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Publications Citing International Satellite Land Surface Climatology Project (ISLSCP II)

The following 260 publications cited the International Satellite Land Surface Climatology Project (ISLSCP II) project.

YearCitationDataset or Project
2024Cho, N., C. Agossou, E. Kim, J. Lim, T. Hwang, and S. Kang. 2024. Evaluating key climatic and ecophysiological parameters of worldwide tree mortality with a process-based BGC model and machine learning algorithms. Ecological Modelling. 491:110688. https://doi.org/10.1016/j.ecolmodel.2024.110688
2024Peng, J., Y. Xue, N. Pan, Y. Zhang, H. Liang, and F. Zhang. 2024. Exploring the Spatiotemporal Alterations in China’s GPP Based on the DTEC Model. Remote Sensing. 16(8):1361. https://doi.org/10.3390/rs16081361
2024Sharples, W., U. Bende-Michl, L. Wilson, A. Shokri, A. Frost, and S. Baron-Hay. 2024. Improving continental hydrological models for future climate conditions via multi-objective optimisation. Environmental Modelling & Software. 176:106018. https://doi.org/10.1016/j.envsoft.2024.106018
2023Beier, F.D., B.L. Bodirsky, J. Heinke, K. Karstens, J.P. Dietrich, C. Müller, F. Stenzel, P.J. von Jeetze, A. Popp, and H. Lotze?Campen. 2023. Technical and Economic Irrigation Potentials Within Land and Water Boundaries. Water Resources Research. 59(4). https://doi.org/10.1029/2021WR031924
2023Di, L. and E. Yu. 2023. Examples of Remote Sensing Applications of Big Data Analytics—Land Cover Time Series Creation. Springer Remote Sensing/Photogrammetry, Remote Sensing Big Data. 261-270. https://doi.org/10.1007/978-3-031-33932-5_16
2023Dong, G., S. Chen, K. Liu, W. Wang, H. Hou, L. Gao, F. Zhang, and H. Su. 2023. Spatiotemporal variation in sensitivity of urban vegetation growth and greenness to vegetation water content: Evidence from Chinese megacities. Science of The Total Environment. 905:167090. https://doi.org/10.1016/j.scitotenv.2023.167090
2023Fluet-Chouinard, E., B.D. Stocker, Z. Zhang, A. Malhotra, J.R. Melton, B. Poulter, J.O. Kaplan, K.K. Goldewijk, S. Siebert, T. Minayeva, G. Hugelius, H. Joosten, A. Barthelmes, C. Prigent, F. Aires, A.M. Hoyt, N. Davidson, C.M. Finlayson, B. Lehner, R.B. Jackson, and P.B. McIntyre. 2023. Extensive global wetland loss over the past three centuries. Nature. 614(7947):281-286. https://doi.org/10.1038/s41586-022-05572-6
2023Jia, R., X. Fang, and Y. Ye. 2023. Gridded reconstruction of cropland cover changes in Northeast China from AD 1000 to 1200. Regional Environmental Change. 23(4). https://doi.org/10.1007/s10113-023-02118-y
2023Klein, K., G. Weniger, P. Ludwig, C. Stepanek, X. Zhang, C. Wegener, and Y. Shao. 2023. Assessing climatic impact on transition from Neanderthal to anatomically modern human population on Iberian Peninsula: a macroscopic perspective. Science Bulletin. 68(11):1176-1186. https://doi.org/10.1016/j.scib.2023.04.025
2023Li, S., G. Wang, C. Zhu, M. Hannemann, R. Poyatos, J. Lu, J. Li, W. Ullah, D.F.T. Hagan, A. García-García, Y. Liu, Q. Liu, S. Ma, Q. Liu, S. Sun, F. Zhao, and J. Peng. 2023. Spatial patterns and recent temporal trends in global transpiration modelled using eco-evolutionary optimality. Agricultural and Forest Meteorology. 342:109702. https://doi.org/10.1016/j.agrformet.2023.109702
