Reliability Ensemble Averaging Method Research Articles

Evaluating the Performance of Satellite-Based Precipitation Products Using Gauge Measurement and Hydrological Modeling: A Case Study in a Dry Basin of Northwest China

AbstractSatellite-based precipitation products are commonly evaluated using gauge measurement, yet their regional evaluation and hydrological applicability have not been sufficiently studied, especially for dry basins. In this study, we evaluated the performance of four state-of-the-art remotely sensed precipitation products (CMORPH, GSMaP, IMERG, and PERSIANN-CDR) and their ensemble products (the reliability ensemble averaging and three-cornered hat methods) over the Heihe River basin, northwest China. Both direct evaluation using gauge measurement during 2001–19 and indirect evaluation using the Soil and Water Assessment Tool (SWAT) model during 2001–10 were conducted. Our results showed that 1) for point-to-pixel evaluation, GSMaP and IMERG products with high spatial resolution effectively captured the quantile distribution of gauge data; 2) compared to the spatially interpolated gauge data, all products underestimated the precipitation, among which GSMaP provided the closest interannual variability to the observations; 3) these products had better detection abilities upstream and during the rainy season, indicating that their performance was affected by the rain intensity—in particular, GSMaP exhibited the best ability; 4) the spatial patterns of individual products were inconsistent, while the ensemble products could reduce the bias with the gauge data; and 5) for hydrological modeling, streamflow simulation driven by GSMaP had the best performance, and the ensemble precipitation using the three-cornered hat method was better than that using the reliability ensemble averaging method. Collectively, these findings illustrated the reliability of GSMaP in representing the precipitation characteristics in similar arid areas and elucidated the advantages of using the three-cornered hat method.

Estimating Regionalized Hydrological Impacts of Climate Change Over Europe by Performance-Based Weighting of CORDEX Projections

Ensemble projections of future changes in discharge over Europe show large variation. Several methods for performance-based weighting exist that have the potential to increase the robustness of the change signal. Here we use future projections of an ensemble of three hydrological models forced with climate datasets from the Coordinated Downscaling Experiment - European Domain (EURO-CORDEX). The experiment is set-up for nine river basins spread over Europe that hold different climate and catchment characteristics. We evaluate the ensemble consistency and apply two weighting approaches; the Climate model Weighting by Independence and Performance (ClimWIP) that focuses on meteorological variables and the Reliability Ensemble Averaging (REA) in our study applied to discharge statistics per basin. For basins with a strong climate signal, in Southern and Northern Europe, the consistency in the set of projections is large. For rivers in Central Europe the differences between models become more pronounced. Both weighting approaches assign high weights to single General Circulation Models (GCMs). The ClimWIP method results in ensemble mean weighted changes that differ only slightly from the non-weighted mean. The REA method influences the weighted mean more, but the weights highly vary from basin to basin. We see that high weights obtained through past good performance can provide deviating projections for the future. It is not apparent that the GCM signal dominates the overall change signal, i.e., there is no strong intra GCM consistency. However, both weighting methods favored projections from the same GCM.

Uncertainty Assessment in Drought Severities for the Cheongmicheon Watershed Using Multiple GCMs and the Reliability Ensemble Averaging Method

The consequence of climate variations on hydrology remains the greatest challenging aspect of managing water resources. This research focused on the quantitative approach of the uncertainty in variations of climate influence on drought pattern of the Cheongmicheon watershed by assigning weights to General Circulation Models (GCMs) based on model performances. Three drought indices, Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI) are used for three durations 3-, 6- and 9-months. This study included 27 GCMs from Coupled Model Intercomparison Project 5 (CMIP5) and considered three future periods (2011–2040, 2041–2070 and 2071–2100) of the concentration scenario of Representation Concentration Pathway (RCP) 4.5. Compared to SPEI and SDI, SPI identified more droughts in severe or extreme categories of shorter time scales than SPEI or SDI. The results suggested that the discrepancy in temperature plays a significant part in characterizing droughts. The Reliability Ensemble Averaging (REA) technique was used to give a mathematical approximation of associated uncertainty range and reliability of future climate change predictions. The uncertainty range and reliability of Root Mean Square Error (RMSE) varied among GCMs and total uncertainty ranges were between 50% and 200%. This study provides the approach for realistic projections by incorporating model performance ensemble averaging based on weights from RMSE.

