Integrated Biosphere Simulator Research Articles

Soil Organic Carbon Lateral Movement Processes Integrated Into a Terrestrial Ecosystem Model

AbstractLateral movement of soil organic carbon (SOC) induced by soil erosion and runoff changes spatial distributions of SOC, and further changes the land‐atmosphere CO2 exchange and terrestrial carbon budget. However, current ecosystem models do not or only poorly integrate the process of SOC lateral movement and cannot simulate the impacts of soil erosion on the carbon cycle. This study integrated SOC erosion and deposition processes into a process‐based ecosystem model (i.e., Integrated BIosphere Simulator (IBIS)), and separately simulated the lateral movements of dissolved organic carbon (DOC) and particulate organic carbon (POC). The model was evaluated in three river basins in Northeast China that are dominated by cropland, forest, and grassland. The results showed that the model reproduced well the production, erosion, and deposition of DOC and POC. The annual SOC lateral movement (1.34–7.22 g C m−2 yr−1) induced by erosion in the three tested basins was 0.27%–1.45% of the annual net primary production. The model developed in this study has great implications for simulating the lateral movements of SOC in terrestrial ecosystems, which can improve model performance in projecting the terrestrial carbon budget.

Assessing the Performance of the South American Land Data Assimilation System Version 2 (SALDAS-2) Energy Balance across Diverse Biomes

Understanding the exchange of energy between the surface and the atmosphere is important in view of the climate scenario. However, it becomes a challenging task due to a sparse network of observations. This study aims to improve the energy balance estimates for the Amazon, Cerrado, and Pampa biomes located in South America using the radiation and precipitation forcing obtained from the Clouds and the Earth’s Radiant Energy System (CERES) and the precipitation CPTEC/MERGE datasets. We employed three surface models—Noah-MP, Community Land Model (CLSM), and Integrated Biosphere Simulator (IBIS)—and conducted modeling experiments, termed South America Land Data Assimilation System (SALDAS-2). The results showed that SALDAS-2 radiation estimates had the smallest errors. Moreover, SALDAS-2 precipitation estimates were better than the Global Land Data Assimilation System (GLDAS) in the Cerrado (MBE = −0.16) and Pampa (MBE = −0.19). Noah-MP presented improvements compared with CLSM and IBIS in 100% of towers located in the Amazon. CLSM tends to overestimate the latent heat flux and underestimate the sensible heat flux in the Amazon. Noah-MP and Ensemble outperformed GLDAS in terms latent and sensible heat fluxes. The potential of SALDAS-2 should be emphasized to provide more accurate estimates of surface energy balance.

Development of a Process‐Based N2O Emission Model for Natural Forest and Grassland Ecosystems

AbstractForests and grasslands play a significant role in global nitrous oxide (N2O) emission, while a sophisticated model is missing to interpret the microbial processes involved in N2O production and consumption in these ecosystems. A new N2O emission model, MicN model, was developed for natural forest and grassland ecosystems by integrating with Integrated BIosphere Simulator (IBIS). This new model includes four major N2O correlated processes: autotrophic nitrification, heterotrophic nitrification, nitrifier denitrification and denitrifier denitrification. Thirty‐nine observation sites, including various forest and grassland ecosystem types, were selected to calibrate and validate IBIS‐MicN model. The IBIS‐MicN model explained 89% and 85% of observed daily variability in N2O emission for all calibration and validation sites, respectively. In addition, the model successfully predicted the concentrations of two important intermediates (i.e., ammonium and nitrate) for N2O production at nine validation sites. Compared with Forest‐DNDC model, IBIS‐MicN model performed better in simulating N2O emission, soil NH4+ and NO3−concentrations. At site scale, the simulated N2O emissions by IBIS‐MicN model positively correlated with annual mean soil temperature and annual precipitation, but negatively correlated with soil pH. This study provides an alternative model to estimate global N2O emission from natural forest and grassland ecosystems and to investigate corresponding environmental regulations.

