Understanding the mediating role of labor out-migration in household income generation and distribution under a reforestation policy in rural China.

Income inequality is a critical issue of socio-economic development, particularly in rural areas where forest-dependent people are often vulnerable to the intervention of forest policies. This paper aims to elucidate income distribution and inequality of rural households influenced by China's largest reforestation policy implemented in early 2000s. Drawing on socioeconomic and demographic data from household surveys in two rural sites, we applied the Gini coefficient to measure income inequality and used a regression-based approach to examine the underlying factors that are associated with income generation among households. We also performed a mediation analysis to test the role of labor out-migration in shaping household income distribution under the reforestation policy. Results show that remittances sent by rural out-migrants substantially contribute to household income but tend to worsen inequality, particularly for households having retired cropland for reforestation. The inequality in total income depends on capital accumulation for land endowment and labor availability that render diversified livelihoods possible. Such linkage reveals regional disparity, which, along with policy-implementing institutions (e.g., rules for tree species choice for reforestation), can influence income generation from a given source (e.g., agriculture). Rural out-migration of female labor significantly mediates the economic benefits of the policy delivered to the households with an estimated mediating share of 11.7%. These findings add value to the knowledge of poverty-environment interrelationships in a sense that supporting rural livelihoods of the more vulnerable and underrepresented groups is essential for securing and sustaining the stewardship of forests. Policymaking for such forest restoration programs needs to integrate strategies for targeted or precise poverty alleviation to strengthen the conservation effectiveness.

Understanding the Effects of China's Agro-Environmental Policies on Rural Households' Labor and Land Allocation with a Spatially Explicit Agent-Based Model.

Understanding household labor and land allocation decisions under agro-environmental policies is challenging due to complex human-environment interactions. Here, we develop a spatially explicit agent-based model based on spatial and socioeconomic data to simulate households' land and labor allocation decisions and investigate the impacts of two forest restoration and conservation programs and one agricultural subsidy program in rural China. Simulation outputs reveal that the forest restoration program accelerates labor out-migration and cropland shrink, while the forest conservation program promotes livelihood diversification via increasing non-farm employment. Meanwhile, the agricultural subsidy program keeps labor for cultivation on land parcels with good quality, but appears less effective for preventing marginal croplands from being abandoned. The policy effects on labor allocation substantially differ between rules based on bounded rational and empirical knowledge of defining household decisions, particularly on sending labor out-migrants and engaging in local off-farm jobs. Land use patterns show that the extent to which households pursue economic benefits through shrinking cultivated land is generally greater under bounded rationality than empirical knowledge. Findings demonstrate nonlinear social-ecological impacts of the agro-environmental policies through time, which can deviate from expectations due to complex interplays between households and land. This study also suggests that the spatial agent-based model can represent adaptive decision-making and interactions of human agents and their interactions in dynamic social and physical environments.

Widespread decline of Congo rainforest greenness in the past decade.

Tropical forests are global epicentres of biodiversity and important modulators of climate change, and are mainly constrained by rainfall patterns. The severe short-term droughts that occurred recently in Amazonia have drawn attention to the vulnerability of tropical forests to climatic disturbances. The central African rainforests, the second-largest on Earth, have experienced a long-term drying trend whose impacts on vegetation dynamics remain mostly unknown because in situ observations are very limited. The Congolese forest, with its drier conditions and higher percentage of semi-evergreen trees, may be more tolerant to short-term rainfall reduction than are wetter tropical forests, but for a long-term drought there may be critical thresholds of water availability below which higher-biomass, closed-canopy forests transition to more open, lower-biomass forests. Here we present observational evidence for a widespread decline in forest greenness over the past decade based on analyses of satellite data (optical, thermal, microwave and gravity) from several independent sensors over the Congo basin. This decline in vegetation greenness, particularly in the northern Congolese forest, is generally consistent with decreases in rainfall, terrestrial water storage, water content in aboveground woody and leaf biomass, and the canopy backscatter anomaly caused by changes in structure and moisture in upper forest layers. It is also consistent with increases in photosynthetically active radiation and land surface temperature. These multiple lines of evidence indicate that this large-scale vegetation browning, or loss of photosynthetic capacity, may be partially attributable to the long-term drying trend. Our results suggest that a continued gradual decline of photosynthetic capacity and moisture content driven by the persistent drying trend could alter the composition and structure of the Congolese forest to favour the spread of drought-tolerant species.

