scholarly journals CM2Mc-LPJmL v1.0: Biophysical coupling of a process-based dynamic vegetation model with managed land to a general circulation model

2021 ◽  
Author(s):  
Markus Drüke ◽  
Werner von Bloh ◽  
Stefan Petri ◽  
Boris Sakschewski ◽  
Sibyll Schaphoff ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Surface ◽  
Cmip5 Models ◽  
Surface Model ◽  
Vegetation Model ◽  
Canopy Layer ◽  
Dynamic Global Vegetation Model ◽  
Acceptable Error ◽  
Dynamic Vegetation Model

Abstract. The terrestrial biosphere is exposed to land-use and climate change, which not only affects vegetation dynamics, but also changes land-atmosphere feedbacks. Specifically, changes in land-cover affect biophysical feedbacks of water and energy, therefore contributing to climate change. In this study, we couple the well established and comprehensively validated Dynamic Global Vegetation Model LPJmL5 to the coupled climate model CM2Mc, which is based on the atmosphere model AM2 and the ocean model MOM5 (CM2Mc-LPJmL). In CM2Mc, we replace the simple land surface model LaD (where vegetation is static and prescribed) with LPJmL5 and fully couple the water and energy cycles using the Geophysical Fluid Dynamics Laboratory (GFDL) Flexible Modeling System (FMS). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. These include a sub-daily cycle for calculating energy and water fluxes, a conductance of the soil evaporation and plant interception, a canopy-layer humidity, and the surface energy balance in order to calculate the surface and canopy layer temperature within LPJmL5. Exchanging LaD by LPJmL5, and therefore switching from a static and prescribed vegetation to a dynamic vegetation, allows us to model important biosphere processes, including fire, mortality, permafrost, hydrological cycling, and the impacts of managed land (crop growth and irrigation). Our results show that CM2Mc-LPJmL has similar temperature and precipitation biases as the original CM2Mc model with LaD. Performance of LPJmL5 in the coupled system compared to Earth observation data and to LPJmL offline simulation results is within acceptable error margins. The historic global mean temperature evolution of our model setup is within the range of CMIP5 models. The comparison of model runs with and without land-use change shows a partially warmer and drier climate state across the global land surface. CM2Mc-LPJmL opens new opportunities to investigate important biophysical vegetation-climate feedbacks with a state-of-the-art and process-based dynamic vegetation model.

scholarly journals CM2Mc-LPJmL v1.0: biophysical coupling of a process-based dynamic vegetation model with managed land to a general circulation model

2021 ◽  
Vol 14 (6) ◽  
pp. 4117-4141
Author(s):  
Markus Drüke ◽  
Werner von Bloh ◽  
Stefan Petri ◽  
Boris Sakschewski ◽  
Sibyll Schaphoff ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Surface ◽  
Ocean Model ◽  
Surface Model ◽  
Vegetation Model ◽  
Canopy Layer ◽  
Dynamic Global Vegetation Model ◽  
Acceptable Error ◽  
Dynamic Vegetation Model

Abstract. The terrestrial biosphere is exposed to land-use and climate change, which not only affects vegetation dynamics but also changes land–atmosphere feedbacks. Specifically, changes in land cover affect biophysical feedbacks of water and energy, thereby contributing to climate change. In this study, we couple the well-established and comprehensively validated dynamic global vegetation model LPJmL5 (Lund–Potsdam–Jena managed Land) to the coupled climate model CM2Mc, the latter of which is based on the atmosphere model AM2 and the ocean model MOM5 (Modular Ocean Model 5), and name it CM2Mc-LPJmL. In CM2Mc, we replace the simple land-surface model LaD (Land Dynamics; where vegetation is static and prescribed) with LPJmL5, and we fully couple the water and energy cycles using the Geophysical Fluid Dynamics Laboratory (GFDL) Flexible Modeling System (FMS). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. These include a sub-daily cycle for calculating energy and water fluxes, conductance of the soil evaporation and plant interception, canopy-layer humidity, and the surface energy balance in order to calculate the surface and canopy-layer temperature within LPJmL5. Exchanging LaD with LPJmL5 and, therefore, switching from a static and prescribed vegetation to a dynamic vegetation allows us to model important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the impacts of managed land (crop growth and irrigation). Our results show that CM2Mc-LPJmL has similar temperature and precipitation biases to the original CM2Mc model with LaD. The performance of LPJmL5 in the coupled system compared to Earth observation data and to LPJmL offline simulation results is within acceptable error margins. The historical global mean temperature evolution of our model setup is within the range of CMIP5 (Coupled Model Intercomparison Project Phase 5) models. The comparison of model runs with and without land-use change shows a partially warmer and drier climate state across the global land surface. CM2Mc-LPJmL opens new opportunities to investigate important biophysical vegetation–climate feedbacks with a state-of-the-art and process-based dynamic vegetation model.

