A new standardized Palmer drought index for hydro‐meteorological use

Accepting the concept of standardization introduced by the standardized precipitation index, similar methodologies have been developed to construct some other standardized drought indices such as the standardized precipitation evapotranspiration index (SPEI). In this study, the authors provided deep insight into the SPEI and recognized potential deficiencies/limitations in relating to the climatic water balance it used. By coupling another well‐known Palmer drought severity index (PDSI), we proposed a new standardized Palmer drought index (SPDI) through a moisture departure probabilistic approach, which allows multi‐scalar calculation for accurate temporal and spatial comparison of the hydro‐meteorological conditions of different locations. Using datasets of monthly precipitation, temperature and soil available water capacity, the moisture deficit/surplus was calculated at multiple temporal scales, and a couple of techniques were adopted to adjust corresponding time series to a generalized extreme value distribution out of several candidates. Results of the historical records (1900–2012) for diverse climates by multiple indices showed that the SPDI was highly consistent and correlated with the SPEI and self‐calibrated PDSI at most analysed time scales. Furthermore, a simple experiment of hypothetical temperature and/or precipitation change scenarios also verified the effectiveness of this newly derived SPDI in response to climate change impacts. Being more robust and preferable in spatial consistency and comparability as well as combining the simplicity of calculation with sufficient accounting of the physical nature of water supply and demand relating to droughts, the SPDI is promising to serve as a competent reference and an alternative for drought assessment and monitoring. Copyright © 2013 John Wiley & Sons, Ltd.     

一种基于地表能量平衡的遥感干旱监测新方法及其在甘肃河东地区干旱监测中的应用初探

目前遥感干旱监测方法的精度普遍不高,探求新的遥感干旱监测方法有助于干旱监测预警技术的提升与发展.波文比是感热通量与潜热通量之比,能综合反映地表水热特征,可尝试将其引入到遥感干旱监测领域加以利用.应用甘肃河东地区的EOS-MODIS卫星资料和同步地面气象资料,基于地表能量平衡原理构建了波文比干旱监测模型,对比分析了波文比(β)指数、温度植被指数(TVX)与土壤水分的相关性,并以典型晴空影像(2014年10月5日)为例初步建立了β的干旱分级标准,对研究区进行了旱情评估.结果表明:β与土壤相对湿度呈现出高度负相关,相比于当下广泛应用的TVX,β与0~20cm平均土壤相对湿度具有更好的相关性,监测精度得到了显著提高.用β干旱分级标准评估的研究区干湿状况与前期降水空间分布吻合得相当好,评估表明2014年10月5日研究区基本为适宜(无旱),与2014年9月的降水距平百分率特征一致.基于地表能量平衡的波文比(β)指数在干旱监测中效果突出,具有很好的应用前景.     

Evaluation of multi-sensor satellite data for monitoring different drought impacts

Drought is a natural disaster that significantly affects human life; therefore, precise monitoring and prediction is necessary to minimize drought damage. Conventional drought monitoring is based predominantly on ground observation stations; however, satellite imagery can be used to overcome the disadvantages of existing monitoring methods and has the advantage of monitoring wide areas. In this research, we assess the applicability of drought monitoring based on satellite imagery, focusing on historic droughts in 2001 and 2014, which caused major agricultural and hydrological issues in South Korea. To assess the applicability and accuracy of the drought index, drought impact areas in the study years were investigated, and spatiotemporal comparative analyses between the calculated drought index and previously affected areas were conducted. For drought monitoring based on satellite imagery, we used hydro-meteorological factors such as precipitation, land surface temperature, vegetation, and evapotranspiration, and applied remote sensing data from various sensors. We verified the effectiveness of using precipitation data for meteorological drought monitoring, vegetation and land surface temperature data for agricultural drought monitoring, and evapotranspiration data for hydrological drought monitoring. Moreover, we confirmed that the Standard Precipitation Index (SPI) can be indirectly applied to agricultural or hydrological drought monitoring by determining the temporal correlation between SPI, calculated for various time scales, and satellite-based drought indices.     