Effects of payments for ecosystem services programs in China on rural household labor allocation and land use: Identifying complex pathways.

Payments for Ecosystem Services (PES) is increasingly used in developing countries to secure the sustainable provision of vital ecosystem services. The largest PES programs in the world are embedded in China's new forest policies, which aim to expand forest cover for soil and water conservation and improve livelihoods of rural people. The objective of this study is to identify the complex pathways of impacts of two PES programs - the Conversion of Cropland to Forest Program (CCFP) and the Ecological Welfare Forest Program (EWFP) - on household livelihood decisions, and to quantify the direct and indirect impacts along the identified pathways. We fulfill this objective by developing an integrated conceptual framework and applying a Partial Least Squares-Structural Equation Model (PLS-SEM), based on household survey data from Anhui, China. Labor allocation (for on-farm work, local paid work, local business, and out-migration) and land use decisions (i.e., rent in, maintain, rent out, or abandon cropland) for participating households are key to understand PES program effects on livelihoods. Results show that the PES programs have only small direct effects but significant indirect effects via the mediating factor of capital assets. Moreover, group heterogeneity analysis shows that lower-income households do not benefit any more than the better-off households from the PES, while households with medium wealth increase dependence on agriculture. In addition, household demographics, individual attributes, and geographic settings differ in their impacts on labor allocation and land use decisions. We conclude that CCFP and EWFP programs would be more efficient in conserving the environment while improving the economic welfare of lower-income households if capital assets were taken into account in the design of compensation schemes.

Rural household income distribution and inequality in China: Effects of payments for ecosystem services policies and other factors.

In the late 1990s, China initiated the Conversion of Croplands to Forest Program (CCFP) and the Ecological Welfare Forest Program (EWFP) based on the Payments for Ecosystem Services (PES) principle. Positive socioeconomic outcomes of the programs are essential for the long-term success of eco-environment conservation. However, there is lack of understanding of their longer-term (over 10 years) impacts on rural livelihoods. In this paper, we examine income distribution and inequality of rural households under CCFP and EWFP in rural Anhui, China after 12 years of program implementation. Results show that CCFP-participating households have higher income inequality than non-participants, while the EWFP does not have an significant effect. Local off-farm work and out-migration with remittances are the two principal income sources and both add to inequality. A regression-based decomposition of inequality shows that the CCFP indirectly alters livelihoods by increasing out-migration with remittances, but it also adds to inequality from shifting livelihoods to non-agricultural activities. Meanwhile, EWFP payments positively affect agricultural incomes and contribute 16% to agricultural income inequality. Finally, human capital, natural capital and physical capital all play important roles in generating income and inequality, but the factors affecting inequality from agricultural and non-agricultural activities are different.

Global-scale patterns of nutrient density and partitioning in forests in relation to climate.

