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Inevitable Connection of River Flow Modeling, GIS, and Hydrogeology

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 19863

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Special Issue Editors

Dr. Bojan Đurin

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Guest Editor

Department of Civil Engineering, University North, Jurja Križanića 31b, 42000 Varaždin, Croatia
Interests: hydrology; water resources; time series analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Danko Markovinović

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Guest Editor

Department of Civil Engineering, University North, Jurja Križanića 31b, 42000 Varaždin, Croatia
Interests: geodesy; geomatics; BIM; GIS; laser scanning; land administration; monitoring; INSPIRE; gravimetry

Dr. Quoc Bao Pham

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Guest Editor

Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska Street 60, 41-200 Sosnowiec, Poland
Interests: hydrology; groundwater; machine learning; water resources management; climate change; GIS; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Regardless of whether it is approached as an input parameter for the sizing of hydrotechnical facilities or as a cause of flooding, there is no doubt that the term river flow requires significant attention. Although there are many available modeling methods, as well as software, field analysis procedures, etc., an interdisciplinary approach and exchange of knowledge and experience are imperative. This is because river flow analysis without GIS modeling, as well as without a good foundation from hydrogeology research, is not reliable, as well as because climate change does not favor an accurate prediction of possible changes in the future.

The aim of the Special Issue is to collect the papers which present new approaches as well as state of the art of the aforementioned issues. Potential topics are river flow, velocity, and depth modeling; hydrology research and application of GIS and remote sensing tools for hydrogeology research; as well as their mutual combinations and variations, i.e.:

Surface water, groundwater, snow, and ice, in all their physical, chemical, and biological processes, their interrelationships, and their relationships to geographical factors, atmospheric processes and climate, and Earth processes including erosion and sedimentation;
River flow modeling;
Flood assessment;
Drought assessment;
Hydrological extremes and their impact;
Hydrological aspects of the use and management of water resources and their change under the influence of human activity;
Water resource systems, including the planning, engineering, management, and economic aspects of applied hydrology;
Modeling, analytical, or visualization approaches to aid water decision making, including novel or emerging approaches to DSS, such as using real-time data and AI.

We welcome submissions from around the world representing a range of disciplines, as well as papers that provide transdisciplinary and interdisciplinary research and perspectives. If you are interested in submitting a manuscript to this Special Issue, please reply to this email: Ms. Alexandra Wang <[email protected]> with a brief description of the manuscript topic, with full manuscripts due by 15 February 2022. All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once a manuscript is accepted, it will go into production and will be simultaneously published in the current regular issue and pulled into the online Special Issue.

Thank you for your time and consideration. We look forward to your contributions!

Prof. Dr. Bojan Đurin
Dr. Danko Markovinović
Dr. Quoc Bao Pham
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

hydrological processes
water resources management
machine learning
erosion
sedimentation
GIS
remote sensing
flooding
groundwater assessment
monitoring

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Published Papers (10 papers)

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Research

20 pages, 11978 KiB

Open AccessArticle

Spatial and Temporal Analysis of Hydrological Modelling in the Beas Basin Using SWAT+ Model

by Suraj Kumar Singh, Shruti Kanga, Bhavneet Gulati, Mirna Raič, Bhartendu Sajan, Bojan Đurin and Saurabh Singh

Water 2023, 15(19), 3338; https://doi.org/10.3390/w15193338 - 22 Sep 2023

Cited by 8 | Viewed by 2398

Abstract

In this research, the SWAT+ model was employed to elucidate hydrological dynamics within the Beas Basin. The primary objectives encompassed the calibration of the SWAT model for accurate water balance quantification, annual simulation of salient hydrological components, and a decadal analysis of trends in fluvial discharge and sediment transport. The methodology encompasses simulating hydrological data with the SWAT+ model, followed by calibration and validation using flow data from Larji and Mahadev hydroelectric plants. The model’s efficacy in depicting streamflow and other hydrological components is corroborated by statistical measures such as the Nash–Sutcliffe efficiency and PBIAS. The water balance analysis delivers insights into the basin’s hydrological characteristics, including surface flow, water yield, and evapotranspiration. The temporal analysis exposes intricate seasonal and interannual variability in flow and sediment discharge, while spatial distribution highlights heterogeneity across the basin. These findings have practical implications for water resource management, including optimizing water allocation, hydroelectric power generation, irrigation, and environmental concerns. Limitations, such as data quality and model simplifications, are acknowledged, and future data collection and observations are recommended for improved model performance. In essence, these researches enhance understanding of the Beas Basin’s hydrology, setting a course for future investigations to integrate more data sources, refine model parameters, and consider climate and land-use changes for a richer comprehension of the basin’s hydrological dynamics. Full article