Socio-economic drought assessment in Lake Mead,USA, based on a multivariate standardized water-scarcity index

Drought events are increasing worldwide. Socio-economic drought is the least investigated type of drought, and is the only type for which water demand is taken into consideration. In this research, socio-economic drought was studied in Lake Mead, USA, using a multivariate standardized water-scarcity index (MSWSI) over the period 1990–2014, combining two water-scarcity indices based on time series of inflow, outflow, reservoir storage, and water demand. The inflow and outflow were determined from streamgage data, and reservoir storage from lake level data; demand was based on water pumped by the Southern Nevada Water Authority. Missing observations in input streamgage data were filled through regression modeling. The results indicate that Lake Mead has been in socio-economic drought since 2000, with the most severe drought occurring between 2006 and 2012, and the highest intensity drought in April–July 2014. The Lake Mead droughts revealed through the MSWSI are consistent with those reported in US Drought Monitor (USDM) products. The temporal behavior of MSWSI provides an insight into the socio-economic effects of droughts not captured by USDM products.     

Application of copulas for derivation of drought severity–duration–frequency curves

This study presents copula‐based multivariate probabilistic approach to model severity–duration–frequency (S‐D‐F) relationship of drought events in western Rajasthan, India. Drought occurrences are analysed using standardized precipitation index computed on monthly mean areal precipitation, aggregated at a time scale of 6 months. After testing with a series of probability density functions, the drought variable severity is found to be better represented with log‐normal distribution, whereas duration is well fitted with exponential distribution. Four different classes of bivariate copulas – Archimedean, extreme value, Plackett, and elliptical families are evaluated for modelling joint distribution of drought characteristics. It is observed that the extreme value copula – Gumbel–Hougaard copula – performed better as compared with other classes of copulas, based on results of various statistical tests and upper tail dependence coefficient. The joint distribution obtained from best performing copula is then employed to determine conditional return period and to derive drought severity‐duration‐frequency (S‐D‐F) curves for the study region. The results of the study suggests that the copula method can be used effectively to derive the drought S‐D‐F curves, which can be helpful in planning and adopting suitable drought mitigation strategies in drought‐prone areas. Copyright © 2011 John Wiley & Sons, Ltd.     

Study of indices for drought characterization in KBK districts in Orissa (India)

Drought is a temporary, random and regional climatic phenomenon, originating due to lack of precipitation leading to water deficit and causing economic loss. Success in drought alleviation depends on how well droughts are defined and their severity quantified. A quantitative definition identifies the beginning, end, spatial extent and the severity of drought. Among the available indices, no single index is capable of fully describing all the physical characteristics of drought. Therefore, in most cases it is useful and necessary to consider several indices, examine their sensitivity and accuracy, and investigate for correlation among them. In this study, the geographical information system‐based Spatial and Time Series Information Modeling (SPATSIM) and Daily Water Resources Assessment Modeling (DWRAM) software were used for drought analysis on monthly and daily bases respectively and its spatial distribution in both dry and wet years. SPATSIM utilizes standardized precipitation index (SPI), effective drought index (EDI), deciles index and departure from long‐term mean and median; and DWRAM employs only EDI. The analysis of data from the Kalahandi and Nuapada districts of Orissa (India) revealed that (a) droughts in this region occurred with a frequency of once in every 3 to 4 years, (b) droughts occurred in the year when the ratio of annual rainfall to potential evapotranspiration (Pae/PET) was less than 0·6, (c) EDI better represented the droughts in the area than any other index; (d) all SPI, EDI and annual deviation from the mean showed a similar trend of drought severity. The comparison of all indices and results of analysis led to several useful and pragmatic inferences in understanding the drought attributes of the study area. Copyright © 2007 John Wiley & Sons, Ltd.     

Development of an operational low-flow index for hydrological drought monitoring over Europe