Knowledge of nutrient storage and partitioning in forests is imperative for ecosystem models and ecological theory. Whether the nutrients (N, P, K, Ca, and Mg) stored in forest biomass and their partitioning patterns vary systematically across climatic gradients remains unknown. Here, we explored the global-scale patterns of nutrient density and partitioning using a newly compiled dataset including 372 forest stands. We found that temperature and precipitation were key factors driving the nutrients stored in living biomass of forests at global scale. The N, K, and Mg stored in living biomass tended to be greater in increasingly warm climates. The mean biomass N density was 577.0, 530.4, 513.2, and 336.7 kg/ha for tropical, subtropical, temperate, and boreal forests, respectively. Around 76% of the variation in biomass N density could be accounted by the empirical model combining biomass density, phylogeny (i.e., angiosperm, gymnosperm), and the interaction of mean annual temperature and precipitation. Climate, stand age, and biomass density significantly affected nutrients partitioning at forest community level. The fractional distribution of nutrients to roots decreased significantly with temperature, suggesting that forests in cold climates allocate greater nutrients to roots. Gymnosperm forests tended to allocate more nutrients to leaves as compared with angiosperm forests, whereas the angiosperm forests distributed more nutrients in stems. The nutrient-based Root:Shoot ratios (R:S), averaged 0.30 for R:SN , 0.36 for R:SP , 0.32 for R:SK , 0.27 for R:SCa , and 0.35 for R:SMg , respectively. The scaling exponents of the relationships describing root nutrients as a function of shoot nutrients were more than 1.0, suggesting that as nutrient allocated to shoot increases, nutrient allocated to roots increases faster than linearly with nutrient in shoot. Soil type significantly affected the total N, P, K, Ca, and Mg stored in living biomass of forests, and the Acrisols group displayed the lowest P, K, Ca, and Mg.

Impacts of China's Grain for Green Program on Migration and Household Income.

In the late 1990s, China's Yangtze and Yellow River Basins suffered devastating natural disasters widely attributed to the degradation of soil and water resources. The Government of China responded with a number of major environmental programs, the most expensive and influential of which, the Grain for Green (GfG) Program, was implemented widely from 1999. Under the GfG Program-also known as the Sloping Land Conversion or Conversion of Cropland to Forest Program-the central government compensates farmers to convert cropland on steep slopes or otherwise ecologically sensitive areas to forest or grassland. Its long-term success depends on households' ability to make sustainable changes to their household income streams and income diversification strategies. In this paper, we use a difference-in-difference estimation approach to examine the role of migration as a household-level response to the GfG Program, testing the extent to which individuals migrate following a reduction in land available for farming. Importantly, we exploit 15 years of data on migration decisions and establish that participating and non-participating households were on parallel migration paths before the program, thus refuting a key threat to causality in a difference-in-difference model. We find that participating families do, in fact, choose migration as an income diversification strategy more frequently than non-participants. The program effects varied over time but peaked post-Great Recession in 2011 when migration rates in GfG households exceeded those of non-GfG households by 5.9% points (p = 0.003) or about 26%. Our findings should encourage policymakers that families are making long-term adjustments to their livelihood strategies to avoid poverty in anticipation of the eventual withdrawal of government supports.

Determinants of Out-migration in rural China: effects of payments for Ecosystem Services.

Rural out-migration has been a hallmark of socioeconomic development in China, but rapid economic development including deforestation also resulted in environmental degradation, leading to disastrous floods and droughts. In response, the Chinese government implemented Payments for Ecosystem Services (PES) programs for both environmental conservation and poverty alleviation, notably the Conversion of Cropland to Forest Program (CCFP) and the Ecological Welfare Forest Program (EWFP). In the context of a full model of the determinants of migration incorporating individual, household and community factors, we investigate the manner in which these programs influenced rural out-migration in a mountainous township in Anhui, China. Results show that the CCFP compensation for switching cropland to trees releases farm labor, leading to out-migration. Meanwhile, the EWFP compensation provides poor rural farmers with large areas of forest with sizable cash subsidies that reduces their motivation to migrate. Out-migration was also found to be affected by a number of individual, household and community characteristics.

Divergent phenological response to hydroclimate variability in forested mountain watersheds.

