Sensor placement strategies for snow water equivalent (SWE) estimation in the American River basin. Issue 2 (16th February 2013)

• Record Type: Journal Article
• Authors: Welch, Stephen C.; Kerkez, Branko; Bales, Roger C.; Glaser, Steven D.; Rittger, Karl; Rice, Robert R.
• Is Part Of: Water resources research. Volume 49:Issue 2(2013:Feb.)
• Page Start: 891
• Page End: 903
• Publication Date: 2013-02-16
• Subjects: sensor networks -- snowpack and hydrologic monitoring -- snow water equivalent -- water information systems -- optimal sensor placement; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/wrcr.20100
• Languages: English
• ISSNs: 0043-1397

Abstract

Key Points: There exists temporally stationary properties of basin‐wide SWE distribution Temporally stationary properties of SWE can be used to inform sensor placement A quantitative sampling method can be implemented to generate sensor placements Abstract : [1] A 11 year data set of spatially distributed snow water equivalent (SWE) was used to inform a quantitative, near‐optimal sensor placement methodology for real‐time SWE estimation in the American River basin of California. Rank‐based clustering was compared with geographically based clustering (subbasin delineation) to determine the existence of stationary covariance structures within the overall SWE data set. The historical SWE data, at 500 × 500 m resolution, were split into 8 years of training and 3 years of validation data. Within each cluster, a quantitative sensor‐placement algorithm, based on maximizing the metric of mutual information, was implemented and compared with random placement. Gaussian process models were then built from validation data points selected by the algorithm to evaluate the efficacy of each placement approach. Rank‐based clusters remained stable interannually, suggesting that rankings of pixel‐by‐pixel SWE exhibit stationary features that can be exploited by a sensor‐placement algorithm, yielding a 200 mm average root‐mean‐square error (RMSE) for 20 randomly selected sensing locations. This outperformed geographic and basin‐wide placement approaches, which generated 460 and 290 mm RMSE, respectively. Mutual information ‐ based sampling provided the best placement strategy, improving RMSE between 0 and 100 mm compared with random placements. Increasing the number of rank‐based clusters consistently lowered average RMSE from 400 mm for 1 cluster to 175 mm for 8 clusters, for 20 total sensors placed. To optimize sensor placement, we recommend a strategy that couples rank‐based clustering with mutual information‐based sampling design.