Near real-time monitoring of hydrological drought requires the implementation of an index capable of capturing the dynamic nature of the phenomenon. Starting from a dataset of modelled daily streamflow data, a low-flow index was developed based on the total water deficit of the discharge values below a certain threshold. In order to account for a range of hydrological regimes, a daily 95th percentile threshold was adopted, which was computed by means of a 31-day moving window. The observed historical total water deficits were statistically fitted by means of the exponential distribution and the corresponding probability values were used as a measure of hydrological drought severity. This approach has the advantage that it directly exploits daily streamflow values, as well as allowing a near real-time update of the index at regular time steps (i.e. 10 days, or dekad). The proposed approach was implemented on discharge data simulated by the LISFLOOD model over Europe during the period 1995–2015; its reliability was tested on four case studies found within the European drought reference database, as well as against the most recent summer drought observed in Central Europe in 2015. These validations, even if only qualitative, highlighted the ability of the index to capture the timing (starting date and duration) of the main historical hydrological drought events, and its good performance in comparison with the commonly used standardized runoff index (SRI). Additionally, the spatial evolution of the most recent event was captured well in a simulated near real-time test case, suggesting the suitability of the index for operational implementation within the European Drought Observatory.     

Regional applicability of seven meteorological drought indices in China

The definition of a drought index is the foundation of drought research. However, because of the complexity of drought, there is no a unified drought index appropriate for different drought types and objects at the same time. Therefore, it is crucial to determine the regional applicability of various drought indices. Using terrestrial water storage obtained from the Gravity Recovery And Climate Experiment, and the observed soil moisture and streamflow in China, we evaluated the regional applicability of seven meteorological drought indices: the Palmer Drought Severity Index (PDSI), modified PDSI (PDSI_CN) based on observations in China, self-calibrating PDSI (scPDSI), Surface Wetness Index (SWI), Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and soil moisture simulations conducted using the community land model driven by observed atmospheric forcing (CLM3.5/ObsFC). The results showed that the scPDSI is most appropriate for China. However, it should be noted that the scPDSI reduces the value range slightly compared with the PDSI and PDSI_CN; thus, the classification of dry and wet conditions should be adjusted accordingly. Some problems might exist when using the PDSI and PDSI_CN in humid and arid areas because of the unsuitability of empiricalparameters. The SPI and SPEI are more appropriate for humid areas than arid and semiarid areas. This is because contributions of temperature variation to drought are neglected in the SPI, but overestimated in the SPEI, when potential evapotranspiration is estimated by the Thornthwaite method in these areas. Consequently, the SPI and SPEI tend to induce wetter and drier results, respectively. The CLM3.5/ObsFC is suitable for China before 2000, but not for arid and semiarid areas after 2000. Consistent with other drought indices, the SWI shows similar interannual and decadal change characteristics in detecting annual dry/wet variations. Although the long-term trends of drought areas in China detected by these seven drought indices during 1961–2013 are consistent, obvious differences exist among the values of drought areas, which might be attributable to the definitions of the drought indices in addition to climatic change.     

Drought assessment using a multivariate drought index in the Luanhe River basin of Northern China

With climate change and the rapid increase in water demand, droughts, whose intensity, duration and frequency have shown an increasing trend in China over the past decades, are increasingly becoming a critical constraint to China’s sustainable socio-economic development, especially in Northern China, even more so. Therefore, it is essential to develop an appropriate drought assessment approach in China. To propose a suitable drought index for drought assessment, the Luanhe river basin in the northern China was selected as a case study site. Based on the Principal Component Analysis of precipitation, evapotranspiration, soil moisture and runoff, the three latter variables of which were obtained by using the Variable Infiltration Capacity land surface macro-scale hydrology model, a new multivariate drought index (MDI) was formulated, and its thresholds were determined by use of cumulative distribution function. To test the applicability of the newly developed index, the MDI, the standardized precipitation index (SPI) and the palmer drought severity index (PDSI) time series on a monthly scale were computed and compared during 1962–1963, 1968 and 1972 drought events. The results show that the MDI exhibited certain advantages over the PDSI and the SPI, i.e. better assessing drought severity and better reflecting drought evolution. The MDI formulated by this paper could provide a scientific basis for drought mitigation and management, and references for drought assessment elsewhere in China.     

Multivariate Standardized Drought Index: A parametric multi-index model

Defining droughts based on a single variable/index (e.g., precipitation, soil moisture, or runoff) may not be sufficient for reliable risk assessment and decision-making. In this paper, a multivariate, multi-index drought-modeling approach is proposed using the concept of copulas. The proposed model, named Multivariate Standardized Drought Index (MSDI), probabilistically combines the Standardized Precipitation Index (SPI) and the Standardized Soil Moisture Index (SSI) for drought characterization. In other words, MSDI incorporates the meteorological and agricultural drought conditions for overall characterization of drought. In this study, the proposed MSDI is utilized to characterize the drought conditions over several Climate Divisions in California and North Carolina. The MSDI-based drought analyses are then compared with SPI and SSI. The results reveal that MSDI indicates the drought onset and termination based on the combination of SPI and SSI, with onset being dominated by SPI and drought persistence being more similar to SSI behavior. Overall, the proposed MSDI is shown to be a reasonable model for combining multiple indices probabilistically.     

