Carnegie-Ames-Stanford Approach Research Articles

Dominant control of climate variations over land-use change on net primary productivity under different urbanization intensities in Beijing, China

Climate variations and land-use change induced by rapid urbanization can lead to crucial impacts on ecosystem Net Primary Productivity (NPP), especially in regions with intensive human activity. However, the relative contributions of land-use change and climate variations to NPP changes under different urbanization intensities are still a topic of debate. This study reports on a case study in Beijing, which is undergoing rapid urbanization, to estimate the effects of land-use change and climate variations on NPP in regions with diverse urbanization intensities in recent decades using remote-sensing data and the Carnegie-Ames-Stanford Approach (CASA) model. The results reveal obvious land-use changes in Beijing, showing an increase in both built-up and forested areas in the two inner, highly urbanized regions and a dramatic conversion from built-up area and grassy bush to forest in the two outer, less urbanized regions. Factorial experiments were performed to estimate the contributions of land-use change and climate variations to NPP changes for different regions and land-use types. An increasing trend in regional NPP was observed with decreasing urbanization intensity in all experiments. However, the increment in NPP between 2002 and 2009 tended to decrease with decreasing urbanization intensity in most experiments. Climate variation was the dominant factor in increasing NPP in the study region during 2002–2009, with a mean contribution of ∼89.5% over different regions and land-use types. However, land-use change contributed to a certain extent to NPP changes for specific land-use types (e.g., ∼28.1% for forests) in regions of intensive urbanization. Eco-engineering approaches such as increasing vegetation cover, especially forest, in built-up areas and reforestation and afforestation in non-built-up areas may be useful in mitigating the impacts of urbanization on NPP, especially in intensively urbanized regions.

Variations and climate constraints of terrestrial net primary productivity over Mongolia

The ecosystems of Mongolia are among the most sensitive to global climate change because they are located in the circumpolar boreal zone, with its arid and semiarid climate. This study used the Carnegie-Ames-Stanford Approach (CASA) model parameterized by meteorological and remote-sensing data to quantify vegetation net primary productivity (NPP) for Mongolia for 1982–2011 and further constructed a map of the relative contributions of climatic constraints to NPP. The results showed that 30-year average NPP in Mongolia varied spatially from 18.1 to 662.8 g C/m2, with an average of 228.2 g C/m2. From the map of climatic constraints on NPP, it was estimated that temperature constrains NPP variation over about 3.9% of Mongolia's land area, whereas precipitation constraints are dominant over about 77.5%. NPP in temperature-constrained regions has continuously increased from 1982 to 2011, with an annual increment of 2.18 g C/m2 (R = 0.61, p < 0.01) with continuous climate warming (0.07 °C/year, R = 0.73, p < 0.01). In contrast, the NPP trend in precipitation-constrained regions has changed from increasing (R = 0.68, p = 0.01) to decreasing (R = -0.38, p = 0.12) since 1994, which can be attributed to a reversal of precipitation trends in 1994. At national scale, NPP appeared stable during 1982–2011, due primarily to the high proportion of precipitation-constrained area in Mongolia, which obscured NPP trends in the temperature-constrained regions. Given that NPP trends clearly varied with different climatic constraints, the importance of differentiating climatic constraints on NPP for exploring NPP trends under different climatic drivers should be emphasized. The seasonal-scale analysis further showed that summer climate and associated vegetation productivity is an important factor regulating NPP over the entire growing season, due primarily to the high proportion of summer NPP and precipitation for the entire growing season, and warmer temperatures in summer relative to those in spring and autumn. In addition to temperature and precipitation, radiation is an important factor responsible for the NPP variations, and the shape of the correlations between radiation and NPP with changing precipitation gradient suggests that the impact process of radiation on NPP with changing water stress is similar to that of temperature, but its impact is weaker than that of temperature.

An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images

The accurate and timely monitoring and evaluation of the regional grain crop yield is more significant for formulating import and export plans of agricultural products, regulating grain markets and adjusting the planting structure. In this study, an improved Carnegie–Ames–Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate winter wheat yield. Firstly, in 2009 the entire growing season of winter wheat in the two districts of Tongzhou and Shunyi of Beijing was divided into 54 stages at five-day intervals. Net Primary Production (NPP) of winter wheat was estimated by the improved CASA model with HJ-1A/B satellite images from 39 transits. For the 15 stages without HJ-1A/B transit, MOD17A2H data products were interpolated to obtain the spatial distribution of winter wheat NPP at 5-day intervals over the entire growing season of winter wheat. Then, an NPP-yield conversion model was utilized to estimate winter wheat yield in the study area. Finally, the accuracy of the method to estimate winter wheat yield with remote sensing images was verified by comparing its results to the ground-measured yield. The results showed that the estimated yield of winter wheat based on remote sensing images is consistent with the ground-measured yield, with R2 of 0.56, RMSE of 1.22 t ha−1, and an average relative error of −6.01%. Based on time-series satellite remote sensing images, the improved CASA model can be used to estimate the NPP and thereby the yield of regional winter wheat. This approach satisfies the accuracy requirements for estimating regional winter wheat yield and thus may be used in actual applications. It also provides a technical reference for estimating large-scale crop yield.

