Water

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17 pages, 53660 KiB

Open AccessArticle

Assessment of Hydrological Responses to Land Use and Land Cover Changes in Forest-Dominated Watershed Using SWAT Model

by Hiyaw Hatiya Ware, Sun Woo Chang, Jeong Eun Lee and Il-Moon Chung

Water 2024, 16(4), 528; https://doi.org/10.3390/w16040528 - 07 Feb 2024

Viewed by 1111

Recognizing how human activities affect hydrological systems is vital for the sustainable preservation and effective management of water resources in the watershed. Hence, this paper focuses on the hydrological response to land use and land cover (LULC) change scenarios in the Anyang watershed, [...] Read more.

Recognizing how human activities affect hydrological systems is vital for the sustainable preservation and effective management of water resources in the watershed. Hence, this paper focuses on the hydrological response to land use and land cover (LULC) change scenarios in the Anyang watershed, South Korea. We obtained LULC data maps for the years 2000, 2013, and 2022 from the local government, revealing significant changes over the years. Agricultural lands experienced a 6.2% increase from 2000 to 2022, and pastureland expanded by 8.67% over two decades. The SWAT model was utilized to assess the impact of LULC on the hydrological components of the study watershed. Model calibration and validation for each LULC change were carried out using the SWAT-CUP program, considering the recorded streamflow information of the region. An excellent agreement was reached between the simulated and measured streamflow in both the calibration and validation stages under various LULC conditions. The Nash–Sutcliffe model efficiency (NSE), the objective function, demonstrated values of 0.9, 0.89, and 0.89 during the calibration for 2000, 2013, and 2022, respectively, in the LULC scenario, while for the validation, we obtained values of 0.82, 0.78, and 0.80 for 2000, 2013, and 2022, respectively. Our findings indicate that the surface runoff rise contributed much to the water yield increase over the two decades compared to the other components in terms of the water yield, while the contribution of evapotranspiration (ET) to the watershed hydrological cycle declined by 1.66% from 2000 to 2022. The southeastern sub-basin part showed a high groundwater recharge distribution due to agricultural land, rice area, and forest area changes. Full article

(This article belongs to the Special Issue Novel Applications of Surface Water–Groundwater Modeling)

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34 pages, 11722 KiB

Open AccessArticle

Mapping Prospects for Artificial Groundwater Recharge Utilizing Remote Sensing and GIS Methods

by Dheeraj Mohan Gururani, Yogendra Kumar, Salwan Ali Abed, Vinod Kumar, Dinesh Kumar Vishwakarma, Nadhir Al-Ansari, Kanwarpreet Singh, Alban Kuriqi and Mohamed A. Mattar

Water 2023, 15(22), 3904; https://doi.org/10.3390/w15223904 - 08 Nov 2023

Cited by 2 | Viewed by 2537

Abstract

The indiscriminate use of groundwater and its overexploitation has led to a significant decline in groundwater resources in India, making it essential to identify potential recharge zones for aquifer recharge. A study was conducted to determine such potential recharge zones in the Nandhour-Kailash [...] Read more.

The indiscriminate use of groundwater and its overexploitation has led to a significant decline in groundwater resources in India, making it essential to identify potential recharge zones for aquifer recharge. A study was conducted to determine such potential recharge zones in the Nandhour-Kailash River watershed. The study area included 1481 streams divided into 12 sub-basins (SWS). The results show that the downstream Saraunj sub-basins (SWS-11) and Odra sub-basins (SWS-12) were high priority and required immediate soil and water conservation attention. Sub catchments Lobchla West (SWS-4), Deotar (SWS-5), Balot South (SWS-8), Nandhour (SWS-9), and Nakoliy (SWS-10) had medium priority and were designated for moderate soil erosion and degradation. In contrast, sub-catchments Aligad (SWS-1), Kundal (SWS-2), Lowarnala North (SWS-3), Bhalseni (SWS-6), and Uparla Gauniyarao (SWS-7) had low priority, indicating a low risk of soil erosion and degradation. Using the existing groundwater level data, the potential map of groundwater was validated to confirm its validity. According to the guidelines provided by the Integrated Mission for Sustainable Development (IMSD), the results of the groundwater potential zones for good to very good zones have been integrated at the slope and stream order. In a 120.94 km² area with a slope of 0–5% in first-order streams, 36 ponds were proposed, and in a 218.03 km² area with a slope of 15% in first- to fourth-order streams, 105 retention dams were proposed and recognized as possible sites for artificial groundwater recharge. The proposed water harvesting structure may aid in continuously recharging these zones and benefit water resource managers and planners. Thus, various governmental organizations can use the results to identify possible future recharge areas. Full article

