Evapotranspiration Deficit Index Research Articles

Prediction of DEDI index for meteorological drought with the VMD-CBiLSTM hybrid model

Drought can cause great harm and loss, so accurate and efficient drought prediction has certain research significance. In previous studies, a single machine learning model is often used to predict the factors related to drought, such as precipitation and temperature, to indirectly explain the influence degree of drought, but the overall prediction accuracy is low, which can not fully and effectively predict the nonlinear and non-stationary drought characteristic information. In this paper, the variational modal decomposition model (VMD) is used to decompose the meteorological drought time series, and the convolution neural network (CNN) and the improved bidirectional long short-term memory neural network (BiLSTM) are combined to construct the meteorological drought hybrid prediction model (VMD-CBiLSTM). The research results show that using VMD-CBiLSTM model to forecast the monthly daily evapotranspiration deficit index (DEDI) of three weather stations, compared with the results predicted by VMD-LSTM, VMD-BiLSTM and VMD-ARIMA models, the average prediction accuracy R2 is increased by 0.076, 0.034 and 0.328 respectively, and the average RMSE is decreased by 0.178, 0.094 and 1.373 respectively. Compared with single model, VMD-CBiLSTM can not only reduce the uncertainty of meteorological drought prediction caused by climate model heterogeneity, but also improve the accuracy of drought prediction in arid and semi-arid regions, which can provide reference for coping with drought occurrence and drought early warning in advance.

Integrated GNSS-derived precipitable water vapor and remote sensing data for agricultural drought monitoring and impact analysis

Agricultural drought is a natural disaster that impacts soil water deficiency, plant water stress, and yield loss. It has several effective drought indices to monitor the impact on agriculture, particularly the evapotranspiration deficit index (ETDI). However, this index has exposed the inconsistency of spatial potential evapotranspiration (PET) because of the restricted spatial distribution of meteorological stations and the influence of spatial heterogeneity. The present study aims to develop the fine spatial PET using the Global Navigation Satellite System-derived Precipitable Water Vapor (GNSS-PWV) and remote sensing data for enhancing the ETDI and determining the impacts of drought on sugarcane yield. The grid PET (GPET) model is developed by the correlation between the land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS LST) and the PET from the Revised Potential Evapotranspiration (RPET) model as the ground observations to estimate daily PET at 30-m spatial resolution using spatial extrapolation technique. In addition, the actual evapotranspiration (AET) was evaluated using the Surface Energy Algorithms for Land (SEBAL) algorithm. Both spatial PET and AET were utilized to compute the ETDI as an agricultural drought index. Then, the ETDI was correlated with sugarcane yield to investigate the impact of drought on yield. The results indicated that the GPET model had a strong correlation with the RPET model (R2 = 0.73 and RMSE = 0.84 mm) and relatively good accuracy (RSR = 0.57 and NSE = 0.68). This proposed model could be applied to compute the ETDI with fine spatial resolution. Moreover, the normalized yield of sugarcane exhibited a negative correlation with ETDI in the period from March to April 2020 with a strong relationship (r = −0.83). Therefore, the ETDI is an appropriate index for drought monitoring and determining the effects of drought on yield. These findings are useful for supporting the decision-makers to enhance the national policies for water management in agriculture.

Assessment of Climate Change Effects of Drought Conditions Using the Soil and Water Assessment Tool

A combination of annual peak water demand due to seasonal population spikes along with small and shallow aquifers has prompted an assessment of the region’s watersheds as operating at a net water deficit. This study uses the Soil and Water Assessment Tool (SWAT) to simulate historical drought conditions in the Chipuxet watershed in Rhode Island, USA. The calibrated and validated model uses the Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) as well as an Indicators of Hydrological Alteration (IHA) calculation to determine the frequency and severity of historical droughts and to simulate climate change conditions developed through a downscaled climate model selection. The output data for the historical and climate change scenarios were analyzed for drought frequency and severity. Results indicate that water stress will increase in both low-emission (RCP4.5) and high-emission (RCP8.5) scenarios. Additionally, the SMDI and ETDI show that RCP8.5 climate scenarios will have more severe deficits. Finally, IHA data indicate that zero-flow days and low-flow durations increase under all climate scenarios.

