Drought Monitoring Research Articles

Soil Moisture-Derived SWDI at 30 m Based on Multiple Satellite Datasets for Agricultural Drought Monitoring

Soil Moisture-Derived SWDI at 30 m Based on Multiple Satellite Datasets for Agricultural Drought Monitoring

A framework for drought monitoring and assessment from a drought propagation perspective under non-stationary environments

According to the coupled influence of climate variation and anthropogenic activities, hydro-meteorological variables are hard to keep stationary in a changing environment. Consequently, the efficacy of traditional standardized drought indices, predicated upon the assumption of stationarity, has been called into question. In China, the challenge of drought monitoring and declaration is exacerbated by the need for multiple drought indices covering meteorological, agricultural, hydrological, and groundwater aspects, often lacking real-time availability. To address these challenges, we developed a framework for drought monitoring and assessment from a drought propagation perspective. Central to this is the Nonstationary Integrated Drought Index (NIDI), which integrates responses from meteorological, agricultural, hydrological, and groundwater droughts, accounting for climate change and anthropogenic influences. First, we analyse the process of drought propagation to select the suitable time scale standardized drought index. Subsequently, significant large-scale climatic indices are selected through linear and nonlinear correlation analyses to identify climate anomalies. Anthropogenic influences are assessed using indicators such as the Normalized Difference Vegetation Index (NDVI), Impervious Surface Ratio (ISR), and population density (POP). Nonstationary probability models are then developed for precipitation, soil moisture, runoff, and groundwater series, incorporating climatic and human-induced factors. Finally, the NIDI is calculated using a D-vine copula model, with parameter estimation and updating facilitated by a genetic algorithm, representing the temporal dependence structure among the variables. A case study in the Hulu River Basin of western China validated the NIDI. Results showed that the NIDI effectively accounts for nonstationary hydro-meteorological variables due to climate change and human activities, accurately reproducing their time-dependent structure. Compared to conventional indices like SPI, SSI, SRI, and SGI, the NIDI identifies more extreme drought events. In conclusion, the presented NIDI offers a more comprehensive approach to drought identification, providing valuable insights for accurate drought detection and effective drought-related policy-making.

A global flash drought inventory based on soil moisture volatility

Flash droughts, characterized by rapid onset and development, present significant challenges to agriculture and climate mitigation strategies. Operational drought monitoring systems, based on precipitation, soil moisture deficits, or temperature anomalies, often fall short in timely detection of these events, underscoring the need for customized identification and monitoring indices that account for the rapidity of flash drought onset. Recognizing this need, this paper introduces a global flash drought inventory from 1990 to 2021 derived using the Soil Moisture Volatility Index (SMVI). Our work expands the application of the SMVI methodology, previously focused on the United States, to a global scale, providing a tool for understanding and predicting these rapidly developing phenomena. The dataset encompasses detailed event characteristics, including onset, duration, and severity, across diverse climate zones. By integrating atmospheric variables through their impact on soil moisture, the inventory offers a platform for analyzing the drivers and impacts of flash droughts, and serves as a large, consistent dataset for use in training and evaluating flash drought prediction models.

Modeling and Application of Drought Monitoring with Adaptive Spatial Heterogeneity Using Eco–Geographic Zoning: A Case Study of Drought Monitoring in Yunnan Province, China

Drought, characterized by frequent occurrences, an extended duration, and a wide range of destruction, has become one of the natural disasters posing a significant threat to both socioeconomic progress and agricultural livelihoods. Large-scale geographical environments often exhibit obvious spatial heterogeneity, leading to significant spatial differences in drought’s development and outcomes. However, traditional drought monitoring models have not taken into account the impact of regional spatial heterogeneity on drought, resulting in evaluation results that do not match the actual situation. In response to the above-mentioned issues, this study proposes the establishment of ecological–geographic zoning to adapt to the spatially stratified heterogeneous characteristics of large-scale drought monitoring. First, based on the principles of ecological and geographical zoning, an appropriate index system was selected to carry out ecological and geographical zoning for Yunnan Province. Second, based on the zoning results and using data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and the Tropical Rainfall Measuring Mission (TRMM) 3B43, the vegetation condition index (VCI), the temperature condition index (TCI), the precipitation condition index (TRCI), and three topographic factors including the digital elevation model (DEM), slope (SLOPE), and aspect (ASPECT) were selected as model parameters. Multiple linear regression models were then used to establish integrated drought monitoring frameworks at different eco–geographical zoning scales. Finally, the standardized precipitation evapotranspiration index (SPEI) was used to evaluate the monitoring effects of the model, and the spatiotemporal variation patterns and characteristics of winter and spring droughts in Yunnan Province from 2008–2019 were further analyzed. The results show that (1) compared to the traditional non-zonal models, the drought monitoring model constructed based on ecological–geographic zoning has a higher correlation and greater accuracy with the SPEI and (2) Yunnan Province experiences periodic and seasonal drought patterns, with spring being the peak period of drought occurrence and moderate drought and light drought being the main types of drought in Yunnan Province. Therefore, we believe that ecological–geographic zoning can better adapt to geographical spatial heterogeneity characteristics, and the zonal drought monitoring model constructed can more effectively identify the actual occurrence of drought in large regions. This research finding can provide reference for the formulation of drought response policies in large-scale regions.