2023Li, S., G. Wang, C. Zhu, M. Hannemann, R. Poyatos, J. Lu, J. Li, W. Ullah, D.F.T. Hagan, A. García-García, Y. Liu, Q. Liu, S. Ma, Q. Liu, S. Sun, F. Zhao, and J. Peng. 2023. Spatial patterns and recent temporal trends in global transpiration modelled using eco-evolutionary optimality. Agricultural and Forest Meteorology. 342:109702. https://doi.org/10.1016/j.agrformet.2023.109702
2023Li, Y., A. Chen, G. Mao, P. Chen, H. Huang, H. Yang, Z. Wang, K. Wang, H. Chen, Y. Meng, R. Zhong, P. Wang, H. Wang, and J. Liu. 2023. Multi-model analysis of historical runoff changes in the Lancang-Mekong River Basin – Characteristics and uncertainties. Journal of Hydrology. 619:129297. https://doi.org/10.1016/j.jhydrol.2023.129297
2023Li, Y., S. Chen, J. Yin, and X. Yuan. 2023. Technical note: A stochastic framework for identification and evaluation of flash drought. Hydrology and Earth System Sciences. 27(5):1077-1087. https://doi.org/10.5194/hess-27-1077-2023
2023Mathur, M. and P. Mathur. 2023. Predictive ecological niche modelling of an important bio-control agent: Trichoderma harzianum (Rifai) using the MaxEnt machine learning tools with climatic and non-climatic predictors. Biocontrol Science and Technology. 33(9):820-854. https://doi.org/10.1080/09583157.2023.2245985
2023Mathur, M. and P. Mathur. 2023. Predictive ecological niche modelling of an important bio-control agent: Trichoderma harzianum (Rifai) using the MaxEnt machine learning tools with climatic and non-climatic predictors. Biocontrol Science and Technology. 33(9):820-854. https://doi.org/10.1080/09583157.2023.2245985
2023Mazzariello, A., R. Albano, T. Lacava, S. Manfreda, and A. Sole. 2023. Intercomparison of recent microwave satellite soil moisture products on European ecoregions. Journal of Hydrology. 626:130311. https://doi.org/10.1016/j.jhydrol.2023.130311
2023Muzylev, E.L. 2023. Utilization of Remote Sensing Data in the Simulation of the Water and Heat Regime of Land Areas: A Review of Publications. Water Resources. 50(5):709-731. https://doi.org/10.1134/S0097807823700021
2023Tang, Q., J. Golaz, L.P. Van Roekel, M.A. Taylor, W. Lin, B.R. Hillman, P.A. Ullrich, A.M. Bradley, O. Guba, J.D. Wolfe, T. Zhou, K. Zhang, X. Zheng, Y. Zhang, M. Zhang, M. Wu, H. Wang, C. Tao, B. Singh, A.M. Rhoades, Y. Qin, H. Li, Y. Feng, Y. Zhang, C. Zhang, C.S. Zender, S. Xie, E.L. Roesler, A.F. Roberts, A. Mametjanov, M.E. Maltrud, N.D. Keen, R.L. Jacob, C. Jablonowski, O.K. Hughes, R.M. Forsyth, A.V. Di Vittorio, P.M. Caldwell, G. Bisht, R.B. McCoy, L.R. Leung, and D.C. Bader. 2023. The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results. Geoscientific Model Development. 16(13):3953-3995. https://doi.org/10.5194/gmd-16-3953-2023
2023Wang, Y., Y. Ma, D. Muñoz-Esparza, J. Dai, C.W.Y. Li, P. Lichtig, R.C. Tsang, C. Liu, T. Wang, and G.P. Brasseur. 2023. Coupled mesoscale–microscale modeling of air quality in a polluted city using WRF-LES-Chem. Atmospheric Chemistry and Physics. 23(10):5905-5927. https://doi.org/10.5194/acp-23-5905-2023