Mountain watersheds are primary sources of freshwater, carbon sequestration, and other ecosystem services. There is significant interest in the effects of climate change and variability on these processes over short to long time scales. Much of the impact of hydroclimate variability in forest ecosystems is manifested in vegetation dynamics in space and time. In steep terrain, leaf phenology responds to topoclimate in complex ways, and can produce specific and measurable shifts in landscape forest patterns. The onset of spring is usually delayed at a specific rate with increasing elevation (often called Hopkins' Law; Hopkins, 1918), reflecting the dominant controls of temperature on greenup timing. Contrary with greenup, leaf senescence shows inconsistent trends along elevation gradients. Here, we present mechanisms and an explanation for this variability and its significance for ecosystem patterns and services in response to climate. We use moderate-resolution imaging spectro-radiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data to derive landscape-induced phenological patterns over topoclimate gradients in a humid temperate broadleaf forest in southern Appalachians. These phenological patterns are validated with different sets of field observations. Our data demonstrate that divergent behavior of leaf senescence with elevation is closely related to late growing season hydroclimate variability in temperature and water balance patterns. Specifically, a drier late growing season is associated with earlier leaf senescence at low elevation than at middle elevation. The effect of drought stress on vegetation senescence timing also leads to tighter coupling between growing season length and ecosystem water use estimated from observed precipitation and runoff generation. This study indicates increased late growing season drought may be leading to divergent ecosystem response between high and low elevation forests. Landscape-induced phenological patterns are easily observed over wide areas and may be used as a unique diagnostic for sources of ecosystem vulnerability and sensitivity to hydroclimate change.

Methods for assessing spillover effects between concurrent green initiatives.

This research presents the methods that are used to examine the dynamics and potential spillover effects of various global environmental conservation programs. We specifically show the data and models that we use to analyze the interactions and mutual influences between the U.S.'s Conservation Reserve Program (CRP) and Environmental Quality Incentives Program (EQIP), as well as those between China's Grain-to-Green Program (GTGP) and Forest Ecological Benefit Compensation (FEBC). Additionally, this study illustrates information about global initiatives, their interconnected impacts, and the associated policy strategies for environmental conservation. By utilizing multivariate regression, logistic regression, eigenvector spatial filtering, and scenario modeling, the research aims to understand the collective influence of these initiatives on broader environmental objectives. The findings of this study provide valuable insights for improving conservation policy designs and effectiveness.•Multivariate and logistic regression analyses to dissect global environmental conservation program interactions and mutual influences.•Eigenvector spatial filtering to address spatial autocorrelation and enhance the accuracy of the model results and our interpretations.•Scenario modeling to project potential future outcomes and impacts.

Remittance from migrants reinforces forest recovery for China's reforestation policy.

Forests play a key role in the mitigation of global warming and provide many other vital ecosystem goods and services. However, as forest continues to vanish at an alarming rate from the surface of the planet, the world desperately needs knowledge on what contributes to forest preservation and restoration. Migration, a hallmark of globalization, is widely recognized as a main driver of forest recovery and poverty alleviation. Here, we show that remittance from migrants reinforces forest recovery that would otherwise be unlikely with mere migration, realizing the additionality of payments for ecosystem services for China's largest reforestation policy, the Conversion of Cropland to Forest Program (CCFP). Guided by the framework that integrates telecoupling and coupled natural and human systems, we investigate forest-livelihood dynamics under the CCFP through the lens of rural out-migration and remittance using both satellite remote sensing imagery and household survey data in two representative sites of rural China. Results show that payments from the CCFP significantly increases the probability of sending remittance by out-migrants to their origin households. We observe substantial forest regeneration and greening surrounding households receiving remittance but forest decline and browning in proximity to households with migrants but not receiving remittance, as measured by forest coverage and the Enhanced Vegetation Index derived from space-borne remotely sensed data. The primary mechanism is that remittance reduces the reliance of households on natural capital from forests, particularly fuelwood, allowing forests near the households to recover. The shares of the estimated ecological and economic additionality induced by remittance are 2.0% (1.4%∼3.8%) and 9.7% (5.0%∼15.2%), respectively, to the baseline of the reforested areas enrolled in CCFP and the payments received by the participating households. Remittance-facilitated forest regeneration amounts to 12.7% (6.0%∼18.0%) of the total new forest gained during the 2003-2013 in China. Our results demonstrate that remittance constitutes a telecoupling mechanism between rural areas and cities over long distances, influencing the local social-ecological gains that the forest policy intended to stimulate. Thus, supporting remittance-sending migrants in cities can be an effective global warming mitigation strategy.

