Tool For Drought Monitoring Research Articles

Assessing the Impact of Agricultural Practices and Urban Expansion on Drought Dynamics Using a Multi-Drought Index Application Implemented in Google Earth Engine: A Case Study of the Oum Er-Rbia Watershed, Morocco

Drought monitoring is a critical environmental challenge, particularly in regions where irrigated agricultural intensification and urban expansion pressure water resources. This study assesses the impact of these activities on drought dynamics in Morocco’s Oum Er-Rbia (OER) watershed from 2002 to 2022, using the newly developed Watershed Integrated Multi-Drought Index (WIMDI), through Google Earth Engine (GEE). WIMDI integrates several drought indices, including SMCI, ESI, VCI, TVDI, SWI, PCI, and SVI, via a localized weighted averaging model (LOWA). Statistical validation against various drought-type indices including SPI, SDI, SEDI, and SMCI showed WIMDI’s strong correlations (r-values up to 0.805) and lower RMSE, indicating superior accuracy. Spatiotemporal validation against aggregated drought indices such as VHI, VDSI, and SDCI, along with time-series analysis, confirmed WIMDI’s robustness in capturing drought variability across the OER watershed. These results highlight WIMDI’s potential as a reliable tool for effective drought monitoring and management across diverse ecosystems and climates.

Spatio-temporal forecasting for the US Drought Monitor

Abstract The US Drought Monitor is the leading drought monitoring tool in the United States. Updated weekly and freely distributed, it records the drought conditions as geo-referenced polygons showing one of six ordered levels. These levels are determined by a mixture of quantitative environmental measurements and local expert opinion across the entire United States. At present, forecasts of the Drought Monitor only convey the expected direction of drought development (i.e. worsen, persist, subside) and do not communicate any uncertainty. This limits the utility of forecasts. In this paper, we describe a Bayesian spatio-temporal ordinal hierarchical model for use in modelling and projecting drought conditions. The model is flexible, scalable, and interpretable. By viewing drought data as areal rather than point-referenced, we reduce the cost of sampling from the posterior by avoiding dense matrix inversion. Draws from the posterior predictive distribution produce future forecasts of actual drought levels—rather than only the direction of drought development—and all sources of uncertainty are propagated into the posterior. Spatial random effects and an autoregressive model structure capture spatial and temporal dependence, and help ensure smoothness in forecasts over space and time. The result is a framework for modelling and forecasting drought levels and capturing forecast uncertainty.

Exploring the utility of the Enhanced Vegetation Index as rainfall and agricultural proxy in a Caribbean case study event

Highly fragile small island states experience disproportionate climate impacts given their limited capacity to implement cost‐effective tools for detecting emerging signals of drying conditions and monitoring systems for sensitive sectors such as agriculture, especially for uncertain, ‘creeping’ events such as droughts. Despite the existence of open‐source Google Earth Engine datasets, untapped potential remains for their full deployment in disaster management infrastructure. Given this gap, this paper explores the utility of the Enhanced Vegetation Index (EVI) for detecting spatio‐temporal variations of Mid‐Summer Drought (MSD) impacts on vegetation in the small island of Jamaica, with emphasis on major historical drought events. Geospatial analyses of EVI datasets from the Terra Moderate‐Resolution Imaging Spectroradiometer (MODIS) between 2000−2015 archived by the United States Geological Survey (USGS), were computed, and validated by station‐based precipitation and production data for selected parishes for historical case study MSD events. Results revealed highly asymmetrical drought impacts, with Jamaica's agriculturally intense Southern coastline displaying the most stressed vegetation (EVI < 0.5). North‐Western and North‐Eastern regions had the healthiest vegetation during the MSD (EVI > 0.6). A ‘fair’ to ‘moderate’ concurrent correlation was found between EVI and precipitation (R > 0.6), with lower correlations vis‐a‐vis agricultural production (R = 0.2–0.4). The results provide evidence of EVI's utility as a drought monitoring tool in a small island context.

