Prediction of biome-specific potential evapotranspiration in mongolia under a scarcity of weather data

We propose practical guidelines to predict biome-specific potential evapotranspiration (ETp) from the knowledge of grass-reference evapotranspiration (ET0) and a crop coefficient (Kc) in Mongolia. A paucity of land-based weather data hampers use of the Penman–Monteith equation (FAO-56 PM) based on the Food and Agriculture Organization (FAO) guidelines to predict daily ET0. We found that the application of the Hargreaves equation provides ET0 estimates very similar to those from the FAO-56 PM approach. The Kc value is tabulated only for crops in the FAO-56 guidelines but is unavailable for steppe grasslands. Therefore, we proposed a new crop coefficient, Kc adj defined by (a) net solar radiation in the Gobi Desert (Kc adjD) or (b) leaf area index in the steppe region (Kc adjS) in Mongolia. The mean annual ETp obtained using our approach was compared to that obtained by FAO-56 guidelines for forages (not steppe) based on tabulated Kc values in 41 lo-cations in Mongolia. We found the differences are acceptable (RMSE of 0.40 mm d−1) in northern Mongolia under high vegetation cover but rather high (RMSE of 1.69 and 2.65 mm d−1) in central and southern Mongolia. The FAO aridity index (AI) is empirically related to the ETp/ET0 ratio. Ap-proximately 80% and 54% reduction of ET0 was reported in the Gobi Desert and in the steppe loca-tions, respectively. Our proposed Kc adj can be further improved by considering local weather data and plant phenological characteristics

IRRIGATED AND RAINFED CROPS Zea mays L. (MAIZE) AND Glycine max (SOYBEAN) ACTING AS A SOURCE OR SINK FOR ATMOSPHERIC WARMING AT MEAD, NEBRASKA

Land Use and Land Cover Change (LULCC) influence the climate at a global and local scale. Using long term microclimate data (2002-2009, 2011-2012) from the Carbon Sequestration Project (CSP), Mead, NE, this study examines how crop selection and water management can mitigate heat in the atmosphere. Mitigation of global warming is dependent on the management of crop lands, and the amount and timing of rainfall during the growing season. Rainfed crops were found to heat the passing air. The irrigated maize crop was able to mitigate 20 to 62% of the sensible heat (H) compared to the rainfed maize counterpart, the lower value for wet years and the larger value for dry years. Soybeans under irrigation, on the other hand, extracted a maximum of 37% of cumulated H in comparison to rainfed soybean. The irrigated maize field can reduce the warming by as much as 76% compared to the rainfed soybean crop. In addition to increasing yields, irrigation of maize greatly reduces the heating of air, thus moderating regional climate in east central Nebraska

Fitting measured evapotranspiration data to the FAO56 dual crop coefficient method

The FAO-56 publication of the UN Food and Agriculture Organization contains guidelines on constructing and applying a ‘dual crop coefficient’ method to characterize the behavior of evapotranspiration (ET) on a day to day basis. The dual crop coefficient (Kc) method substantially improves the ability to fit simulated with measured data, as compared to the ‘single’ Kc method, by partitioning evaporation from soil (Es) from transpiration from vegetation. This permits the separate estimation of Es when there are known wetting events from precipitation and irrigation and assists in explaining behavior of measured data. The application of the dual Kc method is relatively straight forward, especially when applied using the straight-line segment method for the basal Kc curve, Kcb. Illustrations are given on fitting the dual Kc method and Kcb curve to daily ET data for irrigated and rainfed corn crops near Mead, Nebraska measured by eddy covariance and sensitivity to various soil and root zone parameters. Assessment of transferring Kcb curve parameters to other fields and years indicates that soil and root zone parameters are relatively transferrable with little modification, whereas lengths of the four crop growth stages do vary from year to year due to differences in cultivar type and possibly differences in weather

Prediction of Biome-Specific Potential Evapotranspiration in Mongolia under a Scarcity of Weather Data

We propose practical guidelines to predict biome-specific potential evapotranspiration (ETp) from the knowledge of grass-reference evapotranspiration (ET0) and a crop coefficient (Kc) in Mongolia. A paucity of land-based weather data hampers use of the Penman–Monteith equation (FAO-56 PM) based on the Food and Agriculture Organization (FAO) guidelines to predict daily ET0. We found that the application of the Hargreaves equation provides ET0 estimates very similar to those from the FAO-56 PM approach. The Kc value is tabulated only for crops in the FAO-56 guidelines but is unavailable for steppe grasslands. Therefore, we proposed a new crop coefficient, Kc adj defined by (a) net solar radiation in the Gobi Desert (Kc adjD) or (b) leaf area index in the steppe region (Kc adjS) in Mongolia. The mean annual ETp obtained using our approach was compared to that obtained by FAO-56 guidelines for forages (not steppe) based on tabulated Kc values in 41 locations in Mongolia. We found the differences are acceptable (RMSE of 0.40 mm d-1) in northern Mongolia under high vegetation cover but rather high (RMSE of 1.69 and 2.65 mm d-1) in central and southern Mongolia. The FAO aridity index (AI) is empirically related to the ETp/ET0 ratio. Approximately 80% and 54% reduction of ET0 was reported in the Gobi Desert and in the steppe locations, respectively. Our proposed Kc adj can be further improved by considering local weather data and plant phenological characteristics