(This article belongs to the Special Issue Novel Applications of Surface Water–Groundwater Modeling)

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13 pages, 3807 KiB

Open AccessArticle

Estimation of Streamflow Depletion Caused by Groundwater Withdrawal in the Bokhacheon Watershed in South Korea Using the Modified SWAT Model

by Jeongwoo Lee, Jeong-Eun Lee and Il-Moon Chung

Water 2023, 15(19), 3336; https://doi.org/10.3390/w15193336 - 22 Sep 2023

Viewed by 811

Abstract

Understanding the effects of groundwater withdrawal on streamflow depletion is important for effectively managing water resources. The Soil and Water Assessment Tool (SWAT) model has a groundwater module to calculate the groundwater budget and groundwater discharge. However, the water pumped from the aquifer [...] Read more.

Understanding the effects of groundwater withdrawal on streamflow depletion is important for effectively managing water resources. The Soil and Water Assessment Tool (SWAT) model has a groundwater module to calculate the groundwater budget and groundwater discharge. However, the water pumped from the aquifer is not considered in the SWAT module that estimates groundwater discharge. Therefore, this module was modified to consider the impact of groundwater pumping on the changes in groundwater discharge in the Bokhacheon watershed, South Korea. The model’s water transfer module was improved to allow water from the aquifer to be transferred to destination locations, such as residential, industrial, and agricultural lands. Using the modified SWAT, streamflow responses to groundwater extraction were simulated for 2011–2019. The groundwater withdrawal induced decreases of 14.6 and 24.2% in low and drought flows, respectively, at the watershed’s outlet. The groundwater withdrawals decreased groundwater flow and total water yield by 23.5% and 9.8%, respectively, and increased surface flow, lateral flow, percolation, soil water, and evapotranspiration owing to the increased soil moisture resulting from the partial re-infiltration of the groundwater pumped for agricultural irrigation. The modified SWAT can effectively estimate streamflow depletion resulting from groundwater pumping without extensive hydrogeological input data and computational time. Full article

(This article belongs to the Special Issue Novel Applications of Surface Water–Groundwater Modeling)

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13 pages, 1670 KiB

Open AccessArticle

Solution to the Unsteady Seepage Model of Phreatic Water with Linear Variation in the Channel Water Level and Its Application

by Dan Wu, Yuezan Tao, Jie Yang and Bo Kang

Water 2023, 15(15), 2834; https://doi.org/10.3390/w15152834 - 05 Aug 2023

Viewed by 694

Abstract

For a semi-infinite aquifer controlled by a river channel boundary, when the Laplace transform is used to solve a one-dimensional unsteady seepage model of phreatic water while considering the influence of the vertical water exchange intensity ε with the change in the river [...] Read more.

For a semi-infinite aquifer controlled by a river channel boundary, when the Laplace transform is used to solve a one-dimensional unsteady seepage model of phreatic water while considering the influence of the vertical water exchange intensity ε with the change in the river channel water level f(t), a complicated and tedious integral transformation process is required. By replacing f(t) with an operator, this study first derived the analytic formula of the ε term based on the properties of the Laplace transform without the direct participation of f(t) in the transformation. By using f(t) in the form of several types of linear functions, the Laplace transform and inverse transform laws were summarized. The analytical solution to the problem was easily obtained by applying the “integral property” of the transformation to the linear function term with time t. The relative error between the numerical solution and the analytical solution of the example was less than 0.2%, which verified the rationality of the model linearization method and the reliability of the analytical solution. For different boundary conditions, the process of establishing and applying the inflection point method and the curve-fitting method for calculating the model parameters by using dynamic monitoring data for phreatic water is presented with examples. Full article