scholarly journals MIROC-INTEG-LAND version 1: a global biogeochemical land surface model with human water management, crop growth, and land-use change

2020 ◽  
Vol 13 (10) ◽  
pp. 4713-4747
Author(s):  
Tokuta Yokohata ◽  
Tsuguki Kinoshita ◽  
Gen Sakurai ◽  
Yadu Pokhrel ◽  
Akihiko Ito ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Management ◽  
Land Use ◽  
Land Use Change ◽  
Land Surface ◽  
Crop Yields ◽  
Land Surface Model ◽  
Climate System ◽  
Surface Model ◽  
Earth System

Abstract. Future changes in the climate system could have significant impacts on the natural environment and human activities, which in turn affect changes in the climate system. In the interaction between natural and human systems under climate change conditions, land use is one of the elements that play an essential role. On the one hand, future climate change will affect the availability of water and food, which may impact land-use change. On the other hand, human-induced land-use change can affect the climate system through biogeophysical and biogeochemical effects. To investigate these interrelationships, we developed MIROC-INTEG-LAND (MIROC INTEGrated LAND surface model version 1), an integrated model that combines the land surface component of global climate model MIROC (Model for Interdisciplinary Research on Climate) with water resources, crop production, land ecosystem, and land-use models. The most significant feature of MIROC-INTEG-LAND is that the land surface model that describes the processes of the energy and water balance, human water management, and crop growth incorporates a land use decision-making model based on economic activities. In MIROC-INTEG-LAND, spatially detailed information regarding water resources and crop yields is reflected in the prediction of future land-use change, which cannot be considered in the conventional integrated assessment models. In this paper, we introduce the details and interconnections of the submodels of MIROC-INTEG-LAND, compare historical simulations with observations, and identify various interactions between the submodels. By evaluating the historical simulation, we have confirmed that the model reproduces the observed states well. The future simulations indicate that changes in climate have significant impacts on crop yields, land use, and irrigation water demand. The newly developed MIROC-INTEG-LAND could be combined with atmospheric and ocean models to develop an integrated earth system model to simulate the interactions among coupled natural–human earth system components.

scholarly journals Land-use change may exacerbate climate change impacts on water resources in the Ganges basin

2018 ◽  
Vol 22 (2) ◽  
pp. 1411-1435 ◽  
Author(s):  
Gina Tsarouchi ◽  
Wouter Buytaert
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Water Resources ◽  
Land Use Change ◽  
Land Surface ◽  
Land Surface Model ◽  
Coupled Model ◽  
Surface Model ◽  
Climate Change Scenarios ◽  
Ganges River

