[1] Very little is known about the vulnerability of dams and reservoirs to man-made alteration of extreme precipitation and floods as we step into the 21st century. This is because conventional dam and reservoir design over the last century has been "one-way" with no acknowledgment of the possible feedback mechanisms affecting the regional water cycle. Although the notion that an impoundment could be built to increase rainfall was suggested more than 60 years ago, dam design protocol in civil engineering continues to assume as "static" the statistical parameters of a low exceedance probability precipitation event during the lifespan of the dam. It is time for us to change our perceptions and embrace a hydrometeorological approach to dam design and operations.

Dams and their impounded waters are among the most common civil infrastructures, with a long heritage of modern design and operations experience. In particular, large dams, defined by the International Commission on Large Dams (ICOLD) as having a height greater than 15 meters from the foundation and holding a reservoir volume of more than 3 million cubic meters, have the potential to vastly transform local climate, landscapes, regional economics, and urbanization patterns. In the United States alone, about 75,000 dams are capable of storing a volume of water equaling almost 1 year's mean runoff of the nation [Graf, 1999]. The World Commission on Dams (WCD) reports that at least 45,000 large dams have been built worldwide since the 1930s. These sheer numbers raise the question of the extent to which large dams and their impounded waters alter patterns that would have been pervasive had the dams not been built.

This paper introduces a remote sensing-based approach to rapidly derive urban morphological characteristics using radar satellite data. The approach is based on the expectation that the magnitude of the synthetic aperture radar (SAR) backscatter can be related to urban canopy parameters (UCPs) describing the height, density, and roughness of buildings, trees, and other objects in cities. This hypothesis was tested with full-feature terrain elevation and SAR datasets for the Houston, Texas, metropolitan area. The backscatter magnitude was found to vary as expected across the city with higher backscatter values in the downtown tall building district relative to adjacent residential and commercial areas. To demonstrate the concept of using radar backscatter to estimate UCPs, relationships were derived between SAR backscatter and mean height, plan area fraction, and frontal area index of roughness elements (e.g., buildings and trees). In addition, SAR backscatter relationships were derived with roughness lengths computed using morphometric approaches. In all cases, the derived relationships were found to provide estimates of UCPs acceptable for use in meteorological models. Further testing using data from the Salt Lake City, Utah, metropolitan area validated the relationships and identified key areas for improvement for future research, including SAR instrument view angle differences and buildings split between SAR pixels.

Abstract. Land Surface Temperature plays a critical role in the land surface-atmosphere exchange processes. In this study, the impacts of land cover changes on land Surface Temperature in bay area, California was analyzed. The key findings of the land cover change impacts study are presented. National Land Cover Database (NLCD) data and Landsat Land Surface Temperature data were used for this study. The time period of analysis is 2001 through 2020. The results of the study indicated that with the increase in built land cover, the land surface temperature in bay area, California exhibited a significant increase.

Abstract. Land cover change is critical to be monitored as land cover change has significant impacts on flooding, ground water recharge, and urban air temperature. In this paper, key findings from a land cover change analysis study performed in the State of California are presented. National Land Cover Database (NLCD) data from the Multi-Resolution Land Characteristics Consortium (MRLC) was used for this study. Time series of NLCD data during the time period of 2001 through 2016 was used for the analysis. NLCD data processing was done in ArcMap 10.6.1. This paper includes the methodology in detail, and the results of the analysis. Results of the study indicate a significant increase in impervious surfaces, and a significant decrease in forest land cover.