Construction and Application of Dynamic Threshold Model for Agricultural Drought Grades Based on Near-Infrared and Short-Wave Infrared Bands for Spring Maize

Maize (Zea mays L.) is one of the most important grain crops in the world. Drought caused by climate change in recent years may greatly threaten water supply and crop production, even if the drought only lasts for a few days or weeks. Therefore, effective daily drought monitoring for maize is crucial for ensuring food security. A pivotal challenge in current related research may be the selection of data collection and the methodologies in the construction of these indices. Therefore, orthorectified reflectance in the short-wave infrared (SWIR) band, which is highly sensitive to variations in vegetation water content, was daily obtained from the MODIS MCD43A4 product. Normalized Difference Water Index (NDWI) calculated using the NIR and SWIR bands and days after planting (DAP) were normalized to obtain the Vegetation Water Index (VWI) and normalized days after planting (NDAP), respectively. The daily dynamic threshold model for different agricultural drought grades was constructed based on the VWI and NDAP with double-logistic fitting functions during the maize growing season, and its specific threshold was determined with historical drought records. Verification results indicated that the VWI had a good effect on the daily agricultural drought monitoring of spring maize in the “Golden Maize Belt” in northeast China. Drought grades produced by the VWI were completely consistent with historical records for 84.6% of the validation records, and 96.2% of the validation records differed by only one grade level or less. The VWI can not only daily identify the occurrence and development process of drought, but also well reflect the impact of drought on the yield of maize. Moreover, the VWI could be used to monitor the spatial evolution of drought processes at both regional and precise pixel scales. These results contribute to providing theoretical guidance for the daily dynamic monitoring and evaluation of spring maize drought in the “Golden Maize Belt” of China.

Spatiotemporal Variation Patterns of Drought in Liaoning Province, China, Based on Copula Theory

Liaoning Province, a crucial agricultural region in Northeast China, has endured frequent drought disasters in recent years, significantly affecting both agricultural production and the ecological environment. Conducting drought research is of paramount importance for formulating scientific drought monitoring and prevention strategies, ensuring agricultural production and ecological safety. This study developed a Comprehensive Joint Drought Index (CJDI) using the empirical Copula function to systematically analyze drought events in Liaoning Province from 1981 to 2020. Through the application of MK trend tests, Morlet wavelet analysis, and run theory, the spatiotemporal variation patterns and recurrence characteristics of drought in Liaoning Province were thoroughly investigated. The results show that, compared to the three classic drought indices, Standardized Precipitation Index (SPI), Evaporative Demand Drought Index (EDDI), and Standardized Precipitation Evapotranspiration Index (SPEI), CJDI has the highest accuracy in monitoring actual drought events. From 1981 to 2020, drought intensity in all regions of Liaoning Province (east, west, south, and north) exhibited an upward trend, with the western region experiencing the most significant increase, as evidenced by an MK test Z-value of −4.53. Drought events in Liaoning Province show clear seasonality, with the most significant periodic fluctuations in spring (main cycles of 5–20 years, longer cycles of 40–57 years), while the frequency and variability of drought events in autumn and winter are lower. Mild droughts frequently occur in Liaoning Province, with joint and co-occurrence recurrence periods ranging from 1.0 to 1.8 years. Moderate droughts have shorter joint recurrence periods in the eastern region (1.2–1.4 years) and longer in the western and southern regions (1.4–2.2 years), with the longest co-occurrence recurrence period in the southern region (3.0–4.0 years). Severe and extreme droughts are less frequent in Liaoning Province. This study provides a scientific foundation for drought monitoring and prevention in Liaoning Province and serves as a valuable reference for developing agricultural production strategies to adapt to climate change.

