Author
Qiaozhen Mu
Other affiliations: University of Maryland, College Park
Bio: Qiaozhen Mu is an academic researcher from University of Montana. The author has contributed to research in topics: Evapotranspiration & Moderate-resolution imaging spectroradiometer. The author has an hindex of 27, co-authored 50 publications receiving 7878 citations. Previous affiliations of Qiaozhen Mu include University of Maryland, College Park.
Papers published on a yearly basis
Papers
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TL;DR: In this article, an improved version of the global evapotranspiration (ET) algorithm based on MODIS and global meteorology data has been proposed, which simplifies the calculation of vegetation cover fraction, calculating ET as the sum of daytime and nighttime components, adding soil heat flux calculation, improving estimates of stomatal conductance, aerodynamic resistance and boundary layer resistance, separating dry canopy surface from the wet and dividing soil surface into saturated wet surface and moist surface.
2,052 citations
TL;DR: An estimate of global land evapotranspiration from 1982 to 2008 is provided using a global monitoring network, meteorological and remote-sensing observations, and a machine-learning algorithm, which suggests that increasing soil-moisture limitations on evapOTranspiration largely explain the recent decline of the global land-evapotranpiration trend.
Abstract: More than half of the solar energy absorbed by land surfaces is currently used to evaporate water. Climate change is expected to intensify the hydrological cycle and to alter evapotranspiration, with implications for ecosystem services and feedback to regional and global climate. Evapotranspiration changes may already be under way, but direct observational constraints are lacking at the global scale. Until such evidence is available, changes in the water cycle on land-a key diagnostic criterion of the effects of climate change and variability-remain uncertain. Here we provide a data-driven estimate of global land evapotranspiration from 1982 to 2008, compiled using a global monitoring network, meteorological and remote-sensing observations, and a machine-learning algorithm. In addition, we have assessed evapotranspiration variations over the same time period using an ensemble of process-based land-surface models. Our results suggest that global annual evapotranspiration increased on average by 7.1 ± 1.0 millimetres per year per decade from 1982 to 1997. After that, coincident with the last major El Ni±o event in 1998, the global evapotranspiration increase seems to have ceased until 2008. This change was driven primarily by moisture limitation in the Southern Hemisphere, particularly Africa and Australia. In these regions, microwave satellite observations indicate that soil moisture decreased from 1998 to 2008. Hence, increasing soil-moisture limitations on evapotranspiration largely explain the recent decline of the global land-evapotranspiration trend. Whether the changing behaviour of evapotranspiration is representative of natural climate variability or reflects a more permanent reorganization of the land water cycle is a key question for earth system science. © 2010 Macmillan Publishers Limited. All rights reserved.
1,756 citations
TL;DR: In this article, the authors developed a global remote sensing evapotranspiration (ET) algorithm based on Cleugh et al.'s [Cleugh, H.A., R. Leuning, Q. Mu, S.W. Running (2007) Regional evaporation estimates from flux tower and MODIS satellite data.
1,424 citations
TL;DR: In this article, two models were evaluated for their ability to estimate land surface evaporation at 16-day intervals using MODIS remote sensing data and surface meteorology as inputs.
664 citations
TL;DR: New evidence acquired from global satellite data to analyse the biophysical effects of forests on local climate is presented and it is shown that tropical forests have a strong cooling effect throughout the year; temperate forests show moderate cooling in summer and moderate warming in winter with net cooling annually.
Abstract: The biophysical effects of forests on climate have been extensively studied with climate models. However, models cannot accurately reproduce local climate effects due to their coarse spatial resolution and uncertainties, and field observations are valuable but often insufficient due to their limited coverage. Here we present new evidence acquired from global satellite data to analyse the biophysical effects of forests on local climate. Results show that tropical forests have a strong cooling effect throughout the year; temperate forests show moderate cooling in summer and moderate warming in winter with net cooling annually; and boreal forests have strong warming in winter and moderate cooling in summer with net warming annually. The spatiotemporal cooling or warming effects are mainly driven by the two competing biophysical effects, evapotranspiration and albedo, which in turn are strongly influenced by rainfall and snow. Implications of our satellite-based study could be useful for informing local forestry policies.
435 citations
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Book Chapter•
01 Jan 2013
TL;DR: The authors assesses long-term projections of climate change for the end of the 21st century and beyond, where the forced signal depends on the scenario and is typically larger than the internal variability of the climate system.
Abstract: This chapter assesses long-term projections of climate change for the end of the 21st century and beyond, where the forced signal depends on the scenario and is typically larger than the internal variability of the climate system. Changes are expressed with respect to a baseline period of 1986-2005, unless otherwise stated.
2,253 citations
TL;DR: In this article, an improved version of the global evapotranspiration (ET) algorithm based on MODIS and global meteorology data has been proposed, which simplifies the calculation of vegetation cover fraction, calculating ET as the sum of daytime and nighttime components, adding soil heat flux calculation, improving estimates of stomatal conductance, aerodynamic resistance and boundary layer resistance, separating dry canopy surface from the wet and dividing soil surface into saturated wet surface and moist surface.