Grassland Dynamics and the Driving Factors Based on Net Primary Productivity in Qinghai Province, China

Qinghai province is an important part of the Tibetan Plateau, and is characterized by extremely fragile ecosystems. In the last few decades, grasslands in this province have been influenced profoundly by climate change, as well as human activities. Here, we use the Carnegie-Ames-Stanford Approach (CASA) model to assess the dynamics of temperate steppe, alpine steppe, temperate meadow, alpine meadow, sparse grassland and herbaceous wetland via actual net primary productivity (NPPa). Our findings showed that: (1) From 2001 to 2016, the average NPPa in Qinghai province showed a fluctuation presented a generally increasing trend. The mean value of NPPa was 114.27 g C m−2 year−1, and the increase rate was 0.47 g C cm−2 year−1. (2) There were NPPa increase rate discrepancies among the six typical grassland biomes. Herbaceous wetland had the highest change rate, closely followed by alpine steppe, temperate steppe, alpine meadow, temperate meadow and sparse grassland. (3) The largest area of restoration mainly impacted by climate change reached 47.08% of the total grassland area, with human activities accounting for 21.74%. By contrast, the deteriorated area induced by human activities accounted for 9.78% of the total grassland. (4) Temperature may have been a greater factor than precipitation in driving grassland change during the study period. Decreasing grazing intensity and implementing effective protection measures were favorable to grassland restoration.

Influences of 1.5 °C and 2.0 °C global warming scenarios on water use efficiency dynamics in the sandy areas of northern China

Water use efficiency (WUE) is an important variable used in hydrometeorology study to reveal the links between carbon-water cycles in sandy ecosystems which are highly sensitive to climate change and can readily reflect the effects of it. In light of the Paris Agreement, it is essential to identify the regional impacts of 0.5 °C of additional global warming to inform climate adaptation and mitigation strategies. Using the modified Carnegie-Ames-Stanford Approach (CASA) and Advection-Aridity (AA) models with global warming values of 1.5 °C and 2.0 °C above preindustrial levels from Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b) datasets, we conducted a new set of climate simulations to assess the effects of climate on WUE (the ratio of net primary productivity (NPP) to actual evapotranspiration (ETa)) in different sandy land types (mobile sandy land, MSL; semimobile/semifixed sandy land, SMSF; and fixed sandy land, FSL) during the period of baseline (1986–2005) and future (2006–2100). The spatiotemporal patterns of ETa, NPP, and WUE mostly showed increasing trends; the value of WUE decreased (6.40%) only in MSL with an additional 0.5 °C of warming. Meteorological and vegetation factors determined the variations in WUE. With warming, only the correlation between precipitation and WUE decreased in the three sandy land types, and the leaf area index (LAI) increased with an additional 0.5 °C of warming. The desertification degree comprehensively reflects the linkages among the standardized precipitation evapotranspiration index (SPEI), LAI and WUE. Simulation results indicated the sandy area extent could potential increase by 20 × 104 km2 per decade on average during 2016–2047 and that the increase could be gradual (2.60 × 104 km2 per decade) after 2050 (2050–2100). These results highlight the benefits of limiting the global mean temperature change to 1.5 °C above preindustrial levels and can help identify the risk of desertification with an additional 0.5 °C of warming.

