Eduardo Jauch

Sensitivity of river fishes to climate change: The role of hydrological stressors on habitat range shifts.

Climate change will predictably change hydrological patterns and processes at the catchment scale, with impacts on habitat conditions for fish. The main goal of this study is to assess how shifts in fish habitat favourability under climate change scenarios are affected by hydrological stressors. The interplay between climate and hydrological stressors has important implications in river management under climate change because management actions to control hydrological parameters are more feasible than controlling climate. This study was carried out in the Tamega catchment of the Douro basin. A set of hydrological stressor variables were generated through a process-based modelling based on current climate data (2008-2014) and also considering a high-end future climate change scenario. The resulting parameters, along with climatic and site-descriptor variables were used as explanatory variables in empirical habitat models for nine fish species using boosted regression trees. Models were calibrated for the whole Douro basin using 254 fish sampling sites and predictions under future climate change scenarios were made for the Tamega catchment. Results show that models using climatic variables but not hydrological stressors produce more stringent predictions of future favourability, predicting more distribution contractions or stronger range shifts. The use of hydrological stressors strongly influences projections of habitat favourability shifts; the integration of these stressors in the models thinned shifts in range due to climate change. Hydrological stressors were retained in the models for most species and had a high importance, demonstrating that it is important to integrate hydrology in studies of impacts of climate change on freshwater fishes. This is a relevant result because it means that management actions to control hydrological parameters in rivers will have an impact on the effects of climate change and may potentially be helpful to mitigate its negative effects on fish populations and assemblages.

Improving remotely sensed actual evapotranspiration estimation with raster meteorological data

Evapotranspiration is a process driven by weather, vegetation, and soil conditions. The complex interrelations among these parameters have been modelled by numerous remote-sensing energy balance algorithms. When estimating evapotranspiration on a regional scale, the spatial variability of the weather parameters is important and thus closer attention to the meteorological input data is required. The aim of this work is to improve the accuracy of estimating actual evapotranspiration by integrating outputs from a meteorological model into a remotely sensed energy balance model. In order to achieve this, a time series of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were processed to retrieve daily evapotranspiration values using raster meteorological data. The ITA-MyWater tool implementing the ReSET-Raster algorithm was used in the Tâmega trans-boundary watershed shared by Portugal and Spain. The results were compared to the global MODIS evapotranspiration products for validation, achieving a coefficient of correlation of 0.61 and a root mean square error of 0.92 mm day–1. Compared with an actual evapotranspiration map that was generated using weather station data, there were improvements in the spatial distribution, especially in dry areas where differences between evapotranspiration estimations of up to 1.88 mm day–1 were noticed. The proposed methodology contributes to the improved estimation of water use, an important parameter of water cycles, using satellite remote-sensing data.