Kamaljit Banger

Global patterns and controls of soil organic carbon dynamics as simulated by multiple terrestrial biosphere models: Current status and future directions

Abstract Soil is the largest organic carbon (C) pool of terrestrial ecosystems, and C loss from soil accounts for a large proportion of land‐atmosphere C exchange. Therefore, a small change in soil organic C (SOC) can affect atmospheric carbon dioxide (CO2) concentration and climate change. In the past decades, a wide variety of studies have been conducted to quantify global SOC stocks and soil C exchange with the atmosphere through site measurements, inventories, and empirical/process‐based modeling. However, these estimates are highly uncertain, and identifying major driving forces controlling soil C dynamics remains a key research challenge. This study has compiled century‐long (1901–2010) estimates of SOC storage and heterotrophic respiration (Rh) from 10 terrestrial biosphere models (TBMs) in the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project and two observation‐based data sets. The 10 TBM ensemble shows that global SOC estimate ranges from 425 to 2111 Pg C (1 Pg = 1015 g) with a median value of 1158 Pg C in 2010. The models estimate a broad range of Rh from 35 to 69 Pg C yr−1 with a median value of 51 Pg C yr−1 during 2001–2010. The largest uncertainty in SOC stocks exists in the 40–65°N latitude whereas the largest cross‐model divergence in Rh are in the tropics. The modeled SOC change during 1901–2010 ranges from −70 Pg C to 86 Pg C, but in some models the SOC change has a different sign from the change of total C stock, implying very different contribution of vegetation and soil pools in determining the terrestrial C budget among models. The model ensemble‐estimated mean residence time of SOC shows a reduction of 3.4 years over the past century, which accelerate C cycling through the land biosphere. All the models agreed that climate and land use changes decreased SOC stocks, while elevated atmospheric CO2 and nitrogen deposition over intact ecosystems increased SOC stocks—even though the responses varied significantly among models. Model representations of temperature and moisture sensitivity, nutrient limitation, and land use partially explain the divergent estimates of global SOC stocks and soil C fluxes in this study. In addition, a major source of systematic error in model estimations relates to nonmodeled SOC storage in wetlands and peatlands, as well as to old C storage in deep soil layers.

Global Methane and Nitrous Oxide Emissions from Terrestrial Ecosystems Due to Multiple Environmental Changes

Abstract Greenhouse gas (GHG)‐induced climate change is among the most pressing sustainability challenges facing humanity today, posing serious risks for ecosystem health. Methane (CH4) and nitrous oxide (N2O) are the two most important GHGs after carbon dioxide (CO2), but their regional and global budgets are not well known. In this study, we applied a process‐based coupled biogeochemical model to concurrently estimate the magnitude and spatial and temporal patterns of CH4 and N2O fluxes as driven by multiple environmental changes, including climate variability, rising atmospheric CO2, increasing nitrogen deposition, tropospheric ozone pollution, land use change, and nitrogen fertilizer use. The estimated CH4 and N2O emissions from global land ecosystems during 1981–2010 were 144.39 ± 12.90 Tg C/yr (mean ± 2 SE; 1 Tg = 1012 g) and 12.52 ± 0.74 Tg N/yr, respectively. Our simulations indicated a significant (P < 0.01) annually increasing trend for CH4 (0.43 ± 0.06 Tg C/yr) and N2O (0.14 ± 0.02 Tg N/yr) in the study period. CH4 and N2O emissions increased significantly in most climatic zones and continents, especially in the tropical regions and Asia. The most rapid increase in CH4 emission was found in natural wetlands and rice fields due to increased rice cultivation area and climate warming. N2O emission increased substantially in all the biome types and the largest increase occurred in upland crops due to increasing air temperature and nitrogen fertilizer use. Clearly, the three major GHGs (CH4, N2O, and CO2) should be simultaneously considered when evaluating if a policy is effective to mitigate climate change.

Complex Spatiotemporal Responses of Global Terrestrial Primary Production to Climate Change and Increasing Atmospheric CO2 in the 21st Century

