ali kalantari oskouei

In any country, the management of water resources in watersheds is very important, and managers try to protect these resources by providing management packages. One of the basins that have been considered by managers in the recent years is the catchment area of Lake Urmia located in the northwestern of Iran. In this study, considering the complexity of the rainfall, temperature, evaporation, discharge of hydrometric stations, water volume of the Lake, the contribution of each factor on the rate of decreasing water volume of Urmia Lake was determined. To evaluate the changes in complexity, Wavelet-Entropy combination method was used. To calculate the value of complexity, 41 years seasonal time series were used (period of 1977-2018) for the selected hydrological units of the south and west of Urmia lake. Then, the time series were divided into three equal parts, and the complexity changes were calculated in these intervals based on the wavelet-entropy criterion (SWS). The results showed that SWS of lake water volume, rivers discharge flow and precipitation were reduced in entire time, however, the SWS of temperature and evaporation increased. Generally, it seems that the rivers discharge flow was the main cause of reducing the volume of lake water, and precipitation, temperature, and evaporation parameters were in the next ranks.

Prevention of water loss for the Urmia Lake due to the drought is environmentally crucial for the lake basin and it seems that, the analysis of the historical process of factors governing the water mass balance equation for the catchment leading to the lake can provide insights on what has to be done. In order to do that, statistical significance for potential breaking points and rate of changes over time points of precipitation and runoff for 25 hydrological basin stations based on the data regarding Annual precipitation and Annual runoff related to the whole span of the lake basin from 1977 to 2019 has been studied and surveyed using Mann-kendall test, Petit test and Sen’s Slope Estimator. For all hydrological basinstations breaking points have been observed in the water discharge time points from 1993 to 2005 and the decline of water discharge. Significant increase in precipitation in the entire Urmia catchment area of about 0.16 mm at a indicates its stability during the study period. The annual runoff of the studied basins into Lake Urmia in the two time periods before and after the discharge drop were estimated at 4.671 and 1.885 billion cubic meters per year, respectively, indicating volume reduction of 2.786 billion cubic meters (59.6%). Looking at the Lake Urmia sub-basins annual discharge reduction data, it can be seen that Zarrinehroud with the largest share of 34.5% and Mahparishai with smallest share of 0.2%, and the rest in between, contribute to the occurrence of draught for Lake Urmia.

Data sharing is one of the key issues in the success of Spatial Data Infrastructure (SDI). Data sharing can prevent the repeated production of spatial data by various organizations and institutions, and provide the reduction of the costs, prevention of the resource losses, helping the economic development and using the capabilities of spatial data in processes of decision-making. But, evidences suggest that the realization of the spatial data sharing has always faced numerous challenges and problems, and that the SDI development goals will not be achieved without solving them. Therefore, the main objective of this research is to provide a strategy for identifying and prioritizing the sharing of spatial data in the country.
The framework of the research is based on surveying, the concept of risk, and the use of a fuzzy inference system. In order to objectify this framework, a case study was conducted with the participation of 19 organizations in East Azarbaijan province. At first, various sources were reviewed and the 25 probabilistic sharing challenges were identified. Then, with the help of a questionnaire, experts’ viewpoints regarding the probability of the occurrence and severity of the impacts of the challenges were investigated. The questionnaire consists of two parts. The first part focuses on the demographic information of the experts such as affiliation, years of experience, and academic degree, which were used to determine the experts’ importance weight. The second section measures the probability and the impact intensity of each identified challenge. To measure the factors of probability and impact intensity, a five level verbal rating scale including, very high, high, medium, low and very low (as verbal terms) was used to maintain the balance between simplicity and comprehensiveness. In the next stage, a fuzzy inference system, with two inputs and one output, 25 fuzzy rules, Guassian membership functions and the field inference engine were developed to process the views of the experts and to calculate the fuzzy scores of each of the challenges using MatlabR software. Having determined the sharing challenges scores, a cluster analysis was carried out to divide them based on the score related to the groups (clusters), so that, the challenges inside a cluster are very similar (but not identical) to one another but very different from the challenges in other clusters. Since there is no need to specify the numbers of clusters in hierarchical methods in advance, the hierarchical method was used as a clustering technique to group the challenges. Then, the results were evaluated by a number of knowledgeable experts.
According to the findings, the most important challenges which had the highest scores and were also in the same cluster, include: lack of a Geoportal for searching, access and evaluation, lost problems or metadata, lack of coordination among different organizations for spatial data sharing, fear of disclosing of organizational spatial data and information, the lack of up-to-date spatial data and information, the tendency to parallel work (the lack of investigation of other organizations for spatial data and information needed by the organization) and the lack of specialist in spatial data and sharing the information. Moreover, 25 challenges of spatial data were categorized into five homogenous groups (clusters) by applying a hierarchical cluster analysis. Based on the results, the overall geometric mean value of the 25 challenges of the spatial data and information sharing was calculated as 62.76% that shows the existence of the important challenges in the realization and implementation of the spatial data sharing and SDI initiatives in organizations. Analyzing the results with regard to the two types of sharing challenges revealed that the organizational challenges with a geometric mean of 55/56% were more important than the technical challenges with a geometric mean of 44/44%. These results may mean that, in order to overcome the organizational challenges, more time and efforts have to be taken into consideration in the planning and development of SDI compared to the technical challenges.
It seems that there is no accurate and complete picture of the concept of the spatial data sharing in the majority of the organizations, and it is often interpreted as putting spatial data and information of the organization in the hands of others. However, a significant part of the sharing issue is associated with the metadata sharing that prevents the repeated works and spending unnecessary credits of the organizations, and make the available data to be accessible with spatial services in different formats after an agreement between data providers and consumers, and to be used in decision making processes. The information gap in this regard is very tangible in the organizations, nevertheless, it would be possible to change the views and behaviors of individuals and organizations by creating capacity, and eventually to be hopeful that the willingness of organizations to participate in data sharing improve. Finally, the following recommendations were suggested in order to improve the status of the data sharing: individual and organizational attitude changes towards the issue of data sharing, increasing technical knowledge and empowering organizations in spatial technologies and clarifying the benefits of spatial data sharing and its socioeconomic roles in society, specifying the leader organization and forcing organizations to create standard spatial databases and metadata.

