Using Spatial Data for Geo-Environmental Studies

The physically-based spatially-distributed model PROMET (Processes
of Radiation, Mass and Energy Transfer) is applied to the Greater
Damascus Basin, which is considered as one of the most important
basins in Syria, to serve as a case study of using spatial data for
Geo-environmental studies. Like most areas of the Middle East, the
study area is characterized by large temporal and spatial
variations in precipitation and by limited water resources. Due to
the increasing water demand caused by the economic development and
the rapid growth of population, the study area is expected to
suffer from further water shortages in the future. This highlights
the necessity of developing an integrated Decision Support System
(DSS) to evaluate strategies for efficient and sustainable water
resources management in the basin, taking into consideration global
environmental changes and socio-economic conditions. The work
presented here represents the first steps toward achieving this
goal through applying a distributed hydrological model (an
important component of any integrated DSS for water resources
management) to the Greater Damascus Basin utilizing different types
of spatial data used as time-dependent (e.g., meteorology) and
time-independent (e.g., topography and soil) input parameters. The
model PROMET, which was developed within the GLOWA-Danube project
as part of the decision support system DANUBIA, is run on an hourly
time step (for the period from 1991 to 2005) and a 180*180m spatial
resolution to simulate the water and energy fluxes in this basin.
The model is embedded within a raster-based GIS-structure which
facilitates the integration of the diverse types of spatial data.
The spatial information related to topography (such as elevation,
slope, and exposition) as well as those related to runoff routing
(such as upstream-area, channel width, and downstream proxel) are
automatically extracted from Digital Elevation Model (Shuttle Radar
Topography Mission, SRTM-90m DEM). The spatial patterns of the
different land use/land cover classes are derived from remote
sensing data (classification of a cloud-free LANDSAT 7 ETM+ image
using the supervised classification algorithm). The spatial fields
of meteorological input data are provided on an hourly basis
through spatiotemporal interpolation of the measurements of the
available weather stations. Spatial information about the soil
texture is provided through generalization and aggregation of the
soil type classes of the Soil Map of Syria (prepared by USAID) and
transferring the soil types to texture classes. Several
pedotransfer functions are then used to estimate the soil hydraulic
properties for each soil texture class (and each soil layer) found
in the study area. While plant physiological parameters (which are
assumed to be static, such as minimum stomatal resistance) are
estimated for each vegetation class using information taken from
literature sources, the temporal evolution of Albedo and Leaf Area
Index (LAI) are derived from five cloud-free LANDSAT-7 images
acquired at different seasons of the year. The goodness of the
results obtained by the model PROMET are verified and/or validated
by comparing them either with their corresponding data observed in
the filed or with remote sensing-derived information (e.g., snow
cover). Two subcatchments are selected for the purpose of
calculating the spatially-distributed annual water balances. The
results indicate that the modelled mean annual runoff volume fits
well with the measured discharge for both chosen subcatchment. In
addition, the simulated discharge is compared to the observed one
(at seven gauge stations) on a monthly basis, covering the whole
simulation period (15 years). The results of the regression
analysis for each of these gauge stations (with slope of regression
line ranges from 0.79 to 1.04; coefficient of determination
0.69-0.90; and Nash-Sutcliffe Coefficient 0.73-0.95) indicate that
there is a good correlation between simulated and observed monthly
mean discharge volumes.