Integrated UAV and Satellite Multi-Spectral for Agricultural Drought Monitoring of Winter Wheat in the Seedling Stage.

Agricultural droughts are a threat to local economies, as they disrupt crops. The monitoring of agricultural droughts is of practical significance for mitigating loss. Even though satellite data have been extensively used in agricultural studies, realizing wide-range, high-resolution, and high-precision agricultural drought monitoring is still difficult. This study combined the high spatial resolution of unmanned aerial vehicle (UAV) remote sensing with the wide-range monitoring capability of Landsat-8 and employed the local average method for upscaling to match the remote sensing images of the UAVs with satellite images. Based on the measured ground data, this study employed two machine learning algorithms, namely, random forest (RF) and eXtreme Gradient Boosting (XGBoost1.5.1), to establish the inversion models for the relative soil moisture. The results showed that the XGBoost model achieved a higher accuracy for different soil depths. For a soil depth of 0-20 cm, the XGBoost model achieved the optimal result (R2 = 0.6863; root mean square error (RMSE) = 3.882%). Compared with the corresponding model for soil depth before the upscaling correction, the UAV correction can significantly improve the inversion accuracy of the relative soil moisture according to satellite remote sensing. To conclude, a map of the agricultural drought grade of winter wheat in the Huaibei Plain in China was drawn up.

Predicting agricultural and meteorological droughts using Holt Winter Conventional 2D-Long Short-Term Memory (HW-Conv2DLSTM).

Drought is an extended shortage of rainfall resulting in water scarcity and affecting a region's social and economic conditions through environmental deterioration. Its adverse environmental effects can be minimised by timely prediction. Drought detection uses only ground observation stations, but satellite-based supervision scans huge land mass stretches and offers highly effective monitoring. This paper puts forward a novel drought monitoring system using satellite imagery by considering the effects of droughts that devastated agriculture in Thanjavur district, Tamil Nadu, between 2000 and 2022. The proposed method uses Holt Winter Conventional 2D-Long Short-Term Memory (HW-Conv2DLSTM) to forecast meteorological and agricultural droughts. It employs Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data precipitation index datasets, MODIS 11A1 temperature index, and MODIS 13Q1 vegetation index. It extracts the time series data from satellite images using trend and seasonal patterns and smoothens them using Holt Winter alpha, beta, and gamma parameters. Finally, an effective drought prediction procedure is developed using Conv2D-LSTM to calculate the spatiotemporal correlation amongst drought indices. The HW-Conv2DLSTM offers a better R2 value of 0.97. It holds promise as an effective computer-assisted strategy to predict droughts and maintain agricultural productivity, which is vital to feed the ever-increasing human population.

Quantifying energy fluxes in Tarai region of India during post-monsoon season: Insights from METRIC model, ET station and remote sensing

Accurate evapotranspiration (ET) assessment is crucial for agricultural water management, encompassing crop water requirements, irrigation scheduling, water budgeting and drought monitoring. This study integrates remote sensing-based surface energy balance model with in-situ ET measurements to evaluate surface energy fluxes and ET in Pantnagar, Tarai region. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model, using high-resolution remote sensing data, was validated against observations from an ET station equipped with large aperture scintillometer and micrometeorological tower, situated in sugarcane farm at Govind Ballabh Pant University of Agriculture & Technology (GBPUA&T), Pantnagar. On November 13, 2021, METRIC and Landsat-9 satellite data estimated an instantaneous ET of 7.39 mm day-1, closely aligned with the observed value of 6.72 mm day-1 recorded by the ET station. The findings confirm the METRIC model's high accuracy for spatial ET estimation and its associated micrometeorological variables. This study underscores the utility of the METRIC model, ET station and remote sensing in determining ET and energy flux which may be further utilised in the estimation of crop water requirement, energy fluxes and irrigation water management for sugarcane cultivation in the Tarai region.

