Root Zone Soil Moisture Research Articles

Flash Drought Teleconnection With the Large‐Scale Climate Drivers in the Homogeneous Rainfall Regions of India

ABSTRACTFlash drought events can be characterised by the quick depletion of crop root zone soil moisture (rapid intensification) and hence can be termed as agricultural flash droughts. These events can have devastating impacts, such as increasing the risk of agricultural yield loss, heatwaves and increased wildfire risk, which further have cascading impacts on the socio‐economic conditions. The regional hotspots of flash droughts are analysed for winter, pre‐monsoon, monsoon and post‐monsoon seasons over India from 1981 to 2020. We assess the impact of the El Niño‐Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on the flash drought frequency (FDF: number of flash drought events). The causal connection of the FDF with the ENSO and IOD is analysed using the PCMCI (Peter and Clark's algorithm combined with the Momentary Conditional Independence) algorithm. The monsoon season (June–September) is found to be more prone to flash droughts with higher spatial/regional average values of total per pixel FDF during the 40‐year period over the Central Northeast (~54) and West Central (~41) regions. It is observed that the fraction of the total number of flash droughts during the El Niño years (38.8%) is higher as compared with that in La Niña years (25.7%). It is also found that the co‐occurrence of positive/negative IOD with the El Niño phase can alter the seasonal fraction of FDF over India, highlighting the high complexity in the ENSO–IOD interactions. The causal analysis shows that only the Southern Peninsula and West Central regions have significant direct and lagged causal links of average per pixel FDF with IOD. Whereas, similar (direct and lagged) causal connections are observed between the ENSO and IOD. This study reveals that flash droughts and their teleconnections vary greatly among the seasons and regions in India, limiting its accurate predictions and increasing the risk to agricultural communities.

Modeling water balance components of conifer species using the Noah-MP model in an eastern Mediterranean ecosystem

Abstract. Few studies have investigated the performance of land surface models for semiarid Mediterranean forests. This study aims to parameterize and test the performance of the Noah-MP land surface model for an eastern Mediterranean ecosystem. To this end, we calibrated the model for root zone soil moisture and transpiration of two conifer species, Pinus brutia, and Cupressus sempervirens, in a plantation forest on the Mediterranean island of Cyprus. The study area has a long-term average annual rainfall of 315 mm. Observations from 4 sap flow and 48 soil moisture sensors, for the period from December 2020 to June 2022, were used for model parameterization. A local sensitivity analysis found that the surface infiltration (REFKDT), hydraulic conductivity (SATDK), and stomatal resistance (RSMIN) parameters had the highest impacts on the water balance components (soil evaporation, tree transpiration, surface runoff, and drainage). The model performed better during the wetter 9-month validation period (379 mm rain) than during the drier 10-month calibration period (175 mm rain). Average soil moisture in the top 60 cm of the soil profile was reasonably well captured for both species (daily Nash–Sutcliffe efficiency > 0.80 for validation). Among the three soil layers, the second layer (20–40 cm) showed better simulation performance during both periods and for both species. The model exhibited limitations with respect to simulating transpiration, particularly during the drier calibration period. The inclusion of a root distribution function in the model, along with the monitoring of soil moisture below the 60 cm soil depth in the field, could improve the accuracy of model simulations in such water-limited ecosystems.

Unlocking the ecohydrological dynamics of vegetation growth's impact on Terrestrial Water Storage trends across the China-Pakistan Economic Corridor

Studying the influence of vegetation dynamics on water storage is fundamental for efficiently managing ecosystems in dryland areas. Therefore, this study was designed to investigate the ecohydrological dynamics of vegetation growth's impact on Terrestrial Water Storage (TWS) trends across the China-Pakistan Economic Corridor (CPEC). Exploring vegetation growth's effect on TWS utilizes remote sensing and statistical methods such as generalized additive model (GAM), random forest, Mann-Kendall test, Sen's slope, and Partial correlation coefficient. Our results revealed a consistent increase in vegetation cover in semi-arid and dry sub-humid regions, especially in croplands, from 1986 to 2020. However, a noticeable finding was that TWS has declined by approximately 39.65 % in the study area. On the other hand, forests have displayed resilience by mitigating the effects of climate change and human activities through hydraulic memory effects. Additionally, soil moisture has decreased in various land cover types, with croplands experiencing the most significant decrease. Similarly, increased vegetation growth in greening drylands significantly negatively impacts terrestrial water storage, with correlations of −0.31 for terrestrial water storage anomaly (TWSA), −0.26 for root zone soil moisture (RZSM), and −0.29 for surface soil moisture (SSM). Meanwhile, evapotranspiration (ET) also had negative correlations with TWSA, RZSM, and SSM, with standardized coefficients of −0.23, −0.16, and −0.11, respectively. These findings exposed the study area's vegetation interaction with land cover and hydrological dynamics. Addressing these hydrological imbalances will help ensure sustainable ecological management in the study area.

