CART), a comprehensive agricultural drought monitoring model at 10 cm, 20 cm, and 50 cm depth in Gansu Province is established. The influence of environmental factors and meteorological factors on the accuracy of the comprehensive model is discussed, and the accuracy of the comprehensive model is evaluated. The results show that the comprehensive model has a significant improvement in accuracy compared to the single variable model, which is a decrease of about 26% and 28% in RMSE and MAPE, respectively, compared to the best MCI model. Environmental factors such as season, DEM, and climate zone, especially the DEM, play a crucial role in improving the accuracy of the integrated model. These three environmental factors can comprehensively reduce the average RMSE of the comprehensive model by about 25%. Compared to environmental factors, meteorological factors have a slightly weaker effect on improving the accuracy of comprehensive models, which is a decrease of about 6.5% in RMSE. The fitting accuracy of the comprehensive model in humid and semi-humid areas, as well as semi-arid and semi-humid areas, is significantly higher than that in arid and semi-arid areas. These research results have important guiding significance for improving the accuracy of agricultural drought monitoring in Gansu Province.
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(This article belongs to the Topic Advances in Hydro-Geological Research in Arid and Semi-Arid Areas)
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Open AccessArticle
Groundwater Characteristics’ Assessment for Productivity Planning in Al-Madinah Al-Munawarah Province, KSA
by
Milad Masoud, Maged El Osta, Nassir Al-Amri, Burhan Niyazi, Abdulaziz Alqarawy and Mohamed Rashed
Hydrology 2024, 11(7), 99; https://doi.org/10.3390/hydrology11070099 - 8 Jul 2024
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In recent times, drilling groundwater wells for irrigation, domestic, and industrial uses is increasing at a high rate in Saudi Arabia, meaning that groundwater is becoming a primary water resource. In the study region, over-exploitation and unsustainable performance severely deteriorate groundwater. Therefore, it
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In recent times, drilling groundwater wells for irrigation, domestic, and industrial uses is increasing at a high rate in Saudi Arabia, meaning that groundwater is becoming a primary water resource. In the study region, over-exploitation and unsustainable performance severely deteriorate groundwater. Therefore, it is important to monitor the groundwater levels and quality as well as to detect the hydraulic parameters in order to plan and maintain groundwater sustainability. Knowledge of aquifer hydraulic parameters and groundwater quality is essential for the productivity planning of an aquifer. Therefore, this study carried out a thorough analysis on measured depth to groundwater data (2017 and 2022), borehole pumping test records, and chemical analysis of the collected water samples, especially in the presence of overexploitation and scarcity of recharge scale. To accomplish this aim, measurements of 113 groundwater wells (including 103 water samples) and analysis of 29 pumping tests between step and long-duration tests were made of all aquifer characteristics. These parameters consist of well loss, formation loss, well efficiency, specific capacity, transmissivity, hydraulic conductivity, resulted drawdown, and physiochemical parameters. Thematic maps were generated for all parameters using the geographic information system (GIS) and diagrams to strategize the groundwater productivity in Al-Madinah Al-Munawarah Province. The estimated hydraulic parameters are highly variable. Four distinct portions were identified for aquifer potentiality based on these varying ranges. Both the north and east of the region are good for groundwater productivity due to good aquifer materials, whereas the southwestern and western portions have relatively poor values. The analyzed groundwater was categorized as fresh to slightly salty water, with two primary chemical types identified showing a prevalence of mixed NaCl and Ca-Mg-SO4/Cl water. Finally, groundwater productivity assessment predicts that the aquifers can support the Al-Madinah Al-Munawarah Province demand for several years if certain well distributions are adopted and for a few hours/day of pumping rate. The maps that have been created can be examined to aid in making decisions related to hydrology.
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Open AccessEditorial
Groundwater Pollution: Sources, Mechanisms, and Prevention
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Pantelis Sidiropoulos
Hydrology 2024, 11(7), 98; https://doi.org/10.3390/hydrology11070098 - 5 Jul 2024
Abstract
Groundwater resources are vital for ecosystems and human health and prosperity [...]
