Meteorological Drought Indices Research Articles

Drought Monitoring Using Landsat Derived Indices and Google Earth Engine Platform: A Case Study from Al-Lith Watershed, Kingdom of Saudi Arabia

Precise assessment of drought and its impact on the natural ecosystem is an arduous task in regions with limited climatic observations due to sparsely distributed in situ stations, especially in the hyper-arid region of Kingdom of Saudi Arabia (KSA). Therefore, this study investigates the application of remote sensing techniques to monitor drought and compare the remote sensing-retrieved drought indices (RSDIs) with the standardized meteorological drought index (Standardized Precipitation Evapotranspiration Index, SPEI) during 2001–2020. The computed RSDIs include Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI), which are derived using multi-temporal Landsat 7 ETM+, Landsat 8 OLI/TIRS satellites, and the Google Earth Engine (GEE) platform. Pearson correlation coefficient (CC) is used to find the extent of agreement between the SPEI and RSDIs. The comparison showed CC values of 0.74, 0.67, 0.57, and 0.47 observed for VHI/SPEI-12, VHI/SPEI-6, VHI/SPEI-3, and VHI/SPEI-1, respectively. Comparatively low agreement was observed between TCI and SPEI with CC values of 0.60, 0.61, 0.42, and 0.37 observed for TCI/SPEI-12, TCI/SPEI-6, TCI/SPEI-3, and TCI/SPEI-1. A lower correlation with CC values of 0.53, 0.45, 0.33 and 0.24 was observed for VCI/SPEI-12, VCI/SPEI-6, VCI/SPEI-3, and VCI/SPEI-1, respectively. Overall, the results suggest that VHI and SPEI are better correlated drought indices and are suitable for drought monitoring in the data-scarce hyper-arid regions. This research will help to improve our understanding of the relationships between meteorological and remote sensing drought indices.

Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models.

Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models. A different combination of machine learning models and variables has been performed for the forecasting of metrological drought based on the SPI-6 and 12months. Models were developed using monthly rainfall data for the period of 2000-2019 at two meteorological stations, namely, Karanjali and Gangawdi, each representing a geographical region of Upper Godavari river basin area in the central India of Maharashtra state which frequently experiences droughts. Historical data from the SPI from 2000 to 2013 was processed to train the model into machine learning model, and the rest of the 2014 to 2019-year data were used for testing to forecast the SPI and metrological drought. The mean square error (MSE), root mean square error (RMSE), adjustedR2, Mallows' (Cp), Akaike's (AIC), Schwarz's (SBC), and Amemiya's PC were used to identify the best combination input model and best subregression analysis for both stations of SPI-6 and 12. The correlation coefficient ([Formula: see text]), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) were used to perform evaluation for SPI-6 and 12months of both stations with RF, RT, and GPR-PUK kernel models during the training and testing scenarios. The results during testing phase revealed that the RF was found as the best model in forecasting droughts with values of [Formula: see text], MAE, RMSE, RAE (%), and RRSE (%) being 0.856, 0.551, 0.718, 74.778, and 54.019, respectively, for SPI-6 while 0.961, 0.361, 0.538, 34.926, and 28.262, respectively, for SPI-12 scales at Gangawdi station. Further, the respective values of evaluators at Karanjali station were 0.913 and 0.966, 0.541 and 0.386, 0.604 and 0.589, 52.592 and 36.959, and 42.315 and 31.394 for PUK kernel and RT models, respectively, during SPI-6 and SPI-12. Machine learning models are potential drought warning techniques because they take less time, have fewer inputs, and are less sophisticated than dynamic or scientific models.

