Abstract
We present a scheme aimed at estimating daily spatial snow water equivalent (SWE) maps in real time and at high spatial resolution from scarce in-situ SWE measurements from Internet of Things (IoT) devices at actual sensor locations and historical SWE maps. The method consists of finding a background SWE field, followed by an update step using ensemble optimal interpolation to estimate the residuals. This novel approach allowed for areas with parsimonious sensors to have accurate estimates of spatial SWE without explicitly discovering and specifying the spatial-interpolation features. The scheme is evaluated across the Tuolumne River basin on a 50 m grid using an existing LiDAR-based product as the historical dataset. Results show a minimum RMSE of 30% at 50 m resolutions. Compared with the operational SNODAS product, reduction in error is up to 80% with historical LiDAR-measured snow depth as input data.
Highlights
Estimating the spatial distribution of snow water equivalent (SWE) at a basin scale and in a timely manner is crucial for efficiently operating downstream water-supply reservoirs and hydropower networks
Existing methods for spatial SWE estimation are outlined in Reference [7] and fall into the following categories: (i) spatial interpolation from in-situ sensors constrained by remote sensing, (ii) SWE reconstruction using snowmelt models given the point of disappearance of snow determined from remote sensing, (iii) global SWE remote sensing based on passive microwave, (iv) a snow model assimilated by dense in-situ sensors or by remote sensing products such as Sentinel-2 [8] or Lansat [9] and (v) emerging methods such as air-borne Light Detection and Ranging (LiDAR) altimetry
Air-borne LiDAR scans combined with snow density values provide SWE estimates at a large spatial scale, but as a stand-alone method are currently expensive to conduct at a frequent temporal scale
Summary
Estimating the spatial distribution of snow water equivalent (SWE) at a basin scale and in a timely manner is crucial for efficiently operating downstream water-supply reservoirs and hydropower networks. This need has become more pressing with a changing climate where past trends no longer predict the future. With The advent and rapid development of accessible cyber-physical systems technology, watershed instrumentation is expected to increase. We find multiple such systems reported in the recent literature [3,4,5].
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