Catchment Scale Evaluation of Multiple Global Hydrological Models from ISIMIP2a over North America

A satisfactory performance of hydrological models under historical climate conditions is considered a prerequisite step in any hydrological climate change impact study. Despite the significant interest in global hydrological modeling, few systematic evaluations of global hydrological models (gHMs) at the catchment scale have been carried out. This study investigates the performance of 4 gHMs driven by 4 global observation-based meteorological inputs at simulating weekly discharges over 198 large-sized North American catchments for the 1971–2010 period. The 16 discharge simulations serve as the basis for evaluating gHM accuracy at the catchment scale within the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). The simulated discharges by the four gHMs are compared against observed and simulated weekly discharge values by two regional hydrological models (rHMs) driven by a global meteorological dataset for the same period. We discuss the implications of both modeling approaches as well as the influence of catchment characteristics and global meteorological forcing in terms of model performance through statistical criteria and visual hydrograph comparison for catchment-scale hydrological studies. Overall, the gHM discharge statistics exhibit poor agreement with observations at the catchment scale and manifest considerable bias and errors in seasonal flow simulations. We confirm that the gHM approach, as experimentally implemented through the ISIMIP2a, must be used with caution for regional studies. We find the rHM approach to be more trustworthy and recommend using it for hydrological studies, especially if findings are intended to support operational decision-making.

Uncertainty of precipitation reference dataset for climate change impact studies

<p><strong>Abstract. </strong></p><p>Climate change impact studies typically require a reference climatological dataset providing a baseline period to assess future changes.  The reference dataset is also used to perform bias correction of climate model outputs.  Various reliable precipitation datasets are now available over regions with a high-density network of weather stations such as over most parts of Europe and in the United States.  In many of the world’s regions, the low-density of observation stations (or lack thereof) renders gauge-based precipitation datasets highly uncertain.  Satellite, reanalysis and merged products can be used to overcome this limitation.   However, each dataset brings additional uncertainty to the reference climate. This study compares ten precipitation datasets over 1091 African catchments to evaluate dataset uncertainty contribution in climate change studies. The precipitation datasets include two gauged-only products (GPCC, CPC), four satellite products (TRMM, CHIRPS, PERSIANN-CDR and TAMSAT) corrected using ground-based observations, three reanalysis products (ERA5, ERA-I, and CFSR) and one merged product of gauge, satellite, and reanalysis (MSWEP).</p><p>Each of those datasets was used to assess changes in future streamflows. The climate change impact study used a top-down modelling chain using 10 CMIP5 GCMs under RCP8.5. Each climate projection was bias-corrected and fed to a lumped hydrological model to generate future streamflows over the 2071-2100 period. A variance decomposition was performed to compare GCM uncertainty and reference dataset uncertainty for 51 streamflow metrics over each catchment. Results show that dataset uncertainty is much larger than GCM uncertainty for most of the streamflow metrics and over most of Africa. A selection of the best performing reference datasets (credibility ensemble) significantly reduced the uncertainty attributed to datasets, but remained comparable to that of GCMs in most cases. Results show also relatively small differences between datasets over a reference period can propagate to generate large amounts of uncertainty in the future climate. </p>

Assessing Hydrologic Impact of Climate Change with Uncertainty Estimates: Bayesian Neural Network Approach

A major challenge in assessing the hydrologic effect of climate change remains the estimation of uncertainties associated with different sources, such as the global climate models, emission scenarios, downscaling methods, and hydrologic models. There is a demand for an efficient and easy-to-use rainfall–runoff modeling tool that can capture the different sources of uncertainties to generate future flow simulations that can be used for decision making. To manage the large range of uncertainties in the climate change impact study on water resources, a neural network–based rainfall–runoff model—namely, Bayesian neural network (BNN)—is proposed. The BNN model is used with Canadian Centre for Climate Modelling and Analysis Coupled GCM, versions 1 and 2 (CGCM1 and CGCM2, respectively) with two emission scenarios, Intergovernmental Panel on Climate Change (IPCC) IS92a and Special Report on Emissions Scenarios (SRES) B2. One widely used statistical downscaling model (SDSM) is used in the analysis. The study is undertaken to simulate daily river flow and daily reservoir inflow in the Serpent and the Chute-du-Diable watersheds, respectively, in northeastern Canada. It is found that the uncertainty bands of the mean ensemble flow (i.e., flow simulated using the mean of the ensemble members of downscaled meteorological variables) is able to mostly encompass all other flows simulated with various individual downscaled meteorological ensemble members whichever CGCM or emission scenario is used. In addition, the uncertainty bands are also able to typically encompass most of the flows simulated with another rainfall–runoff model, namely, Hydrologiska Byråns Vattenbalansavdelning (HBV). The study results suggest that the BNN model could be used as an effective hydrological modeling tool in assessing the hydrologic effect of climate change with uncertainty estimates in the form of confidence intervals. It could be a good alternative method where resources are not available to implement the general multimodel ensembles approach. The BNN approach makes the climate change impact study on water resources with uncertainty estimate relatively simple, cost effective, and time efficient.