Application of the Reconstructed Solar-Induced Chlorophyll Fluorescence by Machine Learning in Agricultural Drought Monitoring of Henan Province, China from 2010 to 2022

Solar-induced chlorophyll fluorescence (SIF) serves as a proxy indicator for vegetation photosynthesis and can directly reflect the growth status of vegetation. Using SIF for drought monitoring offers greater potential compared to traditional vegetation indices. This study aims to develop and validate a novel approach, the improved Temperature Fluorescence Dryness Index (iTFDI), for more accurate drought monitoring in Henan Province, China. However, the low spatial resolution, data dispersion, and short temporal sequence of SIF data hinder its direct application in drought studies. To overcome these challenges, this study constructs a random forest SIF downscaling model based on the TROPOspheric Monitoring Instrument SIF (TROPOSIF) and the Moderate-resolution Imaging Spectroradiometer (MODIS) data. Assuming an unchanging spatial scale relationship, an improved SIF (iSIF) product with a temporal resolution of 500 m over the period March to September, 2010–2022 was obtained for Henan Province. Subsequently, using the retrieved iSIF and the surface temperature difference data, the iTFDI was proposed, based on the assumption that under the same vegetation cover conditions, lower soil moisture and a greater diurnal temperature range of the surface indicate more severe drought. Results showed that: (1) The accuracy of the TROPOSIF downscaling model achieved coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) values of 0.847, 0.073 mW m−2 nm−1 sr−1, and 0.096 mW m−2 nm−1 sr−1, respectively. (2) The 2022 iTFDI drought monitoring results indicated favorable soil moisture in Henan Province during March, April, July, and August, while extensive droughts occurred in May, June, and September, accounting for 70.27%, 71.49%, and 43.61%, respectively. The monitored results were consistent with the regional water conditions measured at ground stations. (3) The correlation between the Standardized Precipitation Evapotranspiration Index (SPEI) and iTFDI at five stations was significantly stronger than the correlation with the Temperature Vegetation Dryness Index (TVDI), with the values −0.631, −0.565, −0.612, −0.653, and −0.453, respectively. (4) The annual Sen’s slope and Mann–Kendall significance test revealed a significant decreasing trend in drought severity in the southern and western regions of Henan Province (6.74% of the total area), while the eastern region showed a significant increasing trend (4.69% of the total area). These results demonstrate that the iTFDI offers a significant advantage over traditional indices, providing a more accurate reflection of regional drought conditions. This enhances the ability to identify drought trends and supports the development of targeted drought management strategies. In conclusion, the iTFDI constructed using the downscaled iSIF data and surface temperature differential data shows great potential for drought monitoring.

Drought sensitivity analysis of meteorological and vegetation indices in Dak Nong, Vietnam

ABSTRACT Drought poses an increasing threat to agricultural sustainability in Dak Nong, Vietnam. This study investigates the sensitivity of meteorological and vegetation indices (VIs) to drought conditions in the region. We analyzed 23 years (2000–2022) of data from 11 meteorological stations and remote sensing imagery covering various crop types. Our methodology involved correlating multiple VIs (VHI, VCI, TCI, and TVDI) with meteorological drought indices (SPI and SPEI) at different time scales. Results reveal a strong correlation (r > 0.7, p < 0.001) between the vegetation health index (VHI) and the standardized precipitation evapotranspiration index (SPEI), particularly at a 4-month time scale (SPEI4). This combination proved most effective for drought monitoring across diverse vegetation types. Spatial analysis identified drought-sensitive zones covering 10.8% of the province, with steep terrain and limited river density. These areas, predominantly occupied by perennial agriculture, annual crops, and production forests, show heightened vulnerability to water scarcity. Our findings provide a scientific basis for targeted drought management strategies, including establishing an early warning system using SPEI4 and VHI, implementing water-efficient agricultural practices, and prioritizing farmer support in high-risk areas. This study enhances drought resilience and sustainable water resource management in Dak Nong and similar tropical regions.

Drought prediction using artificial intelligence models based on climate data and soil moisture

