Developed Drought Indices Research Articles

An enhanced Standardized Precipitation–Evapotranspiration Index (SPEI) drought-monitoring method integrating land surface characteristics

Abstract. Atmospheric evaporative demand is a key metric for monitoring agricultural drought. Existing ways of estimating evaporative demand in drought indices do not faithfully represent the constraints imposed by land surface characteristics and become less accurate over nonuniform land surfaces. This study proposes incorporating surface vegetation characteristics, such as vegetation dynamics data, aerodynamic parameters, and physiological parameters, into existing potential-evapotranspiration (PET) methods. This approach is implemented across the continental United States (CONUS) for the period from 1981–2017 and is tested using a recently developed drought index, the Standardized Precipitation–Evapotranspiration Index (SPEI). We show that activating realistic maximum surface conductance and aerodynamic conductance could improve the prediction of soil moisture dynamics and drought impacts by 29 %–41 % on average compared to more simple, widely used methods. We also demonstrate that this is especially effective in forests and humid regions, with improvements of 86 %–89 %. Our approach only requires a minimal amount of ancillary data while allowing for both historical reconstruction and real-time drought forecasting. This offers a physically meaningful yet easy-to-implement way to account for vegetation control in drought indices.

Differences in drought characteristics, progression, and recession across ecosystem types in the pantropical region of the Lancang-Mekong River Basin

Exploring the development and impacts of drought across different ecosystems can offer new insights for mitigating the adverse effects of drought events. Using the pantropical Lancang-Mekong River Basin as the study region, we investigated the agricultural, ecological, and hydrological drought characteristics and explored their drought progression and recession rates across four vegetation ecosystem types: tropical forests, subtropical forests, shrubs, and crops. We utilized newly developed drought indices based on the ERA5-Land reanalysis dataset, GOSIF chlorophyll fluorescence data, and modified Moderate Resolution Imaging Spectroradiometer (MODIS) land cover data. The results showed that agricultural and hydrological droughts exhibited increasing trends from 2001 to 2021, whereas ecological drought displayed a decreasing trend over the same period. The cropland region experienced the fewest drought events, shortest drought durations, slowest progression rates, and lowest recession rates. By contrast, the two evergreen, broadleaf forest ecosystems (subtropical and tropical forests) experienced the highest number of drought events and fastest progression and recession rates. The findings suggest a trade-off relationship between vegetation resistance and recovery, where faster drought onset is associated with faster drought recession for ecological drought. Given the more severe challenges posed by agricultural and hydrological droughts, the riparian countries in the Lancang-Mekong River Basin should adopt proactive financial and management measures to mitigate the adverse impacts of these drought types. The insights gained from this study can inform the development of targeted strategies for drought monitoring, preparedness, and response across diverse ecosystems.

A drought index based on groundwater quantity and quality: Application of multivariate copula analysis

In this paper, a new Groundwater Quantity-Quality-based Drought Index (GQQI) is developed based on multivariate Copula analysis of groundwater quantity and quality indicators. For evaluating the developed index, its temporal and spatial distribution is studied and compared to those of some other indices, such as Standardized Salinity Index (SSI), Standardized Groundwater Index (SGI), and Standardized Water level Index (SWI). The proposed index is applied for the temporal and spatial evaluation of the quantity and quality of groundwater in theLake Urmia basin, having 1084 piezometric and 935 groundwater quality monitoring wells. For a more comprehensive analysis of drought, 24 marginal and 26 joint probability distribution functions are driven. Based on the results, the developed GQQI is correlated with drought indices of SSI, SGI, and SWI by 88%, 86%, and 61%, respectively. Moreover, the values of the developed drought index indicates the occurrence of more severe droughts, compared to those detected by other drought indices. This can be resulted from considering the combined effects of groundwater quantity and quality using the proposed index. In addition, the multivariate GQQI can spatially and temporally represent the drought severity over the basin, and identify severe and extreme drought conditions better than other univariate drought indices.

