Explore the vast range of titles available.

Land use and land cover change (LULCC) have profoundly altered land surface properties and ecosystem functions, including carbon and water production. While mapping these changes from local to global scales has become more achievable due to advancements in earth observations and remote sensing, linking land cover changes to ecosystem functions remains challenging, especially at regional scale. Our study attempts to fill this gap by employing a computationally efficient method and two types of widely used high-resolution satellite images. We first investigated the contribution of landscape composition to ecosystem function by examining how land cover and proportion affected gross primary production (GPP) and evapotranspiration (ET) at six macro-landscapes in Mongolia and Kazakhstan. We hypothesized that both ecosystem and landscape GPP and ET are disproportionate to their composition and, therefore, changes in land cover will have asymmetrical influences on landscape functions. We leveraged a computational-friendly linear downscaling approach to align the coarse spatial resolution of MODIS (500 m) with a fine-grain and localized land cover map developed from Landsat (30 m) for six provinces in countries where intensive LULCC occurred in recent decades. By establishing two metrics—function to composition ratio (F/C) and function to changes in composition change (ΔF/ΔC)—we tested our hypothesis and evaluated the impact of land cover change on ecosystem functions within and among the landscapes. Our results show three major themes. (1) The five land cover types have signature downscaled ET and GPP that appears to vary between the two countries as well as within each country. (2) F/C of ET and GPP of forests is statistically greater than 1 (i.e., over-contributing), whereas F/C of grasslands and croplands is close to or slightly less than 1 (i.e., under-contribution). F/C of barrens is clearly lower than 1 but greater than zero. Specifically, a unit of forest generates 1.085 unit of ET and 1.123 unit of GPP, a unit of grassland generates 0.993 unit of ET and GPP, and a unit of cropland produces 0.987 unit of ET and 0.983 unit of GPP. The divergent F/C values among the land cover classes support the hypothesis that landscape function is disproportionate to its composition. (3) ΔET/ΔC and ΔGPP/ΔC of forests and croplands showed negative values, while grasslands and barrens showed positive values, indicating that converting a unit of forest to other land cover leads to a decrease in ET and GPP, while converting units of grassland or barren to other land cover classes will result in increased ET and GPP. This linear downscaling approach for calculating F/C and ΔF/ΔC is labor-saving and cost-effective for rapid assessment on the impact of land use land cover change on ecosystem functions.

The replacement of natural lands with urban structures has multiple environmental consequences, yet little is known about the magnitude and extent of albedo-induced warming contributions from urbanization at the global scale in the past and future. Here, we apply an empirical approach to quantify the climate effects of past urbanization and future urbanization projected under different shared socioeconomic pathways (SSPs). We find an albedo-induced warming effect of urbanization for both the past and the projected futures under three illustrative scenarios. The albedo decease from urbanization in 2018 relative to 2001 has yielded a 100-year average annual global warming of 0.00014 [0.00008, 0.00021] °C. Without proper mitigation, future urbanization in 2050 relative to 2018 and that in 2100 relative to 2018 under the intermediate emission scenario (SSP2-4.5) would yield a 100-year average warming effect of 0.00107 [0.00057,0.00179] °C and 0.00152 [0.00078,0.00259] °C, respectively, through altering the Earth’s albedo. Albedo changes caused by projected future urban land expansion will contribute to global warming without proper mitigation. This warming effect will be larger under higher emission scenarios than under lower emission scenarios.

Assessments of changes in landscape patterns and functions during urban development need to factor urban fringes (UPs) as part of the overall social-environmental system, especially in regions with poor transportation systems where urban functions depend heavily on surrounding suburbs. In this study, we use net primary production (NPP) as an integrative measure to delineate UPs and to measure the expansion in 15 urban areas in the remote Qinghai-Tibet Plateau. Using a logistic curve fitting model based on NPP to delineate differences between the UF and rural landscapes, we explore how NPP-inferred UF expansions may have changed with increase in urban population and the secondary and tertiary industrial production. The UF width (area) was 17.4 km (950.67 km 2 ) in 2000 but increased to 27.0 km (2289.06 km 2 ) in 2019 for Lhasa. For Xining, this was from 28.0 km (2461.76 km 2 ) to 36.0 km (4069.44 km 2 ) during 2000–2019. For the prefecture-level cities, the rate increased from 2–16 km (12.56–803.84 km 2 ) to 7–17 km (153.86–907.46 km 2 ). More importantly, the ratio between UF width and population during the five study periods showed a linear decreasing trend, but an exponential decrease with economic measures. The urban expansion due to population increase changed from 26 m in 2000 to 21 m in 2019 for every increase of 1000 residents, while the expansion due to economic changes was significantly reduced from 732 m per billion RMB (Ren Min Bi) in 2000 to 52 m per billion RMB in 2019. We confirm a hypothesis that the ratio of expansion of UFs was more dependent on economic growth in early stages of urbanization than in later stages, whereas urban population promoted expansions over the entire study period.