A novel feature extraction-selection technique for long lead time agricultural drought forecasting

Long-term drought forecasting plays a crucial role in mitigating drought risks by providing early warnings. Researchers have long been interested in achieving accurate long-term drought forecasting, which is challenging since accuracy generally decreases by increasing the forecasting period. The primary aim of this research is to propose a new method for high-accuracy long lead time drought forecasting by combining various Feature Extraction (FE) and selection techniques. In this study, monthly time-series datasets encompassing precipitation, potential evapotranspiration, actual evapotranspiration, runoff, surface and root-zone soil moisture—were utilized to forecast SPEI-6 over various lead times including 1-, 3-, 6-, 9-, 12-, 18-, and 24-months using global gridded products with a 0.5O × 0.5O spatial resolution spanning the years January 1980 to December 2022. The method was evaluated using two different approaches, namely Gaussian Process Regression (GPR) as a simple machine learning technique and Long Short-Term Memory (LSTM) as a deep learning method. The findings provided improved accuracy, particularly for long-term forecasting when employing the proposed methodology. When utilizing LSTM with FE instead of the original datasets as inputs, the error reduced from RMSE = 0.16 to RMSE = 0.07 (a 56 % decrease), while the correlation increased from R = 0.65 to R = 0.90 (a 38 % increase) when forecasting SPEI-6 12 months ahead. The results showed that the GPR with FE and selection model outperformed the LSTM with original datasets model for SPEI-6 (t + 24) with a correlation coefficient (R) of 0.9811 and a Normalized Root Mean Square Error (NRMSE) of 0.1380, compared to R = 0.6517 and NRMSE = 0.4307 for the LSTM with original datasets. These findings can offer valuable insights for early agricultural drought warning in arid areas.

Hysteresis in seasonal land-atmospheric interactions over India and its characteristics across croplands and forests

Abstract Satellite-derived vegetation optical depth and soil moisture (SM) data reveal the critical role of the soil-vegetation continuum in storing rainwater during the Indian Summer Monsoon and supporting evapotranspiration (ET) during the dry non-monsoon season. During the non-monsoon drier period, the climatologically estimated spatial mean of ET exceeds precipitation input, a phenomenon known as the soil–vegetation capacitor effect, which is pivotal in maintaining ecosystem productivity. Notably, our analysis reveals significant variations in the capacitor period between croplands and forests, with croplands exhibiting a ∼77 d longer due to dual crop seasons influenced by regional precipitation. The well-recognized hysteresis curves, observed in magnetization and soil–water characteristic curves, highlight phenomena where a system’s state is influenced by its historical inputs or states and are integral to our findings. We report a previously undocumented seasonal hysteresis in the relationship between the evaporative fraction (EVF) and SM for Indian croplands and forests. We further found that the croplands SM-EVF relation exhibits a reversal in hysteresis in the case of root-zone SM. The surface SM-EVF hysteresis is not present in forests with large root depths and reduced soil evaporation due to high canopy shading, and yet it is present for the root-zone SM. With its reversal for croplands, the newly found hysteresis must be addressed in redefining the critical SM threshold to demarcate the energy and water-limiting regimes. It should be incorporated in the land surface modeling parameterization. Additionally, we observed hysteresis in the SM-gross primary productivity relationship across both land covers and soil profiles (surface and root-zone), underscoring the need to investigate such processes to consider their dynamics in future ecological and hydrological models.

