Simulation of water reliability on a small scale water harvesting agrisystem

Water harvesting techniques are being studied to determine its potential as an alternative reclamation practice. To assist planners and decision makers in evaluating the water harvesting potential, a procedure is developed by which precipitation and its distribution are simulated and incorporated with a runoff model to forecast the long term availability of harvested water. The output from the stochastic precipitation model and statistically- derived runoff model provide a simulated 100 years of annual precipitation and runoff events. Two reservoirs were designed and evaluated separately using varied seasonal irrigation demands. The sensitivity analysis, of varying the demand, revealed yearly water reliability decreased as the total seasonal irrigation demand approached the mean annual runoff. It was also shown that an appropriate reservoir size could be chosen using the probability distributions of the number of dry reservoir days and the number of days the reservoir overflows.hydrology collectio

OPERATIONS/EDUCATION/RESEARCH Swanton Pacific Ranch: A Cal Poly living and learning laboratory

Swanton Pacific, a unique educational field facility, presents on using their land to study the effectiveness of sustainable resource management, while incorporating over 20 disciplines. Students developed a monitoring system to track impacts of a LEED Certified Housing Project and an electrical engineering course incorporated analysis of sustainability factors into each experiment. Lastly we will learn about the use of Green Chemistry in an undergraduate teaching lab and the broader implications of Green Chemistry for the campus

An Approach to Study the Effect of Harvest and Wildfire on Watershed Hydrology and Sediment Yield in a Coast Redwood Forest

The Little Creek watershed, within California State Polytechnic University’s Swanton Pacific Ranch, is the location of a paired and nested watershed study to investigate the watershed effects of coast redwood forest management. Streamflow, suspended sediment, and stream turbidity have been collected during storms at two locations on the North Fork Little Creek and at the outlet of South Fork Little Creek from 2002 until present. In 2008, the watershed area between the two monitoring stations on the North Fork Little Creek watershed was harvested with an individual tree selection silvicultural system within the Santa Cruz County Rules of the California Forest Practice Rules. The South Fork Little Creek was left unharvested to serve as a control. In 2009, the Little Creek watershed was burned by a wildfire. The wildfire eliminated our control watersheds for the proposed Before After Control Intervention (BACI) study design. We present an alternative approach at detecting harvest and fire effects that uses rainfall/runoff models, soil erosion models, and sediment runoff relations to simulate runoff and sediment yield from the watersheds. The models and sediment runoff relationships will be developed within the framework of an uncertainty assessment to simulate pre-harvest and pre-fire conditions for the North and South Forks of Little Creek. The modeled results will be used as the control for the study which had been eliminated due to the wildfire in 2009. We use the HBV hydrologic model and sediment runoff relations to demonstrate our approach. An example of post-harvest and post-fire runoff and sediment changes within the uncertainty of the approach are demonstrated

A Calibration Procedure Using Topmodel to Determine Suitability for Evaluating Potential Climate Change Effects on Water Yield

An evaluation was conducted on three forested upland watersheds in the northeastern U.S. to test the suitability of TOPMODEL for predicting water yield over a wide range of climatic scenarios. The analysis provides insight of the usefulness of TOPMODEL as a predictive tool for future assessments of potential long-term changes in water yield as a result of changes in global climate. The evaluation was conducted by developing a calibration procedure to simulate a range of climatic extremes using historical temperature, precipitation, and streamfiow records for years having wet, average, and dry precipitation amounts from the Leading Ridge (Pennsylvania), Fernow (West Virginia), and Hubbard Brook (New Hampshire) Experimental Watersheds. This strategy was chosen to determine whether the model could be successfully calibrated over a broad range of soil moisture conditions with the assumption that this would be representative of the sensitivity necessary to predict changes in streamfiow under a variety of climate change scenarios. The model calibration was limited to a daily time step, yet performed reasonably well for each watershed. Model efficiency, a least squares measure of how well a model performs, averaged between 0.64 and 0.78. A simple test of the model whereby daily temperatures were increased by 1.7°C, resulted in annual water yield decreases of 4 to 15 percent on the three watersheds. Although these results makes the assumption that the model components adequately describe the system, this version of TOPMODEL is capable to predict water yield impacts given subtle changes in the temperature regime. This suggests that adequate representations of the effects of climate change on water yield for regional assessment purposes can be expected using the TOPMODEL concept

Forest Roads Mapped Using LiDAR in Steep Forested Terrain

LiDAR-derived digital elevation models can reveal road networks located beneath dense forest canopy. This study tests the accuracy of forest road characteristics mapped using LiDAR in the Santa Cruz Mountains, CA. The position, gradient, and total length of a forest haul road were accurately extracted using a 1 m DEM. In comparison to a field-surveyed centerline, the LiDAR-derived road exhibited a positional accuracy of 1.5 m, road grade measurements within 0.53% mean absolute difference, and total road length within 0.2% of the field-surveyed length. Airborne LiDAR can provide thorough and accurate road inventory data to support forest management and watershed assessment activities

Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDAR

Aerial and satellite imagery are widely used to assess the severity and impact of wildfires. Light detection and ranging (LiDAR) is a newer remote sensing technology that has demonstrated utility in measuring vegetation structure. Combined use of imagery and LiDAR may improve the assessment of wildfire impacts compared to imagery alone. Estimation of tree mortality at the plot scale could serve for more rapid, broad-scale, and lower cost post-fire assessments than feasible through field assessment. We assessed the accuracy of classifying color-infrared imagery in combination with post-fire LiDAR, and with differenced (pre- and post-fire) LiDAR, in estimating plot percent mortality in a second-growth coast redwood forest near Santa Cruz, CA. Percent mortality of trees greater than 25.4 cm DBH in 47 permanent 0.08 ha plots was categorized as low (<25%), moderate (25%–50%), or high (>50%). The model using Normalized Difference Vegetation Index (NDVI) from National Agricultural Imagery Program (NAIP) was 74% accurate; the model using NDVI and post-fire LiDAR was 85% accurate, while the model using NDVI and differenced LiDAR was 83% accurate. The addition of post-fire LiDAR data provided a modest increase in accuracy compared to imagery alone, which may not justify the substantial cost of data acquisition. The method demonstrated could be applied to rapidly estimate tree mortality resulting from wildfires at fine to moderate scale