(This article belongs to the Special Issue Inevitable Connection of River Flow Modeling, GIS, and Hydrogeology)

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27 pages, 8903 KiB

Open AccessArticle

Understanding the Climate Change and Land Use Impact on Streamflow in the Present and Future under CMIP6 Climate Scenarios for the Parvara Mula Basin, India

by Usman Mohseni, Prasit G. Agnihotri, Chaitanya B. Pande and Bojan Durin

Water 2023, 15(9), 1753; https://doi.org/10.3390/w15091753 - 2 May 2023

Cited by 14 | Viewed by 4342

Abstract

Understanding the likely impacts of climate change (CC) and Land Use Land Cover (LULC) on water resources (WR) is critical for a water basin’s mitigation. The present study intends to quantify the impact of (CC) and (LULC) on the streamflow (SF) of the Parvara Mula Basin (PMB) using SWAT. The SWAT model was calibrated and validated using the SWAT Calibration Uncertainty Program (SWAT-CUP) for the two time periods (2003–2007 and 2013–2016) and (2008–2010 and 2017–2018), respectively. To evaluate the model’s performance, statistical matrices such as R², NSE, PBIAS, and RSR were computed for both the calibrated and validated periods. For both these periods, the calibrated and validated results of the model were found to be very good. In this study, three bias-corrected CMIP6 GCMs (ACCESS-CM2, BCC-CSM2-MR, and CanESM5) under three scenarios (ssp245, ssp370, and ssp585) have been adopted by assuming no change in the existing LULC (2018). The results obtained from the SWAT simulation at the end of the century show that there will be an increase in streamflow (SF) by 44.75% to 53.72%, 45.80% to 77.31%, and 48.51% to 83.12% according to ACCESS-CM2, BCC-CSM2-MR, and CanESM5, respectively. A mean ensemble model was created to determine the net change in streamflow under different scenarios for different future time projections. The results obtained from the mean ensembled model also reveal an increase in the SF for the near future (2020–2040), mid future (2041–2070), and far future (2071–2100) to be 64.19%, 47.33%, and 70.59%, respectively. Finally, based on the obtained results, it was concluded that the CanESM5 model produces better results than the ACCESS-CM2 and BCC-CSM2-MR models. As a result, the streamflow evaluated with this model can be used for the PMB’s future water management strategies. Thus, this study’s findings may be helpful in developing water management strategies and preventing the pessimistic effect of CC in the PMB. Full article

(This article belongs to the Special Issue Inevitable Connection of River Flow Modeling, GIS, and Hydrogeology)

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21 pages, 15276 KiB

Open AccessArticle

Groundwater Potential Zone Mapping in the Ghaggar River Basin, North-West India, Using Integrated Remote Sensing and GIS Techniques

by Ritambhara K. Upadhyay, Gaurav Tripathi, Bojan Đurin, Sanja Šamanović, Vlado Cetl, Naval Kishore, Mukta Sharma, Suraj Kumar Singh, Shruti Kanga, Md Wasim, Praveen Kumar Rai and Vinay Bhardwaj

Water 2023, 15(5), 961; https://doi.org/10.3390/w15050961 - 2 Mar 2023

Cited by 20 | Viewed by 5588

Abstract

The immense dependence of the growing population on groundwater has resulted in depletion at a fast pace can be seen nowadays. Identifying a groundwater potential zone can be proved as an aid to provide insight to the decision-makers and local authorities for planning purposes. This study evaluated the delineation of groundwater potential zones using integrated remote sensing and GIS approach. Various thematic layers such as geology, geomorphology, lineament, slope, drainage, soil, land use/land cover, and rainfall were considered in this study as these have influence on the occurrence of groundwater and its cycle, and maps have been prepared in GIS domain. Afterward, appropriate weights were assigned to these layers based on multi-criteria decision analysis, i.e., Analytical Hierarchy Process (AHP). Groundwater potentiality has been delineated in different zones (low, moderate, high, and very high) in the study region based on weighted overlay analysis. The study reveals zones with different groundwater prospects viz. low (1.27%), moderate (15.65%), high (75.54%), and very high (7.29%). The ground survey data provided by CGWB (Central Ground Water Board) of nearly 100 wells/dug wells/borewells/piezometers have been used for validation purposes, showing comparable results with the groundwater prospects zones. It also confirms that the majority of these wells fall under very high or high groundwater potential zones. They were also found to be thereby indicating that there is the existence of a permeable reservoir with considerable water storage in the subsurface. One of the most important issues for users and governments is groundwater depletion. Planning for the available groundwater resource is made easier by identifying the potential for groundwater (low to high). Full article

