Drivers and mechanisms of forest change in the Himalayas

Mountains are critical ecosystems that have a strong influence far beyond their topographic boundaries. More than 50 million people inhabit the Himalayas, and more than one billion people depend on the ecosystem services they provide. Anthropogenic activities have driven concurrent deforestation and regeneration in the Himalayas, and interventions to reduce forest loss and promote forest recovery require a synthetic understanding of the complex and interacting drivers of forest change. We conducted a systematic review of case studies from 1984 to 2020 (n = 137) and combined a system dynamics approach with a causal network analysis to identify, map and articulate the relationships between the drivers, actors and mechanisms of forest change across the entirety of the Himalayan mountain range. In total, the analysis revealed five proximate drivers, 12 underlying drivers, two institutional factors and five ‘other’ factors connected by a total of 221 linkages. Forest change dynamics have been dominated by widespread smallholder agriculture, extensive non-timber forest product extraction, widespread commercial and non-commercial timber extraction, and high rates of agricultural abandonment. Underlying drivers include population growth, poor agricultural productivity, international support for development projects, and successful community forest management systems. Contradictory linkages emerge from a combination of contextual factors, which can have negative impacts on conservation goals. Global processes such as shifts in governance, transnational infrastructure-development programs, economic slowdowns, labor migrations and climate change threaten to destabilize established dynamics and change forest trajectories. The underlying and proximate drivers interact through multiple pathways that can be utilized to achieve conservation goals. Based on this analysis, we highlight five thematic focus areas to curtail forest loss and promote recovery: (1) decreasing the population pressure, (2) sustainable increase of agricultural productivity, (3) strengthening of forest management institutions, (4) leveraging tourism growth and sustainable infrastructure expansion, and (5) fuel transition and establishing firewood plantations on degraded land. The broader adoption of systems thinking, and specifically a system dynamics approach and causal network analysis, will greatly enhance the rigour of policy development, help design site-specific interventions at multiple spatial scales which can respond to local and global changes, and guide deeper inquiry to enhance our understanding of driver-forest dynamics.     

Subnational institutions and power of landholders drive illegal deforestation in a major commodity production frontier

Deforestation is a main threat to the biosphere due to its contribution to biodiversity loss, carbon emissions, and land degradation. Most deforestation is illegal and continues unabated, representing around half of the total deforestation in the tropics and subtropics. Quantifying illegal deforestation is challenging, let alone assessing the social and institutional processes underlying its occurrence. We tackle this challenge by quantifying the relative influence of individual (i.e., landholders’ power, landholding size) and contextual (i.e., subnational institutions, agricultural suitability) factors on the type and size of illegal deforestation in the Argentine Dry Chaco, a major commodity production frontier and global deforestation hotspot. We build a Bayesian network fed with data of 244 illegal deforestation events, obtained from journalistic articles, grey literature, key informant interviews, and geospatial analyses. The results reveal that more powerful landholders were associated with larger illegal deforestation events. Policy simulations suggest that higher concentration of land in the hands of powerful landholders and more flexible subnational forest regulations would escalate illegal deforestation. This points to the need for a smart policy mix that integrates across economic, agricultural, and environmental sectors to halt illegal deforestation at commodity production frontiers. A land tenure reform can facilitate forest protection, while incentives to land-use diversification and the criminal prosecution of illegal deforestation are critical to shift landholder behavior towards more balanced production and conservation outcomes.     

HEST2007珠峰北坡风廓线观测研究

为了研究青藏高原南部喜马拉雅山区局地大气环流系统,继2006年HEST2006大气科学实验之后,2007年中国科学院大气物理研究所和中国科学院青藏高原研究所在珠穆朗玛峰北坡实施HEST2007综合观测.本文使用该观测实验中LAP-3000风廓线仪获得的绒布河谷内三维风场观测资料,并结合地面辐射资料,分析研究了该地区观测期间局地大气环流的日变化和逐日变化过程.研究表明,该地区局地环流系统,特别是沿河谷的轴向风,与其上空西风环流间存在非常紧密的联系,这种联系似乎与不同天气条件下喜马拉雅山区的大气辐射状况有关,即高层西风环流较强的阶段,地面辐射较强,激发出的局地环流也较强,反之亦然.     

