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Hao Z, Zheng J, Ge Q, Bai M. A 2.5° × 2.5° gridded drought/flood grades dataset for eastern China during the last millennium. Sci Data 2023; 10:202. [PMID: 37041161 PMCID: PMC10090067 DOI: 10.1038/s41597-023-02110-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 03/27/2023] [Indexed: 04/13/2023] Open
Abstract
Hydroclimate reconstruction for the last millennium is essential to understand the differences in hydroclimate extremes and their causes under cold/warm conditions. In this study, the first gridded drought/flood grades (D/F grades) dataset in eastern China (EC) during the last millennium was generated. This D/F grades dataset mainly consisted of two components. The first section was created by interpolating drought/flood grades from 1500 to 2000 using the angular distance weight method. Sampling error estimates were employed to assess the effects of the interpolated dataset. The second section for the D/F grades dataset during 960-1500 was generated by constructing best subset regression models using selected tree-ring chronologies in the United States through atmospheric teleconnection. The validation parameters of the calibration equations were also derived, including the adjusted R2, predicted R2, RE, and CE. This dataset provides critical support for investigating the characteristics and causes of hydroclimate extremes in EC at various spatiotemporal scales, as well as the relationship with climate modes, such as El Niño-Southern Oscillation, Pacific Decadal Oscillation, and East Asia Summer Monsoon.
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Affiliation(s)
- Zhixin Hao
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Jingyun Zheng
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Quansheng Ge
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Mengxin Bai
- Beijing Municipal Climate Center, Beijing Meteorological Bureau, Beijing, 100089, China.
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2
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Guo S, Huang J, Zhang X, Qiao W, Zhang M. Ecological sensitivity study of the South-North Water Transfer central line water source area based on land use change. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:384. [PMID: 36763310 DOI: 10.1007/s10661-023-11012-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
Land use change and ecological sensitivity studies are relevant to the dynamic stability of the overall regional ecological environment. In this study, Nanyang City, a South-North Water Transfer water source area, was used as the study area to analyse land use changes and landscape pattern changes in Nanyang City from 1990 to 2020 based on land use data. Hierarchical analysis (AHP method) was used and combined with six sensitivity factors to conduct a multi-factor comprehensive ecological sensitivity evaluation of Nanyang City. The results show that: (1) between 1990 and 2020, the transfer of water and construction land in Nanyang City was larger, increasing by 30.25% and 99.56% respectively, with a small decrease in the area of cultivated land and a small increase in the area of forest land; the overall rate of land use change in Nanyang City during the 30-year period was first slow and then fast. (2) At the level of patch types, the fragmentation of cultivated land and construction land is the highest; during the study period, the overall fragmentation of the study area tends to decrease; at the level of landscape, each landscape type develops in the direction of equilibrium. (3) The ecological sensitivity increases gradually from the urban cluster in the central plain area to the periphery, with the highly sensitive areas distributed in the water area, the medium sensitive areas in the mountainous area, and the low sensitive areas and insensitive areas in the cultivated land and town planning area.
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Affiliation(s)
- Shaolei Guo
- Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
| | - Jiafeng Huang
- Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
| | - Xianqi Zhang
- Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
- Collaborative Innovation Center of Water Resources Efficient Utilization and Protection Engineering, Zhengzhou, 450046, China.
- Technology Research Center of Water Conservancy and Marine Traffic Engineering, Zhengzhou, 450046, Henan Province, China.