2023Yu, H., Y. Duan, J. Mulder, P. Dörsch, W. Zhu, Xu-Ri, K. Huang, Z. Zheng, R. Kang, C. Wang, Z. Quan, F. Zhu, D. Liu, S. Peng, S. Han, Y. Zhang, and Y. Fang. 2023. Universal temperature sensitivity of denitrification nitrogen losses in forest soils. Nature Climate Change. 13(7):726-734. https://doi.org/10.1038/s41558-023-01708-2
2023Yuan, S., W. Lei, Q. Liu, R. Liu, J. Liu, J. Fu, and Y. Han. 2023. Distribution and environmental impact of microalgae production potential under the carbon-neutral target. Energy. 263:125584. https://doi.org/10.1016/j.energy.2022.125584
2023Yuan, S., W. Lei, Y. Cen, Q. Liu, J. Liu, J. Fu, and Y. Han. 2023. Economic analysis of global microalgae biomass energy potential. Science of The Total Environment. 899:165596. https://doi.org/10.1016/j.scitotenv.2023.165596
2022Bastos, A., P. Ciais, S. Sitch, L.E.O.C. Aragão, F. Chevallier, D. Fawcett, T.M. Rosan, M. Saunois, D. Günther, L. Perugini, C. Robert, Z. Deng, J. Pongratz, R. Ganzenmüller, R. Fuchs, K. Winkler, S. Zaehle, and C. Albergel. 2022. On the use of Earth Observation to support estimates of national greenhouse gas emissions and sinks for the Global stocktake process: lessons learned from ESA-CCI RECCAP2. Carbon Balance and Management. 17(1). https://doi.org/10.1186/s13021-022-00214-w
2022Braghiere, R.K., J.B. Fisher, K. Allen, E. Brzostek, M. Shi, X. Yang, D.M. Ricciuto, R.A. Fisher, Q. Zhu, and R.P. Phillips. 2022. Modeling Global Carbon Costs of Plant Nitrogen and Phosphorus Acquisition. Journal of Advances in Modeling Earth Systems. 14(8). https://doi.org/10.1029/2022MS003204
2022Chen, X., Y. Huang, C. Nie, S. Zhang, G. Wang, S. Chen, and Z. Chen. 2022. A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms. Scientific Data. 9(1). https://doi.org/10.1038/s41597-022-01520-1
2022Ekberzade, B., O. Yetemen, O.L. Sen, and H.N. Dalfes. 2022. Simulating the potential forest ranges in an old land: the case for Turkey’s forests. Biodiversity and Conservation. 31(13-14):3217-3236. https://doi.org/10.1007/s10531-022-02485-8
2022Fang, J., H.H. Shugart, F. Liu, X. Yan, Y. Song, and F. Lv. 2022. FORCCHN V2.0: an individual-based model for predicting multiscale forest carbon dynamics. Geoscientific Model Development. 15(17):6863-6872. https://doi.org/10.5194/gmd-15-6863-2022
2022Guirado, E., M. Delgado-Baquerizo, J. Martínez-Valderrama, S. Tabik, D. Alcaraz-Segura, and F.T. Maestre. 2022. Climate legacies drive the distribution and future restoration potential of dryland forests. Nature Plants. 8(8):879-886. https://doi.org/10.1038/s41477-022-01198-8
2022Hauser, E., P.L. Sullivan, A.N. Flores, D. Hirmas, and S.A. Billings. 2022. Global?Scale Shifts in Rooting Depths Due To Anthropocene Land Cover Changes Pose Unexamined Consequences for Critical Zone Functioning. Earth's Future. 10(11). https://doi.org/10.1029/2022EF002897
2022Hauser, E., P.L. Sullivan, A.N. Flores, D. Hirmas, and S.A. Billings. 2022. Global?Scale Shifts in Rooting Depths Due To Anthropocene Land Cover Changes Pose Unexamined Consequences for Critical Zone Functioning. Earth's Future. 10(11). https://doi.org/10.1029/2022EF002897
