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Abstract
Land surface models and large-scale hydrological models provide the basis for studying impacts of climate and anthropogenic changes on continental- to regional-scale hydrology. Hence, there is a need for comparison and validation of simulated characteristics of spatial and temporal dynamics with independent observations. This study introduces a novel validation framework that relates to common hydrological design measures. The framework is tested by comparing anomalies of runoff from a high-resolution climate-model simulation for Europe with a large number of streamflow observations from small near-natural basins. The regional climate simulation was performed as a “poor man’s reanalysis,” involving a dynamical downscaling of the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) with the Danish “HIRHAM5” model. For 19 different anomaly levels, two indices evaluate the temporal agreement (i.e., the occurrence and frequency of dry and wet events based on daily anomalies), whereas two other indices compare the interannual variability and trends based on annual anomalies. Benchmarks on each index facilitated a comparison across indices, anomaly levels, and basins. The lowest agreement of observed and simulated anomalies was found for dry anomalies. Weak to moderately wet anomalies agreed best, but agreement dropped again for the wettest anomalies. The results could guide the decision on thresholds if this regional climate model were used for the assessment of climate change scenario impacts on flood and drought statistics. Indices vary across Europe, but a gradient with decreasing correspondence between observed and simulated runoff characteristics from west to east, from lower to higher elevations, and from fast to slowly responding basins can be distinguished. The suggested indices can easily be adapted to other study areas and model types to assist in assessing the reliability of predictions of hydrological change.
Abstract
Land surface models and large-scale hydrological models provide the basis for studying impacts of climate and anthropogenic changes on continental- to regional-scale hydrology. Hence, there is a need for comparison and validation of simulated characteristics of spatial and temporal dynamics with independent observations. This study introduces a novel validation framework that relates to common hydrological design measures. The framework is tested by comparing anomalies of runoff from a high-resolution climate-model simulation for Europe with a large number of streamflow observations from small near-natural basins. The regional climate simulation was performed as a “poor man’s reanalysis,” involving a dynamical downscaling of the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) with the Danish “HIRHAM5” model. For 19 different anomaly levels, two indices evaluate the temporal agreement (i.e., the occurrence and frequency of dry and wet events based on daily anomalies), whereas two other indices compare the interannual variability and trends based on annual anomalies. Benchmarks on each index facilitated a comparison across indices, anomaly levels, and basins. The lowest agreement of observed and simulated anomalies was found for dry anomalies. Weak to moderately wet anomalies agreed best, but agreement dropped again for the wettest anomalies. The results could guide the decision on thresholds if this regional climate model were used for the assessment of climate change scenario impacts on flood and drought statistics. Indices vary across Europe, but a gradient with decreasing correspondence between observed and simulated runoff characteristics from west to east, from lower to higher elevations, and from fast to slowly responding basins can be distinguished. The suggested indices can easily be adapted to other study areas and model types to assist in assessing the reliability of predictions of hydrological change.
Abstract
Future climate model scenarios depend crucially on the models’ adequate representation of the hydrological cycle. Within the EU integrated project Water and Global Change (WATCH), special care is taken to use state-of-the-art climate model output for impacts assessments with a suite of hydrological models. This coupling is expected to lead to a better assessment of changes in the hydrological cycle. However, given the systematic errors of climate models, their output is often not directly applicable as input for hydrological models. Thus, the methodology of a statistical bias correction has been developed for correcting climate model output to produce long-term time series with a statistical intensity distribution close to that of the observations. As observations, global reanalyzed daily data of precipitation and temperature were used that were obtained in the WATCH project. Daily time series from three GCMs (GCMs) ECHAM5/Max Planck Institute Ocean Model (MPI-OM), Centre National de Recherches Météorologiques Coupled GCM, version 3 (CNRM-CM3), and the atmospheric component of the L’Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4) coupled model (called LMDZ-4)—were bias corrected. After the validation of the bias-corrected data, the original and the bias-corrected GCM data were used to force two global hydrology models (GHMs): 1) the hydrological model of the Max Planck Institute for Meteorology (MPI-HM) consisting of the simplified land surface (SL) scheme and the hydrological discharge (HD) model, and 2) the dynamic global vegetation model called LPJmL. The impact of the bias correction on the projected simulated hydrological changes is analyzed, and the simulation results of the two GHMs are compared. Here, the projected changes in 2071–2100 are considered relative to 1961–90. It is shown for both GHMs that the usage of bias-corrected GCM data leads to an improved simulation of river runoff for most catchments. But it is also found that the bias correction has an impact on the climate change signal for specific locations and months, thereby identifying another level of uncertainty in the modeling chain from the GCM to the simulated changes calculated by the GHMs. This uncertainty may be of the same order of magnitude as uncertainty related to the choice of the GCM or GHM. Note that this uncertainty is primarily attached to the GCM and only becomes obvious by applying the statistical bias correction methodology.