| | - Wenbao Qiao
- Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
| | - Minghui Zhang
- Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
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3
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Mahnken M, Cailleret M, Collalti A, Trotta C, Biondo C, D'Andrea E, Dalmonech D, Marano G, Mäkelä A, Minunno F, Peltoniemi M, Trotsiuk V, Nadal-Sala D, Sabaté S, Vallet P, Aussenac R, Cameron DR, Bohn FJ, Grote R, Augustynczik ALD, Yousefpour R, Huber N, Bugmann H, Merganičová K, Merganic J, Valent P, Lasch-Born P, Hartig F, Vega Del Valle ID, Volkholz J, Gutsch M, Matteucci G, Krejza J, Ibrom A, Meesenburg H, Rötzer T, van der Maaten-Theunissen M, van der Maaten E, Reyer CPO. Accuracy, realism and general applicability of European forest models. GLOBAL CHANGE BIOLOGY 2022; 28:6921-6943. [PMID: 36117412 DOI: 10.1111/gcb.16384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 06/01/2022] [Accepted: 06/19/2022] [Indexed: 06/15/2023]
Abstract
Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests.
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Affiliation(s)
- Mats Mahnken
- Potsdam Institute for Climate Impact Research (PIK), Leibniz Association, Potsdam, Germany
- Forest Growth and Woody Biomass Production, TU Dresden, Tharandt, Germany
| | - Maxime Cailleret
- UMR RECOVER, INRAE, Aix-Marseille University, Aix-en-Provence, France
- Forest Dynamics Unit, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
| | - Alessio Collalti
- Forest Modelling Lab, National Research Council of Italy, Institute for Agriculture and Forestry Systems in the Mediterranean (CNR-ISAFOM), Perugia, Italy
- Department of Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
- Division Impacts on Agriculture, Forests and Ecosystem Services (IAFES), Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Viterbo, Italy
| | - Carlo Trotta
- Department of Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
- Division Impacts on Agriculture, Forests and Ecosystem Services (IAFES), Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Viterbo, Italy
| | - Corrado Biondo
- Department of Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
- Division Impacts on Agriculture, Forests and Ecosystem Services (IAFES), Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Viterbo, Italy
| | - Ettore D'Andrea
- Forest Modelling Lab, National Research Council of Italy, Institute for Agriculture and Forestry Systems in the Mediterranean (CNR-ISAFOM), Perugia, Italy
| | - Daniela Dalmonech
- Forest Modelling Lab, National Research Council of Italy, Institute for Agriculture and Forestry Systems in the Mediterranean (CNR-ISAFOM), Perugia, Italy
| | - Gina Marano
- Forest Modelling Lab, National Research Council of Italy, Institute for Agriculture and Forestry Systems in the Mediterranean (CNR-ISAFOM), Perugia, Italy
- Department of Environmental Systems Science, Forest Ecology, Institute of Terrestrial Ecosystems, ETH Zurich, Zurich, Switzerland
| | - Annikki Mäkelä
- Department of Forest Sciences, Institute for Atmospheric and Earth System Research (INAR) and Faculty of Agriculture and Forestry, University of Helsinki, Helsinki, Finland
| | - Francesco Minunno
- Department of Forest Sciences, Institute for Atmospheric and Earth System Research (INAR) and Faculty of Agriculture and Forestry, University of Helsinki, Helsinki, Finland
| | | | - Volodymyr Trotsiuk
- Forest Dynamics Unit, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
| | - Daniel Nadal-Sala
- Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany
- Ecology Section, Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona (UB), Barcelona, Spain
| | - Santiago Sabaté
- Ecology Section, Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona (UB), Barcelona, Spain
- CREAF (Center for Ecological Research and Forestry Applications), Cerdanyola del Vallès, Spain
| | - Patrick Vallet
- LESSEM, INRAE, Univ. Grenoble Alpes, St-Martin-d'Hères, France
| | | | - David R Cameron
- UK Centre for Ecology and Hydrology, Penicuik, Midlothian, UK
| | - Friedrich J Bohn
- Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
| | - Rüdiger Grote
- Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany
| | | | - Rasoul Yousefpour
- Forestry Economics and Forest Planning, University of Freiburg, Freiburg, Germany
- Institute of Forestry and Conservation, John Daniels Faculty of Architecture, Landscape and Design, University of Toronto, Toronto, Ontario, Canada
| | - Nica Huber
