Decoupling of forest water supply and agricultural water demand attributable to deforestation in North Korea.

This study focused on changes in water balance attributable to severe deforestation in North Korea. The forest water supply and agricultural water demand of North Korea were quantified to identify their decoupling over the past three decades. Forest water supply and agricultural water demand before and after deforestation were estimated using the InVEST-WY (Integrated Valuation of Ecosystem Services and Trade-offs - Water Yield) and EPIC (Environmental Policy Integrated Climate) models, respectively. Analysis of land cover change before and after deforestation showed that area under forests decreased by 25%, whereas that under cropland increased by 63%, and that the conversion from forest to cropland was the largest for the study period. As a result, agricultural water demand increased and forest water supply decreased, significantly. Analysis of the net impact of deforestation on water budgets using recent climate and two land covers showed that forest water supply decreased by 43% and agricultural water demand increased by 62%. An assessment of the water balance at the watershed level showed that the Taedong, Ryesong, and Tumen Rivers suffered the largest negative change in terms of the large gross impact of deforestation on water resources. The water balance of the entire North Korea has declined by 51% and this is attributable to deforestation. In contrast, South Korea has experienced success in national-scale afforestation in recent decades, and North Korea can emulate this. The restoration of forests in North Korea promises more than environmental benefits; it will provide a new growth engine for the prosperity of the Korean Peninsula as a whole.

Construction of land-use change matrix and estimation of greenhouse gas inventory focusing on settlements in South Korea.

BACKGROUND: Five ministries are involved in estimating the greenhouse gas (GHG) inventory in the South Korean land use, land-use change, and forestry (LULUCF) sectors. However, these ministries have not established a consistent land classification standard between land-use categories. Therefore, the GHG inventory is estimated at the approach 1 level with no spatial clarity between land-use categories. Moreover, the settlements category is not estimated because activity data and the spatial scope are lacking. This study proposed a methodology for constructing a land-use change (LUC) matrix in the LULUCF sector for improving approach level and estimating the GHG inventory in the settlements. RESULT: We examined 10 sets of spatiotemporal data in South Korea to construct a LUC matrix. To maintain consistency in the spatial land classification, we constructed a LUC matrix using cadastral maps, which provide useful data for consistent land-use classification in South Korea. The LUC matrix was divided into remaining and land-converted settlements between 2005 and 2019 with estimated areas of 878,393.17 and 203,260.42 ha, respectively. CO2 emissions, according to Intergovernmental Panel Climate Change's Guideline Tier 1, were estimated at 18.94 MtCO2 for 15 years, with an annual CO2 emission of 1.26 MtCO2 yr-1. CO2 emission by land conversion type was found to be the largest at 16.93 MtCO2 in the case of forest converted to settlements. In addition, the area with the largest CO2 emission density was Sejong-si at 7.59 tCO2/ha. CONCLUSION: Based on reviewing available spatial data in South Korea, it is possible to improve Approach 3, which is more advanced than previous Approach 1 in the settlement category. In addition, the national GHG inventory also can be estimated by our constructed LUC matrix and activity data in this study. Under the many discussions about developing the Approach system, this study can provide in-detail information on developing LUC in South Korea in the settlement category as well as suggesting a methodology for constructing the LUC matrix for countries with similar problems to South Korea.

Application of integrated Korean forest growth dynamics model to meet NDC target by considering forest management scenarios and budget.

BACKGROUND: Forests are atmospheric carbon sinks, whose natural growth can contribute to climate change mitigation. However, they are also affected by climate change and various other phenomena, for example, the low growth of coniferous forests currently reported globally, including in the Republic of Korea. In response to the implementation of the Paris Agreement, the Korean government has proposed 2030 greenhouse gas roadmap to achieve a Nationally Determined Contribution (NDC), and the forest sector set a sequestration target of 26 million tons by 2030. In this study, the Korean forest growth model (KO-G-Dynamic model) was used to analyze various climate change and forest management scenarios and their capacity to address the NDC targets. A 2050 climate change adaptation strategy is suggested based on forest growth and CO2 sequestration. RESULTS: Forest growth was predicted to gradually decline, and CO2 sequestration was predicted to reach 23 million tons per year in 2050 if current climate and conditions are maintained. According to the model, sequestrations of 33 million tCO2 year-1 in 2030 and 27 million tCO2 year-1 in 2050 can be achieved if ideal forest management is implemented. It was also estimated that the current forest management budget of 317 billion KRW (264 million USD) should be twice as large at 722 billion KRW (602 million USD) in the 2030s and 618 billion KRW (516 million USD) in the 2050s to achieve NDC targets. CONCLUSIONS: The growth trend in Korea's forests transitions from young-matured stands to over-mature forests. The presented model-based forest management plans are an appropriate response and can increase the capacity of Korea to achieve its NDC targets. Such a modeling can help the forestry sector develop plans and policies for climate change adaptation.