Scaling up of CO\u3csub\u3e2\u3c/sub\u3e fluxes from leaf to canopy in maize-based agroecosystems

Carbon dioxide fluxes are being measured in three maize-based agroecosystems in eastern Nebraska in an effort to better understand the potential for these systems to sequester carbon in the soil. Landscape-level fluxes of carbon, water and energy were measured using tower eddy covariance systems. In order to better understand the landscape-level results, measurements at smaller scales, using techniques promoted by John Norman, were made and scaled up to the landscape-level. Single leaf gas exchange properties (CO2 assimilation rate and stomatal conductance) and optical properties, direct and diffuse radiation incident on the canopy, and photosynthetically active radiation (PAR) reflected and transmitted by the canopy were measured at regular intervals throughout the growing season. In addition, soil surface CO2 fluxes were measured using chamber techniques. From leaf measurements, the responses of net CO2 assimilation rate to relevant biophysical controlling factors were quantified. Single leaf gas exchange data were scaled up to the canopy level using a simple radiative model that considers direct beam and diffuse PAR penetration into the canopy. Canopy level photosynthesis was estimated, coupled with the soil surface CO2 fluxes, and compared to measured net ecosystem CO2 exchange (NEE) values from the eddy covariance approach. Estimated values of canopy level absorbed PAR was also compared to measured values. The agreement between estimated and observed values increases our confidence in the measured carbon pools and fluxes in these agroecosystems and enhances our understanding of biophysical controls on carbon sequestration

Relationship between ecosystem productivity and photosynthetically-active radiation for northern peatlands

We analyzed the relationship between net ecosystem exchange of carbon dioxide (NEE) and irradiance (as photosynthetic photon flux density or PPFD), using published and unpublished data that have been collected during midgrowing season for carbon balance studies at seven peatlands in North America and Europe. NEE measurements included both eddy-correlation tower and clear, static chamber methods, which gave very similar results. Data were analyzed by site, as aggregated data sets by peatland type (bog, poor fen, rich fen, and all fens) and as a single aggregated data set for all peatlands. In all cases, a fit with a rectangular hyperbola (NEE = α PPFD Pmax/(α PPFD + Pmax) + R) better described the NEE-PPFD relationship than did a linear fit (NEE = β PPFD + R). Poor and rich fens generally had similar NEE-PPFD relationships, while bogs had lower respiration rates (R = −2.0μmol m−2s−1 for bogs and −2.7 μmol m−2s−1 for fens) and lower NEE at moderate and high light levels (Pmax = 5.2 μmol m−2s−1 for bogs and 10.8 μmol m−2s−1 for fens). As a single class, northern peatlands had much smaller ecosystem respiration (R = −2.4 μmol m−2s−1) and NEE rates (α = 0.020 and Pmax = 9.2μmol m−2s−1) than the upland ecosystems (closed canopy forest, grassland, and cropland) summarized by Ruimy et al. [1995]. Despite this low productivity, northern peatland soil carbon pools are generally 5–50 times larger than upland ecosystems because of slow rates of decomposition caused by litter quality and anaerobic, cold soils

Modeling Gross Primary Production of Midwestern US Maize and Soybean Croplands with Satellite and Gridded Weather Data

Gross primary production (GPP) is a useful metric for determining trends in the terrestrial carbon cycle. To estimate daily GPP, the cloud-adjusted light use efficiency model (LUEc) was developed by adapting a light use efficiency (LUE, ε) model to include in situ meteorological data and biophysical parameters. The LUEc uses four scalars to quantify the impacts of temperature, water stress, and phenology on ε. This study continues the original investigation in using the LUEc, originally limited to three AmeriFlux sites (US-Ne1, US-Ne2, and US-Ne3) by applying gridded meteorological data sets and remotely sensed green leaf area index (gLAI) to estimate daily GPP over a larger spatial extent. This was achieved by including data from four additional AmeriFlux locations in the U.S. Corn Belt for a total of seven locations. Results show an increase in error (RMSE = 3.5 g C m−2 d−1) over the original study in which in situ data were used (RMSE = 2.6 g C m−2 d−1). This is attributed to poor representation of gridded weather inputs (vapor pressure and incoming solar radiation) and application of gLAI algorithms to sites in Iowa, Minnesota, and Illinois, calibrated using data from Nebraska sites only, as well as uncertainty due to climatic variation. Despite these constraints, the study showed good correlation between measured and LUEc-modeled GPP (R2 = 0.80 and RMSE of 3.5 g C m−2 d−1). The decrease in model accuracy is somewhat offset by the ability to function with gridded weather datasets and remotely sensed biophysical data. The level of acceptable error is dependent upon the scope and objectives of the research at hand; nevertheless, the approach holds promise in developing regional daily estimates of GPP