ALBERT

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: As an initial step in assessing the prospect of using global hydrological models (GHMs) for hydrological forecasting, this study investigates the skill of the GHM PCR-GLOBWB in reproducing the occurrence of past extremes in monthly discharge on a global scale. Global terrestrial hydrology from 1958 until 2001 is simulated by forcing PCR-GLOBWB with daily meteorological data obtained by downscaling the CRU dataset to daily fields using the ERA-40 reanalysis. Simulated discharge values are compared with observed monthly streamflow records for a selection of 20 large river basins that represent all continents and a wide range of climatic zones. We assess model skill in three ways all of which contribute different information on the potential forecasting skill of a GHM. First, the general skill of the model in reproducing hydrographs is evaluated. Second, model skill in reproducing significantly higher and lower flows than the monthly normals is assessed in terms of skill scores used for forecasts of categorical events. Third, model skill in reproducing flood and drought events is assessed by constructing binary contingency tables for floods and droughts for each basin. The skill is then compared to that of a simple estimation of discharge from the water balance (P−E). The results show that the model has skill in all three types of assessments. After bias correction the model skill in simulating hydrographs is improved considerably. For most basins it is higher than that of the climatology. The skill is highest in reproducing monthly anomalies. The model also has skill in reproducing floods and droughts, with a markedly higher skill in floods. The model skill far exceeds that of the water balance estimate. We conclude that the prospect for using PCR-GLOBWB for monthly and seasonal forecasting of the occurrence of hydrological extremes is positive. We argue that this conclusion applies equally to other similar GHMs and LSMs, which may show sufficient skill to forecast the occurrence of monthly flow extremes.

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: 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: The representation of hydrological processes in land surface schemes (LSSs) has recently been improved. In this study, the usability of GCM runoff for river discharge modeling is evaluated by validating the mean, timing, and amplitude of the modeled annual discharge cycles against observations. River discharge was calculated for six large rivers using runoff, precipitation, and actual evaporation from the GCMs ECHAM5 and Hadley Centre Global Environmental Model version 2 (HadGEM2). Four methods were applied: 1) accumulation of GCM runoff, 2) routing of GCM runoff, 3) routing of GCM runoff combined with temporal storage of subsurface runoff, and 4) offline hydrological modeling with the global distributed hydrological model PCRaster Global Water Balance (PCR-GLOBWB) using meteorological data from the GCMs as forcing. The quality of discharge generated by all four methods is highly influenced by the quality of the GCM data. In small catchments, the methods that include runoff routing perform equally well, although offline modeling with PRC-GLOBWB outperforms the other methods for ECHAM5 data. For larger catchments, routing introduces realistic travel times, decreased day-to-day variability, and it reduces extremes. Complexity of the LSS of both GCMs is comparable to the complexity of the hydrological model. However, in HadGEM2 the absence of subgrid variability for saturated hydraulic conductivity results in a large subsurface runoff flux and a low seasonal variability in the annual discharge cycle. The analysis of these two GCMs shows that when LSSs are tuned to reproduce realistic water partitioning at the grid scale and a routing scheme is also included, discharge variability and change derived from GCM runoff could be as useful as changes derived from runoff obtained from offline simulations using large-scale hydrological models.

Description: As an initial step in assessing the prospect of using macro-scale hydrological models (MHMs) for hydrological forecasting, this study investigates the skill of the MHM PCR-GLOBWB in reproducing past discharge extremes on a global scale. Global terrestrial hydrology from 1958 until 2001 is simulated by forcing PCR-GLOBWB with daily meteorological data obtained by downscaling the CRU dataset to daily fields using the ERA-40 reanalysis. Simulated discharge values are compared with observed monthly streamflow records for a selection of 20 large river basins that represent all continents and a wide range of climatic zones. We assess model skill in three ways. First, the general performance of the model in reproducing hydrographs is evaluated. Second, model skill in reproducing significantly higher and lower flows than the monthly normals is assessed in terms of skill scores used for forecasts of categorical events. Third, model skill in reproducing flood and drought events is assessed by constructing binary contingency tables for floods and droughts for each basin. The results show that the model has skill in all three types of hindcasting. After bias correction the model skill in simulating hydrographs is improved considerably. For most basins it is much higher than that of the climatology. The skill in hindcasting monthly anomalies is high compared to that of an imaginary unskilled system. The model also performs better than an unskilled system in hindcasting floods and droughts, with a markedly higher skill in floods. We conclude that the prospect for using PCR-GLOBWB for monthly and seasonal hydrological forecasting is positive. Our results which we argue are representative for other similar MHMs, show that MHMs have sufficient skill for use in forecasting flow extremes.

Description: Forested tropical peatlands in Southeast Asia store at least 42 000 Million metric tonnes (Mt) of soil carbon. Human activity and climate change threatens the stability of this large pool which has been decreasing rapidly over the last few decades owing to deforestation, drainage and fire. In this paper we estimate the carbon dioxide (CO2) emissions resulting from drainage of lowland tropical peatland for agricultural and forestry development which dominates the perturbation of the carbon balance in the region. Present and future emissions from drained peatlands are quantified using data on peatland extent and peat thickness, present and projected land use, water management practices and decomposition rates. Of the 27.1 Million hectares (Mha) of peatland in Southeast Asia, 12.9 Mha had been deforested and mostly drained by 2006. This latter area is increasing rapidly as a result of increasing land development pressures. Carbon dioxide (CO2) emission caused by decomposition of drained peatlands was between 355 and 855 Mt y