Deficiencies of Phenology Models in Simulating Spatial and Temporal Variations in Temperate Spring Leaf Phenology

AbstractSpring leaf phenology and its response to climate change have crucial effects on surface albedo, carbon balance, and the water cycle of terrestrial ecosystems. Based on long‐term (period 1963–2014) in situ observations of budburst date and leaf unfolding date of more than 300 deciduous woody species from 32 sites across the temperate zone in China, we conducted model‐data comparison of spatial and temporal variations for spring leaf phenology calculated using the phenology modules that were embed into 10 existing terrestrial ecosystem models. Our results suggested that ORganizing Carbon and Hydrology in Dynamic EcosystEms and Spatially Explicit Individual‐Based performed the best in reproducing the spatial patterns of spring leaf phenology, but tended to underestimate the temporal variations in responding to temperature warming, showing low interannual variability (IAV) and temperature sensitivity (ST). In contrast, the performances of Vegetation Integrated SImulator for Trace Gases were the best in modeling IAV and ST. BIOME3, Lund‐Potsdam‐Jena model, Joint UK Land Environment Simulator, BioGeochemical Cycles, Community Land Model, Integrated Biosphere Simulator, and Commonwealth Scientific and Industrial Research Organisation Atmosphere Biosphere Land Exchange Model failed to reproduce both the spatial and temporal patterns. Using temperature series (1960–2100) form Coupled Model Intercomparison Project Number 6 scenarios to force the 10 phenology modules, our results highlighted large uncertainties in predicting spring leaf phenology changes with the warming climate, and more work is required to deal with the deficiencies of phenology model parameters and algorithms.

Terrestrial Ecosystem Modeling with IBIS: Progress and Future Vision

Dynamic Global Vegetation Models (DGVM) are powerful tools for studying complicated ecosystem processes and global changes. This review article synthesizes the developments and applications of the Integrated Biosphere Simulator (IBIS), a DGVM, over the past two decades. IBIS has been used to evaluate carbon, nitrogen, and water cycling in terrestrial ecosystems, vegetation changes, land-atmosphere interactions, land-aquatic system integration, and climate change impacts. Here we summarize model development work since IBIS v2.5, covering hydrology (evapotranspiration, groundwater, lateral routing), vegetation dynamics (plant functional type, land cover change), plant physiology (phenology, photosynthesis, carbon allocation, growth), biogeochemistry (soil carbon and nitrogen processes, greenhouse gas emissions), impacts of natural disturbances (drought, insect damage, fire) and human induced land use changes, and computational improvements. We also summarize IBIS model applications around the world in evaluating ecosystem productivity, carbon and water budgets, water use efficiency, natural disturbance effects, and impacts of climate change and land use change on the carbon cycle. Based on this review, visions of future cross-scale, cross-landscape and cross-system model development and applications are discussed.

Weakening of carbon sink on the Qinghai–Tibet Plateau

Cold regions contain a large amount of soil organic carbon, and the warming-accelerated loss of this carbon pool could cause important feedback to climatic change. The changes of carbon budgets in cold regions are poorly quantified especially for the Qinghai–Tibet Plateau (QTP) due to limited field observation data. By considering the soil freeze–thaw process and establishing new plant functional types with localized parameters, we used the Integrated Biosphere Simulator (IBIS) model to simulate the changes of carbon budget on the QTP during 1980–2016. The model was calibrated and validated using carbon flux data from eddy covariance observations at 16 sites. The results showed that the QTP has assimilated 43.16 Tg C/yr during 1980–2016, with permafrost and non-permafrost regions accounting for approximately 15% and 85% of the carbon sink, respectively. During the past four decades, the gross primary production and ecosystem respiration have increased by 1.74 and 2.04 Tg C/yr2, resulting in that the carbon sink on the QTP has weakened during 1980–2016. Moreover, the weakening of carbon sink is more pronounced in the non-permafrost regions. We project that the ecosystems will release 12.30 and 24.40 Tg C by 2080–2100 under the moderate and high shared socio-economic pathways (SSP 370 and SSP 585), respectively. This could largely offset the carbon sink and even shift the carbon sink to carbon source on the QTP.

A Multi-Perspective Assessment Method with a Dynamic Benchmark for Human Activity Impacts on Alpine Ecosystem under Climate Change