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(This article belongs to the Special Issue Groundwater Pollution: Sources, Mechanisms, and Prevention)
Open AccessArticle
Jucazinho Dam Streamflow Prediction: A Comparative Analysis of Machine Learning Techniques
by
Erickson Johny Galindo da Silva, Artur Paiva Coutinho, Jean Firmino Cardoso and Saulo de Tarso Marques Bezerra
Hydrology 2024, 11(7), 97; https://doi.org/10.3390/hydrology11070097 - 4 Jul 2024
Abstract
The centuries-old history of dam construction, from the Saad el-Kafara Dam to global expansion in the 1950s, highlights the importance of these structures in water resource management. The Jucazinho Dam, built in 1998, emerged as a response to the scarcity of water in
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The centuries-old history of dam construction, from the Saad el-Kafara Dam to global expansion in the 1950s, highlights the importance of these structures in water resource management. The Jucazinho Dam, built in 1998, emerged as a response to the scarcity of water in the Agreste region of Pernambuco, Brazil. After having less than 1% of its water storage capacity in 2016, the dam recovered in 2020 after interventions by the local water utility. In this context, the reliability of influent flow prediction models for dams becomes crucial for managers. This study proposed hydrological models based on artificial intelligence that aim to generate flow series, and we evaluated the adaptability of these models for the operation of the Jucazinho Dam. Data normalization between 0 and 1 was applied to avoid the predominance of variables with high values. The model was based on machine learning and employed support vector regression (SVM), random forest (RF) and artificial neural networks (ANNs), as provided by the Python Sklearn library. The selection of the monitoring stations took place via the Brazilian National Water and Sanitation Agency’s (ANA) HIDROWEB portal, and we used Spearman’s correlation to identify the relationship between precipitation and flow. The evaluation of the performance of the model involved graphical analyses and statistical criteria such as the Nash–Sutcliffe model efficiency coefficient (NSE), the percentage of bias (PBIAS), the coefficient of determination (R2) and the root mean standard deviation ratio (RSR). The results of the statistical coefficients for the test data indicated unsatisfactory performance for long-term predictions (8, 16 and 32 days ahead), revealing a downward trend in the quality of the fit with an increase in the forecast horizon. The SVM model stood out by obtaining the best indices of NSE, PBIAS, R2 and RSR. The graphical results of the SVM models showed underestimation of the flow values with an increase in the forecast horizon due to the sensitivity of the SVM to complex patterns in the time series. On the other hand, the RF and ANN models showed hyperestimation of the flow values as the number of forecast days increased, which was mainly attributed to overfitting. In summary, this study highlights the relevance of artificial intelligence in flow prediction for the efficient management of dams, especially in water scarcity and data-scarce scenarios. A proper choice of models and the ensuring of reliable input data are crucial for obtaining accurate forecasts and can contribute to water security and the effective operation of dams such as Jucazinho.
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(This article belongs to the Section Hydrological and Hydrodynamic Processes and Modelling)
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Open AccessArticle
Measurement and Calculation of Sediment Transport on an Ephemeral Stream
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Loukas Avgeris, Konstantinos Kaffas and Vlassios Hrissanthou
Hydrology 2024, 11(7), 96; https://doi.org/10.3390/hydrology11070096 - 30 Jun 2024
Abstract
Sediment transport remains a significant challenge for researchers due to the intricate nature of the physical processes involved and the diverse characteristics of watercourses worldwide. A type of watercourse that is of particular interest for study is the ephemeral streams, found primarily in
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Sediment transport remains a significant challenge for researchers due to the intricate nature of the physical processes involved and the diverse characteristics of watercourses worldwide. A type of watercourse that is of particular interest for study is the ephemeral streams, found primarily in semiarid and arid regions. Due to their unique nature, a new measurement algorithm was created and a modified bed load sampler was built. Measurement of the bed load transport rate and calculation of the water discharge were conducted in an ephemeral stream in Northeastern Greece, where the mean calculated streamflow rate ranged from 0.019 to 0.314 m3/s, and the measured sediment load transport rates per unit width varied from 0.00001 to 0.00213 kg/m/s. The sediment concentration was determined through various methods, including nonlinear regression equations and formulas developed by Yang, with the coefficients of these formulas calibrated accordingly. The results demonstrated that the equations derived from Yang’s multiple regression analysis offered a superior fit compared to the original equations. As a result, two modified versions of Yang’s stream sediment transport formulas were developed and are presented to the readership. To assess the accuracy of the modified formulas, a comparison was conducted between the calculated total sediment concentrations and the measured total sediment concentrations based on various statistical criteria. The analysis shows that none of Yang’s original formulas fit the available data well, but after optimization, both modified formulas can be applied to the specific ephemeral stream. The results indicate also that the formulas derived from the nonlinear regression can be successfully used for the determination of the total sediment concentration in the ephemeral stream and have a better fit compared to Yang’s formulas. The correlation from the nonlinear regression equations suggests that total sediment transport is primarily influenced by water discharge and rainfall intensity, with the latter showing a high correlation coefficient of 0.998.
