遥感地表水储量显示出与SWAT模拟数据不同的模式

摘要(中文译文)

量化与预测湖泊和湿地蓄水的下游效益需要流域水文模型，而这类模型通常使用静态数字高程模型（DEM）数据来参数化地形洼地中的地表水蓄水。利用外部数据集对模拟动态进行校准和验证并不常见，尤其是在大型流域尺度上，模型校准通常集中于观测流量。本研究开发并评估了一种基于遥感（RS）的新型数据集（Sentinel-1和Sentinel-2），用于验证上密西西比河流域（UMRB；约440,000 km²）水土评估工具（SWAT）模型的模拟估算值。结果表明，基于静态DEM的参数化以及仅基于流量的模型校准均不足以捕捉UMRB中的空间和时间动态。SWAT估算的平均蓄水量比遥感蓄水量高74%±122%（均值±标准差），其中SWAT在湿地丰富的子流域中低估了地表水蓄水，而在农业排水暗管子流域中则高估了蓄水。仅在38.8%的子流域中，SWAT蓄水量与遥感蓄水量的时间序列呈正相关。由于遥感蓄水量同样易受误差影响，研究将蓄水估算值与选定小型湿地的测深数据进行了比较。尽管遥感从范围到蓄水量的转换仍存在不确定性，但本文所提出的方法和数据集为改进SWAT及其他基于过程的水文模型中蓄水过程的参数化和校准提供了有前景的选择。进一步考虑这些蓄水过程可能有助于提高湿地丰富模型区域中模拟河川流量的准确性。

Abstract

Quantifying and projecting the downstream benefits of water stored in lakes and wetlands ( ) requires watershed hydrologic models, which often parameterize surface water storage in topographic depressions using static digital elevation model (DEM) data. Calibration and validation of modeled dynamics using external data sets is uncommon, particularly across major river basins, with model calibration typically focused on observed discharge. Here, we develop and assess a novel remote sensing-based (RS) data set (Sentinel-1 and Sentinel-2) for verifying simulated estimates from a Soil and Water Assessment Tool (SWAT) model of the Upper Mississippi River Basin (UMRB; ~440,000 km). Our results suggest that static DEM-based parameterization as well as model calibration based solely on discharge do not adequately capture spatial and temporal dynamics in the UMRB. Mean as estimated by SWAT was 74% ± 122% (mean ± standard deviation) higher than RS , where SW storage in SWAT was underestimated in wetland-rich subbasins and overestimated in agricultural, tile-drained subbasins. Time series of SWAT and RS were positively correlated in only 38.8% of subbasins. As RS is also vulnerable to error, storage estimates were compared to bathymetric data in select small wetlands. While uncertainty remains in the conversion from extent to storage for RS , the method and data set presented here are a promising option for improved parameterization and calibration of processes in SWAT and other process-based hydrologic models. Further consideration of these storage processes can potentially improve the accuracy of simulated streamflow in wetland-rich model domains.

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