A Review of User Perceptions of Drought Indices and Indicators Used in the Diverse Climates of North America

Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought-monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America and to enhance the utility of drought-monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impact information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and its indicators are changing over time as the climate warms and by recommending ways to strengthen the use of indices and indicators in drought decision making.

Drought Assessment in Greece Using SPI and ERA5 Climate Reanalysis Data

The present work aims to assess the spatial variability and the trends of the annual rainfall and meteorological drought in the entire territory of Greece utilising the ERA5 reanalysis precipitation dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF), which spans from January 1940 to December 2022 (an 83-year period). Drought assessment took place based on the Standardized Precipitation Index (SPI) for timescales ranging from 1 month to 12 months. Evaluation was carried out by calculating SPI using observed rainfall data from five meteorological stations. The annual rainfall and drought severity trends for timescales of 1 (SPI-1), 3 (SPI-3), 6 (SPI-6) and 12 (SPI-12) months were analysed using the Theil–Sen slope method and the Mann–Kendall trend test. The results indicate significant, both increasing and decreasing, annual precipitation trends at the 95% significance level for the Aegean Islands, western Crete and western mainland of Greece. The results also indicate significant drought trends for SPI-12 for the Aegean Islands and western Peloponnese. Trend analysis for SPI-1, SPI-3 and SPI-6 indicate a mixture of non-significantly increasing wetting trends and increasing drought trends at the national scale. In conclusion, the ERA5 dataset seems to be a valuable tool for drought monitoring at the spatial scale.

A drought monitor for Australia

The UniSQ Drought Monitor and Australian Combined Drought Indicator (CDI) are introduced. The objective of these products is to use a multi-index approach to fully capture the long- and short-term consequences of drought as an information service for the public. Since drought is a complex phenomenon relying on multiple variables such as rainfall evaporative demand, and antecedent soil moisture conditions, monitoring drought using a single index or indicator is not always appropriate.The Drought Monitor was developed using a normalized linear combination with weighting determined by PCA. It was evaluated against observed wheat yield as well as total pasture growth simulated by the AussieGRASS model. The results indicate that there was a significant positive correlation with both. The Drought Monitor was also well-received by survey respondents and has the potential to become a valuable drought monitoring tool for identifying drought impacts and related risks.

Copula-Based Joint Drought Index Using Precipitation, NDVI, and Runoff and Its Application in the Yangtze River Basin, China

Drought monitoring ensures the Yangtze River Basin’s social economy and agricultural production. Developing a comprehensive index with high monitoring precision is essential to enhance the accuracy of drought management strategies. This study proposes the standardized comprehensive drought index (SCDI) using a novel approach that utilizes the joint distribution of C-vine copula to effectively combine three critical drought factors: precipitation, NDVI, and runoff. The study analyzes the reliability and effectiveness of the SCDI in detecting drought events through quantitative indicators and assesses its applicability in the Yangtze River Basin. The findings are as follows: (1) The SCDI is a highly reliable and applicable drought index. Compared to traditional indices like the SPI, VCI, and SRI, it has a consistency rate of over 67% and can detect drought events in more sensitive months by over 51%. It has a low false negative rate of only 2% and a false positive rate of 0%, making it highly accurate. The SCDI is also applicable to all the third-level sub-basins of the Yangtze River Basin, making it a valuable tool for regional drought monitoring. (2) The time lag effect of the NDVI can affect the sensitivity of the SCDI. When the NDVI time series data are shifted forward by one month, the sensitivity of the SCDI in detecting agricultural drought improves from 47.8% to 53%. (3) The SDCI can assist in monitoring drought patterns in the Yangtze River Basin. From 2001 to 2018, the basin saw fluctuations in drought intensity, with the worst in December 2008. The western region had less frequent but more intense and prolonged droughts, while the eastern part had more frequent yet less severe droughts.