Evaluating Drought Indices for Alaska

Abstract Drought is a recurrent natural phenomenon, but there is concern that climate change may increase the frequency or severity of drought in Alaska. Because most common drought indices were designed for lower latitudes, it is unclear how effectively they characterize drought in Alaska’s diverse, high-latitude climates. Here, we compare three commonly used meteorological drought indices [the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the self-calibrating Palmer drought severity index (scPDSI)] with each other and with streamflow across Alaska’s 13 climate divisions. All of the drought indices identify major droughts, but the severity of the drought varies depending on the index used. The SPI and the SPEI are more flexible and often better correlated with streamflow than the scPDSI, and we recommend using them. Although SPI and SPEI are very similar in energy-limited climates, the drought metrics do diverge in drier locations in recent years, and consideration of the impact of temperature on drought may grow more important in the coming decades. Hargreaves potential evapotranspiration (PET) estimates appeared more physically realistic than the more commonly used Thornthwaite equation and are equally easy to calculate, so we suggest using the Hargreaves equation when PET is estimated from temperature. This study, one of the first to evaluate drought indices for high-latitude regions, has the potential to improve drought monitoring and representation within the U.S. Drought Monitor, leading to more informed decision-making during drought in Alaska, and it improves our ability to track changes in drought driven by rising temperatures. Significance Statement Tracking drought at high latitudes is challenging because we have not adequately studied drought impacts in cold climates, and the primary meteorological drought indices were designed for lower latitudes and may not accurately estimate evaporative demand and the influence of snow. We investigate three common drought indices and recommend using the standardized precipitation index (SPI) or the standardized precipitation evapotranspiration index (SPEI) because they can track short and long droughts. The SPEI may be more useful because comparisons between the SPI and SPEI show that, in recent decades, temperature has made noticeable contributions to drought in drier parts of Alaska. If using the SPEI, we suggest the Hargreaves potential evapotranspiration rather than the Thornthwaite because it is more physically realistic.

Predictive association between meteorological drought and climate indices in the state of Sinaloa, northwestern Mexico

Predictive association between meteorological drought and climate indices in the state of Sinaloa, northwestern Mexico

Comparison of four precipitation based meteorological drought indices in the Yesilirmak Basin, Turkey

Drought, which is often defined as not enough precipitation, does not a mean simple lack of precipitation. This condition, which occurs when humidity is less than the average value for many years, is caused by a disrupted balance between precipitation and evaporation in a region. It is very difficult to predict the start and the end time of drought. In the present study, the drought conditions of the stations selected from Yesilirmak Basin between 1970 and 2014 were determined by using Z-Score Index (ZSI), China-Z Index (CZI), Modified China-Z Index (MCZI), and Standard Precipitation Index (SPI), and the compliance of these indices to the SPI was investigated. It was determined that these indices gave parallel results to each other, and SPI detected drought earlier than other indices.

Drought assessment for peri urban region: A case study of Malegaon city

Drought occurs due to less rainfall over an extended period or some time of the year, disturbing all meteorological and hydrological parameters which results in a water shortage to human being needs. The occurrence of drought in Malegaon is more frequent, affecting a large number of people in it. The unplanned water resources present around the study area, adding more in drought vulnerability impact. The topography, soil type, water demand and cropping pattern in the study area play an important role while drought monitoring in the selected study region. The demand of water for growing populations, irrigation, and industrial development has increased rapidly in the study area. The current study focus on performance of two important meteorological drought indices; SPI and RDI for 3, 6, 12 month time scale in Malegaon. The measures to cope up with frequent drought has been suggested for the present study as an outcome of research, change in cropping pattern, land use and alternative sources of water for the region has been recommended to cope up with growing water demand for the peri urban area.

Assessment of Spatiotemporal Characteristic of Droughts Using In Situ and Remote Sensing-Based Drought Indices