The global climate change mitigation strategy REDD: monitoring costs and uncertainties jeopardize economic benefits

REDD (Reducing Emissions from Deforestation and Forest Degradation) has been suggested as a climate change mitigation strategy that is based on the philosophy to reward countries for reducing their deforestation and forest degradation by financial benefits via the generation of carbon credits. While the potential of REDD has been widely discussed, minor attention has been drawn to the implication of uncertainties and costs associated with the estimation of carbon stock changes. To raise awareness of these issues, we conducted a simulation study for a set of countries that show high to low deforestation rates, which demonstrates that the potential to generate benefits from REDD depends highly on the magnitude of the total error while assessment costs and the price of carbon credits play a minor role. For countries with low deforestation rates REDD is obviously not an option for generating benefits as they would need to implement monitoring systems that are able to estimate carbon stock changes with a total error well below 1 %. Total errors feasible under operational monitoring systems are only sufficient to gain revenues from REDD-regimes under high deforestation rates.     

Linkages between soybean and neotropical deforestation: Coupling and transient decoupling dynamics in a multi-decadal analysis

Soybean expansion, driven by growing global meat demand, has accompanied neotropical deforestation in past decades. A recent decoupling between soybean production and deforestation in Brazil is taken as evidence of efficient deforestation regulation. Here, we assessed the relationships between soybean economy, livestock production and deforestation from 1972 to 2011 in Northern Argentina Dry Chaco. We used Panel Analysis to evaluate the relationship between soybean cultivated and deforested area in different periods and we used high resolution time series analysis of a deforestation hotspot, to explore links between soybean economy, cattle ranching and deforestation. In northern Argentina, 2.7 millions ha were deforested from 1972 to 2011, 56% of which occurred after 2002. The results of the Panel analysis indicate a strong link between soybean expansion and deforestation but with variation among periods mediated by the links between soybean and livestock productions. Deforestation was strongly coupled with soybean expansion during the 1972–1997 and 2002–2011 periods; but was largely decoupled between 1997 and 2002, when strong increments in production were accompanied by low deforestation. The high resolution analysis also indicated contrasting levels of association after and before 1997. The soybean deforestation decoupled periods in Brazil and Argentina shared similarly weak economic incentives for soybean production, rapid technological innovation and preceding high deforestation periods. In the Argentine case, when economic incentives turned positive after a 5-years decoupled period, new government measures were unable to regulate deforestation. Our study suggests that macroeconomic factors can be a much stronger deforestation force compared with domestic legal frameworks. Effectiveness of neotropical deforestation regulation should be carefully monitored and interpreted with caution paying special attention to global economic context for soybean expansion.     

Centralization in the global avoided deforestation collaboration network

Reducing Emissions from Deforestation and Forest Degradation projects currently cover an area approximately twice the size of Germany and challenge traditional concepts of centralization and decentralization in studies of environmental governance. Emerging from the interactions of a complex network of actors, Reducing Emissions from Deforestation and Forest Degradation demonstrates that transnational governance networks of organizations can become spatially centralized. Using a historical analysis of the development of Reducing Emissions from Deforestation and Forest Degradation, we argue that the evolution of Reducing Emissions from Deforestation and Forest Degradation policy has been directed primarily from donor countries, especially in North America and Europe. Adopting a social network analysis approach, we present findings from a new dataset of collaboration on 276 Reducing Emissions from Deforestation and Degradation, avoided deforestation, and sustainable forest management projects that began some on-the-ground operations between 1989 and June 2012, finding that organizations in donor countries have from the beginning been the central actors in the Reducing Emissions from Deforestation and Forest Degradation network. We conclude that Reducing Emissions from Deforestation and Forest Degradation exhibits spatial centralization within transnational governance architectures despite institutional fragmentation, raising important normative questions about participation in transnational forest governance.     

Community forest management in Indonesia: Avoided deforestation in the context of anthropogenic and climate complexities

