The Application of Remote Sensing and Hydrological model on Water Conservation Capacity Estimation For Reservior Watershed Area
Hydrologic Factors( Parameters) Analysis
From previous research output, those affect water conservation capacity of reservoir watershed in Taiwan can be identified, which include the vegetation factors, topographic factor, soil factor, hydrometerological factor... etc. In this study, some general variables and data are used as show in table 1.
| Factor | Data requirements |
| Vegetation | Land cover/use (forest type and density) |
| Topography | Slope/aspect,elevation |
| Soil | Soil moisture content, antecedent mosture content, soil profile |
| Hydrometerology | Precipitation, water level and discharge volume at gauging stations |
Table 1. Data requirements for water conversation capacity esrimation
Environmental Database Establishment for Watershed Area
All parameters for hydrological model, include vegetation, topography, land cover/use. Slope/aspect, elevation and gauge station distributing map... etc., can be built into a physical Environment database by the assist of GIS and RS techniques.
This physical Environmental database includes basic data such as geographical features, geological characteristics, hydrological information, and land use/cover information. Among those data set, the geology. Digital Elevation Model (DEM) data, forest stands polygons and land use information were available in digital format. These data were transformed into transverse Mercator 2-degree projection, which is the coordination system currently used in Taiwan
Satellite Image Source
SPOT image were used to detect land use change in this study due to better spatial resolution. Three image from different seasons were chose for this study, one was taken on December 8,1993, another was on Aril 17,1996, and the other was taken on November
19,1997.
Land Use Classification
The land use classification process was focused in identifying vegetation area. First unsupervised classification was used to group the nature categories in the image. Then candidate training sites for supervised classification were first identified from aerial photos topographic maps. Field observations were also performed to check the ground truths and to select final training sites.
These SPOT satellite image respectively were used to identify the land use/cover condition in Dar-pu watershed area. Through these different season images. The land use changes output and land use distribution information can serve as the input parameters for the hydrological model of water conservation capacity estimation .using satellite image to get the land use patterns and land use changes for large area is efficient in both time and cost. The result from image classification are show in table 2.
Image date Land use/cover |
12/8/1993
Area (hectare) | 4/17/1996
Area (hectare) | 11/19/1997
Area (hectare) |
| Forest | 6410.13 | 6181.88 | 6102.68 |
| Orchard | 1799.83 | 2070.52 | 2329.28 |
| Crop field | 773.49 | 483.98 | 425.74 |
| Paddy rice | 377.47 | 431.83 | 465.04 |
| Water | 185.59 | 150.62 | 211.38 |
| Road | 145.76 | 145.57 | 146.30 |
| Build up area | 203.59 | 423.77 | 473.53 |
| Bare land/landslides | 77.14 | 379.45 | 255.47 |
| Grass land | 493.23 | 175.83 | 52.47 |
| Undistinguished | 0 | 22.77 | 4.31 |
| Total | 10466.22 | 10466.22 | 10466.22 |
Table 2. Land use/cover identification from satellite images classification
Hydrological Model Analysis
Assuming the reservoir watershed is persuadable, this study applied PWATER subroutine from PERLAND module of HSPF model. The PWATER structure is shown in figure 2. the main function is to simulate total runoff from percolating area. This research focused on the simulation result from PERLAND module for surface runoff from groundwater changes in reservoir watershed.
Figure 2. Structure Diagram of PWATER subroutine from HSPF model
