In response to a congressional mandate, the US Global Change Research Program organized a National Assessment of Climate Change focusing on geographic regions (e.g. Alaska, Great Plains) and sectors ...(e.g. public health, agriculture, water resources). This paper describes methodology and results of a study by researchers at the Pacific Northwest National Laboratory contributing to the water sector analysis. The subsequent paper makes use of the water supply results to estimate the climate change impacts on irrigated agriculture. The vulnerability of water resources in the conterminous US to climate changes in 10-year periods centered on 2030 and 2095 as projected by the Hadley/United Kingdom Meteorological Office (UKMO) general circulation model (GCM; HadCM2) were modeled using the Hydrologic Unit Model for the United States (HUMUS). HUMUS, a biophysically based hydrology model, consists of a Geographical Information System (GIS) that provides data on soils, land use and climate to drive the Soil Water Assessment Tool (SWAT). The modeling was done at the scale of the eight-digit United States Geological Survey (USGS) Hydrologic Unit Area (HUA) of which there are 2101 in the conterminous US. Results are aggregated to the four- and two-digit (major water resource region, MWRR) scales for various purposes. Daily records of maximum/minimum temperature and precipitation (PPT) from 1961 to 1990 provided the baseline climate. Water yields (WY), used as a measure of water supply for irrigation, increases from the 1961–1990 baseline period over most of the US in 2030 and 2095. In 2030, WY increases in the western US and decreases in the central and southeast regions. Notably, WY increases by 139
mm (35%) from baseline in the Pacific Northwest. Driven by higher temperatures and reduced precipitation, WY is projected to decrease in the Lower Mississippi and Texas Gulf basins. The HadCM2 (2095) scenario projects a climate significantly wetter than baseline, resulting in water yield increases of 38% on average. Water yield increases are projected to be significant throughout the eastern US—39% in the Ohio basin, for example. Water yields increase significantly in the western US, as well—57 and 76% in the Upper and Lower Colorado, respectively. Climate change also affects the seasonality of the hydrologic cycle. Early snowmelt is induced in western basins, leading to dramatically increased water yields in late winter and early spring. The simulations were run at current (365
ppm) and elevated (560
ppm) atmospheric CO
2 concentrations CO
2 to account for the potential impacts of the ‘CO
2-fertilization’ effect. The effects of climate change scenario were considerably greater than those due to elevated CO
2 but the latter, overall, decreased losses and augmented increases in water yield.
Improved practices in agriculture, forestry, and land management could be used to increase soil carbon and thereby significantly reduce the concentration of atmospheric carbon dioxide. Understanding ...biological and edaphic processes that increase and retain soil carbon can lead to specific manipulations that enhance soil carbon sequestration. These manipulations, however, will only be suitable for adoption if they are technically feasible over large areas, economically competitive with alternative measures to offset greenhouse gas emissions, and environmentally beneficial. Here we present the elements of an integrated evaluation of soil carbon sequestration methods.
Nitrous oxide fluxes from soils are inherently variable in time and space. An improved understanding of this variability is needed to make accurate estimates of N2O fluxes at a regional scale. The ...objectives of this work were to (i) characterize the influence of soil-landscape combinations and N application rates on N2O emissions and to (ii) determine the contribution of these influences on the estimation of N2O emissions at the field scale. We used static chambers and gas chromatography methods to measure N2O fluxes and collected ancillary data (mineral N, water soluble C, soil water content, soil temperature) in Canada at Mundare (AB) in the aspen parkland ecoregion and at Swift Current (SK) in the short-grass prairie ecoregion. At Mundare, measurements were taken in 1995 and 1996 by landscape position and land use. At Swift Current, data were collected in 1999 and 2000 by landscape position and N rate. At Mundare, landscape position affected N2O emissions but the pattern varied seasonally. During a 46-d period in summer 1995, a flux of 430 g N2O-N ha(-1) measured in a backslope was greater than the 60 g N2O-N ha(-1) measured on average in shoulder and depressional areas. The flux pattern changed during a 43-d spring thaw of 1996 when fluxes from depressional areas were greatest (1710 g N2O-N ha(-1)). Nitrous oxide emissions from natural areas were small. The emission pattern during summer 1996 was similar to that of 1995 but the fluxes were an order of magnitude larger. At Swift Current, N2O fluxes in summer 1999 were affected by topography and N rate. Fluxes were greatest in depressional areas receiving N at 110 kg ha(-1) (3140 g N2O-N ha(-1)). Use of the area fraction occupied by each landscape position to calculate N2O flux increased the estimates of N2O fluxes at the field scale in five out of six cases. Further research of N2O fluxes in variable landscapes should help elucidate factors controlling N2O fluxes from pedon to field scale and thus translate into improved flux estimates at regional scales.