- Department of Environmental Systems Science, Forest Ecology, Institute of Terrestrial Ecosystems, ETH Zurich, Zurich, Switzerland
- Remote Sensing, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
| | - Harald Bugmann
- Department of Environmental Systems Science, Forest Ecology, Institute of Terrestrial Ecosystems, ETH Zurich, Zurich, Switzerland
| | - Katarina Merganičová
- Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Praha, Czech Republic
- Department of Biodiversity of Ecosystems and Landscape, Institute of Landscape Ecology, Slovak Academy of Sciences, Nitra, Slovakia
| | - Jan Merganic
- Faculty of Forestry, Technical University in Zvolen, Zvolen, Slovak Republic
| | - Peter Valent
- Faculty of Forestry, Technical University in Zvolen, Zvolen, Slovak Republic
| | - Petra Lasch-Born
- Potsdam Institute for Climate Impact Research (PIK), Leibniz Association, Potsdam, Germany
| | - Florian Hartig
- Theoretical Ecology, University of Regensburg, Regensburg, Germany
| | | | - Jan Volkholz
- Potsdam Institute for Climate Impact Research (PIK), Leibniz Association, Potsdam, Germany
| | - Martin Gutsch
- Potsdam Institute for Climate Impact Research (PIK), Leibniz Association, Potsdam, Germany
| | - Giorgio Matteucci
- Forest Modelling Lab, National Research Council of Italy, Institute for Agriculture and Forestry Systems in the Mediterranean (CNR-ISAFOM), Perugia, Italy
| | - Jan Krejza
- Global Change Research Institute CAS, Brno, Czech Republic
- Department of Forest Ecology, Mendel University in Brno, Brno, Czech Republic
| | - Andreas Ibrom
- Department of Environmental Engineering, Technical University of Denmark (DTU), Lyngby, Denmark
| | | | - Thomas Rötzer
- Forest Growth and Yield Science, TU München, Freising, Germany
| | | | | | - Christopher P O Reyer
- Potsdam Institute for Climate Impact Research (PIK), Leibniz Association, Potsdam, Germany
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Ma F, Wang C, Zhang Y, Chen J, Xie R, Sun Z. Development of Microbial Indicators in Ecological Systems. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13888. [PMID: 36360768 PMCID: PMC9654993 DOI: 10.3390/ijerph192113888] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/22/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Indicators can monitor ecological environment changes and help maintain ecological balance. Bioindicators are divided into animal, plant, and microbial indicators, of which animal and plant indicators have previously been the most researched, but microbial indicators have drawn attention recently owing to their high sensitivity to the environment and their potential for use in monitoring environmental changes. To date, reviews of studies of animals and plants as indicator species have frequently been conducted, but reviews of research on microorganisms as indicator species have been rare. In this review, we summarize and analyze studies using microorganisms as indicator species in a variety of ecosystems, such as forests, deserts, aquatic and plateau ecosystems, and artificial ecosystems, which are contained in wetlands, farmlands, and mining ecosystems. This review provides useful information for the further use of microorganisms as indicators to reflect the changes in different environmental ecosystems.
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Affiliation(s)
- Fangzhou Ma
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China
| | - Chenbin Wang
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China
| | - Yanjing Zhang
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China
| | - Jing Chen
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China
| | - Rui Xie
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China
| | - Zhanbin Sun
- School of Light Industry, Beijing Technology and Business University, Beijing 100048, China
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5
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Fisher JB, Sikka M, Block GL, Schwalm CR, Parazoo NC, Kolus HR, Sok M, Wang A, Gagne‐Landmann A, Lawal S, Guillaume A, Poletti A, Schaefer KM, El Masri B, Levy PE, Wei Y, Dietze MC, Huntzinger DN. The Terrestrial Biosphere Model Farm. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 2022; 14:e2021MS002676. [PMID: 35860620 PMCID: PMC9285607 DOI: 10.1029/2021ms002676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 01/13/2022] [Accepted: 01/17/2022] [Indexed: 06/15/2023]
Abstract
Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901-2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.