Spatiotemporal changes in snow cover and their relationship with drought events in the Lake Urmia basin

ABSTRACT Using a MODIS daily snow-cover product, MOD10A1F, the spatiotemporal pattern of the snow cover area (SCA) in the Lake Urmia Basin and its relationship with drought was analysed from October 2000 to September 2020. There was high inter-annual variability in the SCA, and the annual trends for the SCA and snow line elevation were non-significant. Moreover, no significant trend was detected for snow depletion curve indices. The role of evaporation and evaporative demand of the atmosphere in reducing the SCA is more significant than the role of precipitation in increasing the SCA. The remarkable result was the high correlation of the SCA with the Standardized Evapotranspiration Deficit Index (SEDI), which shows the ability of the SEDI to represent the simultaneous effect of evaporation and evaporative demand of the atmosphere in controlling the SCA and reveals the importance of this index in monitoring and forecasting snow cover.

A global daily evapotranspiration deficit index dataset for quantifying drought severity from 1979 to 2022

Droughts cause multiple ecological and social damages. Drought indices are key tools to quantify drought severity, but they are mainly limited to timescales of monthly or longer. However, shorter-timescale (e.g., daily) drought indices enable more accurate identification of drought characteristics (e.g., onset and cessation time) and help timely potential mitigation of adverse effects. Here, we propose a dataset of a daily drought index named daily evapotranspiration deficit index (DEDI), which is produced for global land areas from 1979 to 2022 using actual and potential evapotranspiration data. Validation efforts show that the DEDI dataset can well identify dry and wet variations in terms of spatial patterns and temporal evolutions when compared with other available drought indices on a daily scale. The dataset also has the capability to capture recent drying trends and to detect ecology- or agriculture-related droughts. Overall, the DEDI dataset is a step forward in facilitating drought monitoring and early warning at higher temporal resolution than other compared existing products.

Assessment of Meteorological and Agricultural Drought Indices under Climate Change Scenarios in the South Saskatchewan River Basin, Canada

Climate change has amplified the severity of droughts with potentially adverse impacts on agriculture in western Canada. This study assessed meteorological and agricultural drought in the Southern Saskatchewan River Basin (SSRB) using an array of drought indices, including the Standardized Precipitation Index (SPI), the Standardized Precipitation-Evapotranspiration Index (SPEI), the Self-Calibrated Palmer Drought Severity Index (scPDSI), the Soil Moisture Deficit Index (SMDI), and the Evapotranspiration Deficit Index (ETDI). These indices were evaluated using multiple regional climate model (RCM) projections assuming 1.5, 2.0, and 3.0 °C thresholds of global warming. A modified Soil and Water Assessment Tool (SWAT-M) was used to simulate the soil water content (SWC), actual evapotranspiration (AET), and potential evapotranspiration. The results of a sensitivity analysis using the SUFI-2 method in SWAT-CUP showed that the model performed well with BIAS lower than 10% and NSE and R higher than 0.7, and the range of SWC output closely matched the observed SWC. According to the RCM projections, the annual precipitation increases for all three global temperature thresholds while the annual mean temperature increases at a greater rate than the rise in global mean temperature. The projected PDSI and the SPEI suggest that drought duration and severity will exceed historical values while SPI will remain largely unchanged. Furthermore, severe drought conditions (SMDI < 2.0) are more frequent under the 3.0 °C global temperature scenario. The mean ETDI was historically 0.58 while the projected value is 0.2, 0.1, and −0.2 for the first to third scenarios, respectively. Simulated values, spatial maps, and heat maps of SMDI and ETDI illustrated that Canesm2.CRCM5 projects the driest conditions among all the RCMs. Agricultural drought indices, which incorporate SWC data, show more significant effects than meteorological drought indices. The increasing dryness will potentially impact agricultural crop production, particularly under the third scenario (3 °C) in the SSRB.

