scholarly journals Interactive comment on “MIROC-INTEG1: A global bio-geochemical land surface model with human water management, crop growth, and land-use change” by Tokuta Yokohata et al.

2020 ◽  
Author(s):  
Tokuta Yokohata
Keyword(s):  
Water Management ◽  
Land Use ◽  
Land Use Change ◽  
Land Surface ◽  
Land Surface Model ◽  
Crop Growth ◽  
Surface 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 Impacts of land surface model and land use data on WRF model simulations of rainfall and temperature over Lake Tana Basin, Ethiopia

Heliyon ◽  
2019 ◽  
Vol 5 (9) ◽  
pp. e02469 ◽  
Author(s):  
Achenafi Teklay ◽  
Yihun T. Dile ◽  
Dereje H. Asfaw ◽  
Haimanote K. Bayabil ◽  
Kibruyesfa Sisay
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Lake Tana ◽  
Model Simulations ◽  
Lake Tana Basin

scholarly journals Representation of fire, land-use change and vegetation dynamics in the Joint UK Land Environment Simulator vn4.9 (JULES)

2019 ◽  
Vol 12 (1) ◽  
pp. 179-193 ◽  
Author(s):  
Chantelle Burton ◽  
Richard Betts ◽  
Manoel Cardoso ◽  
Ted R. Feldpausch ◽  
Anna Harper ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Vegetation Cover ◽  
Land Surface ◽  
Vegetation Dynamics ◽  
Fire Disturbance ◽  
Substantial Contribution ◽  
Surface Model ◽  
The Impact

Abstract. Disturbance of vegetation is a critical component of land cover, but is generally poorly constrained in land surface and carbon cycle models. In particular, land-use change and fire can be treated as large-scale disturbances without full representation of their underlying complexities and interactions. Here we describe developments to the land surface model JULES (Joint UK Land Environment Simulator) to represent land-use change and fire as distinct processes which interact with simulated vegetation dynamics. We couple the fire model INFERNO (INteractive Fire and Emission algoRithm for Natural envirOnments) to dynamic vegetation within JULES and use the HYDE (History Database of the Global Environment) land cover dataset to analyse the impact of land-use change on the simulation of present day vegetation. We evaluate the inclusion of land use and fire disturbance against standard benchmarks. Using the Manhattan metric, results show improved simulation of vegetation cover across all observed datasets. Overall, disturbance improves the simulation of vegetation cover by 35 % compared to vegetation continuous field (VCF) observations from MODIS and 13 % compared to the Climate Change Initiative (CCI) from the ESA. Biases in grass extent are reduced from −66 % to 13 %. Total woody cover improves by 55 % compared to VCF and 20 % compared to CCI from a reduction in forest extent in the tropics, although simulated tree cover is now too sparse in some areas. Explicitly modelling fire and land use generally decreases tree and shrub cover and increases grasses. The results show that the disturbances provide important contributions to the realistic modelling of vegetation on a global scale, although in some areas fire and land use together result in too much disturbance. This work provides a substantial contribution towards representing the full complexity and interactions between land-use change and fire that could be used in Earth system models.

scholarly journals Implementation of dynamic crop growth processes into a land surface model: evaluation of energy, water and carbon fluxes under corn and soybean rotation

2013 ◽  
Vol 10 (12) ◽  
pp. 8039-8066 ◽  
Author(s):  
Y. Song ◽  
A. K. Jain ◽  
G. F. McIsaac
Keyword(s):  
Latent Heat ◽  
Land Surface ◽  
Root Distribution ◽  
Carbon Allocation ◽  
Land Surface Model ◽  
Crop Growth ◽  
Surface Model ◽  
Energy Fluxes ◽  
Growth Processes ◽  
Dynamic Crop Growth

Abstract. Worldwide expansion of agriculture is impacting the earth's climate by altering carbon, water, and energy fluxes, but the climate in turn is impacting crop production. To study this two-way interaction and its impact on seasonal dynamics of carbon, water, and energy fluxes, we implemented dynamic crop growth processes into a land surface model, the Integrated Science Assessment Model (ISAM). In particular, we implemented crop-specific phenology schemes and dynamic carbon allocation schemes. These schemes account for light, water, and nutrient stresses while allocating the assimilated carbon to leaf, root, stem, and grain pools. The dynamic vegetation structure simulation better captured the seasonal variability in leaf area index (LAI), canopy height, and root depth. We further implemented dynamic root distribution processes in soil layers, which better simulated the root response of soil water uptake and transpiration. Observational data for LAI, above- and belowground biomass, and carbon, water, and energy fluxes were compiled from two AmeriFlux sites, Mead, NE, and Bondville, IL, USA, to calibrate and evaluate the model performance. For the purposes of calibration and evaluation, we use a corn–soybean (C4–C3) rotation system over the period 2001–2004. The calibrated model was able to capture the diurnal and seasonal patterns of carbon assimilation and water and energy fluxes for the corn–soybean rotation system at these two sites. Specifically, the calculated gross primary production (GPP), net radiation fluxes at the top of the canopy, and latent heat fluxes compared well with observations. The largest bias in model results was in sensible heat flux (SH) for corn and soybean at both sites. The dynamic crop growth simulation better captured the seasonal variability in carbon and energy fluxes relative to the static simulation implemented in the original version of ISAM. Especially, with dynamic carbon allocation and root distribution processes, the model's simulated GPP and latent heat flux (LH) were in much better agreement with observational data than for the static root distribution simulation. Modeled latent heat based on dynamic growth processes increased by 12–27% during the growing season at both sites, leading to an improvement in modeled GPP by 13–61% compared to the estimates based on the original version of the ISAM.

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