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Remote Sens. 2017, 9(12), 1280; https://doi.org/10.3390/rs9121280

The Effects of Forest Area Changes on Extreme Temperature Indexes between the 1900s and 2010s in Heilongjiang Province, China

1
Key Laboratory of Remote Sensing Monitoring of Geographic Environment, Harbin Normal University, Harbin 150025, China
2
Key Laboratory of Land Surface Process and Climate Change in Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
3
Innovation and Opening Laboratory of Regional Eco-Meteorology in Northeast, China Meteorological Administration, Harbin 150030, China
4
Meteorological Academician Workstation of Heilongjiang Province, Harbin 150030, China
5
Heilongjiang Province Institute of Meteorological Sciences, Harbin 150030, China
*
Author to whom correspondence should be addressed.
Received: 29 October 2017 / Revised: 5 December 2017 / Accepted: 6 December 2017 / Published: 9 December 2017
(This article belongs to the Special Issue Remote Sensing of Forest Growth in a Changing Climate)
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Abstract

Land use and land cover changes (LUCC) are thought to be amongst the most important impacts exerted by humans on climate. However, relatively little research has been carried out so far on the effects of LUCC on extreme climate change other than on regional temperatures and precipitation. In this paper, we apply a regional weather research and forecasting (WRF) climate model using LUCC data from Heilongjiang Province, that was collected between the 1900s and 2010s, to explore how changes in forest cover influence extreme temperature indexes. Our selection of extreme high, low, and daily temperature indexes for analysis in this study enables the calculation of a five-year numerical integration trail with changing forest space. Results indicate that the total forested area of Heilongjiang Province decreased by 28% between the 1900s and 2010s. This decrease is most marked in the western, southwestern, and northeastern parts of the province. Our results also reveal a remarkable correlation between change in forested area and extreme high and low temperature indexes. Further analysis enabled us to determine that the key factor explaining increases in extreme high temperature indexes (i.e., calculated using the number of warm days, warm nights, as well as tropical nights, and summer days) is decreasing forest area; data also showed that this factor caused a decrease in extreme low temperature indexes (i.e., calculated using the number of cold days and cold nights, as well as frost days, and ice days) and an increase in the maximum value of daily minimum temperature. Spatial data demonstrated that there is a significant correlation between forest-to-farmland conversion and extreme temperature indexes throughout most of our study period. Spatial data demonstrated that there is a significant correlation between forest-to-farmland conversion and extreme temperature indexes throughout most of our study period. Positive correlations are also present between decreasing forest area, the more frequent occurrence of extreme high temperature events, and a rise in the maximum value of daily minimum temperature. At the same time, we found clear negative correlations between decreasing forest area and less frequent occurrence of extreme low temperature events. View Full-Text
Keywords: extreme temperature index; forest cover change; Heilongjiang Province extreme temperature index; forest cover change; Heilongjiang Province
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Zhang, L.; Pan, T.; Zhang, H.; Li, X.; Jiang, L. The Effects of Forest Area Changes on Extreme Temperature Indexes between the 1900s and 2010s in Heilongjiang Province, China. Remote Sens. 2017, 9, 1280.

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