Interactions between precipitation, evapotranspiration and soil-moisture-based indices to characterize drought with high-resolution remote sensing and land-surface model data

Abstract. The Iberian Peninsula is prone to drought due to the high variability in the Mediterranean climate with severe consequences for drinking water supply, agriculture, hydropower and ecosystem functioning. Because of the complexity and relevance of droughts in this region, it is necessary to increase our understanding of the temporal interactions of precipitation, evapotranspiration and soil moisture that originate from drought within the Ebro basin, in northeastern Spain, as the study region. Remote sensing and land-surface models provide high-spatial-resolution and high-temporal-resolution data to characterize evapotranspiration and soil moisture anomalies in detail. The increasing availability of these datasets has the potential to overcome the lack of in situ observations of evapotranspiration and soil moisture. In this study, remote sensing data of evapotranspiration from MOD16A2 and soil moisture data from SMOS1km as well as SURFEX-ISBA land-surface model data are used to calculate the evapotranspiration deficit index (ETDI) and the soil moisture deficit index (SMDI) for the period 2010–2017. The study compares the remote sensing time series of the ETDI and SMDI with the ones estimated using the land-surface model SURFEX-ISBA, including the standardized precipitation index (SPI) computed at a weekly scale. The study focuses on the analysis of the time lags between the indices to identify the synchronicity and memory of the anomalies between precipitation, evapotranspiration and soil moisture. Lag analysis results demonstrate the capabilities of the SPI, ETDI and SMDI drought indices computed at a weekly scale to give information about the mechanisms of drought propagation at distinct levels of the land–atmosphere system. Relevant feedback for both antecedent and subsequent conditions is identified, with a preeminent role of evapotranspiration in the link between rainfall and soil moisture. Both remote sensing and the land-surface model show capability to characterize drought events, with specific advantages and drawbacks of the remote sensing and land-surface model datasets. Results underline the value of analyzing drought with dedicated indices, preferably at a weekly scale, to better identify the quick self-intensifying and mitigating mechanisms governing drought, which are relevant for drought monitoring in semi-arid areas.

Joint impacts of ozone pollution and climate change on yields of Chinese winter wheat

Ozone (O3), soil water deficit, and climate warming affect crop yields, but previous studies examined the individual effects of these stressors on only specific crops at some regions. The combined effects of these stressors on crop yields can reveal more environmentally realistic impacts, but relevant studies are scarce. Here, we used statistical modeling based on historical data to assess the joint impacts of O3, agricultural drought (evapotranspiration deficit index, ETDI), and maximum air temperature (Tmax) on actual yields of winter wheat in main production areas in China. We found that the negative effects of ETDI and Tmax were weakening due to wetting and warming during 1982–2012. However, the O3-induced yield loss for winter wheat was 8.02% (95% CI: 2.97%–14.35%), including the O3-Tmax and O3-ETDI interaction effects in the model. O3-Tmax interaction significantly resulted in a median yield loss of 3.08% (95% CI: 0.33%–7.47%), whereas no significant yield loss was induced by O3-ETDI interaction. The results suggest that Tmax worsens the negative effect of O3. To protect against such O3-driven yield losses, reducing O3-precursor emission and breeding more heat-tolerant and O3-resistant cultivars are needed to increase world grain production.

The efficiency of the Standardized Evapotranspiration Deficit Index (SEDI) in assessing the impact of drought on vegetation cover.

It is important to choose an indicator that can optimally demonstrate the effects of drought intensity on soil moisture access, evapotranspiration and the changes in vegetation cover at a regional scale. Therefore, herein, SEDI was developed by the fit of the experimental distribution of Gringorten on evapotranspiration deficit based on TerraClimate data at the time scales of 1, 3, 6 and 12months, and its relationship with Standardized Precipitation-Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSMI), Normalized Ecosystem Drought Index (NEDI) and Normalized Difference Vegetation Index (NDVI) were investigated. The results indicated that SEDI has the highest significant correlation (above 95%) with NEDI and SPEI, especially for the 1-month time scale. This index also revealed the lowest correlation (less than 25%) with SPI on short-term time scales. The relationship between SEDI and SSMI indicated the high sensitivity of SEDI to the cumulative reduction of low amounts of soil moisture. According to the findings, the 6-month SEDI with NDVI showed the highest correlation with a 1-month delay (r = 0.64) and the best fit between them occurred in wet months. However, in the dry months, the relationship between SEDI with NDVI was affected via water availability stresses, grazing intensity and pest infestation. Finally, the use of SEDI at a regional scale, especially in arid and semi-arid regions like Lorestan, could be recognized as an important index in depicting the effects of drought on vegetation cover, due to the use of the actual evapotranspiration factor.