RETRACTED: Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China

Estimating the net primary production (NPP) of vegetation is essential for eco-environment conservation and carbon cycle research. Remote sensing techniques, combined with algorithm models, have been proven to be promising methods for NPP estimation. High-precision and real-time NPP monitoring in heterogeneous areas requires high spatio-temporal resolution remote sensing data, which are not easy to acquire by single remote sensors, especially in cloudy weather. This study proposes to fuse images of different sensors to provide high spatio-temporal resolution data for NPP estimation in cloud-prone areas. Firstly, the time series Normalized Difference Vegetation Index (NDVI) with a spatial resolution of 30 m and a temporal resolution of 16 days, are obtained by the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Then, the time series NDVI data, combined with meteorological data are input into an improved Carnegie–Ames–Stanford Approach (CASA) model for NPP estimation. This method is validated by a case study of a heavily urbanized area, in the middle reaches of the Yangtze River in China. The results indicate that the NPP estimated by the fused NDVI data has more detailed spatial information than by using the MODIS data. The results show a strong correlation between the actual Landsat8 NDVI and the fused NDVI images, which means that the accuracy of synthetic NDVI images (a 16 day interval and a 30 m resolution) is reliable, and it can provide superior inputs for accurate estimations of a NPP time series. The correlation coefficient (R) and root mean square error between the NPP, based on the fused NDVI and the measured NPP, are 0.66 and 14.280 g C/(m2·yr), respectively, indicating a good consistency. The small discrepancy is caused by the uncertainties of fused NDVI, measurement errors, conversion errors, and other factors in the CASA model. In this study, we achieved NPP with high spatial and temporal resolutions, which can provide higher accuracies of NPP data for analyzing the carbon cycling heavily urbanized areas, compared with similar studies using mono-temporal NPP data. The spatio-temporal fusion technique is an effective way of generating high spatio-temporal resolution images from different sensors, thereby providing enough data for NPP monitoring in urbanized areas.

2000-2015年宁夏草地净初级生产力时空特征及其气候响应

PDF HTML阅读 XML下载 导出引用 引用提醒 2000-2015年宁夏草地净初级生产力时空特征及其气候响应 DOI: 10.5846/stxb201801220171 作者: 作者单位: 宁夏大学西北土地退化与生态系统恢复省部共建国家重点实验室培育基地,宁夏大学西北土地退化与生态系统恢复省部共建国家重点实验室培育基地,宁夏大学西北土地退化与生态系统恢复省部共建国家重点实验室培育基地,宁夏大学西北土地退化与生态系统恢复省部共建国家重点实验室培育基地,宁夏大学西北土地退化与生态系统恢复省部共建国家重点实验室培育基地,宁夏大学西北土地退化与生态系统恢复省部共建国家重点实验室培育基地,宁夏大学西北土地退化与生态系统恢复省部共建国家重点实验室培育基地,宁夏大学西北土地退化与生态系统恢复省部共建国家重点实验室培育基地 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41661003）；宁夏高等学校一流学科（生态学）建设项目（NXYLXK2017B06）；中国科学院西部之光人才计划项目（XAB2017AW01） Spatiotemporal characteristics of grassland net primary production in Ningxia Province from 2000 to 2015 and its response to climate change Author: Affiliation: Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwest China,Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwest China,Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwest China,Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwest China,Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwest China,Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwest China,Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwest China,Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwest China Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:草地是宁夏陆地生态系统的重要组成部分，估算其净初级生产力（NPP）对宁夏草地可持续利用与管理至关重要。采用MODIS数据和CASA模型对2000-2015年间宁夏草地生态系统NPP进行了估算，通过一元线性回归趋势分析、Hurst指数等方法研究草地NPP的时空变化规律及未来演变趋势，并分析草地NPP与气象因子的相关性。结果表明：（1）基于CASA模型的宁夏草地NPP模拟精度高，其估算值与实测多年草地NPP均值具有良好的线性关系（R=0.93，P < 0.01），与MOD17产品的草地NPP空间分布基本一致。（2）近16 a宁夏草地年均NPP为148.28 g C m-2 a-1，且存在波动上升的趋势，其线性增长率为3.84 g C m-2 a-1（P < 0.01）。（3）宁夏草地NPP整体处于上升趋势，草地NPP增长的草地面积达98%，且其增率自南向北递减；宁夏草地NPP的Hurst指数在0.27-0.81之间，均值为0.53，大部分草地的NPP变化趋势具有较强同向持续性。（4）在年时间尺度上，宁夏草地NPP主要受降水量的影响，与气温的相关性较弱；在月时间尺度上，生长季草地NPP与月总降水量的相关性高，且不存在时间滞后响应现象，而与月均温的响应则存在1个月的时间滞后性，宁夏大面积分布的干草原与荒漠草原NPP对气温响应滞后是导致这一现象发生的主要原因。 Abstract:Net primary productivity (NPP) is an important indicator of terrestrial ecosystems and accurately estimating NPP is fundamental in terrestrial carbon cycle research. Ningxia Province is dominated by grassland and the estimation of grassland NPP can provide a scientific basis for the sustainable use of grassland resources and the improvement of grassland management. In this study, the grassland ecosystem NPP in Ningxia Province was estimated using a Carnegie-Ames-Stanford Approach (CASA) model and Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2000 to 2015. To study the spatiotemporal characteristics and future evolution trend of grassland NPP, the correlation between the grassland NPP and meteorological factors was analyzed using a simple linear regression model and the Hurst index. The results showed that the CASA model accurately simulated the grassland NPP in Ningxia Province. The correlation coefficient between estimated NPP by the CASA model and measured NPP was 0.93 (P < 0.01), and the spatial characteristics of the estimated NPP image were similar to that of the NPP image of MOD17. In the whole province, the average annual grassland NPP estimated by the CASA model was 148.28 g C m-2 a-1 during 2000-2015. Meanwhile, the average annual grassland NPP increased with fluctuations during the past 16 years and the linear growth rate was 3.84 g C m-2 a-1 (P < 0.01). Regarding the spatial variation of grassland NPP in Ningxia Province, the NPP in 98% of the grassland increased during the past 16 years and this increasing rate decreased from South to North. The Hurst index of grassland NPP in the past 16 years ranged from 0.27 to 0.81 and the mean value was 0.53, which indicated the grassland NPP will maintain the same trend of change in the future. At an annual scale, the average grassland NPP in Ningxia Province was mainly affected by annual precipitation, and had a weak correlation with the average annual temperature. At a monthly scale, there was also a weak relationship between the grassland NPP and temperature in the growing season; however, the monthly total precipitation exerted an important influence on the grassland NPP accumulation. Furthermore, there was a one-month time lag in the monthly NPP response to mean temperature, but this phenomenon did not exist between the monthly NPP and total precipitation. The main reason for this phenomenon was possibly the hysteresis response to temperature in the steppe and desert grasslands, which were the main types of grassland in Ningxia Province. 参考文献 相似文献 引证文献

