Spatiotemporal monitoring of drought using satellite precipitation products: The case of Borena agro-pastoralists and pastoralists regions, South Ethiopia

Drought is increasingly affecting farmers in agro-pastoralist and pastoralists region. It is one of the most harmful natural disasters that significantly affects rain-fed agriculture in developing countries. Drought assessment is an important component of drought risk management. This study used CHIRPS rainfall data to monitor the characteristics of drought in Borena Zone in southern Ethiopia. The standardized precipitation index (SPI) is used to calculate the magnitude, intensity, and severity of drought during the rainy season. Results show that severe and extreme droughts were detected in the first rainy season (March to May) and second wet season (September to November). Severe and extreme droughts were detected in the first rainy/wet season in 1992, 1994, 1999, 2000, 2002-2004, 2008,2009, 2011, 2019-2021. The spatial and temporal variability of drought is highly influenced by El Nino-Southern Oscillation (ENSO) in Ethiopia. Results revealed that most of the first rainy season was dry. 2011 was the driest year during the first wet season. Drought risk events in the first wet season were greater than in the second wet season. Results show that drought more frequently occurred in the northern and southern part in the first wet season. In the second rainy season extreme drought was detected in 1990, 1992, 1993, 1994, 1996, and 1997. The results of this study will promote the importance of early warning measures, drought risk management, and food security management in the study area.

Changes in the relationship between vapour pressure deficit and water use efficiency with the drought recovery time: A case study of the Yellow River Basin

Drought is a major driver of interannual variability in the gross primary productivity (GPP) of global terrestrial ecosystems, and drought recovery time has been widely used to assess ecosystem responses to drought. However, the response of the carbon-water coupled cycle to drought, especially changes in the correlation between drought intensity and carbon-water coupling throughout the recovery time, remains unclear. In this study, the Yellow River Basin (YRB) located mostly in drylands was the study area. We assessed the correlation between the standardized water vapour pressure deficit (VPD) and the water use efficiency of ecosystems (WUEe) and water use efficiency of canopies (WUEc) every month with the drought recovery time of GPP. We found that the drought intensity in the middle reach of the YRB (MYRB) was greater and the drought recovery time was longer than those in the upper reach (UYRB) and lower reach (LYRB) during the period from 2003 to 2017. In terms of the correlation between drought intensity and carbon-water coupling, the greater the VPD was, the lower the WUEc. In addition, the correlation of WUEc with VPD was higher than that of WUEe in most areas of the YRB, especially in the LYRB. On the watershed level, the correlation between the two types of WUE and VPD increased gradually with the recovery time, while the correlation between WUEc and VPD (mostly negative) changed more than the correlation between WUEe and VPD (mostly positive). Therefore, the response of WUEc to meteorological drought should be given more attention, especially during the middle and late stages of drought, since it exhibited an opposite signal compared to that of WUEe during drought recovery.

Functional traits and their plasticity shift from tolerant to avoidant under extreme drought

Under climate change, extreme droughts will limit water availability for plants. However, the species-specific responses make it difficult to draw general conclusions. We hypothesized that changes in species' abundance in response to extreme drought can be best explained by a set of water economic traits under ambient conditions in combination with the ability to adjust these traits towards higher drought resistance. We conducted a four-year field experiment in temperate grasslands using rainout shelters with 30% and 50% rainfall reduction. We quantified the response as the change in species abundance between ambient conditions and the rainfall reduction. Abundance response to extreme drought was best explained by a combination of traits in ambient conditions and their functional adjustment, most likely reflecting plasticity. Smaller leaved species decreased less in abundance under drought. With increasing drought intensity, we observed a shift from drought tolerance, i.e. an increase in leaf dry matter content, to avoidance, i.e. a less negative turgor loss point (TLP) in ambient conditions and a constancy in TLP under drought. We stress the importance of using a multidimensional approach of variation in multiple traits and the importance of considering a range of drought intensities to improve predictions of species' response to climate change.

Meta-analysis shows non-uniform responses of above- and belowground productivity to drought

Terrestrial productivity underpins ecosystem carbon (C) cycling and multi-trophic diversity. Despite the negative impacts of drought on terrestrial C cycling, our understanding of the responses of above- and belowground productivity to drought remains incomplete. Here, we synthesized the responses of terrestrial productivity and soil factors (e.g., soil moisture, soil pH, soil C, soil nitrogen (N), soil C:N, fungi:bacteria ratio, and microbial biomass C) to drought via a global meta-analysis of 734 observations from 107 studies. Our results revealed that the productivity variables above- and belowground (i.e., net primary productivity, aboveground net primary productivity, belowground net primary productivity, total biomass, aboveground biomass, root biomass, gross ecosystem productivity, and net ecosystem productivity) were decreased across all ecosystems. However, drought did not significantly affect litter mass across all ecosystems, and the responses of above- and belowground productivity to drought were non-uniform. Furthermore, the responses of these productivity variables to drought were more pronounced with drought intensity and duration, and consistent across ecosystem types and background climates. Drought significantly decreased soil moisture, soil C concentrations, soil C:N ratios, and microbial biomass C, whereas it enhanced soil pH values and fungi:bacteria ratios. Moreover, the negative effects of drought on above- and belowground productivity variables were correlated mostly with the response of soil pH to drought among all soil factors. Our study indicated that litter biomass, which mostly represents productivity levels via traditional ecosystem models, was not able to predict the responses of terrestrial ecosystem productivity to drought. The strong relationship between the responses of soil pH and terrestrial productivity to drought suggests that the incorporation of soil pH into Earth system models might facilitate the prediction of terrestrial C cycling and its feedbacks to drought.

Assessing agricultural drought in summer over Oklahoma Mesonet sites using the water-related vegetation index from MODIS

Agricultural drought, a common phenomenon in most parts of the world, is one of the most challenging natural hazards to monitor effectively. Land surface water index (LSWI), calculated as a normalized ratio between near infrared (NIR) and short-wave infrared (SWIR), is sensitive to vegetation and soil water content. This study examined the potential of a LSWI-based, drought-monitoring algorithm to assess summer drought over 113 Oklahoma Mesonet stations comprising various land cover and soil types in Oklahoma. Drought duration in a year was determined by the number of days with LSWI <0 (DNLSWI) during summer months (June-August). Summer rainfall anomalies and LSWI anomalies followed a similar seasonal dynamics and showed strong correlations (r ² = 0.62-0.73) during drought years (2001, 2006, 2011, and 2012). The DNLSWI tracked the east-west gradient of summer rainfall in Oklahoma. Drought intensity increased with increasing duration of DNLSWI, and the intensity increased rapidly when DNLSWI was more than 48 days. The comparison between LSWI and the US Drought Monitor (USDM) showed a strong linear negative relationship; i.e., higher drought intensity tends to have lower LSWI values and vice versa. However, the agreement between LSWI-based algorithm and USDM indicators varied substantially from 32 % (D ₂ class, moderate drought) to 77 % (0 and D ₀ class, no drought) for different drought intensity classes and varied from ∼30 % (western Oklahoma) to >80 % (eastern Oklahoma) across regions. Our results illustrated that drought intensity thresholds can be established by counting DNLSWI (in days) and used as a simple complementary tool in several drought applications for semi-arid and semi-humid regions of Oklahoma. However, larger discrepancies between USDM and the LSWI-based algorithm in arid regions of western Oklahoma suggest the requirement of further adjustment in the algorithm for its application in arid regions.