Consistency of agricultural drought characterization over Upper Greater Horn of Africa (1982-2013): Topographical, gauge density, and model forcing influence

A regional stocktake of maize yield vulnerability to droughts in the Horn of Africa

Climate projections in sub-Saharan Africa predict increased frequency of droughts with parallel impacts on crop yield. The Horn of Africa is among the most vulnerable regions in Africa to these changes because agriculture in general and maize production in particularly is highly climate driven, and rain-fed. Current research approaches have mostly focused on the climatic and biophysical drivers of crop yield without including the socio-economic drivers of crop yield. This study fills this gap by investigating the vulnerability of maize yield in the Horn of Africa to climate and socio-economic indicators. The hypothesis is that there is an inverse relationship between vulnerability and adaptive capacity. The vulnerability index is a composite index that integrates sensitivity, exposure, and adaptive capacity sub-indices. Maize yield data to compute the sensitivity index were collected from FAOSTAT, precipitation data to compute the exposure index were collected from the Climate Research Unit (CRU), and the data for the proxies of adaptive capacity were collected from the readiness index database on figshare. From the results, Somalia records the highest vulnerability index of 1.15, followed by Ethiopia with a vulnerability index of 0.61. Kenya records the lowest vulnerability index of 0.33. Also, there is a positive relationship between the vulnerability, sensitivity, and the exposure indices and an inverse relationship between the vulnerability index and the adaptive capacity index. The high vulnerability index recorded in Somalia is accentuated by a low adaptive capacity index of 0.44 that is anchored on low literacy and high poverty rates. As Somalia records the lowest adaptive capacity index of 0.44, Ethiopia and Kenya record 0.91 and 0.99 respectively. This study has shown that to better understand vulnerability, a shift from the old paradigm that focuses on the climatic variables to integrating socio-economic variables or proxies of adaptive capacity which enhances our understanding of vulnerability. Though leveraging the benefits of climatic and non-climatic variables is important, the challenge so far has been on how to integrate these in the same model; a challenge this work has succinctly overcome by integrating adaptive capacity in the vulnerability equation.

Continued decline of global soil moisture content, with obvious soil stratification and regional difference

Soil is an important component connecting atmosphere and vegetation, and is an important 'regulator' of slope hydrological process. Global warming accelerates the global water cycle, and Soil Moisture Content (SMC) will change, but this change is not yet clear. Here, we study the global trend of SMC at different depths over the past 70 years and the next 70 years, based on the GLDAS-NOAH025 dataset and precipitation and temperature data from 15 CMIP6 models. We found that compared with the long-term average of 70 years, the global 0-200 cm SMC is decreasing at a rate of 1.284 kg/m² per year from 2000 to 2020, and the area showing a significant decreasing trend accounts for 31.67 % of the global. Over the past decade, 0-200 cm SMC reduction rate (2.251 kg/m²) doubled. Global warming and precipitation reduction are the main reasons for the attenuation of SMC at different depths in the global from 2000 to 2020. Under the SSP126, SSP245, SSP370 and SSP585 scenarios, the global 0-200 cm SMC will continue to decay in the future, and the area showing a significant reduction trend accounts for 22.73-49.71 % of the global, but the stratified soil and regional differences are obvious. The attenuation of SMC will further aggravate the global water cycle and enhance the variability of extreme meteorological disasters. We will face more severe soil drought problems.

Analysis of Smallholders’ Livelihood Vulnerability to Drought across Agroecology and Farm Typology in the Upper Awash Sub-Basin, Ethiopia

Assessing the magnitude of smallholder farmers’ livelihood vulnerability to drought is an initial step in identifying the causal factors and proposing interventions that mitigate the impacts of drought. This study aimed to assess smallholders’ livelihood vulnerability to the drought in the upper Awash sub-basin, Ethiopia. Household (HH) and climate data were used for indicators related to sensitivity, exposure, and adaptive capacity that define vulnerability to drought. The vulnerability of farmers’ livelihood to drought was compared among the studies agroecological zone (AEZ) and farm typologies. The result illustrated a diverse magnitude of vulnerability index (VI) ranging from −1.956 to −4.253 for AEZ. The highest magnitude of VI was estimated for livelihood in the lowland AEZ, while the lowest magnitude of VI was estimated in midland AEZ. This could be accounted for by the fact that lowland farmers shown the highest exposure (0.432) and sensitivity (0.420) and the lowest adaptive capacity (0.288). A closer look at farmers’ livelihood typology, in each of the AEZ, showed substantial diversity of farmers’ livelihood vulnerability to drought, implying potential aggregations at AEZ. Accordingly, the vulnerability index for livestock and on-farm-income-based livelihood and marginal and off-farm-income-based livelihood typologies were higher than the intensive-irrigation-farming-based smallholders’ livelihood typology. Based on the result, we concluded that procedures for smallholders’ livelihood resilience-building efforts should better target AEZ to prioritize the focus region and farmers’ livelihood typology to tailor technologies to farms. Although the result emphasizes the importance of irrigation-based livelihood strategy, the overall enhancement of farmers adaptive capacity needs to focus on action areas such as reducing the sensitivity and exposure of the households, improving farmers usage of technologies, diversify farmers’ livelihood options, and, hence, long-term wealth accumulation to strengthen farmers’ adaptive capacity toward drought impacts.

Sensitive Feature Evaluation for Soil Moisture Retrieval Based on Multi-Source Remote Sensing Data with Few In-Situ Measurements: A Case Study of the Continental U.S

Soil moisture (SM) plays an important role for understanding Earth’s land and near-surface atmosphere interactions. Existing studies rarely considered using multi-source data and their sensitiveness to SM retrieval with few in-situ measurements. To solve this issue, we designed a SM retrieval method (Multi-MDA-RF) using random forest (RF) based on 29 features derived from passive microwave remote sensing data, optical remote sensing data, land surface models (LSMs), and other auxiliary data. To evaluate the importance of different features to SM retrieval, we first compared 10 filter or embedded type feature selection methods with sequential forward selection (SFS). Then, RF was employed to establish a nonlinear relationship between the in-situ SM measurements from sparse network stations and the optimal feature subset. The experiments were conducted in the continental U.S. (CONUS) using in-situ measurements during August 2015, with only 5225 training samples covering the selected feature subset. The experimental results show that mean decrease accuracy (MDA) is better than other feature selection methods, and Multi-MDA-RF outperforms the back-propagation neural network (BPNN) and generalized regression neural network (GRNN), with the R and unbiased root-mean-square error (ubRMSE) values being 0.93 and 0.032 cm3/cm3, respectively. In comparison with other SM products, Multi-MDA-RF is more accurate and can well capture the SM spatial dynamics.