Net primary productivity dynamics and associated hydrological driving factors in the floodplain wetland of China's largest freshwater lake

Wetlands are thought to be the most unique ecosystem in the world which plays an important role in water and material circulation. However, investigation of ecosystem dynamics in those lake floodplain wetlands that suffering rapid and significant short-term water level fluctuation is quite a challenge. In this study, the short- and long-term characteristics of vegetation NPP (net primary productivity) and their driving mechanism were investigated in the Poyang Lake floodplain wetland, an important international wetland that listed in the Global Eco-region by the World Wildlife Fund (WWF). Attempts were achieved through validating the Carnegie-Ames-Stanford Approach (CASA) model based on observed biomasses of different vegetation types and reconstructed continuous high spatiotemporal resolution (30 m and 16 days) of NDVI data during 2000–2015 according to the fused Landsat and MODIS data. Major result indicates that the intra-annual variation of NPP of most vegetation types shows two peaks in a year due to combined effects of vegetation growth rhythm and seasonal exposure of the lake floodplain. Annual NPP of the lake floodplain ranges in 360.09–735.94 gC/m2 and shows an increasing trend during the study period. The change of NPP in space indicates that the distribution elevation of the major vegetation types decreased and evoluted toward the center lake floodplain. Different from the terrestrial ecosystem, inundation duration is the dominant factor that controls NPP dynamics in the lake floodplain, while the influences of other meteorological factors are much weakened. Recent decline of lake water level was the major reason for the spatio-temporal evolution of annual and seasonal vegetation NPP in the lake floodplain.

Spatial Responses of Net Ecosystem Productivity of the Yellow River Basin under Diurnal Asymmetric Warming

The net ecosystem productivity (NEP) of drainage basins plays an important role in maintaining the carbon balance of those ecosystems. In this study, the modified CASA (Carnegie Ames Stanford Approach) model and a soil microbial respiration model were used to estimate net primary productivity (NPP) and NEP of the Yellow River Basin’s (YRB) vegetation in the terrestrial ecosystem (excluding rivers, floodplain lakes and other freshwater ecosystems) from 1982 to 2015. After analyzing the spatiotemporal variations in the NEP using slope analysis, the coefficient of variation, and the Hurst exponent, precipitation was identified as the main factor limiting vegetation growth in the YRB. Hence, precipitation was treated as the control variable and a second-order partial correlation method was used to determine the correlation between diurnal asymmetric warming and the YRB’s NEP. The results indicate that: (i) diurnal asymmetric warming occurred in the YRB from 1982 to 2015, with nighttime warming (Tmin) being 1.50 times that of daytime warming (Tmax). There is a significant correlation between variations in NPP and diurnal warming; (ii) the YRB’s NEP are characterized by upward fluctuations in terms of temporal variations, large differences between the various vegetation types, high values in the western and southeastern regions but low values in the northern region in terms of spatial distribution, overall relative stability in the YRB’s vegetation cover, and changes in the same direction being more dominant than those in the opposite direction (although the former is not sustained); and (iii) positive correlations between the NEP and nighttime and daytime warming are approximately 48.37% and 67.51% for the YRB, respectively, with variations in nighttime temperatures having more extensive impacts on vegetation cover.