Global hidden spillover effects among concurrent green initiatives.

Concurrently implemented green initiatives to combat global environmental crises may be curtailed or even sacrificed given the ongoing global economic contraction. We collected empirical data and information about green initiatives from 15 sites or countries worldwide. We systematically explored how specific policy, intended behaviors, and gains of given green initiative may interact with those of other green initiatives concurrently implemented in the same geographic area or involving the same recipients. Surprisingly, we found that spillover effects were very divergent: one initiative could reduce the gain of another by 22 % âˆ¼ 100 %, representing alarming losses, while in other instances, substantial co-benefits could arise as one initiative can increase the gain of another by 9 % âˆ¼ 310 %. Leveraging these effects will help countries keep green initiatives with significant co-benefits but stop initiatives with substantial spillover losses in the face of widespread budget cuts, better meeting the United Nations' sustainable development goals.

Impacts of COVID-19 pandemic on the livelihoods of rural households in the community forestry landscape in the Middle Hills of Nepal.

The novel coronavirus disease (COVID-19) has severely affected all sectors of the economy, and the impacts are expected to last-long. One major impact is that migrants return to their original households in rural communities due to loss of jobs. Since rural communities are highly dependent on forest and agriculture for livelihoods, an influx of return migrants likely increases the consumption of forest products and intensifies the agriculture practices, increasing the pressure on forest resources. Based on in-person interview of 215 in 2018 before the pandemic and a phone interview of the same 215 rural households in 2021 at the peak of the pandemic in Kavrepalanchowk district in Nepal, this study addresses the following research questions: (1) Does COVID-19 exert differential impacts among the socio-economic groups? (2) How do return migrants affect the rural land use? (3) Do return migrants put additional pressure on forests resources? The rare before-and-after dataset provide a precious opportunity to assess the COVID-19 impacts on the livelihoods of rural households in the community forestry landscape in the Middle Hills of Nepal. We found that the impacts of COVID-19 were severe on the households with larger family size, those belonging to the marginalized caste groups, having lower number of livestock, low wellbeing index, those who rely on daily wage-based occupation, with low level of education, and the households with return migrants. A significant number of migrants were found to return to their village of origin. As a result, there was a decrease in abandoned land and an increase in the livestock number and forest product use. These findings provide timely insights for the post-pandemic recovery efforts in better targeting needy household with limited resource in the community forestry landscape in the Middle Hills of Nepal.

Urban warming increases the temperature sensitivity of spring vegetation phenology at 292 cities across China.

Urban spring phenology changes governed by multiple biological and environmental factors significantly impact urban ecosystem functions and services. However, the temporal changes in spring phenology (i.e., the start of the vegetation growing season, SOS) and the magnitude of SOS sensitivity to temperature in urban settings are not well understood compared with natural ecosystems. Therefore, we explored warming impacts on SOS across 292 rural and urban areas from 2001 to 2016. We found that warming occurred in 79.9% of urban areas and 61.3% of rural areas. This warming advanced SOS in 78.3% of the urban settings and 72.8% of the rural areas. The accelerated rate of SOS in urban settings was significantly higher (-0.52 ± 0.86 days/year) than in rural areas (-0.09 ± 0.69 days/year). Moreover, SOS was significantly more sensitive to warming in urban areas (-2.86 ± 3.57 days/°C) than in rural areas (-1.57 ± 3.09 days/°C), driven by urban-rural differences in climatic (precipitation, temperature, and warming speed) and vegetation factors. Precipitation contributed the most had the highest relative importance for controlling SOS, at 45% and 63% for urban and rural areas, respectively. These findings provide a new understanding of the impacts of urbanization and climate change on vegetation phenology. Moreover, our results have implications for urban environment impacts on ecosystems and human health.