Abstract. Quantifying how land-use change and climate change affect water resources is a challenge in hydrological science. This work aims to quantify how future projections of land-use and climate change might affect the hydrological response of the Upper Ganges river basin in northern India, which experiences monsoon flooding almost every year. Three different sets of modelling experiments were run using the Joint UK Land Environment Simulator (JULES) land surface model (LSM) and covering the period 2000–2035: in the first set, only climate change is taken into account, and JULES was driven by the CMIP5 (Coupled Model Intercomparison Project Phase 5) outputs of 21 models, under two representative concentration pathways (RCP4.5 and RCP8.5), whilst land use was held fixed at the year 2010. In the second set, only land-use change is taken into account, and JULES was driven by a time series of 15 future land-use pathways, based on Landsat satellite imagery and the Markov chain simulation, whilst the meteorological boundary conditions were held fixed at years 2000–2005. In the third set, both climate change and land-use change were taken into consideration, as the CMIP5 model outputs were used in conjunction with the 15 future land-use pathways to force JULES. Variations in hydrological variables (stream flow, evapotranspiration and soil moisture) are calculated during the simulation period. Significant changes in the near-future (years 2030–2035) hydrologic fluxes arise under future land-cover and climate change scenarios pointing towards a severe increase in high extremes of flow: the multi-model mean of the 95th percentile of streamflow (Q5) is projected to increase by 63 % under the combined land-use and climate change high emissions scenario (RCP8.5). The changes in all examined hydrological components are greater in the combined land-use and climate change experiment. Results are further presented in a water resources context, aiming to address potential implications of climate change and land-use change from a water demand perspective. We conclude that future water demands in the Upper Ganges region for winter months may not be met.

scholarly journals Contributions of Climate Change, CO2, Land-Use Change, and Human Activities to Changes in River Flow across 10 Chinese Basins

2018 ◽  
Vol 19 (11) ◽  
pp. 1899-1914 ◽  
Author(s):  
Yi Xi ◽  
Shushi Peng ◽  
Philippe Ciais ◽  
Matthieu Guimberteau ◽  
Yue Li ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Water Resources ◽  
Land Use Change ◽  
Water Consumption ◽  
Land Surface ◽  
River Flow ◽  
Yellow River ◽  
Co2 Concentration ◽  
Surface Model

Abstract As an essential source of freshwater river flow comprises ~80% of the water consumed in China. Per capita water resources in China are only a quarter of the global average, and its economy is demanding in water resources; this creates an urgent need to quantify the factors that contribute to changes in river flow. Here, we used an offline process-based land surface model (ORCHIDEE) at high spatial resolution (0.1° × 0.1°) to simulate the contributions of climate change, rising atmospheric CO2 concentration, and land-use change to the change in natural river flow for 10 Chinese basins from 1979 to 2015. We found that climate change, especially an increase in precipitation, was responsible for more than 90% of the changes in natural river flow, while the direct effect of rising CO2 concentration and land-use change contributes at most 6.3%. Nevertheless, rising CO2 concentration and land-use change cannot be neglected in most basins as these two factors significantly change transpiration. From 2003 to 2015, the increase in water consumption offset more than 30% of the increase in natural river flow in northern China, especially in the Yellow River basin (~140%), but it had little effect on observed river flow in southern China. Although the uncertainties of rainfall data and the statistical water consumption data could propagate the uncertainties in simulated river flow, this study could be helpful for water planning and management in China under the context of global warming.

scholarly journals Land-use change may exacerbate climate change impacts on water resources in the Ganges basin

2017 ◽  
pp. 1-33
Author(s):  
Gina Tsarouchi ◽  
Wouter Buytaert
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Soil Moisture ◽  
Water Resources ◽  
Land Use Change ◽  
River Basin ◽  
Land Surface ◽  
Surface Model ◽  
Climate Change Scenarios ◽  
Northern India