Community forest management has been identified as a win-win option for reducing deforestation while improving the welfare of rural communities in developing countries. Despite considerable investment in community forestry globally, systematic evaluations of the impact of these policies at appropriate scales are lacking. We assessed the extent to which deforestation has been avoided as a result of the Indonesian government’s community forestry scheme, Hutan Desa (Village Forest). We used annual data on deforestation rates between 2012 and 2016 from two rapidly developing islands: Sumatra and Kalimantan. The total area of Hutan Desa increased from 750 km² in 2012 to 2500 km² in 2016. We applied a spatial matching approach to account for biophysical variables affecting deforestation and Hutan Desa selection criteria. Performance was assessed relative to a counterfactual likelihood of deforestation in the absence of Hutan Desa tenure. We found that Hutan Desa management has successfully achieved avoided deforestation overall, but performance has been increasingly variable through time. Hutan Desa performance was influenced by anthropogenic and climatic factors, as well as land use history. Hutan Desa allocated on watershed protection forest or limited production forest typically led to a less avoided deforestation regardless of location. Conversely, Hutan Desa granted on permanent or convertible production forest had variable performance across different years and locations. The amount of rainfall during the dry season in any given year was an important climatic factor influencing performance. Extremely dry conditions during drought years pose additional challenges to Hutan Desa management, particularly on peatland, due to increased vulnerability to fire outbreaks. This study demonstrates how the performance of Hutan Desa in avoiding deforestation is fundamentally affected by biophysical and anthropogenic circumstances over time and space. Our study improves understanding on where and when the policy is most effective with respect to deforestation, and helps identify opportunities to improve policy implementation. This provides an important first step towards evaluating the overall effectiveness of this policy in achieving both social and environmental goals.     

Brazil beyond 2020: from deforestation to the energy challenge

The main assumptions and findings are presented on a comparative analysis of three GHG long-term emissions scenarios for Brazil. Since 1990, land-use change has been the most important source of GHG emissions in the country. The voluntary goals to limit Brazilian GHG emissions pledged a reduction in between 36.1% and 38.9% of GHG emissions projected to 2020, to be 6–10% lower than in 2005. Brazil is in a good position to meet the voluntary mitigation goals pledged to the United Nations Framework Convention on Climate Change (UNFCCC) up to 2020: recent efforts to reduce deforestation have been successful and avoided deforestation will form the bulk of the emissions reduction commitment. In 2020, if governmental mitigation goals are met, then GHG emissions from the energy system would become the largest in the country. After 2020, if no additional mitigation actions are implemented, GHG emissions will increase again in the period 2020–2030, due to population and economic growth driving energy demand, supply and GHG emissions. However, Brazil is in a strong position to take a lead in low-carbon economic and social development due to its huge endowment of renewable energy resources allowing for additional mitigation actions to be adopted after 2020.

Policy relevance

The period beyond 2020 is now relevant in climate policy due to the Durban Platform agreeing a ‘protocol, legal instrument or agreed outcome with legal force’ that will have effect from 2020. After 2020, Brazil will be in a situation more similar to other industrialized countries, faced with a new challenge of economic development with low GHG energy-related emissions, requiring the adoption of mitigation policies and measures targeted at the energy system. Unlike the mitigation actions in the land-use change sector, where most of the funding will come from the national budgets due to sovereignty concerns, the huge financial resources needed to develop low-carbon transport and energy infrastructure could benefit from soft loans channelled to the country through nationally appropriate mitigation actions (NAMAs).     

Forest law enforcement in the Brazilian Amazon: Costs and income effects

Despite recent success in reducing forest loss in the Brazilian Amazon, additional forest conservation efforts, for example, through ‘Reducing Emissions from Deforestation and Forest Degradation’ (REDD+), could significantly contribute to global climate-change mitigation. Economic incentives, such as payments for environmental services could promote conservation, but deforestation often occurs on land without crucial tenure-security prerequisites. Improving the enforcement of existing regulatory disincentives thus represents an important element of Brazil's anti-deforestation action plan. However, conservation law enforcement costs and benefits have been much less studied than for conditional payments. We develop a conceptual framework and a spatially explicit model to analyze field-based regulatory enforcement in the Brazilian Amazon. We validate our model, based on historical deforestation and enforcement mission data from 2003 to 2008. By simulating the current conservation law enforcement practice, we analyze the costs of liability establishment and legal coercion for alternative conservation targets, and evaluate corresponding income impacts. Our findings suggest that spatial patterns of both deforestation and inspection costs markedly influence enforcement patterns and their income effects. Field-based enforcement is a highly cost-effective forest conservation instrument from a regulator's point of view, but comes at high opportunity costs for land users. Payments for environmental services could compensate costs, but will increase budget outlays vis-à-vis a command-and-control dominated strategy. Both legal and institutional challenges have to be overcome to make conservation payments work at a larger scale. Decision-makers may have to innovatively combine incentive and disincentive-based policy instruments in order to make tropical forest conservation both financially viable and socially compatible.     

