Integrated Drought Index Research Articles

Integrated Drought Monitoring and Evaluation through Multi-Sensor Satellite-Based Statistical Simulation

To proactively respond to changes in droughts, technologies are needed to properly diagnose and predict the magnitude of droughts. Drought monitoring using satellite data is essential when local hydrogeological information is not available. The characteristics of meteorological, agricultural, and hydrological droughts can be monitored with an accurate spatial resolution. In this study, a remote sensing-based integrated drought index was extracted from 849 sub-basins in Korea’s five major river basins using multi-sensor collaborative approaches and multivariate dimensional reduction models that were calculated using monthly satellite data from 2001 to 2019. Droughts that occurred in 2001 and 2014, which are representative years of severe drought since the 2000s, were evaluated using the integrated drought index. The Bayesian principal component analysis (BPCA)-based integrated drought index proposed in this study was analyzed to reflect the timing, severity, and evolutionary pattern of meteorological, agricultural, and hydrological droughts, thereby enabling a comprehensive delivery of drought information.

Hydroclimatic aggregate drought index (HADI): a new approach for identification and categorization of drought in cold climate regions

Drought identification is crucial to water resources management and planning. Different drought indices have been developed and their complexity and applicability vary. The objectives of this research are to develop a new integrated drought index with the capability of identification of drought and to further customize drought categorization for cold climate regions. Specifically, a new hydroclimatic aggregate drought index (HADI) is developed by coupling with a grid-based hydrologic model and applying the R-mode correlation-based principal component analysis. The HADI is a composite drought index, which assesses the anomalies of rainfall, surface runoff, snowmelt, and soil moisture in the root zone. Furthermore, joint probability distribution function of drought frequencies and classes as well as conditional expectation are used for drought categorization. The HADI was applied to the Red River of the North Basin (RRB) and its performance was evaluated by comparing with the Palmer Drought Severity Index (PDSI) and the U.S. Drought Monitor (USDM) products. Based on the impacts of drought on agriculture, the HADI outperformed the PDSI in identification of droughts in the RRB. Although the HADI and USDM showed a good agreement in identification of drought periods, the drought area coverages for each drought category from the two methods differed. The new customized drought categorization based on variable threshold levels accounted for the variations in both time and geographical locations. The new HADI, together with the customized drought categorization, is able to provide more accurate drought identification and characterization, especially for cold climate regions.

Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index

Satellite remote sensing provides unprecedented information on near-surface soil moisture at a global scale, enabling a wide range of studies such as drought monitoring and forecasting. Data Assimilation (DA) has been recognized as an effective means to incorporate such observations into hydrologic models to better predict and forecast hydroclimatic variables. In this study, we use a recently developed Evolutionary Particle Filter with Markov Chain Monte Carlo (EPFM) approach to assimilate Soil Moisture Active Passive (SMAP) soil moisture data into Variable Infiltration Capacity (VIC) hydrologic model to provide more reliable topsoil layer moisture (0~–5 cm) over the entire Continental United States (CONUS). The EPFM outperformed an Ensemble Kalman filter (EnKF) in terms of correlations and the unbiased root mean square error (ubRMSE) with in situ measurements from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN). Also, we used a multivariate probability distribution based on a Copula function to integrate the posterior soil moisture, precipitation (from the North American Land Data Assimilation System (NLDAS)) and evapotranspiration (from the Moderate Resolution Imaging Spectroradiometer (MODIS)) information to develop a new integrated drought index, i.e. SPESMI. To validate the usefulness of the developed integrated drought index, we compared the drought events detected by this index with those reported by the United States Drought Monitor (USDM). The results indicated a strong temporal consistency of the drought areas detected by our approach and the USDM over the entire period of study (April 2015 to June 2018). In addition to such promising results, we noticed that our approach could capture the flash drought in 2017 in the U.S. Northern Plains earlier than the USDM, and could identify some severe to extreme drought events that had been underestimated by the USDM. Moreover, the SPESMI has a high correlation with the yield loss of spring and winter wheat in the United States. This novel drought monitoring framework can serve as an independent and potentially complementary drought monitoring system.