(This article belongs to the Special Issue Inevitable Connection of River Flow Modeling, GIS, and Hydrogeology)

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22 pages, 9348 KiB

Open AccessArticle

Comparative Study of Coupling Models of Feature Selection Methods and Machine Learning Techniques for Predicting Monthly Reservoir Inflow

by Jakkarin Weekaew, Pakorn Ditthakit, Quoc Bao Pham, Nichnan Kittiphattanabawon and Nguyen Thi Thuy Linh

Water 2022, 14(24), 4029; https://doi.org/10.3390/w14244029 - 9 Dec 2022

Cited by 2 | Viewed by 2069

Abstract

Effective reservoir operation under the effects of climate change is immensely challenging. The accuracy of reservoir inflow forecasting is one of the essential factors supporting reservoir operations. This study aimed to investigate coupling models of feature selection (FS) and machine learning (ML) algorithms to predict the monthly reservoir inflow. The study was carried out using data from the Huai Nam Sai reservoir in southern Thailand. Eighteen years of monthly recorded data (i.e., reservoir inflow, reservoir storage, rainfall, and regional climate indices) with up to a 12-month time lag were utilized. Three ML techniques, i.e., multiple linear regression (MLR), support vector regression (SVR), and artificial neural network (ANN)were compared in their capabilities. In addition, two FS techniques, i.e., genetic algorithm (GA) and backward elimination (BE) methods, were studied with four predictable time intervals, consisting of 3, 6, 9, and 12 months in advance. Ten-fold cross-validation was used for model evaluation. Study results revealed that FS methods (i.e., GA and BE) Could improve the performance of SVR and ANN for predicting monthly reservoir inflow forecasting, but they have no effects on MLR. Different developed forecasting models were suitable for different reservoir inflow forecasting time-step-ahead. BE-ANN provided the best performance for three-time-ahead (T + 3) and nine-time-ahead (T + 9) by giving an OI of 0.9885 and 0.8818, NSE of 0.9546 and 0.9815, RMSE of 1.3155 and 1.2172 MCM/month, MAE of 0.9568 and 0.9644 MCM/month, and r of 0.9796 and 0.9804, respectively. The GA-ANN model showed the highest prediction accuracy for six-time-ahead (T + 6), with an OI of 0.8997, NSE of 0.9407, RMSE of 2.1699 MCM/month, MAE of 1.7549 MCM/month, and r of 0.9759. The ANN model showed the best prediction accuracy for twelve-time-ahead (T + 12), with an OI of 0.9515, NSE of 0.9835, RMSE of 1.1613 MCM/month, MAE of 0.9273 MCM/month, and r of 0.9835. Full article

(This article belongs to the Special Issue Inevitable Connection of River Flow Modeling, GIS, and Hydrogeology)

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19 pages, 3983 KiB

Open AccessArticle

Spatial-Temporal Pattern Analysis of Land Use and Water Yield in Water Source Region of Middle Route of South-to-North Water Transfer Project Based on Google Earth Engine

by Pengtao Niu, Enchao Zhang, Yu Feng and Peihao Peng

Water 2022, 14(16), 2535; https://doi.org/10.3390/w14162535 - 18 Aug 2022

Cited by 12 | Viewed by 2947

Abstract

The water source area of the middle route of the South-to-North Water Diversion Project is an important water conservation and ecological protection area in China. Based on remote sensing data, this paper analyzed the evolution process of land use/cover change in water source region in the past 35 years. Then, based on the InVEST model, the spatial-temporal patterns of water yield in the water source region were calculated with land use cover, meteorology and soil data as inputs. The impacts of climate factors such as precipitation and temperature and land use change on water yield were discussed, and the responses of water yield to these two changes were also discussed. The results show that from 1985 to 2020, the average water yield depth in the middle route of the South-to-North Water Diversion Project increases first and then decreases, from 615 mm in 1985 to 738 mm in 2000, and then decreases to 521 mm in 2020. The spatial heterogeneity of the water-producing capacity is obvious. The high value of the water-producing capacity is concentrated in the Daba Mountain area in the south, while the low values are concentrated in the Hanzhong Basin, Ankang Basin and the eastern plain area. The spatial pattern of water producing depth has no obvious change. The average water yield depth of forest, grassland and shrub in the region was the largest, and forest and cultivated land were the main contributors to the total water yield of the region, providing 82% and 14% of the total water yield in 2020. Precipitation has a significant effect on water yield, while land use/cover change has a small effect on water yield. Full article