Impacts of Drought and Climatic Factors on Vegetation Dynamics in the Yellow River Basin and Yangtze River Basin, China

Understanding the impacts of drought and climate change on vegetation dynamics is of great significance in terms of formulating vegetation management strategies and predicting future vegetation growth. In this study, Pearson correlation analysis was used to investigate the correlations between drought, climatic factors and vegetation conditions, and linear regression analysis was adopted to investigate the time-lag and time-accumulation effects of climatic factors on vegetation coverage based on the standardized evapotranspiration deficit index (SEDI), normalized difference vegetation index (NDVI), and gridded meteorological dataset in the Yellow River Basin (YLRB) and Yangtze River Basin (YTRB), China. The results showed that (1) the SEDI in the YLRB showed no significant change over time and space during the growing season from 1982 to 2015, whereas it increased significantly in the YTRB (slope = 0.013/year, p < 0.01), and more than 40% of the area showed a significant trend of wetness. The NDVI of the two basins, YLRB and YTRB, increased significantly at rate of 0.011/decade and 0.016/decade, respectively (p < 0.01). (2) Drought had a significant impact on vegetation in 49% of the YLRB area, which was mainly located in the northern region. In the YTRB, the area significantly affected by drought accounted for 21% of the total area, which was mainly distributed in the Sichuan Basin. (3) In the YLRB, both temperature and precipitation generally had a one-month accumulated effect on vegetation conditions, while in the YTRB, temperature was the major factor leading to changes in vegetation. In most of the area of the YTRB, the effect of temperature on vegetation was also a one-month accumulated effect, but there was no time effect in the Sichuan Basin. Considering the time effects, the contribution of climatic factors to vegetation change in the YLRB and YTRB was 76.7% and 63.2%, respectively. The explanatory power of different vegetation types in the two basins both increased by 2% to 6%. The time-accumulation effect of climatic factors had a stronger explanatory power for vegetation growth than the time-lag effect.

An evapotranspiration deficit-based drought index to detect variability of terrestrial carbon productivity in the Middle East

The primary driver of the land carbon sink is gross primary productivity (GPP), the gross absorption of carbon dioxide (CO2) by plant photosynthesis, which currently accounts for about one-quarter of anthropogenic CO2 emissions per year. This study aimed to detect the variability of carbon productivity using the standardized evapotranspiration deficit index (SEDI). Sixteen countries in the Middle East (ME) were selected to investigate drought. To this end, the yearly GPP dataset for the study area, spanning the 35 years (1982–2017) was used. Additionally, the Global Land Evaporation Amsterdam Model (GLEAM, version 3.3a), which estimates the various components of terrestrial evapotranspiration (annual actual and potential evaporation), was used for the same period. The main findings indicated that productivity in croplands and grasslands was more sensitive to the SEDI in Syria, Iraq, and Turkey by 34%, 30.5%, and 29.6% of cropland area respectively, and 25%, 31.5%, and 30.5% of grass land area. A significant positive correlation against the long-term data of the SEDI was recorded. Notably, the GPP recorded a decline of >60% during the 2008 extreme drought in the north of Iraq and the northeast of Syria, which concentrated within the agrarian ecosystem and reached a total vegetation deficit with 100% negative anomalies. The reductions of the annual GPP and anomalies from 2009 to 2012 might have resulted from the decrease in the annual SEDI at the peak 2008 extreme drought event. Ultimately, this led to a long delay in restoring the ecosystem in terms of its vegetation cover. Thus, the proposed study reported that the SEDI is more capable of capturing the GPP variability and closely linked to drought than commonly used indices. Therefore, understanding the response of ecosystem productivity to drought can facilitate the simulation of ecosystem changes under climate change projections.

The increasing contribution of potential evapotranspiration to severe droughts in the Yellow River basin

