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This study examines the characteristics of drought in the Volta River Basin (VRB), investigates the influence of drought on the streamflow, and projects the impacts of future climate change on the drought. A combination of observation data and regional climate simulations of past and future climates (1970-2013, 2046-2065, and 2081-2100) were analyzed for the study. The Standardized Precipitation Index (SPI) and Standardized Precipitation and Evapotranspiration (SPEI) were used to characterize drought while the Standardized Runoff Index (SRI) were used to quantify runoff. Results of the study show that the historical pattern of drought is generally consistent with previous studies over the Basin and most part of West Africa. RCA ensemble medians (RMED) give realistic simulations of drought characteristics and area extent over the Basin and the sub-catchments in the past climate. Generally, an increase in drought intensity and spatial extent are projected over VRB for SPEI and SPI, but the magnitude of increase is higher with SPEI than with SPI. Drought frequency (events per decade) may be magnified by a factor of 1.2, (2046-2065) to 1.6 (2081-2100) compared to the present day episodes in the basin. The coupling between streamflow and drought episodes was very strong (P < 0.05) for the 1-16-year band before the 1970 but showed strong correlation all through the time series period for the 4-8 -years band. Runoff was highly sensitive to precipitation in the VRB and a 2-3 month time lag was found between drought indices and streamflow in the Volta River Basin. Results of this study may guide policymakers in planning how to minimize the negative impacts of future climate change that could have consequences on agriculture, water resources and energy supply.
This study examines the variations of climate variables and rice yield and quantifies the relationships among them using multiple linear regression, principal component analysis, and support vector machine (SVM) analysis in southwest Nigeria. The climate and yield data used was for a period of 36 years between 1980 and 2015. Similar to the observed decrease (P < 0.001) in rice yield, pan evaporation, solar radiation, and wind speed declined significantly. Eight principal components exhibited an eigenvalue > 1 and explained 83.1% of the total variance of predictor variables. The SVM regression function using the scores of the first principal component explained about 75% of the variance in rice yield data and linear regression about 64%. SVM regression between annual solar radiation values and yield explained 67% of the variance. Only the first component of the principal component analysis (PCA) exhibited a clear long-term trend and sometimes short-term variance similar to that of rice yield. Short-term fluctuations of the scores of the PC1 are closely coupled to those of rice yield during the 1986-1993 and the 2006-2013 periods thereby revealing the inter-annual sensitivity of rice production to climate variability. Solar radiation stands out as the climate variable of highest influence on rice yield, and the influence was especially strong during monsoon and post-monsoon periods, which correspond to the vegetative, booting, flowering, and grain filling stages in the study area. The outcome is expected to provide more in-depth regional-specific climate-rice linkage for screening of better cultivars that can positively respond to future climate fluctuations as well as providing information that may help optimized planting dates for improved radiation use efficiency in the study area.