Assessing extreme precipitation from a regional climate model in different spatial–temporal scales: A hydrological perspective in South America

Abstract

Global and regional climate models (GCM and RCM respectively) are the current mathematical tools used to project alterations on precipitation regimes given different greenhouse gases emissions scenarios. However, these models have specific resolutions, physical equations and numerical approaches that provide a diverse set of performances across different regions and spatialtemporal scales. In South America, most hydrological impact studies have used the Eta RCM to yield precipitation projections without a proper uncertainty analysis. It is important to acknowledge its uncertainties prior to any hydrological assessment to adequately support climate change investigations and related water decision making. Therefore, we aim to investigate how Eta extreme precipitation biases vary in different spatial-temporal scales from a hydrological perspective. Thus, we evaluate the extreme precipitation generated by the Eta RCM driven by four different GCMs. It is investigated Eta biases across different temporal (3 hr-5 days) and spatial scales (0.2-1.0 ) and how those errors affect river streamflow simulations. It is used local intensityduration-frequency (IDF) curves and gridded precipitation datasets (MSWEP and ERA5-Land) as references for Eta assessment. In general, Eta underestimates subdaily extreme precipitation across South America, regardless of the driven GCM. The median bias of a 10-year return period daily precipitation is −36 mm (1st and 3rd quantiles −58 and −17 mm) compared to MSWEP and −26 mm (1st and 3rd quantiles −45 and −11 mm) compared to ERA5-Land. However, the relative errors reduce with temporal and spatial aggregation. For example, the average bias of extreme precipitation decreases 8.4 and 5.4 percentage points from 1-to 5-day duration compared to MSWEP and ERA5-Land, respectively. The negative biases observed for precipitation ( ≈ 20%) are propagated to the flood discharges ( ≈ 40%), and these errors reduce with the drainage area. In general, there are greater biases in extreme discharges for small basins, but these errors considerably reduce for basins larger than 30,000 km 2 compared to MGB-MSWEP simulations. Compared to MGB-ERA5-Land simulations, MGB-Eta presents relatively similar errors for

Comparative Evaluation of Five Hydrological Models in a Large-Scale and Tropical River Basin

“…The selection of these models aims to compare numerical approaches that have been used to characterize the hydrological conditions in Brazilian river basins (SMAP and MGB-IPH model), with other models that have been developed and are commonly used in regions with different hydrological and climatological conditions (GR4J, HYMOD, and HBV). For instance, the SMAP model was adopted by the Brazilian National Electrical System Operator (ONS) [32] to simulate daily and monthly inflows for all hydropower reservoirs in Brazil, and the MGB-IPH model was developed and broadly apply to represent different Brazilian and South American watersheds [33][34][35]. This work also reinforces the analysis initially made by previous studies [9,36,37] that compare the use of hydrological models with different structure and parameter complexities.…”

“…The MGB-IPH (Large Basins Model-Modelo de Grandes Bacias) is a large-scale and semi-distributed hydrological model that uses physical-and conceptual-based equations to simulate continental hydrological cycles [55]. This model has been developed with a focus on large South American basins, considering the typical low spatial density and limited duration of hydrological records available in the region [34,35,56]. The MGB-IPH has been previosly applied to represent the hydrological of different tropical river basins [57,58], as well for the implementation of operational flood forecasting systems [59,60].…”

Climate Change Flood Risk Analysis: Application of Dynamical Downscaling and Hydrological Modeling

“…Studies integrating climate model outputs into hydrological modeling have grown substantially to investigate the impact of climate change on the hydrological regime [10][11][12][13]. For this purpose, in South America, one of the most applied RCMs to yield precipitation projections is the Eta-CPTEC [14], which has been used even in relatively small drainage basins (<5000 km 2 ) [11,15,16].…”

“…Studies integrating climate model outputs into hydrological modeling have grown substantially to investigate the impact of climate change on the hydrological regime [10][11][12][13]. For this purpose, in South America, one of the most applied RCMs to yield precipitation projections is the Eta-CPTEC [14], which has been used even in relatively small drainage basins (<5000 km 2 ) [11,15,16]. The National Institute for Space Research (INPE) coordinated sets of downscaled products based on the Eta RCM for climate research in South America nested in four different GCMs with a horizontal resolution of 20 km [14,17] and one of 5 km for Southeast Brazil, which was utilized with HadGEM2-ES [18].…”