پیش‌یابی بارش سالانه در دو اقلیم خشک و نیمه خشک

برآورد میزان بارش، موفقیت در مدیریت بهتر منابع آب و حوضه های آبخیز به منظور استفاده بهینه و پایدار از این منابع را تضمین می کند. در این تحقیق با استفاده از آمار بارش و دمای روزانه ایستگاه های مختلف هواشناسی استان های چهارمحال و بختیاری (اقلیم نیمه خشک) و یزد (اقلیم خشک) که دارای آمار روزانه بلند مدت بودند، پارامترهای مختلفی محاسبه شد. از میان این پارامترها، مجموع بارش شش ماهه اول سال آبی (r6m1، میلی متر)، طول فاصله زمانی وقوع 47.5 میلی متر بارش از ابتدای پاییز (t47.5، روز)، میانگین بلند مدت بارش (rm، میلی متر)، متوسط دمای تابستان قبل از هر سال آبی (tsu، درجه سانتی گراد) و متوسط دمای تابستان سال آبی قبل و پاییز هر سال آبی (tsu.au، درجه سانتی گراد) که هر یک همبستگی بالایی با بارش سالانه داشتند، در مدل های خطی ساده و روش شبکه عصبی مصنوعی برای پیش بینی بارش سالانه بکار گرفته شد. مدل های بدست آمده در سطح احتمال کمتر از 0.01 معنی دار بودند. نتایج نشان داد هر یک از این روش ها می تواند بارش سالانه را با دقت مناسبی برآورد نماید. ارزیابی مدل ها با شاخص ریشه میانگین مربعات خطای نرمال شده (nrmse) کمتر از 0.3 و شاخص تطابق (d) بالاتر از 0.8 نیز کارایی مدل ها را تصدیق کرد. دمای بالاتر در تابستان و پاییز حاکی از بارش سالانه بیشتر در استان چهارمحال و بختیاری بود و بارش کمتر در استان یزد بود. همچنین در دو استان هر چه فاصله زمانی وقوع 47.5 میلی متر باران از ابتدای پاییز به تاخیر بیفتد بارش سالانه کمتر خواهد بود.

Annual precipitation forecast in two arid and semi-arid regions

IntroductionAnnual precipitation forecasting usually guarantees success in sustainable management of water resources and watersheds in cases like determination of rainfed and water cultivation area and water resources consumption planning. Although precipitation does not follow a specified pattern; however, it has a correlation with some climatic parameters. If these parameters were easy to find, simple applicable models could be developed to predict annual precipitation. One of the simplest methods for predicting annual precipitation is regression models. The block box models such as Artificial Neural Network (ANN) has also been used for precipitation forecasting. This study was an attempt to predict annual precipitation using some climatic parameters by multiple linear regression and ANN models. Materials and MethodsChaharmahalBakhtiari and Yazd provinces are located in the southwestern and central part of Iran with semiarid and arid climate, respectively. Mean annual precipitation of these two provinces are 560 and 110 mm, respectively. Meteorological data of the weather stations belong to 20012011 in ChaharmahalBakhtiari and 20032013 in Yazd province; respectively, were analyzed. Various parameters were calculated for predicting annual precipitation using longterm daily precipitation and temperature data of the meteorological stations. Among the parameters, total precipitation in the first half of the water year (R6m, mm), time to 47.5 mm cumulative precipitation since the beginning of autumn (t47.5, day), longterm mean annual precipitation (Rm, mm), average summer temperature of the preceding water year (Tsu, °C) and average temperature of preceding summer and current autumn of water year (Tsu.au, °C) that had a high correlation with annual precipitation, were used in multiple linear regression (MLR) models and artificial neural network (ANN) techniques. normalize mean square error (NRMSE) and degree of agreement (d) were used to evaluate accuracy of the models for predicting annual precipitation.Results and DiscussionResults showed that the obtained MLR models were significant at a probability level of less than 0.01. Results also showed that both methods (MLR and ANNN) could accurately estimate the annual precipitation. Evaluation and verification of the models with NRMSE values less than 0.3 and d values greater than 0.8 confirmed the performance of the models. The best topology of the ANN network in the study was a multiple layer perceptron network with one hidden layer and two neurons and sigmoidal activation function. The findings of the study support the fact that higher temperature in summer and autumn was a sign of higher and lower annual precipitation in ChaharmahalBakhtiari and Yazd Provinces, respectively. Besides, higher time period to 47.5 mm cumulative precipitation from the beginning of autumn implies fewer amount of annual precipitation. ConclusionThis study showed that annual precipitation could be predicted by MLR and ANN methods in both arid and semiarid regions with acceptable accuracy. According to the results, a rainy water year will be expected continuing a warmer summer and autumn in ChaharmahalBakhtiari Province; however, there will be a dry water year in Yazd Province followed by these conditions. Furthermore, if 47.5 mm cumulative precipitation takes a longer time since the beginning of autumn annual precipitation will decrease.