Remote Sensing Indexes Assessment for Drought Monitoring Using Sentinel Satellite Imagery: A Case Study from Natanz County, Iran

In recent years, the problem of climate change and decrease in rainfall has become a big challenge in Iran, so it’s monitoring and control is very vital and necessary. In addition to the classical methods, nowadays with the progress of the space industry in the world, the use of remote sensing techniques has greatly helped to detect and monitor drought. The science of remote sensing and the use of satellite images is considered a useful and optimal tool for drought monitoring. In this project, we investigated the occurrence of drought using satellite images and remote sensing techniques. In this way, we have calculated the drought-related remote sensing indicators, including NDVI, VCI, TCI, and VHI, in the six-year period between 2015 and 2021 for Natanz county located in Isfahan province. In order to calculate the indicators, Sentinel satellite data was used, and to calculate the Standard Rainfall Index (SPI), data from the synoptic station of Natanz county was used. According to the general results obtained from the indicators, currently the drought situation in the southwestern region of this area has been increasing compared to the last five years, and in other regions we are witnessing the growth and occurrence of drought sporadically. The results of this research show that the best index for calculating and monitoring drought is the VCI index because it has a higher correlation with meteorological data and the SPI index.

SPEI and multi-threshold run theory based drought analysis using multi-source products in China

With the accelerated climate change, the selection of globe-warming related drought monitoring tools is vitally significant. To select the most suitable multi-source precipitation product for drought monitoring, this study comprehensively evaluated the performance of 7 latest version of multi-source precipitation estimate products (MPEs, including PERSIANN-CDR, CHIRPS, ERA5, MERRA2, APHRODITE, GPCC and MSWEP) and compared their drought monitoring applicability combined with GLEAM potential evapotranspiration (ETP) product based on the standardized precipitation evapotranspiration index (SPEI) during 1983–2015 over China. The SPEI estimated by the China monthly Precipitation Analysis Product (CPAP) was used as the reference to assess their performance. In addition, the drought area ratio, frequency, duration and intensity were analyzed based on the multi-threshold run theory. The results showed that: (1) The precision of MPEs was higher in eastern China but lower in western China, and the overall accuracy of MPEs summarized as 4 levels from top to end respectively was GPCC and APHRODITE, MERRA2 and MSWEP, CHIRPS and PERSIANN-CDR as well as ERA5. GLEAM ETP performed well in eastern China but poor in western China with large underestimations. (2) The applicability of MPEs in drought monitoring was regional-specific (low in parts of Northwest Desert and Qinghai-Tibet Plateau, but high in other areas). The accuracy of estimated SPEI for most MPEs dropped with expanding timescale in most sub-regions. Among of MPEs, GPCC product comprehensively performed best in drought monitoring. (3) Drought events occurring in northern China were usually with larger area ratio, lower frequency and longer duration than other regions. Drought events had an exacerbating trend in southeastern China. Overall, this study provides significant information to choose suitable datasets for globe-warming related drought studies.

Ground Hyper-Spectral Remote-Sensing Monitoring of Wheat Water Stress during Different Growing Stages

Monitoring agricultural drought via ground hyper-spectral remote sensing has always been a hot topic in the fields of agriculture and meteorology. In this study, a greenhouse experiment was conducted on wheat subjected to water stress during its different growth stages, namely tillering, jointing, and milk maturity. An instrument (HOBO ware PRO) used to continuously measure soil moisture was employed to measure the soil water content (SWC). An analytical spectral device (ASD) was utilized to obtain the spectral curve of wheat subject to different water treatment methods. The canopy temperature was obtained using thermal infrared sensors (METER SI-400). The relationships between the SWC, wheat drought stage, canopy temperature, and spectral response characteristics were elucidated. The results showed that the significant differences in spectral characteristics were due to water stress during the different growth stages of wheat. Red-edge parameters of red-valley position (RVP) and red-edge position (REP) both changed by 21 nm for the tillering-stage drought and the jointing-stage drought; however, the RVP and REP values for the milk maturity stage drought and the treatment under no water stress changed by 2 nm. Further, it was proved that the red-edge blue-shift phenomenon was affected not only by the different wheat growth processes, but also by the water stress at different growth stages. Red-edge reflectance clearly reflects wheat water stress at different growth stages. From SWC and canopy temperature analysis results, SWC and canopy temperature had a significant difference between wheat drought at different growth stages, and the canopy temperature at the jointing stage drought had the strongest change. The water index (WI) based on eleven vegetation water indexes exhibited a good performance for distinguishing wheat water stress at different growth stages. In conclusion, ground-based hyperspectral remote sensing can provide a large amount of high temporal and spectral resolution data on vegetation and its surrounding environment, making it an important technical tool for wheat drought monitoring, which has a great significance on the monitoring and early warning of wheat drought, reducing drought-related yield losses, and ensuring food security.

