Cho, Nanghyun
(Department of Environmental Science, Kangwon National University)
,
Kim, Eunsook
(Forest Ecology and Climate Change Division, National Institute of Forest Science)
,
Lim, Jong-Hwan
(Forest Ecology and Climate Change Division, National Institute of Forest Science)
,
Seo, Bumsuk
(Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research)
,
Kang, Sinkyu
(Department of Environmental Science, Kangwon National University)
Background: The phenomenon of tree dieback in forest ecosystems around the world, which is known to be associated with high temperatures that occur simultaneously with drought, has received much attention. Korea is experiencing a rapid rise in temperature relative to other regions. Particularly in t...
Background: The phenomenon of tree dieback in forest ecosystems around the world, which is known to be associated with high temperatures that occur simultaneously with drought, has received much attention. Korea is experiencing a rapid rise in temperature relative to other regions. Particularly in the growth of evergreen conifers, temperature increases in winter and spring can have great influence. In recent years, there have been reports of group dieback of Pinus densiflora trees in Korea, and many studies are being conducted to identify the causes. However, research on techniques to diagnose and monitor drought stress in forest ecosystems on local and regional scales has been lacking. Results: In this study, we developed and evaluated an index to identify drought and high-temperature vulnerability in Pinus densiflora forests. We found the Drought Stress Index (DSI) that we developed to be effective in generally assessing the drought-reactive physiology of trees. During 2001-2016, in Korea, we refined the index and produced DSI data from a 1 × 1-km unit grid spanning the entire country. We found that the DSI data correlated with the event data of Pinus densiflora mass dieback compiled in this study. The average DSI value at times of occurrence of Pinus densiflora group dieback was 0.6, which was notably higher than during times of nonoccurrence. Conclusions: Our combination of the Standard Precipitation Index and growing degree days evolved and short- and long-term effects into a new index by which we found meaningful results using dieback event data. Topographical and biological factors and climate data should be considered to improve the DSI. This study serves as the first step in developing an even more robust index to monitor the vulnerability of forest ecosystems in Korea.
Background: The phenomenon of tree dieback in forest ecosystems around the world, which is known to be associated with high temperatures that occur simultaneously with drought, has received much attention. Korea is experiencing a rapid rise in temperature relative to other regions. Particularly in the growth of evergreen conifers, temperature increases in winter and spring can have great influence. In recent years, there have been reports of group dieback of Pinus densiflora trees in Korea, and many studies are being conducted to identify the causes. However, research on techniques to diagnose and monitor drought stress in forest ecosystems on local and regional scales has been lacking. Results: In this study, we developed and evaluated an index to identify drought and high-temperature vulnerability in Pinus densiflora forests. We found the Drought Stress Index (DSI) that we developed to be effective in generally assessing the drought-reactive physiology of trees. During 2001-2016, in Korea, we refined the index and produced DSI data from a 1 × 1-km unit grid spanning the entire country. We found that the DSI data correlated with the event data of Pinus densiflora mass dieback compiled in this study. The average DSI value at times of occurrence of Pinus densiflora group dieback was 0.6, which was notably higher than during times of nonoccurrence. Conclusions: Our combination of the Standard Precipitation Index and growing degree days evolved and short- and long-term effects into a new index by which we found meaningful results using dieback event data. Topographical and biological factors and climate data should be considered to improve the DSI. This study serves as the first step in developing an even more robust index to monitor the vulnerability of forest ecosystems in Korea.
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문제 정의
Through this study, we revealed the potential of DSI using only climate data. Although the strategy based on the climate–productivity–respiration relationship previously studied for developing such an index seemed useful, we also found that the limitations of the DSI for some events made it very low in explanatory power.
제안 방법
In this study, different weighting values (α, β) were assigned to investigate how the DSI values varied over SPI, HI, and short- and longterm effects.
(2016) as an index considering high temperature and drought stress physiology of trees. In this study, only climatic variables were used as the initial stage of a strategic approach to the development of the index, but geographical and biological factors will be added to develop a more useful and effective drought stress index.