Multivariate drought characteristics using trivariate Gaussian and Student t copulas

This study aims to investigate the changing properties of drought events in Weihe River basin, China, by modeling the multivariate joint distribution of drought duration, severity and peak using trivariate Gaussian and Student t copulas. Monthly precipitations of Xi'an gauge are used to illustrate the meta‐elliptical copula‐based methodology for a single‐station application. Gaussian and Student t copulas are found to produce a better fit comparing with other six symmetrical and asymmetrical Archimedean copulas, and, checked by the goodness‐of‐fit tests based on a modified bootstrap version of Rosenblatt's transformation, both of them are acceptable to model the multivariate joint distribution of drought variables. Gaussian copula, the best fitting, is employed to construct the dependence structures of positively associated drought variables so as to obtain the multivariate joint and conditional probabilities of droughts. A Kendall's return period (KRP) introduced by Salvadori and De Michele (2010) is then adopted to assess the multivariate recurrent properties of drought events, and its spatial distributions indicate that prolonged droughts are likely to break out with rather short recurrence intervals in the whole region, while drought status in the southeast seems to be severer than the northwest. The study is of some merits in terms of multivariate drought modeling using a preferable copula‐based method, the results of which could serve as a reference for regional drought defense and water resources management. Copyright © 2011 John Wiley & Sons, Ltd.     

Drought hotspot analysis and risk assessment using probabilistic drought monitoring and severity–duration–frequency analysis

Drought hotspot identification requires continuous drought monitoring and spatial risk assessment. The present study analysed drought events in the agriculture‐dominated mid‐Mahanadi River Basin in Odisha, India, using crop water stress as a drought indicator. This drought index incorporated different factors that affect crop water deficit such as the cropping pattern, soil characteristics, and surface soil moisture. The drought monitoring framework utilized a relevance vector machine model‐based classification that provided the uncertainty associated with drought categorization. Using the proposed framework, drought hotspots are identified in the study region and compared with indices based on precipitation and soil moisture. Further, a bivariate copula is employed to model the agricultural drought characteristics and develop the drought severity–duration–frequency (S–D–F) relationships. The drought hotspot maps and S–D–F curves are developed for different locations in the region. These provided useful information on the site‐specific drought patterns and the characteristics of the devastating droughts of 2002 and 2012, characterized by an average drought duration of 7 months at several locations. The site‐specific risk of short‐ and long‐term agricultural droughts are then investigated using the conditional copula. The results suggest that the conditional return periods and the S–D–F curves are valuable tools to assess the spatial variability of drought risk in the region.     

An insight into the Palmer drought mechanism based indices: comprehensive comparison of their strengths and limitations

Considering the drawbacks of the original Palmer drought severity index (PDSI) in terms of its simplified hydrologic algorithm and spatio-temporal inconsistency, we compare six variants of PDSI derived from different combinations of two hydrologic algorithms and three standard processes so as to provide deep insights into the individual impacts of hydrological processing and standardization on final PDSI values as well as their combined effects. Investigations are conducted in whole Yellow River basin. On basis of 52 years’ (1961–2012) hydro-meteorological data, comprehensive analysis on multiple drought characteristics are carried out for each PDSI variant, combined with comparison of three crucial intermediate variables of PDSI. Results show that variable infiltration capacity (VIC) model based modification in the hydrologic accounting section significantly improve drought trends with more reasonable spatial distributions presented. For the statistical characteristics of drought areas and frequency, comparable performance is found between VIC-based modification and self-calibrating standard procedure-based modification, though they are derived from different mechanisms. However, in case of the coupling of these two modifications, indices derived from combined modifications perform poorly than single modification-based indices with unexpected high frequency of extreme events detected in certain regions. This reflects the complicated mechanism of PDSI and it is essential to propose an appropriate standardization to match the hydrological algorithm and further improve the performance of relevant drought index. With the crucial findings mentioned above, this study is promising to provide some theoretical supports and serve as a competent reference for future PDSI based researches.     

