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首页> 外文期刊>Journal of Geophysical Research. Biogeosciences >Macroscale hydrological modeling using remotely sensed inputs: Application to the Ohio River basin
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Macroscale hydrological modeling using remotely sensed inputs: Application to the Ohio River basin

机译：使用遥感输入的宏观水文模拟：在俄亥俄河流域的应用

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摘要

Predictions of water and energy budgets at the land surface are central to climate simulation and numerical weather prediction, as well as to water resources planning and management. Macroscale hydrological models provide a new tool for simulating surface water and energy balances at the scale of large continental river basins, However, these models are limited by the scarcity of in situ meteorological forcing data. Remote sensing data provide an alternative to in situ data, with observations that are, in some cases, at a higher spatial and temporal resolution than those available from traditional surface sources. Nonetheless, there remain important questions as to whether the accuracy of remotely sensed surface variables is sufficient to serve as forcings for surface hydrological models. This question is addressed through comparison of hydrologic simulations for the Ohio River basin with the variable infiltration capacity (VIC) macroscale hydrology model, using in situ and remotely sensed data. In situ data consist of gridded (at 1/2 degree latitude-longitude spatial resolution) precipitation, temperature, and wind, with downward solar and longwave radiation inferred from the diurnal temperature range. Remotely sensed observations include incident solar radiation, air temperature, and vapor pressure deficit inferred from the Geostationary Operational Environmental Satellite (GOES), the advanced very high resolution radiometer (AVHRR) and the TIROS Operational Vertical Sounder (TOVS), respectively. Precipitation, in all cases, is from gridded station data. The modeled streamflows and evapotranspiration rates are quite similar for the two cases. The largest differences in predicted surface hydrology are associated with differences in modeled snow cover accumulation and snowmelt, and result from a warm bias in the remotely sensed temperature data. [References: 54]
机译：陆地表面水和能源预算的预测对于气候模拟和数值天气预报以及水资源规划和管理至关重要。宏观水文模型提供了一种新的工具，可以模拟大型大陆河流盆地规模的地表水和能量平衡，但是，这些模型受到现场气象强迫数据稀缺性的限制。遥感数据提供了现场数据的一种替代方法，在某些情况下，其观测值的空间和时间分辨率要比传统地表资源更高。然而，关于遥感地表变量的准确性是否足以作为地表水文模型的强制，仍然存在重要的问题。通过使用原位和遥感数据将俄亥俄州河流域的水文模拟与可变渗透能力（VIC）宏观水文模型进行比较，可以解决该问题。实地数据包括栅格化（在纬度/经度为1/2度的空间分辨率）下的降水，温度和风，并从昼夜温度范围推断出向下的太阳辐射和长波辐射。遥感观测包括分别从对地静止运行环境卫星（GOES），先进的超高分辨率辐射计（AVHRR）和TIROS垂直运行测深仪（TOVS）推断的太阳辐射，气温和蒸气压赤字。在所有情况下，降水都是来自栅格化的站台数据。对于这两种情况，模型化的流量和蒸散速率非常相似。预测的地表水文学的最大差异与模拟积雪和融雪的差异有关，这是由遥感温度数据中的温暖偏差造成的。 [参考：54]

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