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利用卫星数据预测无资料流域径流的生态水文模型。

Ecohydrologic model with satellite-based data for predicting streamflow in ungauged basins.

作者信息

Choi Jeonghyeon, Kim Ungtae, Kim Sangdan

机构信息

Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology (KICT), Goyang-Si, Gyeonggi-Do 10223, Republic of Korea.

Department of Civil and Environmental Engineering, Cleveland State University, Cleveland, OH 44115, USA.

出版信息

Sci Total Environ. 2023 Dec 10;903:166617. doi: 10.1016/j.scitotenv.2023.166617. Epub 2023 Aug 28.

Abstract

Information on water availability in basins can be crucial for making decisions for effective water resource management in basins. As the operation of hydrometric stations in Korea is mainly focused on flood season and large rivers, most basins have lack or no observed data. Consequently, this complicates water resource planning and management. Remote sensing data is emerging as a powerful alternative to hydrological information in ungauged basins. This study investigated the applicability of Satellite-Remote Sensed Data (SRSD) as a source for model calibration in Prediction in Ungauged Basins (PUB) through modeling. Remote sensed leaf area index (LAI), actual evapotranspiration, and soil moisture data were used. Each SRSD was used alone to calibrate a hydrologic model to predict the daily streamflow for 28 basins in Korea. A vegetation module was added to the existing hydrologic model to use LAI. Among the SRSDs tested, the model calibrated with LAI had the most robust performance, predicting streamflow with acceptable accuracy compared to the traditional calibration based on streamflow. In particular, since the model account for vegetation actively interacting with evapotranspiration and soil moisture in the season of low flow, the LAI-calibrated model showed an advantage in improving the flow prediction performance. Although further research is required to utilize evapotranspiration and soil moisture data, the overall results of the LAI-based calibration were promising for predicting streamflow in ungauged basins where observations are scarce or absent, given that the satellite-derived LAI data were used alone without any preprocessing such as a bias correction. However, the prediction performance of the LAI-calibrated model was found to have a statistically significant relationship with local conditions. Therefore, by evaluating and improving the potential of SRSD in different region and climatic conditions, it is expected that the application of the SRSD-only calibration method can be extended to various ungauged basins.

摘要

流域水资源可用性信息对于做出有效管理流域水资源的决策至关重要。由于韩国水文站的运行主要集中在汛期和大型河流,大多数流域缺乏或没有观测数据。因此,这使得水资源规划和管理变得复杂。遥感数据正在成为无观测流域水文信息的有力替代来源。本研究通过建模调查了卫星遥感数据(SRSD)作为无观测流域预测(PUB)模型校准数据源的适用性。使用了遥感叶面积指数(LAI)、实际蒸散量和土壤湿度数据。每个SRSD单独用于校准水文模型,以预测韩国28个流域的日流量。在现有的水文模型中添加了一个植被模块以使用LAI。在所测试的SRSD中,用LAI校准的模型性能最为稳健,与基于流量的传统校准相比,其预测流量的准确性可以接受。特别是,由于该模型考虑了植被在枯水期与蒸散和土壤湿度的积极相互作用,基于LAI校准的模型在改善流量预测性能方面具有优势。尽管利用蒸散和土壤湿度数据还需要进一步研究,但基于LAI校准的总体结果对于预测观测稀少或没有观测的无观测流域的流量很有前景,因为卫星衍生的LAI数据在没有任何诸如偏差校正等预处理的情况下单独使用。然而,发现基于LAI校准的模型的预测性能与当地条件存在统计学上的显著关系。因此,通过评估和提高SRSD在不同区域和气候条件下的潜力,预计仅使用SRSD的校准方法的应用可以扩展到各种无观测流域。

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