Under global warming, droughts have become more severe and more frequent. In this context, it is widely accepted that both water supply (e.g., precipitation, PRE) and demand (e.g., potential evapotranspiration, PET) should be considered in drought assessments due to increasing evapotranspiration. China’s Yellow River basin is located mainly in arid and semi-arid regions that suffer severe droughts. Analyzing the temporal and spatial patterns of droughts in this area and exploring the relative effects of supply and demand on drought occurrence and variations are thus necessary to adapt to increased droughts. Here, we compared three kinds of drought indices, including the standardized precipitation evapotranspiration index (SPEI), the standardized precipitation index (SPI), and the standardized evapotranspiration deficit Index (SEDI), and used SPEI to identify droughts in the Yellow River basin during the six decades from 1956 to 2016. We identified droughts and described drought dynamics based on drought duration, intensity, frequency, and spatial extent. We then evaluated the relative contributions of PRE and PET to droughts of varying severity and long-term trend, as well as the relationships between ocean signals and droughts. Our results show that most of the basin has undergone progressively drier conditions since the 1950 s. This increased drought occurrence has manifested as longer, more frequent droughts affecting a larger spatial extent. PRE was the dominant driver (64%) of drought occurrence in an area of ∼ 86% of the basin, whereas PET was the main driver (55%) in the remaining 14% of the basin. However, changes in PET contributed more than PRE to drought variation, i.e., long-term SPEI trends, in about ∼ 60% of the area. And effects of PET increased during severer droughts due to the intensive interaction between soil moisture and atmospheric water demand in highly arid conditions. Physically, droughts were closely related to several ocean signals, such as the Multivariate ENSO Index (MEI), the North Atlantic Oscillation (NAO), the Atlantic Multidecadal Oscillation index (AMO), and the Arctic Oscillation index (AO), at different time scales. Understandings of drought dynamics, their physical mechanisms, and the effects of PRE and PET on droughts of varying severity are essential for water resource management, especially in arid regions.

Decline in net primary productivity caused by severe droughts: evidence from the Pearl River basin in China

AbstractUnderstanding the spatiotemporal characteristics of drought events and their impacts on terrestrial net primary productivity (NPP) is crucial for drought mitigation and environmental protection. This study, by taking the Pearl River basin as the case region, investigated drought duration, severity, intensity, affected area, and centroids during 1960–2015 based on the Standardized Evapotranspiration Deficit Index and three-dimensional clustering algorithm and then revealed how these drought characteristics have affected NPP. Results showed that there were altogether 32 severe drought events lasting at least 3 months in the basin, with half lasting longer than 6 months. The total NPP loss significantly correlated with drought severity and intensity. Most drought events caused a reduction in NPP across more than half of the drought-affected area; specifically, the February–December drought in 2011 has cut NPP by 31.85 Tg C, accounting for 11.7% of the regional annual mean NPP, while the September 2009–September 2010 drought caused a decrease of 20.26 Tg C in NPP. Our research improves the insight into the relationship between NPP and drought, which helps decision-makers manage droughts and provides guidance for drought-related studies across other regions.

Spatiotemporal Drought Risk Assessment Considering Resilience and Heterogeneous Vulnerability Factors: Lempa Transboundary River Basin in The Central American Dry Corridor

Drought characterization and risk assessment are of great significance due to drought’s negative impact on human health, economy, and ecosystem. This paper investigates drought characterization and risk assessment in the Lempa River basin in Central America. We applied the Standardized Evapotranspiration Deficit Index (SEDI) for drought characterization and drought hazard index (DHI) calculation. Although SEDI’s applicability is theoretically proven, it has been rarely applied. Drought risk is generally derived from the interactions between drought hazard (DHI) and vulnerability (DVI) indices but neglects resilience’s inherent impact. Accordingly, we propose incorporating DHI, DVI, and drought resilience index (DREI) to calculate drought risk index (DRI). Since system factors are not equally vulnerable, i.e., they are heterogeneous, our methodology applies the Analytic Hierarchy Process (AHP) to find the weights of the selected factors for the DVI computation. Finally, we propose a geometric mean method for DRI calculation. Results show a rise in DHI during 2006–2010 that affected DRI. We depict the applicability of SEDI via its relationship with El Nino-La Nina and El Salvador’s cereal production. This research provides a systematic drought risk assessment approach that is useful for decision-makers to allocate resources more smartly or intervene in Drought Risk Reduction (DRR). This research is also useful for those interested in socioeconomic drought.

Evaluation of evapotranspiration deficit index for agricultural drought monitoring in North China