2022Liu, J., P. Fang, Y. Que, L. Zhu, Z. Duan, G. Tang, P. Liu, M. Ji, and Y. Liu. 2022. A dataset of lake-catchment characteristics for the Tibetan Plateau. Earth System Science Data. 14(8):3791-3805. https://doi.org/10.5194/essd-14-3791-2022
2022Liu, X., T. Zhou, P. Shi, Y. Zhang, H. Luo, P. Yu, Y. Xu, P. Zhou, and J. Zhang. 2022. Uncertainties of soil organic carbon stock estimation caused by paleoclimate and human footprint on the Qinghai Plateau. Carbon Balance and Management. 17(1). https://doi.org/10.1186/s13021-022-00203-z
2022Liu, X., T. Zhou, P. Shi, Y. Zhang, H. Luo, P. Yu, Y. Xu, P. Zhou, and J. Zhang. 2022. Uncertainties of soil organic carbon stock estimation caused by paleoclimate and human footprint on the Qinghai Plateau. Carbon Balance and Management. 17(1). https://doi.org/10.1186/s13021-022-00203-z
2022Makarieva, A.M., A.V. Nefiodov, A.D. Nobre, D. Sheil, P. Nobre, J. Pokorný, P. Hesslerová, and B. Li. 2022. Vegetation impact on atmospheric moisture transport under increasing land-ocean temperature contrasts. Heliyon. 8(10):e11173. https://doi.org/10.1016/j.heliyon.2022.e11173
2022Nijzink, R.C., J. Beringer, L.B. Hutley, and S.J. Schymanski. 2022. Does maximization of net carbon profit enable the prediction of vegetation behaviour in savanna sites along a precipitation gradient?. Hydrology and Earth System Sciences. 26(2):525-550. https://doi.org/10.5194/hess-26-525-2022
2022Ren, S., X. Chen, and C. Pan. 2022. Temperature-precipitation background affects spatial heterogeneity of spring phenology responses to climate change in northern grasslands (30°N-55°N). Agricultural and Forest Meteorology. 315:108816. https://doi.org/10.1016/j.agrformet.2022.108816
2022Roobaert, A., L. Resplandy, G.G. Laruelle, E. Liao, and P. Regnier. 2022. A framework to evaluate and elucidate the driving mechanisms of coastal sea surface <i>p</i>CO<sub>2</sub> seasonality using an ocean general circulation model (MOM6-COBALT). Ocean Science. 18(1):67-88. https://doi.org/10.5194/os-18-67-2022
2022Sato, H., and T. Ise. 2022. Predicting global terrestrial biomes with the LeNet convolutional neural network. Geoscientific Model Development. 15(7):3121-3132. https://doi.org/10.5194/gmd-15-3121-2022
2022Scott, H.G., and N.G. Smith. 2022. A Model of C 4 Photosynthetic Acclimation Based on Least?Cost Optimality Theory Suitable for Earth System Model Incorporation. Journal of Advances in Modeling Earth Systems. 14(3). https://doi.org/10.1029/2021MS002470
2022Shu, Mi, and Du, Shihong. 2022. Forty Years' Progress and Challenges of Remote Sensing in National Land Survey. Journal of Geo-Information Science. 24(4):597-616. https://doi.org/10.12082/dqxxkx.2022.210512
2022Shu, Mi, and Du, Shihong. 2022. Forty Years' Progress and Challenges of Remote Sensing in National Land Survey. Journal of Geo-Information Science. 24(4):597-616. https://doi.org/10.12082/dqxxkx.2022.210512
2022Stephens, R.B., A.P. Ouimette, E.A. Hobbie, and R.J. Rowe. 2022. Reevaluating trophic discrimination factors ( ?? 13 C and ?? 15 N ) for diet reconstruction. Ecological Monographs. 92(3). https://doi.org/10.1002/ecm.1525