Intense human activities and rapid climate changes both have obvious impacts on alpine ecosystems. However, the magnitudes and directions of the impacts by these two drivers remain uncertain due to a lack of a reasonable assessment method to distinguish between them. The impact of natural resilience is also generally included in the dynamics of a disturbed ecosystem and is liable to be mixed into the impact of human activity. It is urgent that we quantitatively discriminate human activity impacts on the ecosystem under climate change, especially for fast-developing alpine regions. Here, we propose an assessment method to determine human activity impacts under a dynamic climate, taking the potential net primary production (NPP) of an ecosystem as a benchmark. The potential NPP (NPPP) series under the changing climate was retrieved by an improved integrated biosphere simulator based on the initial disturbed ecosystem status of the assessment period. The actual NPP (NPPA) series monitored by remote sensing was considered as the results derived from the joint impacts of climate change, natural resilience and human activity. Then, the impact of human activity was quantified as the difference between the NPPP and NPPA. The contributions of human activity and natural forces to ecosystem NPP dynamics were then calculated separately and employed to explore the dominant driver(s). This assessment method was demonstrated in a typical alpine ecosystem in Northwest China. The results indicate that this method capably revealed the positive impacts of local afforestation and land-use optimization and the negative impacts caused by grazing during the assessment period of 2001–2017. This assessment method provides a quantitative reference for assessing the performances of ecological protections or human damage to alpine ecosystems at the regional scale.

Active forest management accelerates carbon storage in plantation forests in Lishui, southern China

China has committed to achieving peak CO2 emissions before 2030 and carbon neutrality before 2060; therefore, accelerated efforts are needed to better understand carbon accounting in industry and energy fields as well as terrestrial ecosystems. The carbon sink capacity of plantation forests contributes to the mitigation of climate change. Plantation forests throughout the world are intensively managed, and there is an urgent need to evaluate the effects of such management on long-term carbon dynamics. We assessed the carbon cycling patterns of ecosystems characterized by three typical plantation species (Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), oak (Cyclobalanopsis glauca (Thunb.) Oerst.), and pine (Pinus massoniana Lamb.)) in Lishui, southern China, by using an integrated biosphere simulator (IBIS) tuned with localized parameters. Then, we used the state-and-transition simulation model (STSM) to study the effects of active forest management (AFM) on carbon storage by combining forest disturbance history and carbon cycle regimes. 1) The carbon stock of the oak plantation was lower at an early age (<50 years) but higher at an advanced age (>50 years) than that of the Chinese fir and pine plantations. 2) The carbon densities of the pine and Chinese fir plantations peaked at 70 years (223.36 ​Mg·ha‒1) and 64 years (232.04 ​Mg·ha‒1), respectively, while the carbon density in the oak plantation continued increasing (>100 years). 3) From 1989 to 2019, the total carbon pools of the three plantation ecosystems followed an upward trend (an annual increase of 0.16–0.22 ​Tg ​C), with the largest proportional increase in the aboveground biomass carbon pool. 4) AFM increased the recovery of carbon storage after 1996 and 2009 in the pine and Chinese fir plantations, respectively, but did not result in higher growth in the oak plantation. 5) The proposed harvest planning is reasonable and conducive to maximizing the carbon sequestration capacity of the forest. This study provides an example of a carbon cycle coupling model that is potentially suitable for simulating China's plantation forest ecosystems and supporting carbon accounting to monitor peak CO2 emissions and reach carbon neutrality.

Modeling watershed carbon dynamics as affected by land cover change and soil erosion

Process-based ecosystem carbon cycle models typically incorporate vegetation growth, vegetation mortality, and soil respiration as well as the biotic and environmental drivers that influence these variables. However, few spatially explicit process models can efficiently incorporate the influence of land cover change and carbon lateral movement at regional scales or high spatial resolution. This study uses the Land Use and Carbon Scenario Simulator (LUCAS) to demonstrate the development of a fast ecosystem model that not only considers the basic carbon cycle but also incorporates the impact of land cover change, soil erosion, and soil deposition. As input to the LUCAS modeling framework, we used the integrated biosphere simulator (IBIS) to simulate a non-spatial reference carbon cycling scenario without considering land cover change for the Nisqually River watershed in the northwestern United States. We then used the Land Change Monitoring, Assessment, and Projection (LCMAP) remotely sensed 30-m sequential land cover data to generate annual land change history for the Nisqually River area from 1985 to 2017 and used the Unit Stream Powered Erosion and Deposition model (USPED) to estimate annual soil carbon lateral movement. Finally, we combined the annual carbon outputs from IBIS, the land change history from LCMAP, and the soil erosion and deposition from USPED within the LUCAS simulation framework. Results showed that from 1985 to 2017, along with the dynamic land cover changes, total ecosystem biomass carbon increased from 11.4 to 18.6 TgC, mainly due to forest growth. Total ecosystem soil carbon declined from 31.7 to 29.7 TgC, but the overall loss in soil carbon was not uniform across land cover types. Forestland (forest sector) and grassland lost carbon, while wetland, developed land and agricultural land gained carbon. Forest, grassland, and developed land lost 0.0553 TgC during the study period (1.73 Gg of C per year; 1 Gg = 0.001 Tg) from erosion, while wetland gained 0.0071 TgC (0.22 Gg C per year) from deposition. Agricultural land was neutral in terms of soil erosion.