Drought is deemed a major natural disaster that can lead to severe economic and social implications. Drought indices are utilized worldwide for drought management and monitoring. However, as a result of the inherent complexity of drought phenomena and hydroclimatic condition differences, no universal drought index is available for effectively monitoring drought across the world. Therefore, this study aimed to develop a new meteorological drought index to describe and forecast drought based on various artificial intelligence (AI) models: decision tree (DT), generalized linear model (GLM), support vector machine, artificial neural network, deep learning, and random forest. A comparative assessment was conducted between the developed AI-based indices and nine conventional drought indices based on their correlations with multiple drought indicators. Historical records of five drought indicators, namely runoff, along with deep, lower, root, and upper soil moisture, were utilized to evaluate the models’ performance. Different combinations of climatic datasets from Alice Springs, Australia, were utilized to develop and train the AI models. The results demonstrated that the rainfall anomaly drought index was the best conventional drought index, scoring the highest correlation (0.718) with the upper soil moisture. The highest correlation between the new and conventional indices was found between the DT-based index and the rainfall anomaly index at a value of 0.97, whereas the lowest correlation was 0.57 between the GLM and the Palmer drought severity index. The GLM-based index achieved the best performance according to its high correlations with conventional drought indicators, e.g., a correlation coefficient of 0.78 with the upper soil moisture. Overall, the developed AI-based drought indices outperformed the conventional indices, hence contributing effectively to more accurate drought forecasting and monitoring. The findings emphasized that AI can be a promising and reliable prediction approach for achieving better drought assessment and mitigation.

EVALUATING REMOTE SENSING-BASED DROUGHT INDICES: STRENGTHS, LIMITATIONS, AND APPLICABILITY ACROSS SUB-SAHARAN AFRICA'S AGRO-ECOLOGICAL ZONES: A REVIEW

This study reviews the application and effectiveness of various remote sensing (RS) indices for drought monitoring in Sub-Saharan Africa (SSA). Given the region’s diverse climatic zones and frequent drought occurrences, accurate and timely assessment tools are crucial. The study examines indices from different spectral regions, including optical, thermal infrared, and microwave bands, focusing on their spatial and temporal resolutions, data availability, strengths, and limitations. Optical indices such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) are effective in semi-arid and sub-humid zones where vegetation density varies. Thermal infrared indices, including the Temperature Condition Index (TCI), the Vegetation Health Index (VHI), and the Temperature Vegetation Dryness Index (TVDI), provide insights into thermal anomalies and vegetation health, with TCI particularly suited for semi-arid zones and TVDI useful in both semi-arid and sub-humid zones. Microwave indices, such as the Normalized Backscatter Moisture Index (NBMI), Vegetation Optical Depth (VOD), and the Microwave Polarization Difference Index (MPDI), excel in capturing soil moisture and vegetation water content, proving useful in humid forest and semi-arid zones. The integration of these indices with other meteorological and hydrological data enhances drought monitoring and management strategies. Recommendations are made for the optimal use of these indices across different SSA agroecological zones.

Improved identification and monitoring of meteorological, agricultural, and hydrological droughts using the modified nonstationary drought indices in the Yellow River Basin of China

Non-stationarity in hydro-meteorological series complicates drought monitoring and assessment. Our study aimed to better identify and monitor meteorological, agricultural and hydrological droughts in the Yellow River Basin (YRB) of China. We constructed nonstationary standardized precipitation index (NSPI), nonstationary standardized precipitation evapotranspiration index (NSPEI), nonstationary standardized soil moisture index (NSSMI) and nonstationary standardized runoff index (NSRI) based on the Generalized Additive Models for Location, Scale, and Shape (GAMLSS). We also investigated the performance of NSPI/NSPEI/NSSMI/NSRI by comparing them with historical drought events. Our results revealed that: (1) The probability distribution function (PDF), smoothing function (SF), and degrees of freedom (DF) significantly influenced the fitting effect of the GAMLSS model. The PDF emerged as the most critical factor in determining fitting accuracy, while SF and DF played crucial roles in preventing overfitting and accurately depicting data variations. (2) Stationary GAMLSS models were primarily suited for time series with lower-scales (1- and 3-month scales), whereas nonstationary models exhibited better suitability for time series with higher-scale (6- and 12-month scales). (3) NSPI and NSPEI demonstrated heightened sensitivity in identifying meteorological droughts, with NSPEI showing greater precision in determining drought intensity and duration. However, compared to SSMI, while NSSMI aligned more closely with historical agricultural drought events, it exhibited limited responsiveness to mild and moderate drought. (4) NSRI indicated that hydrological droughts in the tributaries of the YRB were significantly influenced by meteorological droughts, and the intensity of hydrological drought was stronger than that in the mainstream of the YRB. In conclusion, the nonstationary drought indices could better identify drought events in the period of climate change, and provide valuable information for drought management in the climate change environment.

