tailieunhanh - GIS for Water Resources and Watershed Management - Chapter 9

Một GIS Đặc điểm đầu nguồn sử dụng để xác định cho sự phát triển của các phương trình hồi quy nhiều cho dự báo lưu lượng thấp trong dòng suối trong phần phía đông của Puerto Rico. Một lựa chọn sơ bộ của 45 lưu vực sông WS gauged dựa trên một bản đồ khu chung sống, và bảo hiểm vector của lượng mưa và nhiệt độ khu vực điển hình của rừng quốc gia Caribbean. Số lượng các lưu vực sông WS Giảm đến 19 bằng cách sử dụng đất đè và bảo hiểm trong một GIS Những. | CHAPTER 9 Watershed Characterization by GIS for Low Flow Prediction Glenn S. Warner Andrés R. García-Martinó Frederick N. Scatena and Daniel L. Civco INTRODUCTION A GIS was used to determine watershed characteristics for development of multiple regression equations for prediction of low flow in streams in the eastern part of Puerto Rico. A preliminary selection of 45 gauged watersheds was based on a general Life Zone Map and vector coverage of precipitation and temperature regions typical of the Caribbean National Forest. The number of watersheds was reduced to 19 by overlaying land use coverage in a GIS and selecting those watersheds having permanent forest cover and available data including digital elevation models DEMs . A total of 53 initial watershed parameters grouped into geology soils geomorphology stream network relief and climate were determined by GIS for each of the 19 watersheds. The number of parameters was reduced to 13 through a two-step process using criteria that included correlation among the parameters correlation with low flows colinearity significance of coefficients and logic. Multiple regression analyses were performed to predict the 7 day-10 yr 30 day-10 yr and 7 day-2 yr low flows using combinations of the 13 parameters. Selection of the final regression model for each low flow was based on five statistical tests. Four parameters drainage density weighted mean slope an aspect parameter and ratio of tributaries to main channel length were found to be the best predictors resulting in R2 from to for the three low flow variables. A non-GIS model was also developed with easier to measure parameters but resulted in a higher SE than the GIS-based models. BACKGROUND Due to the complexity involved in the generation of low flow in streams few if any physically based models have been developed for prediction of low flows. Instead measured low flows are usually regressed against physical parameters that characterize the watersheds involved.