Drought is a major natural hazard that causes significant economic loss across the world. Drought indexes derived from remote sensing data can provide dynamic information in real time, facilitating drought assessments. The evapotranspiration (ET) deficit index (ETDI) is derived from observations of ET and potential ET. ETDI is an indicator of agricultural drought. In this study, actual ET was modeled using the Boreal Ecosystem Productivity Simulator with remote sensing data and ground-station observation data as input. We investigated the efficacy of ETDI to indicate regional agricultural drought in a major region of winter wheat production in Northern China. Spatiotemporal changes in ETDI agreed well with the standardized precipitation index (SPI) on a 5 month scale and with the Palmer drought severity index (PDSI). ETDI also agreed well with the composite index of yield reduction (CI), which represents the provincial percentage area of cropland subject to drought stress. CI performed better in agricultural regions where water was supplied by precipitation rather than irrigation. Changes in winter wheat yield due to meteorological events were highly correlated with ETDI, rather than SPI or PDSI, averaged across specific growth stages. Weather conditions had significant impacts in April (jointing and boot stages of winter wheat development) in Hebei province, in May (heading and grain-filling stages) in Shanxi province, and from January through April in other provinces. These results demonstrate that ETDI is a suitable index for indicating regional agricultural drought in order to inform drought mitigation activities.

Sensitivity of the Evapotranspiration Deficit Index to Its Parameters and Different Temporal Scales

Sound estimates of drought characteristics are very important for planning intervention measures in drought-prone areas. Due to data scarcity, many studies are increasingly using less data-intensive approaches, such as the evapotranspiration deficit index (ETDI), in estimations of agricultural droughts. However, little is known about the sensitivity of this specific ETDI formula to its parameters, and to data at different temporal scales. In this study, a general ETDI formula, homologous to the specific ETDI formula, was introduced and used to test the sensitivity of the ETDI to its parameters and to data at different temporal scales. The tests used time series of remotely sensed evapotranspiration data in the Ruvu River basin in Tanzania. The parameter sensitivity tests revealed that ETDI is sensitive to its parameters, and different parameter combinations resulted in different drought characteristics. The temporal scale sensitivity test showed that drought characteristics, such as the number of drought events and the total drought durations, decreased as the temporal scale increased. Thus, an inappropriate temporal scale may lead to the misrepresentation of drought characteristics. To reduce uncertainty and increase the accuracy of ETDI-based agricultural drought characteristics, ETDI requires parameter calibration and the use of data with small temporal scales, respectively. These findings are useful for improving estimations of ETDI-based agricultural droughts.

Uncertainty of drought information in a data-scarce tropical river basin

Study regionThe Wami River basin in east-central Tanzania features the Eastern Arc Mountains and dichotomous climate regimes. Study focusDrought characteristics are vital in planning and managing water resources in river basins. However, the estimation of drought characteristics from the evapotranspiration deficit index (ETDI) is susceptible to the uncertainty associated with alternative datasets from hydrological models and remote sensing images. Thus, this study investigated scenario uncertainty in ETDI-based drought characteristics in the river basin using evapotranspiration and potential evapotranspiration data from a classical Soil and Water Assessment Tool (SWAT) and the Moderate Resolution Imaging Spectroradiometer (MODIS) images between the years 2000 and 2010. New hydrological insightsThe findings showed a substantial scenario uncertainty in drought duration, severity, and intensity at various locations in the river basin. Localized severe MODIS-based overall drought intensity (MODIS-ODI) in the middle of the basin was attributed to prolonged low rainfall amounts. In the downstream, localized severe SWAT-based overall drought intensity (SWAT-ODI) was attributed to an imperfection of the SWAT model in simulating the true hydrologic cycle of the river basin. However, at the basin level, both SWAT-ODI and MODIS-ODI between the years 2000 and 2010 were moderate. These findings can be used as the baseline information in improving drought estimation in the river basin. Moreover, this study also demonstrates the importance of assessing scenario uncertainty in drought characterization.

Water Use Efficiency‐Based Multiscale Assessment of Ecohydrological Resilience to Ecosystem Shifts Over the Continent of Africa During 1992–2015