The main objective of this researchwas to determine Iran's National Spatial Data Infrastructures (NSDI) readiness index, with the aim of identifying the basic restrictions that impede NSDI development. The framework of the research is constructed on the basis of survey and SDI readiness model. In this research, Iran's NSDI according toa fuzzy-based modelhas been assessed using 16 decision criteria and five factors, including organisational, people, financial, information and technology factors.The data were collected through the questionnaire and interview with the experienced experts, who work in the government organizations, Universities, and private sector. The results of the implementation of the model demonstrated that Iran's NSDI readiness composite index was 0.44. In addition, the assessment of the factors of Iran's NSDI readiness showed that except technology factor, the rest of them were not developed well. Moreover, the status of the 75% of the assessment criteria was not satisfactory. Enterprise and private sector funding,individual leadership, telecommunication infrastructure, institutional leadership, umbrella legal agreement, politician's vision regarding SDI, metadata availability, SDI culture/education, were identified as the main limiting criteria in the development of the NSDI. Despite the different types of the limiting factors of the NSDI development, Web connectivity, digital Geospatial data availability, human capital and Geospatial software criteria were evaluated at the suitable level of development.Besides, it seems that the non-technical factors (organisational, financial, human resources) were more important limiting factors than the technology factor in Iran's NSDI development. At the end of the paper, some recommendations were presented to overcome the NSDI limiting factors.

Planning and management of water resources in arid regions are recognized as two important issues. The purpose of this research is to study the potential of water resources in the basin Meshnaq Chay. To do this study, meteorological and hydrometric data from the study area were collected and organized for subsequent processing. To locate and determine the appropriate location of the dam axis, 40 geophysical probe, and 7 piezometric well was implemented. Basin water balance based on meteorological, hydrometric and land use data was estimated. Then, using Geoelectrics and Piezometric information, the volume of water that can be stored in the proposed location of underground dams along with Regulatory volume of reservoir were calculated. The results showed that in case of rain as 330.2 mm, drought climatic conditions of the area would be within the normal range. Excess water in the non-cultivated season was estimated about 305,683 cubic meters that can be stored by groundwater dam. Annually Storage capacity of aquifers would be more than 100,000 cubic meters of reservoir regulation volume if farmers simultaneously make use of stored water, which due to water shortage in the Meshnaq village, the lack of the required water volume of the village lands can be compensated.