Agricultural drought monitoring in maligavila gn division based on normalized difference moisture index (NDMI) and vegetation health index (VHI)

Agricultural drought monitoring in maligavila gn division based on normalized difference moisture index (NDMI) and vegetation health index (VHI)

Identification of propagation characteristics from meteorological drought to hydrological drought using daily drought indices and lagged correlations analysis

Study regionThe Juam Dam area in Suncheon, Jeollanam-do, South Korea Study focusThis study aims to analyze drought propagation characteristics using time-lagged correlation analysis based on daily drought index, determining the lag time from meteorological drought to propagation into hydrological drought. The target period for correlation analysis is the onset date of the dry spell preceding the occurrence of hydrological drought and the termination date of the dry spell following the termination of the drought for each drought event. The Standardized Precipitation Index (SPI) at various time-scales for meteorological drought and the Standardized Reservoir Supply Index (SRSI) for hydrological drought were applied. New hydrological insights for the regionThis study presents the objectivity and accuracy of drought onset and termination date for drought propagation analysis through daily lagged correlation analysis. Through ROC analysis, SPI90, SPI180, and SPI365 are shown to increase by an average of 16.0 %, 8.8 %, and 6.0 %, respectively. From 1993–2023, long-term hydrological droughts lasting 2 years occurred 5 times, with a maximum duration of 408 days, magnitude −629, and severity −1.76. The daily lag between the multi-scale SPIs and SRSI of individual drought events presents the possibility of predicting hydrological drought through multiple regression analysis. This research provides insights for improving hydrological drought monitoring, prediction, and response strategies through results of individual propagation characteristics of drought events.

Development of a leaf area index-based relative threshold method for identifying agricultural drought areas

Considering the global aggravated agricultural drought condition, the availability of reliable historical agricultural drought information with high spatiotemporal resolution is crucial for accurate drought monitoring, effective water resources management, and sustainable agricultural development. However, the coarse spatiotemporal resolution of available historical agricultural drought datasets imposes great limitations on drought prevention and mitigation. Recognizing the research gap, this study developed a novel approach of leaf area index (LAI) relative thresholds to generate a 500 m-resolution agricultural drought areas dataset in the North China Plain (NCP) spanning the timeframe from 2006 to 2019. A range of key LAI relative thresholds were established to capture various levels of agricultural drought severity. Subsequently, a 500 m-resolution agricultural drought area dataset was generated, encompassing critical parameters of drought-covered area, drought-damaged area, and crop failure area for both summer-harvest and autumn-harvest seasons in each year. The relative thresholds for drought-covered area, drought-damaged area, and crop failure area yielded percentages of 56 %, 51 %, 34 % for summer-harvest crops and 45 %, 41 %, 28 % for autumn-harvest crops, respectively. To validate the credibility of the generated approach, historical agricultural drought areas data from the Bulletin of Flood and Drought Disasters in China were juxtaposed across various harvest seasons and multiple years. The spatial verification in the Hebei Province (one main province of the NCP) revealed a remarkable consistency between the newly generated dataset and the authentic dataset, demonstrating correlation efficiency estimates ranging from 0.70 to 0.83. The developed approach gives insights into the spatial distribution and coherence of agricultural drought impacted areas and sheds light on revealing the inter-annual dynamics of crop growing seasons. It can support for impact-based agricultural drought monitoring and prediction, and subsequently assist in optimizing agricultural water management and ensuring global food security.

Evaluation of global seamless soil moisture products over China: A perspective of soil moisture sensitivity to precipitation