Drought Onset and Termination in India

AbstractDespite the implications of meteorological drought propagation to agricultural, hydrological, and groundwater droughts, the focus of previous studies has been primarily on meteorological droughts in India. We use the well‐calibrated and evaluated Variable Infiltration Capacity‐ Simple Groundwater Model (VIC‐SIMGM) to simulate soil moisture, runoff, and groundwater storage variability in India for the 1951–2016 period. The Integrated Drought Index (IDI) that combines meteorological, agricultural, hydrological, and groundwater droughts was developed for the 1951–2016 period for India. Using a spatial clustering algorithm based on the traditional interpoint distance metric, eight homogeneous clusters based on IDI were identified. The majority of clusters in India experience the onset and termination of droughts during the summer monsoon season (June–September). The analysis of moisture back trajectories using the Hybrid Single‐Particle Lagrangian Integrated Trajectory (HYSPLIT) model showed that the Arabian Sea and Bay of Bengal are the two major moisture sources for the identified clusters in India. We performed the Empirical Orthogonal Function (EOF) and Maximum Covariance Analysis (MCA) using monthly IDI and Sea Surface Temperature (SST) to evaluate the influence of long‐term climate variability on droughts in India. Droughts based on 1‐month IDI that affect a majority of drought clusters are associated with the positive phase of El Nino Southern Oscillations (ENSO) and Indian Ocean Dipole (IOD). On the other hand, drought clusters in the Gangetic Plain and peninsular India are affected by the SST warming over the Indian Ocean. Overall, drought clusters based on IDI, their moisture source, and large‐scale teleconnection can assist in drought management and assessment in India.

Assessing Meteorological and Agricultural Drought in Chitral Kabul River Basin Using Multiple Drought Indices

Drought is a complex and poorly understood natural hazard in complex terrain and plains lie in foothills of Hindukush-Himalaya-Karakoram region of Central and South Asia. Few research studied climate change scenarios in the transboundary Chitral Kabul River Basin (CKRB) despite its vulnerability to global warming and importance as a region inhabited with more than 10 million people where no treaty on use of water exists between Afghanistan and Pakistan. This study examines the meteorological and agricultural drought between 2000 and 2018 and their future trends from 2020 to 2030 in the CKRB. To study meteorological and agricultural drought comprehensively, various single drought indices such as Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI) and Vegetation Condition Index (VCI), and combined drought indices such as Scaled Drought Condition Index (SDCI) and Microwave Integrated Drought Index (MIDI) were utilized. As non-microwave data were used in MIDI, this index was given a new name as Non-Microwave Integrated Drought Index (NMIDI). Our research has found that 2000 was the driest year in the monsoon season followed by 2004 that experienced both meteorological and agricultural drought between 2000 and 2018. Results also indicate that though there exists spatial variation in the agricultural and meteorological drought, but temporally there has been a decreasing trend observed from 2000 to 2018 for both types of droughts. This trend is projected to continue in the future drought projections between 2020 and 2030. The overall study results indicate that drought can be properly assessed by integration of different data sources and therefore management plans can be developed to address the risk and signing new treaties.

Integrated Drought Index (IDI) for Drought Monitoring and Assessment in India

AbstractDrought monitoring and declaration in India are challenging due to the requirement of multiple drought indices representing meteorological, hydrological, and agricultural droughts that are often not available in near real‐time. In addition, the current drought monitoring efforts do not consider groundwater storage variability. To overcome this, we develop an Integrated Drought Index (IDI) that combines the response of meteorological, hydrological, and agricultural droughts and accounts for groundwater storage. We use the Gaussian copula to integrate the 12‐month Standardized Precipitation Index (SPI), 4‐month Standardized Runoff Index (SRI), 1‐month Standardized Soil moisture Index (SSI), and 1‐month Standardized Groundwater Index (SGI) to develop IDI. Hydrologic variables (total runoff, soil moisture, and groundwater) required in IDI were simulated using the Variable Infiltration Capacity (VIC) with SIMple Groundwater Model (VIC‐SIMGM). We evaluated IDI against the Drought Severity Index (DSI), terrestrial and groundwater storage anomalies from the Gravity Recovery and Climate Experiment (GRACE) satellites, groundwater well, and streamflow anomalies. Moreover, we identify the three major droughts with the highest severity (based on IDI) that occurred in 1965, 1987, and 2002 in the Sabarmati river basin. The three most severe droughts occurred in 1966, 1979, and 2010 in the Brahmani basin. Notwithstanding the large intermodel uncertainty, which arises primarily from precipitation projections, the drought frequency based on IDI is projected to decline in Sabarmati while it increases in Brahmani basin under the warming climate. Our results show that IDI can be effectively used for drought monitoring and assessment under retrospective and future climate in India.

