Performance evaluation of various hydrological models with respect to hydrological responses under climate change scenario: a review
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Cited by 2 publications
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Assessing the Hydrological Response to Land Use Changes Linking SWAT and CA ‐Markov Models
Land use change, as a major driving factor of watershed hydrological process, has a significant influence on watershed hydrological change. In addition, a series of hydrological models, as important tools for simulating hydrological impacts, are widely employed in studying land use change. However, when employing hydrological model to analyse the hydrological impacts of land use changes, most previous studies focused on the evolution of historical land use change and lacked reasonable predictions of future land use. Therefore, it is necessary to extend such studies to future scenarios to cope with possible future hydrological variations in the basin. Given this, this paper making the Wuwei section of Shiyang River Basin as the study area, coupled the SWAT (Soil and Water Assessment Tool) model for hydrological simulation with the CA‐Markov (cellular automata‐Markov chain) model for future land use prediction to analyse the regional hydrological effects caused by historical and future land use changes. In addition, the general CA‐Markov model directly uses a system‐generated suitability atlas. In contrast, this study applied logistic regression and Multi‐criteria evaluation (MCE) methods to construct the suitability atlas, thereby establishing the Logistic‐CA‐Markov and MCE‐CA‐Markov models. Based on the model results, the main results are as follows: (1) The land use in study area is mainly grassland and barren, accounting for more than 80%. Additionally, forest is changing at the highest rate among all land use types. (2) In terms of the percentage of grassland and forest, the future land use predicted by MCE‐CA‐Markov (Multi‐criteria evaluation‐cellular automata‐Markov chain) has the largest forest and grassland coverage (57.78%), whereas the future land use predicted by Logistic CA‐Markov has the lowest (54.69%), indicating that the former pays more attention to the sustainable development of ecological environment. (3) The study area's R2 = 0.83, NSE = 0.79, PBIAS = −18.6%, and validation R2 = 0.81, NSE = 0.76, PBIAS = −17.8% demonstrate the favourable application of the SWAT model. (4) Based on simulated runoff results under historical and future land use scenarios, the amount of increasing grassland and forest coverage in the study area would eventually rise water yield (WYLD) by increasing lateral runoff (LATQ), increasing subsurface runoff (GWQ), and reducing surface runoff (SURQ). The study contributes to a better understanding of the impact of land use change on regional water resources and water balance, thus guiding regional water resources management and sustainable development.
Assessing the Hydrological Response to Land Use Changes Linking SWAT and CA ‐Markov Models
Land use change, as a major driving factor of watershed hydrological process, has a significant influence on watershed hydrological change. In addition, a series of hydrological models, as important tools for simulating hydrological impacts, are widely employed in studying land use change. However, when employing hydrological model to analyse the hydrological impacts of land use changes, most previous studies focused on the evolution of historical land use change and lacked reasonable predictions of future land use. Therefore, it is necessary to extend such studies to future scenarios to cope with possible future hydrological variations in the basin. Given this, this paper making the Wuwei section of Shiyang River Basin as the study area, coupled the SWAT (Soil and Water Assessment Tool) model for hydrological simulation with the CA‐Markov (cellular automata‐Markov chain) model for future land use prediction to analyse the regional hydrological effects caused by historical and future land use changes. In addition, the general CA‐Markov model directly uses a system‐generated suitability atlas. In contrast, this study applied logistic regression and Multi‐criteria evaluation (MCE) methods to construct the suitability atlas, thereby establishing the Logistic‐CA‐Markov and MCE‐CA‐Markov models. Based on the model results, the main results are as follows: (1) The land use in study area is mainly grassland and barren, accounting for more than 80%. Additionally, forest is changing at the highest rate among all land use types. (2) In terms of the percentage of grassland and forest, the future land use predicted by MCE‐CA‐Markov (Multi‐criteria evaluation‐cellular automata‐Markov chain) has the largest forest and grassland coverage (57.78%), whereas the future land use predicted by Logistic CA‐Markov has the lowest (54.69%), indicating that the former pays more attention to the sustainable development of ecological environment. (3) The study area's R2 = 0.83, NSE = 0.79, PBIAS = −18.6%, and validation R2 = 0.81, NSE = 0.76, PBIAS = −17.8% demonstrate the favourable application of the SWAT model. (4) Based on simulated runoff results under historical and future land use scenarios, the amount of increasing grassland and forest coverage in the study area would eventually rise water yield (WYLD) by increasing lateral runoff (LATQ), increasing subsurface runoff (GWQ), and reducing surface runoff (SURQ). The study contributes to a better understanding of the impact of land use change on regional water resources and water balance, thus guiding regional water resources management and sustainable development.
Sensitivity of Streamflow to Changing Rainfall and Evapotranspiration in Catchments Across the Nile Basin
This research focuses on the complex dynamics governing the sensitivity of streamflow to variations in rainfall and potential evapotranspiration (PET) within the Nile basin. By employing a hydrological model, our study examines the interrelationships between meteorological variables and hydrological responses across six catchments (Blue Nile, El Diem, Kabalega, Malaba, Mpanga, and Ribb) and explores the intricate balance between rainfall, PET, and streamflow. Nash Sutcliffe Efficiency (NSE) for calibration of the hydrological model ranged from 0.636 (Ribb) to 0.831 (El Diem). For validation, NSE ranged from 0.608 (Ribb) to 0.811 (Blue Nile). With rainfall kept constant while PET was increased by 5%, the streamflows of the Blue Nile, El Diem, Kabalega, Malaba, Mpanga, and Ribb decreased by 7.00, 5.08, 2.49, 4.10, 1.84, and 7.67%, respectively. With the original PET data unchanged, increasing rainfall of the Blue Nile, El Diem, Kabalega, Malaba, Mpanga, and Ribb by 5% led to an increase in streamflow by 9.02, 9.87, 5.38, 4.34, 6.58, and 8.32%, respectively. The research reveals that the rate at which a catchment losing water to the atmosphere (determined by PET) substantially influences its drying rate. Utilizing linear models, we demonstrate that the surplus rainfall available for increasing streamflow (represented by model intercepts) amplifies with higher rainfall intensities. This highlights the pivotal role of rainfall in shaping catchment water balance dynamics. Moreover, our study stresses the varied sensitivities of catchments within the basin to changes in PET and rainfall. Catchments with lower PET exhibit heightened responsiveness to increasing rainfall, accentuating the influence of evaporative demand on streamflow patterns. Conversely, regions with higher PET rates necessitate refined management strategies due to their increased sensitivity to changes in evaporative demand. Understanding the intricate interplay between rainfall, PET, and streamflow is paramount for developing adaptive strategies amidst climate variability. By examining these relationships, our research contributes essential knowledge for sustainable water resource management practices at both the catchment and regional scales, especially in regions susceptible to varying sensitivities of catchments to climatic conditions.
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