ALBERT

Description: El Niño Southern Oscillation (ENSO) is the most dominant interannual signal of climate variability and has a strong influence on climate over large parts of the world. In turn, it strongly influences many natural hazards (such as hurricanes and droughts) and their resulting socioeconomic impacts, including economic damage and loss...

Description: Data from General Circulation Models (GCMs) are often used to investigate hydrological impacts of climate change. However GCM data are known to have large biases, especially for precipitation. In this study the usefulness of GCM data for hydrological studies, with focus on discharge variability and extremes, was tested by using bias-corrected daily climate data of the 20CM3 control experiment from a selection of twelve GCMs as input to the global hydrological model PCR-GLOBWB. Results of these runs were compared with discharge observations of the GRDC and discharges calculated from model runs based on two meteorological datasets constructed from the observation-based CRU TS2.1 and ERA-40 reanalysis. In the first dataset the CRU TS 2.1 monthly timeseries were downscaled to daily timeseries using the ERA-40 dataset (ERA6190). This dataset served as a best guess of the past climate and was used to analyze the performance of PCR-GLOBWB. The second dataset was created from the ERA-40 timeseries bias-corrected with the CRU TS 2.1 dataset using the same bias-correction method as applied to the GCM datasets (ERACLM). Through this dataset the influence of the bias-correction method was quantified. The bias-correction was limited to monthly mean values of precipitation, potential evaporation and temperature, as our focus was on the reproduction of inter- and intra-annual variability. After bias-correction the spread in discharge results of the GCM based runs decreased and results were similar to results of the ERA-40 based runs, especially for rivers with a strong seasonal pattern. Overall the bias-correction method resulted in a slight reduction of global runoff and the method performed less well in arid and mountainous regions. However, deviations between GCM results and GRDC statistics did decrease for Q, Q90 and IAV. After bias-correction consistency amongst models was high for mean discharge and timing (Qpeak), but relatively low for inter-annual variability (IAV). This suggests that GCMs can be of use in global hydrological impact studies in which persistence is of less relevance (e.g. in case of flood rather than drought studies). Furthermore, the bias-correction influences mean discharges more than extremes, which has the positive consequence that changes in daily rainfall distribution and subsequent changes in discharge extremes will also be preserved when the bias-correction method is applied to future GCM datasets. However, it also shows that agreement between GCMs remains relatively small for discharge extremes. Because of the large deviations between observed and simulated discharge, in which both errors in climate forcing, model structure and to a lesser extent observations are accumulated, it is advisable not to work with absolute discharge values for the derivation of future discharge projections, but rather calculate relative changes by dividing the absolute change by the absolute discharge calculated for the control experiment.

Description: Climate change is likely to have significant effects on the hydrology. The Ganges-Brahmaputra river basin is one of the most vulnerable areas in the world as it is subject to the combined effects of glacier melt, extreme monsoon rainfall and sea level rise. To what extent climate change will impact river flow in the Brahmaputra basin is yet unclear, as climate model studies show ambiguous results. In this study we investigate the effect of climate change on both low and high flows of the lower Brahmaputra. We apply a novel method of discharge-weighted ensemble modeling using model outputs from a global hydrological models forced with 12 different global climate models (GCMs). Our analysis shows that only a limited number of GCMs are required to reconstruct observed discharge. Based on the GCM outputs and long-term records of observed flow at Bahadurabad station, our method results in a multi-model weighted ensemble of transient stream flow for the period 1961–2100. Using the constructed transients, we subsequently project future trends in low and high river flow. The analysis shows that extreme low flow conditions are likely to occur less frequent in the future. However a very strong increase in peak flows is projected, which may, in combination with projected sea level change, have devastating effects for Bangladesh. The methods presented in this study are more widely applicable, in that existing multi-model streamflow simulations from global hydrological models can be weighted against observed streamflow data to assess at first order the effects of climate change for specific river basins.