Generation of root zone soil moisture from the integration of an all-weather satellite surface soil moisture estimates and an analytical model: A preliminary result in China

In addition to surface soil moisture (SSM) data, root zone soil moisture (RZSM) is particularly significant for irrigation decision-making and agricultural drought warnings since water uptake generally occurs through the root systems of plants. In this study, a daily/0.05° RZSM dataset covering the major land areas of China was generated from the synergistic use of satellite-derived SSM estimates and a RZSM-SSM analytical model. First, based on the SSM and RZSM datasets generated from the fifth generation of atmospheric reanalysis (ERA5-Land), the Markov Chain Monte Carlo algorithm was applied to optimize the soil moisture analytical relationship (SMAR) model to determine four essential parameters pixel-to-pixel over the study area. In addition, the optimized SMAR parameters were integrated with satellite-derived daily/0.05° SSM to predict the daily/0.05° RZSM in China. Finally, due to the lack of in situ RZSM measurements ground truth at a spatial resolution of 0.05°, two RZSM products, namely the Soil Moisture Active Passive (SMAP)-based RZSM and the China Meteorological Administration Land Data Assimilation System (CLDAS)-based RZSM, were used as references to preliminarily assess the estimated data. The results showed overall fair correlations between SMAP_RZSM and estimated RZSM (correlation coefficient R varying from 0.557 to 0.655), and between CLDAS_RZSM and estimated RZSM (correlation coefficient R varying from 0.496 to 0.596) over four typical days representing spring, summer, autumn, and winter in 2019. Although the proposed approach may overestimate or underestimate RZSM compared to the two referenced products in different seasons, an overall acceptable accuracy with unbiased root mean square error of ∼ 0.070–0.090 cm3/cm3 was obtained.

CNN based approach for root zone soil moisture prediction by ground penetrating radar

Abstract Traditional soil moisture prediction methods often face challenges in providing spatially and temporally comprehensive root zone soil moisture (RZSM) with complex soil environment. In this paper, a novel method is proposed for predicting RZSM using images obtained from ground penetrating radar (GPR) data and processed with the convolutional neural network (CNN). The CNN encompasses two major stages: classification and regression. Initially, a CNN is tailored to classify tree root images obtained by GPR into distinct water content categories. Subsequently, through transfer learning, the pre-trained classification network is adapted for regression to predict continuous soil moisture levels. Then, the field experimental results demonstrate the efficacy and robustness of our approach, offering promising avenues for enhancing root zone water management and sustainability.

Integrating multi-source remote sensing and machine learning for root-zone soil moisture and yield prediction of winter oilseed rape (Brassica napus L.): A new perspective from the temperature-vegetation index feature space

Accurately assessing root-zone soil moisture is crucial for precision irrigation, as it directly influences crop yield. The Temperature-Vegetation Index (Ts-VI) Feature Space, which combines land surface temperature (Ts) and vegetation index (VI), is widely used to evaluate root-zone soil moisture in vegetated areas. However, its effectiveness in estimating crop yield remains unclear. Therefore, the objectives of this study are: (1) to collect multispectral and thermal infrared remote sensing data from a two-year (2021–2023) field experiment on winter oilseed rape (Brassica napus L.), and to optimize and evaluate the fitting methods of the dry and wet edges of the Ts-VI feature space based on the selected vegetation indices; (2) to analyze the spatiotemporal patterns of the Temperature Vegetation Dryness Index (TVDI) derived from the optimized Ts-VI feature space and estimate root-zone soil moisture (SM) and crop yield; and (3) to precisely invert the SM and yield of winter oilseed rape in the 0–60 cm root-zone using three machine learning algorithms—Support Vector Regression (SVR), Extreme Gradient Boosting Regression (XGBR), and Random Forest Regression (RFR)—based on the optimized TVDI. Results indicate that, among the various fitting methods, the polynomial fitting method shows the best performance. The performance of the root-zone soil moisture prediction models across different growth stages follows the order of budding stage > seedling stage > flowering stage, and with the increase of soil depth, the performance of the model gradually deteriorates.In the yield inversion of winter oilseed rape, TVDI effectively predicts yield, with the coefficient of determination (R2) ranging from 0.430 to 0.480 and RMSE ranging from 213.399 to 267.212 kg ha−1 during the seedling stage, R2 ranging from 0.640 to 0.747 and RMSE ranging from 110.712 to 178.133 kg ha−1 during the budding stage, and R2 ranging from 0.680 to 0.773 and RMSE ranging from 83.815 to 147.301 kg ha−1 during the flowering stage. The flowering stage effectively reflects crop yield trends and allows for accurate yield prediction of winter oilseed rape up to two months in advance. A comparison of the modeling results from XGBR, SVR, and RFR shows that XGBR provides the best fit for both root-zone soil moisture and yield predictions. Compared to linear regression models, the three machine learning models significantly improve accuracy and fit, providing more precise evaluations of root-zone soil moisture and yield. In addition, through the comparison and verification of this method in other regions, it shows that the results also have certain reference value. The combination of the Ts-VI feature space and machine learning algorithms not only enables precise monitoring of root-zone soil moisture conditions but also predicts future crop yield trends, offering valuable insights for water resource management and irrigation decision-making in precision agriculture.