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(This article belongs to the Special Issue Advances in Catchments Hydrology and Sediment Dynamics)
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Open AccessArticle
Estimation of Evapotranspiration in South Eastern Afghanistan Using the GCOM-C Algorithm on the Basis of Landsat Satellite Imagery
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Emal Wali, Masahiro Tasumi and Otto Klemm
Hydrology 2024, 11(7), 95; https://doi.org/10.3390/hydrology11070095 - 30 Jun 2024
Abstract
This study aims to assess the performance of the Global Change Observation Mission—Climate (GCOM-C) ETindex estimation algorithm to estimate the actual evapotranspiration (ETa) in southeastern Afghanistan. Here, the GCOM-C ETindex algorithm was adopted to estimate the monthly ETa for the period
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This study aims to assess the performance of the Global Change Observation Mission—Climate (GCOM-C) ETindex estimation algorithm to estimate the actual evapotranspiration (ETa) in southeastern Afghanistan. Here, the GCOM-C ETindex algorithm was adopted to estimate the monthly ETa for the period from November 2016 to October 2017 using a series of Landsat 8, Thermal Infrared Sensor (TIRS) Band 10 satellite imagery. The estimation accuracy was evaluated by comparing the results with other estimates of ETa, namely the mapping evapotranspiration with the internalized calibration (METRIC) model, the MODIS Global Evapotranspiration Project (MOD16), the surface energy balance system (SEBS) tools, and with the crop evapotranspiration under standard conditions (ETc) as estimated by the FAO-56 procedure. The evaluation was made for irrigated wheat, maize, rice, and orchards and for non-irrigated bare soil land. The comparison of ETa values showed good correlation among the GCOM-C, METRIC, and FAO-56, while the MOD16 and SEBS showed significantly lower values of ETa. The agreement with the METRIC ETa implies that the simple GCOM-C algorithm successfully estimated the ETa in the region and that the precision was similar to that of the METRIC. This study provides the first high-quality evapotranspiration data with the spatial resolution of Landsat Band 10 data for the southeastern part of Afghanistan. The estimation procedure is straightforward, and its results are anticipated to enhance the understanding of regional hydrology.
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(This article belongs to the Special Issue GIS Modelling of Evapotranspiration with Remote Sensing)
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Open AccessArticle
Effects of Forest Logging Systems on the River Flow Regime Indices Using Graphical Techniques: A Case Study in a Small Natural Forest
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Farshad Keivan Behjou, Raoof Mostafazadeh and Nazila Alaei
Hydrology 2024, 11(7), 94; https://doi.org/10.3390/hydrology11070094 - 28 Jun 2024
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This study aims to investigate the impact of forest exploitation methods on monthly discharge and hydrological indices of river flow using graphical methods in a forested watershed in North Iran. To achieve this, 10 hydrological index changes related to river flow regime influenced
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This study aims to investigate the impact of forest exploitation methods on monthly discharge and hydrological indices of river flow using graphical methods in a forested watershed in North Iran. To achieve this, 10 hydrological index changes related to river flow regime influenced by the Shelterwood/clear cutting, Femel cutting, and the Near Nature approach forest cutting methods were assessed. According to the results, it can be stated that the Shelterwood/clear cutting method influenced monthly flow indices by increasing the coefficient of variations and intensifying runoff production, while the Femel cutting and the Near Nature approach methods contributed to regulating the flow regime and sustaining river flow. The influence of various tree-cutting techniques on river flow values and fluctuations is more evident during the wettest months compared to low-water months. The period of Shelterwood/clear cutting disrupted the natural correlation between precipitation and runoff production. Furthermore, the shift from Shelterwood/clear cutting to Femel cutting and the Near Nature approach progressively diminished the slope of the curve, indicating a reduction in monthly runoff at both measurement stations. In conclusion, opting for an appropriate method, such as the Near Nature approach, is preferable from both ecological and hydrological perspectives when managing forest areas in the study region and similar conditions involving comparable topography, climate, soil, and forest stands. The index-based coupled with graphical methodology employed appropriately demonstrates the influence of logging techniques on monthly flow patterns, which provides valuable insights into evaluating the repercussions of alternative management interventions on river flow dynamics.