Quantitative information on the response of global terrestrial net primary production (NPP) to climate change and increasing atmospheric CO2 is essential for climate change adaptation and mitigation in the 21st century. Using a process-based ecosystem model (the Dynamic Land Ecosystem Model, DLEM), we quantified the magnitude and spatiotemporal variations of contemporary (2000s) global NPP, and projected its potential responses to climate and CO2 changes in the 21st century under the Special Report on Emission Scenarios (SRES) A2 and B1 of Intergovernmental Panel on Climate Change (IPCC). We estimated a global terrestrial NPP of 54.6 (52.8–56.4) PgC yr−1 as a result of multiple factors during 2000–2009. Climate change would either reduce global NPP (4.6%) under the A2 scenario or slightly enhance NPP (2.2%) under the B1 scenario during 2010–2099. In response to climate change, global NPP would first increase until surface air temperature increases by 1.5°C (until the 2030s) and then level-off or decline after it increases by more than 1.5°C (after the 2030s). This result supports the Copenhagen Accord Acknowledgement, which states that staying below 2°C may not be sufficient and the need to potentially aim for staying below 1.5°C. The CO2 fertilization effect would result in a 12%–13.9% increase in global NPP during the 21st century. The relative CO2 fertilization effect, i.e. change in NPP on per CO2 (ppm) bases, is projected to first increase quickly then level off in the 2070s and even decline by the end of the 2080s, possibly due to CO2 saturation and nutrient limitation. Terrestrial NPP responses to climate change and elevated atmospheric CO2 largely varied among biomes, with the largest increases in the tundra and boreal needleleaf deciduous forest. Compared to the low emission scenario (B1), the high emission scenario (A2) would lead to larger spatiotemporal variations in NPP, and more dramatic and counteracting impacts from climate and increasing atmospheric CO2.

Methane emissions from global rice fields: Magnitude, spatiotemporal patterns, and environmental controls

Given the importance of the potential positive feedback between methane (CH4) emissions and climate change, it is critical to accurately estimate the magnitude and spatio-temporal patterns of CH4 emissions from global rice fields and better understand the underlying determinants governing the emissions. Here, we used a coupled biogeochemical model in combination with satellite-derived contemporary inundation area to quantify the magnitude and spatio-temporal variation of CH4 emissions from global rice fields and attribute the environmental controls of CH4 emissions during 1901-2010. Our study estimated that CH4 emissions from global rice fields varied from 18.3 ± 0.1 Tg CH4/yr (Avg. ± 1 std. dev.) under intermittent irrigation to 38.8 ± 1.0 Tg CH4/yr under continuous flooding in the 2000s, indicating that the magnitude of CH4 emissions from global rice fields was largely dependent on different water schemes. Over the past 110 years, our simulated results showed that global CH4 emissions from rice cultivation increased 85%. The expansion of rice fields was the dominant factor for the increasing trends of CH4 emissions, followed by elevated CO2 concentration, and nitrogen fertilizer use. On the contrary, climate had the negative effect on the cumulative CH4 emissions for most of the years over the study period. Our results imply that CH4 emissions from global rice fields could be reduced through implementing optimized irrigation practices. Therefore, the future magnitude of CH4 emissions from rice fields will be determined by the human demand for rice production as well as the implementation of optimized water management practices.

Impacts of climate variability and extremes on global net primary production in the first decade of the 21st century

A wide variety of studies have estimated the magnitude of global terrestrial net primary production (NPP), but its variations, both spatially and temporally, still remain uncertain. By using an improved process-based terrestrial ecosystem model (DLEM, Dynamic Land Ecosystem Model), we provide an estimate of global terrestrial NPP induced by multiple environmental factors and examine the response of terrestrial NPP to climate variability at biome and global levels and along latitudes throughout the first decade of the 21st century. The model simulation estimates an average global terrestrial NPP of 54.6 Pg C yr–1 during 2000–2009, varying from 52.8 Pg C yr–1 in the dry year of 2002 to 56.4 Pg C yr–1 in the wet year of 2008. In wet years, a large increase in terrestrial NPP compared to the decadal mean was prevalent in Amazonia, Africa and Australia. In dry years, however, we found a 3.2% reduction in global terrestrial NPP compared to the decadal mean, primarily due to limited moisture supply in tropical regions. At a global level, precipitation explained approximately 63% of the variation in terrestrial NPP, while the rest was attributed to changes in temperature and other environmental factors. Precipitation was the major factor determining inter-annual variation in terrestrial NPP in low-latitude regions. However, in mid- and high-latitude regions, temperature variability largely controlled the magnitude of terrestrial NPP. Our results imply that projected climate warming and increasing climate extreme events would alter the magnitude and spatiotemporal patterns of global terrestrial NPP.

Climate extremes dominating seasonal and interannual variations in carbon export from the Mississippi River Basin

Knowledge about the annual and seasonal patterns of organic and inorganic carbon (C) exports from the major rivers of the world to the coastal ocean is essential for our understanding and potential management of the global C budget so as to limit anthropogenic modification of global climate. Unfortunately our predictive understanding of what controls the timing, magnitude, and quality of C export is still rudimentary. Here we use a process-based coupled hydrologic/ecosystem biogeochemistry model (the Dynamic Land Ecosystem Model) to examine how climate variability and extreme events, changing land use, and atmospheric chemistry have affected the annual and seasonal patterns of C exports from the Mississippi River basin to the Gulf of Mexico. Our process-based simulations estimate that the average annual exports of dissolved organic C (DOC), particulate organic C (POC), and dissolved inorganic C (DIC) in the 2000s were 2.6 ± 0.4 Tg C yr−1, 3.4 ± 0.3 Tg C yr−1, and 18.8 ± 3.4 Tg C yr−1, respectively. Although land use change was the most important agent of change in C export over the past century, climate variability and extreme events (such as flooding and drought) were primarily responsible for seasonal and interannual variations in C export from the basin. The maximum seasonal export of DIC occurred in summer while for DOC and POC the maximum occurred in winter. Relative to the 10 year average (2001–2010), our modeling analysis indicates that the years of maximal and minimal C export cooccurred with wet and dry years (2008: 32% above average and 2006: 32% below average). Given Intergovernmental Panel on Climate Change-predicted changes in climate variability and the severity of rain events and droughts of wet and dry years for the remainder of the 21st century, our modeling results suggest major changes in the riverine link between the terrestrial and oceanic realms, which are likely to have a major impact on C delivery to the coastal ocean.

