تعیین اهمیت عوامل موثر در مکان یابی محل های جمع ‌آوری آب باران درجا در حوزه آبخیز تجره کاشان

جمع آوری آب باران گزینه ای مناسب جهت ذخیره رواناب سطحی جهت کاربرد های بعدی طی دوره های با محدودیت دسترسی به آب است. مهم ترین مرحله در به کارگیری سامانه‌ های جمع ‏ آوری آب باران، مکان یابی عرصه های مناسب است. لذا با شناسایی محل های مناسب برای این منظور، در زمان و هزینه صرفه جویی می شود. در این پژوهش، برای مکان یابی روش جمع ‏آوری آب باران درجا در حوزه آبخیز تجره از مدل رگرسیون چند متغیره و همچنین سامانه اطلاعات جغرافیایی استفاده شد. بدین منظور لایه های تاج پوشش، لاشبرگ، سنگ و سنگ ریزه، خاک ل خت، شماره منحنی، بارش، شیب و عمق خاک به‌عنوان متغیر مستقل و عامل نفوذ نیز به‌عنوان متغیر وابسته در نظر گرفته شد. سپس با توجه به نقشه های تهیه شده، مقادیر آن ها به صورت متوسط برای هر یک از زیرحوضه های 27 گانه محاسبه شد. همچنین برای بررسی ارتباط بین این متغیر ها و وزن دهی به هر یک، عوامل موثر از رگرسیون چند متغیره به روش گام‌به‌گام استفاده شد. نتایج نشان داد که مدل رگرسیون چند متغیره خطی با ضریب تبیین 0/993  به خوبی توانسته است مقادیر عامل  نفوذ را برآورد نماید. از لحاظ درجه اهمیت نیز متغیر های شماره منحنی با ضریب 2/433-، عمق خاک با ضریب 0/348 و درصد سنگ و سنگ ریزه با ضریب 0/057 به ترتیب دارای بیشترین اهمیت بوده و سایر عوامل دارای اهمیت معنی داری نبودند. مقایسه نقشه حاصل از مکان یابی رگرسیون چند متغیره در این تحقیق با برخی معیار های توصیه شده سوابق پژوهشی مختلف نشان داد که کلاس های پیش بینی شده با تناسب خوب در بخش های مرکزی حوضه و بسیار خوب مناطق بالادست حوضه که در قسمت شرقی و جنوب شرقی حوضه دارای هم پوشانی با مناطق توصیه شده با این معیار ها است.  

determine the importance of effective factors in site selection rainwater harvesting in the tajerre kashan basin

rainrainwater harvesting is an appropriate option for storing surface runoff for subsequent use during periods with limited access to water. the most important step in the application of rainwater harvesting systems (rwh) is the selection of suitable areas. by identifying suitable sites for this purpose, time and cost can be saved. in this research, a multivariate regression model and gis were used to select suitable sites for rainwater harvesting in the tajarre watershed. the layers considered for this purpose included crown cover, litter, rock and stones, soil, curve number, rainfall, slope, and depth of field as independent variables, and infiltration as the dependent variable. the values of these variables were calculated in average for each of the 27 sub-basins, based on the maps. additionally, the stepwise method was used to investigate the relationship between these variables and assign weights to each of the effective layers of the multivariate regression. the results showed that the linear multivariate regression model, with an explanation coefficient of 0.993, was able to estimate the penetration factor values accurately. in terms of importance, the curve number variable had a coefficient of -2.433, the depth of soil had a coefficient of 0.3488, and the rubble and gravel percentage had a coefficient of 0.057. these factors were found to be the most significant, while other factors were deemed insignificant. a comparison of the map resulting from the multivariate regression site selection in this research with some recommended criteria from various other studies showed that the predicted classes fit well with the recommended areas in the central and upstream parts of the basin. in the eastern and southeastern part of the basin, the predicted classes overlapped well with the recommended areas based on these criteria.water harvesting is an appropriate option for storing surface runoff for subsequent uses during periods with limited access to water. the most important step in the application of rainwater harvesting systems (rwh) is the site selection suitable areas. therefore, by identifying suitable sites for this purpose, time and cost will be saved . in this research, multivariate regression model and gis were used to site selection in situ (rwh) in tajare watershed. for this purpose, layers such as crown cover, litter, rock and stones, soil, curve number, rainfall, slope and depth of field as independent variable and infiltration were considered as the dependent variable. then, according to the maps, their values were calculated in average for each of the 27 sub-basins. also, to investigate the relationship between these variables and weighting, each of the effective layers of multi-variable regression was used by the stepwise method the results showed that the linear multivariate regression model with an explanation coefficient of 0.993 was able to estimate the penetration factor values well in terms of grade of importance, the curve number variables with a coefficient of 2.433, depth of soil with a coefficient of 0.3488, and rubble and gravel percent with a coefficient of 0.057, were the most important, and other factors were not significant. comparison of the map from the site selection of multivariate regression in this research with some recommended criteria of various research studies showed that the predicted classes with good in the central parts of the basin and very good in the upstream areas of the basin which in the eastern and southeastern part of the basin fit have a good overlap with the recommended areas with these criteria.