Constructing high-resolution groundwater drought at spatio-temporal scale using GRACE satellite data based on machine learning in the Indus Basin

The complicated phenomenon induced by inadequate precipitation is a drought that impacts water resources and human life. Traditional methods to assess groundwater drought events are hindered due to sparse groundwater observations on a spatio-temporal scale. These groundwater drought events are not well studied in the study area of the Indus Basin Irrigation System (IBIS) holistically. This study applied four machine learning models to the training datasets of Gravity Recovery and Climate Experiment (GRACE) Terrestrial Water Storage (TWS) and Groundwater Storage (GWS) data to improve resolution to 0.25° from 1°. The Extreme Gradient Boosting (XGBoost) model outperformed the four models and results showed Pearson correlation (R) (0.99), Nash Sutcliff Efficiency (NSE) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm). The GRACE Groundwater Drought Index (GGDI) was calculated by normalizing XGBoost-downscaled GWS. The trend characteristics, the temporal evolution, and spatial distribution of GGDI were analyzed across the IBIS from 2003 to 2016. The wavelet coherence approach was used to evaluate the relationship between teleconnection factors and GGDI. The XGBoost downscaling model can accurately reproduce local groundwater behavior, with the acceptable correlation of coefficient values for validation (ranging from 0.02 to 0.84). The accumulated Standardized Precipitation Evapotranspiration Index (SPEI) with the time of 1, 3, and 6 months, and self-calibrated Palmer Drought Severity Index (sc-PDSI) were used to validate GGDI. The findings have demonstrated that GGDI has comparable drought patterns to SPEI-3 and SPEI-6 and sc-PDSI. The teleconnection factors have a significant impact on the GGDI shown by the wavelet coherence technique. The impact of the sea surface temperature index (namely, NINO3.4) on GGDI was observed significantly high among other teleconnection factors in the IBIS. The proposed framework can serve as a useful tool for drought monitoring and a better understanding of extreme hydroclimatic conditions in the IBIS and other similar climatic regions.

Climate change and groundwater overdraft impacts on agricultural drought in India: Vulnerability assessment, food security measures and policy recommendation

The problem of drought in India is a major issue in terms of various adverse impacts on livelihood of society. Drought Early Warning System (DEWS), a real-time drought-monitoring tool, has reported that over a fifth of India's geographical area (21.06 %) is suffering drought-like situations. This is 62 % larger than the drought-affected area during the same period last year, which was 7.86 %. Drought affects 21.06 %, with conditions ranging from unusually dry to extremely dry. While 1.63 % and 1.73 % of the area are experiencing ‘extreme’ or ‘exceptional’ dry conditions, 2.17 % is experiencing ‘severe’ dry conditions. Under ‘moderate’ dry circumstances, up to 8.15 % is possible. In this perspective groundwater vulnerability assessment in the overall country is needed for implementing the sustainable and long-term strategies for escaping from this type of hazardous situation. The main objective of this study is to estimate the drought vulnerability in changing climate which eventually influences the food security of India. The groundwater overdraft is one of the crucial elements in agricultural drought vulnerability. Various related parameters have been selected for estimating the drought vulnerability and its impact to food security in India. Here, MaxEnt (maximum entropy) and ANN (analytical neural network) has been considered in this perspective. The AUC values for the training datasets in the ANN and MaxEnt model are 0.891 and 0.921, respectively. The AUC values in ANN and MaxEnt model for the validation datasets are 0.876 and 0.904, respectively. Here MaxEnt model is most optimal than ANN considering predictive accuracy. From this study analysis it is established that western, south and middle portion of country is very much prone to drought vulnerability. So, special emphases in terms of the regional planning have to be taken into consideration for sustainable planning.