The past five decades have witnessed a rapid growth of computer models for simulating ecosystem functions and dynamics. This has been fueled by the availability of remote sensing data, computation capability, and cross-disciplinary knowledge. These models contain many submodules for simulating different processes and forcing mechanisms, albeit it has become challenging to truly understand the details due to their complexity. Most ecosystem models, fortunately, are rooted in a few core biophysical foundations, such as the widely recognized Farquhar model, Ball-Berry-Leuning and Medlyn family models, Penman-Monteith equation, Priestley-Taylor model, and Michaelis-Menten kinetics. After an introduction of biophysical essentials, four chapters present the core algorithms and their behaviors in modeling ecosystem production, respiration, evapotranspiration, and global warming potentials. Each chapter is composed of a brief introduction of the literature, in which model algorithms, their assumptions, and performances are described in detail. Spreadsheet (or Python codes) templates are included in each chapter for modeling exercises with different input parameters as online materials, which include datasets, parameter estimation, and real-world applications (e.g., calculations of global warming potentials). Users can also apply their own datasets. The materials included in this volume serve as effective tools for users to understand model behaviors and uses with specified conditions and in situ applications.

Drylands in East Asia (DEA) are home to more than one billion people with an environment vulnerable to natural and anthropogenic changes. One of the critical needs in the region is to fully understand how dryland ecosystems respond to the changing climate and human activities in order to develop strategies to cope with continued climate change. This book provides state-of-the-art knowledge and information on drylands ecosystem dynamics, changing climate, society, and land use in the region. In addition to the synthesis of the existing research and knowledge of DEA, the book provides a role model for regional ecological assessment. With a wide spectrum of contributions from experts around the globe, the book should be of interest to researchers and students both internationally and in East Asia. Lessons learned from this synthesis effort in DEA should be useful for developing climate adaptation strategies for other similar regions around the globe.

The past five decades have witnessed a rapid growth of computer models for simulating ecosystem functions and dynamics. This has been fuelled by the availability of remote sensing data, computation capability, and cross-disciplinary knowledge. These models contain many submodules for simulating different processes and forcing mechanisms, albeit it has become challenging to truly understand the details due to their complexity. Most ecosystem models, fortunately, are rooted in a few core biophysical foundations, such as the widely recognized Farquhar model, Ball-Berry-Leuning and Medlyn family models, Penman-Monteith equation, Priestley-Taylor model, and Michaelis-Menten kinetics.an introduction of biophysical essentials, four chapters present the core algorithms and their behaviors in modeling ecosystem production, respiration, evapotranspiration, and global warming potentials. Each chapter is composed of a brief introduction of the literature, in which model algorithms, their assumptions, and performances are described in detail. Spreadsheet (or Python codes) templates are included in each chapter for modeling exercises with different input parameters as online materials, which include datasets, parameter estimation, and real-world applications (e.g., calculations of global warming potentials). Users can also apply their own datasets. The materials included in this volume serve as effective tools for users to understand model behaviours and uses with specified conditions and in situ applications.

The timing and magnitude of the early Cenozoic surface uplift of the Tibetan Plateau is controversial due to a scarcity of unaltered terrestrial sediments required for palaeoaltimetry techniques. Such information is critical, however, for constraining the geodynamic and palaeoclimatic evolution of the Indian and Eurasian continents and for interpreting global climate, biodiversity and biogeochemical cycles since the Cenozoic. We find that substantial uplift occurred by 63 to 61 million years ago, before the collision of the Indian and Eurasian continental plates, based on comparison of triple oxygen isotopes of modern meteoric waters with epithermal Ag–Pb–Zn deposit quartz veins from the Palaeocene Gangdese Arc in southern Lhasa. Low δ18O and δ17O quartz values are consistent with precipitation from meteoric waters influenced by a large degree of topographic rainout. We show that by 63 to 61 Ma, the Gangdese Arc reached an elevation of ~3.5 km, suggesting that the Gangdese Arc achieved >60% of its current elevation before continent–continent collision. This uplift was probably caused by crustal shortening in response to low-angle subduction of Neo-Tethyan oceanic lithosphere. This early high palaeoelevation estimate for the Himalaya–Tibetan system challenges previous assumptions that southern Tibet uplift required continent–continent collision to achieve substantial topography.The triple oxygen isotope composition of quartz veins indicates that the southern Tibetan Plateau was already around 3.5 km high by 60 million years ago, showing that substantial surface uplift started before collision of the Eurasian and Indian plates.

The Tibetan Plateau has experienced higher-than-global-average climate warming in recent decades, resulting in many significant changes in ecosystem structure and function. Among them is albedo, which bridges the causes and consequences of land surface processes and climate. The plateau is covered by snow/ice and vegetation in the non-growing season (nGS) and growing season (GS), respectively. Based on the MODIS products, we investigated snow/ice cover and vegetation greenness in relation to the spatiotemporal changes of albedo on the Tibetan Plateau from 2000 through 2013. A synchronous relationship was found between the change in GSNDVI and GSalbedo over time and across the Tibetan landscapes. We found that the annual average albedo had a decreasing trend, but that the albedo had slightly increased during the nGS and decreased during the GS. Across the landscapes, the nGSalbedo fluctuated in a synchronous pattern with snow/ice cover. Temporally, monthly snow/ice coverage also had a high correspondence with albedo, except in April and October. We detected clear dependencies of albedo on elevation. With the rise in altitude, the nGSalbedo decreased below 4000 m, but increased for elevations of 4500-5500 m. Above 5500 m, the nGSalbedo decreased, which was in accordance with the decreased amount of snow/ice coverage and the increased soil moisture on the plateau. More importantly, the decreasing albedo in the most recent decade appeared to be caused primarily by lowered growing season albedo.