Development of a Non-Linear Integrated Drought Index (NDI) for Managing Drought and Water Resources Forecasting in the Upper Tana River Basin-Kenya

This article uses the non-linear integrated drought index (NDI) for managing drought and water resources forecasting in a tropical river basin. The NDI was formulated using principal component analysis (PCA). The NDI used hydro-meteorological data and forecasted using recursive multi-step neural networks. In this article, drought forecasting and projection is adopted for planning ahead for mitigation and for the adaptation of adverse effects of droughts and food insecurity in the river basin. Results that forecasting ability of NDI model using ANNs decreased with increase in lead time. The formulated NDI as a tool for projecting into the future.

Satellite observed reversal in trends of tropical and subtropical water availability

Understanding the spatially and temporally non-uniform water availability trends is key to assess changes of tropical and subtropical ecosystem productivity. However, their detailed spatial-temporal patterns and the relative role of local to the overall trend are not well known. This study provides a comprehensive investigation in characteristics of the time-varying water availability trend using the ensemble empirical mode decomposition (EEMD) method based on state-of-the-art satellite microwave observation derived microwave integrated drought index (MIDI) from 1998 to 2016. The results indicated an increased area with wetting trend since 1998, and the overall water availability reversed a drying tendency to a wetting trend around 2005. Also, the Northern Hemisphere became wetter and the Southern Hemisphere became dryer in 2016, demonstrating a transformation of favorable climate from the Southern Hemisphere to the Northern Hemisphere. Spatial patterns of the trend shape determined spatial distributions of the trend contributions, showing an opposite contribution between subtropics and tropics. Additionally, tropical rainforests and shrublands governed the overall trend reversely. This analysis helps to understand the non-uniform patterns of water availability trend and related ecosystem productivity over tropics and subtropics.

Remote Sensing-based Agricultural Drought Monitoring using Hydrometeorological Variables

A new drought index, the agricultural dry condition index (ADCI), was developed to combine various hydrometeorological variables associated with agricultural droughts. It was calculated by applying weights to the soil moisture, vegetation activity, and land surface temperature data, which are used to monitor agricultural droughts. The vegetation health index (VHI) and microwave integrated drought index (MIDI) are also used to monitor agricultural droughts; these were calculated using satellite image data collected between 2001 and 2015 in South Korea and their spatiotemporal variations were analyzed. In order to compare the ADCI with actual agricultural drought conditions, land in South Korea was divided into two classes (rice paddies and croplands) and the ADCI values were compared to the corresponding crop yields (rice from rice paddies, potatoes and soybeans from croplands). There was no significant correlation between the ADCI and crop yield for the rice paddies because the water supply is controlled by irrigation. However, in the croplands there was a high degree of correlation with correlation coefficients of 0.83 and 0.80 for potatoes and soybeans, respectively. In order to confirm agreement with the actual affected areas, a receiver operating characteristic analysis was conducted for 2001 and 2015 when there was severe drought. This analysis found that the ADCI peaked at 0.68 in 2001 (June) 0.64 in 2015 (June). The ADCI was found to be highly applicable to the assessment of agricultural drought conditions. The VHI responded positively to land surface temperature while the MIDI responded to rainfall. However, the ADCI showed the best results because it is a weighted index of the input data, such as the land surface temperature, soil moisture, and vegetation activity, and their combination. The results confirmed that soil moisture, vegetation activity, and land surface temperature are the most important variables associated with droughts and that the ADCI can be effectively used to monitor agricultural droughts.