Description: Potential evaporation (PET) is one of the main inputs of hydrological models. Yet, there is limited consensus on which PET equation is most applicable in hydrological climate impact assessments. In this study six different methods to derive global scale reference PET daily time series from Climate Forecast System Reanalysis (CFSR) data are compared: Penman-Monteith, Priestley-Taylor and original and re-calibrated versions of the Hargreaves and Blaney-Criddle method. The calculated PET time series are (1) evaluated against global monthly Penman-Monteith PET time series calculated from CRU data and (2) tested on their usability for modeling of global discharge cycles. A major finding is that for part of the investigated basins the selection of a PET method may have only a minor influence on the resulting river flow. Within the hydrological model used in this study the bias related to the PET method tends to decrease while going from PET, AET and runoff to discharge calculations. However, the performance of individual PET methods appears to be spatially variable, which stresses the necessity to select the most accurate and spatially stable PET method. The lowest root mean squared differences and the least significant deviations (95% significance level) between monthly CFSR derived PET time series and CRU derived PET were obtained for a cell-specific re-calibrated Blaney-Criddle equation. However, results show that this re-calibrated form is likely to be unstable under changing climate conditions and less reliable for the calculation of daily time series. Although often recommended, the Penman-Monteith equation applied to the CFSR data did not outperform the other methods in a evaluation against PET derived with the Penman-Monteith equation from CRU data. In arid regions (e.g. Sahara, central Australia, US deserts), the equation resulted in relatively low PET values and, consequently, led to relatively high discharge values for dry basins (e.g. Orange, Murray and Zambezi). Furthermore, the Penman-Monteith equation has a high data demand and the equation is sensitive to input data inaccuracy. Therefore, we recommend the re-calibrated form of the Hargreaves equation which globally gave reference PET values comparable to CRU derived values for multiple climate conditions. The resulting gridded daily PET time series provide a new reference dataset that can be used for future hydrological impact assessments in further research, or more specifically, for the statistical downscaling of daily PET derived from raw GCM data. The dataset can be downloaded from http://opendap.deltares.nl/thredds/dodsC/opendap/deltares/FEWS-IPCC.

Description: This study makes a thorough global assessment of the effects of climate change on hydrological regimes and their accompanying uncertainties. Meteorological data from twelve GCMs (SRES scenarios A1B and control experiment 20C3M) are used to drive the global hydrological model PCR-GLOBWB. This reveals in which regions of the world changes in hydrology can be detected that have a high likelihood and are consistent amongst the ensemble of GCMs. New compared to existing studies is: (1) the comparison of spatial patterns of regime changes and (2) the quantification of notable consistent changes calculated relative to the GCM specific natural variability. The resulting consistency maps indicate in which regions the likelihood of hydrological change is large. Projections of different GCMs diverge widely. This underscores the need of using a multi-model ensemble. Despite discrepancies amongst models, consistent results are revealed: by 2100 the GCMs project consistent decreases in discharge for southern Europe, southern Australia, parts of Africa and southwestern South-America. Discharge decreases strongly for most African rivers, the Murray and the Danube while discharge of monsoon influenced rivers slightly increases. In the Arctic regions river discharge increases and a phase-shift towards earlier peaks is observed. Results are comparable to previous global studies, with a few exceptions. Globally we calculated an ensemble mean discharge increase of more than ten percent. This increase contradicts previously estimated decreases, which is amongst others caused by the use of smaller GCM ensembles and different reference periods.

Description: Data from General Circulation Models (GCMs) are often used in studies investigating hydrological impacts of climate change. However GCM data are known to have large biases, especially for precipitation. In this study the usefulness of GCM data for hydrological studies was tested by applying bias-corrected daily climate data of the 20CM3 control experiment from an ensemble of twelve GCMs as input to the global hydrological model PCR-GLOBWB. Results are compared with discharges calculated from a model run based on a reference meteorological dataset constructed from the CRU TS2.1 data and ERA-40 reanalysis time-series. Bias-correction was limited to monthly mean values as our focus was on the reproduction of runoff variability. The bias-corrected GCM based runs resemble the reference run reasonably well, especially for rivers with strong seasonal patterns. However, GCM derived discharge quantities are overall too low. Furthermore, from the arctic regimes it can be seen that a few deviating GCMs can bias the ensemble mean. Moreover, the GCMs do not well represent intra- and inter-year variability as exemplified by a limited persistence. This makes them less suitable for the projection of future runoff extremes.