Identification of Drought Events in Major Basins of Africa from GRACE Total Water Storage and Modeled Products

Terrestrial water storage (TWS) plays a vital role in climatological and hydrological processes. Most of the developed drought indices from the Gravity Recovery and Climate Experiment (GRACE) over Africa neglected the influencing roles of individual water storage components in calculating the drought index and thus may either underestimate or overestimate drought characteristics. In this paper, we proposed a Weighted Water Storage Deficit Index for drought assessment over the major river basins in Africa (i. e., Nile, Congo, Niger, Zambezi, and Orange) with accounting for the contribution of each TWS component on the drought signal. We coupled the GRACE data and WaterGAP Global Hydrology Model through utilizing the component contribution ratio as the weight. The results showed that water storage components demonstrated distinctly different contributions to TWS variability and thus drought signal response in onset and duration. The most severe droughts over the Nile, Congo, Niger, Zambezi, and Orange occurred in 2006, 2012, 2006, 2006, and 2003, respectively. The most prolonged drought of 84 months was observed over the Niger basin. This study suggests that considering the weight of individual components in the drought index provides more reasonable and realistic drought estimates over large basins in Africa from GRACE.

Application of meteorological, hydrological and remote sensing data to develop a hybrid index for drought assessment

ABSTRACT In this study, a hybrid index called the aggregated drought index (ADI) is developed for drought assessment integrating various meteorological, hydrological, and agricultural features of the region. To this end, precipitation, reservoir storage, discharge, temperature, potential evapotranspiration, and the degree of vegetation based on remote sensing images are utilized to quantify ADI. Then, the performance of two models, auto-regressive integrated moving average (ARIMA) and adaptive neuro-fuzzy inference system (ANFIS), is investigated in predicting the developed drought index. The proposed framework is applied to the Aharchay watershed located in East Azarbaijan Province, Iran. The results demonstrate that the region has experienced normal and mild drought conditions during the investigated time period. ADI is also applied in another watershed (Ajichay) to show ADI’s capability in different climatic settings. Regarding the predictive capability, the ANFIS model outperforms ARIMA in drought prediction, particularly in severe weather conditions. The ADI benefits planning for drought mitigation and preparedness by incorporating several different aspects of the region.

A CONUS-Wide Standardized Precipitation–Evapotranspiration Index for Major U.S. Row Crops

Abstract Agricultural droughts afflicting the contiguous United States (CONUS) are serious and costly natural hazards. Widespread damage to a single cash crop may be crippling to rural communities that produce it. While drought is insidious in nature, drought indices derived from meteorological data and drought impact reports both provide essential guidance to decision-makers about the location and intensity of developing and ongoing droughts. However, response to dry meteorological conditions is not consistent from one crop type to the next, making crop-specific drought appraisal difficult using weather data alone. Additionally, drought impact reports are often subjective, latent, or both. To rectify this, we developed drought indices using meteorological data, and phenological information for the row crops most commonly grown over CONUS: corn, soybeans, and winter wheat. These are referred to as crop-specific standardized precipitation–evapotranspiration indices (CSPEIs). CSPEIs correlate more closely with end-of-season yields than traditional meteorological indicators for the eastern two thirds of CONUS for corn, and offer an advantage in predicting winter wheat yields for the High Plains. CSPEIs do not always explain a higher fraction of variance than traditional meteorological indicators. In such cases, results provide insight on which meteorological indicators to use to most effectively supplement impacts information.

Development of the Agricultural Water Demand and Supply Drought Index (AWDSDI) in Evaluating Daily Agricultural Drought in Small Administrative Districts

Drought is caused by a long period of lack of rainfall and water resources, and has a great impact on the life and ecosystem of both humans and animals. It is particularly important for the agricultural sector, which is closely related to food security. Global warming, urbanization, and industrialization have led to a gradually increasing demand for agricultural water. In response, Korea has steadily developed its agricultural water sources to reduce rising damage to the agricultural sector due to climate change. The severity of drought is evaluated by using meteorological, agricultural, hydrological, and socioeconomic drought indexes. The agricultural drought index is evaluated using soil moisture and crop dryness, and the hydrological drought index is evaluated based on water shortage by comparing demands with water resources available for supply, such as rivers and groundwater, reservoirs and dams. However, these methods were found to over- or under-estimate the relatively low sensitivity of agricultural water as they assess the shortage of water for life, industry, and agriculture. Therefore, in this study, we developed the Agricultural Water Demand and Supply Drought Index (AWDSDI), which evaluates agricultural drought by analyzing water supply systems such as agricultural reservoirs, pumping stations and drainage, groundwater systems, and demands for agricultural water. In order to review the applicability of the developed drought index, AWDSDI was applied to 32 Eps and Myuns in three cities, including Jincheongun, Changnyeonggun, and Jangseonggun in the period June-August 2017, when drought damage was significant. It was found that AWDSDI reproduced the daily agricultural drought well in small administrative districts such as Eps and Myuns. In addition, in order to verify the developed drought index, the evaluation results of AWDSDI, the previously developed agricultural drought index and the hydrological drought index were compared together. The comparison found that the AWDSDI reproduced the drought period and drought depth in 32 Eps and Myuns in three cities better than previously developed drought indices.

Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

Drought is a major natural disaster that creates a negative impact on socio-economic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001–2018 period, along with the Mann–Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p < 0.01). Additionally, a high performance of the new developed drought index, showing a strong correlation between EDSI and meteorological drought indices (i.e., standardized precipitation index (SPI) or the reconnaissance drought index (RDI)), measured at meteorological stations, giving r > 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions.

Non-stationary and copula-based approach to assess the drought characteristics encompassing climate indices over the Himalayan states in India

The most of the available drought indices do not incorporate the environmental changes in the present scenario of climate change. In an attempt to encompass the climate variability in the computation of meteorological drought, a non-stationary gamma distribution with climate indices in its location parameter as a covariate is proposed. The performance of the non-stationary drought is evaluated based on the statistical performance as compared to the stationary drought. Focusing on two Himalayan states in India, the meteorological drought events are described and assessed based on the stationary and non-stationary drought index. Moreover, the bivariate analysis of different drought properties is carried out and compared with the univariate analysis. The management indices such as reliability, resilience, and vulnerability are also computed based on the developed drought index. The results in the study indicate that in most of the cases the non-stationary drought index is capable of capturing the drought characteristics over the study areas. The variability in the probability density of different drought properties is observed under 12-month drought scale in most of the cases. During bivariate analysis, a compare difference is noticed between secondary and primary return periods. Moreover, higher reliability and resilience is noticed during 12-month scale drought period. The newly developed drought index and the copula-based analysis of drought properties provide a new concept for robust and effective management practices in the changing environment.

Assessment of Probabilistic Multi-Index Drought Using a Dynamic Naive Bayesian Classifier

The proper consideration of all plausible feature spaces of the hydrological cycle and inherent uncertainty in preceding developed drought indices is inevitable for comprehensive drought assessment. Therefore, this study employed the Dynamic Naive Bayesian Classifier (DNBC) for multi-index probabilistic drought assessment by integrating various drought indices (i.e., Standardized Precipitation Index (SPI), Streamflow Drought Index (SDI), and Normalized Vegetation Supply Water Index (NVSWI)) as indicators of different feature spaces (i.e., meteorological, hydrological, and agricultural) contributing to drought occurrence. The overall results showed that the proposed model was able to account for various physical forms of drought in probabilistic drought assessment, to accurately detect a drought event better than (or occasionally equal to) any single drought index, to provide useful information for assessing potential drought risk, and to precisely capture drought persistence in terms of drought state transition probability in drought monitoring. This easily produced an alternative method for comprehensive drought assessment with combined use of different drought indices.

Integrated Methodological Framework for Assessing the Risk of Failure in Water Supply Incorporating Drought Forecasts. Case Study: Andean Regulated River Basin

Hydroclimatic drought conditions can affect the hydrological services offered by mountain river basins causing severe impacts on the population, becoming a challenge for water resource managers in Andean river basins. This study proposes an integrated methodological framework for assessing the risk of failure in water supply, incorporating probabilistic drought forecasts, which assists in making decisions regarding the satisfaction of consumptive, non-consumptive and environmental requirements under water scarcity conditions. Monte Carlo simulation was used to assess the risk of failure in multiple stochastic scenarios, which incorporate probabilistic forecasts of drought events based on a Markov chains (MC) model using a recently developed drought index (DI). This methodology was tested in the Machangara river basin located in the south of Ecuador. Results were grouped in integrated satisfaction indexes of the system (DSIG). They demonstrated that the incorporation of probabilistic drought forecasts could better target the projections of simulation scenarios, with a view of obtaining realistic situations instead of optimistic projections that would lead to riskier decisions. Moreover, they contribute to more effective results in order to propose multiple alternatives for prevention and/or mitigation under drought conditions.