Evaluating the variability of ANPP in central Iranian arid and semi-arid rangelands using CASA model and its relationship with climatic factors

Aboveground Net Primary Production (ANPP) plays an important role in regulating ecological processes and carbon cycle in arid and semi-arid rangelands. Hence, this study aimed to investigate the spatio-temporal variability of rangeland ANPP in seven bioclimatic zones in Isfahan Province using the Carnegie-Ames-Stanford Approach (CASA) ecosystem model fitted with MODIS-NDVI and climatic data during 2000–2016. The model evaluation indicated a good agreement between the estimated and observed ANPP (R2 = 0.917). In concomitant with the precipitation gradient, the mean annual ANPP increased from east towards west and southwest of the region from zero to 160 g C m–2 yr–1. The maximum and minimum ANPP values occurred in humid and cold as well as hyper-arid and warm zone, respectively. The annual temporal variability of ANPP was analyzed in response to climatic factors (precipitation and temperature), Temperature Vegetation Dryness Index (TVDI) and Standard Precipitation Index (SPI) drought indices. The drought was found as the most important factor affecting ANPP. The minimum and maximum ANPP values were observed in 2000 (23.23 g C m–2 yr–1) and 2014 (41.73 g C m–2 yr–1). The maximum ANPP occurred in May and June during which temperature, as an important factor in plant growth, reached its optimum and precipitation affected with a time lag. The annual rangeland ANPP was positively associated with precipitation and negatively with temperature.

Modelling and analysis of net primary productivity and its response mechanism to climate factors in temperate grassland, northern China

ABSTRACTGrassland productivity is the most direct indicator of grassland’s ecological status; thus, precise and rapid estimation of grassland productivity has an important significance for evaluating regional ecological carrying capacity, utilizing and developing natural resources reasonably. In this study, based on Moderate Resolution Imaging Spectroradiometer remote-sensing data, meteorological data, and ground measured data, the light utilization efficiency parameter of Carnegie–Ames–Stanford Approach model was optimized, and the optimized model was used to estimate net primary productivity (NPP) and analyse its spatio-temporal distribution of Xilingol grassland between 2005 and 2014. In addition, we investigated the NPP response mechanism to climate factors from two aspects, i.e. the accumulation amount and time of NPP. The results indicated that (i) Maximum light utilization efficiency was calculated to be 0.539 g C MJ−1 for Xilingol grassland according to the least error criterion. (ii) Validation against NPP observations from 30 sites showed good performance of the optimized model in the research area, with an overall coefficient of determination (R2) of 0.72 and mean relative estimation error of 0.29. (iii) The annual mean NPP of Xilingol grassland was 161.23 g C m–2 year–1, with a decreasing trend from the northeast to the southwest. (iv) NPP presented increasing trend with a rate of 3.89 g C m–2 year–1 overall during 20052014, and the largest increase in NPP was found in temperate meadow-steppe. However, desert ecosystems showed the largest increasing rate after NPP standardization. (v) Precipitation was the main factor for driving carbon accumulation at the inter-annual scale, and the time to maximum NPP was significantly correlated with spring precipitation (R2 = 0.637), NPP peaked early as the spring precipitation increasing.

Variation of Net Primary Production and Its Correlation with Climate Change and Anthropogenic Activities over the Tibetan Plateau

