A process-based model designed for filling of large data gaps in tower-based measurements of net ecosystem productivity

In this paper we present a simple hybrid gap-filling model (GFM) designed with a minimum number of parameters necessary to capture the ecological processes important for filling medium-to-large gaps in Flux data. As the model is process-based, the model has potential to be used in filling large gaps exhibiting a broad range of micro-meteorological and site conditions. The GFM performance was evaluated using “Punch hole” and extrapolation experiments based on data collected in west-central New Brunswick. These experiments indicated that the GFM is able to provide acceptable results (r² > 0.80) when >500 data points are used in model parameterization. The GFM was shown to address daytime evolution of NEP reasonably well for a wide range of weather and site conditions. An analysis of residuals indicated that for the most part no obvious trends were evident; although a slight bias was detected in NEP with soil temperature. To explore the portability of the GFM across ecosystem types, a transcontinental validation was conducted using NEP and ancillary data from seven ecosystems along a north-south transect (i.e., temperature–moisture gradient) from northern Europe (Finland) to the Middle East (Israel). The GFM was shown to explain over 75% of the variability in NEP measured at most ecosystems, which strongly suggests that the GFM maybe successfully applied to forest ecosystems outside Canada.     

Modelling and mapping topographic variations in forest soils at high resolution: A case study

This article examines the utility of a digitally derived cartographic depth-to-water (DTW) index to model and map variations in drainage, vegetation and soil type and select soil properties within a forested area (40 ha) of the Swan Hills, Alberta, Canada. This index was derived from a LiDAR (Light Detection and Ranging) derived digital elevation model (DEM), with at least 1 ground return per m². The resulting DTW pattern was set to be zero along all DEM-derived flow channels, each with a 4 ha flow-initiation threshold. Soil type (luvisol, gleysol, mesisol), drainage type (very poor to excessive), vegetation type (hydric to xeric) and forest floor depth were determined along hillslope transects. These determinations conformed more closely to the DEM-derived log₁₀(DTW) variations (R² > 60%) than to the corresponding variations of the widely used topographic wetness index (TWI) (R² < 25%). Setting log₁₀(DTW) thresholds to represent the wet to moist to dry transitions between vegetation, drainage and soil type enabled a high-resolution mapping of these types across the study area. Also determined were soil moisture content, coarse fragment and soil particle composition (sand, silt, clay), pH, total C, N, S, P, Ca, Mg, K, Fe, Al, Mn, Zn, and available Ca, Mg, K, P and NH₄, by soil layer type and depth. Most of these variables were also more correlated with log₁₀(DTW) than with TWI, with and without soil layer depth as an additional regression variable. These variables are, therefore, subject to topographic controls to at least some extent, and can be modelled and mapped accordingly, as illustrated for soil moisture, forest floor depth and pH across the study area, from ridge tops to depressions. Further examinations revealed that the DEM-produced DTW and TWI patterns complemented one another, with DTW delineating soils in relation to local water-table influences, and with TWI delineating where the water would flow and accumulate.     

Using the soil and water assessment tool to estimate achievable water quality targets through implementation of beneficial management practices in an agricultural watershed

Runoff from crop production in agricultural watersheds can cause widespread soil loss and degradation of surface water quality. Beneficial management practices (BMPs) for soil conservation are often implemented as remedial measures because BMPs can reduce soil erosion and improve water quality. However, the efficacy of BMPs may be unknown because it can be affected by many factors, such as farming practices, land-use, soil type, topography, and climatic conditions. As such, it is difficult to estimate the impacts of BMPs on water quality through field experiments alone. In this research, the Soil and Water Assessment Tool was used to estimate achievable performance targets of water quality indicators (sediment and soluble P loadings) after implementation of combinations of selected BMPs in the Black Brook Watershed in northwestern New Brunswick, Canada. Four commonly used BMPs (flow diversion terraces [FDTs], fertilizer reductions, tillage methods, and crop rotations), were considered individually and in different combinations. At the watershed level, the best achievable sediment loading was 1.9 t ha(-1) yr(-1) (89% reduction compared with default scenario), with a BMP combination of crop rotation, FDT, and no-till. The best achievable soluble P loading was 0.5 kg ha(-1) yr(-1) (62% reduction), with a BMP combination of crop rotation and FDT and fertilizer reduction. Targets estimated through nonpoint source water quality modeling can be used to evaluate BMP implementation initiatives and provide milestones for the rehabilitation of streams and rivers in agricultural regions.     