Drought dynamics in California and Mississippi: A wavelet analysis of meteorological to agricultural drought transition

Understanding the transition from meteorological to agricultural drought is crucial for developing effective drought management strategies and early warning systems. This study provides a unique perspective by utilizing hybrid drought indices to explore the temporal and spatial complexities of drought propagation across two large watersheds—California and Mississippi—that feature distinct agro-climatic conditions and irrigation practices. We assess the links between meteorological drought, measured by the Standardized Precipitation Index (SPI), and agricultural drought using three indicators: Vegetation Drought Response Index (VegDRI), GRACE Root Zone Soil Moisture Percentile (SMI), and the Evaporative Demand Drought Index (EDDI). By comparing watersheds with different irrigation systems, we also consider the role of irrigation in modifying drought dynamics. Our analysis identified the 14-day SPI as particularly effective in capturing drought dynamics. Soil moisture showed the earliest response to rainfall anomalies, with a lag of 41 days in California and 59 days in Mississippi. Vegetation stress followed, with a lag of 137 days in California and 75 days in Mississippi, while atmospheric conditions lagged by 143 and 139 days, respectively. Irrigation was found to delay drought impacts by up to two months. This framework offers a finer temporal resolution and a more nuanced understanding of drought propagation, potentially informing more targeted drought mitigation strategies than traditional methods.

On the Use of SMAP Soil Moisture for Forecasting NDVI Over CONUS Cropland Regions

AbstractVegetation health forecasting (NDVI as a proxy) informs decision‐makers about the end of season crop yield productivity but is not well‐documented. This study tests improvements in vegetation health forecasting by developing a data‐driven Dynamic Agricultural Productivity Indicator (DAPI), which simultaneously incorporates satellite‐based root zone soil moisture (RZSM) and satellite‐based NDVI data. RZSM is estimated via data assimilation of satellite based SMAP SM dataset. We employ the proposed DAPI forecast across four cropland types in the CONUS, including corn, cotton, soybeans, and wheat. Results demonstrate superior performance of the DAPI forecasts compared to climatology‐based NDVI forecasts, with the largest improvements in water‐limited regions. DAPI shows particularly good performance during hydrologic disturbances such as floods and droughts. To this end, the DAPI approach is useful in estimating future vegetation health for identifying potential food‐insecure areas, predicting crop price changes, and projecting expected commodities market trends.

Estimating Rootzone Soil Moisture by Fusing Multiple Remote Sensing Products with Machine Learning

Estimating Rootzone Soil Moisture by Fusing Multiple Remote Sensing Products with Machine Learning

Spatial-temporal simulation and prediction of root zone soil moisture based on Hydrus-1D and CNN-LSTM-Attention in the Yutian Oasis, Southern Xinjiang, China

Root zone soil moisture (RZSM) plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth. However, the correlation between RZSM and its associated variables, including surface soil moisture (SSM), often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques. Therefore, this study presents a CNN-LSTM-Attention (CLA) model for predicting RZSM. Owing to the scarcity of soil moisture observation data, the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatiotemporal vertical soil moisture. Meteorological data and MODIS vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model. The results of the CLA model for soil moisture prediction within the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models. This was particularly notable at depth of 80–100 cm, where the coefficient of determination (R²) reached nearly 0.9298. Moreover, the RMSE was reduced by 49% and 57% compared with those of the LSTM and CNN-LSTM models, respectively. This study demonstrates that the integration of physical modeling and deep learning techniques provides a more comprehensive and accurate understanding of spatial and temporal soil moisture variations in the root zone.

