An integrated package for drought monitoring, prediction and analysis to aid drought modeling and assessment

Hao, Zengchao; Hao, Fang; Singh, Vijay P.; Ouyang, Wei; Cheng, Hongguang

doi:10.1016/j.envsoft.2017.02.008

Cited by 79 publications

(49 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For the regression model, the probabilistic forecast of the predictand can be constructed through distributional assumptions of error terms (Hao, Hong, et al, ; Hwang & Carbone, ). For the conditional probability model, probabilistic forecasts can be achieved by constructing conditional distribution functions of the predictand with respect to predictors (Hao, Hao, Singh, Ouyang, & Cheng, ; Liu et al, ). Probabilistic drought prediction provides more values than a deterministic forecast and is easier for decision makers to understand.…”

Section: Future Prospectsmentioning

confidence: 99%

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Hao

Singh

Xia

2018

Reviews of Geophysics

Self Cite

394

232

View full text Add to dashboard Cite

Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large‐scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high‐quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

show abstract

Section: Future Prospectsmentioning

confidence: 99%

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Hao

Singh

Xia

2018

Reviews of Geophysics

Self Cite

394

232

View full text Add to dashboard Cite

show abstract

“…The drought risk evaluation method suggested herein may be useful for predicting drought and establishing realistic coping strategies for extreme drought in a changing climate. Climate change is regarded as one of the most significant risks facing current and future generations and may have deleterious effects on both civilizations and ecosystems (Ranjan et al, 2006;Pelt and Swart, 2011;Hao et al, 2017;Kang et al, 2017;Kim et al, 2017;Choi et al, 2018). About 9.5 million people experienced severe food shortages due to droughts in eastern Africa from 2010 to 2011; this led to numerous deaths from malnutrition (Hillier and Dempsey, 2012).…”

Section: Funding Informationmentioning

confidence: 99%

Extreme drought hotspot analysis for adaptation to a changing climate: Assessment of applicability to the five major river basins of the Korean Peninsula

Lee

Park

Kim

et al. 2018

Intl Journal of Climatology

View full text Add to dashboard Cite

This study proposes a quantitative approach for extreme drought hotspot assessment related to climate change, the hotspot drought risk index (HDRI), and evaluates the response of extreme drought to climate change in the five major river basins of the Korean Peninsula. According to an analysis of seasonal and regional drought characteristics on the Korean Peninsula, drought occurs most frequently in spring, and the central and southern areas are generally more vulnerable to drought. Drought risk analysis under various climate change scenarios indicates significant drought risk over the entire Korean Peninsula excepting some parts of the Han and Nakdong river basins from 2011 to 2040. Extreme drought may be particularly severe in the mid‐west area as these basins lack available water resources. Drought risk decreases in the southern Korean Peninsula in the 2011–2040 and 2071–2099 periods, during which the area vulnerable to drought also decreases. However, some parts of central Korea and the western and eastern coastlines remain susceptible to drought. The drought risk evaluation method suggested herein may be useful for predicting drought and establishing realistic coping strategies for extreme drought in a changing climate.

show abstract

“…Usually, one is interested in the likelihood of a hot extreme given the antecedent dry or wet condition, which can be expressed from a statistical perspective based on the conditional probability distribution. Given the antecedent dry condition (e.g., X ≤ x 0 ) or wet condition (e.g., X > x 0 ), the probability of extreme temperature higher than a certain threshold y can be expressed as [ Hao et al ., ; Yue and Rasmussen , ]

P_{Y > y true∣ X \leq x_{0}} = \frac{u - C ((),, u, v)}{u}

P_{Y > y true∣ X > x_{0}} = \frac{1 - u - v + C ((),, u, v)}{1 - u}

…”

Section: Methodsmentioning

confidence: 99%

“…The conditional RP of extreme temperature exceeding a certain threshold given drought indicator X = x can be expressed as [ Brunner et al ., ; Hao et al ., ; Yue and Rasmussen , ]

T_{2} = \frac{1}{P (()|, Y > y, X = x)} = \frac{1}{1 - P (()|, Y \leq y, X = x)}

…”

Section: Methodsmentioning

confidence: 99%

Quantitative risk assessment of the effects of drought on extreme temperature in eastern China

Hao

Singh

et al. 2017

JGR Atmospheres

Self Cite

View full text Add to dashboard Cite

Hot extremes may lead to disastrous impacts on human health and agricultural production. Previous studies have revealed the feedback between drought and hot extremes in large regions of eastern China, while quantifying the impact of antecedent drought on hot extremes has been limited. This study aims at quantitatively assessing the risk of extreme temperature conditioned on the antecedent drought condition represented by Standardized Precipitation Index (SPI) during summer time in eastern China. A copula‐based model is proposed to construct the joint probability distribution of extreme temperature and drought based on 6 month SPI (SPI6). Accordingly, the conditional probability distribution is employed to quantify impacts of antecedent dry (and wet) conditions on the exceedance probability of extreme temperature. Results show that the likelihood of extreme temperature exceeding high quantiles is higher given antecedent dry conditions than that given antecedent wet conditions in large regions from southwestern to northeastern China. Specifically, the conditional probability difference of temperature exceeding 80th percentile given SPI6 lower than or equal to −0.5 and SPI6 higher than 0.5 is around 0.2–0.3. The case study of the 2006 summer hot extremes and drought in Sichuan and Chongqing region shows that the conditional return period of extreme temperature conditioned on antecedent drought is around 5–50 years shorter than univariate return period. These results quantify the impact of antecedent drought on subsequent extreme temperature and highlight the important role of antecedent drought in intensifying hot extremes in these regions.

show abstract

An integrated package for drought monitoring, prediction and analysis to aid drought modeling and assessment

Cited by 79 publications

References 87 publications

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Extreme drought hotspot analysis for adaptation to a changing climate: Assessment of applicability to the five major river basins of the Korean Peninsula

Quantitative risk assessment of the effects of drought on extreme temperature in eastern China

Contact Info

Product

Resources

About