(This article belongs to the Special Issue Novel Applications of Surface Water–Groundwater Modeling)

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19 pages, 6799 KiB

Open AccessFeature PaperArticle

Assessment of Spatiotemporal Groundwater Recharge Distribution Using SWAT-MODFLOW Model and Transient Water Table Fluctuation Method

by Hiyaw Hatiya Ware, Tarekegn Dejen Mengistu, Bisrat Ayalew Yifru, Sun Woo Chang and Il-Moon Chung

Water 2023, 15(11), 2112; https://doi.org/10.3390/w15112112 - 02 Jun 2023

Cited by 4 | Viewed by 2204

Abstract

Recharge is a crucial section of water balance for both surface and subsurface models in water resource assessment. However, quantifying its spatiotemporal distribution at a regional scale poses a significant challenge. Empirical and numerical modeling are the most commonly used methods at the [...] Read more.

Recharge is a crucial section of water balance for both surface and subsurface models in water resource assessment. However, quantifying its spatiotemporal distribution at a regional scale poses a significant challenge. Empirical and numerical modeling are the most commonly used methods at the watershed scales. However, integrated models inherently contain a vast number of unknowns and uncertainties, which can limit their accuracy and reliability. In this work, we have proposed integrated SWAT-MODFLOW and Transient Water Table Fluctuation Method (TWTFM) to evaluate the spatiotemporal distribution of groundwater recharge in Anyang watershed, South Korea. Since TWTFM also uses SWAT model percolation output data, calibration was performed for individual models and a coupled model. The coupled model was calibrated using daily streamflow and hydraulic head. The SWAT-MODFLOW model performed well during the simulation of streamflow compared to the SWAT model. The study output showed that the study watershed had significant groundwater recharge variations during the simulated period. A significant amount of recharge happens in the wet season. It contributes a significant amount of the average annual precipitation of the region. The direct flow components (surface and lateral) showed significant contributions when the water balance components were evaluated in the region. TWTFM showed a glimpse to estimate recharge, which requires representative monitoring wells in the study region. Comprehensively, the SWAT-MODFLOW model estimated groundwater recharge with reasonable accuracy in the region. Full article

(This article belongs to the Special Issue Novel Applications of Surface Water–Groundwater Modeling)

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21 pages, 6652 KiB

Open AccessArticle

Machine Learning Algorithms for the Estimation of Water Quality Parameters in Lake Llanquihue in Southern Chile

by Lien Rodríguez-López, David Bustos Usta, Lisandra Bravo Alvarez, Iongel Duran-Llacer, Andrea Lami, Rebeca Martínez-Retureta and Roberto Urrutia

Water 2023, 15(11), 1994; https://doi.org/10.3390/w15111994 - 24 May 2023

Cited by 3 | Viewed by 1875

Abstract

The world’s water ecosystems have been affected by various human activities. Artificial intelligence techniques, especially machine learning, have become an important tool for predicting the water quality of inland aquatic ecosystems. As an excellent biological indicator, chlorophyll-a was studied to determine the state [...] Read more.

The world’s water ecosystems have been affected by various human activities. Artificial intelligence techniques, especially machine learning, have become an important tool for predicting the water quality of inland aquatic ecosystems. As an excellent biological indicator, chlorophyll-a was studied to determine the state of water quality in Lake Llanquihue, located in southern Chile. A 31-year time series (1989 to 2020) of data collected in situ was used to determine the evolution of limnological parameters at eight spaced stations covering all of the main points of the lake, and the year, month, day, and hour time intervals were selected. Using machine learning techniques, out of eight estimation algorithms that were applied with real data to estimate chlorophyll-a, three models showed better performance (XGBoost, LightGBM, and AdaBoost). The results for the best models show excellent performance, with a coefficient of determination between 0.81 and 0.99, a root-mean-square error of between 0.03 ug/L and 0.46 ug/L, and a mean bias error of between 0.01 and 0.27 ug/L. These models are scalable and applicable to other lake systems of interest that present similar conditions and can support decision making related to water resources. Full article