Uncertainty and Reliability Analysis of CMIP5 Climate Projections in South Korea Using REA Method

Weighting criteria methods achieved popularity in the quantification of uncertainty in the first decade of 21st century. In this study, Reliability Ensemble Averaging (REA) method is used to provide a quantitative estimate of uncertainty range and reliability of future climate projections over Han river basin in Korea simulated by 18 GCMs under CMIP5 Project. The root mean square error (RMSE) was used to measure uncertainty range. The result indicates that historical simulations project 43% decrease in precipitation while future scenarios simulations of GCMs projects a moderate increase in mean annual temperature and precipitation. The future projections showed a reduction in uncertainty range of about 150% as compared to simple ensemble average approach. The results suggest the viability of REA method by incorporating the model performance and model convergence criteria.

Building hazard maps of extreme daily rainy events from PDF ensemble, via REA method, on Senegal River Basin

Abstract. The Sudano-Sahelian zone of West Africa, one of the poorest of the Earth, is characterized by high rainfall variability and rapid population growth. In this region, heavy storm events frequently cause extensive damage. Nonetheless, the projections for change in extreme rainfall values have shown a great divergence between Regional Climate Models (RCM), increasing the forecast uncertainty. Novel methodologies should be applied, taking into account both the variability provided by different RCMs, as well as the non-stationary nature of time series for the building of hazard maps of extreme rainfall events. The present work focuses on the probability density functions (PDFs)-based evaluation and a simple quantitative measure of how well each RCM considered can capture the observed annual maximum daily rainfall (AMDR) series on the Senegal River basin. Since meaningful trends have been detected in historical rainfall time series for the region, non-stationary probabilistic models were used to fit the PDF parameters to the AMDR time series. In the development of PDF ensemble by bootstrapping techniques, Reliability Ensemble Averaging (REA) maps were applied to score the RCMs. The REA factors were computed using a metric to evaluate the agreement between observed -or best estimated- PDFs, and that simulated with each RCM. The assessment of plausible regional trends associated to the return period, from the hazard maps of AMDR, showed a general rise, owing to an increase in the mean and the variability of extreme precipitation. These spatial-temporal distributions could be considered by Organization for the Development of the Senegal River (Organisation pour la mise en valeur du fleuve Sénégal, OMVS), in such a way as to reach a better balance between mitigation and adaptation.

Extracting information from an ensemble of GCMs to reliably assess future global runoff change

Future runoff projections derived from different global climate models (GCMs) show large differences. Therefore, within this study the, information from multiple GCMs has been combined to better assess hydrological changes. For projections of precipitation and temperature the Reliability ensemble averaging (REA) method was introduced to calculate weighted average ensemble change with an accompanying uncertainty range. In this study the original REA method is compared with three other methods that calculate runoff change by selecting or weighting GCMs on their inter-model similarity for the current and future climate. All methods are applied to distributed runoff fields calculated with the hydrological model PCR-GLOBWB forced with meteorological data from an ensemble of 12 GCMs. Differences between weighted and non-weighted average runoff changes for 2100 are small. Within a validation experiment, where GCM ensemble mean change is derived between the time-slices 1961–1975 and 1976–1990, the non-weighted ensemble change resembled observed change best. Yet, both the weighted and non-weighted average change were too conservative. This underscores the importance of considering an uncertainty range alongside the ensemble average change. In this study the uncertainty range (or 95% confidence interval) is defined by four times the root mean square difference around the ensemble mean change. The uncertainty range derived with the non-weighted method is relatively wide, upper and lower uncertainty bounds show large biases from observed change. However, the uncertainty range was reliably be reduced by using only a selection of GCMs which show high inter-model similarity for the current and future climate.

Probability of regional climate change based on the Reliability Ensemble Averaging (REA) method

We present an extension of the Reliability Ensemble Averaging, or REA, method [Giorgi and Mearns, 2002] to calculate the probability of regional climate change exceeding given thresholds based on ensembles of different model simulations. The method is applied to a recent set of transient experiments for the A2 and B2 IPCC emission scenarios with 9 different atmosphere‐ocean General Circulation Models (AOGCMs). Probabilities of surface air temperature and precipitation change are calculated for 10 regions of subcontinental scale spanning a range of latitudes and climatic settings. The results obtained from the REA method are compared with those obtained with a simpler but conceptually similar approach [Räisänen and Palmer, 2001]. It is shown that the REA method can provide a simple and flexible tool to estimate probabilities of regional climate change from ensembles of model simulations for use in risk and cost assessment studies.