Hydrological yearbooks, especially in developing countries, are full of gaps in flow data series. Filling missing records is needed to make feasibility studies, potential assessment, and real-time decision making. In this research project, it was tried to predict the missing data of gauging stations using data from neighboring sites and a relevant architecture of artificial neural networks (ANN) as well as adaptive neuro-fuzzy inference system (ANFIS). To be able to evaluate the results produced by these new techniques, two traditionally used methods including the normal ratio method and the correlation method were also employed. According to the results, although in some cases all four methods presented acceptable predictions, the ANFIS technique presented a superior ability to predict missing flow data especially in arid land stations with variable and heterogeneous data. Comparing the results, ANN was also found as an efficient method to predict the missing data in comparison to the traditional approaches.

Drought is one of the most important natural hazards in Iran. Therefore, drought monitoring has become a point of concern for most of the researchers. In the present study, the changes and trend of drought was surveyed, under the current global climate changes, by non parametric Mann-Kendall statistical test for 42 synoptic stations at different places of Iran. Standardized Precipitation Index (SPI) was calculated to recognize the drought condition at different time scales (3, 6, 9, 12, 18 and 24 months' time series) for analyzing the drought trend in the recent 30 years. The obtained results have indicated a significant negative trend of drought in many parts of Iran, especially the South-East, West and South-West regions of the country. According to the results, although some parts of Iran such as North (around the Caspian Sea) and Northeast show no significant trend but in other parts of country, the severity of drought has increased during the last 30 years.

Drought is one of the most important natural hazards in Iran and frequently affects a large number of people, causing tremendous economic losses, environmental damages and social hardships. Especially, drought has a strong impact on water resources in Iran. This situation has made more considerations toward the study and management of drought. The present study is focused on two important indices; SPI and RDI, for 3, 6, 9, 12, 18 and 24 months time scales in 40 meteorological synoptic stations in Iran. In the case of RDI computation, potential evapotranspiration was an important factor toward drought monitoring. So, evapotranspiration was calculated by Penman-Monteith equation. The correlation of RDI and SPI was also surveyed. Drought severity maps for SPI and RDI were also presented in the driest year (1999)(2000). The present results have shown that the correlation of SPI and RDI was more considerable in the 3, 6 and 9 months than longer time scales. Furthermore, drought severity maps have shown that during 1999-2000, the central, eastern and south-eastern parts of Iran faced extremely dry conditions. While, according to SPI and RDI trends, other parts of the country suffered from severe drought. The SPI and RDI methods showed approximately similar results for the effect of drought on different regions of Iran. Since, RDI resolved more climatic parameters, such as evapotranspiration, into account which had an important role in water resource losses in the Iranian basins, it was worthwhile to consider RDI in drought monitoring in Iran, too.

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Estimation of the design flood flow for hydraulic structures is often performed by adjusting probabilistic models to daily mean flow series. In most cases, this may cause under design of the structure capacity with possible risks of failure because instantaneous peak flows may be considerably larger than the daily averages. As there is often a lack of instantaneous flow data at a given site of interest, the peak flow has to be estimated. This paper develops new machine-learningbased methods to estimate the instantaneous peak flow from mean daily flow data where long daily data series exist but the instantaneous peak data series are short. However, the presented methods cannot be used where only daily flow data are available. Developed methodologies have been successfully applied to series of flow information from different gauging stations in Iran, with important improvements compared to traditional empirical methods available in the literature.Reliable results produced by the machine-learning-based models compared to the traditional methods show the superior ability of these techniques to solve the problem of inadequate measured peak flow data periods, especially in developing countries where it is difficult to find sufficiently long instantaneous peak flow data series.

Irrigation water quantity and quality limitation is the main problem of agricultural development in the research area (Rafsanjan pistachio orchards in Iran). Optimization of the irrigation system is one of the most important factors to enhance water use efficiency in this region. This research project was designed to compare the applicability of two different types of irrigation, including traditionally used surface irrigation and a simple and relatively cheap subsurface drip irrigation (using a perforated pipe covered with plastic cloth). For this purpose two plots, each containing 39 pistachio trees and 720 m 2 in area, were selected in an orchard and were both irrigated using an exactly equal quantity and quality of water for 3 years. At the end of the second year the yield in the plots was harvested separately and compared. The ratio of the weight of fresh and also dried crop in the subsurface irrigation plot to those of surface irrigation plot was respectively 1.895 and 2 for the second year, and 2.17 and 2.12 for the third year. Another parameter measured for the trees of the two plots was annual shoot growth. The value of the plot growth index (PGI) in the surface irrigation plot was calculated as 2238 cm, whereas in the subsurface irrigation plot it was 4580 cm. In addition, the dried weight of weed grown in the surface irrigation plot was 82 kg but was only 21 kg in the subsurface irrigation plot. These results show the considerable difference in efficiency of the two irrigation systems, and relatively higher preference for a subsurface system over the traditionally used surface method.