Responses of global terrestrial water use efficiency to climate change and rising atmospheric CO2 concentration in the twenty-first century

ABSTRACT Terrestrial ecosystems play a significant role in global carbon and water cycles because of the substantial amount of carbon assimilated through net primary production and large amount of water loss through evapotranspiration (ET). Using a process-based ecosystem model, we investigate the potential effects of climate change and rising atmospheric CO2 concentration on global terrestrial ecosystem water use efficiency (WUE) during the twenty-first century. Future climate change would reduce global WUE by 16.3% under high-emission climate change scenario (A2) and 2.2% under low-emission climate scenario (B1) during 2010–2099. However, the combination of rising atmospheric CO2 concentration and climate change would increase global WUE by 7.9% and 9.4% under A2 and B1 climate scenarios, respectively. This suggests that rising atmospheric CO2 concentration could ameliorate climate change-induced WUE decline. Future WUE would increase significantly at the high-latitude regions but decrease at the low-latitude regions under combined changes in climate and atmospheric CO2. The largest increase of WUE would occur in tundra and boreal needleleaf deciduous forest under the combined A2 climate and atmospheric CO2 scenario. More accurate prediction of WUE requires deeper understanding on the responses of ET to rising atmospheric CO2 concentrations and its interactions with climate.

Contemporary land cover and land use patterns in India estimated by different regional and global data sets

Discrepancies in various land cover and land use (LCLU) data sets could induce big uncertainties in assessing interactions among human activities, climate, and ecosystem. In this study, we analyzed inventory LCLU records from Department of Economics and Statistics (DES) and remote-sensing-based data sets obtained from Resourcesat-1, MODIS, Globcover, and HYDE 3.1 for 2005 in India. Based on the DES and Resourcesat-1 data sets, the best estimate of agricultural area was 143 million ha which was lower than MODIS and Globcover data sets (158–167 million ha). Global data sets have underestimated forest area by 20–30 million ha, comparing to DES and Resourcesat-1 data sets (63–66 million ha). All the remote sensing data sets showed a wide range for urban area (1.5–8.4 million ha) which was lower than inventory data sets. Our results caution scientific community and policymakers to carefully use global LCLU data sets that are significantly different from DES records in India.

Simulating nitrogen management impacts on maize production in the U.S. Midwest

Nutrient loss reduction strategies have recently been developed in the U.S. Midwest to decrease the environmental footprint associated with nitrogen (N) fertilizer use. Although these strategies generally suggest decreasing N rates and shifting the timing of N application from fall to spring, the spatiotemporal impacts of these practices on maize yield and fertilizer N use efficiency (NUE, kg grain yield increase per kg N applied) have not been assessed at the watershed scale using crop simulation models. We simulated the effects of N fertilizer rate (0, 168, 190, 224 kg N ha-1) and application timing [fall-applied N (FN): 100% N applied on 1 December; spring-applied N (SN): 100% N applied 10 days before planting; split N: 66% N applied on 1 December + 34% N applied 10 days before planting] on maize grain yield (GY) across 3042 points in Illinois during 2011–2015 using the DSSAT-CERES-Maize model. When simulations were scaled up to the watershed level, results suggest that increases in average maize GY for SN compared to FN occurred in years with higher than average winter rainfall (2011, 2013), whereas yields were similar (+/- 4%) in 2012, 2014, and 2015. Accordingly, differences in NUE for SN compared to FN were small (0.0–1.4 kg GY/kg N) when cumulative winter rainfall was < 300 mm, but increased to 0.1–9.2 kg GY/kg N when winter rainfall was > 500 mm at both 168 kg N ha-1 and 224 kg N ha-1. The combined practice of reducing N fertilizer amounts from 224 kg N ha-1 to 190 kg N ha-1 and shifting from FN to SN resulted in a wide range of yield responses during 2011–2015, with the probability of increasing yields varying from <10% to >70% of simulation points within a watershed. Positive impacts on both GY and NUE occurred in only 60% of simulations for this scenario, highlighting the challenge of simultaneously improving yield and NUE with a 15% N rate reduction in this region.