Shengli Huang

Mapping the mean annual precipitation of China using local interpolation techniques

Spatially explicit precipitation data are required in the research of hydrology, agriculture, ecology, and environmental sciences. In this study, two established techniques of local ordinary linear regression (OLR) and geographically weighted regression (GWR) and two new local hybrid interpolation techniques of local regression-kriging (LRK) and geographically weighted regression kriging (GWRK) were compared to predict the spatial distribution of mean annual precipitation of China. Precipitation data from 684 meteorological stations were used in the analysis, and a stepwise regression analysis was used to select six covariates, including longitude, latitude, elevation, slope, surface roughness, and river density. The four spatial prediction methods (OLR, GWR, LRK, and GWRK) were implemented with local regression techniques with different number of neighbors (50, 100, 150, and 200). The prediction accuracy was assessed at validation sites with the root mean squared deviation, mean estimation error, and R-square values. The results showed that LRK outperforms OLR and GWRK outperforms GWR, indicating that adding the kriging of regression residuals can help improve the prediction performance. GWRK gives the best prediction but the accuracy of estimation varies with the number of neighborhood points used for modeling. Although LRK is outperformed by GWRK, LRK is still recommended as a powerful and practical interpolation method given its computation efficiency. However, if LRK and GWRK are used to extrapolate prediction values, post-processing in the areal interpolation will be needed.

Simulating the water budget of a Prairie Potholes complex from LiDAR and hydrological models in North Dakota, USA

Abstract Hydrological processes of the wetland complex in the Prairie Pothole Region (PPR) are difficult to model, partly due to a lack of wetland morphology data. We used Light Detection And Ranging (LiDAR) data sets to derive wetland features; we then modelled rainfall, snowfall, snowmelt, runoff, evaporation, the “fill-and-spill” mechanism, shallow groundwater loss, and the effect of wet and dry conditions. For large wetlands with a volume greater than thousands of cubic metres (e.g. about 3000 m3), the modelled water volume agreed fairly well with observations; however, it did not succeed for small wetlands (e.g. volume less than 450 m3). Despite the failure for small wetlands, the modelled water area of the wetland complex coincided well with interpretation of aerial photographs, showing a linear regression with R2 of around 0.80 and a mean average error of around 0.55 km2. The next step is to improve the water budget modelling for small wetlands. Editor Z.W. Kundzewicz; Associate editor X. Chen Citation Huang, S.L., Young, C., Abdul-Aziz, O.I., Dahal, D., Feng, M., and Liu, S.G., 2013. Simulating the water budget of a Prairie Potholes complex from LiDAR and hydrological models in North Dakota, USA. Hydrological Sciences Journal, 58 (7), 1434–1444.

The General Ensemble Biogeochemical Modeling System (GEMS) and its Applications to Agricultural Systems in the United States

The General Ensemble Biogeochemical Modeling System (GEMS) was es in individual models, it uses multiple site-scale biogeochemical models to perform model simulations. Second, it adopts Monte Carlo ensemble simulations of each simulation unit (one site/pixel or group of sites/pixels with similar biophysical conditions) to incorporate uncertainties and variability (as measured by variances and covariance) of input variables into model simulations. In this chapter, we illustrate the applications of GEMS at the site and regional scales with an emphasis on incorporating agricultural practices. Challenges in modeling soil carbon dynamics and greenhouse emissions are also discussed.

Spatially explicit surface energy budget and partitioning with remote sensing and flux measurements in a boreal region of Interior Alaska

Extrapolating energy fluxes between the ground surface and the atmospheric boundary layer from point-based measurements to spatially explicit landscape estimation is critical to understand and quantify the energy balance components and exchanges in the hydrosphere, atmosphere, and biosphere. This information is difficult to quantify and are often lacking. Using a Landsat image (acquired on 5 August 2004), the flux measurements from three eddy covariance flux towers (a 1987 burn, a 1999 burn, and an unburned control site) and a customized satellite-based surface energy balance model of Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC), we estimated net radiation, sensible heat flux (H), latent heat flux (LE), and soil heat flux (G) for the boreal Yukon River Basin of Interior Alaska. The model requires user selection of two extreme conditions present within the image area to calibrate and anchor the sensible flux output. One is the “hot” condition which refers to a bare soil condition with specified residual evaporation rates. Another one is the “cold” condition which refers to a fully transpiring vegetation such as full-cover agricultural crops. We selected one bare field as the “hot” condition while we explored three different scenarios for the “cold” pixel because of the absence of larger expanses of agricultural fields within the image area. For this application over boreal forest, selecting agricultural fields whose evapotranspiration was assumed to be 1.05 times the alfalfa-based reference evapotranspiration as the “cold” pixel could result in large errors. Selecting an unburned flux tower site as the “cold” pixel could achieve acceptable results, but uncertainties remain about the energy balance closure of the flux towers. We found that METRIC performs reasonably well in partitioning energy fluxes in a boreal landscape.

Projecting the spatiotemporal carbon dynamics of the Greater Yellowstone Ecosystem from 2006 to 2050

BackgroundClimate change and the concurrent change in wildfire events and land use comprehensively affect carbon dynamics in both spatial and temporal dimensions. The purpose of this study was to project the spatial and temporal aspects of carbon storage in the Greater Yellowstone Ecosystem (GYE) under these changes from 2006 to 2050. We selected three emission scenarios and produced simulations with the CENTURY model using three General Circulation Models (GCMs) for each scenario. We also incorporated projected land use change and fire occurrence into the carbon accounting.ResultsThe three GCMs showed increases in maximum and minimum temperature, but precipitation projections varied among GCMs. Total ecosystem carbon increased steadily from 7,942 gC/m2 in 2006 to 10,234 gC/m2 in 2050 with an annual rate increase of 53 gC/m2/year. About 56.6% and 27% of the increasing rate was attributed to total live carbon and total soil carbon, respectively. Net Primary Production (NPP) increased slightly from 260 gC/m2/year in 2006 to 310 gC/m2/year in 2050 with an annual rate increase of 1.22 gC/m2/year. Forest clear-cutting and fires resulted in direct carbon removal; however, the rate was low at 2.44 gC/m2/year during 2006–2050. The area of clear-cutting and wildfires in the GYE would account for 10.87% of total forested area during 2006–2050, but the predictive simulations demonstrated different spatial distributions in national forests and national parks.ConclusionsThe GYE is a carbon sink during 2006–2050. The capability of vegetation is almost double that of soil in terms of sequestering extra carbon. Clear-cutting and wildfires in GYE will affect 10.87% of total forested area, but direct carbon removal from clear-cutting and fires is 109.6 gC/m2, which accounts for only 1.2% of the mean ecosystem carbon level of 9,056 gC/m2, and thus is not significant.