Evaluation of trends and predictability of short‐term droughts in three meteorological subdivisions of India using multivariate EMD‐based hybrid modelling

This paper presented trend analysis of droughts in Kerala, Telangana, and Orissa meteorological subdivisions in India and proposed a framework for drought prediction by employing the Empirical Mode Decomposition (EMD)‐based prediction models. The study used 3‐month standardized precipitation index (SPI3) for drought analysis. The trend analysis of SPI3 series for the period 1871–2012 using Mann–Kendall method showed statistically significant increasing trend in Kerala and Telangana subdivisions and a decreasing trend in Orissa subdivision. In addition, the non‐linear trend component extracted from EMD showed statistically significant changes in all the three subdivisions. Then, the study proposed a hybrid approach for prediction of short‐term droughts by coupling multivariate extension of EMD (MEMD) with stepwise linear regression (SLR) and genetic programming (GP) methods. First, the multivariate dataset comprising the SPI3 series of current and lagged time steps are decomposed using the MEMD. Then, SLR/GP models are developed to predict each subseries of SPI3 of desired time step considering the subseries of predictor variables at the corresponding timescales as inputs. The resulting models at different timescales are recombined to obtain the SPI3 values of the desired time step. The method is applied for prediction of short‐term droughts in the three subdivisions. The results obtained by the hybrid models are compared with that obtained by conventional prediction models using M5 Model Trees and GP. The rigorous performance evaluation based on multiple statistical criteria clearly exhibited the superiority of the hybrid approaches (i.e., prediction models with MEMD‐based decomposition over models without decomposition) for prediction of SPI3 in three subdivisions. Further, the study found that MEMD‐GP model performs marginally better than the MEMD‐SLR model due to its efficacy in modelling high frequency modes.     

Drought monitoring

Drought detection, monitoring and indices are closely related to its definition. The specific definition chosen for a particular drought analysis will affect the procedures one uses in drought detection and monitoring. The traditional Palmer Drought Severity Index (PDSI) has been proven to be ineffective in regions of predominantly irrigated agriculture.The recently developed ALERT (Automated Local Evaluation in Real Time) system is proposed for use in monitoring the spatial and temporal variations of drought in real time. The ALERT system uses standardized instruments, radio frequencies, software and hardware. It was originally developed as a flash flood waming system by local flood control districts and the National Weather Service. However, now it has expanded to over 100 other uses in the areas of natural and man-made disaster detection and warning. The successful ALERT system indicates the need for the continued development of a national drought monitoring index that is applicable to a wide range of climate, hydrologic and water resource environments.     

Drought monitoring

Drought detection, monitoring and indices are closely related to its definition. The specific definition chosen for a particular drought analysis will affect the procedures one uses in drought detection and monitoring. The traditional Palmer Drought Severity Index (PDSI) has been proven to be ineffective in regions of predominantly irrigated agriculture.The recently developed ALERT (Automated Local Evaluation in Real Time) system is proposed for use in monitoring the spatial and temporal variations of drought in real time. The ALERT system uses standardized instruments, radio frequencies, software and hardware. It was originally developed as a flash flood waming system by local flood control districts and the National Weather Service. However, now it has expanded to over 100 other uses in the areas of natural and man-made disaster detection and warning. The successful ALERT system indicates the need for the continued development of a national drought monitoring index that is applicable to a wide range of climate, hydrologic and water resource environments.     

