Scholarlink

<p>One of the significant challenges in hydrological modeling is the spatial variability of rainfall. Even slight changes in rainfall patterns can lead to fundamental differences in runoff responses, impacting the accuracy of predictions. Additionally, the complexity of rainfall patterns, influenced by topography, often results in mispredictions. This study investigates the spatial distribution of rainfall and analyzes the impact of raindrop data dispersion on rainfall-runoff modeling, using data from two gridded precipitation products, PERSIANN-CDR and ERA5. The analysis is conducted at both daily and monthly scales with the semi-distributed SWAT model. Raindrop data from four stations—Talezang, Tang-e Panj Bakhtiari, Sepid Dasht Sezar, and the Dez Dam Rain Gauge Station—along with the Tele-Zang hydrometry station, were utilized for the period from 2008 to 2019. The simulation results, compared with observed data, indicate that the inadequacy of the raingauge network and spatial rainfall distribution hindered the SWAT rainfall-runoff simulation model’s ability to correlate rainfall amounts with their corresponding discharge values. This lack of correlation resulted in suboptimal modeling outcomes, as demonstrated by the NSE coefficient values of 0.52 and -0.01 for daily calibration and validation periods, respectively. In contrast, monthly periods yielded more suitable results with NSE values of 0.76 and 0.82 for calibration and validation, respectively. In daily hydrological evaluations, the PERSIANN-CDR dataset outperformed others, achieving NSE values of 0.29 and 0.59 for calibration and validation periods, respectively. Conversely, the ERA5 dataset exhibited superior performance in monthly hydrological evaluations for rainfall-runoff modeling, with calibration and validation coefficients of 0.77 and 0.82, respectively. The spatially distributed nature of gridded data allows for a more accurate representation of rainfall patterns across a given watershed, resulting in improved predictions of surface runoff. Enhanced precipitation input facilitates better assessments of hydrological processes by accounting for variations in land use, topography, and soil characteristics. Incorporating these high-resolution rainfall datasets increases the reliability of runoff simulations, ultimately allowing for more effective water resource management, flood forecasting, and planning.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Analysis of spatial distribution of precipitation using hydrological modeling of watersheds (Case Study: Dez Dam Watershed)

  • 期刊论文

  • 发表日期：

    2025年1月8日

查看全文
  • Ali Gorjizade,
  • Ali Shahbazi

摘要

One of the significant challenges in hydrological modeling is the spatial variability of rainfall. Even slight changes in rainfall patterns can lead to fundamental differences in runoff responses, impacting the accuracy of predictions. Additionally, the complexity of rainfall patterns, influenced by topography, often results in mispredictions. This study investigates the spatial distribution of rainfall and analyzes the impact of raindrop data dispersion on rainfall-runoff modeling, using data from two gridded precipitation products, PERSIANN-CDR and ERA5. The analysis is conducted at both daily and monthly scales with the semi-distributed SWAT model. Raindrop data from four stations—Talezang, Tang-e Panj Bakhtiari, Sepid Dasht Sezar, and the Dez Dam Rain Gauge Station—along with the Tele-Zang hydrometry station, were utilized for the period from 2008 to 2019. The simulation results, compared with observed data, indicate that the inadequacy of the raingauge network and spatial rainfall distribution hindered the SWAT rainfall-runoff simulation model’s ability to correlate rainfall amounts with their corresponding discharge values. This lack of correlation resulted in suboptimal modeling outcomes, as demonstrated by the NSE coefficient values of 0.52 and -0.01 for daily calibration and validation periods, respectively. In contrast, monthly periods yielded more suitable results with NSE values of 0.76 and 0.82 for calibration and validation, respectively. In daily hydrological evaluations, the PERSIANN-CDR dataset outperformed others, achieving NSE values of 0.29 and 0.59 for calibration and validation periods, respectively. Conversely, the ERA5 dataset exhibited superior performance in monthly hydrological evaluations for rainfall-runoff modeling, with calibration and validation coefficients of 0.77 and 0.82, respectively. The spatially distributed nature of gridded data allows for a more accurate representation of rainfall patterns across a given watershed, resulting in improved predictions of surface runoff. Enhanced precipitation input facilitates better assessments of hydrological processes by accounting for variations in land use, topography, and soil characteristics. Incorporating these high-resolution rainfall datasets increases the reliability of runoff simulations, ultimately allowing for more effective water resource management, flood forecasting, and planning.