Grasslands in the Tibetan Plateau are claimed to be sensitive and vulnerable to climate change and anthropogenic activities. Quantifying the impacts of climate change and anthropogenic activities on grassland growth is an essential step for developing sustainable grassland ecosystem management strategies under the background of climate change and increasing anthropogenic activities occurring in the plateau. Net primary productivity (NPP) is one of the key components in the carbon cycle of terrestrial ecosystems, and can serve an important role in the assessment of vegetation growth. In this study, a modified Carnegie–Ames–Stanford Approach (CASA) model, which considers remote sensing information for the estimation of the water stress coefficient and time-lag effects of climatic factors on NPP simulation, was applied to simulate NPP in the Tibetan Plateau from 2001 to 2015. Then, the spatiotemporal variations of NPP and its correlation with climatic factors and anthropogenic activities were analyzed. The results showed that the mean values of NPP were 0.18 kg∙C∙m−2∙a−1 and 0.16 kg∙C∙m−2∙a−1 for the original CASA model and modified CASA model, respectively. The modified CASA model performed well in estimating NPP compared with field-observed data, with root mean square error (RMSE) and mean absolute error (MAE) of 0.13 kg∙C∙m−2∙a−1 and 0.10 kg∙C∙m−2∙a−1, respectively. Relative RMSE and MAE decreased by 45.8% and 44.4%, respectively, compared to the original CASA model. The variation of NPP showed gradients decreasing from southeast to northwest spatially, and displayed an overall decreasing trend for the study area temporally, with a mean value of −0.02 × 10−2 kg∙C∙m−2∙a−1 due to climate change and increasing anthropogenic activities (i.e., land use and land cover change). Generally, 54% and 89% of the total pixels displayed a negative relationship between NPP and mean annual temperature, as well as annual cumulative precipitation, respectively, with average values of –0.0003 (kg∙C∙m−2 a−1)/°C and −0.254 (g∙C∙m−2∙a−1)/mm for mean annual temperature and annual cumulative precipitation, respectively. Additionally, about 68% of the total pixels displayed a positive relationship between annual cumulative solar radiation and NPP, with a mean value of 0.038 (g∙C∙m−2·a−1)/(MJ m−2). Anthropogenic activities had a negative effect on NPP variation, and it was larger than that of climate change, implying that human intervention plays a critical role in mitigating the degenerating ecosystem. In terms of human intervention, ecological destruction has a significantly negative effect on the NPP trend, and the absolute value was larger than that of ecological restoration, which has a significantly positive effect on NPP the trend. Our results indicate that ecological destruction should be paid more attention, and ecological restoration should be conducted to mitigate the overall decreasing trend of NPP in the plateau.

Parameter calibration in global soil carbon models using surrogate-based optimization

Abstract. Soil organic carbon (SOC) has a significant effect on carbon emissions and climate change. However, the current SOC prediction accuracy of most models is very low. Most evaluation studies indicate that the prediction error mainly comes from parameter uncertainties, which can be improved by parameter calibration. Data assimilation techniques have been successfully employed for the parameter calibration of SOC models. However, data assimilation algorithms, such as the sampling-based Bayesian Markov chain Monte Carlo (MCMC), generally have high computation costs and are not appropriate for complex global land models. This study proposes a new parameter calibration method based on surrogate optimization techniques to improve the prediction accuracy of SOC. Experiments on three types of soil carbon cycle models, including the Community Land Model with the Carnegie–Ames–Stanford Approach biogeochemistry submodel (CLM-CASA') and two microbial models show that the surrogate-based optimization method is effective and efficient in terms of both accuracy and cost. Compared to predictions using the tuned parameter values through Bayesian MCMC, the root mean squared errors (RMSEs) between the predictions using the calibrated parameter values with surrogate-base optimization and the observations could be reduced by up to 12 % for different SOC models. Meanwhile, the corresponding computational cost is lower than other global optimization algorithms.

The impacts of land conversion and management measures on the grassland net primary productivity over the Loess Plateau, Northern China

In the 1990s, the Chinese government began implementation of a series of national-scale restoration programs to combat environmental degradation. As one of most important arid and semiarid regions of China, the Loess Plateau has attracted attention related to the effectiveness of these initiatives. The present study analyzed land use and cover change (LUCC) of the grassland in the Loess Plateau and the consequent change in net primary productivity (NPP) based on a consecutive land use data derived from the European Space Agency Climate Change Initiative land cover maps and the CASA (Carnegie-Ames-Stanford Approach) model driven by MODIS-NDVI data. The contributions of climate variation and human activities (including land conversion and management measures) to these changes were also quantitatively differentiated. The results indicated that the area of the Loess Plateau grassland experienced a net increase of 0.43 × 104 km2 over the study period. The total NPP of the Loess Plateau grassland increased by 11,325.13 Gg C·yr−1, of which the human activities and climate variation were responsible for 78.45% and 21.55%, respectively. The land conversion reduced the grassland NPP by 308.60 Gg C·yr−1, whereas management measures increased the NPP by 9197.97 Gg C·yr−1 in the otherwise unmodified grassland. Overall, ecological restoration programs have effectively increased grassland NPP in the Loess Plateau. However, human activities played both positive and negative impacts in this process.