Drought has been identified as one of the significant complicated natural disasters exacerbated by land degradation and climate change. Hence, monitoring drought and evaluating its spatiotemporal dynamics are essential to manage regional drought conditions and protecting the natural environment. In this study, various single remote sensing-based drought indices including soil moisture condition index (SMCI), precipitation condition index (PCI), temperature condition index (TCI), and vegetation condition index (VCI) and combined RS-based drought Indices including optimized meteorological drought index (OMDI) and synthesized drought index (SDI) have been used to investigate the spatiotemporal variations of meteorological and agricultural droughts between 2000 and 2021 in Iran. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> drought indices, including the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) series of 1, 3, 6, 12, and 24 months were utilized to verify remote sensing-based drought indices and evaluate their applicability for analyzing drought conditions. The results indicated that the correlation coefficients of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> drought indices with the combined drought indices are higher than the RS-based single drought indexes. Generally, single-factor drought indexes, including VCI, TCI, PCI, and SMCI, have specific characteristics. The PCI and SMCI have an acceptable correlation with the short-term SPI and SPEI and are more applicable to monitoring short-term drought conditions. Further, the TCI has better performance in monitoring long-term drought conditions in Iran. This research concluded that the central, eastern, and southeastern parts of Iran mainly were experiencing exceptional and extreme drought conditions as the worst agricultural and meteorological drought conditions observed in the years 2008 and 2021 in the region during the last 20 years. The results also showed that, in 2019 and 2020, most areas of Iran had higher OMDI and SDI values and the severity of the drought has decreased in these years. Particularly, this research provides an essential reference for reasonably choosing RS-based drought indices for monitoring meteorological and agricultural droughts from a local to global scale.

Multi-temporal SPI and RDI Based on the Modified Meteorological Draught and Hydrological Drought of Marathwada Region of Maharashtra, India

The meteorological and hydrological drought indices are mostly applying as a tool for observing changes in drought situations. In the present research work, evaluated the performance of two modified meteorological drought indices i.e. modified standardized precipitation index (SPIe) and modified recoinnaissance drought index (RDIe) and hydrological drought indices i.e stream-flow drought index (SDI). The effective precipitation was used instead of precipitation for modification of meteorological drought indices. The assessment of drought trends at multi-temporal such as 1, 3, 6 and 12 months was done by using popular Mann-Kendall test and the probability and return period of occurrence of severe and extreme drought condition of the meteorological and hydrological drought events. Finally results were non-significant trend (either positive or negative) at 95% confidence limit and the 43 years reoccurrence interval of extreme meteorological drought for Latur and Parabhani districts, the 10.75, 21.5 and 14.33 years return period of severe meteorological drought for Latur, Nanded and Parabhani districts. Also, the 18, 18 and 43 years reoccurrence interval of severe hydrological drought for Latur, Nanded and Parabhani districts was calculated.

Characterization of hydrological droughts in Brazil using a novel multiscale index from GNSS

Drought severity is not only related to drought intensity but also depends on drought duration. To accurately characterize short- and long-term droughts and comprehend drought propagation mechanisms, it is essential to ascertain the multiscale features of drought extremes. Here, we estimate terrestrial water storage changes (TWSC) using Global Navigation Satellite System (GNSS) vertical crustal displacement and then develop a novel Multiscale GNSS-based Drought Index (MGDI) to characterize hydrological droughts in Brazil from 2010 to 2021. MGDI is a probability-based drought index, and we employ a normal distribution to fit the TWSC result. The comparisons of temporal variations indicate that the MGDI is similar to other multiscale drought indices (i.e., standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and standardized terrestrial water storage index (STI)) at different time scales. Due to the longer response time to meteorological variables, hydrological drought indicators (i.e., MGDI and STI) generally lag behind meteorological drought indicators (i.e., SPI and SPEI) but with less noise on the short scale. The spatial distribution of MGDI-derived drought frequency reveals that hydrological droughts are most frequent in northeastern Brazil and near the Parana River Basin, with a frequency of up to 0.38 (about 55 months) on the 1-month scale. The spatiotemporal evolutions of the 2015–2016 drought event imaged by 3-month MGDI and 12-month SPI are generally consistent, and 3-month MGDI shows good temporal continuity of evolution. Investigation of drought propagation in various basins shows that 1-month MGDI correlates well with SPI on the specific time scales (e.g., 6-month, 11-month, 13-month, and 14-month), with all correlation coefficients above 0.55. In the Amazon Basin, we also find that MGDI on the 1.5–3 years scale is strongly correlated with El Niño-Southern Oscillation (ENSO) events, suggesting hydrological droughts are likely to be modulated by ENSO. Our findings imply that MGDI can characterize hydrological deficits more comprehensively and offer some critical insights for multiscale drought research.

