Hydrologic Drought Atlas for Texas

Rajsekhar, Deepthi; Singh, Vijay P.; Mishra, Ashok K.

doi:10.1061/(asce)he.1943-5584.0001074

Cited by 35 publications

(20 citation statements)

References 47 publications

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“…Equation (11) can thus be rewritten as (Shiau and Modarres, ; Rajsekhar et al , )

T_{(|, S) D} ((), s (|, d)) = \frac{1}{γ ([], 1 - C_{(|, F_{S}) F_{D}} ((), (|, F_{S} ((), s)) F_{D} ((), d)))}

…”

Section: Methodsmentioning

confidence: 99%

“…severity, duration and frequency of occurrence) in a single diagram and is regarded as a useful multivariate tool in water resources management (Shiau and Modarres, ; Mishra and Singh, ; Reddy and Ganguli, ; Kim et al , ). This curve is similar to intensity‐duration‐frequency (IDF) curves investigated for design storms (Rajsekhar et al , ). There are several applications of SDF curves in drought analysis, for example, Todisco et al () concluded that station‐level information obtained from SDF curves can be interpolated to obtain severity maps for fixed return period, in order to investigate the spatial variability of drought characteristics (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Shiau and Modarres () used Copula to construct the SDF curves of two different climatic regions in Iran to compare the drought severity in different stations for given drought duration and recurrence interval. Rajsekhar et al () established SDF curves for preparing the drought atlas that depicts the drought severity for any drought duration and return period at any location in Texas. Among these studies, the Copula function, which overcomes the shortcomings and constraints in the choice of marginal distribution and allows the marginal distributions belong to different families, has been widely used to model the multivariable joint distribution (Shiau, ; Zhang and Singh, ; Kao and Govindaraju, ; Jaewon et al , ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wet and dry spell analysis using copulas

et al. 2015

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Climate variability modulates spatio‐temporal variability of dry spells (DSs) and wet spells (WSs) within a river basin and will affect water resources management practices leading to various impacts on the socio‐economic development in river basins. In this study, we evaluated spatio‐temporal variability of DS and WS in Huai River basin (HRB), China, by developing copula‐based severity‐duration‐frequency (SDF) curves. The result shows that the upper reach and the southern part of middle reach of HRB are prone to both DS and WS; however, the duration and severity of WS are comparatively higher in comparison to DS. It was observed that DS is more frequent in spring and summer, whereas WS in summer and autumn. The choice of copula plays an important role in deriving the SDF curves, and an inappropriately chosen copula function may result in a large bias of SDF estimation. The arch12 copula was found to be the best choice in the majority of stations for deriving the SDF curves. The constructed SDF curves primarily shows two major patterns for DS and WS, i.e. concave down pattern and convex up pattern. The frequency of extreme DS decreases from 1960s to 1990s, and increases after 2000s, while the frequency of extreme WS increases from 1970s to 1990s and then decreases from 1990s to 2000s. The results in this study can provide useful information for designing conservation structures and to develop water allocation strategies at different temporal scales.

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“…Equation (11) can thus be rewritten as (Shiau and Modarres, ; Rajsekhar et al , )

T_{(|, S) D} ((), s (|, d)) = \frac{1}{γ ([], 1 - C_{(|, F_{S}) F_{D}} ((), (|, F_{S} ((), s)) F_{D} ((), d)))}

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wet and dry spell analysis using copulas

et al. 2015

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“…Therefore, we expect that hydrological drought characteristics derived based on streamflow likely to have a nonlinear dependence due to the complex interaction between climate and catchment processes within a watershed. In addition to that, the evolution of hydrological drought is often clustered based on neighboring catchments due to the similarity in climate and catchment characteristics (Rajasekhar et al, ; Zhang et al, ). Hence, it is important to gain a deeper understanding of the dominant linear and nonlinear controls resulting in distinct drought regimes using robust nonparametric techniques.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Climate and Catchment Control on Hydrological Drought in the Continental United States

Konapala

Mishra

2020

Water Resources Research

118

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The evolution of hydrological drought events is a result of complex (nonlinear) interactions between climate and catchment processes. To investigate such nonlinear relationship, we integrated a machine learning modeling framework based on the random forest (RF) algorithms with an interpretation framework to quantify the role of climate and catchment controls on hydrological drought. More particularly, our framework interprets a built RF machine-learning model to identify dominant variables and visualize their functional dependence and interaction effects on hydrological drought characteristics utilizing concepts of minimal depth, interactive depth, and partial dependence. We test our proposed modeling framework based on a set of 652 continental United States catchments with minimal human interference for a period of 1979-2010. Application of this framework indicated presence of three distinct drought regimes, which includes, Regime 1: droughts with longer duration, less frequent and lesser intensity; Regime 2: droughts with moderate duration, moderate frequency, and moderate intensity; and Regime 3: droughts with shorter duration, more frequent, and more intense. RF algorithm was able to accurately model the drought characteristics (intensity, duration, and number of events) for all the three drought regimes as a function of selected variables. It was observed that the type of dominant variables as well as their nonlinear functional relationship with hydrological droughts characteristics can vary between three selected regimes. Our interpretation framework indicated that catchment characteristics have a significant role in controlling the hydrologic drought for catchments (regime 1), whereas both climate and catchment characteristics control hydrological drought in regimes 2 and 3.

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“…Many studies have been performed for the research of drought, such as the definition and evaluation of droughts, including the spatiotemporal structure and statistical characteristic analysis, [3,4,9,10] and the multivariable probability distributions jointly considering multiple drought variables (such as duration, severity, and magnitude) simultaneously [11][12][13]. Knowledge about the anticipated changes in drought frequency or magnitude by means of persistent daily precipitation deficiency (i.e., dry spell of precipitation) has attracted more and more interest [8,[14][15][16][17] and is considered to be necessary for the design and application of valid and reasonable drought resistance strategies.…”

Section: Introductionmentioning

confidence: 99%

Regional Frequency Analysis of Extreme Dry Spells during Rainy Season in the Wei River Basin, China

She

Xia

Zhang

et al. 2016

Advances in Meteorology

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Our research analyzes the regional changes of extreme dry spell, represented by the annual maximum dry spell length (noted as AMDSL) during the rainy season in the Wei River Basin (WRB) of China for 1960-2014 using the L-moments method. The mean AMDSL values increase from the west to the east of the WRB, suggesting a high dry risk in the east compared to the west in the WRB. To investigate the regional frequency more reasonably, the WRB is clustered into four homogenous subregions via the Kmeans method and some subjective adjustments. The goodness-of-fit test shows that the GEV, PE3, and GLO distribution can be accepted as the "best-fit" model for subregions 1 and 4, subregion 2, and subregion 3, respectively. The quantiles of AMDSL under various return levels figure out a similar spatial distribution with mean AMDSL. We also find that the dry risk in subregion 2 and subregion 4 might be higher than that in subregion 1. The relationship between ENSO events and extreme dry spell events in the rainy season with cross wavelet analysis method proves that ENSO events play a critical role in triggering extreme dry events during rainy season in the WRB.

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Hydrologic Drought Atlas for Texas

Cited by 35 publications

References 47 publications

Wet and dry spell analysis using copulas

Wet and dry spell analysis using copulas

Quantifying Climate and Catchment Control on Hydrological Drought in the Continental United States

Regional Frequency Analysis of Extreme Dry Spells during Rainy Season in the Wei River Basin, China

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