An improved IBIS model for simulating NPP dynamics in alpine mountain ecosystems: A case study in the eastern Qilian Mountains, northeastern Tibetan Plateau

The Integrated Biosphere Simulator (IBIS) model is an effective means of simulating potential vegetation dynamics driven by climate change. However, this model neglects certain key processes and the influences of terrain and soil thickness in alpine mountain ecosystems, which leads to a poor accuracy of net primary production (NPP) simulation. Taking the eastern Qilian Mountains as a case, we revised the IBIS model by integrating terrain influences, water distribution processes, and vegetation and soil characteristics. Terrain effects on solar radiation and precipitation intensity were executed using a hill-shade value and a terrain correction factor, respectively. Then, the re-infiltration process of surface runoff within a pixel was integrated into water distribution sub-module. In the revised model (IBISi), soil hydraulic conductivity was dynamically updated based on the saturated hydraulic conductivity of soil texture and soil-pore water content, instead of a constant value. Inverse migration of soil water due to surface evaporation was also integrated to simulate its impacts on soil moisture. Considering spatial heterogeneity of soil thickness in alpine mountains, the IBISi adopts a thickness-based soil layer structure instead of a 4-meter one. The leaf area index base in herbaceous areas was optimized by a germination coefficient. A measured CO2 concentration series was utilized as an alternative to the calculated values by model prediction. Meanwhile, the key parameters of alpine mountain ecosystems were set based on field survey from the case study area. The NPP values simulated by the IBISi model were compared with the ones measured by remote sensing in areas with relatively less human activities. The results indicated that the IBISi model had a high reliability and accuracy. Thus, it is a good option for simulating NPP dynamics in alpine mountain ecosystems under climate change, and could also provide a scientific basis for assessing human activity impacts at the regional scale.

Attribution analyses of changes in alpine grasslands on the Qinghai-Tibetan Plateau

Alpine grasslands are one of the major ecosystem types on the Qinghai-Tibetan Plateau. In the past decades, the ecological structure and function of alpine grasslands have undergone great changes with intensified climate change and human activities. However, the relative contributions of natural and human factors to such changes are controversial. Based on the optimized residual method, this study evaluated the relative anthropogenic contribution to the changes in alpine grasslands on the Qinghai-Tibetan Plateau in the past twenty years (1990−2013). The Lund-potsdam-jena model (LPJ), Integrated Biosphere Simulator model (IBIS) and Terrestrial ecosystem model (TEM) were selected to simulate grassland productivity driven by climate factors. We found that the temperature on the plateau was on the rise at a rate of about 0.5°C per decade. Precipitation increased slightly, but with a large spatial difference. The population of the Tibetan Plateau increased sharply during the study period. During the two score years (1990−2013), the productivity of alpine grasslands on the Qinghai-Tibetan Plateau increased greatly, mainly (up to 74.0%) contributed by human activities. Where the change was dominated by human activities, the area with increased grassland net primary product (NPP) was greater than that with decreased NPP, indicating that the over-utilization of alpine grasslands has been effectively contained and reversed into moderate protection. For five out of the first ten years (1990−1999), human activities dominated the change of alpine grasslands (relative anthropogenic contribution around 60.1%), the NPP increased for 3 years and decreased for 2 years. After 2000, the impact of human activities increased sharply (up to 84.6%) and the anthropogenic dominating area expanded substantially, indicating that the large ecological restoration project implemented may have increased the productivity of alpine grasslands in the same period (9 out of 13 years for NPP increase). Spatial analyses comparing the two periods showed that 36.7% of areas with changed grassland NPP was dominated first by climate change and then by human activities, among which 10.1% had increased NPP and 26.6% had decreased NPP. In one word, the Qinghai-Tibetan Plateau may be approaching the turning point of the Environmental Kuznets Curve. Therefore, with further strengthened construction of ecological civilization, the ecological function of alpine grasslands would continue to be improved on the Plateau. However, we should also pay attention to the ever larger areas of human activity-induced NPP decrease. Therefore, the restoration and treatment of degraded grasslands is still pivotal to the construction of ecological security barriers and adaptative ecosystem management of grasslands on the Qinghai-Tibetan Plateau.

