Biome-specific soil respiration (Rs) has important yet different roles in both the carbon cycle and climate change from regional to global scales. To date, no comparable studies related to global biome-specific Rs have been conducted applying comprehensive global Rs databases. The goal of this study was to develop artificial neural network (ANN) models capable of spatially estimating global Rs and to evaluate the effects of interannual climate variations on 10 major biomes. We used 1976 annual Rs field records extracted from global Rs literature to train and test the ANN models. We determined that the best ANN model for predicting biome-specific global annual Rs was the one that applied mean annual temperature (MAT), mean annual precipitation (MAP), and biome type as inputs (r 2 = 0.60). The ANN models reported an average global Rs of 93.3 ± 6.1 Pg C yr −1 from 1960 to 2012 and an increasing trend in average global annual Rs of 0.04 Pg C yr −1 . Estimated annual Rs increased with increases in MAT and MAP in cropland, boreal forest, grassland, shrubland, and wetland biomes. Additionally, estimated annual Rs decreased with increases in MAT and increased with increases in MAP in desert and tundra biomes, and only significantly decreased with increases in MAT (r 2 = 0.87) in the savannah biome. The developed biome-specific global Rs database for global land and soil carbon models will aid in understanding the mechanisms underlying variations in soil carbon dynamics and in quantifying uncertainty in the global soil carbon cycle.

No abstract

No abstract

No abstract

Model prediction of soil drainage classes based on digital elevation model parameters and soil attributes from coarse resolution soil maps. Can. J. Soil Sci. 88: 787Á799. High-resolution soil drainage maps are important for crop production planning, forest management, and environmental assessment. Existing soil classification maps tend to only have information about the dominant soil drainage conditions and they are inadequate for precision forestry and agriculture planning purposes. The objective of this research was to develop an artificial neural network (ANN) model for producing soil drainage classification maps at high resolution. Soil profile data extracted from coarse resolution soil maps (1:1 000 000 scale) and topographic and hydrological variables derived from digital elevation model (DEM) data (1:35 000 scale) were considered as candidates for inputs. A high-resolution soil drainage map (1:10 000) of the Black Brook Watershed (BBW) in northwestern New Brunswick (NB), Canada, was used to train and validate the ANN model. Results indicated that the best ANN model included average soil drainage classes, average soil sand content, vertical slope position (VSP), sediment delivery ratio (SDR) and slope steepness as inputs. It was found that 52% of model-predicted drainage classes were exactly the same as field assessment observations and 94% of model-predicted drainage classes were within91 class. In comparison, only 12% of maps indicated drainage classes were the same as field assessment observations based on coarse resolution soil maps and only 55% of points were within 91 class of field assessed drainage classes. Results indicated that the model could be used to produce high-resolution soil drainage maps at relatively low cost.Key words: Soil drainage, artificial neural network model, ANN model, high-resolution soil maps, DEM, hydrology model Zhao, Z., Chow, T. L., Yang, Q., Rees, H. W., Benoy, B., Xing, Z. et Meng, F. R. 2008. Mode`le pre´visionnel des classes de drainage du sol d'apre`s les parame`tres du MAN et les attributs des cartes pe´dologiques a`faible re´solution. Can. J. Soil Sci. 88: 787Á799. Les cartes de drainage du sol a`haute re´solution reveˆtent une grande importance pour la planification des cultures, la gestion des foreˆts et l'e´valuation de l'environnement. Les cartes de classification des sols actuelles ont tendance a`ne fournir de renseignements que sur les principales conditions de drainage et ne conviennent pas a`la planification de la foresterie et de l'agriculture de pre´cision. L'e´tude devait cre´er un mode`le a`re´seau neuronal artificiel (RNA) permettant une classification a`haute re´solution du drainage du sol. Les auteurs ont examine´si on pouvait utiliser a`cette fin les donne´es sur le profil du sol tire´es des cartes a`faible re´solution (e´chelle 1:1 000 000) ainsi que les variables topographiques et hydrologiques du mode`le altime´trique nume´rique (MAN) (e´chelle 1:35 000). Ils ont ensuite utilise´la carte a`haute re´solution de drainage du sol (1:10 000) du bassin...