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Open AccessArticle
Analysis of Landscape Pattern Evolution and Impact Factors in the Mainstream Basin of the Tarim River from 1980 to 2020
by
Lili Jiang and Yating Li
Hydrology 2024, 11(7), 93; https://doi.org/10.3390/hydrology11070093 - 27 Jun 2024
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The mainstream basin of the Tarim River serves as a vital ecological security barrier that prevents the merging and expansion of deserts and an important strategic corridor directly linking Qinghai and Xinjiang. With society’s development and climate change, ecological issues such as river
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The mainstream basin of the Tarim River serves as a vital ecological security barrier that prevents the merging and expansion of deserts and an important strategic corridor directly linking Qinghai and Xinjiang. With society’s development and climate change, ecological issues such as river interruption, vegetation degradation, and land desertification in the basin have notably intensified, and the ecological security is facing a critical test. Exploring the characteristics of landscape changes and their driving factors within the basin is crucial in improving the ecological environment system’s management. Based on land use data from 1980 to 2020, this study analyzed the characteristics of the spatiotemporal changes and pattern evolution of the landscape through a landscape transfer matrix and landscape pattern indices. It further revealed the impact factors of the landscape pattern through canonical correspondence analysis. The results showed that (1) in 1980–2020, the areas of desert, forest, farmland, and settlement landscapes increased, while the area of grassland landscape decreased, and the water landscape showed an “increasing–decreasing–recovery” pattern. The landscape transition types mainly included the transition from grassland to desert; mutual transitions among farmland, grassland, and forests; mutual transitions between water and grassland; and the transition from farmland to settlements. (2) The overall landscape pattern demonstrated increased fragmentation, shape complexity, and evenness with decreased aggregation. Furthermore, different landscapes exhibited distinct characteristics of landscape pattern changes; for instance, grassland landscape showed severe fragmentation, while desert landscape displayed the strongest dominance. (3) The landscape pattern was a result of the combined impact of natural and human factors, with the soil thickness (SOT), road density (ROD), annual actual evapotranspiration (AAE), population density (POD), and mean annual temperature (MAT) exhibiting significant influences. Specifically, the settlement and farmland landscapes were mainly influenced by the mean annual relative humidity (MAH), POD, GDP density (GDP), and distance to artificial water (DAW); the forest, grassland, and water landscapes were mainly influenced by the SOT, soil organic matter content (SOM), AAE, ROD, elevation (ELE), MAT, slope (SLP), and distance to natural water (DNW); and the desert landscape was mainly influenced by the DAW, DNW, SLP, AAE, SOT, SOM, and ROD. These findings can provide a scientific reference for landscape management and restoration, as well as sustainable social and economic development, in the mainstream basin of the Tarim River.