2022Tamoffo, A.T., A. Dosio, L.K. Amekudzi, and T. Weber. 2022. Process-oriented evaluation of the West African Monsoon system in CORDEX-CORE regional climate models. Climate Dynamics. https://doi.org/10.1007/s00382-022-06502-y
2022Tamoffo, A.T., L.K. Amekudzi, T. Weber, D.A. Vondou, E.I. Yamba, and D. Jacob. 2022. Mechanisms of Rainfall Biases in Two CORDEX-CORE Regional Climate Models at Rainfall Peaks over Central Equatorial Africa. Journal of Climate. 35(2):639-668. https://doi.org/10.1175/JCLI-D-21-0487.1
2022Wang, H., H. Yan, Y. Hu, Y. Xi, and Y. Yang. 2022. Consistency and Accuracy of Four High-Resolution LULC Datasets—Indochina Peninsula Case Study. Land. 11(5):758. https://doi.org/10.3390/land11050758
2022Yang, X., H. Xu, and M. Tan. 2022. Downscaling estimates of land carbon opportunity costs for agricultural products to provincial level in China. Journal of Cleaner Production. 376:134267. https://doi.org/10.1016/j.jclepro.2022.134267
2022Ye, Y., J. Li, X. Fang, D. Zhang, Z. Zhao, Z. Wu, Y. Lu, and B. Li. 2022. Reconstruction of cropland change in European countries using integrated multisource data since AD 1800. Boreas. 52(1):60-77. https://doi.org/10.1111/bor.12598
2021Anande, D.M. and M.S. Park. 2021. Impacts of Projected Urban Expansion on Rainfall and Temperature during Rainy Season in the Middle-Eastern Region in Tanzania. Atmosphere. 12(10):1234. https://doi.org/10.3390/atmos12101234
2021Aziz, T. 2021. Changes in land use and ecosystem services values in Pakistan, 1950-2050. Environmental Development. 37:100576. https://doi.org/10.1016/j.envdev.2020.100576
2021Karp, A.T., J.W. Andrae, F.A. McInerney, P.J. Polissar, and K.H. Freeman. 2021. Soil Carbon Loss and Weak Fire Feedbacks During Pliocene C 4 Grassland Expansion in Australia . Geophysical Research Letters. 48(2):. https://doi.org/10.1029/2020GL090964
2021Kuipers, K.J.J., R. May, and F. Verones. 2021. Considering habitat conversion and fragmentation in characterisation factors for land-use impacts on vertebrate species richness. Science of The Total Environment. 801:149737. https://doi.org/10.1016/j.scitotenv.2021.149737
2021Linyucheva, A. and P. Kindlmann. 2021. A review of global land cover maps in terms of their potential use for habitat suitability modelling. EUROPEAN JOURNAL OF ENVIRONMENTAL SCIENCES. 11(1):46-61. https://doi.org/10.14712/23361964.2021.6
2021Randazzo, N.A., A.M. Michalak, C.E. Miller, S.M. Miller, Y.P. Shiga, and Y. Fang. 2021. Higher Autumn Temperatures Lead to Contrasting CO 2 Flux Responses in Boreal Forests Versus Tundra and Shrubland . Geophysical Research Letters. 48(18):. https://doi.org/10.1029/2021GL093843
2021Son, R., H. Kim, S.Y.S. Wang, J.H. Jeong, S.H. Woo, J.Y. Jeong, B.D. Lee, S.H. Kim, M. LaPlante, C.G. Kwon, and J.H. Yoon. 2021. Changes in fire weather climatology under 1.5 degC and 2.0 degC warming. Environmental Research Letters. 16(3):034058. https://doi.org/10.1088/1748-9326/abe675
2021Thompson, D.R., P.G. Brodrick, K. Cawse-Nicholson, K. Dana Chadwick, R.O. Green, B. Poulter, S. Serbin, A.N. Shiklomanov, P.A. Townsend, and K.R. Turpie. 2021. Spectral Fidelity of Earth's Terrestrial and Aquatic Ecosystems. Journal of Geophysical Research: Biogeosciences. 126(8):. https://doi.org/10.1029/2021JG006273