In this study, we developed a new drought index to monitor and predict stressed Pinus densiflora forests from abnormal climate factors such as high temperature and drought, called the Drought Stress Index, which is calculated from a combination of the Standardized Precipitation Index (SPI) (McKee et al. 1993) and growing degree days (GDD). Both SPI and GDD are indices that have long been used successfully in various fields and have the characteristics of being flexible in terms of time scales.
In this study, we have proposed a new index, the DSI, combining the advantages of SPI and HI. Both the temporal flexibility of SPI and the ecological utilization of HI were considered.
By interpolating and combining data on precipitation and temperature, we produced raster data at a spatial resolution of 1 km × 1 km across South Korea. The precipitation and temperature data were standardized and converted to SPI and HI, and the DSI was calculated through a simple linear combination of SPI and HI. The DSI was compared to and analyzed with dieback event data compiled in this study.
1993). To calculate SPI, we first derive the moving average of the monthly precipitation data at the desired time scale (such as 3, 9, or 12 months), calculate the appropriate monthly probability distribution based on the moving average, obtain the cumulative probability, and estimate the z-value corresponding to that of cumulative probability assuming a standard normal distribution. The SPI value indicates the intensity of the drought; the below-zero value signals the beginning of the drought, and the smaller its value, the severe is the drought.
To consider the effects of temperature, the monthly total GDD was derived from daily average temperature data and used to estimate the DSI. Representing the cumulative value above the threshold temperature, GDD has been used successfully in agriculture since the 1970s to refer to the amount of heat needed for plants to move on to the next development phase in their life cycle (McMaster and Wilhelm 1997).
If the DSI shows a high ranking for the tree dieback occurrence years, it can be seen as having a high level of explanatory power, and if the ranking is low, it can be considered as a low level of explanatory power. Using the ranking frequency method, the analysis for 16 Pinus densiflora dieback events from 10 regions in Korea was conducted.
We calculated the mean DSI value for the tier when dieback events occurred during 2004–2016 in the 10 target locations and then performed a ranking frequency analysis (Fig. 3 and Table 6).
대상 데이터
In this study, different weighting values (α, β) were assigned to investigate how the DSI values varied over SPI, HI, and short- and longterm effects. A total of five models were designed. Model 1 and Model 2 (only considering short-term effect) are only different in temperature and precipitation contribution value (α, β) in the calculation of the DSI.
kr/). Considering the continuity and location of weather data, we chose 357 sites (62 WS and 295 AWS locations) to use in this study (Fig. 1).
The SPI value indicates the intensity of the drought; the below-zero value signals the beginning of the drought, and the smaller its value, the severe is the drought. In this study, SPI6 and SPI48 were used for DSI estimate based on 6 months and 48 months using the SPI program provided by the U.S. National Drought Mitigation Center (at the University of Nebraska; the software is available for the free download at https:// drought.unl.edu/droughtmonitoring/SPI.aspx).
데이터처리
The Statistical Package for Social Sciences and the R statistical program were used for basic statistical analysis and data processing, respectively. To evaluate the DSI, we conducted a ranking frequency analysis, which compares rankings at a specific time for the entire period. This method is to rank the DSI values (ordered by the highest DSI value) for each year for a target region and compare the ranking in the tree dieback occurrence year and non-dieback year.
이론/모형
The DSI is based on simplifying the climate–productivity–respiration relationship presented by Kang et al. (2016) as an index considering high temperature and drought stress physiology of trees.
성능/효과
Kriging was performed using such parameter values as nugget, sill, and range calculated from the variogram. Based on the cross-checking results, the RMSE(rootmean-square error) for precipitation showed 51 to 66 mm from July to September, and a much lower RMSE values were confirmed in other periods. For temperature, RMSE showed a range of 0.
It was especially severe in the southern region (Gyeongsang, Jeolla province). The average DSI value of nine locations in Gyeongsang province (Gimhae, Miryang, Geoje, Sacheon, Changwon, Gyeongsan, Uljin, Pohang, and Yeongcheon), where Pinus densiflora group dieback events were reported was 1.48, the highest value during the entire study period.
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