(This article belongs to the Special Issue Inevitable Connection of River Flow Modeling, GIS, and Hydrogeology)

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19 pages, 6478 KiB

Open AccessArticle

Availability and Accessibility of Hydrography and Hydrogeology Spatial Data in Europe through INSPIRE

by Danko Markovinović, Vlado Cetl, Sanja Šamanović and Olga Bjelotomić Oršulić

Water 2022, 14(9), 1499; https://doi.org/10.3390/w14091499 - 7 May 2022

Cited by 2 | Viewed by 2338

Abstract

Hydrography and hydrogeology data contain spatial references and as such are part of spatial data infrastructure. On the European level, these data are part of European spatial data infrastructure, well known as INSPIRE. The objective of INSPIRE is to make public spatial data available and accessible for a broad range of users in a simple, interoperable, and efficient way. Spatial data play an important role in facilitating data integration, enabling data-driven decision making on where and why things happen and easing communication through intuitive visualizations. Within this paper, we take the opportunity to reflect on the development and implementation of INSPIRE, with the main focus on the availability and accessibility of hydrography and hydrogeology data. By availability, we aim for the existence of metadata describing spatial data, while by accessibility, we aim for the existence of related services for spatial data viewing and downloading. The overall findings, based on the analysis in the INSPIRE Geoportal, shows that the data are still not fully available, although the deadline for INSPIRE implementation has already passed. Data accessibility is also an issue. Data that are even available in the infrastructure are sometimes not accessible. However, technological developments and recent policy initiatives could be drivers for future improvement. Full article

(This article belongs to the Special Issue Inevitable Connection of River Flow Modeling, GIS, and Hydrogeology)

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18 pages, 8000 KiB

Open AccessArticle

Efficiency of Geospatial Technology and Multi-Criteria Decision Analysis for Groundwater Potential Mapping in a Semi-Arid Region

by Ahmed M. Masoud, Quoc Bao Pham, Ahmed K. Alezabawy and Sherif A. Abu El-Magd

Water 2022, 14(6), 882; https://doi.org/10.3390/w14060882 - 11 Mar 2022

Cited by 30 | Viewed by 4133

Abstract

The increasing water demand in Egypt causes massive stress on groundwater resources. The high variability in the groundwater depth, aquifer properties, terrain characteristics, and shortage of rainfall make it necessary to identify the groundwater potentiality in semi-arid regions. This study used the possibilities of multi-criteria decision approaches (MCDA), geographical information system (GIS), and groundwater field data to delineate potential groundwater zones in the Tushka area, west of Lake Nasser, South Egypt. Furthermore, groundwater potentiality identification can help decision-makers better plan and manage the water resources in this promising area. Eight controlling factors were utilized to achieve the objective of the present work using multi-criteria decision analysis (MCDA) approaches, namely the analytical hierarchy process (AHP) and frequency ratio (FR) models. The controlling parameters were integrated with the geographic information system (GIS) to develop the zones of groundwater potentialities. The results revealed that high and moderate-potential zones cover approximately 61% and 52% of the total area in the AHP and FR models, respectively. A total of 44 groundwater production wells along with the well yield were collected and used to validate the models. The results were evaluated using the receiver operating characteristics (ROC) curve. The best-performing prediction rates achieved by AHP and FR were 83% and 81%, respectively. Finally, the obtained results indicated that the AHP model achieved better performance than the FR model. Full article

(This article belongs to the Special Issue Inevitable Connection of River Flow Modeling, GIS, and Hydrogeology)

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17 pages, 4012 KiB

Open AccessArticle

Estimating Yield and Water Productivity of Tomato Using a Novel Hybrid Approach

by Hossein Dehghanisanij, Somayeh Emami, Mohammed Achite, Nguyen Thi Thuy Linh and Quoc Bao Pham

Water 2021, 13(24), 3615; https://doi.org/10.3390/w13243615 - 16 Dec 2021

Cited by 8 | Viewed by 3111

Abstract

Water productivity (WP) of crops is affected by water–fertilizer management in interaction with climatic factors. This study aimed to evaluate the efficiency of a hybrid method of season optimization algorithm (SO) and support vector regression (SVR) in estimating the yield and WP of tomato crops based on climatic factors, irrigation–fertilizer under the drip irrigation, and plastic mulch. To approve the proposed method, 160 field data including water consumption during the growing season, fertilizers, climatic variables, and crop variety were applied. Two types of treatments, namely drip irrigation (DI) and drip irrigation with plastic mulch (PMDI), were considered. Seven different input combinations were used to estimate yield and WP. R², RMSE, NSE, SI, and σ criteria were utilized to assess the proposed hybrid method. A good agreement was presented between the observed (field monitoring data) and estimated (calculated with SO–SVR method) values (R² = 0.982). The irrigation–-fertilizer parameters (PMDI, F) and crop variety (V) are the most effective in estimating the yield and WP of tomato crops. Statistical analysis of the obtained results showed that the SO–SVR hybrid method has high efficiency in estimating WP and yield. In general, intelligent hybrid methods can enable the optimal and economical use of water and fertilizer resources. Full article