An improved phenology-based CASA model for estimating net primary production of forest in central China based on Landsat images

ABSTRACTThe optimum temperature () in the current Carnegie–Ames–Stanford Approach (CASA) model was defined as the mean temperature of the month when normalized difference vegetation index (NDVI) reaches its maximum. However, it requires improvements from a comprehensive perspective due to that the stability of the maximum NDVI acquisition is subjected to a variety of factors. The article proposed an improved CASA model by redefining the optimum temperature based on phenology () to model the net primary production (NPP) of forest in Shennongjia, central China, and analysed the relationship between annual mean NPP and topography. Logistic function was used to model the phenological phases of forest and was redefined as the mean temperature during the period of maturity stability. The improved was lower than the for five forest types. Specifically, the average of evergreen broadleaf forest, deciduous broadleaf forest, evergreen needleleaf forest, deciduous needleleaf forest, and mixed forest were 22.72°C, 23.31°C, 24.05°C, 23.41°C, and 23.18°C, respectively, whereas the corresponding average were 24.42°C, 24.90°C, 24.54°C, 24.57°C, and 24.43°C, respectively. The NPP observations transformed from field measured biomass were used to evaluate the accuracy of NPP estimated from the -based CASA model and the -based CASA model. The result indicated that the accuracy of the -based CASA model was higher than that of the -based CASA model, with the coefficients of determination of 0.837 (root mean square error (RMSE) = 75 g C m–2 year–1) and 0.632 (RMSE = 122 g C m–2 year–1), respectively. The total NPP of forest in Shennongjia modelled by the -based CASA model and the -based CASA model were 1.40 and 1.35 Tg C year–1, respectively. The relationship between the annual mean NPP and altitude showed a quadratic polynomial function at the altitude from 500 to 3000 m, while the relationship between the annual mean NPP and aspect showed a sine function when aspect in the range of 4.5–360.0°. The results demonstrate that the improvement of CASA model (-based CASA model) is of great significance in phenology and plays as a promising alternative method to model NPP for forest.

Assessing the Driving Forces in Vegetation Dynamics Using Net Primary Productivity as the Indicator: A Case Study in Jinghe River Basin in the Loess Plateau

An objective and effective method to distinguish the influence of climate change and human activities on vegetation dynamics has great significance in the design and implementation of ecosystem restoration projects. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) remote data and the Miami and Carnegie–Ames–Stanford Approach (CASA) model, this study simulated and used net primary productivity (NPP) as an indicator to identify vegetation dynamics and their driving forces in the Jinghe River basin from 2000 to 2014. The results showed that: (1) The vegetation in the Jinghe River basin, which accounted for 84.4% of the study area, showed an increasing trend in NPP; (2) Human activities contributed most to vegetation restoration, which accounted for 54.5% of the areas; 24.0% of the areas showed an increasing trend in the NPP that was dominated by climate factors. Degradation dominated by human activities accounted for 4.3% of the study area, and degradation dominated by climate factors resulted in 17.2%; (3) The rate of vegetation degradation in areas dominated by climate factors rose with increased slope, where the arid climate caused shortages of water resources, and the human-dominated vegetation restoration activities exacerbated the vegetation’s water demand further, which surpassed the carrying capacity of regional water resources and led ultimately to vegetation degradation. We recommend that future ecological restoration programs pay more attention to maintaining the balance between ecosystem restoration and water resource demand to maximize the benefits of human activities and ensure the vegetation restoration is ecologically sustainable.

Assimilating multi-source remotely sensed data into a light use efficiency model for net primary productivity estimation

High spatiotemporal resolution satellite data are necessary for the retrieval of vegetation indexes, such as Normalized Difference Vegetation Index (NDVI), to be assimilated into the Carnegie-Ames-Stanford Approach (CASA) model for net primary productivity (NPP) estimation, especially in the growing season. However, current remotely sensed data cannot accurately monitor vegetation changes at high spatiotemporal resolution. To consider both temporal and spatial information, spatiotemporal fusion models have been developed to obtain the temporal information from high temporal resolution data (e.g., MODIS) together with the spatial information from high spatial resolution data (e.g., Landsat). In this paper, synthetic NDVI images with the spatial resolution of Landsat data and the temporal resolution of MODIS data were first produced using spatiotemporal fusion models. Next, phenological features were extracted from synthetic NDVI time series data to improve land cover classification accuracy. Finally, we evaluated the approach of assimilating the synthetic NDVI and land cover classification map into the CASA model for synthetic NPP estimation. The results revealed that the accuracy of the synthetic NPP was better than NPP estimation from non-fusion NDVI data, and improving the land cover classification accuracy could improve the accuracy of the synthetic NPP estimation. Furthermore, the monthly synthetic NPP showed a significant exponential relationship with the temperature, rainfall, and solar radiation of the current and previous month.