Corrigendum to "Mann-Kendall Monotonic Trend Test and Correlation Analysis using Spatio-temporal Dataset: the case of Asia using vegetation greenness and climate factors" [MethodsX 5 (2018) 803-807].

[This corrects the article DOI: 10.1016/j.mex.2018.07.006.][This corrects the article DOI: 10.1016/j.scitotenv.2017.09.145.].

Long-term trend of and correlation between vegetation greenness and climate variables in Asia based on satellite data.

Satellite data has been used to ascertain trends and correlations between climate change and vegetation greenness in Asia. Our study utilized 33-year (1982-2014) AVHRR-GIMMS (Advanced Very High Resolution Radiometer-Global Inventory Modelling and Mapping Studies) NDVI3g and CRU TS (Climatic Research Unit Time Series) climate variable (temperature, rainfall, and potential evapotranspiration) time series. First, we estimated the overall trends for vegetation greenness and climate variables and analyzed trends during summer (April-October), winter (November-March), and the entire year. Second, we carried out correlation and regression analyses to detect correlations between vegetation greenness and climate variables. Our study revealed an increasing trend (0.05-0.28) in temperature in northeastern India (bordering Bhutan), Southeast Bhutan, Yunnan Province of China, Northern Myanmar, Central Cambodia, northern Laos, southern Vietnam, eastern Iran, southern Afghanistan, and southern Pakistan. However, a decreasing trend in temperature (0.00 to -0.04) was noted for specific areas in southern Asia including Central Myanmar and northwestern Thailand and the Guangxi, Southern Gansu, and Shandong provinces of China. The results also indicated an increasing trend for evapotranspiration and air temperature accompanied by a decreasing trend for vegetation greenness and rainfall. Increases in both the mean annual signal and annual cycle occurred in the forest, herbaceous, and cropland areas of India, Northwest China, and eastern Kazakhstan. The temperature was found to be the main driver of the changing vegetation greenness in Kazakhstan, northern Mongolia, Northeast and Central China, North Korea, South Korea, and northern Japan, showing an indirect relationship (R = 0.84-0.96). •Temperature is the main climatic variable affecting vegetation greenness.•A downward trend in vegetation greenness was observed during summer (April-October).•Temperature showed an upward trend across many areas of Asia during the study period.•In winter, rainfall showed downward and upward trends in different parts of Asia.

Long-term trend and correlation between vegetation greenness and climate variables in Asia based on satellite data.

Satellite data has been used to ascertain trends and correlations between climate change and vegetation greenness in Asia. Our study utilized 33-year (1982-2014) AVHRR-GIMMS (Advanced Very High Resolution Radiometer - Global Inventory Modelling and Mapping Studies) NDVI3g and CRU TS (Climatic Research Unit Time Series) climate variable (temperature, rainfall, and potential evapotranspiration) time series. First, we estimated the overall trends for vegetation greenness, climate variables and analyzed trends during summer (April to October), winter (November to March), and the entire year. Second, we carried out correlation and regression analyses to detect correlations between vegetation greenness and climate variables. Our study revealed an increasing trend (0.05 to 0.28) in temperature in northeastern India (bordering Bhutan), Southeast Bhutan, Yunnan Province of China, Northern Myanmar, Central Cambodia, northern Laos, southern Vietnam, eastern Iran, southern Afghanistan, and southern Pakistan. However, a decreasing trend in temperature (0.00 to -0.04) was noted for specific areas in southern Asia including Central Myanmar and northwestern Thailand and the Guangxi, Southern Gansu, and Shandong provinces of China. The results also indicated an increasing trend for evapotranspiration and air temperature accompanied by a decreasing trend for vegetation greenness and rainfall. The temperature was found to be the main driver of the changing vegetation greenness in Kazakhstan, northern Mongolia, Northeast and Central China, North Korea, South Korea, and northern Japan, showing an indirect relationship (R=0.84-0.96).