Extreme Drought Events over Brazil from 2011 to 2019

Drought-related disasters are among the natural disasters that are able to cause large economic and social losses. In recent years, droughts have affected different regions of Brazil, impacting water, food, and energy security. In this study, we used the Integrated Drought Index (IDI), which combines a meteorological-based drought index and remote sensing-based index, to assess the drought events from 2011 to 2019 over Brazil. During this period, drought events were observed throughout the country, being most severe and widespread between the years 2011 and 2017. In most of the country, the 2014/15 hydrological year stands out due to the higher occurrence of severe and moderate droughts. However, drought intensity and observed impacts were different for each region, which is shown by the different case studies, assessing different types of impacts caused by drought in Brazil. Thus, it is fundamental to evaluate the impacts of droughts in a continental country such as Brazil, where a variety of vegetation, soil, land use, and especially different climate regimes predominate.

Assessment of drought evolution characteristics based on a nonparametric and trivariate integrated drought index

Drought index is an important tool for drought research. The development of a reliable and simple integrated drought index is a critical step of current drought monitoring and risk assessments. The Gringorten point position formula was adopted to develop a trivariate standardized drought index (TSDI) based on precipitation (meteorology), runoff (hydrology), and soil moisture (agriculture) information in this study. This index can be used to characterize droughts from an integrated meteorological, hydrological, and agricultural point of view, and explore drought evolution characteristics. The possible driving forces of drought were explored by examining the associations between sunspot activities and large-scale atmospheric circulation anomalies. A typical Loess Plateau watershed, the Wei River Basin (WRB), was selected as a case study. Results showed that: 1) the TSDI is better at characterizing real drought conditions than other traditional single-variable indices; although it is similar to the parametric trivariate drought index based on copula function (Serinaldi and Grimaldi., 2007; Yang et al., 2018), it is much simpler to calculate and more suitable for characterizing drought of different regions; 2) the value of integrated drought index gradually increased in all areas, and its change point occurred in the early 1970s and 1990s mainly due to variations in runoff; 3) the evolution of the integrated drought appeared to be strongly influenced by sunspot activities and abnormal atmospheric circulation factors, where sunspots activities are dominant. This study provides a valuable reference for the development of an integrated drought index, which might be applied to local drought monitoring, preparedness and mitigation.

A new station-enabled multi-sensor integrated index for drought monitoring

Remote sensing data are frequently incorporated into drought indices used widely by research and management communities to assess and diagnose current and historic drought events. The integrated drought indices combine multiple indicators and reflect drought conditions from a range of perspectives (i.e., hydrological, agricultural, meteorological). However, the success of most remote sensing based drought indices is constrained by geographic regions since their performance strongly depends on environmental factors such as land cover type, temperature, and soil moisture. To address this limitation, we propose a framework for a new integrated drought index that performs well across diverse climate regions. Our framework uses a geographically weighted regression model and principal component analysis to composite a range of vegetation and meteorological indices derived from multiple remote sensing platforms and in-situ drought indices developed from meteorological station data. Our new index, which we call the station-enabled Geographically Independent Integrated Drought Index (GIIDI_station), compared favorably with other common drought indices such as Microwave Integrated Drought Index (MIDI), Optimized Meteorological Drought Index (OMDI), Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Vegetation Condition Index (VCI). Using Pearson correlation analyses between remote sensing and in-situ drought indices during the growing season (April to October) from 2002 to 2011, we show that GIIDI_station had the best correlations with in-situ drought indices. Across the entire study region of the continental United States, the performance of GIIDI_station was not affected by common environmental factors such as precipitation, temperature, land cover and soil conditions. Taken together, our results suggest that GIIDI_station has considerable potential to improve our ability of monitoring drought at regional scales, provided local meteorological station data are available.