Comparison of the capability of the Meteorological and Remote Sensing Drought Indices

Comparison of the capability of the Meteorological and Remote Sensing Drought Indices

Responses of the Remote Sensing Drought Index with Soil Information to Meteorological and Agricultural Droughts in Southeastern Tibet

The Temperature–Vegetation–Precipitation–Drought Index (TVPDI) has a good performance in drought monitoring in China. However, different regions have different responses to droughts due to terrain differences. In southeastern Tibet, the drought monitoring capacity of some drought indices without soil information has to be assessed on account of the poor sensitivity between temperature and soil humidity. Therefore, soil moisture was added to calculate a new drought index based on TVPDI in southeastern Tibet, named the Temperature–Vegetation–Soil-Moisture–Precipitation–Drought Index (TVMPDI). Then, the TVMPDI was validated by using the Standardized Precipitation Evapotranspiration Index (SPEI) and other remote sensing drought indices, including the Vegetation Health Index (VHI) and Scale Drought Conditions Index (SDCI), during the growing seasons of 2003–2018. The Standardized Precipitation Index (SPI) and SPEI were used to represent meteorological drought and Gross Primary Productivity (GPP) was used to represent agricultural drought. The relation between TVMPDI and these drought indices was compared. Finally, the time trends of TVMPDI were also analyzed. The relation coefficients of TVMPDI and SPEI were above 0.5. The correlations between TVMPDI and drought indices, including the Vegetation Health Index (VHI) and Scale Drought Conditions Index (SDCI), also had a good performance. The correlation between the meteorological drought indices (SPI and SPEI) and TVMPDI were not as good as for the TVPDI, but the temporal correlation between the TVMPDI and GPP was greater than that between the TVPDI and GPP. This indicates that the TVMPDI is more suitable for monitoring agricultural drought than the TVPDI. In addition, historical drought monitoring had values that were consistent with those of the actual situation. The trend of the TVMPDI showed that drought in the study area was alleviated from 2003 to 2018. Furthermore, GPP was negatively correlated with SPEI (r = −0.4) and positively correlated with Soil Moisture (SM) drought index (TVMPDI, SMCI) (r = 0.4) in the eastern part of the study area, which suggests that SM, rather than precipitation, could promote the growth of vegetation in the region. A correct understanding of the role of soil information in drought comprehensive indices may monitor meteorological drought and agricultural drought more accurately.

Development of PCA-based composite drought index for agricultural drought assessment using remote- sensing

The present study was conducted in the Saurashtra region of Gujarat to demonstrate the development and validation of location and crop-specific composite drought index (CDI) using a linear combination of three parameters including meteorological drought index, vegetation drought index and inverse of maximum consecutive dry days%for major Kharif crops of the region i.e. cotton and groundnut. The performance of nine drought indices including six meteorological and three remote sensing-based vegetation indices wasevaluated in terms of correlation with district scale crop yields.The district-wise expressions of CDI were developed by assigning principal component analysis (PCA) based weights to parameters.Standardized Precipitation Evapotranspiration Index (SPEI)/ Reconnaissance Drought Index (RDI) among meteorological indices and NDVI Anomaly Index (NDVIA)/ Vegetation Condition Index (VCI) among vegetation indices were found suitable for generating district specific CDI expressions. The developed CDI showed higher correlation with Kharif cotton and Groundnut crop yields as compared to various meteorological as well as vegetation indices used in the study and effectively quantified major historic agricultural droughts.The average correlation coefficients of developed CDI with cotton and groundnut yields were 0.71 and 0.77 respectively. The correlations of CDI and crop yields for all CDI expression were highly significant with p&lt;0.01. The method developed in the study will be useful to generate crop and region-specific multi-scalar drought indices by the amalgamation of multiple drought indices for assessing crop production losses.