AbstractThe spatial‐temporal representation is a crucial key for understanding at what extent terrestrial ecosystems can maintain their structure and functions and recover from various ecosystem shifts (drought/wet). Hence, it is vital for sustainable ecosystem management, adaptation, and mitigation practices. This study assessed ecohydrological resilience to ecosystem shifts at contrast water‐limited and well‐watered regions at multiple scales (i.e., country scale, basin scale, climate type scale, and land cover scale) over Africa during the period 1992–2015, based on Standard Evapotranspiration Deficit Index (SEDI) and water use efficiency (WUE) at ecosystem scales, which was obtained from satellite‐derived evapotranspiration (ET) and simulated gross primary productivity (GPP) using the Carnegie‐ames‐stanford approaches (CASA) model. SEDI values indicated that 11 years were characterized by dry conditions, while 13 were wetter years during 1992–2015. WUE, ET, and GPP values indicated increased tendencies mostly in central to central west of African continent. The ecohydrological resilience exhibited that about 68.78% of African terrestrial ecosystems were highly, moderately, or slightly resilient, and about 31.22% were not resilient to ecosystem shifts with 11.28% vulnerably exposed to high risk of ecosystem shift and severely nonresilient. The extent areas exposed to severe ecohydrological nonresilience are distributed in Savannas and barelands land cover types, temperate‐dry summer‐hot summer (Csa), climate type, Mediterranean South Coast (B14), African‐North interior (B3), and Red Sea‐Golf of Aden Coast (b5) basins; and the four countries, namely, Egypt, Mauritania, Niger, and Tunisia. These findings may provide insightful scientific information about vulnerable regions for policy making and ecosystem management and increase the necessary awareness for taking reasonable adaptation mechanisms, such as afforesting open lands to increase the area of vegetation cover (i.e., choosing the drought or wet‐tolerant plant species) and water conservation and management measures to ensure the sustainability of terrestrial ecosystems under ecosystem shifts over the African continent.

A new framework for tracking flash drought events in space and time

Flash drought is a space–time phenomenon with rapid intensification nature that poses a series of challenges for early warning systems and drought relief. Traditional works do not consider the space–time dynamic processes of flash droughts, unable to provide important information such as how fast an event spreads in space that hampers governors and stakeholders from making timely drought mitigation operations. Here we introduced a novel framework for tracking flash droughts that fully accounts of their dynamic space–time behavior, with focus upon the instantaneous intensification/recovery rate (IIR/IRR) and spatial propagation. Flash drought events were defined by a space–time coherent set of grids where pentad-scale standardized evapotranspiration deficit index is below a prescribed threshold. The drought development/recovery stage is identified in terms of intensity, and IIR/IRR between two consecutive drought patches is determined based on the variable motion relationship of speed-time process from the physics perspective. A single space–time drought event is extracted when the duration, averaged intensification rate and IIR all reach prescribed standards. Using daily meteorological station data and daily root zone (0–100 cm) gridded soil moisture data, the framework is demonstrated by analyzing the flash droughts in the Pearl River basin over China from 1960 to 2015. Results indicate that the framework can well capture space–time structure of flash droughts including the severity and dynamic spatial propagation. Most of the identified flash drought events last 5–6 pentads but affect over half of the basin, and the top seven events have affected over 90% of the basin which intensify in two pentads showing rapid intensification nature. Moreover, flash droughts are associated with precipitation, humidity, temperature, and sunshine duration. The framework is conducive to better understanding of flash drought processes that help guide effective monitoring and development of early warning systems.

Analysis of Agricultural Drought Using Remotely Sensed Evapotranspiration in a Data-Scarce Catchment

Understanding spatial drought characteristics is vital for planning adaptation and mitigation measures in river catchments. In many parts of the world, spatial drought information is not available due to lack of adequate evenly distributed data for spatial drought analyses. This study elucidates a spatial drought analysis in a data-scarce tropical catchment using remote sensing actual evapotranspiration (ET) and potential evapotranspiration (PET) data. Firstly, the time series of 690 images of remotely sensed ET and PET between the years 2000 and 2014 were spatially analyzed using the evapotranspiration deficit index (ETDI) approach to obtain ETDIs in the Kilombero River catchment (Tanzania). Then, spatio-temporal patterns of ETDIs were used to characterize drought frequency, total drought durations, total drought severity, and drought intensity. The frequency, durations, severity, and intensity of drought increased from the year 2000 towards 2014, causing substantial drought changes in the catchment. However, drought intensity revealed that those changes were mainly from no drought and mild drought to moderate drought. Between the years 2000 and 2014, no-drought areas and mild drought areas declined from 10% to 0% and from 42% to 19%, respectively, whereas moderate drought areas increased from 47% to 81% of the catchment size. Those changes of drought conditions were partly attributed to anthropogenic land cover change, especially in the southwest grasslands, and were partly attributed to meteorological factors in other parts of the catchment. This information is crucial for further land cover change and climate change investigations, as well as planning water and land resources in the Kilombero River catchment. Moreover, the study also demonstrates the potential of using publicly available remote sensing ET products and the ETDI approach for spatially characterizing drought in ungauged regions.