Tracking Major Sources of Water Contamination Using Machine Learning.

Current microbial source tracking techniques that rely on grab samples analyzed by individual endpoint assays are inadequate to explain microbial sources across space and time. Modeling and predicting host sources of microbial contamination could add a useful tool for watershed management. In this study, we tested and evaluated machine learning models to predict the major sources of microbial contamination in a watershed. We examined the relationship between microbial sources, land cover, weather, and hydrologic variables in a watershed in Northern California, United States. Six models, including K-nearest neighbors (KNN), Naïve Bayes, Support vector machine (SVM), simple neural network (NN), Random Forest, and XGBoost, were built to predict major microbial sources using land cover, weather and hydrologic variables. The results showed that these models successfully predicted microbial sources classified into two categories (human and non-human), with the average accuracy ranging from 69% (Naïve Bayes) to 88% (XGBoost). The area under curve (AUC) of the receiver operating characteristic (ROC) illustrated XGBoost had the best performance (average AUC = 0.88), followed by Random Forest (average AUC = 0.84), and KNN (average AUC = 0.74). The importance index obtained from Random Forest indicated that precipitation and temperature were the two most important factors to predict the dominant microbial source. These results suggest that machine learning models, particularly XGBoost, can predict the dominant sources of microbial contamination based on the relationship of microbial contaminants with daily weather and land cover, providing a powerful tool to understand microbial sources in water.

Divergent socioeconomic-ecological outcomes of China's Conversion of Cropland to Forest Program in the subtropical mountainous area and the semi-arid Loess Plateau.

China's Conversion of Cropland to Forest Program (CCFP) is one of the world's largest Payments for Ecosystem Services (PES) programs. Its socioeconomic-ecological effects are of great interest to both scholars and policy-makers. However, little is known about how the socioeconomic-ecological outcomes of CCFP differ across geographic regions. This study integrates household survey data, satellite imagery, and statistical models to examine labor migration and forest dynamics under CCFP. The investigation is carried out at two mountainous sites with distinct biophysical and socioeconomic conditions, one in a subtropical mountainous region (Anhui) and the other in the semi-arid Loess Plateau (Shanxi). We found divergent CCFP outcomes on migration behavior, stimulating both local- and distant-migration in the Anhui site while discouraging distant-migration in the Shanxi site, after controlling for factors at the individual, household, community and regional levels. Forest recovery is positively associated with distant-migration in Anhui but with local-migration in Shanxi. Contextual factors interact with demographic-socioeconomic factors to influence household livelihoods in both areas, leading to various socio-ecological pathways from CCFP participation to enhanced forest sustainability. Regional differences should therefore be taken into account in the design of future large-scale PES programs.

Feedback effect of crop raiding in payments for ecosystem services.

Payments for ecosystem services (PES) may alter dynamics in coupled human and natural systems, producing reciprocal feedback effects on socioeconomic and environmental outcomes. As forests recover following China's two nation-wide PES programs, wildlife-related crop raiding has been increasingly affecting rural people's livelihoods. We evaluate the feedback effect of crop raiding on people's intention to convert their cropland plots into forests under different PES program scenarios in the Tianma National Nature Reserve. Increases in crop raiding, conservation payment amounts, and program duration significantly increased local people's intention to enroll their cropland plots in future PES programs. Our results suggest that a substantial portion of economic benefit from the current PES programs was offset by the feedback effect of crop raiding promoted by these programs. Therefore, such complex human-environment interactions should be incorporated into the design and evaluation of China's PES practices and other PES programs around the world.

Understanding the relationship between urban blue infrastructure and land surface temperature.

