Estimation of rainfall spatial distribution is an important step in water resources studies. Preliminary assessment of existing rain gage networks show that the spatial distribution of the gages is not suitable as many are just located in populated areas. Optimization of rain gage locations can improve the accuracy of water balance studies. Several methods have been proposed for such tasks. Kriging is a geostatistical method which can estimate the variance error based on a pre-determined semivariogram with no reliance on historical record. One can therefore apply Kriging method to estimate the reduction in error variance due to any change in the network such as addition of a new gage or moving an existing gage. In this paper, the objective is to optimize gage locations based on error variance and topography of the region. This is done such that no extra cost is incurred; i.e. only omission or moving the stations is allowed. The results indicated that the number and the network structure is sufficient for estimation of rainfall distribution in dry months. However, in case of wet months and the annual rainfall, moving some of the stations is necessary. In all, 17 stations may be moved to reduce the average error by 10 percent. It must be noted that the error is quite high in extrapolation regions, as expected.

To Adequately design density and distribution of rain- gauges in rainfall networks of each region, is an effective step toward success of water projects, regional programming and proper use of information. In this research, the locations of new rain- gauges in rainfall network of Gav-khuni basin have been determined using transinformation entropy concept based on annual rainfall data of stations (1356-1385).Sequential and genetic algorithms have been used in order to seleet the proper rain- gauges sites. Two objectives of maximizing the minimum transinformation entropy and maximizing the mean of transinformation entropy have been defined for each algorithm. Then the performances of different models have been compared. The results imply on the better performance and relative supremacy of genetic algorithm, with maximizing the minimum entropy (maximum supremacy is +1.31, +1.34 and +0.12% in zone 1, 2 and 3, respectively) and maximizing the mean entropy (maximum supremacy is +0.35, +0.21 and +0.02% in zone 1, 2 and 3 respectively) objectives to sequential algorithm.

Introduction: The prediction of the occurrence of floods and the reduction of damages caused by it is strongly influenced by the modeling of physical phenomena and the spatial-temporal distribution of precipitation. The purpose of the research was to optimize the rainfall gauging network in Kurdistan province using Kriging estimation variance and taking into account the topography of the area. In this study, to optimize the rain gauging network in Kurdistan province, rainfall data of the rain gauging, synoptic, and climatology stations were used. In order to reduce the costs, stations close to each other that are located in the same height range and also have the same error variance, were removed from the existing network. In order to reduce the maintenance cost of the stations, after clustering of the area, 8 stations whose removal had little impact on the accuracy of the data, were identified in the province. Then. In order to strengthen the network, the optimization of new stations was put on the agenda and 28 points were set as the proposed stations....

Quality of rainfall information for a given watershed or region is primary information needed for the sustainable designing and operation of water resources systems. For an optimal rainfall network design, they should be reviewed periodically based on the information needs and future water resources development plans. This study evaluated regional values of rain gages in great Karoun basin located in Khozestan, Iran, using the discrete entropy. Discrete entropy can overcome limits of data normality assumption in past research and applications for hydrological variables. To determine the regional value of each station within a region, several information parameters such as marginal entropy, joint entropy and trans information index between stations, were calculated to identify essential rain gauge and critical area. Sensitivity analysis to number of discrete intervals showed that the entropy is sensitive to changes, but the ranks based on entropy indices appear to be less sensitive. Finally, results confirm that the entropy is useful to characterize the essential and excess station in the region and density of 31 rain gauge network is optimal and other rain gauge can be removed from monitoring network.

Introduction: Rainfall data are required for planning, designing, developing and managing water resources projects as well as hydrological studies. Some previous studies have suggested increasing the density of the rain gauge network to reduce the estimation error. However, more operational stations require more installation costs and monitoring. Some common techniques including statistical methods, spatial interpolation, information-based theory and combination are used to evaluate and design the network. Chaharmahal va Bakhtiari province is a mountainous region,hence, a denser rainfall network is expected in this mountainous environment. The aim of this study was to evaluate the condition of rain gauge stations in Chaharmahal va Bakhtiari province using two approaches, i. e. geostatistical methods and entropy theory. Materials and Methods: The main required data set for this study is a time series of rainfall data. These data were collected on a daily scale from the Regional Water Company of Chaharmahal va Bakhtiari. After performing statistical tests, the annual data series was prepared for 46 rain gauge stations. A statistical period of 2000 to 2016 was used. The homogeneity of data was investigated by double mass test and histogram drawing methods using Excel and SPSS software, and the existence of trend in the time series of data was investigated by applying a Spearman test. Then, the adequacy of rain gauges in the gauging network was investigated. Annual rainfall interpolation maps and their standard error maps were prepared using the kriging method. Contribution of each station in reducing or increasing the error in the rain gauge network was investigated by removing each station in a cross validation procedure. The efficiency of the rain gauge network was evaluated using the concept of discrete entropy and the values of entropy indices. The value of keeping the rain gauge stations was determined using the net exchange information index. Results and Discussion: There was no homogeneity problem and significant trend in the data series. Considering the permissible error percentage of 5%, there is a need to add 15 new rain gauge stations to the network. To apply the geostatistical method, we applied it once without deleting any station,then, the kriging interpolation error was calculated for the precipitation data. Then, only one station was removed at each stage, and both the error and the contribution of each station in increasing or decreasing the error compared to the case without Station deletion were obtained. The results indicated that Ab-Turki, Shahrekord, Borujen and Barez stations were more important than other stations. Two stations namely Chaman-Goli and Ben stations can also be considered as the influential stations in error due to the density of stations in the region and error maps. Similarly, the results of the entropy theory method were found effective in evaluating the design of the rain gauge network. The highest value of H(x) was observed in the data of Armand station (3. 26) and the lowest value was observed in Abbasabad station (2. 28). Since H(x) shows the uncertainty of measuring data, the maximum and minimum uncertainty were found for Armand and Abbasabad sites, respectively. Based on the Net Exchange Information Index, Bardeh, Bareh Mardeh and Dezkabad stations were ranked 1 to 3, respectively, indicating that they transmit and receive more information than other stations. On the other hand, a number of stations including Dorak anari, Abtorki and Chelo stations had the lowest values. Conclusion: Due to the vast extent of the area and also considering the permissible error percentage of 5%, the number of the stations in this area was found to be insufficient. Thus, although calculating the kriging error maps showed that some stations do not have a significant share in increasing the error, removing the stations is not recommendable. Regarding the new stations, new 15 rain gauge stations are needed to check out the error maps. According to the field observations, the higher priority should be given to the northwestern area (which had the largest interpolation error) in the first place. For the regions with lower error, such as northeast, east, southeast, west and southwest that do not have rain gauge stations, additional rain gauge stations should be constructed.