Spatio-temporal characteristics of drought structure across China using an integrated drought index

Previous studies have focused on drought duration, intensity and frequency without explicitly investigating the timing of droughts, such as the drought onset and demise, as well as their transition periods, which is of importance for agricultural production. Furthermore, large discrepancies exist for those studies using a single-variable based drought index. In this study, a nonparametric multivariate standardized drought index (NMSDI) that combines information about precipitation and streamflow was applied to investigate the spatial and temporal features of the drought structure in China. Subsequently, the relationships between El Niño-Southern Oscillation (ENSO) events and NMSDI variations were examined. The results indicate that: the NMSDI exhibited comparable performance when compared to the corresponding Standardized Precipitation Index and Standardized Streamflow Index, and importantly, it is more sensitive to capturing the onset, persistence, and termination of droughts; the preferred season of drought onset is in summer, with drought demise occurring in winter; the average drought duration in China is nearly 6 months, which is longer than the transition periods of drought onset (3 months) and demise (3 months); ENSO events have a strong influence on drought variations, with the effects depending on climate zones across China. Our results on the timing of droughts have important implications for drought mitigation and adaptation in China, which is helpful for agricultural production under the context of climate warming.

Improving meteorological drought monitoring capability over tropical and subtropical water-limited ecosystems: evaluation and ensemble of the Microwave Integrated Drought Index

The monitoring of meteorological drought is critically important for tropical and subtropical water-limited ecosystems, which play key roles in the global carbon cycle and ecosystem services. Satellite remote sensing is the only practical approach for tracking drought changes with high resolution and an instantaneous response. However, the application of remote sensing drought indices derived from optical and infrared bands has been constrained due to cloud contamination. Here the Microwave Integrated Drought Index (MIDI), a multi-sensor microwave remote sensing drought index integrating state-of-the-art multiple satellite microwave-derived precipitation, soil moisture and land surface temperature information, was processed to improve the meteorological drought monitoring capability of satellite remote sensing. The index was evaluated against the 1 to 12 month field-based drought indicators Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) and anomalies in the satellite-derived Enhanced Vegetation Index (EVI) from 1998 to 2014. MIDI with an optimal ensemble of multiple satellite precipitation products showed a better improvement in meteorological drought monitoring capability than MIDI based on an individual satellite precipitation dataset, and had statistically strong correlation in particular with 1 month SPI/SPEI over a wide range of bio-climatic zones spanning from arid to humid. Moreover it showed significantly higher correlations with EVI anomalies than SPI/SPEI. The results demonstrated the reliability and superiority of MIDI in monitoring meteorological drought with the ability to work in all weather conditions. Therefore, MIDI provides a gateway to employing satellite microwave remote sensing for reliable meteorological drought monitoring, with the potential for long-term drought assessments as well as near-real-time drought monitoring using operational satellites.

A new multi-sensor integrated index for drought monitoring

Drought is one of the most expensive but least understood natural disasters. Remote sensing based integrated drought indices have the potential to describe drought conditions comprehensively, and multi-criteria combination analysis is increasingly used to support drought assessment. However, conventional multi-criteria combination methods and most existing integrated drought indices fail to adequately represent spatial variability. An index that can be widely used for drought monitoring across all climate regions would be of great value for ecosystem management. To this end, we proposed a framework for generating a new integrated drought index applicable across diverse climate regions. In this new framework, a local ordered weighted averaging (OWA) model was used to combine the Temperature Condition Index (TCI) from the Moderate-resolution Imaging Spectroradiometer (MODIS), the Vegetation Condition Index (VCI) developed using the Vegetation Index based on Universal Pattern Decomposition method (VIUPD), the Soil Moisture Condition Index (SMCI) derived from the Advanced Microwave Scanning Radiometer–Earth Observation System (AMSR-E), and the Precipitation Condition Index (PCI) derived from the Tropical Rainfall Measuring Mission (TRMM). This new index, which we call the “Geographically Independent Integrated Drought Index (GIIDI),” was validated in diverse climate divisions across the continental United States. Results showed that GIIDI was better correlated with in-situ PDSI, Z-index, SPI-1, SPI-3 and SPEI-6 (overall r-value = 0.701, 0.794, 0.811, 0.733, 0.628; RMSE = 1.979, 0.810, 0.729, 1.049 and 1.071, respectively) when compared to the Microwave Integrated Drought Index (MIDI), Optimized Meteorological Drought Index (OMDI), Scaled Drought Condition Index (SDCI), PCI, TCI, SMCI, and VCI. GIIDI also performed well in most climate divisions for both short-term and long-term drought monitoring. Because of the superior performance of GIIDI across diverse temporal and spatial scales, GIIDI has considerable potential for improving our ability to monitor drought across a range of biomes and climates.

