Climate change impact assessment on the hydrology of a large river basin in Ethiopia using a local-scale climate modelling approach.

Local-scale climate change adaptation is receiving more attention to reduce the adverse effects of climate change. The process of developing adaptation measures at local-scale (e.g., river basins) requires high-quality climate information with higher resolution. Climate projections are available at a coarser spatial resolution from Global Climate Models (GCMs) and require spatial downscaling and bias correction to drive hydrological models. We used the hybrid multiple linear regression and stochastic weather generator model (Statistical Down-Scaling Model, SDSM) to develop a location-based climate projection, equivalent to future station data, from GCMs. Meteorological data from 24 ground stations and the most accurate satellite and reanalysis products identified for the region, such as Climate Hazards Group InfraRed Precipitation with Station Data were used. The Soil Water Assessment Tool (SWAT) was used to assess the impacts of the projected climate on hydrology. Both SDSM and SWAT were calibrated and validated using the observed climate and streamflow data, respectively. Climate projection based on SDSM, in one of the large and agricultural intensive basins in Ethiopia (i.e., Awash), show high variability in precipitation but an increase in maximum (Tmax) and minimum (Tmin) temperature, which agrees with global warming. On average, the projection shows an increase in annual precipitation (>10%), Tmax (>0.4 °C), Tmin (>0.2 °C) and streamflow (>34%) in the 2020s (2011-2040), 2050s (2041-2070), and 2080s (2071-2100) under RCP2.6-RCP8.5. Although no significant trend in precipitation is found, streamflow during March-May and June-September is projected to increase throughout the 21 century by an average of more than 1.1% and 24%, respectively. However, streamflow is projected to decrease during January-February and October-November by more than 6%. Overall, considering the projected warming and changes in seasonal flow, local-scale adaptation measures to limit the impact on agriculture, water and energy sectors are required.

Hydrogeochemical and mixing processes controlling groundwater chemistry in a wastewater irrigated agricultural system of India.

In many parts of the world, wastewater irrigation has become a common practice because of freshwater scarcity and to increase resource reuse efficiency. Wastewater irrigation has positive impacts on livelihoods and at the same time, it has adverse impacts related to environmental pollution. Hydrochemical processes and groundwater behaviour need to be analyzed for a thorough understanding of the geochemical evolution in the wastewater irrigated systems. The current study focuses on a micro-watershed in the peri-urban Hyderabad of India, where farmers practice intensive wastewater irrigation. To evaluate the major factors that control groundwater geochemical processes, we analyzed the chemical composition of the wastewater used for irrigation and groundwater samples on a monthly basis for one hydrological year. The groundwater samples were collected in three settings of the watershed: wastewater irrigated area, groundwater irrigated area and upstream peri-urban area. The collected groundwater and wastewater samples were analyzed for major anions, cations and nutrients. We systematically investigated the anthropogenic influences and hydrogeochemical processes such as cation exchange, precipitation and dissolution of minerals using saturated indices, and freshwater-wastewater mixtures at the aquifer interface. Saturation indices of halite, gypsum and fluorite are exhibiting mineral dissolution and calcite and dolomite display mineral precipitation. Overall, the results suggest that the groundwater geochemistry of the watershed is largely controlled by long-term wastewater irrigation, local rainfall patterns and water-rock interactions. The study results can provide the basis for local decision-makers to develop sustainable groundwater management strategies and to control the aquifer pollution influenced by wastewater irrigation.

Long-term trends in rainfall and temperature using high-resolution climate datasets in East Africa.

Detecting changes in climate is a prerequisite for a better understanding of the climate and developing adaptation and mitigation measures at a regional and local scale. In this study long-term trends in rainfall and maximum and minimum temperature (T-max and T-min) were analysed on seasonal and annual time scales for East Africa. High resolution gridded rainfall (1981-2016) and temperature (1979-2010) data from international databases like the Climate Hazards Group are used. Long-term seasonal trend analysis shows a non-significant (except for small areas), decreasing (increasing) trend in rainfall in eastern (western) parts of Ethiopia and Kenya and a decreasing trend in large parts of Tanzania during the long rainy season. On the other hand, a non-significant increasing trend in large parts of the region is observed during the short rain season. With regard to annual trends, results largely confirm seasonal analyses: only a few significant trends in rainfall, but significant increasing trends in T-max (up to 1.9 °C) and T-min (up to 1.2 °C) for virtually the whole region. Our results demonstrate the need and added value of analysing climate trends based on data with high spatial resolution allowing sustainable adaptation measures at local scales.

Impacts of projected change in climate on water balance in basins of East Africa.