Dynamics of meteorological and hydrological drought: The impact of groundwater and El Niño events on forest fires in the Amazon

Over recent decades, anthropogenic forest fires have significantly altered vegetation dynamics in the Amazon region. While human activities primarily initiate these fires, their escalation is intricately linked to climatic conditions, particularly droughts induced by the warm El Niño phase. This study investigates the impact of meteorological and hydrological drought on forest fires in the Amazon, focusing on the role of groundwater and El Niño events. Utilizing comprehensive drought indicators at various soil depths and standardized precipitation indexes, the research spans from 2004 to 2016, revealing a consistent decrease in humidity conditions across surface soil moisture, root zone soil moisture, and groundwater storage levels. With its slower response to precipitation changes, groundwater emerges as a crucial factor influencing hydrological drought patterns in the Amazon. The spatial distribution of drought conditions is explored, highlighting areas with lower humidity concentrations in the northeast and a correlation between forest fires and positive rates of change in burned area fraction during El Niño events. Notably, the study underscores the substantial increase in burned area during the 2015–2016, characterized by a very strong El Niño. This nuanced understanding of groundwater dynamics and its interplay with El Niño events provides critical insights for developing a tailored fire risk index in the ecologically significant and vulnerable Amazon basin, subsidizing strategies for mitigating fire risk and enhancing preparedness.

Non‐Stationary Flash Drought Analysis and Its Teleconnection With Low‐Frequency Climatic Oscillations

ABSTRACTFlash droughts, characterised by their rapid onset and significant impacts on local communities and agriculture, pose challenges for monitoring and mitigation efforts due to their unpredictable nature. Therefore, this study aims to investigate the occurrence, characteristics and influencing factors of flash droughts in the Ganga River Basin (GRB) for the period 1981–2020. Flash droughts are identified using the pentad averaged root zone soil moisture (PRZSM). The Mann‐Kendall trend test is used to determine the spatial and temporal pattern of flash drought characteristics. Furthermore, a multivariate flash drought index (MFDI) is developed to account for the combined effects of flash drought characteristics. Finally, wavelet coherence analysis evaluates the relationship between climatic oscillations and MFDI at the sub‐basin scale. Utilising a revised flash drought identification approach incorporating non‐stationary cumulative distribution functions (CDFs), the study identifies flash droughts in the GRB, particularly emphasising higher occurrences in the Chambal and Upper Yamuna Sub‐basins. Analysis of flash drought characteristics under stationary and non‐stationary conditions reveals increased frequency, severity and decline rates, highlighting the impact of evaporation and latent heat flux. Furthermore, the Upper Ganga Sub‐basin demonstrates coherence with the DMI at shorter time scales (1 to 4‐year time scales), while the Lower Ganga Sub‐basin displays a pronounced association with the NINO3.4 index (5.65‐year time scale), indicating the impact of climate oscillations on flash drought dynamics in these regions. These findings provide valuable insights for drought monitoring, prediction and management strategies in a changing climate, emphasising the need for integrated approaches to address the complex interplay between climate variability and flash drought occurrences in the GRB.

Rootzone Soil Moisture Dynamics Using Terrestrial Water‐Energy Coupling

AbstractA lack of high‐density rootzone soil moisture (θRZ) observations limits the estimation of continental‐scale, space‐time contiguous θRZ dynamics. We derive a proxy of daily θRZ dynamics — active rootzone degree of saturation (SRZ) — by recursive low‐pass (LP) filtering of surface soil moisture (θS) within a terrestrial water‐energy coupling (WEC) framework. We estimate the LP filter parameters and WEC thresholds for the piecewise‐linear coupling between SRZ and evaporative fraction (EF) at remote sensing and field scale over the Contiguous U.S. We use θS from the Soil Moisture Active‐Passive (SMAP) satellite and 218 in‐situ stations, with EF from the Moderate Resolution Imaging Spectroradiometer. The estimated SRZ compares well against SMAP Level‐4 estimates and in‐situ θRZ, at the corresponding scale. The instantaneous hydrologic state (SRZ) vis‐à‐vis the WEC thresholds is proposed as a rootzone soil moisture stress index (SMSRZ) for near‐real‐time operational agricultural drought monitoring and agrees well with established drought metrics.