Meeting the Drought Information Needs of Midwest Perennial Specialty Crop Producers

Abstract Agricultural production in the U.S. Midwest is vulnerable to drought, and specialty crop producers are an underserved audience for monitoring information and decision-support tools. We investigate the decision-making needs of apple, grape, and cranberry growers using a participatory process to develop crop-specific decision calendars. The process highlights growers’ decisions and information needs during the winter dormant, growing, harvest, and postharvest seasons. Apple, grape, and cranberry growers tend to be concerned with the effects of short-term drought on current crop quality and quantity, while also considering the long-term drought effect on the health of perennial plants and future years’ production. We find gaps in drought information particularly for tactical and strategic decision-making. We describe the use of decision calendars to identify points of entry for existing drought monitoring resources and tools, and to highlight where additional research and tool development is needed. Significance Statement While drought causes agricultural losses in the U.S. Midwest, more is known about the impacts and decision-support needs of commodity row crop growers in the region than those of perennial specialty crop growers. We find opportunities for climate information providers to tailor drought information delivery to perennial fruit growers according to the season, the parameters that are relevant to their decisions, and the timeframe of information needed for operational, tactical, and strategic decision-making.

A comprehensive validation for GPM IMERG precipitation products to detect extremes and drought over mainland China

This study provides a comprehensive validation of Integrate Multi-SatellitE Retrievals of (IMERG) Global Precipitation Measurement (GPM) products in detecting extremes and drought over mainland China. The estimated values of extreme precipitation and drought are provided by three runs (early, late and final) of the latest IMERG products (V06) and 696 in-situ gauges over mainland China during 2008–2017. The results demonstrate that the three runs of IMERG V06 exhibit a relatively good performance in detecting the spatial patterns of extreme precipitation volume. The early and late runs present limited capability to capture extreme precipitation events, while the final run performs slightly better. Based on the extreme value theory, the three runs show better performances in estimating extreme precipitation within short return period (10-yr) compared to 50-yr and 100-yr return periods. All runs consistently underestimate extreme precipitation in all investigated return periods over eastern China. The standardized precipitation index (SPI) is used as a drought monitoring tool. The SPI of three runs of IMERG is well aligned with the in-situ data in southern and eastern China at both time and space scales, which demonstrates that the great potential of IMERG V06 in monitoring drought in these regions. However, the three runs of IMERG V06 products have inferior performances in monitoring drought over western China at four investigated timescales (1-, 3-, 6- and 12-month). This study highlights discrepancies in the capability of the IMERG products to estimate major extreme climatic variables and suggests potential avenues of research to improve the algorithm associated with the products. Meanwhile, the results of this study can benefit both developers and users of the new generation GPM IMERG products.

Yield loss assessment of grapes using composite drought index derived from landsat OLI and TIRS datasets

Drought is one of the most challenging natural phenomena and occurs due to inadequate and erratic precipitation (snowfall and rainfall) as well as the effects of high temperature on agronomical and horticultural crops. Among horticultural crops, grapes suffer extensively from inadequate berry formation due to drought. Therefore, developing a comprehensive drought monitoring tool with a drought assessment system that incorporates multiple agrometeorological variables into a single drought indicator is critical for supporting growers. In this context, the objective of this research was to assess drought severity using the composite drought index (CDI) and determine the variations in grape yields in drought-affected vineyards. In this study, the CDI was developed for the years 2016–2020 by comprising five indices: the vegetation condition index (VCI), temperature condition index (TCI), deviation of NDVI from the long-term mean (NDVI DEV), normalized difference moisture index (NDMI) and precipitation condition index (PCI). Moreover, the quantitative principal component analysis (PCA) approach was used to assign a weight for each input parameter, and then the weights of all the indices were combined into one composite drought index. Finally, Bayesian regularized artificial neural networks (BRANNs) were used to evaluate the yield variation in each affected vineyard. The results were validated with the standard precipitation index (SPI) from 2016 to 2020. Moderate to severe drought was detected with the CDI across Kabul Province during 2016 and 2018. The correlation coefficient between the CDI and SPI-1 in the active growth stages (April to October) was relatively high, at 0.64. A validation was performed with the correlating yield losses, and losses of 3.4 ton/ha and 4.7 ton/ha were found in 2016 and 2018, respectively, under severe drought conditions. The findings suggested that the CDI can be used for vineyard drought detections and agricultural organization support to more precisely aid farmers during severe drought conditions in grape-growing regions based on satellite remote sensing datasets.