Carbon fluxes resulting from U.S. private timberland management

A carbon budget model was developed to examine the effects of forest management practices on carbon storage in U.S. private timberlands. The model explicitly incorporates the demand for wood products and its impact on harvesting and other management decisions. Forest carbon is divided into four components: carbon stored in trees, soils, forest litter, and understory vegetation. Changes in the forest carbon inventory result from tree growth and management activities, in particular harvesting. Harvesting of timber for wood products is determined by demand and supply forces. The model then tracks carbon in timber removals through primary and secondary processing and disposal stages. Harvesting also has effects on carbon in soils, forest litter, and understory vegetation. A base-run scenario projects increases in carbon storage in U.S. private timberlands by 2040; however, this increase is offset by carbon emissions resulting from harvesting.     

Regional- and district-level drivers of timber harvesting in European Russia after the collapse of the Soviet Union

After the collapse of the Soviet Union, the forestry sector in Russia underwent substantial changes: the state forestry sector was decentralized, the timber industry was privatized, and timber use rights were allocated through short- and long-term leases. To date, there has been no quantitative assessment of the drivers of timber harvesting in European Russia following these changes. In this paper we estimate an econometric model of timber harvesting using remote sensing estimations of forest disturbance from 1990–2000 to 2000–2005 as our dependent variable. We aggregate forest disturbance to administrative districts – equivalent to counties in the United States – and test the impact of several biophysical and economic factors on timber harvesting. Additionally, we examine the impact that regions – equivalent to states in the United States and the main level of decentralized governance in Russia – have on timber harvesting by estimating the influence of regional-level effects on forest disturbance in our econometric model. Russian regions diverged considerably in political and economic conditions after the collapse of the Soviet Union, and the question is if these variations impacted timber harvesting after controlling for district-level biophysical and economic drivers. We find that the most important drivers of timber harvesting at the district level are road density, the percent of evergreen forest, and the total area of forest. The influence of these variables on timber harvesting changed over time and there was more harvesting closer to urban areas in 2000–2005. Even though district-level variables explain more than 70 percent of the variation in forest disturbance in our econometric model, we find that regional-level effects remain statistically significant. While we cannot identify the exact mechanism through which regional-level effects impact timber harvesting, our results suggest that sub-national differences can have a large and statistically significant impact on land-use outcomes and should be considered in policy design and evaluation.     

A carbon budget for Brazil: Influence of future land-use change

Because of its large area of high C density forests and high deforestation rate, Brazil may play an important role in the global C cycle. The study reported here developed an estimate of Brazil's biotic CO₂-C budget for the period 1990–2010. The analysis used a spreadsheet C accounting model based on three major components: a conceptual model of ecosystem C cycling, a recently completed vegetation classification developed from remote-sensing data, and published estimates of C density for each of the vegetation classes. The dynamics of the model came from estimates of disturbance to ecosystems that release C and estimates of recovery from past disturbance that store C. The model was projected into the future with three alternative estimates of the rate of future land use change. Under all three deforestation scenarios Brazil was a C source in the range of about 3–5 × 10⁹ MgC over the 20-yr study period.The research described in this article has been funded wholly by the U.S. Environmental Protection Agency. This document has been prepared at the EPA National Health and Environmental Effects Research Laboratory in Corvallis, OR, U.S.A., through contract number 68-C8-0006 to ManTech Environmental Research Services, Corp. It has been subjected to the Agency's peer and administrative review and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.     

Sustainable agricultural intensification or Jevons paradox? The role of public governance in tropical South America

The process of global deforestation calls for urgent attention, particularly in South America where deforestation rates have failed to decline over the past 20 years. The main direct cause of deforestation is land conversion to agriculture. We combine data from the FAO and the World Bank for six tropical Southern American countries over the period 1970–2006, estimate a panel data model accounting for various determinants of agricultural land expansion and derive elasticities to quantify the effect of the different independent variables. We investigate whether agricultural intensification, in conjunction with governance factors, has been promoting agricultural expansion, leading to a “Jevons paradox”. The paradox occurs if an increase in the productivity of one factor (here agricultural land) leads to its increased, rather than decreased, utilization. We find that for high values of our governance indicators a Jevons paradox exists even for moderate levels of agricultural productivity, leading to an overall expansion of agricultural area. Agricultural expansion is also positively related to the level of service on external debt and population growth, while its association with agricultural exports is only moderate. Finally, we find no evidence of an environmental Kuznets curve, as agricultural area is ultimately positively correlated to per-capita income levels.     