Evaluation of drought using SPEI drought class transitions and log-linear models for different agro-ecological regions of India

Markov chain and 3-dimensional log-linear models were attempted to model drought class transitions derived from the newly developed drought index the Standardized Precipitation Evapotranspiration Index (SPEI) at a 12 month time scale for six major drought prone areas of India. Log-linear modelling approach has been used to investigate differences relative to drought class transitions using SPEI-12 time series derived form 48 yeas monthly rainfall and temperature data. In this study, the probabilities of drought class transition, the mean residence time, the 1, 2 or 3 months ahead prediction of average transition time between drought classes and the drought severity class have been derived. Seasonality of precipitation has been derived for non-homogeneous Markov chains which could be used to explain the effect of the potential retreat of drought. Quasi-association and Quasi-symmetry log-linear models have been fitted to the drought class transitions derived from SPEI-12 time series. The estimates of odds along with their confidence intervals were obtained to explain the progression of drought and estimation of drought class transition probabilities. For initial months as the drought severity increases the calculated odds shows lower value and the odds decreases for the succeeding months. This indicates that the ratio of expected frequencies of occurrence of transition from drought class to the non-drought class decreases as compared to transition to any drought class when the drought severity of the present class increases. From 3-dimensional log-linear model it is clear that during the last 24 years the drought probability has increased for almost all the six regions. The findings from the present study will immensely help to assess the impact of drought on the gross primary production and to develop future contingent planning in similar regions worldwide.

Probabilistic Forecasting of Drought Events Using Markov Chain- and Bayesian Network-Based Models: A Case Study of an Andean Regulated River Basin

The scarcity of water resources in mountain areas can distort normal water application patterns with among other effects, a negative impact on water supply and river ecosystems. Knowing the probability of droughts might help to optimize a priori the planning and management of the water resources in general and of the Andean watersheds in particular. This study compares Markov chain- (MC) and Bayesian network- (BN) based models in drought forecasting using a recently developed drought index with respect to their capability to characterize different drought severity states. The copula functions were used to solve the BNs and the ranked probability skill score (RPSS) to evaluate the performance of the models. Monthly rainfall and streamflow data of the Chulco River basin, located in Southern Ecuador, were used to assess the performance of both approaches. Global evaluation results revealed that the MC-based models predict better wet and dry periods, and BN-based models generate slightly more accurately forecasts of the most severe droughts. However, evaluation of monthly results reveals that, for each month of the hydrological year, either the MC- or BN-based model provides better forecasts. The presented approach could be of assistance to water managers to ensure that timely decision-making on drought response is undertaken.

Characterizing Drought Using the Reliability-Resilience-Vulnerability Concept

This study borrows the measures developed for the operation of water resources systems as a means of characterizing droughts in a given region. It is argued that the common approach of assessing drought using a univariate measure (severity or reliability) is inadequate as decision makers need assessment of the other facets considered here. It is proposed that the joint distribution of reliability, resilience, and vulnerability (referred to as RRV in a reservoir operation context), assessed using soil moisture data over the study region, be used to characterize droughts. Use is made of copulas to quantify the joint distribution between these variables. As reliability and resilience vary in a nonlinear but almost deterministic way, the joint probability distribution of only resilience and vulnerability is modeled. Recognizing the negative association between the two variables, a Plackett copula is used to formulate the joint distribution. The developed drought index, referred to as the drought management index (DMI), is able to differentiate the drought proneness of a given area when compared to other areas. An assessment of the sensitivity of the DMI to the length of the data segments used in evaluation indicates relative stability is achieved if the data segments are 5 years or longer. The proposed approach is illustrated with reference to the Malaprabha River basin in India, using four adjoining Climate Prediction Center grid cells of soil moisture data that cover an area of approximately 12,000 km2.

Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring

Drought is one of the major natural hazards that bring about billions of dollars in loss to the farming community around the world each year. Drought is most often caused by a departure of precipitation from the normal amount, and agriculture is often the first sector to be affected by the onset of drought due to its dependence on water resources and soil moisture reserves during various stages of crop growth. Currently used drought indices like the Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) have coarse spatial (7000–100,000 km 2) and temporal resolution (monthly). Hence, the distributed hydrologic model SWAT was used to simulate soil moisture and evapotranspiration from daily weather data at a high spatial resolution (16 km 2) using GIS. Using this simulated data the drought indices Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) were developed based on weekly soil moisture deficit and evapotranspiration deficit, respectively. SMDI was computed at four different levels, using soil water available in the entire soil profile, then soil water available at the top 2 ft. (SMDI-2), 4 ft. (SMDI-4), and 6 ft. (SMDI-6). This was done because the potential of the crop to extract water from depths varies during different stages of the crop growth and also by crop type. ETDI and SMDI-2 had less auto-correlation lag, indicating that they could be used as good indicators of short-term drought. The developed drought indices showed high spatial variability (spatial standard deviation ∼1.00) in the study watersheds, primarily due to high spatial variability of precipitation. The wheat and sorghum crop yields were highly correlated ( r > 0.75) with the ETDI and SMDI's during the weeks of critical crop growth stages, indicating that the developed drought indices can be used for monitoring agricultural drought.