Quantifying how land-use change and climate change affect water resources is a challenge in hydrological science. The Upper Ganges (UG) river basin in northern India experiences monsoon flooding almost every year. Studies have shown evidence of strong coupling between the land surface (soil moisture) and atmosphere (precipitation) in northern India, which means that regional climate variations and changes in land use/cover could influence the temporal dynamics of land-atmosphere interactions. <br><br> This work aims to quantify how future projections of land-use and climate change are affecting the hydrological response of the UG river basin. Two different sets of modelling experiments were run using the JULES Land Surface Model and covering the period 2000&amp;ndash;2035: In the first set, climate change is taken into account, as JULES was driven by the CMIP5 (Coupled Model Intercomparison Project Phase 5) outputs of 21 models, under two Representative Concentration Pathways (RCP4.5 &amp; RCP8.5), whilst land use was kept constant at year 2010. In the second set, both climate change and land-use change were taken into consideration, as apart from the CMIP5 model outputs, JULES was also forced with a time-series of 15 future land-use scenarios, based on Landsat satellite imagery and Markov chain simulation. Variations in hydrological variables (stream flow, evapotranspiration and soil moisture) are calculated during the simulation period. <br><br> Significant changes in the near-future (years 2030&amp;ndash;2035) hydrologic fluxes arise under future land cover and climate change scenarios pointing towards a severe increase in high extremes of flow: the multi-model mean of the 95th percentile of streamflow [Q<sub>5</sub>] is projected to increase by 63&amp;thinsp;% under the combined land-use and climate change high emissions scenario [RCP8.5]. The changes in all examined hydrological components are greater in the combined land-use and climate change experiment. <br><br> Results are further presented in a water resources context, aiming to address potential implications of climate change from a water-demand perspective, highlighting that that demand thresholds in the UG region are projected to be exceeded in the future winter months (Dec&amp;ndash;Feb).

scholarly journals Climate pattern-scaling set for an ensemble of 22 GCMs – adding uncertainty to the IMOGEN version 2.0 impact system

2018 ◽  
Vol 11 (2) ◽  
pp. 541-560 ◽  
Author(s):  
Przemyslaw Zelazowski ◽  
Chris Huntingford ◽  
Lina M. Mercado ◽  
Nathalie Schaller
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Land Surface ◽  
Land Surface Model ◽  
Individual Performance ◽  
Balance Model ◽  
Surface Model ◽  
Near Surface ◽  
Global Circulation Models ◽  
Version 2.0

Abstract. Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energy-balance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34.25±5.21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85.44±4.37 and 14.98±4.61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed.

Pastoralists-farmers’ conflicts in Nigeria’s mid-Benue Trough: Socio-ecological drivers and pathways to addressing the conflicts.

2021 ◽  
Author(s):  
Chukwudi Njoku ◽  
Francis Okpiliya ◽  
Joel Efiong ◽  
Chinwe Ifejika Speranza
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Surface ◽  
Land Development ◽  
Policy Formulation ◽  
Landsat 8 ◽  
Land Resource ◽  
Hotspot Analysis ◽  
Benue Trough ◽  
Year 2000

&lt;p&gt;Violent conflicts related to pastoralists-farmers&amp;#8217; interactions in Nigeria have assumed an unprecedented dimension, causing loss of lives and livelihoods. The mid-Benue trough (Benue and Taraba States) has suffered most from the conflicts. This study aims to provide knowledge on the socio-ecological drivers of pastoralists-farmers&amp;#8217; conflicts in the mid-Benue trough from the year 2000 to 2020 and to identify pathways to solving them. First, data from the Armed Conflict Location and Event Data Project were used to map the conflicts. Second, to understand the nexus of climate change, land use and the conflicts, the study analyzed satellite data of Land Surface Temperature (LST) as a proxy for climate change, using data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite and Land Use Land Cover (LULC), using LandSat 7 ETM and LandSat 8 ETM+ data, then linked them to the mapped conflicts. Third, to understand causes and impacts of the conflict on pastoralists and farmers&amp;#8217; livelihoods, 100 interviews were conducted, 50 for each group and analyzed using content analysis and descriptive statistics. Results showed that there were 2532 fatalities from 309 conflict events between pastoralists and farmers. The incidents exhibited statistically significant clustering and were minimal between the year 2000 and 2012, increasing gradually until the year 2013 when it began to rise geometrically. The Getis-Ord Gi hotspot analysis revealed the conflict hotspots to include Agatu, Oturkpo, Gwer East and Gashaka Local Government Areas. The results from the LST analysis showed that the area coverage of high LST increased from 30 percent in 2000 to 38 percent in 2020, while extremely high LST area also increased from 14 to 16 percent. A significantly high percentage of the conflicts (87 percent) occurred in areas with high LST (&gt;30&amp;#8304;C). In addition, the LULC analyses showed that built-up land area increased by 35 km&lt;sup&gt;2 &lt;/sup&gt;(0.1 percent) and dense forests reduced by 798 km&lt;sup&gt;2&lt;/sup&gt; (0.1 percent). Notably, shrublands and grasslands, which are the resource domains of the pastoralists reduced by 11,716 km&lt;sup&gt;2&amp;#160; &lt;/sup&gt;(13.1 percent) and croplands of farmers increased by 12,316 km&lt;sup&gt;2 &lt;/sup&gt;(13.8 percent)&lt;strong&gt;. &lt;/strong&gt;This presents an apparent transition of LULC from shrublands and grasslands to croplands in the area. Further analyses showed that 63 percent of the conflicts occurred in croplands and 16 percent in shrublands and grasslands. Hence, the reduction of land resource available to pastoralists and their subsequent cropland encroachment were identified as major causes of the conflict. It was therefore concluded that land development for other purposes is a major driver of pastoralists-farmers&amp;#8217; conflicts in the study area. There is thus a need to integrate conflict maps, LST and LULC dynamics to support dialogue, land use planning and policy formulation for sustainable land management to guide pastoral and farming activities.&lt;/p&gt;

