ALBERT

Description: People across Peru are vulnerable and exposed to a wide range of hazards, and studies demonstrate that these hazards are key drivers of migration in the country. Hydrometeorological hazards resulting in excessive amounts of water (in such forms as torrential rainfalls and floods) – or the lack thereof (in the form of, for example, drought or glacier retreat) – are particularly salient to migration. Climate change has intensified these hazards and will continue to do so, possibly resulting in new and unparalleled impacts on migration. IOM and the Potsdam Institute for Climate Impact Research have partnered to produce this report, which seeks to shed light on the available evidence on the environment, climate change and migration nexus in Peru. The study puts into perspective various climate risks and hazards that affect communities in the country’s main topographical zones: the coast, the highlands, and the rainforest or jungle. The report provides a systematic review of the complex interaction between climate and other factors driving migration in the country. It discusses the necessity to understand climate migration patterns and improve planning and policies in the short term to the mid-term, in view of several “no-analog threats” – that is, those with unprecedented, large impacts – that could occur towards the end of the century.

Description: Too Much, Too Little Water (policy brief) People across Peru are vulnerable and exposed to a wide range of hazards, and studies demonstrate that these hazards are key drivers of migration in the country. Hydrometeorological hazards resulting in excessive amounts of water (in such forms as torrential rainfalls and floods) – or the lack thereof (such as drought or glacier retreat) – are particularly salient to migration. Climate change has intensified these hazards and will continue to do so, possibly resulting in new and unparalleled impacts on migration. This policy brief, based on a systematic review of the literature and expert interviews, assesses available scientific evidence on the nexus between climate risks and migration in Peru. It discusses the necessity to understand climate migration patterns and improve planning and policies in the short term to the mid-term, in view of several “no-analog threats” – that is, those with unprecedented, large impacts – that could occur towards the end of the century. Recent policy developments in the country, such as the National Plan of Action on Climate Migration and the National Adaptation Plan (NAP), can break new ground in addressing these challenges.

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: Most hydrological studies rely on a model calibrated using discharge alone. However, judging the model reliability based on such calibration is problematic, as it does not guarantee the correct representation of internal hydrological processes. This study aims (a) to develop a comprehensive multi-objective calibration framework using remote sensing vegetation data and hydrological signatures (flow duration curve, FDC, and baseflow index) besides discharge, and (b) to apply this framework for calibration of the Soil and Water Assessment Tool (SWAT) in a typical Andean catchment. Overall, our calibration approach outperformed traditional discharge-based and FDC signature-based calibration strategies in terms of vegetation, streamflow, and flow partitioning simulation. New hydrological insights for the region are: baseflow is the main component of the streamflow sustaining the long dry-season flow, and pasture areas offer higher water yield and baseflow than other land covers. The proposed approach could be used in other data-scarce regions with complex topography.

Description: A novel approach for estimating precipitation patterns is developed here and applied to generate a new hydrologically corrected daily precipitation dataset, called RAIN4PE (for ‘Rain for Peru and Ecuador’), at 0.1° spatial resolution for the period 1981-2015 covering Peru and Ecuador. It is based on the application of a) the random forest method to merge multi-source precipitation estimates (gauge, satellite, and reanalysis) with terrain elevation, and b) observed and modeled streamflow data to firstly detect biases and secondly further adjust gridded precipitation by inversely applying the simulated results of the eco-hydrological model SWAT (Soil and Water Assessment Tool). Hydrological results using RAIN4PE as input for the Peruvian and Ecuadorian catchments were compared against the ones when feeding other uncorrected (CHIRP and ERA5) and gauge-corrected (CHIRPS, MSWEP, and PISCO) precipitation datasets into the model. For that, SWAT was calibrated and validated at 72 river sections for each dataset using a range of performance metrics, including hydrograph goodness of fit and flow duration curve signatures. Results showed that gauge-corrected precipitation datasets outperformed uncorrected ones for streamflow simulation. However, CHIRPS, MSWEP, and PISCO showed limitations for streamflow simulation in several catchments draining into the Paċific Ocean and the Amazon River. RAIN4PE provided the best overall performance for streamflow simulation, including flow variability (low-, high- and peak-flows) and water budget closure. The overall good performance of RAIN4PE as input for hydrological modeling provides a valuable criterion of its applicability for robust countrywide hydrometeorological applications, including hydroclimatic extremes such as droughts and floods.

Description: Here, we present BASD-CMIP6-PE, a high-resolution (1d, 10 km) climate dataset for Peru and Ecuador based on the bias-adjusted and statistically downscaled CMIP6 climate projections of 10 GCMs. 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 (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. The BASD performance was evaluated using observational data and through hydrological modeling across Peruvian and Ecuadorian river basins in the historical period. Results demonstrated that BASD significantly reduced biases between CMIP6-GCM simulations and observational data, enhancing long-term statistical representations, including mean and extreme values, and seasonal patterns. Furthermore, the hydrological evaluation highlighted the appropriateness of adjusted GCM simulations for simulating streamflow, including mean, low, and high flows. These findings underscore the reliability of BASD-CMIP6-PE in assessing regional climate change impacts on agriculture, water resources, and hydrological extremes.

Description: The new climate dataset, BASD-CMIP6-PE, for Peru and Ecuador is based on bias-adjusted and statistically downscaled CMIP6 climate projections from 10 GCMs. It addresses the need for reliable high-resolution (1d, 10km) climate data covering Peru and Ecuador. This dataset includes 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 using observational data and through hydrological modeling across Peruvian and Ecuadorian river basins in the historical period. The evaluation demonstrated the dataset’s reliability in describing spatial patterns of atmospheric variables and streamflow simulation, including mean, low, and high flows. This suggests the usefulness of the new dataset for assessing regional climate change impacts on agriculture, water resources, and hydrological extremes. The BASD-CMIP6-PE data are available for the domain covering Peru and Ecuador, located between 19°S and 2°N and 82°W to 67°W, with a spatial resolution of 0.1° and a daily temporal resolution. The unit for precipitation is millimeters (mm), and for temperature, it is degrees Celsius (°C). The BASD-CMIP6-PE dataset is organized within a "daily" folder, denoting its availability at a 1 daily temporal resolution. Within this directory, four subfolders are present: "historical" containing historical data, "ssp126" for SSP1-2.6, "ssp370" for SSP3-7.0, and "ssp585" for SSP5-8.5. Each of these subfolders further includes ten distinct folders, corresponding to different GCMs: CanESM5, IPSL–CM6A–LR, UKESM1–0–LL, CNRM–CM6–1, CNRM–ESM2–1, MIROC6, GFDL–ESM4, MRI–ESM2–0, MPI–ESM1–2–HR, and EC–Earth3. These folders store the data in the NetCDF format arranged by model, model member, experiment, variable, temporal resolution, and subset period, resulting in file names like "canesm5_r1i1p1f1_ssp126_pr_daily_2015_2020.nc". For a detailed description of the BASD-CMIP6-PE development and evaluation, readers are advised to read Fernandez-Palomino et al. (2023), for which this dataset is supplementary material.