Cascading spatial drought network: A complex networks approach to track propagation of meteorological droughts to agricultural droughts

Meteorological droughts often propagate to agricultural (and other) droughts, both spatially and temporally. The present study proposes a novel complex networks-based cascading spatial drought network to examine the spatial propagation of meteorological droughts in a region to agricultural droughts in other regions. This is done through: (1) establishing stable homogeneous drought communities; (2) investigating inter-community drought propagation; (3) locating drought sources; and (4) evaluating drought connections within major crop belts. The approach is implemented to study droughts in the Indian-subcontinent during the period 1948–2022. Monthly precipitation and root-zone soil moisture data from GLDAS (Global Land Data Assimilation System) are used to compute the standardized precipitation index (SPI) for meteorological droughts and standardized soil moisture index (SSI) for agricultural droughts. Primarily, the drought network is demarcated into several subsets of network communities within which clusters of localized propagation take place. Multi-community subgraphs combining different communities are also formed to understand the long-distance inter-community drought linkages. Using network centrality measures, such as degree, closeness, and clustering coefficient, network properties of scale-freeness, small-worldness, and presence of rich-clubs are checked. Although the overall network does not exhibit any of these properties, certain subgraphs have significant small-worldness, rich-clubs, and partial scale-freeness. Some of the crucial nodes that support these network properties lie in the monsoon pathways (in the Western Ghats), and others have a strong association with El Niño Southern Oscillation (ENSO) teleconnections, thus validating the ability of the drought network to capture seasonal and climatic features. Additionally, subgraphs of nodes with high productivity of different food crops are created to study drought propagation within crop belts. Barring potential shortcomings related to data dependencies, the cascading spatial drought network helps identify an impending agricultural drought that could strengthen our ability to forecast droughts.

Enhanced root zone soil moisture monitoring using multitemporal remote sensing data and machine learning techniques

Accurate root zone soil moisture (RZSM) estimation using remote sensing (RS) in areas with dense vegetation is essential for real-time field monitoring and precise irrigation scheduling. Traditional methods often face challenges due to the dense crop cover and the complexity of soil and climate interactions. These challenges include the coarse spatial resolution of available soil moisture products, the influence of vegetation and surface roughness, and the difficulty of estimating RZSM from surface data. Aiming to overcome these limitations, two RZSM estimation methods were developed by combining synthetic aperture radar (SAR) data from Sentinel-1 (VV and VH polarizations) and optical and thermal RS data from Landsat-8. These data sources were used in conjunction with various machine learning (ML) models such as M5-pruned (M5P), support vector regression (SVR), extreme gradient boosting (XGBoost), and random forest regression (RFR) to improve the accuracy of soil moisture estimation. In addition to RS data, soil physical and hydraulic properties, meteorological variables, and topographical parameters were selected as inputs to the ML models for estimating the RZSM of sugarcane crops in Khuzestan, Iran. This study identified the temperature vegetation dryness index (TVDI) as a critical parameter for estimating RZSM in combination with the Sentinel-1 SAR data under high vegetation conditions. In both methods, the RFR algorithm outperformed, with similar performance, the XGBoost, SVR, and M5P algorithms in estimating soil surface moisture (R2 = 0.89, RMSE = 0.04 cm3cm−3). However, the accuracy of the RFR algorithm decreased with increasing depth for both the optical-thermal and combined SAR and optical-thermal RS data. This decrease was more pronounced in the combined approach, particularly for the root zone, where the RMSE reached approximately 0.073 cm3cm−3. Accordingly, the key findings demonstrated that the optical-thermal RS data outperformed the SAR RS data for retrieving RZSM in high-vegetated areas. However, combining TVDI with SAR data is a substantial improvement that opens a new path in radar-based RZSM estimation methods under high vegetation conditions.

The optimal applications of scPDSI and SPEI in characterizing meteorological drought, agricultural drought and terrestrial water availability on a global scale

