Hydrology

16 pages, 762 KiB

Open AccessArticle

Geostatistical Analysis of Groundwater Data in a Mining Area in Greece

by E. Diamantopoulou, A. Pavlides, E. Steiakakis and E. A. Varouchakis

Hydrology 2024, 11(7), 102; https://doi.org/10.3390/hydrology11070102 - 11 Jul 2024

Viewed by 127

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 [...] Read more.

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 (

ρ = 65 %

for Cd and

ρ = 52 %

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. Full article

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17 pages, 13176 KiB

Open AccessArticle

Influence of Slope Aspect and Vegetation on the Soil Moisture Response to Snowmelt in the German Alps

by 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

Viewed by 228

Abstract

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, [...] Read more.

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. Full article

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20 pages, 2575 KiB

Open AccessArticle

Agricultural Drought Model Based on Machine Learning Cubist Algorithm and its Evaluation

by 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

Viewed by 223

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, [...] Read more.

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. Full article

(This article belongs to the Topic Advances in Hydro-Geological Research in Arid and Semi-Arid Areas)

19 pages, 4478 KiB

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

Viewed by 379

Abstract

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 [...] Read more.

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. Full article

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5 pages, 241 KiB

Open AccessEditorial

Groundwater Pollution: Sources, Mechanisms, and Prevention

by Pantelis Sidiropoulos

Hydrology 2024, 11(7), 98; https://doi.org/10.3390/hydrology11070098 - 5 Jul 2024

Viewed by 181

Abstract

Groundwater resources are vital for ecosystems and human health and prosperity [...] Full article

(This article belongs to the Special Issue Groundwater Pollution: Sources, Mechanisms, and Prevention)

20 pages, 5878 KiB

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

Viewed by 453

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 [...] Read more.

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. Full article

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

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18 pages, 5340 KiB

Open AccessArticle

Measurement and Calculation of Sediment Transport on an Ephemeral Stream

by Loukas Avgeris, Konstantinos Kaffas and Vlassios Hrissanthou

Hydrology 2024, 11(7), 96; https://doi.org/10.3390/hydrology11070096 - 30 Jun 2024

Viewed by 288

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advances in Catchments Hydrology and Sediment Dynamics)

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15 pages, 4339 KiB

Open AccessArticle

Estimation of Evapotranspiration in South Eastern Afghanistan Using the GCOM-C Algorithm on the Basis of Landsat Satellite Imagery

by Emal Wali, Masahiro Tasumi and Otto Klemm

Hydrology 2024, 11(7), 95; https://doi.org/10.3390/hydrology11070095 - 30 Jun 2024

Viewed by 275

Abstract

This study aims to assess the performance of the Global Change Observation Mission—Climate (GCOM-C) ET_index estimation algorithm to estimate the actual evapotranspiration (ETa) in southeastern Afghanistan. Here, the GCOM-C ET_index algorithm was adopted to estimate the monthly ETa for the period [...] Read more.

This study aims to assess the performance of the Global Change Observation Mission—Climate (GCOM-C) ET_index estimation algorithm to estimate the actual evapotranspiration (ETa) in southeastern Afghanistan. Here, the GCOM-C ET_index 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. Full article

(This article belongs to the Special Issue GIS Modelling of Evapotranspiration with Remote Sensing)

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21 pages, 4339 KiB

Open AccessArticle

Effects of Forest Logging Systems on the River Flow Regime Indices Using Graphical Techniques: A Case Study in a Small Natural Forest

by Farshad Keivan Behjou, Raoof Mostafazadeh and Nazila Alaei

Hydrology 2024, 11(7), 94; https://doi.org/10.3390/hydrology11070094 - 28 Jun 2024

Viewed by 237

Abstract

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 [...] Read more.

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. Full article

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23 pages, 1218 KiB

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

Viewed by 225

Abstract

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 [...] Read more.

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. Full article

28 pages, 1804 KiB

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

Viewed by 742

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 [...] Read more.

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. Full article

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

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22 pages, 5413 KiB

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

Viewed by 633

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Trends and Variations in Hydroclimatic Variables)

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20 pages, 1585 KiB

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

Viewed by 591

Abstract

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 [...] Read more.

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. Full article

20 pages, 8660 KiB

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

Viewed by 550

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 [...] Read more.

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 (ET_o), based only on temperature data (T_min, T_max, T_mean), by comparing their daily ET_o 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 ET_o models’ comparisons, it was observed that all regression-based machine learning models possess the capability to accurately estimate daily ET_o 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 ET_o, 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. Full article

(This article belongs to the Special Issue GIS Modelling of Evapotranspiration with Remote Sensing)

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22 pages, 15379 KiB

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

Viewed by 490

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Groundwater Pollution: Sources, Mechanisms, and Prevention)

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