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  • Atmospheric Sciences  (1)
  • high-resolution  (1)
  • random forest  (1)
  • 1
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    How evaluation of hydrological models influences results of climate impact assessment—an editorial (2020)
    Springer Netherlands
    Details
    Publication Date: 2023-06-16
    Description: This paper introduces the Special Issue (SI) “How evaluation of hydrological models influences results of climate impact assessment.” The main objectives were as follows: (a) to test a comprehensive model calibration/validation procedure, consisting of five steps, for regional-scale hydrological models; (b) to evaluate performance of global-scale hydrological models; and (c) to reveal whether the calibration/validation methods and the model evaluation results influence climate impacts in terms of the magnitude of the change signal and the uncertainty range. Here, we shortly describe the river basins and large regions used as case studies; the hydrological models, data, and climate scenarios used in the studies; and the applied approaches for model evaluation and for analysis of projections for the future. After that, we summarize the main findings. The following general conclusions could be drawn. After successful comprehensive calibration and validation, the regional-scale models are more robust and their projections for the future differ from those of the model versions after the conventional calibration and validation. Therefore, climate impacts based on the former models are more trustworthy than those simulated by the latter models. Regarding the global-scale models, using only models with satisfactory or good performance on historical data and weighting them based on model evaluation results is a more reliable approach for impact assessment compared to the ensemble mean approach that is commonly used. The former method provides impact results with higher credibility and reduced spreads in comparison to the latter approach. The studies for this SI were performed in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).
    Description: Potsdam-Institut für Klimafolgenforschung (PIK) e.V. (3500)
    Keywords: ddc:551.48 ; Atmospheric Sciences ; Climate Change/Climate Change Impacts
    Language: English
    Type: doc-type:article
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    CITATION GEO-LEO
  • 2
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    Rain for Peru and Ecuador (RAIN4PE) (2021)
    GFZ Data Services
    Details
    Publication Date: 2021-12-22
    Description: Abstract
    Description: RAIN4PE is a novel daily gridded precipitation dataset obtained by merging multi-source precipitation data (satellite-based Climate Hazards Group InfraRed Precipitation, CHIRP (Funk et al. 2015), reanalysis ERA5 (Hersbach et al. 2020), and ground-based precipitation) with terrain elevation using the random forest regression method. Furthermore, RAIN4PE is hydrologically corrected using streamflow data in catchments with precipitation underestimation through reverse hydrology. Hence, RAIN4PE is the only gridded precipitation product for Peru and Ecuador, which benefits from maximum available in-situ observations, multiple precipitation sources, elevation data, and is supplemented by streamflow data to correct the precipitation underestimation over páramos and montane catchments. The RAIN4PE data are available for the terrestrial land surface between 19°S-2°N and 82-67°W, at 0.1° spatial and daily temporal resolution from 1981 to 2015. The precipitation dataset is provided in netCDF format. For a detailed description of the RAIN4PE development and evaluation of RAIN4PE applicability for hydrological modeling of Peruvian and Ecuadorian watersheds, readers are advised to read Fernandez-Palomino et al. (2021).
    Description: Other
    Description: Acknowledgements The authors thank the East Africa Peru India Climate Capacities (EPICC) Project for funding this research within the International Climate Initiative (IKI) funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU).
    Keywords: Andes ; Amazon ; Peru ; precipitation ; streamflow ; random forest ; reverse hydrology ; EARTH SCIENCE 〉 ATMOSPHERE 〉 PRECIPITATION
    Type: Dataset , Dataset
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    DATA GFZ
  • 3
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    BASD-CMIP6-PE: bias-adjusted and statistically downscaled CMIP6 projections over Peru and Ecuador (2023)
    GFZ Data Services
    Details
    Publication Date: 2023-12-06
    Description: Abstract
    Description: The new climate dataset BASD-CMIP6-PE for Peru and Ecuador based on the bias-adjusted and statistically downscaled CMIP6 projections of 10 GCMs addresses the need for reliable high-resolution (1d, 10km) climate data covering Peru and Ecuador. This dataset includes both historical simulations (1850-2014) and future projections (2015-2100) for precipitation and minimum, mean, and maximum temperature under three Shared Socioeconomic Pathways (SSPs; SSP1-2.6, SSP3-7.0, and SSP5-8.5). The BASD-CMIP6-PE climate data were generated using the trend-preserving Bias Adjustment and Statistical Downscaling (BASD) method (Lange, 2019, 2021) and data from regional observational datasets such as RAIN4PE (Fernandez-Palomino et al., 2021a, b) for precipitation and PISCO-temperature (Huerta et al., 2018) for temperatures as reference data. The Reliability of the BASD-CMIP6-PE was evaluated through hydrological modeling across Peruvian and Ecuadorian river basins in the historical period. The evaluation showed that the BASD-CMIP6-PE is reliable for describing the spatial patterns of atmospheric variables and streamflow simulation, including low and high flows. This suggests the usefulness of the new dataset for climate change impact assessment studies in Peru and Ecuador. The BASD-CMIP6-PE data are available for the domain covering Peru and Ecuador, located between 19°S and 2°N and 82-67°W, at 0.1° spatial and daily temporal resolution. The precipitation unit is mm, and the temperature is in °C. The data are in the NetCDF format and arranged by model, model member, experiment, variable, temporal resolution, and subset period (e.g., canesm5_r1i1p1f1_ssp126_pr_daily_2015_2020.nc).
    Keywords: CMIP6 ; projections ; climate models ; climate change ; high-resolution ; precipitation ; temperature ; Peru ; Ecuador ; Andes ; Amazon ; EARTH SCIENCE 〉 ATMOSPHERE 〉 ATMOSPHERIC TEMPERATURE ; EARTH SCIENCE 〉 ATMOSPHERE 〉 PRECIPITATION
    Type: Dataset , Dataset
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    DATA GFZ
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