RESUMO
Climate change has the potential to affect climate parameters like rainfall and temperature which lead to a change in the irrigation water requirement of the irrigation system. As irrigation water requirement is highly dependent on precipitation and potential evapotranspiration, climate change impact studies are necessary. Therefore, this study aims to assess the impact of climate change on the irrigation water requirement of the Shumbrite irrigation project. For this study, climate variables of precipitation and temperature were generated from CORDEX-Africa simulations downscaled from MPI Global Circulation Model (GCM) under three emission scenarios (RCP2.6, RCP4.5, and RCP8.5). The climate data covers from 1981 to 2005 for the baseline period and 2021-2045 for the future period for all scenarios. Future precipitation shows a decrease for all scenarios with a maximum decrease under RCP2.6 (4.2%) and temperature show an increase in the future as compared to the baseline period. The reference evapotranspiration and Irrigation Water Requirements (IWR) were calculated by using CROPWAT 8.0 software. Results showed that the mean annual reference evapotranspiration is expected to increase in the future by 2.7%, 2.6%, and 3.3% for RCP2.6, RCP4.5, and RCP8.5 respectively as compared to the baseline period. Mean annual irrigation water requirement shows an increase of 2.58%, 0.74%, and 8.4% for the future under RCP2.6, RCP4.5, and RCP8.5 respectively. The Crop Water Requirement (CWR) also increases for the future period under all RCP scenarios, with maximum CWR for tomato, potato, and pepper crops. To ensure the sustainability of the project, crops with high irrigation water requirements should be replaced by other crops with low water requirements.
RESUMO
Climate change's influence on water resource availability in watersheds must be evaluated to ensure food and water security. Using an ensemble of two global climate models (MIROC and MPI) and one regional climate model (RCA4), the impact of climate change on the availability of water in the Kiltie watershed was evaluated under the RCP4.5 and RCP8.5 scenarios for the year 2040s and 2070s. The flow was simulated using the HBV hydrological model, which needs fewer data and is typically employed in data-scarce settings. The model calibration and validation result, show RVE (relative volume error) of -1.27% and 6.93%, and NSE of 0.63 and 0.64 respectively. Seasonal Water Supply in the Future Under the RCP4.5 Scenario for the 2040s increased between 1.1 mm and 33.2 mm showing maximum incremental in August and a decrease in a range from 0.23 mm to 6.89 mm with a maximum decrease in September. While in the 2070s, water availability increases between 7.2 mm and 56.9 mm, with the largest increases occurring in October and the smallest reductions occurring in July by 9 mm. Future water availability increases under the RCP8.5 scenario during the 2040s period between 4.1 mm and 38.8 mm, with the highest increase occurring in August, and falls between 9.8 mm and 31.2 mm, with the maximum declines occurring in the spring seasons. Water availability in the 2070s, according to the RCP8.5 scenario, increases between 2.7 mm and 42.4 mm with the highest increments in August, and it decreases between 1.8 mm and 80.3 mm with maximum decreases in June. According to this study, climate change would make it easier to access water during the rainy season, necessitating the construction of water storage facilities so that surplus water can be used for dry farming. A watershed-level integrated water resource management strategy should be created quickly as future water supply will decline during the dry seasons.
RESUMO
This study was conducted to quantify the association between meteorological variables and incidence of Plasmodium falciparum in areas with unstable malaria transmission in Ethiopia. We used morbidity data pertaining to microscopically confirmed cases reported from 35 sites throughout Ethiopia over a period of approximately 6-7 years. A model was developed reflecting biological relationships between meteorological and morbidity variables. A model that included rainfall 2 and 3 months earlier, mean minimum temperature of the previous month and P. falciparum case incidence during the previous month was fitted to morbidity data from the various areas. The model produced similar percentages of over-estimation (19.7% of predictions exceeded twice the observed values) and under-estimation (18.6%, were less than half the observed values). Inclusion of maximum temperature did not improve the model. The model performed better in areas with relatively high or low incidence (>85% of the total variance explained) than those with moderate incidence (55-85% of the total variance explained). The study indicated that a dynamic immunity mechanism is needed in a prediction model. The potential usefulness and drawbacks of the modelling approach in studying the weather-malaria relationship are discussed, including a need for mechanisms that can adequately handle temporal variations in immunity to malaria.
