Probabilistic monitoring of meteorological drought impacts on water quality of major rivers in South Korea using copula models.

The impacts of drought range from water supply for humans to ecosystems. Drought affects river water quality by disturbing the hydrological regime in a variety of ways, and can degrade water quality by reducing surface and groundwater availability. In particular, drought-induced low flows, reduced nutrient dilution, and extreme increases in water temperature affect various water quality parameters in streams. Furthermore, the effects of drought on stream water quality may vary from season to season and from stream segment to stream segment, which requires careful investigation. In this study, Environmental Drought Condition Index - water quality (EDCI-wq) is proposed using a bivariate copula joint probability model between meteorological drought index and river water quality. Using this, environmental drought with respect to water quality is defined, and it is confirmed that environmental drought with respect to water quality can be routinely monitored through time series analysis and mapping of the proposed EDCI-wq. In addition, in order to express the environmental drought condition more explicitly to the general public, the environmental drought condition is graded into four classes based on the EDCI-wq. Furthermore, the sensitivity of river water quality to meteorological drought was estimated using the copula joint probability model, which allowed us to identify river segments that are relatively more sensitive to meteorological drought events.

A copula model to identify the risk of river water temperature stress for meteorological drought.

Drought is a natural phenomenon that can occur in all climatic zones, and is persistent and regionally widespread. Extreme drought caused by climate change can have serious consequences for freshwater ecosystems, which can have significant social and economic impacts. In this study, the effect of meteorological drought on river water temperature was analyzed probabilistically in order to identify the risk of river water temperature stress experienced by the aquatic ecosystem when a meteorological drought occurs. Meteorological drought is divided into a situation in which moisture is insufficiently supplied from the atmosphere and a situation in which the atmosphere requires excessive moisture from the earth's surface. Using the copula theory, a joint probabilistic model between the river water temperature and each meteorological drought caused by two causes is proposed. In order to consider the propagation time from meteorological drought to river water temperature, the optimal time-scale meteorological drought index is adopted through correlation analysis between the meteorological drought index calculated at various time-scales and the river water temperature. The optimal copula function of the drought index and river water temperature is determined using AIC analysis. Using the proposed model, a risk map is drawn for the river water temperature stress experienced by the aquatic ecosystem under the user-defined meteorological drought severity. The risk map identifies the stream sections where the river water temperature is relatively more sensitive to meteorological drought. The identified stream sections appear differently depending on the cause of the meteorological drought, the region, and the season.

A copula model integrating atmospheric moisture demand and supply for vegetation vulnerability mapping.

Drought caused by various meteorological factors negatively affects vegetation. Constructing a joint probability distribution between vegetation and drought information may be appropriate to understand the vulnerability of vegetation to drought. In this study, a copula-based trivariate joint probability model is proposed to investigate the effects of various aspects of meteorological drought on vegetation (vegetation drought). Because drought can be caused by insufficient precipitation or excessive evapotranspiration, the meteorological drought risk for vegetation was divided into two aspects (atmospheric moisture supply and moisture demand). The vulnerability of vegetation drought was mapped when two aspects of meteorological drought occurred separately or simultaneously at high spatial resolution using remote sensing data. The results revealed that the response of vegetation was significantly different depending on the climatic stressors. Although the sensitivity of vegetation to each drought condition varied from region to region, it was found that vegetation was more vulnerable to drought caused by atmospheric moisture demand in most regions of Far East Asia. It has also been shown that drought conditions, which overlapped with insufficient precipitation and excessive evapotranspiration, can drive vegetation to a far more lethal level. Meanwhile, through comparison with the existing VTCI, the proposed Normalized Vegetation Temperature Condition Index (nVTCI) was found to be able to more rationally monitor vegetation drought in the Far East Asian region.

Copula-based Joint Drought Index using SPI and EDDI and its application to climate change.

The drought index, which mainly focuses on the moisture supply side of the atmosphere, which has been mainly used in the field of drought monitoring, has limitations that cannot reflect drought caused by changes in various climate variables such as an increase in surface air temperature due to global warming. To overcome these limitations, various evaporation demand-based drought indices have been proposed, focusing on the aspect of atmospheric moisture demand. However, drought indices that consider only precipitation or the demand for atmospheric evaporation are difficult to comprehensively interpret drought caused by various climatic factors. The novelty of this study is to propose a new drought index to simultaneously monitor droughts occurring in terms of atmospheric moisture supply and demand. The proposed Copula-based Joint Drought Index (CJDI) combines the Standardized Precipitation Index and the Evaporative Demand Drought Index using copula. Since CJDI reflects the correlation between the two drought indices, it is shown that CJDI can better monitor Korea's past droughts than other drought indices. It is found that quantification of past drought using CJDI can be used to objectively recognize the level of drought currently in progress by combining with drought severity-duration-frequency curves derived from partial duration series. As a result of analyzing the future drought pattern in Korea, it was revealed that the drought would be alleviated by about 11% in the case of SPI and SPEI, but the drought would intensify by about 89% in the case of EDDI. In the case of CJDI, it is projected that the drought is likely to intensify to about 17%. From the perspective of better reproducing past droughts and projecting a more convincing future drought than other drought indices, CJDI is expected to be fully utilized as a drought index to monitor droughts and establish climate change adaptation policies.