Jeollanamdo Environmental Industries Promotion Institute (JEIPI), Jeollanam-do, 59205, Republic of Korea.
Jeollanamdo Environmental Industries Promotion Institute (JEIPI), Jeollanam-do, 59205, Republic of Korea.
J Environ Manage. 2021 Jan 1;277:111393. doi: 10.1016/j.jenvman.2020.111393. Epub 2020 Oct 16.
Among the input data of the watershed model for simulating changes of flowrate in the watershed, weather input data, especially input data related to rainfall, are the most important. Therefore, it is important to ensure the accuracy of rainfall input data to increase the accuracy of the watershed model results. Securing rainfall measurements with finer spatial and temporal resolutions is important in predicting flowrate variations at a sub-catchment, especially as they relate to global and local climate changes in weather conditions such as rainfall depth, rainfall intensity, etc. In this study, adjusted radar-rainfall estimates were suggested as alternative input data for watershed modeling. Through a statistical analysis of the representativeness of a ground rainfall measurement (10 km × 10 km grid), the necessity of radar-rainfall estimates (2 km × 2 km grid) was identified. By applying calibration factors to initial radar-rainfall estimates and comparing adjusted radar-rainfall estimates with ground rainfall measurements, it was proven that adjusted radar-rainfall estimates could be used as input data for watershed simulations (NSE > 0.92; n = 12). Adjusted radar-rainfall estimates and ground rainfall measurements were used as input data of the Soil and Water Assessment Tool model to predict flowrate variations at the outlets of a tributary and the entire watershed. As a result, the accuracies of the simulation results were improved for the outlets of a tributary and the entire watershed (NSE: 0.33 to 0.48 and 0.19 to 0.55, respectively). To obtain more reliable rainfall data, radar images easily accessible to users were applied, and the accuracy of the data was increased by applying simple equations to numerical data extracted from radar image processing. Additionally, the applicability of the adjusted radar-rainfall estimates was demonstrated by comparing the modeling results using the suggested rainfall data and existing ground-based rainfall data. The suggested methodologies are expected to contribute to more accurately predict the possibility of flood disasters in other regions and countries lacking infrastructure related to rainfall measurements and to establish appropriate countermeasures.
在模拟流域流量变化的流域模型输入数据中，气象输入数据，特别是与降雨相关的输入数据是最重要的。因此，确保降雨输入数据的准确性对于提高流域模型结果的准确性非常重要。在预测子流域的流量变化时，特别是在预测与降雨深度、降雨强度等天气条件相关的全球和局部气候变化时，以更精细的时空分辨率获取降雨测量数据非常重要。在本研究中，提出了调整后的雷达降雨估计值作为流域建模的替代输入数据。通过对地面降雨测量值（10km×10km 网格）的代表性进行统计分析，确定了雷达降雨估计值（2km×2km 网格）的必要性。通过对初始雷达降雨估计值应用校准因子，并将调整后的雷达降雨估计值与地面降雨测量值进行比较，证明调整后的雷达降雨估计值可用作流域模拟的输入数据（NSE＞0.92；n=12）。将调整后的雷达降雨估计值和地面降雨测量值用作 Soil and Water Assessment Tool 模型的输入数据，以预测支流出口和整个流域的流量变化。结果表明，支流出口和整个流域的模拟结果的准确性得到了提高（NSE：0.33 到 0.48 和 0.19 到 0.55）。为了获得更可靠的降雨数据，应用了用户易于访问的雷达图像，并通过将简单方程应用于从雷达图像处理中提取的数值数据，提高了数据的准确性。此外，通过比较使用建议的降雨数据和现有的地面降雨数据的建模结果，验证了调整后的雷达降雨估计值的适用性。建议的方法有望有助于更准确地预测其他缺乏与降雨测量相关的基础设施的地区和国家发生洪水灾害的可能性，并制定适当的对策。
