Water levels of Malheur Lake in southeastern Oregon fluctuate widely with seasonal and cyclic climatic changes. Seven years of severe flooding from 1978 to 1984 produced the highest water levels in recorded history and covered almost all marsh vegetation. Seven years of drought followed the flooding, and by 1992 the water level had dropped to the lowest point in nearly 60 years. A survey of vegetation colonizing the lakeshore as flood water receded was conducted from 1989 to 1992 to describe the reestablishment of marsh vegetation. Six transects were placed in three different ecological units of the lake. Frequency and cover data for each plant species were recorded. Recruitment from seed banks produced germination the first year of annual, mud flat species followed the second year by perennial emergent seedlings. The emergent seedlings generally did not survive the drought as water levels continued to recede. The seeds of introduced Eurasian species were distributed by wind, became lodged in the cracks of drying mud flats and then germinated following winter rains. The central ecological unit, fed by both the Blitzen and Silvies Rivers, did not show severe effects of drought and species of emergent vegetation grew without apparent signs of drought stress.

Two perspectives are debated in current United States water policy model development. One perspective calls for policy based on normative values, such as an environmental ethic. The second perspective calls for policy based on empirical, quantifiable values, for instance, economic benefits and costs. This theoretical debate arises from differing assumptions about what is problematic in contemporary water policy, and in turn gives rise to many water policy models. Developing such models ostensibly provides frameworks useful for developing real-world water policies. This paper proposes that these water policy models are not in fact useful frameworks for policy applications because the models do not accurately account for the actual circumstances confronting water policy makers. In order to illustrate this hypothesis, a comparison of two water policy models with a set of real-world policy circumstances is made here. The two models, each representing one of the dominant theoretical perspectives, are taken from David Lewis Feldman's Water resources manaiement: In search of an environmental ethic (1991) and Peter Rogers' America's water: Federal roles and responsibilities (1993). Feldman's model was selected to represent the normative perspective, and Rogers' model is selected to represent the empirical perspective. The real-world water policy circumstances selected for this study are those of Malheur Lake Basin, Oregon. This basin was selected because it provides the opportunity to consider a range of water policy issues and problems. This study shows that these two models do not offer adequate frameworks for applications. If water policy models are to provide useful frameworks for applications, model development must more closely consider actual cases.

Land cover change over time can be a useful indicator of variations in a watershed, such as the patterns of drought in an area. I present a case study using remotely sensed images from Landsat satellites for over a 30-year period to generate classifications representing land cover categories, which I use to quantify land cover change in the watershed areas that contribute to Malheur, Mud, and Harney Lakes. I selected images, about every 4 to 6 years from late June to late July, in an attempt to capture the peak vegetation growth and to avoid cloud cover. Complete coverage of the watershed required that I selected an image that included the lakes, an image to the North, and an image to the West of the lakes to capture the watershed areas for each chosen year. I used the watershed areas defined by the HUC-8 shapefiles. The relevant watersheds are called: Harney-Malheur Lakes, Donner und Blitzen, Silver, and Silvies watershed. To summarize the land cover classes that could be discriminated from the Landsat images in the area, I used an unsupervised classification algorithm called Iterative Self-Organizing Data Analysis Technique (ISODATA) to identify different classes from the pixels. I then used the ISODATA results and visual inspection of calibrated Landsat images and Google Earth imagery, to create Regions of Interest (ROI) with the following land cover classes: Water, Shallow Water, Vegetation, Dark Vegetation, Salty Area, and Bare Earth. The ROIs were used in the following supervised classification algorithms: maximum likelihood, minimum distance, and Mahalanobis distance, to classify land cover for the area. Using ArcGIS, I removed most of the misclassified area from the classified images by the use of the Landsat CDR, combined the main, north, and west images and then extracted the watersheds from the combined image. The area in acres for each land cover class and watershed was computed and stored in graphs and tables.After comparing the three supervised classifications using the amount of area classified into each category, normalized area in each category, and the raster datasets, I determined that the minimum distance classification algorithm produced the most accurate land cover classification. I investigated the correlation of the land cover classes with the average precipitation, average discharge, average summer high temperature, and drought indicators. For the most part, the land cover changes correlate with the weather. However, land use changes, groundwater, and error in the land cover classes may have accounted for the instances of discrepancy. The correlation of land cover classes, except Dark Vegetation and Bare Earth, are statistically significant with weather data. This study shows that Landsat imagery has the necessary components to create and track land cover changes over time. These results can be useful in hydrological studies and can be applied to models.