Evaluating nitrogen removal by vegetation uptake using satellite image time series in riparian catchments.

Nitrogen (N) removal by vegetation uptake is one of the most important functions of riparian buffer zones in preventing non-point source pollution (NSP), and many studies about N uptake at the river reach scale have proven the effectiveness of plants in controlling nutrient pollution. However, at the watershed level, the riparian zones form dendritic networks and, as such, may be the predominant spatially structured feature in catchments and landscapes. Thus, assessing the functions of riparian system at the basin scale is important. In this study, a new method coupling remote sensing and ecological models was used to assess the N removal by riparian vegetation on a large spatial scale. The study site is located around the Guanting reservoir in Beijing, China, which was abandoned as the source water system for Beijing due to serious NSP in 1997. SPOT 5 data was used to map the land cover, and Landsat-5 TM time series images were used to retrieve land surface parameters. A modified forest nutrient cycling and biomass model (ForNBM) was used to simulate N removal, and the modified net primary productivity (NPP) module was driven by remote sensing image time series. Besides the remote sensing data, the necessary database included meteorological data, soil chemical and physical data and plant nutrient data. Pot and plot experiments were used to calibrate and validate the simulations. Our study has proven that, by coupling remote sensing data and parameters retrieval techniques to plant growth process models, catchment scale estimations of nitrogen uptake rates can be improved by spatial pixel-based modelling.

Evaluation of soil nitrogen emissions from riparian zones coupling simple process-oriented models with remote sensing data.

Riparian ecosystems have critical impacts on controlling the non-point source pollution and maintaining the health of aquatic ecosystems. In this study, a process oriented soil denitrification model was extended with algorithms from a simple nitrogen (N) cycle model and coupled to land surface remote sensing data to enhance its performance in spatial and temporal prediction of gaseous N emissions from soils in the riparian buffer zone surrounding the Guanting reservoir (China). The N emission model is based on chemical and physical relationships that govern the heat budget, soil moisture variations and nitrogen movement in soils. Besides soil water and heat processes, it includes nitrification, denitrification and ammonia (NH(3)) volatilization. SPOT-5 and Landsat-5 TM satellite data were used to derive spatial land surface information and the temporal variation in land cover parameters was also used to drive the model. A laboratory-scale anaerobic incubation experiment was used to estimate the soil denitrification model parameters for the different soil types. An in situ field-scale experiment was conducted to calibrate and validate the soil temperature, moisture and nitrogen sub-models. An indirect method was used to verify simulated N emissions, resulting in a coefficient of determination of R(2)=0.83 between simulated and observed values. Then the model was applied to the whole riparian buffer zone catchment, using the spatial resolution (10m) of the SPOT-5 image. Model sensitivity analysis showed that soil moisture was the most sensitive parameter for gaseous N emissions and soil denitrification was the main process affecting N losses to the atmosphere in the riparian area. From the aspect of land use management around the Guanting reservoir, the spatial structure and distribution of land cover and land use types in the riparian area should be adapted, to enhance faster ecological restoration of the wetland ecological system surrounding this strategically important water resource.

[Effect of nitrogen removal simulated by RIP_ N model to a riparian zone in Guangting Reservoir catchment].

An eco-hydrological model system (RIP_ N) was constructed to simulate the nitrogen (N) removal by riparian zone in reservoir catchment scale. As a case study, the N removal in Guanting Reservoir riparian zone from March to September in 2007 was estimated. At same time, the field simulated experiment was carried out in Yanqing experimental station, which lies in the northeast part of the catchment. With the experimental data and previous studies, the RIP_ N model was calibrated. RIP_ N model was consisted with two parts, which were soil chemical process modeling and plant growth modeling. Soil chemical processes considered the soil denitrification, nitrification and ammonium volatilization. Plant growth included net primary productivity (NPP) module, plant production allocation module and nutrition uptake module. The research indicated that the correlation coefficient between simulated value and monitored value was larger than 0.5, which proved the effectiveness and reliability of RIP_ N model in catchment scale simulating. The simulated results showed that the N removal loss by riparian zone in Guanting Reservoir catchment from March to September was 5.91 x 10(3) t. The model also identified the N removal functions of different land use. At present land use condition, the bottomland, forest land and grassland contributed positively environmental benefits and removed most of N. In the temporal scale, the N removal from March to September consisted 76.5% of annual removal load. On the contrary, the wetlands just removed 5.9% of N of whole watershed. By comparison, the riparian zone was recognized as critical location for non point source pollution prevention.