توسعه بهينه شبكه باران‌سنجي با استفاده از روش كريجينگ و آنتروپي در محيط GIS (مورد مطالعه: حوضه آبريز كرخه)

Optimal Development of Rain Gauge Network using Kriging and Entropy in Geographic Information System (GIS), (Case Study of Karkhe Basin)

با توجه به محدوديت امكان توسعه ايستگاه‌هاي باران‌سنجي، تعيين بارش در تمام نقاط و به تبع آن تخمين دقيق بارش منطقه‌اي امكان‌پذير نيست. لذا، بايد در انتخاب محل و تعداد ايستگاه‌ها در تخمين بارش منطقه‌اي دقت كافي به عمل آيد. در اين تحقيق از آنتروپي انتقال اطلاعات و واريانس تخمين بارش منطقه‌اي براي تعيين نقاط بهينه توسعه ساختار موجود شبكه باران‌سنجي استفاد شده است. در ساختار پيشنهادي، نقاط داراي حداكثر واريانس تخمين و حداقل آنتروپي انتقال اطلاعات در سطح حوضه كانديد ايستگاه‌هاي جديد در نظر گرفته مي‌شود. مدل بهينه‌سازي براي تركيب نتايج اين دو روش توسعه داده شده و در نهايت موقعيت پيشنهادي تاسيس ايستگاه‌هاي جديد تعيين مي‌شود. در ساختار پيشنهادي، از محيط GIS براي ارايه بهتر نتايج تحليل‌هاي مكاني استفاده شده است. حوضه آبريز كرخه با توجه به اهميت بالايي كه از بعد منابع آبي كشور دارد، مطالعه موردي اين تحقيق در نظر گرفته شده است. نتايج بررسي‌ها و تحليل‌هاي صورت‌گرفته در اين تحقيق نشان‌دهنده اين است كه با استفاده از ايستگاه‌هاي پيشنهادي در حوضه كرخه كه هفده موردند مي‌توان دقت نتايج تحليل مكاني بارش را به ميزان زيادي افزايش داد.

Rainfall, as the main representative factor of every regionʹs natural and hydro-climatic specifications, has a high degree of importance in water resources planning and management. Rainfall events have noticeable spatial and temporal variations. Rain gauges of various types are used for the measurement of spatial variations of precipitation. Considering the limitation of possibility of expanding these rain-gauge stations to all locations, the precise estimation of a regional rainfall is not possible, and therefore, sufficient care needs to be taken into choosing the location and number of stations for achieving sufficient precision in regional rainfall estimation. Considering numerous factors that are involved in determining the optimal location of these stations, development of an optimal structure for placement of the stations or expansion of an existing rain-gauge network, seems necessary. In this research, information transformation entropy and estimation variance of regional rainfall are used for determining optimal locations for expansion of an existing rain-gauge network structure. In the proposed structure, points with maximum estimation variance and minimum information transformation entropy over a region are considered as candidates for new stations. An optimization model for combining the results of these two methods is developed and finally suggested locations for establishing new stations are determined. In the proposed structure, GIS environment is used for better illustration of results of spatial analysis. Given its high importance in terms of national water resources, the KARKHE basin is considered as a case study in this research. The results of the investigation and analysis performed in this research show that using 17 suggested new stations in the KARKHE basin, the precision of results of rainfall spatial analysis can be enhanced significantly. Methodolgoy This proposed methodology in this study involves the following steps: (1) Data collection and analysis (2) application of kriging to existing rainfall data to calculate the rainfall spatial analysis variance (3) calculating the transformation entropy in the basin surface (4) selection of candidates points for rain gauge development considering the minimum transformation entropy and the maximum rainfall estimation error (5) presentation of rain gauge network final map. In this paper, the best combination of sampling stations in a monitoring network is selected using the entropy theory by considering the maximum uncertainty (minimum redundant information in the system) and the maximum rainfall estimation Kriging error. Hence, in this study, a new model composed of variance estimation and entropy is proposed to relocate the rainfall network and to obtain the optimal design with the minimum number of rain gauges. Results and Discussion The rainfall data of the 49 stations in the study region are for the period of October to April are utilized.The correlation coefficient higher than 0.6 in rainfall and height, Cokriging method was used to analyze the spatial rainfall. Kolmogorov Smirnov test (K-S) with a confidence level of 95% of normal monthly precipitation data is verified. In case of non-normal data conversion Cox - Box or log normal distribution, the data are close to normal distribution. The estimated variance is calculated for each month. After calculating variance estimates for each month, the layers can be weighted according to the average rainfall. The final layer of the overlapping layers are obtained and as a measure of the objective function be considered. The transformation entropy layer such as variance estimation layer obtained. A new model composed of variance estimation and entropy is proposed to relocate the rainfall network to obtain the optimal design with the minimum number of rain gauges. As a case study, the application of the proposed method to an existing rain network over the Karkhe catchment region under a minimum transformation entropy of 30% and maximum Kriging error of 60% resulted in 17 new rain stations to be added to the original network. Cnclusions In this study a methodology is proposed to suggest new locations for rain gauges development using kriging and entropy methods. On the basis of the rainfall data from the current rain gauge stations, the rainfall of the candidate rain gauge stations are generated by estimation Kriging error. The information entropy is based on the concept of probability to measure uncertainties. A network optimization model based on minimizing the estimated variance and by rain gauge data suggest that the implementation of this new model, 17 stations were added to the network location. Most of the stations in the eastern and north-eastern border of the basin, in the highlands and in places where the space station is too high, they were located. The results show that using the theory of Entropy with geostatistical methods, a higher accuracy in rainguage network development, can provid. By combining the two methods can be used to determine the best places established stations, so that the two factors cover each other. Spatial design using model proposed in this paper, the best combination for rainguage stations using the minimum transformation entropy and the maximum rainfall estimation Kriging error is selected.