Estimation of Net Rice Production through Improved CASA Model by Addition of Soil Suitability Constant (ħα)

Net primary production (NPP) is an important indicator of the supply of food and wood. We used a hierarchy model and real time field observations to estimate NPP using satellite imagery. Net radiation received by rice crop canopies was estimated as 27,428 Wm−2 (215.4 Wm−2 as averaged) throughout the rice cultivation period (RCP), including 23,168 Wm−2 (118.3 Wm−2 as averaged) as shortwave and 4260 Wm−2 (34.63 Wm−2 as averaged) as longwave radiation. Soil, sensible and latent heat fluxes were approximated as 3324 Wm−2, 16,549 Wm−2, and 7554 Wm−2, respectively. Water stress on rice crops varied between 0.5838 and 0.1218 from the start until the end of the RCP. Biomass generation declined from 6.09–1.03 g/m2 in the tillering and ripening stages, respectively. We added a soil suitability constant (ħα) into the Carnegie-Ames-Stanford Approach (CASA) model to achieve a more precise estimate of yield. Classification results suggest that the total area under rice cultivation was 8861 km2. The spatial distribution of rice cultivation as per suitability zone was: 1674 km2 was not suitable (NS), 592 km2 was less suitable (LS), 2210 km2 was moderately suitable (MS) and 4385 km2 was highly suitable (HS) soil type with ħα ranges of 0.05–0.25, 0.4–0.6, 0.7–0.75 and 0.85–0.95 of the CASA based yield, respectively. We estimated net production as 1.63 million tons, as per 0.46 ton/ha, 1.2 ton/ha 1.9 ton/ha and 2.4 ton/ha from NS, LS, MS and HS soil types, respectively. The results obtained through this improved CASA model, by addition of the constant ħα, are likely to be useful for agronomists by providing more accurate estimates of NPP.

Response of net primary production to land use and land cover change in mainland China since the late 1980s

Land use and land cover patterns in mainland China have substantially changed in the recent decades under the economic reform policies of the government. The terrestrial carbon cycle, particularly the net primary productivity (NPP), has been substantially changed on both local and national scales. With the growing concern over the effects of the terrestrial carbon cycle on global climate changes, the impacts of land use and cover change (LUCC) on NPP need to be understood. In this study, variations in NPP caused by LUCC (e.g., urbanization and conversion of other land use to forest and grassland) in mainland China from the late 1980s to 2015 were evaluated based on land cover datasets and NPPs simulated from the Carnegie–Ames–Stanford Approach model. The results indicate that the national total losses in NPP attributed to urbanization reached 1.695 TgC between the late 1980s and 2015. A large proportion (63.02%) of the total losses was due to the transformation from cropland to urban land. Urban expansion decreased the monthly and total NPPs over southern China, which includes the South China Region, Southwest China Region, and the middle and lower regions of the Yangtze River. However, the total NPP increased in the majority of urbanized areas in Northern China, including the Huang–Huai–Hai Region, Inner Mongolia Region (MGR), Gan-Xin Region (GXR), and Northeast China Region; monthly NPP in GXR and MGR increased throughout the year. By contrast, the conversion to grassland or forestland increased the monthly and total NPPs of Northern China, suggesting that returning to forestland and grassland could increase the carbon sequestration capacity of terrestrial ecosystems in mainland China. Among the sub-regions, the Loess Plateau Region contributed the largest increase in NPP, which was prompted by the conversion to grassland and forestland.

Impact of drought on vegetation carbon storage in arid and semi-arid regions

Drought is a common phenomenon in arid and semi-arid regions. In global carbon cycle, semi-arid ecosystem plays a crucial role in interannual variability and greening trend. The vegetation carbon storage and its relation with drought in India are not well understood. The impact of drought on vegetation carbon storage is investigated using Standardized Precipitation Evapotranspiration Index (SPEI) and CASA (Carnegie-Ames-Stanford Approach) derived Net Primary Production (NPP) during 2001–2013. A strong positive correlation is found between regional NPP and SPEI. CASA derived NPP shows good agreement (R2 = 0.57, significance > 95%) with field based observation in arid and semi-arid regions of Gujarat. A significant Increasing trend of 0.2 TgC Yr−1 (about 2 % per year) is observed during the period of 2001–2013. The highest decreased of 24% (2.79 TgC Yr−1) in NPP was found during 2002 and a notable increase of 17% (1.61 TgC Yr−1) was seen during 2013 due to consecutive drought and surplus rainfall years respectively. The study suggests that consecutive droughts have brought an immense impact on vegetation productivity. During the drought years, dry sub-humid region was the most vulnerable as it was dominated by agriculture land, while semi-arid region was less affected due to forest cover. Despite many droughts, some part of north-west and south-west regions had shown an increase in vegetation carbon storage, which indicates that these parts are not affected by the drought. The study also suggests that in addition to rainfall, heat wave also played a major limiting factor in vegetation productivity. The study will be useful in identifying the regions which are more vulnerable to drought. Present study, will also be helpful in combating the regional climate change as vegetation carbon storage is directly linked with agricultural productivity.