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Affiliation(s)
- Joshua B. Fisher
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
- Schmid College of Science and TechnologyChapman UniversityOrangeCAUSA
| | - Munish Sikka
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Gary L. Block
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | | | | | - Hannah R. Kolus
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Malen Sok
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Audrey Wang
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | | | - Shakirudeen Lawal
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | | | - Alyssa Poletti
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Kevin M. Schaefer
- National Snow and Ice Data CenterCooperative Institute for Research in Environmental SciencesUniversity of ColoradoBoulderCOUSA
| | - Bassil El Masri
- Department of Earth and Environmental SciencesMurray State UniversityMurrayKYUSA
| | | | - Yaxing Wei
- Environmental Sciences DivisionOak Ridge National LaboratoryClimate Change Science InstituteOak RidgeTNUSA
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Paciorek CJ, Cogbill CV, Peters JA, Williams JW, Mladenoff DJ, Dawson A, McLachlan JS. The forests of the midwestern United States at Euro-American settlement: Spatial and physical structure based on contemporaneous survey data. PLoS One 2021; 16:e0246473. [PMID: 33571316 PMCID: PMC7877788 DOI: 10.1371/journal.pone.0246473] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/19/2021] [Indexed: 11/18/2022] Open
Abstract
We present gridded 8 km-resolution data products of the estimated stem density, basal area, and biomass of tree taxa at Euro-American settlement of the midwestern United States during the middle to late 19th century for the states of Minnesota, Wisconsin, Michigan, Illinois, and Indiana. The data come from settlement-era Public Land Survey (PLS) data (ca. 0.8-km resolution) of trees recorded by land surveyors. The surveyor notes have been transcribed, cleaned, and processed to estimate stem density, basal area, and biomass at individual points. The point-level data are aggregated within 8 km grid cells and smoothed using a generalized additive statistical model that accounts for zero-inflated continuous data and provides approximate Bayesian uncertainty estimates. The statistical modeling smooths out sharp spatial features (likely arising from statistical noise) within areas smaller than about 200 km2. Based on this modeling, presettlement Midwestern landscapes supported multiple dominant species, vegetation types, forest types, and ecological formations. The prairies, oak savannas, and forests each had distinctive structures and spatial distributions across the domain. Forest structure varied from savanna (averaging 27 Mg/ha biomass) to northern hardwood (104 Mg/ha) and mesic southern forests (211 Mg/ha). The presettlement forests were neither unbroken and massively-statured nor dominated by young forests constantly structured by broad-scale disturbances such as fire, drought, insect outbreaks, or hurricanes. Most forests were structurally between modern second growth and old growth. We expect the data product to be useful as a baseline for investigating how forest ecosystems have changed in response to the last several centuries of climate change and intensive Euro-American land use and as a calibration dataset for paleoecological proxy-based reconstructions of forest composition and structure for earlier time periods. The data products (including raw and smoothed estimates at the 8-km scale) are available at the LTER Network Data Portal as version 1.0.
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Affiliation(s)
- Christopher J. Paciorek
- Department of Statistics, University of California, Berkeley, California, United States of America
- * E-mail:
| | - Charles V. Cogbill
- Harvard Forest, Harvard University, Petersham, Massachusetts, United States of America
| | - Jody A. Peters
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - John W. Williams
- Department of Geography, University of Wisconsin, Madison, Wisconsin, United States of America
- Center for Climatic Research, University of Wisconsin, Madison, Wisconsin, United States of America
| | - David J. Mladenoff
- Department of Forest and Wildlife Ecology, University of Wisconsin, Madison, Wisconsin, United States of America
| | - Andria Dawson
- Department of General Education, Mount Royal University, Calgary, Alberta, Canada
| | - Jason S. McLachlan
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
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