2021Wei, X., M. Widgren, B. Li, Y. Ye, X. Fang, C. Zhang, and T. Chen. 2021. Dataset of 1 km cropland cover from 1690 to 1999 in Scandinavia. Earth System Science Data. 13(6):3035-3056. https://doi.org/10.5194/essd-13-3035-2021
2021Yang, Y., T.R. McVicar, D. Yang, Y. Zhang, S. Piao, S. Peng, and H.E. Beck. 2021. Low and contrasting impacts of vegetation CO<sub>2</sub> fertilization on global terrestrial runoff over 1982-2010: accounting for aboveground and belowground vegetation-CO<sub>2</sub> effects. Hydrology and Earth System Sciences. 25(6):3411-3427. https://doi.org/10.5194/hess-25-3411-2021
2021Yao, P.J., D.Y. Gong, and M. Meng. 2021. Changes in spring vegetation greenness over Siberia associated with weather disturbances during 1982-2015. International Journal of Climatology. https://doi.org/10.1002/joc.7095
2021Zhu, D., E.D. Galbraith, V. Reyes-Garcia, and P. Ciais. 2021. Global hunter-gatherer population densities constrained by influence of seasonality on diet composition. Nature Ecology & Evolution. 5(11):1536-1545. https://doi.org/10.1038/s41559-021-01548-3
2020Doi, T., G. Sakurai, and T. Iizumi. 2020. Seasonal Predictability of Four Major Crop Yields Worldwide by a Hybrid System of Dynamical Climate Prediction and Eco-Physiological Crop-Growth Simulation. Frontiers in Sustainable Food Systems. 4:. https://doi.org/10.3389/fsufs.2020.00084
2020Munchak, S.J., S. Ringerud, L. Brucker, Y. You, I. de Gelis, and C. Prigent. 2020. An Active-Passive Microwave Land Surface Database From GPM. IEEE Transactions on Geoscience and Remote Sensing. 58(9):6224-6242. https://doi.org/10.1109/TGRS.2020.2975477
2020Phrampus, B.J., T.R. Lee, and W.T. Wood. 2020. A Global Probabilistic Prediction of Cold Seeps and Associated SEAfloor FLuid Expulsion Anomalies (SEAFLEAs). Geochemistry, Geophysics, Geosystems. 21(1). https://doi.org/10.1029/2019GC008747
2020Restreppo, G.A., W.T. Wood, and B.J. Phrampus. 2020. Oceanic sediment accumulation rates predicted via machine learning algorithm: towards sediment characterization on a global scale. Geo-Marine Letters. 40(5):755-763. https://doi.org/10.1007/s00367-020-00669-1
2020Rinnan, R., L.L. Iversen, J. Tang, I. Vedel-Petersen, M. Schollert, and G. Schurgers. 2020. Separating direct and indirect effects of rising temperatures on biogenic volatile emissions in the Arctic. Proceedings of the National Academy of Sciences. 117(51):32476-32483. https://doi.org/10.1073/pnas.2008901117
2020Stahl, M.O., J. Gehring, and Y. Jameel. 2020. Isotopic variation in groundwater across the conterminous United States - Insight into hydrologic processes . Hydrological Processes. 34(16):3506-3523. https://doi.org/10.1002/hyp.13832
2020Tian, X., F. Minunno, T. Cao, M. Peltoniemi, T. Kalliokoski, and A. Makela. 2020. Extending the range of applicability of the semi-empirical ecosystem flux model PRELES for varying forest types and climate. Global Change Biology. 26(5):2923-2943. https://doi.org/10.1111/gcb.14992
2020van Natijne, A.L., R.C. Lindenbergh, and T.A. Bogaard. 2020. Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting. Sensors. 20(5):1425. https://doi.org/10.3390/s20051425
