Quantitative Assessment Of The Net Primary Product Of Global Grassland And Driving Factors Analysis Using Modified CASA Model

Grasslands are among the largest biomes in the world,accounting for nearly 25%of the land surface on earth.Grasslands have many ecological functions,such as wind prevention,sand fixation,soil and water conservation,climate adjustment and air clearness.Grasslands significantly contribute to food security by providing food for ruminants,which are sources of meat and milk for human consumption.Due to the largest distribution,grassland ecosystems also play a key role in balancing the concentrations of global atmospheric greenhouse gases,reducing the effects of greenhouse gases through carbon storage and sequestration.With the global climate change and intensification of human activities,structures and functions of grassland ecosystems have been changing accordingly.Assessing the effects of climate change on the spatiotemporal distribution and dynamic of grassland ecosystems and driving factors do not only help to understand in the interactions of global change and grassland ecosystems,but provide baselines for grassland carbon cycle and global terrestrial carbon cycle assessment.Global grassland is the research object,IGBP data was employed to obtain global grassland area.In additional,model simulation is the main research methods,remote sensing and meteorological data was used to drive the model,study period is from 1982 to 2008.Based on these works,this study accomplished the following tasks:(1)based on the CASA model,we modified the model parameters of the maximum light use efficiency and the optimum temperature in temperature stress algorithm.Using the modified CASA model to simulate the global grassland net primary productivity from 1982 to 2008,and we used the measured data to verifies the accuracy of the modified model.(2)simulate the spatial distribution and its variation characteristics of global grassland Net primary productivity(NPP)during the 1982-2008 period.(3)we analyze the the spatial distribution and interannual characteristic of global temperature and precipitation from 1982 to 2008.and explore the correlation between global grassland NPP and temperature and precipitation.(4)By using the scPDSI drought index,we analyze the drought of global grassland from 1982 to 2008.Through analysis the spatial and temporal characteristics and evolution of global grassland scPDSI drought index,we analyze its impact on the global grassland NPP.(5)Using CASA model to simulate the spatial distribution of the global potential NPP,actual NPP and the NPP due to the impact of human activities.Based on the change rate of three kind NPP,we use the scenario simulation method of quantitative analysis to analyze the climate and human factors of the grassland NPP.The main research conclusion as following:(1)This study is based on CASA model,we modified the model parameters of the maximum light use efficiency and the optimum temperature in temperature stress algorithm.Among them,we used the temperature corresponds to the maximum NDVI and the largest change rate of NDVI to determine the temperature of the upper and lower.And we calculated the maximum light use efficiency based on NDVI and LAI.Using the modified CASA model to simulate the global grassland net primary productivity from 1982 to 2008,and we used the measured data to assessment the accuracy of the improved model.Results show that the modified CASA model can be used in the estimating of grassland NPP simulation,and the correlation between simulated values with the measured data reached significant level(R2=0.77,P<0.05).(2)This study used modified CASA model to simulate the global spatial distribution of grassland NPP from 1982 to 2008.We found that the average of global grassland total NPP is 24.73±0.27 Pg C/yr.The global grassland NPP present more significantly growth trend(P<0.05)from 1982 to 2008,and the growth rate is 0.0254 Pg C/yr.Based on the piecewise linear regression model,the global grassland NPP showed significant growth trend(P<0.05)from 1982 to 1995,and the annual growth rate is 0.0554 Pg C/yr.It showed no significant downtrend with the annual growth rate of0.0337 Pg C/yr from 1996 to 2008.By analysis different grassland types.we found that savannas has the highest NPP,which was 560.07 g C/m2/yr,followed by woody savannas(474.45 g C/m2/yr),closed shrubland and non woody grassland were 328.58 and 237.78 g C/m2/yr,and the lowest was open shrubland,whichi was 162.53 g C/m2/yr.(3)By analyzing the spatiotemporal dynamics of temperature and precipitation in global grassland,we found that global average temperature of grassland presents a rising trend during the period of 1982-2008,and have a significant increase trend after 1995.During the period of 1982-1995,the precipitation presented a nonsignificant decrease trend.After 1995.the precipitation showed a rising trend of global grassland,and had a significant increase trend of 1999-2006.Through the analysis of the interannual correlation between NPP and the temperature and precipitation,we found that the positive correlation between temperature and NPP was 0.47 and the negative correlation was 0.48;the positive correlation between precipitation and NPP was 0.49,and the negative correlation was 0.43.Therefore,precipitation is the main climate factor of grassland NPP in terms of global scale.The temperature influence on the grassland NPP will become more prominent in some area or regional scale.