عنوان المقالة:تحسين منحنى المساحة والحجم والعمق باستخدام البيان ات الرادارية ونظم المعلومات الجغرافية لحصاد المياه في المناطق الجافة Optimization of area–volume–elevation curve using GIS–SRTM method for rainwater harvesting in arid areas
Khamis Naba Sayl • Nur Shazwani Muhammad1 • Ahmed El-Shafie2
الملخص العربي
استخدام البيانات الرادارية ونظم المعلومات الجغرافية للحصول على منحنى العلاقة بين الحجم والمساحة مع العمق واستخدام هذا المنحنى كمؤشر للتفضيل بين المواقع المختاره لانشاء السدود في المنطقة الغربيه من العراق
الملخص الانجليزي
The major limitation in planning water harvesting
is the lack of knowledge in the estimation of surface
area and storage volume at any depth of dam reservoir. The
area–volume–elevation (AVE) curve of a reservoir plays a
key role in estimating the most suitable depth, optimum
surface area and highest capacity of reservoir storage. The
existing methods to estimate the AVE curve are costly and
time-consuming and require laborious work. This study
attempts to develop a method to optimize the AVE curve
for earth dams, using the digital elevation model generated
by the Shuttle Radar Topography Mission (SRTM) data,
and integrate it with the geographic information system
(GIS), known as the GIS–SRTM. The proposed method
was tested using field data in the Western Desert of Iraq,
which is an arid environment. Three constructed small
earth dams were selected for this study. The AVE curves
were extracted for Horan 2 (H2), Al-gara 2 (G2) and Algara
4 (G4) earth dams. Comprehensive analyses have been
carried out to evaluate the performance of the AVE curves
using the proposed GIS–SRTM method and the field data.
From the comparison, the proposed GIS–SRTM method
was able to produce reliable AVE curves with a relative
error less than 20%. Additionally, the proposed methodwas less time-consuming and the AVE curves can be
visualized immediately. The proposed GIS–SRTM method
is relatively supportive in analyzing spatial data to select
the optimal site for rainwater harvesting and prevent
excessive evaporation losses.