In East Africa, climate change and variability have shown a strong impact on sectors such as agriculture, energy, and water. To allow mitigation and adaptation of the possible impacts of the projected change in climate, this study applies a Statistical Downscaling Model (SDSM) to generate a high-resolution climate projection, equivalent to future station data, to drive impact assessment models in selected, agricultural intensive, basins of Ethiopia (EthShed), Kenya (KenShed), and Tanzania (TanShed). Observed and large-scale climate variables (predictors) are obtained from the national meteorological agency of Ethiopia and international databases. BROOK90, a physical-based hydrological model, is used to assess the impacts of the projected change in precipitation and maximum and minimum temperature (T-max, and T-min) on the water balance. Based on SDSM, the results show an increase in precipitation, relative to the baseline period (1961-1990), in EthShed (14% - 50%) and KenShed (15% - 86%) and a decrease in TanShed (1.3% - 6.3%) in the 20s (2011-2040), 50s (2041-2070), and 80s (2071-2100) under the three Representative Concentration Pathways (RCPs; RCP2.6, RCP4.5, and RCP8.5). T-max (anomalies up to 3.7 °C) and T-min (anomalies up to 2.76 °C) will be warmer than the baseline period throughout the 21 century in all three basins. In line with the projected change in precipitation and temperature, an increase (decrease) in seasonal and annual streamflow, soil-water, and evaporation in EthShed and KenShed (TanShed) is projected in the 20s, 50s, and 80s. In general, sustainable adaptation measures are required to be developed in a site-specific manner, considering the projected increase in temperature and evaporation in all three basins and a decrease in soil-water and streamflow in TanShed.

Statistically downscaled climate dataset for East Africa.

For many regions of the world, current climate change projections are only available at coarser spatial resolution from Global Climate Models (GCMs) that cannot directly be used in impact assessment and adaptation studies at regional and local scale. Impact assessment studies require high-resolution climate data to drive impact assessment models. To overcome this data challenge, we produced a station based climate projection (precipitation and maximum and minimum temperature) for Ethiopia, Kenya, and Tanzania using observed daily data from 211 stations obtained from the National Meteorological Agency of Ethiopia and international databases. Moreover, 26 large-scale climate variables derived from the National Centers for Environmental Prediction reanalysis data (1961-2005) and second generation Canadian Earth System Model (CanESM2, 1961-2100) are used. Statistical Down-Scaling Model (SDSM) is used to produce the required high-resolution climate projection by developing a statistical relationship between the large- and local-scale climate variables. The predictors are analysed more than 16458 times and we provided 20 ensembles for the current (1961-2005) and future (2006-2100, under RCP2.6, RCP4.5, and RCP8.5) climate.

A phenomenological approach shows a high coherence of warming patterns in dimictic aquatic systems across latitude.

To predict the coherence in local responses to large-scale climatic forcing among aquatic systems, we developed a generalized approach to compare long-term data of dimictic water bodies based on phenomenologically defined hydrographic events. These climate-sensitive phases (inverse stratification, spring overturn, early thermal stratification, summer stagnation) were classified in a dual code (cold/warm) based on threshold temperatures. Accounting for a latitudinal gradient in seasonal timing of phases derived from gradients in cumulative irradiation (2.2 days per degree latitude), we found a high spatial and temporal coherence in warm-cold patterns for six lakes (84 %) and the Baltic Sea (78 %), even when using the same thresholds for all sites. Similarity to CW-codes for the North Sea still was up to 72 %. The approach allows prediction of phase-specific warming trends and resulting instantaneous or time-delayed ecological responses. Exemplarily, we show that warming during early thermal stratification controls food-web-mediated effects on key species during summer.

Can overwintering versus diapausing strategy in Daphnia determine match-mismatch events in zooplankton-algae interactions?

Mismatches between predator and prey due to climate change have now been documented for a number of systems. Ultimately, a mismatch may have far-reaching consequences for ecosystem functioning as decoupling of trophic relationships results in trophic cascades. Here, we examine the potential for climate change induced mismatches between zooplankton and algae during spring succession, with a focus on Daphnia and its algal food. Whereas the development of an overwintering population of daphnids may parallel shifts in phytoplankton phenology due to climate warming, changes in the photoperiod-temperature interaction may cause the emerging population of daphnids to hatch too late and mismatch their phytoplankton prey. A decoupling of the trophic relationship between the keystone herbivore Daphnia and its algal prey can result in the absence of a spring clear water phase. We extended an existing minimal model of seasonal dynamics of Daphnia and algae and varied the way the Daphnia population is started in spring, i.e., from free swimming individuals or from hatching resting eggs. Our model results show that temperature affects the timing of peak abundance in Daphnia and algae, and subsequently the timing of the clear water phase. When a population is started from a small inoculum of hatching resting eggs, extreme climate warming (+6 degrees C) results in a decoupling of trophic relationships and the clear water phase fails to occur. In the other scenarios, the trophic relationships between Daphnia and its algal food source remain intact. Analysis of 36 temperate lakes showed that shallow lakes have a higher potential for climate induced match-mismatches, as the probability of active overwintering daphnids decreases with lake depth. Future research should point out whether lake depth is a direct causal factor in determining the presence of active overwintering daphnids or merely indicative for underlying causal factors such as fish predation and macrophyte cover.