(This article belongs to the Special Issue Inevitable Connection of River Flow Modeling, GIS, and Hydrogeology)

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15 pages, 5449 KiB

Open AccessFeature PaperArticle

Estimating Baseflow and Baseflow Index in Ungauged Basins Using Spatial Interpolation Techniques: A Case Study of the Southern River Basin of Thailand

by Pakorn Ditthakit, Sarayod Nakrod, Naunwan Viriyanantavong, Abebe Debele Tolche and Quoc Bao Pham

Water 2021, 13(21), 3113; https://doi.org/10.3390/w13213113 - 4 Nov 2021

Cited by 6 | Viewed by 3259

Abstract

This research aims to estimate baseflow (BF) and baseflow index (BFI) in ungauged basins in the southern part of Thailand. Three spatial interpolation methods (namely, inverse distance weighting (IDW), kriging, and spline) were utilized and compared in regard to their performance. Two baseflow separation methods, i.e., the local minimum method (LM) and the Eckhardt filter method (EF), were investigated. Runoff data were collected from 65 runoff stations. These runoff stations were randomly selected and divided into two parts: 75% and 25% for the calibration and validation stages, respectively, with a total of 36 study cases. Four statistical indices including mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (r), and combined accuracy (CA), were applied for the performance evaluation. The findings revealed that monthly and annual BF and BFI calculated by EF were mostly lower than those calculated by LM. Furthermore, IDW gave the best performance among the three spatial interpolation techniques by providing the highest r-value and the lowest MAE, RMSE, and CA values for both the calibration and validation stages, followed by kriging and spline, respectively. We also provided monthly and annual BF and BFI maps to benefit water resource management. Full article

(This article belongs to the Special Issue Inevitable Connection of River Flow Modeling, GIS, and Hydrogeology)

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20 pages, 5737 KiB

Open AccessArticle

Analysis of Net Erosion Using a Physics-Based Erosion Model for the Doam Dam Basin in Korea

by Minho Yeon, Seongwon Kim, Hongjoon Shin, Hyunuk An, Daeeop Lee, Sungho Jung and Giha Lee

Water 2021, 13(19), 2663; https://doi.org/10.3390/w13192663 - 27 Sep 2021

Cited by 2 | Viewed by 2506

Abstract

In Korea, approximately 70% of the country is mountainous, with steep slopes and heavy rainfall in summer from June to September. Korea is classified as a high-risk country for soil erosion, and the rate of soil erosion is rapidly increasing. In particular, the operation of Doam dam was suspended in 2001 because of water quality issues due to severe soil erosion from the upstream areas. In spite of serious dam sediment problems in this basin, in-depth studies on the origin of sedimentation using physic-based models have not been conducted. This study aims to analyze the spatial distribution of net erosion during typhoon events using a spatially distributed physics-based erosion model and to improve the model based on a field survey. The spatially uniform erodibility constants of the surface flow detachment equation in the original erosion model were replaced by land use erodibility constants based on benchmarking experimental values to reflect the effect of land use on net erosion. The results of the upgraded model considering spatial erodibility show a significant increase in soil erosion in crop fields and bare land, unlike the simulation results before model improvement. The total erosion and deposition for Typhoon Maemi in 2003 were 36,689.0 and 9893.3 m³, respectively, while the total erosion and deposition for Typhoon Rusa in 2002 were 142,476.6 and 44,806.8 m³, respectively, despite about twice as much rainfall and 1.2 times as high rainfall intensity. However, there is a limitation in quantifying the sources of erosion in the study watershed, since direct comparison of the simulated net erosion with observed spatial information from aerial images, etc., is impossible due to nonperiodic image photographing. Therefore, continuous monitoring of not only sediment yield but also periodic spatial detection on erosion and deposition is critical for reducing data uncertainty and improving simulation accuracy. Full article

(This article belongs to the Special Issue Inevitable Connection of River Flow Modeling, GIS, and Hydrogeology)

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