Dissolved organic carbon concentrations and fluxes in forest catchments and streams: DOC-3 model

Dissolved organic carbon (DOC) concentrations in south-western Nova Scotia streams, sampled at weekly to biweekly intervals, varied across streams from about 3 to 40 mg L⁻¹, being highest mid-summer to fall, and lowest during winter to spring. A 3-parameter model (DOC-3) was proposed to project daily stream DOC concentrations and fluxes from modelled estimates for daily soil temperature and moisture, year-round, and in relation to basin size and wetness. The parameters of this model refer to (i) a basin-specific DOC release parameter “k_DOC^”, related to the wet- and open-water area percentages per basin, (ii) the lag time “τ” between DOC production and subsequent stream DOC emergence, related to the catchment area above the stream sampling location; and (iii) the activation energy “Ea”, to deal with the temperature effect on DOC production. This model was calibrated with the 1988-2006 DOC concentration data from three streams (Pine Marten, Moosepit Brook, and the Mersey River sampled at or near Kejimkujik National Park, or KNP), and was used to interpret the biweekly 1999-2003 DOC concentrations data (stream, ground and lake water, soil lysimeters) of the Pockwock-Bowater Watershed Project near Halifax, Nova Scotia. The data and the model revealed that the DOC concentrations within the streams were not correlated to the DOC concentrations within the soil- and groundwater, but were predictable based on (i) the hydrologically inferred weather-induced changes in soil moisture and temperature next to each stream, and (ii) the topographically inferred basin area and wet- and open-water area percentages associated with each stream (R² = 0.53; RMSE = 3.5 mg L⁻¹). Model-predicted fluxes accounted 74% of the hydrometrically determined DOC exports at KNP.     

Litter decomposition and nitrogen mineralization from an annual to a monthly model

The forest litter decomposition model (FLDM) described in this paper provides an important basis for assessing the impacts of forest management on seasonal stream water quality and export of dissolved organic carbon (DOC). By definition, models with annual time steps are unable to capture seasonal, within-year variation. In order to simulate seasonal variation in litter decomposition and DOC production and export, we have modified an existing annual FLDM to account for monthly dynamics of decomposition and residual mass in experimental litterbags placed in 21 different forests across Canada.The original annual FLDM was formulated with three main litter pools (fast, slow, and very slow decomposing litter) to address the fact that forest litter is naturally composed of a mixture of organic compounds that decompose at different rates. The annual FLDM was shown to provide better simulations than more complex models like CENTURY and SOMM.The revised monthly model retains the original structure of the annual FLDM, but separates litter decomposition from nitrogen (N) mineralization. In the model, monthly soil temperature, soil moisture, and mean January soil temperature are shown to be the most important controlling variables of within-year variation in decomposition. Use of the three variables in a process-based definition of litter decomposition is a significant departure from the empirical definition in the annual model. The revised model is shown to give similar calculations of residual mass and N concentration as the annual model (r² = 0.91, 0.78), despite producing very different timeseries of decomposition over six years. It is shown from a modelling perspective that (i) forest litter decomposition is independent of N mineralization, whereas N mineralization is dependent on litter decomposition, and (ii) mean January soil temperature defines litter decomposition in the summer because of winter-temperatures’ role in modifying forest-floor microorganism community composition and functioning in the following summer.     

How do soil and water conservation practices influence climate change impacts on potato production? Evidence from eastern Canada

We used a stochastic production function method together with a farm-level dataset covering 18 farms over a 23-year period to assess the role that soil and water conservation practices play in affecting the climate change impacts on potato yield in northwestern New Brunswick, Canada. Our analysis accounted for the yield effects of farm inputs, farm technologies, farm-specific factors, seasonal climatic variables, soil and water conservation practices, and a series of interaction terms between soil and water conservation practices and climatic variables. Regression results were used in combination with three climate change scenarios developed by the Intergovernmental Panel on Climate Change (A2, A1B, B1) and four general circulation model predictions over three 30-year time periods (2011–2040, 2041–2070, and 2071–2100) to estimate a range of potato yield projections over these time periods. Results show that accounting for soil and water conservation practices in climate–yield relationships increased the impacts of climate change on potato yield, with yield increases of up to 38 % by the 2071–2100 period. These findings provide evidence that adoption of soil and water conservation practices can help boost potato production in a changing Canadian climate.     

Modification of an ecosystem model for filling medium-sized gaps in tower-based estimates of net ecosystem productivity

Gap filling of flux data is necessary to assist with periodic interruptions in the measurement data stream. The gap-filling model (GFM), first described in Xing et al. [Xing, Z., Bourque, C.P.-A., Meng, F.-R., Zha, T.-S., Cox, R.M., Swift, E., 2007. A simple net ecosystem productivity model for gap filling of tower-based fluxes: an extension of Landsberg's equation with modifications to the light interception term. Ecol. Model. 206, 250–262], was modified to account for the day-to-day control of net ecosystem productivity (NEP) by incorporating air and soil temperature as new controlling variables in the calculation of NEP. To account for the multiple-phase influences of air and soil temperature on plant growth we model ecosystem respiration as a function of soil and canopy respiration. The paper presents model development in an incremental fashion in order to quantify the contribution of individual model enhancements to the prediction of NEP during periods when air and soil temperature variations are important.