Criteria for effective zero-deforestation commitments

Zero-deforestation commitments are a type of voluntary sustainability initiative that companies adopt to signal their intention to reduce or eliminate deforestation associated with commodities that they produce, trade, and/or sell. Because each company defines its own zero-deforestation commitment goals and implementation mechanisms, commitment content varies widely. This creates challenges for the assessment of commitment implementation or effectiveness. Here, we develop criteria to assess the potential effectiveness of zero-deforestation commitments at reducing deforestation within a company supply chain, regionally, and globally. We apply these criteria to evaluate 52 zero-deforestation commitments made by companies identified by Forest 500 as having high deforestation risk. While our assessment indicates that existing commitments converge with several criteria for effectiveness, they fall short in a few key ways. First, they cover just a small share of the global market for deforestation-risk commodities, which means that their global impact is likely to be small. Second, biome-wide implementation is only achieved in the Brazilian Amazon. Outside this region, implementation occurs mainly through certification programs, which are not adopted by all producers and lack third-party near-real time deforestation monitoring. Additionally, around half of all commitments include zero-net deforestation targets and future implementation deadlines, both of which are design elements that may reduce effectiveness. Zero-net targets allow promises of future reforestation to compensate for current forest loss, while future implementation deadlines allow for preemptive clearing. To increase the likelihood that commitments will lead to reduced deforestation across all scales, more companies should adopt zero-gross deforestation targets with immediate implementation deadlines and clear sanction-based implementation mechanisms in biomes with high risk of forest to commodity conversion.     

Is a signature of socio-economic impact written on the climate?

Surging population associated with large-scale colonization, tropical deforestation, and industrialization in parts of Asia that constitute over 60% of the global population may lead to changes in the climate of that region. Identifying such changes is of great importance to scientists and policy makers. Concerning this, an approach is made here to assess the chemical composition in the troposphere over the region that happens to be the globe's longest belt of largest population density (LBLPD) and to assess the long term rainfall pattern of a tropical region lying along the belt of mountain ranges where an intense deforestation has been taking place on a large scale for several decades. Further, this paper reports the long term temperature and rainfall pattern of highly industriatized cities that have one of the fastest population growth rates. The tropospheric levels of CH₄, CO and O₃ over LBLPD are found to be remarkably higher than those over the stations lying outside the belt. The long term rainfall data of the belt of high mountain ranges shows a significant decreasing trend, whereas the data for adjacent coastal belt, which is normally the upwind side of the mountain belt, does not show any kind of trend. Surface air temperature and rainfall data for industrial cities with population greater than ten million shows a definite increasing trend whereas no trend is seen in data for adjacent non-industrialized towns.     

What spatial resolution do we need for a route-based road weather decision support system?

Since their implementation, road weather information systems have mostly relied on point measurements from outstations to initiate and verify daily forecasts. Initially, spatial extrapolation was achieved by thermal mapping, but this is gradually being replaced by route-based forecasting techniques. Both techniques are similar in the sense that they use a point measurement, often taken from an outstation, to provide a spatial forecast of road surface temperatures around the road network at varying resolutions. A substantial research effort has been undertaken to understand and model the complex environmental conditions and mechanisms responsible for the variation in road surface temperatures around the road network. In particular, the interaction of varying geographical parameters around the road network (e.g. altitude, land use, road construction, topography, etc.) has been used to develop local climatological models and route-based forecasting products. By considering the needs of winter maintenance engineers, this paper reviews the current state of the art and takes a critical look at the embedding of forecast products into decision support systems. This is achieved by considering a case study of how road surface temperature and condition vary across the width of a road profile, instead of just lengthways along a road. It is shown that temperature and condition both vary significantly across the profile, which immediately raises questions about the validity of current surveying and modelling practices. This has implications for both the resolution of route-based forecasting products as well as user confidence in automated decision support systems.     

Population, income and ecological conditions as determinants of forest area variation in the tropics

The underlying causes of forest area variation were studied by using data from the original forest assessments between 1970 and 1991 of FAO FORIS database representing 477 subnational geographical units in 67 tropical countries. Multiple regression modelling was applied to measure the effects. Five ecological variables were used to control the varying ecological conditions in the subnational units. Three variables were used to control the varying reliability of forest inventory data. Population and income variables were found to be significant factors explaining forest area variation after controlling for ecological variation. In particular population density and income per capita turned out to be significant underlying factors of deforestation. The overall conclusion is that determining the factors behind forest area variation helps explaining the causes of deforestation.     