Substantially Greater Carbon Emissions Estimated Based on Annual Land-Use Transition Data

Quantifying land-use and land-cover change (LULCC) effects on carbon sources and sinks has been very challenging because of the availability and quality of LULCC data. As the largest estuary in the United States, Chesapeake Bay is a rapidly changing region and is affected by human activities. A new annual land-use and land-cover (LULC) data product developed by the U.S. Geological Survey Land Change Monitoring and Analysis Program (LCMAP) from 2001 to 2011 was analyzed for transitions between agricultural land, developed land, grassland, forest land and wetland. The Land Use and Carbon Scenario Simulator was used to simulate effects of LULCC and ecosystem disturbance in the south of the Chesapeake Bay Watershed (CBW) on carbon storage and fluxes, with carbon parameters derived from the Integrated Biosphere Simulator. We found that during the study period: (1) areas of forest land, disturbed land, agricultural land and wetland decreased by 90, 82, 57, and 65 km2, respectively, but developed lands gained 293 km2 (29 km2 annually); (2) total ecosystem carbon stock in the CBW increased by 13 Tg C from 2001 to 2011, mainly due to carbon sequestration of the forest ecosystem; (3) carbon loss was primarily attributed to urbanization (0.224 Tg C·yr−1) and agricultural expansion (0.046 Tg C·yr−1); and (4) estimated carbon emissions and harvest wood products were greater when estimated with the annual LULC input. We conclude that a dense time series of LULCC, such as that of the LCMAP program, may provide a more accurate accounting of the effects of land use change on ecosystem carbon, which is critical to understanding long-term ecosystem carbon dynamics.

A Processes‐Based Dynamic Root Growth Model Integrated Into the Ecosystem Model

AbstractPlant roots play a critical role in regulating the uptake of soil water and nutrients. Shifts in root growth and biomass distribution resulting from climate change can impact ecosystem water and carbon cycling. Such interactions between root growth and ecosystem functioning have not been integrated into current terrestrial ecosystem models. Here a three‐dimensional dynamic root model (DyRoot) was developed and implemented into the Integrated Biosphere Simulator (IBIS) ecosystem model for simulating root growth, architecture (i.e., fine and coarse roots), and distribution driven by soil water and nitrogen availabilities. Field root biomass observations were used for model calibration and validation. Results showed that DyRoot was able to simulate the root biomass distribution across five forest ecosystem types with good performance (R2 = 0.79, P &lt; 0.01). The validations of root distribution in three forest chronosequences suggested that DyRoot captured the changes in rooting depth with the increase in stand age. The sensitivity of simulated root distribution to soil water availability was compared against root biomass observations from two precipitation reduction experiments. Results indicated that DyRoot reasonably represented the decrease of root biomass in topsoil layers in response to the deficits of soil moisture simulated by (IBIS). The DyRoot model algorithm in this study has great implications for representing an optimized root distribution in ecosystem models, which can improve model performance by simulating the feedbacks of root evolution in response to climate change. Furthermore, allowing explicit root architecture in the DyRoot model also sheds light on depicting the responses of root traits to environmental changes.

Satellite L–band vegetation optical depth is directly proportional to crop water in the US Corn Belt

NASA's Soil Moisture Active Passive (SMAP) and ESA's Soil Moisture Ocean Salinity (SMOS) carry satellite L-band radiometers whose primary missions are to measure soil moisture. However, they also allow retrieving of vegetation optical depth (VOD), the degree to which vegetation attenuates microwave radiation. Because attenuation is primarily a function of the total amount of liquid water in a vegetation canopy that is contained within vegetation tissue, VOD could be used to monitor seasonal changes in this quantity, which we call crop water, in major agricultural regions such as the US Corn Belt. There are two main advantages of L-band VOD: it observes the entire canopy volume because soil moisture sensitivity is maintained throughout the growing season; and since it is unaffected by cloud cover there are close to daily measurements. To show its value, we compare SMAP and SMOS VOD to satellite-scale estimates of crop productivity created using the Agricultural Integrated BIosphere Simulator (Agro-IBIS) and observed weather at the South Fork SMAP Core Validation Site in the Corn Belt state of Iowa. We find that SMAP and SMOS VOD are directly proportional to crop water. New empirical models that relate crop water to crop dry mass were required to make this finding. We created these models with in situ data spanning multiple years and stages of crop development. The value of the proportionality constant (or “b-parameter”) relating VOD to crop water at the satellite scale is about half as large as previous estimates. Because L-band VOD is directly proportional to crop water at the satellite scale, and because we understand the relationship between crop water and crop dry mass, SMAP and SMOS have the potential to evaluate the large-scale performance of crop models in the Corn Belt on a near daily basis.

