On Spatio-Temporal Modelling of Stream Network Initiation

被引：1
作者：
Papageorgaki I. [1 ]
Nalbantis I. [1 ]
机构：
[1] Laboratory of Reclamation Works and Water Resources Management, School of Rural and Surveying Engineering, National Technical University of Athens, Athens
来源：
Environmental Processes | 2018年 / 5卷 / Suppl 1期
关键词：
Channel initiation; Critical support area (CSA); Digital elevation model (DEM); Geomorphometry; Regression analysis; Ungauged basin; Upslope contributing area threshold;
D O I：
10.1007/s40710-018-0338-z
中图分类号：
学科分类号：
摘要：
A hydrographic network consists of perennial and ephemeral streams, the latter expanding or contracting seasonally according to the occurrence of precipitation. Critical Support Area (CSA) is known as the upslope contributing area threshold for channel initiation, which normally varies in both time and space. The aim of this study is to investigate and model CSA and its variation in time and space. The spatio-temporal variation of CSA is studied in three river basins in mainland Greece. Conventional maps at the scale 1:50000 are used to identify stream heads and the corresponding values of CSA for two seasons of the hydrological year: the wet and dry season. A Digital Elevation Model (DEM) with a cell size of about 30 m is used, which is based on the Shuttle Radar Topography Mission (SRTM), while a Geographic Information System (GIS) is employed for data processing. For each stream head indicated on map, raw data are extracted for five variables: CSA and statistics (mean and standard deviation) of the ground slope and square root of ground slope. Various regression models are tested by regressing CSA on one or more of the remaining variables, either untransformed or log-transformed. Each one of the tested models is set to predict the upslope contributing area threshold for a drainage basin of a group of basins with similar hydrological response. Although none of the regression models showed satisfactory accuracy in predicting CSA, the mean value of the ground slope for the upslope contributing area proved to be the most significant explanatory variable in all tested models. Stepwise regression analysis was found to be particularly helpful in finding the best fit models. Given the low predictive capacity of the best fit models and the current practice of using arbitrary constant values for CSA, we propose and discuss using the median of map-derived CSAs for each season. © 2018, Springer Nature Switzerland AG.
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页码：239 / 257
页数：18
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