An integrated model of soil, hydrology, and vegetation for carbon dynamics in wetland ecosystems

Abstract

[1] Wetland ecosystems are an important component in global carbon (C) cycles and may exert a large influence on global climate change. Predictions of C dynamics require us to consider interactions among many critical factors of soil, hydrology, and vegetation. However, few such integrated C models exist for wetland ecosystems. In this paper, we report a simulation model, Wetland-DNDC, for C dynamics and methane (CH 4 ) emissions in wetland ecosystems. The general structure of Wetland-DNDC was adopted from PnET-N-DNDC, a process-oriented biogeochemical model that simulates C and N dynamics in upland forest ecosystems. Several new functions and algorithms were developed for Wetland-DNDC to capture the unique features of wetland ecosystems, such as water table dynamics, growth of mosses and herbaceous plants, and soil biogeochemical processes under anaerobic conditions. The model has been validated against various observations from three wetland sites in Northern America. The validation results are in agreement with the measurements of water table dynamics, soil temperature, CH 4 fluxes, net ecosystem productivity (NEP), and annual C budgets. Sensitivity analysis indicates that the most critical input factors for C dynamics in the wetland ecosystems are air temperature, water outflow parameters, initial soil C content, and plant photosynthesis capacity. NEP and CH 4 emissions are sensitive to most of the tested input variables. By integrating the primary drivers of climate, hydrology, soil and vegetation, the Wetland-DNDC model is capable of predicting C biogeochemical cycles in wetland ecosystems.

In situ measurements of the thermal properties of a northern peatland: Implications for peatland temperature models

“…For example, methane (CH 4 ) production [Dunfield, 1993;Crill, 1991] is related exponentially to temperature, and CH 4 oxidation is exponentially/linearly related to temperature [Dunfield et al, 1993;Whalen et al, 1990] provided oxygen is not limiting [Grant, 1999]. Current peatland temperature models do not adequately simulate soil temperatures [Zhang et al, 2002;Kellner, 2001;Granberg et al, 1999]. Errors in these models may result from errors in their parameterization or errors in the representation of heat transfer processes (i.e., the model may ignore important mechanisms of heat transfer or only partly describe a mechanism).…”

“…While all peatland temperature models incorporate energy transfer by conduction, the advective liquid heat flux is incorporated within some peatland temperature models [Kellner, 2001;Grant and Roulet, 2002], but is excluded from others [Zhang et al, 2002;Granberg et al, 1999]. The vertical advective liquid heat flux has not been directly measured within poorly decomposed Sphagnum peat.…”

“…Accurate simulation of temperatures is particularly important in the upper peat layers which are the most important for peat decomposition and CH 4 production [e.g., Daulat and Clymo, 1998]. Until now, measurements of the thermal properties of peat have been limited to well-decomposed peat [de Vries, 1963;Farouki, 1986;Lauren, 1997], and the empirical equations used to parameterize current peatland temperature models have not been independently tested within poorly decomposed Sphagnum peat: the type of peat most often found near the peatland surface [Zhang et al, 2002;Kellner, 2001;Granberg et al, 1999]. Therefore detailed in situ measurements of the thermal properties of poorly decomposed Sphagnum peat are required to parameterize accurately peatland temperature models.…”

Simulating CO2 Emissions from Tropical Forest Soils by Denitrification-Nitrification Decomposition (DNDC) Model

“…Forest-DNDC was developed by integrating an upland forest model, PnET-N-DNDC [11]- [12], with a hydrology-driven model, Wetlands-DNDC [13]. The input information required for the model simulation include parameters such as the daily maximum and minimum air temperature, precipitation, humus layer type, litter layer and mineral soil pH, stone content, texture, organic matter content, forest age and type.…”

An evaluation of flood inundation mapping from MODIS and ALOS satellites for Pakistan

“…Additionally, Xiao et al (2002) used the temporal normalized difference vegetation index and the normalized difference water index derived from the SPOT VEGETATION sensor to detect the inundation and transplantation of rice. Zhang et al (2002) detected inundated areas during the flooding season in Cambodia using MODIS-based vegetation cover product. Unlike optical (visible/infrared) sensors, synthetic aperture radar (SAR) sensors, because of their capability to operate during daytime/night time and in almost all weather conditions, have emerged as one of the most important tools for providing reliable and near-real-time information on flood disasters.…”