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Recent flood events in the Prairie Pothole Region of North America have stimulated interest in modeling water storage capacities of wetlands and their surrounding catchments to facilitate flood mitigation efforts. Accurate estimates of basin storage capacities have been hampered by a lack of high-resolution elevation data. In this paper, we developed a 0.5 m bare-earth model from Light Detection And Ranging (LiDAR) data and, in combination with National Wetlands Inventory data, delineated wetland catchments and their spilling points within a 196 km 2 study area. We then calculated the maximum water storage capacity of individual basins and modeled the connectivity among these basins. When compared to field survey results, catchment and spilling point delineations from the LiDAR bare-earth model captured subtle landscape features very well. Of the 11 modeled spilling points, 10 matched field survey spilling points. The comparison between observed and modeled maximum water storage had an R 2 of 0.87 with mean absolute error of 5564 m 3. Since maximum water storage capacity of basins does not translate into floodwater regulation capability, we further developed a Basin Floodwater Regulation Index. Based upon this index, the absolute and relative water that could be held by wetlands over a landscape could be modeled. This conceptual model of floodwater downstream contribution was demonstrated with water level data from 17 May 2008.

Spatiotemporal variations of wetland water in the Prairie Pothole Region are controlled by many factors; two of them are temperature and precipitation that form the basis of the Palmer Drought Severity Index (PDSI). Taking the 196 km 2 Cottonwood Lake area in North Dakota as our pilot study site, we integrated PDSI, Landsat images, and aerial photography records to simulate monthly water surface. First, we developed a new Wetland Water Area Index (WWAI) from PDSI to predict water surface area. Second, we developed a water allocation model to simulate the spatial distribution of water bodies at a resolution of 30 m. Third, we used an additional procedure to model the small wetlands (less than 0.8 ha) that could not be detected by Landsat. Our results showed that i) WWAI was highly correlated with water area with an R 2 of 0.90, resulting in a simple regression prediction of monthly water area to capture the intra-and inter-annual water change from 1910 to 2009; ii) the spatial distribution of water bodies modeled from our approach agreed well with the water locations visually identified from the aerial photography records; and iii) the R 2 between our modeled water bodies (including both large and small wetlands) and those from aerial photography records could be up to 0.83 with a mean average error of 0.64 km 2 within the study area where the modeled wetland water areas ranged from about 2 to 14 km 2. These results indicate that our approach holds great potential to simulate major changes in wetland water surface for ecosystem service; however, our products could capture neither the short-term water change caused by intensive rainstorm events nor the wetland change caused by human activities.

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Cathie, "A modeling and spatio-temporal analysis framework for monitoring environmental change using NPP as an ecosystem indicator" (2008 We present and describe a modeling and analysis framework for monitoring protected area (PA) ecosystems with net primary productivity (NPP) as an indicator of health. It brings together satellite data, an ecosystem simulation model (NASA-CASA), spatial linear models with autoregression, and a GIS to provide practitioners a low-cost, accessible ecosystem monitoring and analysis system (EMAS) at landscape resolutions. The EMAS is evaluated and assessed with an application example in Yellowstone National Park aimed at identifying the causes and consequences of drought. Utilizing five predictor covariates (solar radiation, burn severity, soil productivity, temperature, and precipitation), spatio-temporal analysis revealed how landscape controls and climate (summer vegetation moisture stress) affected patterns of NPP according to vegetation functional type, species cover type, and successional stage. These results supported regional and national trends of NPP in relation to carbon fluxes and lag effects of climate. Overall, the EMAS provides valuable decision support for PAs regarding informed land use planning, conservation programs, vital sign monitoring, control programs (fire fuels, invasives, etc.), and restoration efforts.

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