Drought Variability and Trends over the Central United States in the Instrumental Record

Abstract We examined drought variability and trends over the last century (1916–2013) over the conterminous United States (CONUS) using observed precipitation P, temperature T, and reconstructed total moisture percentiles (TMP) and runoff from four land surface models. We used an integrated drought index (IDI), which we defined as the equally weighted mean of the 3-month standardized runoff index (SRI3) and TMP from four land surface models mapped onto a uniform probability distribution. Using a definition of drought as IDI less than 0.3 for 6 months or longer, we identified 16 drought events, which we termed great droughts that covered more than 50% of the CONUS during our study period. We examined the properties of great droughts and compared these with the 2012 event. The great droughts were located at least partially over the central United States (30°–42°N, 85°–110°W). We found that 12 of these great droughts occurred when cold sea surface temperature anomalies (SSTAs) were located in the tropical Pacific with warm SSTAs in the North Atlantic. We also found a predominance of decreasing trends in IDI; droughts occurred less often and events were less severe as time progressed. In particular, only 2 of the 16 great droughts (2012 and 1988) occurred in the second half of the record.

Analyzing drought history using Fuzzy Integrated Drought Index (FIDI): a case study in the Neyshabour basin, Iran

In arid to semi-arid climates, monitoring drought is very complicated because of different hydrometeorology variables effect on it. It is proposed in this paper to develop Fuzzy Integrated Drought Index (FIDI) which combines most important effective factors in developing drought. At first, Variable Infiltration Capacity (VIC) model calibrated simulated runoff to outlet basin runoff data for years 1993–1995. Results represent high performance of model in simulating runoff of outlet basin. Then, Precipitation Anomaly Percentage Index (PAPI), actual Evapotranspiration Anomaly Percentage Index (EAPI), Runoff Anomaly Percentage Index (RAPI), and Soil Moisture Anomaly Percentage Index (SMAPI) were constructed. FIDI was compared with the PAPI, RAPI and SMAPI for the period of 1985 to 2014. The results indicate that (1) the FIDI has more ability in determining start and persistence of drought event compared with PAPI, RAPI, and SMAPI; (2) in the low time scales, PAPI and SMAPI have high correlation with FIDI, and in the higher time scales, RAPI has the high correlation with FIDI; (3) spatially, the middle, west, and portion of north have higher drought risk in the Neyshabour basin.

Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices

Numerous drought indices have been developed to monitor drought conditions. However, different drought indices have differing characteristics, and are suitable for specific environments. The aim of this study was to compare the occurrence of drought, as detected by remote sensing across the Continental United States (CONUS). We used drought events during 2011 and 2012 to compare various indices developed for the study of drought phenomena. Three in situ drought indices, the Palmer drought severity index (PDSI), Z-index and standardized precipitation indices (SPI) with different time scales were used to evaluate drought conditions in different climate divisions. The drought indices compared in this study include the vegetation condition index (VCI), the temperature condition index (TCI), the perpendicular drought index (PDI) and modified PDI (MPDI) derived from moderate-resolution imaging spectroradiometer (MODIS) data, the precipitation condition index (PCI) derived from tropical rainfall measuring mission (TRMM) data, and the soil moisture condition index (SMCI) derived from advanced microwave scanning radiometer - earth observing system (AMSR-E). Other synthesized drought indices, which combine indices such as VCI, TCI, SMCI and PCI, were also compared in this study. These included the vegetation health index (VHI), temperature vegetation dryness index (TVDI), scaled drought condition index (SDCI), the microwave integrated drought index (MIDI), the synthesized drought index (SDI), the optimized meteorological drought index (OMDI), and the optimized vegetation drought index (OVDI). The results include a wide variety of drought conditions based on different drought indices. Meteorological drought indices indicated that more regions were under severe drought than agricultural drought indices did. Drought indices appear to be more similar to standard drought indices than PDI, MPDI and TVDI. The results also indicate that different indices have strengths and weaknesses in different climates across CONUS. SMCI has a good correlation with short-term SPI, and the sensitivity of SMCI is strongly dependent on terrain, as it performs worse in regions with heavy tree cover than in regions with a low density of vegetation. TCI, VCI, PDI and MPDI are more similar to 3-month SPI data, but correlate weakly with station-based indices located in areas of high precipitation, higher soil permeability, large-scale agriculture and forests. PCI is more strongly correlated with short-term drought conditions in almost all climate divisions than other single indices. VCI would be more reliable if there were only red and NIR surface reflectance bands available, while VHI would be a better choice if only NDVI and LST data were available. Condition index-based drought indices (PCI, TCI, VCI, SMCI, VHI, SDCI, SDCI, MIDI, OVDI, OMDI) performed better than other categories of drought indices, and the use of time series analysis may be a contributing factor to this difference in performance.

Copula-based drought risk assessment combined with an integrated index in the Wei River Basin, China

It is critical to assess drought risk based on a reliably integrated drought index incorporating comprehensive information of meteorology, hydrology and agriculture drought indices, which is of great value for further understanding the future drought tendency, prevention and mitigation. Thus, the primary objective of this study was to focus on constructing a multivariate integrated drought index (MIDI) by coupling four drought indices (i.e., Precipitation Anomaly Percentage (PAP), Runoff Anomaly Percentage (RAP), Standardized Precipitation Index with 6-month aggregation time step (SPI6) and Modified Palmer Drought Severity Index (MPDSI)) to objectively and comprehensively investigate drought risk. The variable fuzzy set theory and entropy weight method are used during the MIDI construction process. Based on the MIDI, a drought event including drought duration and severity is redefined using run theory. Then copula-based drought risk is fully assessed through the joint probability distribution of drought duration and severity. Results indicate the following: (1) the constructed MIDI is consistent with the Standardized Precipitation Index (SPI) and Runoff Anomaly Percentage (RAP) series, and it is more sensitive and effective to capture historical drought events; (2) the drought characteristics present noticeable spatial variability among five subzones, and the entire basin has 49 droughts with the longest drought duration spanning 8.55months; and (3) the mainstream, especially the middle and lower reaches, has higher occurrences of severe droughts for approximately every 10years.

Transitional variations and risk of hydro-meteorological droughts in the Tarim River basin, China

The Tarim River basin, China, is a typical arid inland river basin in China. Management of water resources and agricultural development rely heavily on the understanding of droughts. In this study, an integrated drought index is proposed, based on the Standardized Precipitation Evapotranspiration Index and the Standardized Runoff Index, and then the changing properties of drought regimes have been analyzed using a Markov Chain model. Results indicate that: (1) the Kaidu and Aksu Rivers are dominated by prompt transition between hydrological and meteorological droughts. Long-lasting droughts heavily impact agricultural development in the Kaidu and Yarkand River basins; (2) the Kaidu and Aksu River basins are influenced mainly by hydro-meteorological droughts and hydrological drought is dominant in the Yarkand River basin; (3) different drought conditions are the results of different sources of river runoff. Increasing precipitation alleviate droughts in the Tarim River basin. However, a drying tendency can still be found in the Kaidu River basin; (4) higher probability is detected for the transition from both meteorological and hydrological wet to both meteorological and hydrological dry, implying that the Tarim River basin is sensitive to both meteorological and hydrological droughts. Results of this study are of practical value for the regional management of water resources, planning of agricultural irrigation, and measures for mitigation of hydrological and meteorological droughts.