Integrated Assessment of the Land Use Change and Climate Change Impact on Baseflow by Using Hydrologic Model

Lee, Jimin; Park, MinJi; Min, Ji Hyun; Na, Eun Hye

doi:10.3390/su151612465

Cited by 7 publications

(1 citation statement)

References 57 publications

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“…Alterations in LULC have an unswerving influence on various hydrological components by altering surface characteristics such as roughness, vegetation density, and soil composition [8]. These modifications can significantly influence the flow of water, runoff patterns, and infiltration rates within the ecosystem [9,10]. For instance, shifts in LULC can impact the ability of surfaces to retain water, affect the rate at which water moves across the landscape, and ultimately influence the overall water balance and hydrological processes within an area [7].…”

Section: Introductionmentioning

confidence: 99%

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

Ware,

Chang,

Lee

et al. 2024

Water

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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.

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Section: Introductionmentioning

confidence: 99%

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

Ware,

Chang,

Lee

et al. 2024

Water

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Machine learning modeling of base flow generation potential: A case study of the combined application of BWM and Fallback bargaining algorithm

Nasiri Khiavi

2024

Journal of Hydrology

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A hybrid deep learning approach for streamflow prediction utilizing watershed memory and process-based modeling

Yifru,

Lim,

Bae

et al. 2024

Hydrology Research

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Accurate streamflow prediction is essential for optimal water management and disaster preparedness. While data-driven methods’ performance often surpasses process-based models, concerns regarding their ‘black-box’ nature persist. Hybrid models, integrating domain knowledge and process modeling into a data-driven framework, offer enhanced streamflow prediction capabilities. This study investigated watershed memory and process modeling-based hybridizing approaches across diverse hydrological regimes – Korean and Ethiopian watersheds. Following watershed memory analysis, the Soil and Water Assessment Tool (SWAT) was calibrated using the recession constant and other relevant parameters. Three hybrid models, incorporating watershed memory and residual error, were developed and evaluated against standalone long short-term memory (LSTM) models. Hybrids outperformed the standalone LSTM across all watersheds. The memory-based approach exhibited superior and consistent performance across training, evaluation periods, and regions, achieving 17–66% Nash–Sutcliffe efficiency coefficient improvement. The residual error-based technique showed varying performance across regions. While hybrids improved extreme event predictions, particularly peak flows, all models struggled at low flow. Korean watersheds’ significant prediction improvements highlight the hybrid models’ effectiveness in regions with pronounced temporal hydrological variability. This study underscores the importance of selecting a specific hybrid approach based on the desired objectives rather than solely relying on statistical metrics that often reflect average performance.

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Integrated Assessment of the Land Use Change and Climate Change Impact on Baseflow by Using Hydrologic Model

Cited by 7 publications

References 57 publications

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

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

Machine learning modeling of base flow generation potential: A case study of the combined application of BWM and Fallback bargaining algorithm

A hybrid deep learning approach for streamflow prediction utilizing watershed memory and process-based modeling

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