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Open AccessReview
Integrating Remote Sensing Methods for Monitoring Lake Water Quality: A Comprehensive Review
by
Anja Batina and Andrija Krtalić
Hydrology 2024, 11(7), 92; https://doi.org/10.3390/hydrology11070092 - 26 Jun 2024
Abstract
Remote sensing methods have the potential to improve lake water quality monitoring and decision-making in water management. This review discusses the use of remote sensing methods for monitoring and assessing water quality in lakes. It explains the principles of remote sensing and the
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Remote sensing methods have the potential to improve lake water quality monitoring and decision-making in water management. This review discusses the use of remote sensing methods for monitoring and assessing water quality in lakes. It explains the principles of remote sensing and the different methods used for retrieving water quality parameters in complex waterbodies. The review highlights the importance of considering the variability of optically active parameters and the need for comprehensive studies that encompass different seasons and time frames. The paper addresses the specific physical and biological parameters that can be effectively estimated using remote sensing, such as chlorophyll-α, turbidity, water transparency (Secchi disk depth), electrical conductivity, surface salinity, and water temperature. It further provides a comprehensive summary of the bands, band combinations, and band equations commonly used for remote sensing of these parameters per satellite sensor. It also discusses the limitations of remote sensing methods and the challenges associated with satellite systems. The review recommends integrating remote sensing methods using in situ measurements and computer modelling to improve the understanding of water quality. It suggests future research directions, including the importance of optimizing grid selection and time frame for in situ measurements by combining hydrodynamic models with remote sensing retrieval methods, considering variability in water quality parameters when analysing satellite imagery, the development of advanced technologies, and the integration of machine learning algorithms for effective water quality problem-solving. The review concludes with a proposed workflow for monitoring and assessing water quality parameters in lakes using remote sensing methods.
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(This article belongs to the Special Issue Hydrodynamics and Water Quality of Rivers and Lakes)
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Open AccessArticle
Reference Evapotranspiration in Climate Change Scenarios in Mato Grosso, Brazil
by
Marlus Sabino, Andréa Carvalho da Silva, Frederico Terra de Almeida and Adilson Pacheco de Souza
Hydrology 2024, 11(7), 91; https://doi.org/10.3390/hydrology11070091 - 26 Jun 2024
Abstract
Our understanding of spatiotemporal variability in reference evapotranspiration (ETo) and its long-term trends is of paramount importance for water cycle studies, modeling, and water resource management, especially in the context of climate change. Therefore, the primary aim of this study is to critically
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Our understanding of spatiotemporal variability in reference evapotranspiration (ETo) and its long-term trends is of paramount importance for water cycle studies, modeling, and water resource management, especially in the context of climate change. Therefore, the primary aim of this study is to critically evaluate the performance of various CMIP5 global climate models in simulating the Penman–Monteith reference evapotranspiration and its associated climate variables (maximum and minimum air temperature, incident solar radiation, relative humidity, and wind speed). This evaluation is based on data from nine climate models and 33 automatic meteorological stations (AWSs) in the state of Mato Grosso, spanning the period 2007–2020, within the areas of the biomes Amazon and Cerrado and around the Pantanal biome. The statistical metrics used for evaluation include bias, root mean square error, and Pearson and Spearman correlation coefficients. The projections of the most accurate model were then used to analyze the spatial and temporal changes and trends in ETo under the Representative Concentration Pathways (RCPs) of 2.6, 4.5, and 8.5 scenarios from 2007 to 2100. The HadGEM2-ES model projections indicate static averages similar to current conditions until the end of the century in the RCP 2.6 scenario. However, in the RCP 4.5 and 8.5 scenarios, there is a continuous increase in ETo, with the most significant increase occurring during the dry period (May to September). The areas of the Amazon biome in the north of Mato Grosso exhibit the largest increases in ETo when comparing the observed (2007–2020) and projected (2020–2100) averages. The trend analysis reveals significant changes in ETo and its variables across the state of Mato Grosso in the RCP 4.5 and 8.5 scenarios. In the RCP 2.6 scenario, significant trends in ETo are observed only in the northern Amazon areas. Despite not being observed in all AWSs, the trend analysis of the observed data demonstrates more intense changes in ETo and the existence of the evapotranspiration paradox, with an increase in the Cerrado areas and reductions in the Pantanal and southern Amazon areas.