Dynamic forest vegetation models for predicting impacts of climate change on forests: An Indian perspective

Understanding climate change vulnerability of Indian forests has received wider attention in recent years and a number of assessments with different approaches have emerged over time. These assessments have mostly used climate-sensitive vegetation models to explain the climate change impacts. In these studies, trees constituting a particular forest are often clubbed together into small number of groups having similar functional traits referred as Plant Functional Types (PFTs). Most of the Forest Vegetation Models (FVMs) are still in their developmental stage and there have been attempts at various levels to develop more versatile and precise models. Several developing countries, including India, still lag behind in developing dynamic vegetation models (DVMs), which could be appropriate for the local applications to predict the impact on forests at regional level. This is restrained mainly because of the lack of long-term observations with respect to various interacting biotic, abiotic and climatic (or environmental) variables in a forest ecosystem, like water and nitrogen use efficiency, response to elevated concentration of CO2, nutrient cycling, net primary productivity, etc. The observations on influence of the environmental variables on forest ecosystems are available in discrete form. Existing FVMs integrate observations more appropriately for their place of origin for which they have been developed. Different types of forests in different climatic zones are supposed to respond differently to climatic changes. Hence, it is imperative that models are developed for the specific biogeographic regions in order to predict the influences more accurately. It may not be wise to use existing FVMs in their pristine form for all of the region without considering the regional influences. Various challenges associated with the usage of the generic models of external origin with special reference to Integrated Biosphere Simulator (IBIS) model - being widely used and accepted in Indian policy documents- is presented in this paper. We also discuss on the need for developing a regional FVM for climate change impact studies, so that the impact prediction is more precise and reliable.

Impact of Climate Change on Vegetation Distribution and Net Primary Productivity of Forests of Himalayan River Basins: Brahmaputra, Koshi and Indus

The impacts of climate change in terms of forest vegetation shifts and Net Primary Productivity (NPP) changes are assessed for Brahmaputra, Koshi and Indus river basins for the mid (2021-2050) and long (2071-2100) terms for RCP4.5 and RCP8.5 scenarios. Two Dynamical Global Vegetation Models (DGVMs), Integrated BIosphere Simulator (IBIS) and (Lund Postdam and Jena (LPJ), have been used for this purpose. The DGVMs are driven by the ensemble mean climate projections from 5 climate models that contributed to the CMIP5 data base. While both DGVMs project vegetation shifts in the forest areas of the basins, there are large differences in vegetation shifts projected by IBIS and LPJ. This may be attributed to differing representation of land surface processes and to differences in the number of vegetation types (Plant Functional Types) defined and simulated in the two models. However, there is some agreement in NPP changes as projected by both IBIS and LPJ, with IBIS mostly projecting a larger increase in NPP for the future scenarios. Despite the uncertainties with respect to climate change projections at river basin level and the differing impact assessments from different DGVMs, it is necessary to assess the “vulnerability” of the forest ecosystems and forest dependent communities to current climate risks and future climate change and to develop and implement resilience or adaptation measures. Assessment of the “vulnerability” and designing of the adaptation strategies could be undertaken for all the forested grids where both IBIS and LPJ project vegetation shifts.

Influence of Soil Properties in Different Management Systems: Estimating Soybean Water Changes in the Agro-IBIS Model

AbstractThe water balance in agricultural cropping systems is dependent on the physical and hydraulic characteristics of the soil and the type of farming, both of which are sensitive to the soil management. Most models that describe the interaction between the surface and the atmosphere do not efficiently represent the physical differences across different soil management areas. In this study, the authors analyzed the dynamics of the water exchange in the agricultural version of the Integrated Biosphere Simulator (IBIS) model (Agro-IBIS) in the presence of different physical soil properties because of the different long-term soil management systems. The experimental soil properties were obtained from two management systems, no tillage (NT) and conventional tillage (CT) in a long-term experiment in southern Brazil in the soybean growing season of 2009/10. To simulate NT management, this study modified the top soil layer in the model to represent the residual layer. Moreover, a mathematical adjustment to the computation of leaf area index (LAI) is suggested to obtain a better representation of the grain fill to the physiological maturity period. The water exchange dynamics simulated using Agro-IBIS were compared against experimental data collected from both tillage systems. The results show that the model well represented the water dynamics in the soil and the evapotranspiration (ET) in both management systems, in particular during the wet periods. Better results were found for the conventional tillage management system for the water balance. However, with the incorporation of a residual layer and soil properties in NT, the model improved the estimation of evapotranspiration by 6%. The ability of the Agro-IBIS model to estimate ET indicates its potential application in future climate scenarios.