Description: Potential evaporation (PET) is one of the main inputs of hydrological models. Yet, there is limited consensus on which PET equation is most applicable in hydrological climate impact assessments. In this study six different methods to derive global scale reference PET time series from CFSR reanalysis data are compared: Penman-Monteith, Priestley-Taylor and original and modified versions of the Hargreaves and Blaney-Criddle method. The calculated PET time series are (1) evaluated against global monthly Penman-Monteith PET time series calculated from CRU data and (2) tested on their usability for modeling of global discharge cycles. The lowest root mean squared differences and the least significant deviations (95 % significance level) between monthly CFSR derived PET time series and CRU derived PET were obtained for the cell specific modified Blaney-Criddle equation. However, results show that this modified form is likely to be unstable under changing climate conditions and less reliable for the calculation of daily time series. Although often recommended, the Penman-Monteith equation did not outperform the other methods. In arid regions (e.g., Sahara, central Australia, US deserts), the equation resulted in relatively low PET values and, consequently, led to relatively high discharge values for dry basins (e.g., Orange, Murray and Zambezi). Furthermore, the Penman-Monteith equation has a high data demand and the equation is sensitive to input data inaccuracy. Therefore, we preferred the modified form of the Hargreaves equation, which globally gave reference PET values comparable to CRU derived values. Although it is a relative efficient empirical equation, like Blaney-Criddle, the equation considers multiple spatial varying meteorological variables and consequently performs well for different climate conditions. In the modified form of the Hargreaves equation the multiplication factor is uniformly increased from 0.0023 to 0.0031 to overcome the global underestimation of CRU derived PET obtained with the original equation. It should be noted that the bias in PET is not linearly transferred to actual evapotranspiration and runoff, due to limited soil moisture availability and precipitation. The resulting gridded daily PET time series provide a new reference dataset that can be used for future hydrological impact assessments or, more specifically, for the statistical downscaling of daily PET derived from raw GCM data.

Description: This study makes a thorough global assessment of the effects of climate change on hydrological regimes and their accompanying uncertainties. Meteorological data from twelve GCMs (SRES scenarios A1B, and control experiment 20C3M) are used to drive the global hydrological model PCR-GLOBWB. We reveal in which regions of the world changes in hydrology can be detected that are significant and consistent amongst the ensemble of GCMs. New compared to existing studies is: (1) the comparison of spatial patterns of regime changes and (2) the quantification of consistent significant change calculatesd relative to both the natural variability and the inter-model spread. The resulting consistency maps indicate in which regions likelihood of hydrological change is large. Projections of different GCMs diverge widely. This underscores the need of using a multi-model ensemble. Despite discrepancies amongst models, consistent results are revealed: by 2100 the GCMs project consistent decreases in discharge for southern Europe, southern Australia, parts of Africa and southwestern South-America. Discharge decreases are large for most African rivers, the Murray and the Danube. While discharge of Monsoon influenced rivers slightly increases. In the Arctic regions river discharge increases and a phase-shift towards earlier peaks is observed. Results are comparable to previous global studies, with a few exceptions. Globally we calculated an ensemble mean discharge increase of more than ten percent. This increase contradicts previously estimated decreases, which is amongst others caused by the use of smaller GCM ensembles and different reference periods.

Description: Climate change is likely to have significant effects on the hydrology. The Ganges-Brahmaputra river basin is one of the most vulnerable areas in the world as it is subject to the combined effects of glacier melt, extreme monsoon rainfall and sea level rise. To what extent climate change will impact river flow in the Brahmaputra basin is yet unclear, as climate model studies show ambiguous results. In this study we investigate the effect of climate change on both low and high flows of the lower Brahmaputra. We apply a novel method of discharge-weighted ensemble modeling using model outputs from a global hydrological models forced with 12 different global climate models (GCMs). Based on the GCM outputs and long-term records of observed flow at Bahadurabad station, our method results in a multi-model weighted ensemble of transient stream flow for the period 1961–2100. Using the constructed transients, we subsequently project future trends in low and high river flow. The analysis shows that extreme low flow conditions are likely to occur less frequent in the future. However a very strong increase in peak flows is projected, which may, in combination with projected sea level change, have devastating effects for Bangladesh. The methods presented in this study are more widely applicable, in that existing multi-model streamflow simulations from global hydrological models can be weighted against observed streamflow data to assess at first order the effects of climate change for specific river basins.