Hydrological drought characterization based on GNSS imaging of vertical crustal deformation across the contiguous United States

Continuous Global Navigation Satellite System (GNSS) measurements allow us to track subtle elastic crustal deformation in the response to hydrological mass variations and provide an additional tool to independently characterize hydrological extremes (e.g., droughts and floods). In this study, we develop a time-varying GNSS imaging strategy that depends on the principal component analysis of GNSS-sensed vertical crustal displacement (VCD) in 2006–2020 and the monthly images of hydrology-induced deformation are generated for drought characterization across the contiguous United States. The first 12 principal components are selected in our time-varying imaging system, which account for 85% of the data variance. Considering that surface water loads are inversely correlated with the induced elastic vertical motions, we reverse the signs of the GNSS-imaged time series in all grids in subsequent studies (referred to as negative VCD (NVCD)). The GNSS-NVCD data generally correlate well with the water estimates from the Gravity Recovery and Climate Experiment (GRACE) and North American Land Data Assimilation System (NLDAS). Using the GNSS-imaged gridded NVCD products, we produce a GNSS-based drought severity index (GNSS-DSI) based on the climatological methodology, which is implemented by standardizing the GNSS NVCD anomalies that deviate from climatological normal. In most regions, strong linear correlations are accessible for GNSS-DSI relative to GRACE-DSI and the self-calibrating Palmer Drought Severity Index (scPDSI). The new drought monitoring tool, which is based solely on GNSS-measured vertical positions, is used for hydrological drought characterization (onset, end, duration, magnitude, intensity, and recovery); it succeeds in identifying well-documented historical droughts from the US drought monitor (USDM). Our study presents a new drought characterization framework using solely GNSS-measured hydrological loading displacements from a dense GNSS network, which has great potential to strengthen operational drought monitoring and assessment.

Validity and reliability of drought reporters in estimating soil water content and drought impacts in central Europe

Increasing drought is considered one of the major threats associated with climate change in central Europe. To provide an objective, quantitative tool that represents current drought conditions, the Czech Drought Monitor System (CzechDM) was established in 2012. Like other drought monitoring systems worldwide, the CzechDM uses several approaches to provide drought data. However, the CzechDM is unique internationally due to its utilization of a network of voluntary reporters (farmers) who complete a weekly online questionnaire to provide information about soil water content and the impacts of drought on crop yield. In this study, the results from the questionnaires from individual farms were aggregated by district. Reporters’ data were compared and validated with the outputs of the SoilClim model (a core tool of the CzechDM) and with other drought monitoring tools, such as the water balance model, the soil water index and the evaporative stress index. The soil water content estimated by the reporters was significantly correlated (on average r = 0.8) with the outputs of the SoilClim model. Conversely, the correlation between the drought impacts on yield estimated by the reporters and the SoilClim outputs was lower (on average r = 0.4), suggesting that in situ observations by farmers provide additional insights into the occurrence of drought impacts. Importantly, it was found that farmers reported significant drought impacts on yield earlier in the season than any other methods (models or remote sensing). The main findings of this study are that the drought monitoring provided by reporters is a useful and reliable component of the CzechDM. We conclude that weekly reports by farmers represent a significant enhancement to drought monitoring and have potential for use in developing automated approaches that combine in situ, modeling and remote sensing data within a data fusion or machine learning framework.

