TY - JOUR
T1 - Validation of drought indices using environmental indicators
T2 - streamflow and carbon flux data
AU - Bhuyan-Erhardt, Upasana
AU - Erhardt, Tobias M.
AU - Laaha, Gregor
AU - Zang, Christian
AU - Parajka, Juraj
AU - Menzel, Annette
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2019/2/15
Y1 - 2019/2/15
N2 - 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.
AB - 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.
KW - Europe
KW - GPP
KW - NEE
KW - SPEI
KW - SPI
KW - Streamflow
KW - VCI
KW - Vine copulas
UR - http://www.scopus.com/inward/record.url?scp=85057204367&partnerID=8YFLogxK
U2 - 10.1016/j.agrformet.2018.11.016
DO - 10.1016/j.agrformet.2018.11.016
M3 - Article
AN - SCOPUS:85057204367
SN - 0168-1923
VL - 265
SP - 218
EP - 226
JO - Agricultural and Forest Meteorology
JF - Agricultural and Forest Meteorology
ER -