Monitoring vegetation condition using microwave remote sensing: the standardized vegetation optical depth index (SVODI)

Abstract. Vegetation conditions can be monitored on a global scale using remote sensing observations in various wavelength domains. In the microwave domain, data from various spaceborne microwave missions are available from the late 1970s onwards. From these observations, vegetation optical depth (VOD) can be estimated, which is an indicator of the total canopy water content and hence of above-ground biomass and its moisture state. Observations of VOD anomalies would thus complement indicators based on visible and near-infrared observations, which are primarily an indicator of an ecosystem's photosynthetic activity. Reliable long-term vegetation state monitoring needs to account for the varying number of available observations over time caused by changes in the satellite constellation. To overcome this, we introduce the standardized vegetation optical depth index (SVODI), which is created by combining VOD estimates from multiple passive microwave sensors and frequencies. Different frequencies are sensitive to different parts of the vegetation canopy. Thus, combining them into a single index makes this index sensitive to deviations in any of the vegetation parts represented. SSM/I-, TMI-, AMSR-E-, WindSat- and AMSR2-derived C-, X- and Ku-band VODs are merged in a probabilistic manner resulting in a vegetation condition index spanning from 1987 to the present. SVODI shows similar temporal patterns to the well-established optical vegetation health index (VHI) derived from optical and thermal data. In regions where water availability is the main control on vegetation growth, SVODI also shows similar temporal patterns to the meteorological drought index scPDSI (self-calibrating Palmer drought severity index) and soil moisture anomalies from ERA5-Land. Temporal SVODI patterns relate to the climate oscillation indices SOI (Southern Oscillation index) and DMI (dipole mode index) in the relevant regions. It is further shown that anomalies occur in VHI and soil moisture anomalies before they occur in SVODI. The results demonstrate the potential of VOD to monitor the vegetation condition, supplementing existing optical indices. It comes with the advantages and disadvantages inherent to passive microwave remote sensing, such as being less susceptible to cloud coverage and solar illumination but at the cost of a lower spatial resolution. The index generation is not specific to VOD and could therefore find applications in other fields. The SVODI products (Moesinger et al., 2022) are open-access under Attribution 4.0 International and available at Zenodo, https://doi.org/10.5281/zenodo.7114654.

The sensitivity of meteorological drought index towards El Nino-Southern Oscillation

El Nino-Southern Oscillation (ENSO) contributes to the regional climates, such as precipitation and droughts. The objectives of the present work were to: (1) identify the severity index; (2) analyze the correlation of SPI and RDI, and; (3) identify the response of SPI and RDI towards ENSO. SPI and RDI were calculated for time scales (3, 6, and 12 months), and these represented the seasonal and annual drought. The identification of the responses of the drought severity index, based on ENSO, consisted of several thresholds, namely weak, moderate, and strong. The correlational value and RMSE only represented the performance of SPI and RDI on different time scales. The drought severity index would decline along with an increase in the time scales. The strong El Nino phase could be significant to the seasonal and annual drought. In other words, ENSO was impactful on the precipitation and dynamics of drought. Drought periods were due to the moderate and strong El Nino phase, while the weak phase led to a normal condition. For this reason, ENSO could be functioned as an indicator to predict drought.

Determining the most appropriate probability distribution function for meteorological drought indices in Urmia Lake Basin, Iran.

Normalization is believed to be one of the most important parts of numerical computation in discrete mathematics. This process aims to transform a wide numerical range into a narrower one. Hence, in a number of fields of study, numerous distribution functions (DF) have been extended based on their applications, one of which is drought calculation. In this research, annual drought was calculated via standard precipitation index (SPI) and China Z Index (CZI) through seven three-parametric DFs (Pearson 5, Weibull, Pearson 3 (gamma), log Pearson, Fréchet, log-logistic, and fatigue life) in order to determine the most appropriate one for each index in Urmia Lake Basin. To this end, the results of both SPI and CZI, with DFs and without them, were compared with statistical analyzers (RMSE, ME, R2, and pearson correlation). The results indicated that Weibull-CZI and Pearson 5-SPI had the highest correlation with the normal ones. Therefore, they could be used as the best DFs for these drought indices in this basin. Moreover, among the studied years, Gelazchay and Daryanchay stations experienced the most severe drought in 2008 and 1999 based on the CZI and SPI, respectively. It should be noted that in another section of the current study, the correlation between the two indices was analyzed and the results showed high correlations between them.