Prediction for Various Drought Classes Using Spatiotemporal Categorical Sequences

Drought frequently spreads across large spatial and time scales and is more complicated than other natural disasters that can damage economic and other natural resources worldwide. However, improved drought monitoring and forecasting techniques can help to minimize the vulnerability of society to drought and its consequent influences. This emphasizes the need for improved drought monitoring tools and assessment techniques that provide information more precisely about drought occurrences. Therefore, this study developed a new method, Model-Based Clustering for Spatio-Temporal Categorical Sequences (MBCSTCS), that uses state selection procedures through finite mixture modeling and model-based clustering. The MBCSTCS uses the functional structure of first-order Markov model components for modeling each data group. In MBCSTCS, the suitable order K of the components is selected by Bayesian information criterion (BIC). In MBCSTCS, the estimated mixing proportions and the posterior probabilities are used to compute probability distribution associated with the future steps of transitions. Furthermore, MBCSTCS predicts drought occurrences in future time using spatiotemporal categorical sequences of various drought classes. The MBCSTCS is applied to the six meteorological stations in the northern area of Pakistan. Moreover, it is found that MBCSTCS provides expeditious information for the long-term spatiotemporal categorical sequences. These findings may be helpful to make plans for early warning systems, water resource management, and drought mitigation policies to decrease the severe effects of drought.

Insights into hydrological drought characteristics using GNSS-inferred large-scale terrestrial water storage deficits

The hydrological loading displacements measured by continuous Global Navigation Satellite System (GNSS) networks can provide critical constraints on total terrestrial water storage (TWS) anomalies. We invert sparsely distributed GNSS vertical positions for daily large-scale water heights based on Slepian basis functions and devise a novel GNSS-based drought severity index (GNSS-DSI) dataset for drought characterization in Brazil. The spatiotemporal patterns of GNSS-inferred water estimates agree with the TWS observations derived from Gravity Recovery and Climate Experiment (GRACE) spherical harmonic solutions. Both GNSS and GRACE capture notable annual water oscillations in the Amazon River Basin, with an annual amplitude close to 500 mm, larger than that of 100–200 mm in the other geographical divisions. The newly-developed monthly GNSS-DSI time series correlate well with the well-accepted GRACE-DSI dataset, and 95% of stations feature moderate-to-strong correlations (greater than 0.50) between these two DSI data sets. The new drought monitoring tool solely based on GNSS-inferred water storage deviations succeeds in identifying the well-documented historical droughts and provides a quantitative characterization of these drought extremes in the four large river basins in Brazil. Our results demonstrate that a sparsely instrumented continuous GNSS network could be taken as an independent tool to remotely monitor large-scale TWS variations and to quantitatively characterize regional-scale hydrological extremes.

Using the Local Drought Data and GRACE/GRACE-FO Data to Characterize the Drought Events in Mainland China from 2002 to 2020

Accurate quantification of drought characteristics helps to achieve an objective and comprehensive analysis of drought events and to achieve early warning of drought and disaster loss assessment. In our study, a drought characterization approach based on drought severity index derived from Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) data was used to quantify drought characteristics. In order to improve drought detection capability, we used the local drought data as calibration criteria to improve the accuracy of the drought characterization approach to determine the onset of drought. Additionally, the local precipitation data was used to test drought severity determined by the calibrated drought characterization approach. Results show that the drought event probability of detection (POD) of this approach in the four study regions increased by 61.29%, 25%, 94.29%, and 66.86%, respectively, after calibration. We used the calibrated approach to detect the drought events in Mainland China (MC) during 2016 and 2019. The results show that CAR of the four study regions is 100.00%, 92.31%, 100.00%, and 100.00%. Additionally, the precipitation anomaly index (PAI) data was used to evaluate the severity of drought from 2002 to 2020 determined by the calibrated approach. The results indicate that both have a strong similar spatial distribution. Our analysis demonstrates that the proposed approach can serve a useful tool for drought monitoring and characterization.