(4)By using scPDSI drought index,we analyzed the drought of global grassland during 1982-2008.Through analysis the spatial and temporal characteristics and evolution of global grassland scPDSI drought index,we found that the scPDSI drought index has a good applicability in global grassland.It can reveal the drought situation of global grassland and different grassland types.The trend of global grassland scPDSI drought index was rising during 1982-2008,and the average speed was 11.9%/10 a.Especially in the year of 2004-2005 and 2008,the scPDSI drought index increased significantly.The drought area had a decreasing trend,and the size order of drought area among different grassland types as follows Savannas>Non woody grassland>closed shrublands>open shrublands>Woody savannas.Through this study,we found that global scPDSI drought index presented a synchronous trend with grassland NPP during the period of 1982-2008,namely the more severe drought,the lower grassland NPP.Where the drought occurred more seriously,the grassland NPP is also lower,and when the drought degree is weakening in some region,the downtrend of grassland NPP was also weakens.The change trend of scPDSI drought index and grassland NPP among different grassland types is synchronous,the increase or decrease performance of closed shrubland is more obvious.Through the analysis of the effects of global grassland area under different drought grades,we found that global level of different drought grassland in the range of fluctuation during 1982-2008,and presented a decrease trend as a whole.The global grassland area affected by medium drought and mild drought is larger.(5)Using the CASA model,we simulated the spatial distribution of the global potential NPP grassland,the actual NPP and the NPP affected by human activities.Based on the changing rate of these three kinds of NPP,we established the quantitative evaluation scene model about the effect of human and climate factors on grassland NPP.The results showed that 54.89%of the global grassland area enhanced at a rate of 1.76 gC/m2/year,by contrast,the 45.10%of the global grassland area decreased at a rate of 0.54 gC/m2/year.Climate change played a leading role in the grassland NPP change,the global grassland NPP caused by climate change was 874.82 TgC.The interaction of climate change and human activities is the main factors of grassland NPP reducing,and it caused 45.84 TgC reduction of the grassland NPP.The quantitative simulation about the influence of climate and human factors on the grassland NPP can help us to understand the global ecosystem carbon cycle,and provide valuable suggestions for improving grassland ecosystem management and decision making.(6)The carbon use efficiency(CUE)of grassland,a ratio of net primary production(NPP)to gross primary productivity(GPP),is an important index representing the capacity of plants to transfer carbon from the atmosphere to terrestrial biomass.In this study,we used the Moderate Resolution Imaging Spectroradiometer(MODIS)data to calculate the global grassland CUE,and explore the spatiotemporal dynamic of global grassland CUE from 2000 to 2013 to discuss the response to climate variations.The results showed that the average annual CUE of different grassland types follows an order of:open shrublands>non-woody grasslands>closed shrublands>woody savannas>savannas.The higher grassland CUE mainly occurred in the regions with cold and dry climate.By contrast,the regions with the lower grassland CUE were mostly in warm and wet environments.Moreover,the CUE exhibited a globally positive correlation with precipitation and a negative correlation with temperature.Therefore,the grassland CUE has considerable spatial variation associated with grassland type,geographical location and climate change.There are several innovations of this study:1.This research paper improves the algorithm of the optimum temperature.We used the temperature corresponds to the maximum NDVI and the largest change rate of NDVI to determine the temperature of the upper and lower.Then,we calculated the partial correlation relationships between temperature and NDVI beside the influence of precipitation based on suitable growth temperature range.Though the significance test,the maximum correlation coefficient of the corresponding temperature value is defined as the optimum temperature.Maximum light use efficiency is one of the most important input parameters of CASA model,its value is different among different vegetation types.Previous study found that the εmax was also affected by leaf area index,thus we assumed maximum light use efficiency has multiplicative factor relations with vegetation index and leaf area index,and it is inverse proportion.But this method has been used on small regions and grassland in China,and has achieved good results.In other areas and even the global scale has not been verified.Therefore,this article applied this method in the global grassland NPP simulation.Through the validation of the measured data,this method has been proved that has a good applicability on a global scale.2.Among the influence factors of NPP in the grassland ecosystem research,research focuses on the impact of climate change trend of long-term average assessment ways,and less research focused on extreme weather events.At the same time,the research about drought mostly focused on the influence of forest ecosystem,few studies have been done on grassland ecosystem,and its study scale is small,lacking of large-scale comprehensive analysis.Therefore,this study used scPDSI drought index to study the effect of drought on global grassland NPP,helps to elucidate climate change especially extreme climate influence on the grassland ecosystem carbon cycle process,and have important significance on disaster prevention and mitigation and adaptation countermeasures of extreme events.3.The quantitative assessment of the impact of climate change and human factors on the grassland NPP is concentrated in the sample area or small regional scale,and the index employed by different scholars is non-unity,this will lead to poor comparability of the result,and the quantitative evaluation research of change driving force on grassland NPP in large scale is less.Therefore,the relative contribution of climate change and human activities to grassland NPP change was quantitatively assessed based on three NPPs-potential NPP,actual NPP and HNPP.The results do not explicit the global grassland NPP status,but determine the respect contribution of driving factors at different regions,and make clear the dominant factors of grassland degradation.The outcomes may also provide valuable suggestions for reasonable adjustment of ecosystem restoration programs and sustainable development of grassland resources.