Meteorological and hydrological drought hazard, frequency and propagation analysis: A case study in southeast Australia

Study regionSoutheast Australia. Study focusWe investigated meteorological and hydrological drought characteristics using Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSFI) and Effective Drought Index (EDI). Drought Hazard Index (DHI) was derived based on the probability of drought occurrence and Thiessen polygons using SPI/EDI, whereas Drought Frequency Index (DFI) was derived based on number of drought events, and data length using SPI, EDI, and SSFI. The modified Mann-Kendall test was applied to detect trends in streamflow data and hydrological droughts. Furthermore, correlation between meteorological and hydrological drought indices for different timesteps was assessed through Pearson’s and Spearman’s rank correlation analysis. Finally, the drought propagation time (DPT) from meteorological to hydrological drought was estimated by ‘theory of run.’ New hydrological insights for the regionOur major findings include: (i) The spatial coverage of DHI and DFI, based on SPI/EDI, illustrate that mainly south and coastal regions of the study area are the most ‘drought-prone’ (ii) A considerable proportion of streamflow stations shows a significant trend of decrease in annual streamflow, with the most dominant year of abrupt change is 1996; (iii) Hydrological droughts are increasing in the study area; (iv) Performance of EDI with SSFI is found to be better than SPI at 3-month timestep; and (v) DPT can be found using ‘theory of run’ however, defined DPT cannot be directly applied to other regions.

Changes in Meteorological Dry Conditions across Water Management Zones in Uganda

This study analysed trends and variability in extreme precipitation and potential evapotranspiration (PET) indices across Uganda. We made use of daily precipitation and temperature datasets from 1979 to 2013 obtained from the Climate Forecast System Reanalysis (CFSR) products of the National Centres for Environmental Prediction (NCEP). The PET was estimated using the Hargreaves method based on the minimum and maximum temperature. Analysis was focused on the level of Water Management Zones (WMZs). Examples of the extracted extreme climatic indices included the annual maximum number of consecutive days with precipitation intensity < 1 mm/day (CDD1) or < 5 mm/day (CDD5), annual sum of PET for days with PET rate > 5 mm/day (SPETD5), and spell of high evaporative demand (or annual maximum number of consecutive days with PET > 5 mm/day) (CDPET5). Attributes of the variability in meteorological dry conditions were investigated. Trend and variability were investigated using an approach based on the cumulative sum of difference (CSD) between exceedance and non-exceedance counts of data points. The magnitudes of the linear decrease or increase in the precipitation and PET indices were determined using Sen's method. To apply these methods, the CSD-based Sub (Trend) and Variability Analysis Tool (CSD-VAT) was used. The long-term mean of the extracted indices showed Kyoga and Victoria to be the driest and wettest WMZs, respectively. An increase in the number of dry days (indicating increasing severity of meteorological dry conditions) was confined to the Karamoja region or the eastern parts of the Upper Nile and Kyoga WMZs. However, the rest of the country generally experienced negative trends in the precipitation and PET indices. Extreme precipitation indices of Kyoga and the Upper Nile WMZs exhibited consecutive positive and negative sub-trends over the early 1980s and from 1985 to 2013, respectively. Victoria and Albert WMZs mainly exhibited negative sub-trends for both precipitation and PET indices especially from the mid-1980s to the end of the data. Across Victoria and Albert WMZs, we found that the variability in extreme precipitation indices such as CDD1 and CDD5 was significant (p<0.05). However, these WMZs were characterized by insignificant (p>0.05) variability of the PET indices such as SPETD5 and CDPET5. Changes in the precipitation and PET indices were significantly (p<0.05) found to be positively linked to the quasi-biennial oscillation. The meteorological drought indicators were significantly (p<0.05) negatively correlated with the Indian Ocean dipole and Atlantic multidecadal oscillation. Our findings based on NCEP's CFSR data show that variation in sub-trends of the meteorological dry conditions across Uganda resonates well with the changes in some indicators of large-scale ocean-atmosphere conditions. This is important for planning predictive adaptation to the impacts of climate variability on water resources applications which are sensitive to the severity of meteorological dry conditions. Apart from our findings, we introduced a number of extreme PET indices which can be used for analysis of meteorological dry conditions or drought.