2020Wenne, R., M. Zbawicka, L. Bach, P. Strelkov, M. Gantsevich, P. Kuklinski, T. Kijewski, J.H. McDonald, K.K. Sundsaasen, M. Arnyasi, S. Lien, A. Kaasik, K. Herkul, and J. Kotta. 2020. Trans-Atlantic Distribution and Introgression as Inferred from Single Nucleotide Polymorphism: Mussels Mytilus and Environmental Factors. Genes. 11(5):530. https://doi.org/10.3390/genes11050530
2020Wu, G., K. Guan, Y. Li, K.A. Novick, X. Feng, N.G. McDowell, A.G. Konings, S.E. Thompson, J.S. Kimball, M.G. De Kauwe, E.A. Ainsworth, and C. Jiang. 2020. Interannual variability of ecosystem iso/anisohydry is regulated by environmental dryness. New Phytologist. 229(5):2562-2575. https://doi.org/10.1111/nph.17040
2020Y Ryu, H-S Ji, S-O, Hwang, J LeeApplication of a Method Estimating Grid Runoff for a Global High-Resolution Hydrodynamic Model. Atmosphere. 30(2):155-167. https://doi.org/10.14191/Atmos.2020.30.2.155
2019Alipour, M.H. and K.M. Kibler. 2019. Streamflow prediction under extreme data scarcity: a step toward hydrologic process understanding within severely data-limited regions. Hydrological Sciences Journal. 64(9):1038-1055. https://doi.org/10.1080/02626667.2019.1626991
2019Evaristo, J. and J.J. McDonnell. 2019. Global analysis of streamflow response to forest management. Nature. 570(7762):455-461. https://doi.org/10.1038/s41586-019-1306-0
2019Falk, S. and A. Sovde Haslerud. 2019. Update and evaluation of the ozone dry deposition in Oslo CTM3 v1.0. Geoscientific Model Development. 12(11):4705-4728. https://doi.org/10.5194/gmd-12-4705-2019
2019Heinke, J., C. Muller, M. Lannerstad, D. Gerten, and W. Lucht. 2019. Freshwater resources under success and failure of the Paris climate agreement. Earth System Dynamics. 10(2):205-217. https://doi.org/10.5194/esd-10-205-2019
2019Hostetler, S., R. Reker, J. Alder, T. Loveland, D. Willard, C. Bernhardt, E. Sundquist, and R. Thompson. 2019. Application of a Regional Climate Model to Assess Changes in the Climatology of the Eastern United States and Cuba Associated With Historic Land Cover Change. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/2019JD030965
2019Norton, A.J., P.J. Rayner, E.N. Koffi, M. Scholze, J.D. Silver, and Y.P. Wang. 2019. Estimating global gross primary productivity using chlorophyll fluorescence and a data assimilation system with the BETHY-SCOPE model. Biogeosciences. 16(15):3069-3093. https://doi.org/10.5194/bg-16-3069-2019
2019Peltola, O., T. Vesala, Y. Gao, O. Raty, P. Alekseychik, M. Aurela, B. Chojnicki, A.R. Desai, A.J. Dolman, E.S. Euskirchen, T. Friborg, M. Gockede, M. Helbig, E. Humphreys, R.B. Jackson, G. Jocher, F. Joos, J. Klatt, S.H. Knox, L. Kutzbach, S. Lienert, A. Lohila, I. Mammarella, D.F. Nadeau, M.B. Nilsson, W.C. Oechel, M. Peichl, T. Pypker, W. Quinton, J. Rinne, T. Sachs, M. Samson, H.P. Schmid, O. Sonnentag, C. Wille, D. Zona, and T. Aalto. 2019. Monthly Gridded Data Product of Northern Wetland Methane Emissions Based on Upscaling Eddy Covariance Observations. Earth System Science Data Discussions. 1-50. https://doi.org/10.5194/essd-2019-28
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