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(This article belongs to the Special Issue Trends and Variations in Hydroclimatic Variables)
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Open AccessArticle
A Modified Xinanjiang Model for Quantifying Streamflow Components in a Typical Watershed in Eastern China
by
Kaibin Wu, Minpeng Hu, Yu Zhang, Jia Zhou and Dingjiang Chen
Hydrology 2024, 11(7), 90; https://doi.org/10.3390/hydrology11070090 - 25 Jun 2024
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An accurate quantification of flow components and an understanding of water source dynamics are essential for effective water resource and quality management. However, the complexity of hydrological processes and the interference of intensive human activities pose significant challenges in precisely separating water discharge
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An accurate quantification of flow components and an understanding of water source dynamics are essential for effective water resource and quality management. However, the complexity of hydrological processes and the interference of intensive human activities pose significant challenges in precisely separating water discharge into distinct components such as surface runoff, interflow, and groundwater. The Xinanjiang (XAJ) model, a conceptual watershed hydrological model, has been developed and successfully implemented for rainfall–runoff simulations and hydrograph separations across various Chinese watersheds. While the model framework is robust, it fails to account for agricultural irrigation water withdrawals and the variations in in-stream water travel times across different hydrological regimes, introducing considerable uncertainty in simulating low-flow conditions. This study introduced modifications to the XAJ model by allowing parameter adjustments across different flow regimes and incorporating irrigation withdrawals into the runoff routing process. Utilizing a decade of hydrometeorological data (2013–2022) from the Yongan River watershed in eastern China, the modified model demonstrated improved efficiency metrics in low- and medium-flow regimes compared to the original model, with a Nash–Sutcliffe coefficient improvement from −4.43~−0.49 to 0.40~0.46, R2 from 0.21~0.36 to 0.53~0.63, and BIAS reduction from 7.60~89.08% to 2.06~12.71%. Furthermore, the modified XAJ model provided a more accurate estimation of the spatial and temporal distribution of streamflow components across sub-watersheds. The original model tended to overestimate groundwater contributions (13%) and underestimate interflow (14%), particularly in low-flow conditions. The enhanced XAJ model, thus, offers a more effective tool for identifying streamflow components, providing essential insights into hydrological processes for better management decisions.
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Open AccessArticle
Performance Evaluation of Regression-Based Machine Learning Models for Modeling Reference Evapotranspiration with Temperature Data
by
Maria J. Diamantopoulou and Dimitris M. Papamichail
Hydrology 2024, 11(7), 89; https://doi.org/10.3390/hydrology11070089 - 21 Jun 2024
Abstract
In this study, due to their flexibility in forecasting, the capabilities of three regression-based machine learning models were explored, specifically random forest regression (RFr), generalized regression neural network (GRNN), and support vector regression (SVR). The above models were assessed for their suitability in
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In this study, due to their flexibility in forecasting, the capabilities of three regression-based machine learning models were explored, specifically random forest regression (RFr), generalized regression neural network (GRNN), and support vector regression (SVR). The above models were assessed for their suitability in modeling daily reference evapotranspiration (ETo), based only on temperature data (Tmin, Tmax, Tmean), by comparing their daily ETo results with those estimated by the conventional FAO 56 PM model, which requires a broad range of data that may not be available or may not be of reasonable quality. The RFr, GRNN, and SVR models were subjected to performance evaluation by using statistical criteria and scatter plots. Following the implementation of the ETo models’ comparisons, it was observed that all regression-based machine learning models possess the capability to accurately estimate daily ETo based only on temperature data requirements. In particular, the RFr model outperformed the others, achieving the highest R value of 0.9924, while the SVR and GRNN models had R values of 0.9598 and 0.9576, respectively. Additionally, the RFr model recorded the lowest values in all error metrics. Once these regression-based machine learning models have been successfully developed, they will have the potential to serve as effective alternatives for estimating daily ETo, under current and climate change conditions, when temperature data are available. This information is crucial for effective water resources management and especially for predicting agricultural production in the context of climate change.