Digital elevation model (DEM) is often used for hydrologic modeling, land use planning, engineering design and environmental protection. Research is required to assess the need of updating existing conventional DEM using higher resolution and more accurate DEMs, including light detection and ranging (LiDAR) DEM. The objective of this study was to evaluate effects of DEM accuracy and resolution on hydrologic parameters and modeling in an agriculture-dominated watershed. DEMs compared included 1 m and 10 m LiDAR based DEMs, and a conventional 10 m DEM obtained with aerial photogrammetry method. Hydrologic parameters assessed included elevation, sub-basin area and boundaries, drainage networks, slope and slope length. DEM derived hydrological parameters were used to estimate soil loss in Black Brook Watershed, New Brunswick using Revised Universal Soil Loss Equation (RUSLE). Results indicated that DEM resolution had substantial influence on the sub-basins boundaries, sub-basin area, and distribution of water flow lines. Field investigation confirmed that most of the water flow lines derived from 1 m LiDAR based DEM were accurate and a number of flow diversion terraces (FDT) failures had been identified with help of LiDAR 1 m DEM. Both Z. Zhao et al. conventional and LiDAR based 10 m DEM could not identify the impacts of soil conservation structures such as diversion terraces. The RUSLE predicted soil loss using 1 m LiDAR based DEM was considered to be better because both conventional and LiDAR based 10 m DEMs could not reflect the impact of FDTs on reducing soil loss.

Abstract:Flow diversion terraces (FDT) are commonly used beneficial management practice (BMP) for soil conservation on sloped terrain susceptible to water erosion. A simple GIS-based soil erosion model was designed to assess the effectiveness of the FDT system under different climatic, topographic, and soil conditions at a sub-basin level. The model was used to estimate the soil conservation support practice factor (P-factor), which inherently considered two major outcomes with its implementation, namely (1) reduced slope length, and (2) sediment deposition in terraced channels. A benchmark site, the agriculture-dominated watershed in northwestern New Brunswick (NB), was selected to test the performance of the model and estimated P-factors. The estimated P-factors ranged from 0Ð38-1Ð0 for soil conservation planning objectives and ranged from 0Ð001 to 0Ð45 in sediment yield calculations for water-quality assessment. The model estimated that the average annual sediment yield was 773 kg ha 1 yr 1 compared with a measured value of 641 kg ha 1 yr 1 . The P-factors estimated in this study were comparable with predicted values obtained with the revised universal soil loss equation (RUSLE2). The P-factors from this study have the potential to be directly used as input in hydrological models, such as the soil and water assessment tool (SWAT), or in soil conservation planning where only conventional digital elevation models (DEMs) are available.

network models to produce soil organic carbon content distribution maps across landscapes. Can. J. Soil Sci. 90: 75Á87. Soil organic carbon (SOC) content is an important soil quality indicator that plays an important role in regulating physical, chemical and biological properties of soil. Field assessment of SOC is time consuming and expensive. It is difficult to obtain highresolution SOC distribution maps that are needed for landscape analysis of large areas. An artificial neural network (ANN) model was developed to predict SOC based on parameters derived from digital elevation model (DEM) together with soil properties extracted from widely available coarse resolution soil maps (1:1 000 000 scale). Field estimated SOC content data extracted from high-resolution soil maps (1:10 000 scale) in Black Brook Watershed in northwestern New Brunswick, Canada, were used to calibrate and validate the model. We found that vertical slope position (VSP) was the most important variable that determines distributions of SOC across the landscape. Other variables such as slope steepness, and potential solar radiation (PSR) also had significant influence on SOC distributions. The prediction of the selected two-input-node SOC model (VSP and coarse resolution soil map recorded SOC as inputs) had a correlation coefficient of 0.92 with measured values, and model predicted SOC values had 47.9% of the total points within 90.5% of the measured values and 70.6% within 91% of the measured values. The prediction of the selected four-input-node model (VSP, slope steepness, PSR and coarse resolution SOC values as inputs) had a correlation coefficient of 0.98 with measured values and model predicted SOC values had 75% of the total points within 90.5% of the measured values and 87% within 91% of the measured values. The prediction of the five-input-nodes model with soil drainage as additional input had a correlation coefficient of 0.99 with measured values, and model predicted SOC values had 87% of the total points within 90.5% of the measured values and 98% of the total points within 91% of the measured values. The calibrated SOC prediction model was used to produce a high-resolution SOC map for the Black Brook Watershed and the resulting SOC distribution map is considered to be realistic. Results indicated that DEM-derived hydrological parameters together with widely available coarse resolution soil map data could be used to produce high-resolution SOC maps with the ANN method. For personal use only.