Process‐based TRIPLEX‐GHG model for simulating N2O emissions from global forests and grasslands: Model development and evaluation

AbstractThe development of the new process‐based TRIPLEX‐GHG model derives from the Integrated Biosphere Simulator (IBIS), which couples nitrification and denitrification processes to quantify nitrous oxide (N2O) emissions from natural forests and grasslands. Sensitivity analysis indicates that the nitrification rate coefficient (COENR) is the most sensitive parameter to simulate N2O emissions. Accordingly, we calibrated this parameter using data from 29 global forest sites (across different latitudes) and grassland sites. The average nitrification rate coefficient gradually increases in the order of tropical forest to grassland to temperate forest to boreal forest, and giving means of 0.009, 0.03, 0.04, and 0.09, respectively. This study validated the mean value for each ecosystem at 52 sites globally. Calibration results both indicate the good performance of the model and its suitability in capturing seasonal variation and magnitude of N2O flux; however, it is limited in modeling N2O uptake and increments during periods of snowmelt. Additionally, validation results indicate that simulated and observed annual or seasonal N2O fluxes are highly correlated (R2 = 0.75; P &lt; 0.01). Consequently, our results suggest that the model is suitable in simulating N2O emissions from different forest and grassland land types under varying environmental conditions on a global scale.

Vulnerability of Forests in India: A National Scale Assessment.

Forests are subjected to stress from climatic and non-climatic sources. In this study, we have reported the results of inherent, as well as climate change driven vulnerability assessments for Indian forests. To assess inherent vulnerability of forests under current climate, we have used four indicators, namely biological richness, disturbance index, canopy cover, and slope. The assessment is presented as spatial profile of inherent vulnerability in low, medium, high and very high vulnerability classes. Fourty percent forest grid points in India show high or very high inherent vulnerability. Plantation forests show higher inherent vulnerability than natural forests. We assess the climate change driven vulnerability by combining the results of inherent vulnerability assessment with the climate change impact projections simulated by the Integrated Biosphere Simulator dynamic global vegetation model. While 46% forest grid points show high, very high, or extremely high vulnerability under future climate in the short term (2030s) under both representative concentration pathways 4.5 and 8.5, such grid points are 49 and 54%, respectively, in the long term (2080s). Generally, forests in the higher rainfall zones show lower vulnerability as compared to drier forests under future climate. Minimizing anthropogenic disturbance and conserving biodiversity can potentially reduce forest vulnerability under climate change. For disturbed forests and plantations, adaptive management aimed at forest restoration is necessary to build long-term resilience.

Global patterns of woody residence time and its influence on model simulation of aboveground biomass

AbstractWoody residence time (τw) is an important parameter that expresses the balance between mature forest recruitment/growth and mortality. Using field data collected from the literature, this study explored the global forest τw and investigated its influence on model simulations of aboveground biomass (AGB) at a global scale. Specifically, τw was found to be related to forest age, annual temperature, and precipitation at a global scale, but its determinants were different among various plant function types. The estimated global forest τw based on the filed data showed large spatial heterogeneity, which plays an important role in model simulation of AGB by a dynamic global vegetation model (DGVM). The τw could change the resulting AGB in tenfold based on a site‐level test using the Monte Carlo method. At the global level, different parameterization schemes of the Integrated Biosphere Simulator using the estimated τw resulted in a twofold change in the AGB simulation for 2100. Our results highlight the influences of various biotic and abiotic variables on forest τw. The estimation of τw in our study may help improve the model simulations and reduce the parameter's uncertainty over the projection of future AGB in the current DGVM or Earth System Models. A clearer understanding of the responses of τw to climate change and the corresponding sophisticated description of forest growth/mortality in model structure is also needed for the improvement of carbon stock prediction in future studies.