scholarly journals The Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM): a diverse approach to representing terrestrial biogeography and biogeochemistry based on plant functional trade-offs

2013 ◽  
Vol 10 (6) ◽  
pp. 4137-4177 ◽  
Author(s):  
R. Pavlick ◽  
D. T. Drewry ◽  
K. Bohn ◽  
B. Reu ◽  
A. Kleidon
Keyword(s):  
Functional Diversity ◽  
Land Surface ◽  
Grid Cell ◽  
Surface Fluxes ◽  
Growth Strategies ◽  
Vegetation Model ◽  
Terrestrial Biosphere ◽  
Dynamic Global Vegetation Model ◽  
Trade Offs ◽  
Global Vegetation

Abstract. Terrestrial biosphere models typically abstract the immense diversity of vegetation forms and functioning into a relatively small set of predefined semi-empirical plant functional types (PFTs). There is growing evidence, however, from the field ecology community as well as from modelling studies that current PFT schemes may not adequately represent the observed variations in plant functional traits and their effect on ecosystem functioning. In this paper, we introduce the Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM) as a new approach to terrestrial biosphere modelling with a richer representation of functional diversity than traditional modelling approaches based on a small number of fixed PFTs. JeDi-DGVM simulates the performance of a large number of randomly generated plant growth strategies, each defined by a set of 15 trait parameters which characterize various aspects of plant functioning including carbon allocation, ecophysiology and phenology. Each trait parameter is involved in one or more functional trade-offs. These trade-offs ultimately determine whether a strategy is able to survive under the climatic conditions in a given model grid cell and its performance relative to the other strategies. The biogeochemical fluxes and land surface properties of the individual strategies are aggregated to the grid-cell scale using a mass-based weighting scheme. We evaluate the simulated global biogeochemical patterns against a variety of field and satellite-based observations following a protocol established by the Carbon-Land Model Intercomparison Project. The land surface fluxes and vegetation structural properties are reasonably well simulated by JeDi-DGVM, and compare favourably with other state-of-the-art global vegetation models. We also evaluate the simulated patterns of functional diversity and the sensitivity of the JeDi-DGVM modelling approach to the number of sampled strategies. Altogether, the results demonstrate the parsimonious and flexible nature of a functional trade-off approach to global vegetation modelling, i.e. it can provide more types of testable outputs than standard PFT-based approaches and with fewer inputs. The approach implemented here in JeDi-DGVM sets the foundation for future applications that will explore the impacts of explicitly resolving diverse plant communities, allowing for a more flexible temporal and spatial representation of the structure and function of the terrestrial biosphere.

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