Assessing hydrological impacts of short-term climate change in the Mara River Basin of East Africa

Authors: ROY, T. - University Of Arizona; VALDES, J.B. - University Of Arizona; LYNO, B. - University Of Maine; Demaria, Eleonora; SERRAT-CAPDEVILA, A. - World Bank; GUPTA, H. - University Of Arizona; VALDES-PINEDA, R. - University Of Arizona; DURCIK, M. - University Of Arizona

Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/24/2018
Publication Date: 8/27/2018
Citation: Roy, T., Valdes, J., Lyno, B., Demaria, E.M., Serrat-Capdevila, A., Gupta, H., Valdes-Pineda, R., Durcik, M. 2018. Assessing hydrological impacts of short-term climate change in the Mara River Basin of East Africa. Journal of Hydrology. 566:818-829. https://doi.org/10.1016/j.jhydrol.2018.08.051.
DOI: https://doi.org/10.1016/j.jhydrol.2018.08.051

Interpretive Summary: Climate model simulations are frequently used to simulate the impact of future climatic conditions on the human and environmental resources of a region. These future projections have the potential to guide water and natural resources manager in the development of adaptation plans for their regions. This study focuses on the Mara river basin in eastern Africa which has been experiencing increases in water demands due to population increases and agriculture expansion. Using a new dataset of climate scenarios termed VARAG (VAR 144 approach with AgMERRA). The strength of the VARAR approach is to allow for the generation of thousands of plausible future climate scenarios, enabling us to build probability distributions of projected climate change that include the important role of unforced climate variability on near term trends. The analysis compares the current precipitation, soil moisture, and streamflows in the near future (period 2020-2050). During the rainy season, there are indications that precipitation will increase in the near future with will lead to more water availability and to higher peak flows during. However, warmer-than-present temperature will increase evapotranspiration as long with reduce precipitation during the non-rainy season will lead to more dryness.

Technical Abstract: We assess the impacts of a range of short-term climate change scenarios (2020-2050) on the hydrology of the Mara River Basin in East Africa using a new high-resolution (0.25°) daily climate dataset. The scenarios combine natural climate variability, as captured by a vector autoregressive (VAR) model, with a range of climate trends calculated from 31 models in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The methodology translates these climate scenarios into plausible daily sequences of climate variables utilizing the Agricultural Modern-Era Retrospective Analysis for Research and Applications (AgMERRA) dataset. The new dataset (VARAG) has several advantages over traditional general circulation model outputs, such as, the statistical representation of short-term natural climate variability, availability at a daily time scale and high spatial resolution, not requiring additional downscaling, and the use of AgMERRA data which is bias-corrected extensively. To assess the associated impacts on basin hydrology, the semi-distributed Variable Infiltration Capacity (VIC) land-surface model is forced with the climate scenarios, after being calibrated for the study area using the fine-resolution (0.05°) merged satellite and in-situ observation-based dataset, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). The climate data are further bias-corrected by applying a non-parametric quantile mapping scheme, where the cumulative distribution functions are approximated using kernel densities. Three different wetness scenarios (dry, average, and wet) are analyzed to see the potential short-term changes in the basin. We find that the precipitation bias correction is more useful in the mountainous sub-basins, one of which also show the maximum difference between the wet and dry scenario streamflows. Precipitation, evapotranspiration, and soil moisture show increasing trends mostly during the primary rainy season, while no trend is found in the corresponding streamflow. The annual values of these variables also do not change much in the coming three decades. The methodology implemented in this study provides a reliable range of possibilities, which can greatly benefit risk analysis and infrastructure designing.