Accurate and timely information about soil moisture (SM) is not only critical to the study of climate change, but is also significant for agricultural production, drought monitoring, and hydrometeorological predictions. With the development of satellite remote sensing techniques, land surface models, and data assimilation techniques, a number of global seamless soil moisture products have been released with highly variable accuracy. A comprehensive evaluation of these products is necessary to select a suitable SM product for use. In this study, we provided such an evaluation of four global seamless SM products (SMAP-L4, ERA5-Land, GLDAS-Noah, and GLEAM) over China based on the stations from the automatic soil moisture observation (ASMO) system. Compared to previous studies that focused on a direct comparison between SM products and in situ SM observations, we specifically evaluated these products from the perspective of SM sensitivity to precipitation. This is because in situ precipitation observations are more spatially representative than in situ SM observations. Precipitation is a regional event, therefore in situ precipitation observations can be better scale-matched with coarse resolution SM products compared to in situ SM observations. Further, in addition to surface soil moisture (SSM), root zone soil moisture (RZSM) was evaluated in this study. The combination of all statistics illustrates that SMAP-L4 and ERA5-Land generally performed better than GLDAS-Noah and GLEAM. Regardless of the SSM or RZSM estimates, SMAP-L4 had the lowest median bias and RMSE of the four products, and ERA5-Land had the lowest median correlation coefficient (R) value. The lowest median ubRMSE was obtained by SMAP-L4 in SSM evaluation and ERA5-Land performed better than other RZSM products with the lowest median ubRMSE. From the perspective of SM sensitivity to precipitation, SMAP-L4 can capture daily precipitation dynamics most effectively, closely followed by ERA5-Land. This partially explains the superiority of SMAP-L4 and ERA5-Land over GLDAS-Noah and GLEAM for SM monitoring. The results of the metrics under different precipitation conditions varied considerably among the products. But while considering of ubRMSE alone, all products performed better under arid and humid conditions than to semi-arid and semi-humid conditions. This may be due to the fact that SM products had a limited ability to capture the SM dynamics under moderate precipitation. These findings may provide new insights into the selection of appropriate SM products and SM estimation in the future.

A multi-sensor drought index for improved agricultural drought monitoring and risk assessment in the heterogeneous landscapes of the China–Pakistan Economic Corridor (CPEC)

Droughts cause significant economic damage worldwide. Evaluating their impacts on crop yield and water resources can help mitigate these losses. Using single variables such as precipitation, temperature, the soil moisture condition index (SMCI) and the vegetation condition index (VCI) to estimate drought impacts does not provide sufficient information on these complex conditions. Therefore, this study uses station-based and remote-sensing-based data to develop new composite drought indexes (CDIs), including the principal component analysis drought index (PSDI) and the gradient boosting method drought index (GBMDI). The first dataset includes historical observations of the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and the self-calibrated Palmer drought severity index (SC-PDSI) at the 1-, 3-, 6-, and 12-month timescales. The second dataset consists of remote-sensing-based data including the VCI, SMCI, temperature condition index (TCI), and precipitation condition index (PCI). We validated the results of PSDI and GBMDI by comparing them with historical drought events, in-situ drought indices, and annual winter wheat crop yield data from 2003 to 2022 using a regression model. Our temporal analysis revealed extreme to severe drought events during1990s and 2010s. GBMDI typically aligned with actual drought events and exhibited stronger correlations with in-situ drought indices than PSDI. We observed that drought intensity in winter were more severe than in summer. GBMDI was the most effective method, followed by PSDI, for assessing drought impacts on winter wheat yields. Thus, the proposed integrated monitoring framework and indexes offered a valuable and innovative approach to addressing the complexities of agricultural drought, particularly in evaluating its effects.

Trivariate copula functions for constructing a comprehensive atmosphere-land surface-hydrology drought index: A case study in the Yellow River basin

The current univariate and multivariate drought indices cannot fully reflect the multiple processes of meteorological-hydrological-agricultural drought. So it is important to construct a comprehensive drought index for drought monitoring in a changing environment from the perspective of the whole process of the natural water cycle “atmosphere-land surface-hydrology”. In this study, based on the standardized indices (SCI) framework and trivariate Copula functions, a multi-scale comprehensive drought index (SPERSI) integrating rainfall, temperature, runoff and soil moisture is proposed, which can synthetically reflect meteorological, hydrological and agricultural droughts. The spatial and temporal variations of drought in the Yellow River Basin (YRB) from 1961 to 2019 were investigated, and then the links between SPERSI and some remotely correlated factors were revealed by using the multiple cross wavelet transform technique. Results show that: (1) the SPERSI constructed by fusing rainfall-temperature-runoff-soil moisture information can sensitively and effectively capture the onset, persistence and termination of drought; (2) the SPERSI exhibits good performance in detecting integrated drought events in the YRB with probability of detection (POD) > 0.75, false alarm rate (FAR) < 0.04 and critical success index (CSI) > 0.73; (3) From 1961 to 2019, drought in different time scales of above Longyangxia (AL) and Longyangxia to Lanzhou (LL) zones in the YRB both show a slight mitigation trend, while the drought in remaining subzones show a trend of intensification; (4) Multiple cross-wavelet results indicate that AO is the dominant factor of drought in the Yellow River Basin, followed by ENSO. In general, SPERSI results are reliable and can provide basis for drought decision-making.