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首页> 外文期刊>Advances in Water Resources >Extending the soil moisture data record of the US Climate Reference Network (USCRN) and Soil Climate Analysis Network (SCAN)
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Extending the soil moisture data record of the US Climate Reference Network (USCRN) and Soil Climate Analysis Network (SCAN)

机译：扩展了美国气候参考网络（USCRN）和土壤气候分析网络（SCAN）的土壤湿度数据记录

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摘要

Soil moisture estimates are valuable for hydrologic modeling, drought prediction and management, climate change analysis, and agricultural decision support. However, in situ measurements of soil moisture have only become available within the past few decades with additional sensors being installed each year. Comparing newer in situ resources with older resources, previously required a period of cross-calibration, often requiring several years of data collection. One new technique to improve this issue is to develop a methodology to extend the in situ record backwards in time using a soil moisture model and ancillary available data sets. This study will extend the soil moisture record of the U.S. Climate Reference Network (USCRN) by calibrating a precipitation-driven model during the most recent few years when soil moisture data are available and applying that model backwards temporally in years where precipitation data are available and soil moisture data are not. This approach is validated by applying the technique to the Soil Climate Analysis Network (SCAN) where the same model is calibrated in recent years and validated during preceding years at locations with a sufficiently long soil moisture record. Results suggest that if two or three years of concurrent precipitation and soil moisture time series data are available, the calibrated model's parameters can be applied historically to produce RMSE values less than 0.033 m(3)/m(3). With this approach, in locations characterized by in situ sensors with short or intermittent data records, a model can now be used to fill the relevant gaps and improve the historical record as well. Published by Elsevier Ltd.
机译：土壤水分估算对于水文建模，干旱预测和管理，气候变化分析以及农业决策支持非常有用。但是，仅在过去的几十年中才可以对土壤水分进行现场测量，并且每年都安装其他传感器。将新的现场资源与旧的资源进行比较，以前需要一段时间的交叉校准，通常需要数年的数据收集。解决此问题的一项新技术是开发一种方法，以利用土壤水分模型和辅助可用数据集及时向后扩展原位记录。这项研究将通过在可获得土壤湿度数据的最近几年中校准降水驱动的模型，并在可获得降水量数据的年份中在时间上向后应用该模型，从而扩展美国气候参考网络（USCRN）的土壤湿度记录。土壤水分数据不是。通过将该技术应用于土壤气候分析网络（SCAN）来验证该方法，该方法在最近几年已对同一模型进行了校准，并在前几年在土壤湿度记录足够长的位置进行了验证。结果表明，如果有两到三年的同时降水和土壤水分时间序列数据可用，则可以在历史上应用校准模型的参数来生成小于0.033 m（3）/ m（3）的RMSE值。通过这种方法，在具有短或断续数据记录的原位传感器特征的位置，现在可以使用模型来填补相关空白并改善历史记录。由Elsevier Ltd.发布

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