ارزیابی اثر اقدامات بیولوژیکی آبخیزداری بر سیل خیزی مطالعه موردی: حوضه آبخیز پردیسان در استان قم

سیل از جمله رخدادهای طبیعی است که وقوع آن سالانه خسارت‌های زیادی به مردم و محیط‌زیست در سراسر جهان وارد می‌کند. اقدامات آبخیزداری راهکاری موثر در راستای کنترل سیل و کاهش خسارت ناشی از آن بوده و ارزیابی اثر این اقدامات بیانگر میزان دستیابی به موفقیت در نائل شدن به هدف کنترل سیلاب است. در این تحقیق هدف آن است که از تلفیق روش شماره منحنی و ahp در arcgis برای تهیه نقشه حساسیت به سیل استفاده شده و نقش اقدامات بیولوژیکی آبخیزداری در حساسیت به سیلاب منطقه با استفاده از این روش و آزمون‌های آماری مورد بررسی قرار گیرد. برای این منظور، حوضه آبخیز پردیسان در قسمت جنوبی شهر قم، با بیشترین سطح کاربری اراضی مرتع انتخاب شد. ده عامل تراکم زهکشی، شیب، بارندگی سالانه، فاصله از رودخانه، ارتفاع، تجمع جریان، شماره منحنی scs ، زمین‌شناسی، ژئوموفولوژی و نقشه سیلاب پیشین منطقه انتخاب و هر عامل براساس تاثیر بر حساسیت سیل‌خیزی منطقه در مقیاس‌­های مختلف طبقه‌بندی شدند. سپس از روش ahp در arcgis  برای محاسبه مقایسه جفتی و تعیین وزن هر عامل استفاده شد. نتایج نشان داد که عامل شماره منحنی دارای بیشترین درصد وزنی (27/44) و نفوذپذیری سنگ‌ها دارای کمترین درصد وزنی (3/20) است. مقایسه کلاس‌های سیل‌خیزی در شرایط فعلی و آینده نشان می‌دهد که با انجام اقدامات بیولوژیکی آبخیزداری، طبقات سیل‌خیزی زیاد و متوسط به‌ترتیب 7/3 و 39/7 درصد کاهش و طبقات با حساسیت کم و خیلی‌کم به‌ترتیب 22/18 و 22/82 درصد افزایش خواهد یافت. انجام آزمون آماری نشانه و ویلکاکسون نیز بیانگر آن است که اختلاف معنی‌دار در طبقات سیل‌خیزی در قبل و بعد از اقدامات آبخیزداری وجود دارد و اقدامات بیولوژیکی تاثیر مثبتی در کاهش سیل دارد.

The evaluation of biological watershed management measures effect on flood susceptibility Case study: Pardisan watershed in Qom province

Extended AbstractIntroduction. Floods are natural disasters which occurrence causes annual great damage to people and environment around the world. So, specifying flood susceptible land is a necessity to reduce and control destructive impacts. Watershed management implementations could affect runoff volume and flood occurrence. The goal of this study is to apply the combination of Curve Number method and AHP in ArcGIS to prepare flood susceptibility map and to investigate the role of biological measures in flood susceptibility of the region through this method and statistical tests.Materials & Methods .For this purpose, Pardisan watershed located in the southern part of Qom city was selected. Ten factors layers viz. drainage density, slope, annual rainfall, distance from river, elevation, flow accumulation, SCS Curve Number, geo infiltration, geomorphology and previous floods were prepared and classified based on flood susceptibility in different scales. Then future Curve Number was determine with assuming the implementation of biological watershed management in different land uses such as rangeland, agriculture, garden and badland. In this study, AHP method in ArcGIS was used to calculate pairwise comparison and determine the weight of each factor. Overlaying current and future Curve Number layers with nine layers using the weights obtained from the hierarchical analysis method led to the preparation of flood susceptibility maps for pre and post watershed management implementation. Results & DiscussionGeo infiltration map showed the proportion area of “low”, “and “very low” infiltration classes were 4.46% and 16.87%, respectively while moderate and high infiltration classes were 39.75% and 38.92%. Slope map indicates that 02%, 25%, 515%, 1535% and 3560% classes comprise 29.87%, 35%, 30.11%, 4.88% and 0.14% of the studied area, respectively. In this region, South parts were steep whereas; north parts were mild. Distance to river is another factor classified in to four groups of 0500, 5001000, 10003000 and 30006500 meter with 38.86%, 24.32%, 29.63% and 7.19% of the region, respectively. Elevation classified map revealed 45.1% of the region were in 9001200 meter range whereas; 36.4%, 14.8%, 3.6% and 0.1% were in 12001500,15001800,18002100 and 21002400 meter classes, respectively. As can be seen in rainfall map, 25.57% of the region was categorized in 140160 mm rainfall class while 35.41%, 20.59% and 18.43% of the whole area were classified in 160180,180200 and 200250mm groups. In the region, South parts have more rainfall volume than north. Also, flow accumulation map indicated that 96.5%, 1.97%, 1.07%, 0.24% and 0.22% were classified as 01500, 15005000, 500015000, 1500025000, 25000100000 values which high flow accumulation pixel range show high flood susceptibility. Drainage density map represents 10.38%, 14.36%, 56.88% and 18.38% of the studied area were grouped in 00.05, 0.050.07, 0.070.09 and 0.090.12 classes. Also, Curve Number (SCS) map for garden, cultivated lands, rangelands and badlands shows that 25.54% of the study area was classified as 1535 CN value while 36.14%, 0.9% and 37.42% were categorized in 3550, 5065 and 6580 classes before performing biological measures. After biological measures in different uses, 1535 Curve Number values are observed in 36.6% of the area and 3550, 5065, 6580 classes comprise 32.05%, 29% and 2.35% of the study area, respectively. The geomorphological map shows that the class with the highest score is visible in 68.96% of the area, while the classes with the lower scores are observed in 3.07, 18.34, 9.37, and 0.26% of the region, respectively. The past flood zoning map of the region also shows that 22.41% of the region exist in low susceptibility class, 36.15% of the region locates in the medium susceptibility class and 41.44% is in the high sensitivity class. For AHP approach, the calculated consistency ratio of this study was less than 0.1. Therefore; the compatibility between ten selected factors was acceptable. AHP results showed that the Curve Number factor has the highest weight percentage (27.44) whereas; the geoinfiltration has the lowest weight percentage (3.20). Comparison of flooding classes for pre and post water management implementation shows that high and medium flooding classes will decrease by 7.3 and 39.7% and low and very low susceptibility classes will increase by 22.18 and 24.82 %, respectively due to the implementation of biological watershed management measures. Also, Sign and Wilcoxon statistical tests indicated the existence of significance difference in flood classes’ for pre and after implementing biological watershed management. ConclusionFlood susceptibility map provision is a necessity in arid and semiarid regions due to insufficient vegetation cover. The results of this study indicate positive effects of biological watershed management in decreasing flood vulnerability. These findings can be considered for future planning of the region and help watershed managers for optimal utilization of water and soil resources and reduction of flood damage.