Designing of water quantity and quality monitoring system has been raised as one of the most complex issues in the field of water resources and the environment. Designing of these systems used to achieve qualitative and quantitative information, while their design process requires basic information. The study area in this research is Urmia Lake basin that located in the North West of Iran. Entropy literally means disorder. In this study used entropy theory to rain gaging monitoring in period of 1984-2011. Also The modified Mann-Kendal test was used to study the trend of the studied parameters The results of the study of the trend of precipitation values of Urmia Lake basin at annual scale showed that rainfall changes in this basin have been decreasing in the annual scale. In order to study and monitoring the rain gauge network using Entropy theory, two methods of support vector regression and kriging were used to estimate precipitation values. Results indicated that the accuracy of the regression model was higher than the Kriging model. The results of the evaluation of the entropy index at the aquifer showed that only 1. 4% of the studied basin had a severe shortage of information that required the construction of a new station in the area. However, since more than 90 percent of the basin area is in terms of data transmission in excess and relatively excessive condition, the study area is relatively good at its monitoring. In general, the results indicated that the accuracy of the optimized method of support vector regression was used to estimate the annual rainfall in the Urmia Lake basin. The results of the stations' ranking in the study area showed that the stations of Jharabad, Badamlou and Orban received ratings ranging from 1 to 3, which indicates the transfer and reception of more information than other stations.

Rainfall is a key factor for water resources management. Raingauge networks with accurate measurement and appropriate density are required for the estimation of the rainfall in ungauged sites in watersheds. This study aimed at assessing a method based on kriging and normal probability distribution function for evaluating raingauge network in GorganRud watershed (114000 km2). The concept is based on criterion that named percentage of the total area with acceptable accuracy (Ap). Spatial variability of annual rainfall is analyzed using dimensionless variogram then using a sequential algorithm 33 raingauges in the network is evaluated. Results showed the base network for annual rainfall comprises 21 gauges and 12 remaining gauges have little contribution to estimate areal rainfall in the watershed. After optimization the non-based raingauges reduced to 7 gauges and will be more effective to estimate areal rainfall in whole of watershed. Furthermore, Results showed that simplifying and using GIS software by developing a toolbox will make it easier for evaluating a raingauge network.

The spatial and temporal fluctuations of the climatic element of precipitation and severe changes will lead to changes in atmospheric patterns. Therefore, the study of precipitation trends in different time and space scales is considered as a topic of interest in climatology. The purpose of this study was to calculate the seasonal precipitation index, using the Markham method in 28 rain gauge stations of Ardabil province during the 30-year recorded period. In this regard, daily rainfall data were analyzed and the Markham method was used to calculate the mean time of occurrence and the seasonality index (SI) of the components S, C and PR (average annual rainfall vector). In the next step, the Seasonality Index (SI) obtained from the PR ratio to the total annual rainfall for all rain gauge stations over the study area. According to the results, the lowest amount of seasonality is related to Sanin and Shamshirkhani stations with a value of 0. 18. While the highest seasonality value is calculated for the Sarein Station with a value of 0. 39. Based on the seasonal pattern of monthly rainfall values, the mean occurrence time of rainfall of 20 rain gauge stations falls into winter season, and the 6 rain gauge stations experience the highest rainfall during spring season and the 2 remaining stations had a rainy autumn season. Distinguishing seasonality pattern of monthly and seasonal rainfall can be used for the prediction of water balance changes, cultivation timing, and flood/drought events in finer time scales.

Obligatory modelling of precipitations in various periods, have a lot of problems and weakness because of their casual nature in time and space. Moreover, their uncertainty in predictions, reduce credibility of estimations which have done via these models. Wavelet is one of the novel and very effective methods in analyzing of time series and signals considered in the hydrology in recent years. In this research, precipitation signal has been decomposed via selected mother wavelet, and then the resulted data are used by fitting direct equations to anticipate the precipitation. These mentioned methods are applied in Zarringol station in Golestan province (Iran) for 33 years predict monthly precipitation with 808 mm annually during 1975-76 until 2007-2008. As a result, decomposed signal via wavelet, correlation among observed and calculated data is 84% and the precipitation prediction can be done with more precise. Meaningless of F test in 90% and above verifies this phenomenon.