Six month–lead downscaling prediction of winter to spring drought in South Korea based on a multimodel ensemble

Sohn, Soo-Jin; Ahn, Joong‐Bae; Tam, Chi‐Yung

doi:10.1002/grl.50133

Cited by 34 publications

(22 citation statements)

References 29 publications

(39 reference statements)

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“…Up to now, the SPEI has been used in several studies for drought monitoring (Fuchs et al, 2012;Potop, 2011;Sohn et al, 2013). Abiodun et al (2013) and Yu et al (2013) investigated climate change with the SPEI.…”

Section: Discussionmentioning

confidence: 99%

Estimating evapotranspiration using the complementary relationship and the Budyko framework

Kim

Kaluarachchi

2017

Journal of Water and Climate Change

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Evapotranspiration is the single most important mechanism of mass and energy exchange between atmosphere, biosphere, and hydrosphere. Among the common approaches to estimating evapotranspiration, the complementary relationship has been the subject of many recent studies given its simplicity and the use of meteorological data only. Recently, a modified version of the complementary relationship, Modified GG, was developed using meteorological data only and had been successfully applied at 34 diverse global sites to provide more accurate information of evapotranspiration. However, the complementary relationship including Modified GG showed weak performance under dry conditions. This dissertation addressed this limitation of the complementary relationship using the Budyko hypothesis and extended its application to drought monitoring. between ET and potential ET (ETP) has been the subject of many studies because it uses only meteorological data as inputs. However, there is an increasing concern that some complementary relationship models perform poorly under dry conditions. To overcome this limitation, this dissertation was designed to extend the latest complementary relationship model, Modified GG, using both meteorological data and vegetation information, NDVI, which is readily available from remote sensing data. The proposed model, Adjusted GG-NDVI, was validated by comparing to other ET models and measured ET data. With Adjusted GG-NDVI, this dissertation addressed the applicability of using ET as a proxy for drought monitoring. As a result, the drought patterns from the proposed drought index, EWDI, were consistent with commonly used USDM in the United States. More importantly, this study described drought conditions by comprehensively considering both precipitation and vegetation conditions. Taken together, these findings have significant implications for the understanding of how ET can assist in water resources management.

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Section: Discussionmentioning

confidence: 99%

Estimating evapotranspiration using the complementary relationship and the Budyko framework

Kim

Kaluarachchi

2017

Journal of Water and Climate Change

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“…The commonly used statistical downscaling method includes bilinear interpolation, quantile mapping (Wood et al, ), analogue approach (Maurer & Hidalgo, ), and Bayesian methods (Luo et al, ), among others. For the meteorological drought prediction, the precipitation forecast from GCMs should generally be downscaled (and bias corrected) to assess the prediction skill at regional scales (Sohn et al, ; Yoon et al, ). For the agricultural and hydrological drought prediction, climate forecast downscaled to finer temporal and spatial resolutions is needed to drive hydrologic models for predicting soil moisture and streamflow.…”

Section: Dynamical Methods For Drought Predictionmentioning

confidence: 99%

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Hao

Singh

Xia

2018

Reviews of Geophysics

389

232

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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.

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“…While SPEI considers the effect of reference evapotranspiration on drought severity, multiple timescales of SPEI can be identified for different drought types. Although SPEI was only recently developed, it has been applied in various studies for analyzing drought variability, severity, and the impacts of climate change on drought condition [23,[37][38][39][40].…”

Section: Downscaled Climate Data In Study Areamentioning

confidence: 99%

Assessment of the Impact of Climate Change on Drought Characteristics in the Hwanghae Plain, North Korea Using Time Series SPI and SPEI: 1981–2100

Lee

Yoo

Choi

et al. 2017

Water

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North Korea is a food-deficit nation in which climate change could have a significant impact on drought. We analyzed drought characteristics in the Hwanghae Plain, North Korea using both the multiple timescales of the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) from 1981 to 2100. The probability of non-exceedance for a one-month SPEI below −1.0 was only 1.1% in the spring season of 1995 but increased to 24.4% in 2085. The SPEI for a ten-year return period varied from −0.6 to −0.9 in 1995 and decreased to −1.18 in 2025. The results indicate that severe drought is more likely to occur in future as a result of climate change. The seasonal drought conditions were also significantly influenced by climate change. The largest decrease in the SPEI occurred in late spring and early summer, both of which are important for rice growth. Drought characteristics include severity, duration, and intensity. Therefore, we applied the time series of SPIs and SPEIs to the runs theory and found that the drought intensity identified by one-month SPEIs in 1995 was at a level of 1.21, which reached 1.39 in 2085, implying that climate change will intensify drought in the future.

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Six month–lead downscaling prediction of winter to spring drought in South Korea based on a multimodel ensemble

Cited by 34 publications

References 29 publications

Estimating evapotranspiration using the complementary relationship and the Budyko framework

Estimating evapotranspiration using the complementary relationship and the Budyko framework

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Assessment of the Impact of Climate Change on Drought Characteristics in the Hwanghae Plain, North Korea Using Time Series SPI and SPEI: 1981–2100

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