Drought projection based on a hybrid drought index using Artificial Neural Networks

The Tarim River Basin is a special endorheic arid drainage basin in Central Asia, characterized by limited rainfall and high evaporation as common in deserts, while water is supplied mainly by glacier and snow melt from the surrounding mountains. The existing drought indices can hardly capture the drought features in this region as droughts are caused by two dominant factors (meteorological and hydrological conditions). To overcome the problem, a new hybrid drought index (HDI), integrating the meteorological and hydrological drought regimes, was developed and tested in the basin in the work. The index succeeded in revealing the drought characteristics and the ensemble influence better than the single standardized precipitation index or the hydrological index. The Artificial Neural Network approach based on temperature and precipitation observations was set up to simulate the HDI change. The method enabled constructing scenarios of future droughts in the region using climate simulation of the GCMs under four RCP scenarios from the latest CMIP5 project. The simulations in the study have shown that the water budget patterns in the Tarim River Basin are more sensitive to temperature than to precipitation. Dominated by temperature rise causing an accelerating snow/glacier melt, the frequency of drought months is projected to decrease by about 14% in the next decades (until 2035). The drought duration is expected to be shortened to 3 months on average, with the severity alleviated. However, the region would still suffer more severe droughts with a high intensity in some years. The general decrease in drought frequency and intensity over the region in the future would be beneficial for water resources management and agriculture development in the oases. Copyright © 2014 John Wiley & Sons, Ltd.     

Teleconnections between oceanic–atmospheric indices and drought over Iran using quantile regressions

ABSTRACT

In this research, the Bayesian quantile regression model is applied to investigate the teleconnections between large oceanic–atmospheric indices and drought standardized precipitation index (SPI) in Iran. The 12-month SPI time series from 138 synoptic stations for 1952–2014 were selected as the drought index. Three oceanic–atmospheric indices, the North Atlantic Oscillation (NAO), the Southern Oscillation Index (SOI) and the Multivariate El Niño/Southern Oscillation Index (MEI), were selected as covariates. The results show that NAO has the weakest impact on drought in different quantiles and different regions in Iran. La Niña conditions amplified droughts through all SPI quantiles in western, Caspian Sea coastal regions and southern regions. The positive phase of MEI significantly modulates low SPI quantiles (i.e. drought conditions) throughout the Zagros region, Caspian Sea coastal regions and southern regions. The study shows that the effect of large oceanic–atmospheric indices have heterogeneous impacts on extreme dry and wet conditions.     

广东省干旱灾害空间分布特征

利用1956-2005年126个雨量站逐月降水资料,采用标准化降雨指数和经验正交函数分解法,探讨了广东省干旱灾害空间分布规律.结果表明:广东省虽然总体比较湿润,但局部干旱时有发生,且在空间上存在东西差异、南北差异、中部差异的特点;春旱大致呈自西向东、自北向南逐渐加重的趋势;秋旱空间分布特点与春旱相反,由东向西、由南向北逐渐加重;春旱、秋旱在中部地区也有微弱的差异.     

Hydrologic model-based Palmer indices for drought characterization in the Yellow River basin,China

The Palmer indices (PIs) that have been most widely used for drought monitoring and assessment are criticized for two main drawbacks: coarse hydrological accounting processes with a simplified two-stage bucket soil water balance model and arbitrary rules for defining drought properties and standardizing index values through limited calibration and comparison. In this study, we introduce a new proposal of the VIC hydrologic model-based Palmer drought scheme, where traditional PIs (e.g. PDSI) can readily be calculated on the basis of distributed finescale hydrologic simulations. Moreover, recent variants of PI (i.e., SPDI and SPDI-JDI) also provide a preferable standardization strategy that allows probabilistic invariability and better spatio-temporal comparability of computed drought indices. Using gridded meteorological forcing, soil and vegetation data to drive the three-layer VIC model, both non-VIC and VIC-based PIs are investigated to examine their performances for drought characterization and detection. Results indicate that VIC hydrologic model would allow for adjustments in statistical properties of computed PDSI and VIC-based SPDI is also preferable to PDSI for better statistical robustness and spatio-temporal consistency/comparability. Moreover, the joint SPDI-JDI has the strength of integrating multi-scale probabilistic properties and drought information released by individual SPDI, providing overall drought conditions that take into account the onset, persistence and termination of droughts. At proposed 0.25° grid scale, the VIC-based SPDI-JDI indicates high frequency and long total time of drought condition in the Yellow River basin (YRB), China. Although no significant temporal trends are found in identified drought duration and severity, both the seasonal and annual drought index values demonstrate a downward trend (higher drought intensity) for considerable proportions of the YRB. These findings imply high drought risk and potential drying stress for this region. The new framework of hydrologic model-based PIs can help to strengthen our knowledge and/or practices in regional drought monitoring and assessment.