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(This article belongs to the Special Issue GIS Modelling of Evapotranspiration with Remote Sensing)
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Open AccessArticle
Determination of Contaminant Transport Parameters for a Local Aquifer by Numerical Modeling of Two Plumes: Trichloroethylene and Hexavalent Chromium
by
Mahade Ibn Salam, Brian Waldron, Scott Schoefernacker and Farhad Jazaei
Hydrology 2024, 11(7), 88; https://doi.org/10.3390/hydrology11070088 - 21 Jun 2024
Abstract
The municipal wellfield in Collierville, Tennessee, is contaminated with trichloroethylene (TCE) and hexavalent chromium (Cr (VI)) due to industrial operations dating back to the 1970s and 1980s. This study aims to elucidate the aquifer’s contaminant transport mechanisms by determining longitudinal and transverse dispersivities
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The municipal wellfield in Collierville, Tennessee, is contaminated with trichloroethylene (TCE) and hexavalent chromium (Cr (VI)) due to industrial operations dating back to the 1970s and 1980s. This study aims to elucidate the aquifer’s contaminant transport mechanisms by determining longitudinal and transverse dispersivities through inverse modeling. Utilizing MT3DMS for contaminant transport simulation, based on a well-calibrated groundwater flow model, and leveraging Python’s multiprocessing library for efficiency, the study employs a trial-and-error methodology. Key findings reveal that longitudinal dispersivity values range from 5.5 m near the source to 20.5 m further away, with horizontal and vertical transverse dispersivities between 0.28 m and 3.88 m and between 0.03 m and 0.08 m, respectively. These insights into the aquifer’s dispersivity coefficients, which reflect the scale-dependent nature of longitudinal dispersivity, are crucial for optimizing remediation strategies and achieving cleanup goals. This study underscores the importance of accurate parameter estimation in contaminant transport modeling and contributes to a better understanding of contaminant dynamics in the Collierville wellfield.
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(This article belongs to the Special Issue Groundwater Pollution: Sources, Mechanisms, and Prevention)
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Open AccessFeature PaperArticle
Estimating Drainage from Forest Water Reclamation Facilities Based on Drain Gauge Measurements
by
Madeline Schwarzbach, Erin S. Brooks, Robert Heinse, Eureka Joshi and Mark D. Coleman
Hydrology 2024, 11(6), 87; https://doi.org/10.3390/hydrology11060087 - 20 Jun 2024
Abstract
A growing human population requires sustainable solutions to regulate and dispose of municipal wastewater. Water treatment facilities in northern Idaho are permitted to apply reclaimed wastewater to forest land during the growing season at specified monthly hydraulic loading rates. We assessed the spatial
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A growing human population requires sustainable solutions to regulate and dispose of municipal wastewater. Water treatment facilities in northern Idaho are permitted to apply reclaimed wastewater to forest land during the growing season at specified monthly hydraulic loading rates. We assessed the spatial and temporal variability of drainage below the rooting zone between non-irrigated (control) and irrigated (effluent) stands during the growing and dormant seasons in 2021. No drainage was observed during the two months of annual seasonal drought, but large magnitudes of drainage were recorded during the dormant season (38–94 cm), which was consistent with seasonal precipitation. The overall effect of effluent treatment on the drain gauge measurements did not differ from the controls, as effluent only increased the drainage at some facilities. The measured drainage averaged from 35 to 62 cm among facilities. We then used the drainage measurements to calibrate hydrological models (Hydrus-1D and Water Erosion Prediction Project [WEPP]) and predict the drainage in 50 measurement plots distributed evenly among five forest water reclamation facilities. As with the observed drainage, there were no statistically significant growing season differences in the predicted monthly drainage during the growing season between the effluent and control plots, suggesting the successful use of hydrologic models to support the measured drainage findings. While both models struggled to accurately predict the quantity of drainage during the dormant season, they both successfully predicted that drainage would continue through May. WEPP also successfully predicted that the treated plots began to drain in September and October following late-season irrigation at some facilities. The models showed that the prescribed crop coefficient used by the Idaho Department of Environmental Quality was adequate in avoiding drainage during the peak summer months.