The soil C balance is determined by the difference between inputs (e.g., plant litter, organic amendments, depositional C) and outputs (e.g., soil respiration, dissolved organic C leaching, and ...eroded C). There is a need to improve our understanding of whether soil erosion is a sink or a source of atmospheric CO 2. The objective of this paper is to discover the long-term influence of soil erosion on the C cycle of managed watersheds near Coshocton, OH. We hypothesize that the amount of eroded C that is deposited in or out of a watershed compares in magnitude to the soil C changes induced via microbial respiration. We applied the erosion productivity impact calculator (EPIC) model to evaluate the role of erosion-deposition processes on the C balance of three small watersheds (approximately 1 ha). Experimental records from the USDA North Appalachian Experimental Watershed facility north of Coshocton, OH were used in the study. Soils are predominantly silt loam and have developed from loess-like deposits over residual bedrock. Management practices in the three watersheds have changed over time. Currently, watershed 118 (W118) is under a corn (Zea mays L.)-soybean (Glycine max L. Merr.) no till rotation, W128 is under conventional till continuous corn, and W188 is under no till continuous corn. Simulations of a comprehensive set of ecosystem processes including plant growth, runoff, and water erosion were used to quantify sediment C yields. A simulated sediment C yield of 43 +/- 22 kg C ha -1 year -1 compared favorably against the observed 31 +/- 12 kg C ha -1 year -1 in W118. EPIC overestimated the soil C stock in the top 30-cm soil depth in W118 by 21% of the measured value (36.8 Mg C ha -1 ). Simulations of soil C stocks in the other two watersheds (42.3 Mg C ha -1 in W128 and 50.4 Mg C ha -1 in W188) were off by 1 Mg C ha -1 . Simulated eroded C re-deposited inside (30-212 kg C ha -1 year -1 ) or outside (73-179 kg C ha -1 year -1 ) watershed boundaries compared in magnitude to a simulated soil C sequestration rate of 225 kg C ha -1 year -1 and to literature values. An analysis of net ecosystem carbon balance revealed that the watershed currently under a plow till system (W128) was a source of C to the atmosphere while the watersheds currently under a no till system (W118 and W188) behaved as C sinks of atmospheric CO 2. Our results demonstrate a clear need for documenting and modeling the proportion of eroded soil C that is transported outside watershed boundaries and the proportion that evolves as CO 2 to the atmosphere. PUBLICATION ABSTRACT
•The mpi_EPIC model employs a parallel design developed for intensive modeling.•The model dramatically accelerated global high-resolution agroecosystem modeling.•A case study of global simulation for ...a crop was conducted for productivity analysis.
Agroecosystem models that can incorporate management practices and quantify environmental effects are necessary to assess sustainability-associated food and bioenergy production across spatial scales. However, most agroecosystem models are designed for a plot scale. Tremendous computational capacity on simulations and datasets is needed when large scales of high-resolution spatial simulations are conducted. We used the message passing interface (MPI) parallel technique and developed a master–slave scheme for an agroecosystem model, EPIC on global food and bioenergy studies. Simulation performance was further enhanced by applying the Vampir framework. On a Linux-based supercomputer, Cray XT7 Titan, we used 2048 cores and successfully shortened the running time from days to 30min for a global 30years of modeling of a bioenergy crop at the resolution of half-degree (62,482 grids) with the message passing interface based EPIC (mpi_EPIC). The results illustrate that mpi_EPIC using parallel design can balance simulation workloads and facilitate large-scale, high-resolution analyses of agricultural production systems, management alternatives and environmental effects.
