Identification of hydrologic drought triggers from hydroclimatic predictor variables

Maity, Rajib; Ramadas, Meenu; Govindaraju, Rao S.

doi:10.1002/wrcr.20346

Cited by 58 publications

(36 citation statements)

References 60 publications

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“…Thus, we have used a copula-based approach to characterize extreme drought severity. The use of copulas has been suggested for precisely this purpose in the literature (Nelsen, 2006, Sklar, 1959 and there are numerous examples of copula applications in the context of drought management (Hao et al, 2014;Kao and Govindaraju, 2010;Maity et al, 2013;Wong et al, 2009). Further, as a step toward translation to actionable insights, copula-based drought severity-duration-frequency curves are generated, which in turn explicitly consider interdependence among drought attributes.…”

Section: Research Questions and Hypothesesmentioning

confidence: 99%

Space-time trends in U.S. meteorological droughts

Ganguli

Ganguly

2016

Journal of Hydrology: Regional Studies

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Understanding droughts in a climate context remains a major challenge. Over the United States, different choices of observations and metrics have often produced diametrically opposite insights. This paper focuses on understanding and characterizing meteorological droughts from station measurements of precipitation. The Standardized Precipitation Index is computed and analyzed to obtain drought severity, duration and frequency. Average drought severity trends are found to be uncertain and data-dependent. Furthermore, the mean and spatial variance do not show any discernible non-stationary behavior. However, the spatial coverage of extreme meteorological droughts in the United States exhibits an increasing trend over nearly all of the last century. Furthermore, the coverage over the last half decade exceeds that of the dust bowl era. Previous literature suggests that climate extremes do not necessarily follow the trends or uncertainties exhibited by the averages. While this possibility has been suggested for droughts, this paper for the first time clearly delineates and differentiates the trends in the mean, variability and extremes of meteorological droughts in the United States, and uncovers the trends in the spatial coverage of extremes. Multiple data sets, as well as years exhibiting large, and possibly anomalous, droughts are carefully examined to characterize trends and uncertainties. Nonlinear dependence among meteorological drought attributes necessitates the use of copula-based tools from probability theory. Severity-duration-frequency curves are generated to demonstrate how these insights may be translated to design and policy. 3

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Section: Research Questions and Hypothesesmentioning

confidence: 99%

Space-time trends in U.S. meteorological droughts

Ganguli

Ganguly

2016

Journal of Hydrology: Regional Studies

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“…The probability-based theory of copulas has become popular in machine learning for the probabilistic modeling of multivariate data [Lopez-Paz et al, 2013]. An important advantage of copulas is that they enable the joint dependence structure of different variables to be built independently from the marginal distribution [Genest and Favre, 2007;Nelsen, 2006;Maity et al, 2013]. There is an increasing number of copula applications in hydrology and climatology, such as for flood frequency analysis, low-flow/drought analysis, identifying drought return periods, rainfall generator, spatial dependence modeling, and geostatistical interpolation [Favre et al, 2004;Salvadori and De Michele, 2004;Zhang and Singh, 2006;Kao and Govindaraju, 2007;Bárdossy and Li, 2008;Serinaldi, 2009;Hobaek Haff et al, 2015].…”

Section: Liu Et Almentioning

confidence: 99%

A multivariate conditional model for streamflow prediction and spatial precipitation refinement

et al. 2015

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The effective prediction and estimation of hydrometeorological variables are important for water resources planning and management. In this study, we propose a multivariate conditional model for streamflow prediction and the refinement of spatial precipitation estimates. This model consists of high dimensional vine copulas, conditional bivariate copula simulations, and a quantile‐copula function. The vine copula is employed because of its flexibility in modeling the high dimensional joint distribution of multivariate data by building a hierarchy of conditional bivariate copulas. We investigate two cases to evaluate the performance and applicability of the proposed approach. In the first case, we generate one month ahead streamflow forecasts that incorporate multiple predictors including antecedent precipitation and streamflow records in a basin located in South China. The prediction accuracy of the vine‐based model is compared with that of traditional data‐driven models such as the support vector regression (SVR) and the adaptive neuro‐fuzzy inference system (ANFIS). The results indicate that the proposed model produces more skillful forecasts than SVR and ANFIS. Moreover, this probabilistic model yields additional information concerning the predictive uncertainty. The second case involves refining spatial precipitation estimates derived from the tropical rainfall measuring mission precipitationproduct for the Yangtze River basin by incorporating remotely sensed soil moisture data and the observed precipitation from meteorological gauges over the basin. The validation results indicate that the proposed model successfully refines the spatial precipitation estimates. Although this model is tested for specific cases, it can be extended to other hydrometeorological variables for predictions and spatial estimations.

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“…The merit of using copula is that it is a multivariate distribution function that joins different univariate marginal distributions (Joe, ; Nelsen, ; Cherubini, Luciano, & Vecchiato, ). In the field of hydrologic extremes, the dependence among multiple variables that govern hydrological processes has been successfully modelled using copula (Fan et al, ; Grimaldi & Serinaldi, ; Kao & Govindaraju, ; Maity, Ramadas, & Govindaraju, ). Similarly, copulas have been extensively used for developing drought S–D–F relationships.…”

Section: Methodsmentioning

confidence: 99%

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

Samantaray

Singh

Ramadas

et al. 2018

Hydrological Processes

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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.

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Identification of hydrologic drought triggers from hydroclimatic predictor variables

Cited by 58 publications

References 60 publications

Space-time trends in U.S. meteorological droughts

Space-time trends in U.S. meteorological droughts

A multivariate conditional model for streamflow prediction and spatial precipitation refinement

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

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