Defining optimal DEM resolutions and point densities for modelling hydrologically sensitive areas in agricultural catchments dominated by microtopography

AbstractDefining critical source areas (CSAs) of diffuse pollution in agricultural catchments depends upon the accurate delineation of hydrologically sensitive areas (HSAs) at highest risk of generating surface runoff pathways. In topographically complex landscapes, this delineation is constrained by digital elevation model (DEM) resolution and the influence of microtopographic features. To address this, optimal DEM resolutions and point densities for spatially modelling HSAs were investigated, for onward use in delineating CSAs. The surface runoff framework was modelled using the Topographic Wetness Index (TWI) and maps were derived from 0.25m LiDAR DEMs (40 bare-earth points m−2), resampled 1m and 2m LiDAR DEMs, and a radar generated 5m DEM. Furthermore, the resampled 1m and 2m LiDAR DEMs were regenerated with reduced bare-earth point densities (5, 2, 1, 0.5, 0.25 and 0.125 points m−2) to analyse effects on elevation accuracy and important microtopographic features. Results were compared to surface runoff field observations in two 10km2 agricultural catchments for evaluation. Analysis showed that the accuracy of modelled HSAs using different thresholds (5%, 10% and 15% of the catchment area with the highest TWI values) was much higher using LiDAR data compared to the 5m DEM (70–100% and 10–84%, respectively). This was attributed to the DEM capturing microtopographic features such as hedgerow banks, roads, tramlines and open agricultural drains, which acted as topographic barriers or channels that diverted runoff away from the hillslope scale flow direction. Furthermore, the identification of ‘breakthrough’ and ‘delivery’ points along runoff pathways where runoff and mobilised pollutants could be potentially transported between fields or delivered to the drainage channel network was much higher using LiDAR data compared to the 5m DEM (75–100% and 0–100%, respectively). Optimal DEM resolutions of 1–2m were identified for modelling HSAs, which balanced the need for microtopographic detail as well as surface generalisations required to model the natural hillslope scale movement of flow. Little loss of vertical accuracy was observed in 1–2m LiDAR DEMs with reduced bare-earth point densities of 2–5 points m−2, even at hedgerows. Further improvements in HSA models could be achieved if soil hydrological properties and the effects of flow sinks (filtered out in TWI models) on hydrological connectivity are also considered

Mapping Potential Foodsheds Using Regionalized Consumer Expenditure Data for Southeastern Minnesota

The theoretical concept of a foodshed is nearly a century old, while the tools used to model them—com­puter software coupled with spatial and statis­tical datasets—are ever-evolving. In a previous study (Galzki, Mulla, & Peters, 2014), foodshed maps have been created in Southeastern Minnesota that display the potential for local food system capacity in the region. Several assumptions were made based on data and software limitations that make the former results quite theoretical; this study attempts to move those results closer to reality by updating, where relevant. We utilized data pro­duced by a model devel­oped at the University of Minnesota to more effec­tively estimate regional food expendi­tures to create a representative diet in the region. We used current land-use data along with site-specific crop yields to analyze the poten­tial food capacity of the region. We used optimiza­tion software to allocate food supplies to 53 cities in an attempt to feed all residents in the region and minimize food transportation distances. Improve­ments in software capacities allowed us to incor­porate larger datasets, resulting in more detailed maps and statistics that better represent the poten­tial of local foods in the region. The optimization model indicated the region is capable of sustaining its population entirely on locally derived foods. Each resident can be fed on approximately one-third of a hectare (0.85 acre) of land in the region. The average distance a unit of food travels from farm to grocery store was found to be 15.6 km (9.7 miles). Results also show that 90% of the cultivated land remains in surplus after meeting the food demands of the region, minimizing the impacts on the local agroeconomic system. The surplus of pasture land is smaller, but over half the pasture land in the region is in surplus after food needs are met. We explore an alternative land-use scenario that removes environmentally sensitive cropland from cultivation to illustrate the impact conserva­tion efforts may have on a potential local food system. The updated results of this study bolster the evocative effect of mapping foodsheds and provide a more realistic illustration of how the region could sustain itself on locally derived foods

Efficient Algorithms for Geographic Watershed Analysis

poster presented at the MSI 2012 Research ExhibitionThis project is to analyze where wetlands and other vegetated buffers can be placed on the landscape to intercept drain waters and help purify them before they reach the natural watershed. The computational problem comes because new LIDAR images have expanded the resolution of geographic digital elevation models (DEMs) up to a thousandfold or more. This in turn has taxed the ability of existing algorithms to process the expanded datasets. Here we explain the project and present new efficient algorithms for parallel and scalar processing that reduce run-times from days on ordinary computers to minutes or second using the new algorithms in a parallel supercomputing environment.Minnesota Supercomputing Institut

Red Sea MODIS Estimates of Chlorophyll a and Phytoplankton Biomass Risks to Saudi Arabian Coastal Desalination Plants

Harmful algal blooms (HABs) and the high biomass associated with them have afflicted marine desalination plants along coastal regions around the world. Few studies of HABs have been conducted in the Red Sea, where desalination plants along the Saudi Arabian Red Sea coast provide drinking water for millions of people. This study was conducted along the Saudi Arabian Red Sea coast from 2014 to 2015 to assess the potential for using Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing of chlorophyll a (Chl a) or fluorescence line height (FLH) to identify risks for biofouling at these desalination plants. Ship-based surveys of phytoplankton were conducted along the Saudi Arabian coastline offshore of desalination plants at Jeddah, Al Shoaibah and Al Qunfudhuh to assess the density of phytoplankton populations and identify any potential HAB species. Ship-based surveys showed low to moderate concentrations of phytoplankton, averaging from 1800–10,000 cells L−1 at Jeddah, 2000–11,000 cells L−1 at Al Shoaibah and 1000–20,500 cells L−1 at Al Qunfudhuh. Sixteen different species of potentially toxigenic HABs were identified through these surveys. There was a good relationship between ship-based total phytoplankton counts and monthly averaged coastal MODIS Chl a (R2 = 0.49, root mean square error (RMSE) = 0.27 mg m−3) or FLH (R2 = 0.47, RMSE = 0.04 mW m−2 µm−1 sr−1) values. Monthly average near shore Chl a concentrations obtained using MODIS satellite imagery were much higher in the Red Sea coastal areas at Al Qunfudhuh (maximum of about 1.3 mg m−3) than at Jeddah or Al Shoaibah (maximum of about 0.4 and 0.5 mg m−3, respectively). Chlorophyll a concentrations were generally highest from the months of December to March, producing higher risks of biofouling desalination plants than in other months. Concentrations decreased significantly, on average, from April to September. Long-term (2005–2016) monthly averaged MODIS Chl a values were used to delineate four statistically distinct zones of differing HAB biomass across the entire Red Sea. Sinusoidal functions representing monthly variability were fit to satellite Chl a values in each zone (RMSE values from 0.691 to 0.07 mg m−3, from Zone 1 to 4). December to January mean values and annual amplitudes for Chl a in these four sinusoidal functions decreased from Zones 1–4. In general, the greatest risk of HABs to desalination occurs during winter months in Zone 1 (Southern Red Sea), while HAB risks to desalination plants in winter months are low to moderate in Zone 2 (South Central Red Sea), and negligible in Zones 3 (North Central) and 4 (Northern)