The potential for mitigating increasing atmospheric carbon dioxide concentrations through the use of terrestrial biological carbon (C) sequestration is substantial. Here, we estimate the amount of C ...being sequestered by natural processes at global, North American, and national US scales. We present and quantify, where possible, the potential for deliberate human actions - through forestry, agriculture, and use of biomass-based fuels - to augment these natural sinks. Carbon sequestration may potentially be achieved through some of these activities but at the expense of substantial changes in land-use management. Some practices (eg reduced tillage, improved silviculture, woody bioenergy crops) are already being implemented because of their economic benefits and associated ecosystem services. Given their cumulative greenhouse-gas impacts, other strategies (eg the use of biochar and cellulosic bioenergy crops) require further evaluation to determine whether widespread implementation is warranted.
Soil carbon sequestration (SCS) has the potential to attenuate increasing atmospheric C(O)2 and mitigate greenhouse warming. Understanding of this potential can be assisted by the use of simulation ...models. We evaluated the ability of the EPIC model to simulate corn (Zea mays L.) yields and soil organic carbon (SOC) at Arlington, WI, during 1958-1991. Corn was grown continuously on a Typic Argiudoll with three N levels: LTN1 (control), LTN2 (medium), and LTN3 (high). The LTN2 N rate started at 56 kg ha-1 (1958), increased to 92 kg ha-1 (1963), and reached 140 kg ha-1 (1973). The LTN3 N rate was maintained at twice the LTN2 level. In 1984, each plot was divided into four subplots receiving N at 0, 84, 168, and 252 kg ha-1. Five treatments were used for model evaluation. Percent errors of mean yield predictions during 1958-1983 decreased as N rate increased (LTN1 = -5.0%, LTN2 = 3.5%, and LTN3 = 1.0%). Percent errors of mean yield predictions during 1985-1991 were larger than during the first period. Simulated and observed mean yields during 1958-1991 were highly correlated (R2 = 0.961, p < 0.01). Simulated SOC agreed well with observed values with percent errors from -5.8 to 0.5% in 1984 and from -5.1 to 0.7% in 1990. EPIC captured the dynamics of SOC, SCS, and microbial biomass. Simulated net N mineralization rates were lower than those from laboratory incubations. Improvements in EPIC's ability to predict annual variability of crop yields may lead to improved estimates of SCS.
The temporal variability of soil-derived N2O emissions presents a major challenge to the accurate quantification of N2O-N losses from agroecosystems. We characterized the seasonal distribution of N2O ...emissions from two agricultural sites in the Parkland region of Alberta during 1993 and 1994. Treatments studied were fallow, and spring wheat (Triticum aestivum L.) with and without urea fertilizer, under conventional till management. Gas samples were collected from vented static soil chambers and were analyzed for N2O with a gas chromatograph equipped with a 63Ni electron capture detector. Soil water content and concentrations of NO3(-)-N, NH4(+)-N, and water-soluble organic C (WSOC) were measured several times during the season. A brief burst of N2O emission was recorded at both sites during and immediately following spring snow melt. A second period of activity occurred between mid-June and mid-July. Between 16 and 60% of estimated annual N2O-N loss occurred during spring thaw, while 80% of cumulative annual N2O-N loss had occurred by mid-July. Mean soil NO3(-)-N concentration explained up to 65% of the temporal variability in geometric mean N2O emissions. A multiple regression model that included fall soil concentrations of N03(-)-N, NH4(+)-N, and WSOC explained 94% of the variability in estimated cumulative N2O-N loss during the following spring thaw. Most N2O-N losses in the Parkland region appear to occur during spring thaw and early summer; therefore, sampling schedules need to focus on these time periods. Management practices that minimize N availability during spring thaw may be an effective mitigation strategy for this region
Concerns over climate change are motivated in large part because of their impact on human
society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it ...requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.
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