Abstract
Future climate model scenarios depend crucially on the models’ adequate representation of the hydrological cycle. Within the EU integrated project Water and Global Change (WATCH), special care is taken to use state-of-the-art climate model output for impacts assessments with a suite of hydrological models. This coupling is expected to lead to a better assessment of changes in the hydrological cycle. However, given the systematic errors of climate models, their output is often not directly applicable as input for hydrological models. Thus, the methodology of a statistical bias correction has been developed for correcting climate model output to produce long-term time series with a statistical intensity distribution close to that of the observations. As observations, global reanalyzed daily data of precipitation and temperature were used that were obtained in the WATCH project. Daily time series from three GCMs (GCMs) ECHAM5/Max Planck Institute Ocean Model (MPI-OM), Centre National de Recherches Météorologiques Coupled GCM, version 3 (CNRM-CM3), and the atmospheric component of the L’Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4) coupled model (called LMDZ-4)—were bias corrected. After the validation of the bias-corrected data, the original and the bias-corrected GCM data were used to force two global hydrology models (GHMs): 1) the hydrological model of the Max Planck Institute for Meteorology (MPI-HM) consisting of the simplified land surface (SL) scheme and the hydrological discharge (HD) model, and 2) the dynamic global vegetation model called LPJmL. The impact of the bias correction on the projected simulated hydrological changes is analyzed, and the simulation results of the two GHMs are compared. Here, the projected changes in 2071–2100 are considered relative to 1961–90. It is shown for both GHMs that the usage of bias-corrected GCM data leads to an improved simulation of river runoff for most catchments. But it is also found that the bias correction has an impact on the climate change signal for specific locations and months, thereby identifying another level of uncertainty in the modeling chain from the GCM to the simulated changes calculated by the GHMs. This uncertainty may be of the same order of magnitude as uncertainty related to the choice of the GCM or GHM. Note that this uncertainty is primarily attached to the GCM and only becomes obvious by applying the statistical bias correction methodology.
Abstract
A modified drought index, named the spatially normalized–standardized precipitation index (SN-SPI), has been developed for assessing meteorological droughts. The SN–SPI is a variant index to the standardized precipitation index and is based on the probability of precipitation at different time scales, but it is spatially normalized for improved assessment of drought severity. Results of this index incorporate the spatial distribution of precipitation and produce improved drought warnings. This index is applied in the island of Crete, Greece, and the drought results are compared to the ones of SPI. A 30-year-long average monthly precipitation dataset from 130 watersheds of the island is used by the above indices for drought classification in terms of its duration and intensity. Bias-adjusted monthly precipitation estimates from an ensemble of 10 regional climate models were used to quantify the influence of global warming to drought conditions over the period 2010–2100. Results based on both indices (calculated for three time scales of 12, 24, and 48 months) from 3 basins in west, central, and east parts of the island show that 1) the extreme drought periods are the same (reaching 7% of time) but the intensities based on SN–SPI are lower; 2) the area covered by extreme droughts is 3% (first time scale), 16% (second time scale), and 25% (third time scale), and 96% (first time scale), 95% (second time scale), and 80% (third time scale) based on the SN–SPI and SPI, respectively; 3) concerning the longest time scale (48 months), more than half of the area of Crete is about to experience drought conditions during 28%, 69%, and 97% for 2010–40, 2040–70, and 2070–2100, respectively; and 4) extremely dry conditions will cover 52%, 33%, and 25% of the island for the future 90-year period using 12-, 24-, and 48-month SN–SPI, respectively.
Abstract
A modified drought index, named the spatially normalized–standardized precipitation index (SN-SPI), has been developed for assessing meteorological droughts. The SN–SPI is a variant index to the standardized precipitation index and is based on the probability of precipitation at different time scales, but it is spatially normalized for improved assessment of drought severity. Results of this index incorporate the spatial distribution of precipitation and produce improved drought warnings. This index is applied in the island of Crete, Greece, and the drought results are compared to the ones of SPI. A 30-year-long average monthly precipitation dataset from 130 watersheds of the island is used by the above indices for drought classification in terms of its duration and intensity. Bias-adjusted monthly precipitation estimates from an ensemble of 10 regional climate models were used to quantify the influence of global warming to drought conditions over the period 2010–2100. Results based on both indices (calculated for three time scales of 12, 24, and 48 months) from 3 basins in west, central, and east parts of the island show that 1) the extreme drought periods are the same (reaching 7% of time) but the intensities based on SN–SPI are lower; 2) the area covered by extreme droughts is 3% (first time scale), 16% (second time scale), and 25% (third time scale), and 96% (first time scale), 95% (second time scale), and 80% (third time scale) based on the SN–SPI and SPI, respectively; 3) concerning the longest time scale (48 months), more than half of the area of Crete is about to experience drought conditions during 28%, 69%, and 97% for 2010–40, 2040–70, and 2070–2100, respectively; and 4) extremely dry conditions will cover 52%, 33%, and 25% of the island for the future 90-year period using 12-, 24-, and 48-month SN–SPI, respectively.
