Rainfall is a key climate parameter that affects most operations that affect human life, especially in the tropics. Therefore, understanding the various factors that affect the distribution and intensity of this rainf...Rainfall is a key climate parameter that affects most operations that affect human life, especially in the tropics. Therefore, understanding the various factors that affect the distribution and intensity of this rainfall is important for effective planning among the different stakeholders in the weather and climate sectors. This study aimed at understanding how intra seasonal rainfall characteristics, especially Consecutive Dry Days (CDD) and Consecutive Wet Days (CWD), in the two major rainfall seasons will change under two future climate scenarios of RCP4.5 and RCP8.5 in Uganda, covering two future periods of 2021-2050 and 2051-2080. The results indicate a high likelihood of reduced consecutive rainfall days, especially over the Northeastern regions of the country, for both 2021-2050 and 2051-2080. However, the trends in the entire country for the two major rainfall seasons, March to May and September to November, are not significant. Nonetheless, the distribution of these days is important for most agricultural activities during different stages of crop growth. The consecutive dry days show a fairly increasing trend in the eastern part of the country, particularly in the second season of September to November. An increase in consecutive dry days implies more frequent dry spells in the midst of the growing season, potentially affecting some crops during critical growth stages.展开更多
Rainfall is crucial for many applications e.g. agriculture, health, water resources, energy among many others. However, quantitative rainfall estimation is normally a challenge especially in areas with sparse rain gau...Rainfall is crucial for many applications e.g. agriculture, health, water resources, energy among many others. However, quantitative rainfall estimation is normally a challenge especially in areas with sparse rain gauge network. This has introduced uncertainties in rainfall projections by climate models. This study evaluates the performance of three representative concentration pathways, RCP i.e. 4.5, 6.0 and 8.5 over Uganda using the Weather Research and Forecasting (WRF) model. It evaluates the model output using observed daily rain gauge data over the period 2006-2018 using Pearson correlation;relative root mean square error;relative mean error and skill scores (accuracy). It also evaluates the potential improvement in the performance of the WRF model with respective RCPs by applying bias correction. The bias correction is carried out using the quantile mapping method. A poor correlation with observed rainfall is generally found (-0.4 to +0.4);error magnitudes in the ranges of 1 to 3.5 times the long-term mean are observed. The RCPs presented different performances over different areas suggesting that no one RCP is universally valid. Application of bias correction did not produce realistic improvement in performance. Largely, the RCPs underestimated rainfall over the study area suggesting that the projected rainfall cases under these RCPs could be seriously underestimated. However, the study found RCP8.5 with slightly better performance and is thus recommended. Due to the general weak performance of the RCPs, the study recommends re-evaluating the assumptions under the RCPs for different regions or attempt to improve them using data assimilation.展开更多
文摘Rainfall is a key climate parameter that affects most operations that affect human life, especially in the tropics. Therefore, understanding the various factors that affect the distribution and intensity of this rainfall is important for effective planning among the different stakeholders in the weather and climate sectors. This study aimed at understanding how intra seasonal rainfall characteristics, especially Consecutive Dry Days (CDD) and Consecutive Wet Days (CWD), in the two major rainfall seasons will change under two future climate scenarios of RCP4.5 and RCP8.5 in Uganda, covering two future periods of 2021-2050 and 2051-2080. The results indicate a high likelihood of reduced consecutive rainfall days, especially over the Northeastern regions of the country, for both 2021-2050 and 2051-2080. However, the trends in the entire country for the two major rainfall seasons, March to May and September to November, are not significant. Nonetheless, the distribution of these days is important for most agricultural activities during different stages of crop growth. The consecutive dry days show a fairly increasing trend in the eastern part of the country, particularly in the second season of September to November. An increase in consecutive dry days implies more frequent dry spells in the midst of the growing season, potentially affecting some crops during critical growth stages.
文摘Rainfall is crucial for many applications e.g. agriculture, health, water resources, energy among many others. However, quantitative rainfall estimation is normally a challenge especially in areas with sparse rain gauge network. This has introduced uncertainties in rainfall projections by climate models. This study evaluates the performance of three representative concentration pathways, RCP i.e. 4.5, 6.0 and 8.5 over Uganda using the Weather Research and Forecasting (WRF) model. It evaluates the model output using observed daily rain gauge data over the period 2006-2018 using Pearson correlation;relative root mean square error;relative mean error and skill scores (accuracy). It also evaluates the potential improvement in the performance of the WRF model with respective RCPs by applying bias correction. The bias correction is carried out using the quantile mapping method. A poor correlation with observed rainfall is generally found (-0.4 to +0.4);error magnitudes in the ranges of 1 to 3.5 times the long-term mean are observed. The RCPs presented different performances over different areas suggesting that no one RCP is universally valid. Application of bias correction did not produce realistic improvement in performance. Largely, the RCPs underestimated rainfall over the study area suggesting that the projected rainfall cases under these RCPs could be seriously underestimated. However, the study found RCP8.5 with slightly better performance and is thus recommended. Due to the general weak performance of the RCPs, the study recommends re-evaluating the assumptions under the RCPs for different regions or attempt to improve them using data assimilation.
