Real‐time estimation of snow water equivalent in the Upper Colorado River Basin using MODIS‐based SWE Reconstructions and SNOTEL data
Real‐time estimation of snow water equivalent in the Upper Colorado River Basin using MODIS‐based SWE Reconstructions and SNOTEL data
复制标题
使用基于 MODIS 的 SWE 重建和 SNOTEL 数据实时估计科罗拉多河流域上游的雪水当量
DOI：
10.1002/2016wr019067
复制
发表时间：
2016
影响因子：
5.4
通讯作者：
N. Molotch
中科院分区：
文献类型：
--
作者：
D. Schneider;N. Molotch
气候变化需要改进积雪信息，以更好地代表雪水当量（SWE）的异常分布，并改善水资源管理。从2001年1月到2012年6月，我们每周通过两种回归技术准实时估计科罗拉多河上游流域SWE的空间分布：仅使用自然地理信息的原位操作测量点SWE的基线回归，以及结合基于遥感的SWE重建模型的自然地理信息和历史SWE模式的这些原位点的回归。我们比较基线回归方法，我们的新回归的背景下，空间积雪调查和业务雪测量站。与独立的分布式积雪调查相比，新的回归将SWE估计值的偏差从-5.5%降至0.8%，SWE估计值的RMSE从0.25 m降至0.23 m。在高山地形中观察到显著的改善，偏差从-38%降至仅3.4%，RMSE降低了13%，从0.47 m降至0.41 m。与基线回归相比，新回归的交叉验证r2平均增加0.22至0.33。r2在任何一年中的最大增幅是0.19，提高了83%。新的回归估计，平均而言，31%以上的SWE深度比基线回归海拔3000米以上的地区，这有助于高达66%的年度SWE量在最干旱的一年。这表明重建的历史SWE模式将信息添加到插值中，超出了SNOTEL网络所表示的自然地理条件。鉴于以前使用SWE重建的工作仅限于必要的回顾性分析，这里提出的工作代表了一个重要的贡献，因为它将SWE重建扩展到真实的时间应用，并说明这样做可以显着提高SWE估计的准确性。
Changes in climate necessitate improved snowpack information to better represent anomalous distributions of snow water equivalent (SWE) and improve water resource management. We estimate the spatial distribution of SWE for the Upper Colorado River basin weekly from January to June 2001–2012 in quasireal‐time by two regression techniques: a baseline regression of in situ operationally measured point SWE using only physiographic information and regression of these in situ points combining both physiographic information and historical SWE patterns from a remote sensing‐based SWE reconstruction model. We compare the baseline regression approach to our new regression in the context of spatial snow surveys and operational snow measuring stations. When compared to independent distributed snow surveys, the new regression reduces the bias of SWE estimates from −5.5% to 0.8%, and RMSE of the SWE estimates by 8% from 0.25 m to 0.23 m. Notable improvements were observed in alpine terrain with bias declining from −38% to only 3.4%, and RMSE was reduced by 13%, from 0.47 to 0.41 m. The mean increase in cross‐validated r2 for the new regression compared to the baseline regression is from 0.22 to 0.33. The largest increase in r2 in any one year is 0.19, an 83% improvement. The new regression estimates, on average, 31% greater SWE depth than the baseline regression in areas above 3000 m elevation, which contributes up to 66% of annual SWE volume in the driest year. This indicates that the historical SWE patterns from the reconstruction adds information to the interpolation beyond the physiographic conditions represented by the SNOTEL network. Given that previous works using SWE reconstructions were limited to retrospective analyses by necessity, the work presented here represents an important contribution in that it extends SWE reconstructions to real‐time applications and illustrates that doing so significantly improves the accuracy of SWE estimates.