Urban heat island (UHI) effect has serious negative impacts on urban ecosystems and human well-being. Mitigation of UHI using nature-based solutions is highly desirable. It was well known that urban green infrastructure (UGI), i.e., urban vegetation, can effectively mitigate UHI effect. However, the potential of urban blue infrastructure (UBI), i.e., urban surface water, on UHI mitigation is not well understood, although its potential to lower UHI effect via evaporation is similar to the biophysical mechanism of evapotranspiration through vegetation. In this paper, we study the relationship between UBI and land surface temperature (LST) in Wuhan city in central China, using a normalized difference water index (NDWI), maximum local cool island intensity and the maximum cooling distance as indicators for the cooling effects of UBI, respectively. We found a significant negative linear relationship between mean LST and NDWI after NDWI passes a critical threshold value. NDWI is an effective biophysical parameter to delineate the spatial distribution of UBI. The cooling effects of UBI are influenced both by its size and shape. Water surface temperature decreased logarithmically with increasing UBI size, critical threshold values of UBI size corresponding to maximum cooling efficiency do exists. Maximum cooling distance and maximum local cool island intensity are also affected by the shape and size of UBI, and exhibit seasonal and spatial variations. These results provide insights for urban landscape planning regarding how to use UBI as a nature-based solution to improve urban thermal environment.

Empirical Evidence for Impacts of Internal Migration on Vegetation Dynamics in China from 1982 to 2000.

Migration is one of the major socio-economic characteristics of China since the country adopted the policy of economic reform in late 1970s. Many studies have been dedicated to understand why and how people move, and the consequences of their welfare. The purpose of this study is to investigate the environmental impacts of the large scale movement of population in China. We analyzed the trend in the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) along with China migration data from the 1 percent national survey during 1982-1987, the 4th national census during 1985-1990 and the 5th national census during1995~2000. We found that the internal migration in China has a statistically significant negative impact on vegetation growth at the provincial scale from 1982 to 2000 even though the overall vegetation abundance increased in China. The impact from migration (R²=0.47, P=0.0001) on vegetation dynamics is the second strongest as among the factors considered, including changes in annual mean air temperature (R²=0.50, P=0.0001) and annual total precipitation (R²=0.30, P=0.0049) and gross domestic production (R²= 0.25, P=0.0102). The negative statistical relationship between the rate of increase in total migration and the change in vegetation abundance is stronger (R²=0.56, P=0.0000) after controlling for the effects of changes in temperature and precipitation. In-migration dominates the impacts of migration on vegetation dynamics. Therefore, it is important for policy makers in China to take the impacts of migration on vegetation growth into account while making policies aiming at sustainable humanenvironment relations.

A Photosynthesis-based Two-leaf Canopy Stomatal Conductance Model for Meteorology and Air Quality Modeling with WRF/CMAQ PX LSM.

A coupled photosynthesis-stomatal conductance model with single layer sunlit and shaded leaf canopy scaling is developed for the Pleim-Xiu land surface model (LSM) option in the meteorology and air quality modeling system - WRF/CMAQ (Weather Research and Forecast model and Community Multiscale Air Quality model). The photosynthesis-based model for the PX LSM (PX PSN) is implemented and evaluated in a diagnostic box model that has evapotranspiration and ozone deposition components taken directly from WRF/CMAQ. We evaluate PX PSN for latent heat (LH) estimation at four FLUXNET sites with different vegetation types and landscape characteristics and at one FLUXNET site with ozone flux measurements against the simple Jarvis approach used in the current PX LSM. Overall, the PX PSN simulates LH as well as the PX Jarvis approach. The PX PSN, however, shows distinct advantages over the PX Jarvis approach on grassland that likely results from its treatment of C3 and C4 plants for CO2 assimilation estimation. Simulations using Moderate Resolution Imaging Spectroradiometer (MODIS) LAI rather than LAI observations assess how the model would perform with the grid averaged data available in the Eulerian grid model (WRF/CMAQ). While MODIS LAI generally follows the seasonality of the observed LAI, it cannot capture the extreme highs and lows of the site measurements. MODIS LAI estimates degrade model performance at all sites but one site having old and tall trees. Ozone deposition velocity and ozone flux along with LH are simulated especially well by PX PSN as compared to significant PX Jarvis overestimation.