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(This article belongs to the Section Water Resources and Risk Management)
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Open AccessArticle
Modeling the Impacts of Sea Level Rise Scenarios on the Amazon River Estuary
by
Jonathan Luz P. Crizanto, Carlos Henrique M. de Abreu, Everaldo B. de Souza and Alan C. da Cunha
Hydrology 2024, 11(6), 86; https://doi.org/10.3390/hydrology11060086 - 20 Jun 2024
Abstract
The rise in the global mean sea level (MSL) is a significant consequence of climate change, attributed to both natural and anthropogenic forces. This phenomenon directly affects the dynamic equilibrium of Earth’s oceanic and estuarine ecosystems, particularly impacting the Amazon estuary. In this
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The rise in the global mean sea level (MSL) is a significant consequence of climate change, attributed to both natural and anthropogenic forces. This phenomenon directly affects the dynamic equilibrium of Earth’s oceanic and estuarine ecosystems, particularly impacting the Amazon estuary. In this study, a numerical model was employed to investigate the long-term impacts of MSL fluctuations on key hydrodynamic parameters crucial to regional environmental dynamics. Our investigation was based on scenarios derived from Representative Concentration Pathways (RCPs) and Coupled Model Intercomparison Project Phase 5 (CMIP5) projections, incorporating MSL variations ranging from 30 to 150 cm above the current mean level. Following careful calibration and validation procedures, which utilized observational and in situ data, notably from field expeditions conducted in 2019, our simulations unveiled significant impacts on certain hydrodynamic parameters. Specifically, we observed a pronounced increase in diurnal tidal amplitude (p < 0.05) within the upstream sections of the North and South channels. Additionally, discernible alterations in water renewal rates throughout the estuary were noted, persisting for approximately 2 days during the dry season (p < 0.05). These findings provide valuable insights into the vulnerability of key parameters to hydrologic instability within the Amazonian coastal region. In conclusion, this study represents a pivotal scientific endeavor aimed at enhancing the preservation of aquatic ecosystems and advancing the environmental knowledge of the Lower Amazon River, with the goal of proactively informing measures to safeguard the current and future sustainability of these vital ecosystems.
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(This article belongs to the Special Issue Climate Change Effects on Coastal Management)
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Open AccessFeature PaperArticle
Water Uptake by Mountain Big Sagebrush (Artemisia tridentata subsp. vaseyana) and Environmental Variables Affecting Water Availability in Semiarid Rangeland Ecosystems
by
Carlos G. Ochoa, Mohamed A. B. Abdallah and Daniel G. Gómez
Hydrology 2024, 11(6), 85; https://doi.org/10.3390/hydrology11060085 - 19 Jun 2024
Abstract
The sagebrush steppe ecosystem plays a critical role in water cycling in arid and semiarid landscapes of the western United States; yet, there is limited information regarding individual sagebrush plant water uptake. We used the stem heat balance (SHB) method to measure transpiration
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The sagebrush steppe ecosystem plays a critical role in water cycling in arid and semiarid landscapes of the western United States; yet, there is limited information regarding individual sagebrush plant water uptake. We used the stem heat balance (SHB) method to measure transpiration in mountain big sagebrush (Artemisia tridentata subsp. vaseyana) plants in a semiarid rangeland ecosystem in central Oregon, Pacific Northwest Region, USA. We evaluated the relationship between sagebrush transpiration and environmental factors from July 2022 to May 2023 for two individual plants representative of the average sagebrush stand height and crown width at the study site; transpiration rates varied by plant and by season. This study encompassed one below-average (2022; 278 mm) and one above-average (2023; 414 mm) precipitation years. Study results showed that the average water use during the entire period of study was 2.1 L d−1 for Plant 1 and 5.0 L d−1 for Plant 2. During the dry year, maximum transpiration was observed during the summer (Plant 1 = 4.8 L d−1; Plant 2 = 11.1 L d−1). For the wet year, both plants showed maximum transpiration levels at the end of the recording period in mid-May (Plant 1 = 9.6 L d−1; Plant 2 = 8.6 L d−1). The highest seasonal transpiration of both plants occurred in summer (2.87 L d−1), whereas the lowest transpiration was obtained in winter (0.21 L d−1). For all seasons but winter, soil moisture (SM), soil temperature (ST), and vapor pressure deficit (VPD) variables generally showed positive correlations with transpiration. Transpiration rates decreased in the summer of 2022 as the surface soil gradually dried. The two plants’ most significant water uptake differences were obtained during the dry year. It is possible that the larger stem diameter of plant 2 may have contributed to its higher transpiration rates during times of limited water availability. The study results add to the understanding of water use by sagebrush and its potential impact on the water balance of cool-climate rangeland ecosystems. The findings also highlight the sensitivity of sagebrush to variations in seasonal soil moisture availability, soil temperature, and vapor pressure deficit. Future research should involve studying the combined effects of water use by various dominant vegetation species and its effects on the water budget at the watershed scale.
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(This article belongs to the Special Issue Climate Change and Human-Induced Changes on Hydrological and Fluvial Process)
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