Hydrological drought impacts on water storage variations: a focus on the role of vegetation changes in the East Africa region. A systematic review.

Drought is one of the most challenging climatic events. Recently, the drought influence in East Africa (EA) total water storage (TWS) is a serious problem, particularly in arid areas with modified natural vegetation relying on water deficit, garnered extensive research interest. Hydro-climatological and vegetation indices and remote sensing datasets derived from Gravity Recovery Climate Experiment (GRACE) mission datasets reveal good performance in analyzing hydrological drought influences in water storage. Over the last decades, studies were considered successful in monitoring the drought influence in the region TWS potential. However, several challenges remained unsolved, hindering the hydrological drought mitigation strategies. This review deals with an overview of drought impact monitoring targeted at the TWS variation with the response of vegetation change for sustainable drought mitigation. To improve the flexibility and adaptive capacities of the water deficit problem, we aim to provide an overview of drought impacts on TWS in the region to redefine the hydro-climatological and vegetation drought indices and improve the understanding of drought impact through remote sensing datasets. This review presents the challenges and prospects and offers a conclusion. Although, we hope that the review can facilitate further study regarding future hydrological drought projection in the development of several scientific research in the field.

Hydrological drought evaluation using GRACE satellite-based drought index over the lake basins, East Africa.

Hydrological drought, a regular phenomenon that could heavily impact natural systems and human life, is aggravated by a water storage deficit. While Gravity Recovery and Climate Experiment (GRACE) satellite databased drought monitoring has been widely studied in East Africa (EA), drought recovery time and anthropogenic factors are still missing, which are prerequisite for drought management. Here, a water storage deficit index (WSDI) and modified WSDI are utilized for analyzing a holistic representation of drought. The results show that the drought events in recent times are well-identified and estimated using this approach over five lake basins in EA from 2002 to 2021. Although, the basin scale drought events are evaluated using the Pearson correlation coefficient (r) from 2002 to 2021. The results showed a significant correlation between WSDI, MWSDI, and the standardized precipitation-evapotranspiration index (SPEI) in all lake basins except in the Tana basin. We show that the presence of anthropogenic forcing has increased the highest peak deficits of -2.57, -3.25, -19.05, -87.2, and -99 km3 over the Tana, Abaya-Chamo, Turkana, Victoria, and Tanganyika basins, respectively. The longest deficit period of 36 months and the highest severity value of -1140 were observed in the Turkana and Victoria basins. The average drought recovery time ranges from 2.4 to 11.2 months and from 1.4 to 12.6 months as obtained by WSDI and MWSDI, respectively. Our findings highlight the importance of the calculated WSD approach to evaluating the hydrological drought characterization and estimate the drought condition at the basin scale.

Projection of future drought and its impact on simulated crop yield over South Asia using ensemble machine learning approach.

Understanding the development mechanism of drought events, characterization of future drought metrics, and its impact on crop yield is crucial to ensure food security globally, and more importantly, in South Asia. Therefore, the present study assessed the changes in future projected drought metrics and evaluated the future risk of yield reduction under drought intensity. We characterized the magnitude, intensity, and duration of future drought by means of the SPEI drought index using CMIP6 (Coupled Model Inter-comparison Phase-6) climate models. The impact of future drought on crop yield was quantified from the ISI-MP (Inter-Sectoral Impact Model Inter-comparison Project) crop model by a proposed non-linear ensemble of Random Forest (RF) and Gradient Boosting Machine (GBM). Results suggested that high drought magnitude with a longer drought duration is projected in some regions of South Asia while high drought intensity comes with a shorter duration. It was also found that Afghanistan, Pakistan, and India will experience a longer drought duration in the future. Our proposed ensemble machine learning (EML) approach had high predictive skill with a minimum value of RMSE (0.358-0.390), MAE (0.222-0.299), and a maximum value of R2 (0.705-0.918) compared to the stand-alone methods of RF and GBM for yield loss risk projection. The drought-driven impact on crop yield demonstrates a high risk of yield loss under extreme drought events, which will encounter 54.15%, 29.30%, and 50.66% loss in the future for rice, wheat, and maize crops, respectively. Furthermore, drought and yield loss risk dynamics suggested a one unit decrease in SPEI value would lead to a 14.2%, 7.5%, and 10.9% decrease in yield for rice, wheat, and maize crops, respectively. This study will provide a notable direction for policy agencies to build resistance to crop production against the drought impact in the regions that are critical to climate change.

Mapping Heat-Related Risks in Northern Jiangxi Province of China Based on Two Spatial Assessment Frameworks Approaches.

Heat-health risk is a growing concern in many regions of China due to the more frequent occurrence of extremely hot weather. Spatial indexes based on various heat assessment frameworks can be used for the assessment of heat risks. In this study, we adopted two approaches-Crichton's risk triangle and heat vulnerability index (HVI) to identify heat-health risks in the Northern Jiangxi Province of China, by using remote sensing and socio-economic data. The Geographical Information System (GIS) overlay and principal component analysis (PCA) were separately used in two frameworks to integrate parameters. The results show that the most densely populated community in the suburbs, instead of city centers, are exposed to the highest heat risk. A comparison of two heat assessment mapping indicates that the distribution of HVI highlights the vulnerability differences between census tracts. In contrast, the heat risk index of Crichton's risk triangle has a prominent representation for regions with high risks. The stepwise multiple linear regression zero-order correlation coefficient between HVI and outdoor workers is 0.715, highlighting the vulnerability of this particular group. Spearman's rho nonparametric correlation and the mean test reveals that heat risk index is strongly correlated with HVI in most of the main urban regions in the study area, with a significantly lower value than the latter. The analysis of variance shows that the distribution of HVI exhibits greater variety across urban regions than that of heat risk index. Our research provides new insight into heat risk assessment for further study of heat health risk in developing countries.