The Palmer Drought Severity Index (scPDSI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are two of the most commonly used drought indices. However, scPDSI and SPEI at a specific scale are often used interchangeably to characterize meteorological drought, agricultural drought, or terrestrial water availability, leading to potential inaccuracies in research outcomes. This study thus presents a global-scale assessment of the applications of scPDSI and SPEI at various timescales (SPEIs) in these contexts. Our findings indicate that scPDSI is more suitable for monitoring agricultural drought than meteorological drought, and highlight the effectiveness of SPEI at one month scale (SPEI01) for meteorological drought. Additionally, SPEI at nine months scale (SPEI09) is more appropriate for agricultural drought. Regarding their relationship with vegetation water stress, scPDSI and SPEI09 are more closely associated with root-zone soil moisture, while SPEI01 is most closely linked to vapor pressure deficit. Furthermore, we evaluate the capability of scPDSI and SPEI in representing terrestrial water availability by analyzing the responses of diverse vegetation indicators to them, including the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Solar-Induced Chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP). All four vegetation indicators show the highest sensitivity of negative response to SPEI01 in cold climate regions, suggesting SPEI01 is most applicable in these regions. In drylands, vegetation indicators exhibit higher sensitivity of positive responses to SPEI at six months scale (SPEI06) and SPEI09, indicating SPEI06 and SPEI09 effectively characterize water availability in such areas. These findings enhance the understanding of scPDSI and SPEI, providing clearer guidelines for their global-scale applications in meteorological drought, agricultural drought, and terrestrial water availability.

Observed flash drought to persist in future over southern Africa

Southern Africa has experienced multiple occurrences of drought episodes, which is projected to persist in the future, considering all climate scenarios. Despite the documented change in a meteorological, agricultural, and hydrological drought situation in the region, few studies are yet to explore the changes in flash drought (hereafter; FD), which is characterized by a rapid reduction in root-zone soil moisture and more substantial intensification in few days to weeks. Here, we analyze the observed FD and related underlying drivers during the past 34 years. Also, we estimate the future changes in FD using the severity-duration magnitude matrix under the middle of the road (SSP2-4.5) and business as usual (SSP5-8.5), scenario. Lastly, the study investigates the role of anthropogenic warming using the fraction of attributable risk (FAR) approach and possible bivariate return periods of FD events. Our results demonstrate that the region has experienced multiple occurrences up to 72 pentads from 1980 to 2014. Underlying mechanisms revealed the compounding influence of Vapor Pressure Deficit (VPD), Potential Evapotranspiration (PET), and precipitation deficit that have a significant impact on the abrupt onset and rapid intensification of FD events and other hot extremes over the SAF region. Under a high emission scenario, the region will experience FD duration lasting for 30 days with >40 % severity projected to impact the region. Anthropogenic climate change and land use and land cover changes remain the most dominant drivers altering the FD events over the SAF region. The return period of FD events under the SSP5-8.5 scenario shows that the SAF region will witness multiple FD events of up to 80 pentads in the far future. These findings reinforce the need to limit the emission of greenhouse gases. Sustained warming of the climate will exacerbate the extreme events and other compounding factors, thus affecting the livelihoods of humans and life-supporting strata.

Estimation of 100 m root zone soil moisture by downscaling 1 km soil water index with machine learning and multiple geodata

Root zone soil moisture (RZSM) is crucial for agricultural water management and land surface processes. The 1 km soil water index (SWI) dataset from Copernicus Global Land services, with eight fixed characteristic time lengths (T), requires root zone depth optimization (Topt) and is limited in use due to its low spatial resolution. To estimate RZSM at 100-m resolution, we integrate the depth specificity of SWI and employed random forest (RF) downscaling. Topographic synthetic aperture radar (SAR) and optical datasets were utilized to develop three RF models (RF1: SAR, RF2: optical, RF3: SAR + optical). At the DEMMIN experimental site in northeastern Germany, Topt (in days) varies from 20 to 60 for depths of 10 to 30 cm, increasing to 100 for 40–60 cm. RF3 outperformed other models with 1 km test data. Following residual correction, all high-resolution predictions exhibited strong spatial accuracy (R ≥ 0.94). Both products (1 km and 100 m) agreed well with observed RZSM during summer but overestimated in winter. Mean R between observed RZSM and 1 km (100 m; RF1, RF2, and RF3) SWI ranges from 0.74 (0.67, 0.76, and 0.68) to 0.90 (0.88, 0.81, and 0.82), with the lowest and highest R achieved at 10 cm and 30 cm depths, respectively. The average RMSE using 1 km (100 m; RF1, RF2, and RF3) SWI increased from 2.20 Vol.% (2.28, 2.28, and 2.35) at 30 cm to 3.40 Vol.% (3.50, 3.70, and 3.60) at 60 cm. These negligible accuracy differences underpin the potential of the proposed method to estimate RZSM for precise local applications, e.g., irrigation management.