@misc{DidovetsLobanovaBronstertetal.2017,
  author    = {Didovets, Iulii and Lobanova, Anastasia and Bronstert, Axel and Snizhko, Sergiy and Maule, Cathrine Fox and Krysanova, Valentina},
  title     = {Assessment of Climate Change Impacts on Water Resources in Three Representative Ukrainian Catchments Using Eco-Hydrological Modelling},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-394956},
  pages     = {18},
  year      = {2017},
  abstract  = {The information about climate change impact on river discharge is vitally important for planning adaptation measures. The future changes can affect different water-related sectors. The main goal of this study was to investigate the potential water resource changes in Ukraine, focusing on three mesoscale river catchments (Teteriv, UpperWestern Bug, and Samara) characteristic for different geographical zones. The catchment scale watershed model—Soil and Water Integrated Model (SWIM)—was setup, calibrated, and validated for the three catchments under consideration. A set of seven GCM-RCM (General Circulation Model-Regional Climate Model) coupled climate scenarios corresponding to RCPs (Representative Concentration Pathways) 4.5 and 8.5 were used to drive the hydrological catchment model. The climate projections, used in the study, were considered as three combinations of low, intermediate, and high end scenarios. Our results indicate the shifts in the seasonal distribution of runoff in all three catchments. The spring high flow occurs earlier as a result of temperature increases and earlier snowmelt. The fairly robust trend is an increase in river discharge in the winter season, and most of the scenarios show a potential decrease in river discharge in the spring.},
  language  = {en}
}
@article{DidovetsLobanovaBronstertetal.2017,
  author    = {Didovets, Iulii and Lobanova, Anastasia and Bronstert, Axel and Snizhko, Sergiy and Maule, Cathrine Fox and Krysanova, Valentina},
  title     = {Assessment of Climate Change Impacts on Water Resources in Three Representative Ukrainian Catchments Using Eco-Hydrological Modelling},
  series = {Water},
  volume    = {9},
  journal   = {Water},
  number    = {3},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2073-4441},
  doi       = {10.3390/w9030204},
  pages     = {18},
  year      = {2017},
  abstract  = {The information about climate change impact on river discharge is vitally important for planning adaptation measures. The future changes can affect different water-related sectors. The main goal of this study was to investigate the potential water resource changes in Ukraine, focusing on three mesoscale river catchments (Teteriv, UpperWestern Bug, and Samara) characteristic for different geographical zones. The catchment scale watershed model—Soil and Water Integrated Model (SWIM)—was setup, calibrated, and validated for the three catchments under consideration. A set of seven GCM-RCM (General Circulation Model-Regional Climate Model) coupled climate scenarios corresponding to RCPs (Representative Concentration Pathways) 4.5 and 8.5 were used to drive the hydrological catchment model. The climate projections, used in the study, were considered as three combinations of low, intermediate, and high end scenarios. Our results indicate the shifts in the seasonal distribution of runoff in all three catchments. The spring high flow occurs earlier as a result of temperature increases and earlier snowmelt. The fairly robust trend is an increase in river discharge in the winter season, and most of the scenarios show a potential decrease in river discharge in the spring.},
  language  = {en}
}
@article{FernandezPalominoHattermannKrysanovaetal.2022,
  author    = {Fernandez-Palomino, Carlos Antonio and Hattermann, Fred and Krysanova, Valentina and Lobanova, Anastasia and Vega-Jacome, Fiorella and Lavado, Waldo and Santini, William and Aybar, Cesar and Bronstert, Axel},
  title     = {A novel high-resolution gridded precipitation dataset for peruvian and ecuadorian watersheds},
  series = {Journal of hydrometeorology},
  volume    = {23},
  journal   = {Journal of hydrometeorology},
  number    = {3},
  publisher = {American Meteorological Soc.},
  address   = {Boston},
  issn      = {1525-755X},
  doi       = {10.1175/JHM-D-20-0285.1},
  pages     = {309 -- 336},
  year      = {2022},
  abstract  = {A novel approach for estimating precipitation patterns is developed here and applied to generate a new hydrologically corrected daily precipitation dataset, called RAIN4PE (Rain for Peru and Ecuador), at 0.1 degrees spatial resolution for the period 1981-2015 covering Peru and Ecuador. It is based on the application of 1) the random forest method to merge multisource precipitation estimates (gauge, satellite, and reanalysis) with terrain elevation, and 2) observed and modeled streamflow data to first detect biases and second further adjust gridded precipitation by inversely applying the simulated results of the ecohydrological 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 Pacific 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. Significance StatementWe developed a novel precipitation dataset RAIN4PE for Peru and Ecuador by merging multisource precipitation data (satellite, reanalysis, and ground-based precipitation) with terrain elevation using the random forest method. Furthermore, RAIN4PE was hydrologically corrected using streamflow data in watersheds with precipitation underestimation through reverse hydrology. The results of a comprehensive hydrological evaluation showed that RAIN4PE outperformed state-of-the-art precipitation datasets such as CHIRP, ERA5, CHIRPS, MSWEP, and PISCO in terms of daily and monthly streamflow simulations, including extremely low and high flows in almost all Peruvian and Ecuadorian catchments. This underlines the suitability of RAIN4PE for hydrometeorological applications in this region. Furthermore, our approach for the generation of RAIN4PE can be used in other data-scarce regions.},
  language  = {en}
}