Evaluation of Drought in the North of Thailand using Meteorological and Satellite-Based Drought Indices

This research attempts to evaluate drought impact on vegetation dynamics using various drought indices including the vegetation condition index (VCI), the temperature condition index (TCI), the vegetation health index (VHI) and The standardized precipitation index (SPI). Monthly VCI, TCI and VHI values were obtained from multi-temporal Terra/MODIS products from 2000 to 2017. Temporal and spatial scales of drought occurrences were detected from seasonal variations and drought severity maps generated from these drought indices. The correlation analysis was analyzed to identify the dominant factors that control vegetation variability. The results found that VHI was better to detect vegetation health in the mountainous areas at higher altitudes and lower temperatures. VCI and SPI captured drought occurrences for paddy fields in the southern part of the region where there was lower rainfall and vegetation cover. The result can be applied for crop and drought early warnings in the region.

OneMap Drought Monitoring Analysis Based on Statistical Models

As the effects of droughts on agriculture and industrial water availability intensify with climate change, developing suitable drought prevention and mitigation measures has become increasingly important. However, measuring drought conditions using different indices leads to disjointed drought management responses by ministries and agencies. Additionally, indices based on only one variable are insufficient to accurately assess drought conditions. Therefore, creating and adopting a OneMap drought index would be beneficial in the assessment of drought conditions and the implementation of appropriate measures. In this study, we used multivariate statistical modeling using Bayesian principal component analysis to develop a OneMap drought index that unifies existing measures of drought conditions, including meteorological, agricultural, and hydrological drought indices. After evaluating the accuracy of the corrected OneMap drought index based on the self-organizing migrating algorithm optimization technique, it was found that the applicability of the OneMap drought index and its ability to regenerate drought were excellent for ground and satellite data. Therefore, the authors recommend implementing step-by-step drought management action plans using the integrated index to generate drought forecasts and warnings, thus promoting concerted and effective responses of local governments and authorities.

Effects of different types of drought on vegetation in Huang-Huai-Hai River Basin, China

Due to climate change and human activities, drought has become one of the most complex natural disasters in the Huang-Huai-Hai River basin, China. The purpose of the current research is to analyze the response of vegetation to drought in the Huang-Huai-Hai River basin by calculating the lag time correlation coefficient between the enhanced vegetation index (EVI) and drought indices. First, the water storage deficit index (WSDI) calculated from the total water storage anomalies (TWSA) was used as the hydrological drought index. The meteorological drought index we used was the self-calibrating Palmer drought severity index (scPDSI) provided by the Climate Research Unit (CRU). Then, the relationship between hydrological and meteorological drought was obtained based on the wavelet coherent change. Finally, the response of different vegetation types to drought was analyzed based on the lag time correlation coefficient. The results showed that: the average time of hydrological drought lagged meteorological drought in the three first-level basins of the Huang-Huai-Hai River basin, respectively, was 50 days (Yellow River basin), 43 days (Huaihe River basin), and 35 days (Haihe River basin); the lag time correlation coefficient of the different time scales (1–12 months) can reflect the sensitivity of vegetation to drought in different vegetation types and regions. The forest was the least sensitive to drought in the Huang-Huai-Hai River basin, followed by cropland, while grassland was the most sensitive to drought. It was discovered that hydrological drought had a huge influence on vegetation rather than meteorological drought by comparing the maximum lag time correlation coefficients of EVI with drought indices (scPDSI and SPEI). This study stresses the importance of taking into account the effects of various types of drought on the vegetation in the Huang-Huai-Hai River basin. This will help us better understand how drought affects vegetation.