2,052 citations
Max Planck Society1, National University of Cordoba2, Centre national de la recherche scientifique3, Macquarie University4, University of Paris-Sud5, University of Minnesota6, University of Western Sydney7, VU University Amsterdam8, University of Arizona9, University of California, Berkeley10, University of Guelph11, Australian National University12, University of Innsbruck13, University of Leeds14, University of Groningen15, Universidade Federal do Rio Grande do Sul16, University of Cape Town17, University of Wollongong18, New Jersey Institute of Technology19, Centro Agronómico Tropical de Investigación y Enseñanza20, Lawrence Berkeley National Laboratory21, University of Alaska Fairbanks22, University of Cambridge23, Kansas State University24, Helmholtz Centre for Environmental Research - UFZ25, Arizona State University26, University of Giessen27, Autonomous University of Barcelona28, University of Maryland, College Park29, Universidad del Tolima30, University of São Paulo31, University of La Réunion32, University of York33, University of Sydney34, Harvard University35, Goethe University Frankfurt36, University of Sheffield37, University of Ulm38, State University of Campinas39, Kenyon College40, Royal Botanic Gardens41, University of Florida42, University of Oldenburg43, University of Nebraska–Lincoln44, Tohoku University45, Northern Arizona University46, University of Wisconsin–Eau Claire47, Naturalis48, James Cook University49, Institut national de la recherche agronomique50, Newcastle University51, University of New South Wales52, Leipzig University53, Columbia University54, Estonian University of Life Sciences55, Polish Academy of Sciences56, Moscow State University57, Kyushu University58, Wageningen University and Research Centre59, Spanish National Research Council60, University of Regensburg61, University of Rennes62, Université du Québec à Trois-Rivières63, Potsdam Institute for Climate Impact Research64, Technical University of Denmark65, University of California, Los Angeles66, Hokkaido University67, Université de Sherbrooke68, Syracuse University69, Empresa Brasileira de Pesquisa Agropecuária70, University of Aberdeen71, Michigan State University72, Oak Ridge National Laboratory73, University of Leicester74, Utah State University75, Smithsonian Institution76, University of Missouri77
TL;DR: TRY as discussed by the authors is a global database of plant traits, including morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs, which can be used for a wide range of research from evolutionary biology, community and functional ecology to biogeography.
Abstract: Plant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69 000 out of the world's 300 000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation – but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models.
2,017 citations
TL;DR: It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales.
Abstract: Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.
2,014 citations
TL;DR: In this article, the authors identify ten contrasting perspectives that shape the vulnerability debate but have not been discussed collectively and present a set of global vulnerability drivers that are known with high confidence: (1) droughts eventually occur everywhere; (2) warming produces hotter Droughts; (3) atmospheric moisture demand increases nonlinearly with temperature during drought; (4) mortality can occur faster in hotter Drought, consistent with fundamental physiology; (5) shorter Drought can become lethal under warming, increasing the frequency of lethal Drought; and (6) mortality happens rapidly
Abstract: Patterns, mechanisms, projections, and consequences of tree mortality and associated broad-scale forest die-off due to drought accompanied by warmer temperatures—“hotter drought”, an emerging characteristic of the Anthropocene—are the focus of rapidly expanding literature. Despite recent observational, experimental, and modeling studies suggesting increased vulnerability of trees to hotter drought and associated pests and pathogens, substantial debate remains among research, management and policy-making communities regarding future tree mortality risks. We summarize key mortality-relevant findings, differentiating between those implying lesser versus greater levels of vulnerability. Evidence suggesting lesser vulnerability includes forest benefits of elevated [CO2] and increased water-use efficiency; observed and modeled increases in forest growth and canopy greening; widespread increases in woody-plant biomass, density, and extent; compensatory physiological, morphological, and genetic mechanisms; dampening ecological feedbacks; and potential mitigation by forest management. In contrast, recent studies document more rapid mortality under hotter drought due to negative tree physiological responses and accelerated biotic attacks. Additional evidence suggesting greater vulnerability includes rising background mortality rates; projected increases in drought frequency, intensity, and duration; limitations of vegetation models such as inadequately represented mortality processes; warming feedbacks from die-off; and wildfire synergies. Grouping these findings we identify ten contrasting perspectives that shape the vulnerability debate but have not been discussed collectively. We also present a set of global vulnerability drivers that are known with high confidence: (1) droughts eventually occur everywhere; (2) warming produces hotter droughts; (3) atmospheric moisture demand increases nonlinearly with temperature during drought; (4) mortality can occur faster in hotter drought, consistent with fundamental physiology; (5) shorter droughts occur more frequently than longer droughts and can become lethal under warming, increasing the frequency of lethal drought nonlinearly; and (6) mortality happens rapidly relative to growth intervals needed for forest recovery. These high-confidence drivers, in concert with research supporting greater vulnerability perspectives, support an overall viewpoint of greater forest vulnerability globally. We surmise that mortality vulnerability is being discounted in part due to difficulties in predicting threshold responses to extreme climate events. Given the profound ecological and societal implications of underestimating global vulnerability to hotter drought, we highlight urgent challenges for research, management, and policy-making communities.
1,786 citations