Extreme Runoff Estimation for Ungauged Watersheds Using a New Multisite Multivariate Stochastic Model MASVC

Climate change causes the river basin water cycle disorders, and rainfall characteristics frequently result in flood disasters. This study aims to simulate and assess the response behavior of basin floods under the influence of rainfall characteristics and land use changes in the Hulu River basin using a 2D hydrological and hydraulic GAST (GPU Accelerated Surface Water Flow and Transport Model). The peak flow rate and water depth during floods were examined by simulating the evolution process of basin floods and related hydraulic elements under the independent effects of various rainfall characteristics or land use and further simulating the response results of basin floods under the combined effects of rainfall characteristics and land use. The seven scenarios were set to quantify the degree of influence that land use and rainfall characteristics have on the basin flood process based on examining changes in land use and rainfall characteristics in the research area. The results from different rainfall characteristics scenarios depicted that as the rainfall return period is shorter, the peak flow rate is higher, and the peak flow rate is lower as the return period is prolonged. Under different rainfall characteristics, the peak flow rate in scenario R8 is 41.30%, 40.00%, and 34.51% higher than the uniform distribution of rainfall, while water depth is decreased by 0.55%, increased by 4.96% and 2.92% as compared to the uniform distribution of rainfall. While under different land use scenarios, it is observed that the change in land use has increased 2.7% in cultivated land and 1.1% in woodland. In addition, the interactive effect of different rainfall characteristics and land use it can be seen that the scenario with the greatest reduction in flood risk due to rainfall characteristics and land use is RL2-4, representing a 12.55% decrease in peak flow and a 37.69% decrease in peak water depth. In this scenario, the rainfall is heavier in the southeast and northwest regions and lighter in the northeast and southwest regions. The land use type is characterized by reforestation and the return of cultivated land to forests. The changes in rainfall distribution and the increase in grassland contribute to the decrease in flood threat. Future research in the erodible parts of the Hulu River basin, planning for water resources, and soil and water conservation can all benefit from the study’s conclusions.

Section: Introductionmentioning

confidence: 99%

Assessment of the Impacts of Rainfall Characteristics and Land Use Pattern on Runoff Accumulation in the Hulu River Basin, China

Imran,

Hou,

Wang

et al. 2024

“…Many scholars have carried out research on surface runoff, including the impact of surface runoff changes on soil erosion and the impact of rainfall, land use, and land cover distribution on surface runoff, soil nutrients [25,26], rainfall, land use, and land cover [27][28][29]. Rainfall intensity and spatial scale have a certain influence on the SCS-CN runoff model [30], and the SCS-CN runoff model has been studied at the urban and watershed levels. For example, Liu et al [31] found that the response of urban surface runoff is more obvious when the rainfall is low in a study of impervious surface and surface runoff in Xuzhou City.…”

Section: Introductionmentioning

confidence: 99%

Evolution Characteristics of Landscape Patterns and the Response of Surface Runoff in a Rapid Urbanization Area: Focus on the Chang–Zhu–Tan Metropolitan Area of China

Li,

Chen,

et al. 2023

With the acceleration of urbanization, the disturbance to urban landscape patterns causes changes to urban surface runoff and increases the risk of urban waterlogging. We studied the response relationship between landscape pattern change and surface runoff in the Chang–Zhu–Tan metropolitan area for the period from 2000 to 2020, analyzing the driving factors that affected surface runoff. The influence of landscape pattern evolution on surface runoff was studied using the SCS-CN flow generation model, the moving window method, and Pearson’s analysis. The analysis showed that between 2000 and 2020, the forest area decreased, while the impermeable land area increased substantially. At the same time, the landscape spread degree (CONTAG) decreased, whereas the landscape fragmentation degree (DIVISION) increased, and the Shannon diversity index (SHDI) and landscape shape index (LSI) increased. The surface runoff in the main urban areas of Chang–Zhu–Tan increased substantially. The results showed that surface runoff is negatively correlated with SHDI, LSI, and DIVISION but displays a positive relationship to CONTAG. Soil texture and precipitation had the greatest impact on surface runoff. This study emphasizes the importance of landscape pattern evolution to surface runoff in rapidly developing metropolitan areas in terms of reducing surface runoff, alleviating urban waterlogging and preventing regional floods. Our research additionally seeks to optimize the landscape pattern of the Chang–Zhu–Tan metropolitan area.

SWAT-Driven Exploration of Runoff Dynamics in Hyper-Arid Region, Saudi Arabia: Implications for Hydrological Understanding

Hussain,

Niyazi,

Elfeki

et al. 2024

Hydrological modeling plays a vital role in water-resource management and climate-change studies in hyper-arid regions. In the present investigation, surface runoff was estimated by a Soil and Water Assessment Tool (SWAT) model for Wadi Al-Aqul, Saudi Arabia. The Sequential Uncertainty Fitting version 2 (SUFI-2) technique in SWAT-CUP was adopted for the sensitivity analysis, calibration, and validation of the SWAT model’s components. The observational runoff data were scarce and only available from 1979 to 1984; such data scarcity is a common problem in hyper-arid regions. The results show good agreement with the observed daily runoff, as indicated by a Pearson Correlation Coefficient (r) of 0.86, a regression (R2) of 0.76, and a Nash–Sutcliffe coefficient (NSE) of 0.61. Error metrics, including the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), were notably low at 0.05 and 0.58, respectively. In the daily validation, the model continued to perform well, with a correlation of 0.76 and regression of 0.58. As a new approach, fitted parameters of daily calibration were incorporated into the monthly simulation, and they demonstrated an even better performance. The correlation coefficient (regression) and Nash–Sutcliffe were found to be extremely high during the calibration period of the monthly simulation, reaching 0.97 (0.95) and 0.73, respectively; meanwhile, they reached 0.99 (0.98) and 0.63 in the validation period, respectively. The sensitivity analysis using the SUFI-2 algorithm highlighted that, in the streamflow estimation, the Curve Number (CN) was found to be the most responsive parameter, followed by Soil Bulk Density (SOL_BD). Notably, the monthly results showed a higher performance than the daily results, indicating the inherent capability of the model in regard to data aggregation and reducing the impact of random fluctuations. These findings highlight the applicability of the SWAT model in predicting runoff and its implication for climate-change studies in hyper-arid regions.