Effects of Amazonian deforestation on climate: a numerical experiment with a coupled biosphere-atmosphere model with soil hydrology

Summary A coupled biosphere-atmosphere statistical-dynamical model (SDM) is used to study the climatic effects of Amazonian deforestation. A soil moisture model based on BATS has been incorporated into the SDM in order to study the biogeophysical feedback of change in surface characteristics to regional climate due to the deforestation. In the control experiment, the mean annual and mean seasonal climate is well simulated by the model when compared with NCEP/NCAR reanalysis data. In the deforestation experiment, the evergreen broadleaf trees in the Amazonian region are substituted by short grass. The effects of Amazonian deforestation on regional climate are analysed taking into account the model simulations for the land portion of the latitude belts comprising the tropical region. Amazonian deforestation results in regional climate changes such as a decrease in evaporation, precipitation, available surface net radiation and soil moisture content, and an increase in temperatures and sensible heat flux. The reduction in transpiration was responsible for the most part of the decrease in total evapotranspiration. The reduction in precipitation was larger than the decrease in evapotranspiration so that runoff was reduced. The simulation of the diurnal cycle of the surface temperature shows an increase in temperature during the day and a decrease at night, which is in agreement with observations, whereas earlier GCM experiments showed an increase both during the day and night. In general, the changes in temperature and energy fluxes are in good agreement with GCM experiments, showing that the SDM is able to simulate the characteristics of the tropical climate that are associated with the substitution of forest by pasture areas.     

Illicit Drivers of Land Use Change: Narcotrafficking and Forest Loss in Central America

Illegal activity, such as deforestation for illicit crops for cocaine production, has been inferred as a cause of land change. Nonetheless, illicit activity is often overlooked or difficult to incorporate into causal inference models of land change. Evidence continues to build that narcotrafficking plays an important, yet often unreported, role in forest loss. This study presents a novel strategy to meet the challenge of estimating the causal effect of illicit activity in land change using consolidated news media reports to estimate the relationship between drug trafficking and accelerated forest loss in Central America. Drug trafficking organizations engage in illegal land transactions, money laundering, and territorial control that can manifest as forest conversion to agriculture or pasture land uses. Longitudinal data on 50 sub-national units over a period of 16 years (2001-2016) are used in fixed effects regressions to estimate the role of narcotrafficking in forest loss. Two narcotrafficking activity proxies were developed as explanatory variables of forest loss: i) an “official” proxy from drug seizures data within 14 sub-national units; and, ii) an “unofficial” proxy developed from georeferenced news media accounts of narcotrafficking events. The effect of narcotrafficking was systematically compared to the other well-known causes of forest loss, such as rural population growth and other conventional drivers. Both proxies indicate narcotrafficking is a statistically significant (p<0.01) contributor to forest loss in the region, particularly in Nicaragua (p<0.05, official proxy), Honduras (p<0.05, media proxy), and Guatemala (p<0.05, media proxy). Narcotrafficking variables explain an additional 5% (media proxy) and 9% (official proxy) of variance of forest loss not captured by conventional models. This study showed the ability of news media data to capture the signal of illicit activity in land use changes such as forest loss. The methods employed here could be used to estimate the causal effect of illicit activities in other land and environmental systems. Our results suggest that current drug policy, which concentrates drug trafficking in remote areas of very high cultural and environmental value, has helped to accelerate the loss of Central America's remaining forests.     

Quantitative Estimation of Air Mass Exchange by Along-Valley Wind in the Rongbuk Valley

For a better understanding of the air mass exchange processes between the surface and free atmos-phere in the Himalayas,a Himalayan exchange between the surface and troposphere 2007 (HEST2007) campaign was carried out in the Rongbuk Valley,on the northern slope of Mt.Qomolangma,in June 2007.The wind,tem-perature and radiation conditions were measured during the campaign.Using these observation data,together with the National Centers for Environmental Prediction/the National Center for Atmospheric Research (NCEP/NCAR) reanalysis data,the air mass exchange between the inside of the valley and the outside of the valley is quantitatively estimated,based on a closed-valley method.The air mass is strongly injected into the Rongbuk Valley in the after-noon,which dominates the diurnal cycle,by a strong downward along-valley wind,with a maximum down-ward transfer rate of 9.4 cm s?1.The total air volume flux injected into the valley was 2.6×1011 m3 d?1 in 24 hours in June 2007,which is 15 times the total volume of the val-ley.The air mass transfer into the valley also exhibited a clear daily variation during the HEST2007 campaign,which can be affected by the synoptic situations through the adjustment of local radiation conditions.