(This article belongs to the Special Issue Novel Applications of Surface Water–Groundwater Modeling)

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23 pages, 5826 KiB

Open AccessArticle

Application of Different Weighting Schemes and Stochastic Simulations to Parameterization Processes Considering Observation Error: Implications for Climate Change Impact Analysis of Integrated Watershed Models

by Eunhee Lee, Hyeonju Lee, Dongkyu Park, Hyoun-Tae Hwang and Changhui Park

Water 2023, 15(10), 1880; https://doi.org/10.3390/w15101880 - 16 May 2023

Viewed by 1171

Abstract

We investigated the potential impact of observation error on the calibration performance of an integrated watershed model. A three-dimensional integrated model was constructed using HydroGeoSphere and applied to the Sabgyo watershed in South Korea to assess the groundwater–surface water interaction process. During the [...] Read more.

We investigated the potential impact of observation error on the calibration performance of an integrated watershed model. A three-dimensional integrated model was constructed using HydroGeoSphere and applied to the Sabgyo watershed in South Korea to assess the groundwater–surface water interaction process. During the model calibration, three different weighting schemes that consider observation error variances were applied to the parameter estimation tool (PEST). The applied weighting schemes were compared with the results from stochastic models, in which observation errors from surface discharges were considered a random variable. Based on the calibrated model, the interactions between groundwater and surface water were predicted under different climate change scenarios (RCP). Comparisons of calibration performance between the different models showed that the observation-error-based weighting schemes contributed to an improvement in the model parameterization. Analysis of the exchange flux between groundwater and surface water highlighted the significance of groundwater in delaying the hydrological response of integrated water systems. Predictions based on different RCP scenarios suggested the increasing role of groundwater in watershed dynamics. We concluded that the comparison of different weighting schemes for the determination of error covariance could contribute to an improved characterization of watershed processes and reduce the model uncertainty arising from observation errors. Full article

(This article belongs to the Special Issue Novel Applications of Surface Water–Groundwater Modeling)

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17 pages, 5637 KiB

Open AccessArticle

A Case Study: Groundwater Level Forecasting of the Gyorae Area in Actual Practice on Jeju Island Using Deep-Learning Technique

by Deokhwan Kim, Cheolhee Jang, Jeonghyeon Choi and Jaewon Kwak

Water 2023, 15(5), 972; https://doi.org/10.3390/w15050972 - 03 Mar 2023

Cited by 3 | Viewed by 1938

Abstract

As a significant portion of the available water resources in volcanic terrains such as Jeju Island are dependent on groundwater, reliable groundwater level forecasting is one of the important tasks for efficient water resource management. This study aims to propose deep-learning-based methods for [...] Read more.

As a significant portion of the available water resources in volcanic terrains such as Jeju Island are dependent on groundwater, reliable groundwater level forecasting is one of the important tasks for efficient water resource management. This study aims to propose deep-learning-based methods for groundwater level forecasting that can be utilized in actual management works and to assess their applicability. The study suggests practical forecasting methodologies through the Gyorae area of Jeju Island, where the groundwater level is highly volatile and unpredictable. To this end, the groundwater level data of the JH Gyorae-1 point and a total of 12 kinds of daily hydro-meteorological data from 2012 to 2021 were collected. Subsequently, five factors (i.e., mean wind speed, sun hours, evaporation, minimum temperature, and daily precipitation) were selected as hydro-meteorological data for groundwater level forecasting through cross-wavelet analysis between the collected hydro-meteorological data and groundwater level data. The study simulated the groundwater level of the JH Gyorae-1 point using the long short-term memory (LSTM) model, a representative deep-learning technique, with the selected data to show that the methodology is adequately applicable. In addition, for its better utilization in actual practice, the study suggests and analyzes (i) a derivatives-based groundwater level learning model which is defined as derivatives-based learning to forecast derivatives (gradients) of the groundwater level, not the target groundwater time series itself, and (ⅱ) an ensemble forecasting methodology in which groundwater level forecasting is performed repetitively with short time intervals. Full article

(This article belongs to the Special Issue Novel Applications of Surface Water–Groundwater Modeling)

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