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Validation of drought indices using environmental indicators: streamflow and carbon flux data

Bhuyan-Erhardt, Upasana, Erhardt, Tobias M., Laaha, Gregor, Zang, Christian, Parajka, Juraj, Menzel, Annette
Agricultural and forest meteorology 2019 v.265 pp. 218-226
atmospheric precipitation, carbon, drought, environmental indicators, evapotranspiration, gross primary productivity, growing season, information sources, models, net ecosystem exchange, probability, regression analysis, stream flow, vines, watersheds, Germany
Aiming for refined drought characterization, the validation of targeted drought indices is of vital importance. In this study, we compared the performance of established drought indices – the SPI (Standardized Precipitation Index) and the SPEI (Standardized Precipitation Evapotranspiration Index) – with standardized drought indices using a recently developed, vine copula based method for the computation of multivariate drought indices (here addressed as VCI). For our validation study, we used several environmental drought indicators: monthly streamflow anomalies and streamflow drought events from a network of 332 catchments across Europe, as well as gross primary production (GPP) and net ecosystem exchange (NEE) for Germany. The novel multivariate VC-Indices can combine two or more user-selected, drought relevant variables to model different drought types, depending on the user-application. Validation with streamflow data showed that the maximum probability of drought detection values for SPEI, SPI and VCI was observed for 12.0%, 25.9% and 62.0% of the catchments, and the minimum false alarm rate values for SPEI, SPI and VCI was observed for 20.5%, 33.4% and 46.1% of the catchments, respectively. Validation with carbon flux data showed that the average R2 values of a pixel-wise linear regression for the growing season for the period 1980 to 2010 between SPEI, SPI and VCI with NEE were 0.26, 0.07 and 0.37, respectively. Similarly, the average R2 values for SPEI, SPI and VCI with GPP were 0.03, 0.04 and 0.14, respectively. Our results emphasize using the VCI as an additional source of information in order to allow better understanding of drought characterization.