Optimal local water resource diversification model for drought vulnerability reduction in water supply system

Kim, Taehyeon; Seo, J.; Kim, Ki-Bum; Hyung, Jinseok; Koo, Ja−Yong

doi:10.5004/dwt.2020.25159

Cited by 1 publication

(2 citation statements)

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“…The intensity and frequency of extreme weather events, such as droughts, is expected to increase in a warmer climate (Cook et al, 2018;Mishra & Singh, 2010;Spinoni et al, 2018). These events can cause extensive damages (Raikes et al, 2019) to a wide range of socio-economic sectors (Naumann et al, 2015) such as agriculture (Bodner et al, 2015;Madadgar et al, 2017), hydropower production (Turner & Voisin, 2022), water supply (Baltas, 2011;Kim et al, 2020aKim et al, , 2020b) and water quality (Delpla et al, 2009;Whitehead et al, 2009). In South Korea, an unusually longer drought lasting from 2013 to 2015 (Kwon et al, 2016) caused severe damages including water restriction in the western part of the country, and nation-wide crop failure.…”

Section: Introductionmentioning

confidence: 99%

“…To our knowledge, whereas many studies have analysed the possibility of producing statistical seasonal forecasts for South Korea at either national or regional scale (Jin et al, 2005; Kim et al, 2020a, 2020b; Kim & Kug, 2018; Lee & Julien, 2016; Noh & Ahn, 2022; Son et al, 2015), there has been no previous attempt to assess the skill of seasonal precipitation forecasts from GCMs across catchments in South Korea. Few studies (Hyun et al, 2020; Kim et al, 2021; Lee et al, 2016) described the GCM‐based forecasting systems run by Korea Meteorology Administration (KMA) and the APEC (Asia‐Pacific Economic Cooperation) Climate Centre (APCC), but they mainly focused on the comparison between different versions of the operational forecasting system and the skill of weather variables at a global scale.…”

Section: Introductionmentioning

confidence: 99%

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Catchment‐scale skill assessment of seasonal precipitation forecasts across South Korea

Lee

Peñuela

Pianosi

et al. 2023

Intl Journal of Climatology

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Climate change is expected to make droughts more frequent and severe all over the world. South Korea is no exception and is already suffering from extreme droughts such as one that prolonged from 2013 to 2015 and caused nation‐wide damage. To mitigate drought damages, better management of existing water infrastructure is essential. A promising opportunity to improve operational decisions is to make use of seasonal weather forecasts that are provided by general circulation models and can be downscaled to the catchment scale. This study hence assesses the skill of seasonal forecasts over 20 catchments in South Korea, where the largest reservoirs are located. Datasets from four weather forecasting centres (ECMWF, UK Met Office, Météo France and DWD) have been evaluated over the period 2011–2020, and their skill quantified using the Continuous Ranked Probability Skill Score (CRPSS). We analyse how skill varies across the seasons and years, and if it can be linked to catchments characteristics. In doing so, we develop a methodology and a Python package to implement it, which is freely available for future applications to other regions. As for the study case, our results showed that among the four forecasting centres, ECMWF's forecasts were the most skilful in South Korea. In particular, seasonal forecasts outperform the climatology for 2 months of lead time and are more skilful during the wet season and in dry years. Linear bias correction is found to be useful to correct systematic seasonal biases, whereas we found no significant correlation between the catchment characteristics and forecast skill. We also investigated the possibility of anticipating dry years from seasonal forecasts and/or ENSO indices but found no significant link.

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