An investigation of near surface wind speed trends in arid and semiarid regions of Iran

Wind plays an important role on the ecosystems and hydrological cycles besides other meteorological parameters such as temperature, precipitation, sunshine, and relative humidity. It strongly affects evapotranspiration, especially in arid and semiarid regions where there are serious problems in regard to water resource management. Evaluating the wind speed trend can provide good information for future agricultural planning. This study was conducted in order to investigate the wind speed trends over 24 synoptic meteorological stations located in arid and semiarid regions of Iran from 1975 to 2005. Near-surface wind speed was trended by nonparametric Mann–Kendall test spatially and temporally in three time scales (annual, seasonal, and monthly). Then, Sen’s slope estimator was used to determine the amount of the changes; furthermore, 10-year moving average low-pass filter was applied to show general trends. Finally, the smoothed time series derived from the mentioned filter were classified in three clusters for each time series and then mapped to show their spatial distribution pattern. Results showed insignificant and significant, increasing and decreasing trends during the surveyed time. Wind speeds in less than 50 % of stations changed statistically in all time scales, and in most cases, the frequency of the upward trends was more than that of downward ones. The spatial distribution of significant wind speed showed that the increase mostly occurred in eastern part. Clustering gave us the turning point around 1990. Clearly, when clusters were mapped, they indicated the same pattern as the Z value maps derived from Mann–Kendall test which meant that the outputs of the mentioned method confirmed the other one. As the wind speed trends in different stations likely to follow the previous evapotranspiration (ET₀) trend results in Iran, it confirms that wind speed was an effective parameter on ET₀, even though other parameters should be considered too.     

An investigation of trends in precipitation volume for the last three decades in different regions of Fars province, Iran

Under condition of climate changes as global warming, monitoring and detecting trend of precipitation volume is essential and will be useful for agricultural sections. Considering the fact that there were not enough research related to precipitation volume, this study aimed to determine trends in precipitation volume, monthly and annually in different regions of Fars province for the last three decades (33?years period; 1978–2010). Fars province is located in arid and semi-arid regions of Iran, and it plays an important role in agricultural production. Inverse distance weighting interpolation method was used to provide precipitation data for all regions. To analyze the trends of precipitation volume, Mann–Kendall test, Sen’s slope estimator, and 10-year moving average low-pass filter (within time series) were used. The negative trends were identified by the Sen’s slope estimator as well as Mann–Kendall test. However, all the trends were insignificant at the surveyed confidence level (95%). With regards to the application of 10-year moving average low-pass filter, a considerable decreasing trend was observed after around year 1994. Since one of the most important restrictions in agricultural development of the Fars province is lack of sufficient water resources, any changes onward to lack of sufficient precipitation impose impressive pressure and stress on valuable resources and subsequently agricultural production.     

A survey of temporal and spatial reference crop evapotranspiration trends in Iran from 1960 to 2005

Reference crop evapotranspiration (ET₀) is one of the most important climatic parameters which plays a key role in estimating crop water demand and scheduling irrigation. Under global warming and climate change conditions, it is needed to survey the trend of ET₀ in Iran. In this study, ET₀ values were determined based on FAO-56 Penman-Monteith equation over 32 synoptic meteorological stations during 1960–2005; and analyzed spatially and temporally in monthly, seasonal and annual time scales. After removing the significant lag-1 serial correlation effect by pre-whitening, non-parametric statistical Mann–Kendall (MK) test was used to detect the trends. The slope of the changes was determined by Sen’s slope estimator. In order to facilitate in trend analysis, the 10 moving average low pass filter were also applied on the normalized annual ET₀ time series. Annual ET₀ time series and filtered ones were then classified by hierarchical clustering in three clusters and then mapped in order to show the patterns of different clusters. Results showed that the significant decreasing trends were more considerable than increasing ones. Among surveyed stations, and on an annual time scale, the highest and lowest annual values of Sen’s slope estimator were observed in Tabas with (+) 72.14 mm per decade and Shahrud with (?) 62.22 mm per decade, respectively. Results also indicated that the clustered map based on normalized and filtered annual ET₀ time series is in accordance with another map which showed spatial distribution of increasing, decreasing and non-significant trends of ET₀ on annually time scale. Exploratory and visual analysis of smoothed time series showed increasing trend in recent years especially after 1980 and 1995. In brief, the upward trend of ET₀ in recent years is a crucial issue with regard to the high cost of dam construction for agricultural aims in arid and semi-arid regions e.g. Iran.     

A feature weighting and selection method for improving the homogeneity of regions in regionalization of watersheds

The method used for feature selection or feature weighting in regionalization of watersheds may affect the results of regionalization methods considerably. It can play a key role in forming hydrologically homogeneous regions for regional flood frequency analysis. In this study, a method based on exploring the nearest and farthest neighbours of data points is introduced for identifying salient features for regionalization of watersheds. The method includes options to relate watershed features to flood data records in order to increase the homogeneity of the regions. The nearest and farthest neighbours are identified based on the criteria such as the mutual information criterion and Spearman's rank correlation coefficient. Then, the watershed features more able to explain the relationships between the nearest and farthest neighbours are identified as salient features to form homogeneous features for regional flood frequency analysis. The results show that the optimum option of the proposed method improves the performances of the hard and fuzzy clustering algorithms in more than half of the cases based on the cluster validity indices. Furthermore, the results reveal that the optimum option can increase the number of the homogeneous regions formed by clustering algorithms to a great extent. By using the optimum option with 5 nearest and 5 farthest neighbours, longitude, drainage area, and run‐off coefficient are identified as the salient features to regionalize Sefidrud basin. The results show that the proposed method can be considered as an efficient method to form homogeneous regions for regional flood frequency analysis.     

Uncertainty analysis in bed load transport prediction of gravel bed rivers by ANN and ANFIS

River flow is a complex dynamic system of hydraulic and sediment transport. Bed load transport have a dynamic nature in gravel bed rivers and because of the complexity of the phenomenon include uncertainties in predictions. In the present paper, two methods based on the Artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are developed by using 360 data points. Totally, 21 different combination of input parameters are used for predicting bed load transport in gravel bed rivers. In order to acquire reliable data subsets of training and testing, subset selection of maximum dissimilarity (SSMD) method, rather than classical trial and error method, is used in finding randomly manipulation of these subsets. Furthermore, uncertainty analysis of ANN and ANFIS models are determined using Monte Carlo simulation. Two uncertainty indices of d factor and 95% prediction uncertainty and uncertainty bounds in comparison with observed values show that these models have relatively large uncertainties in bed load predictions and using of them in practical problems requires considerable effort on training and developing processes. Results indicated that ANFIS and ANN are suitable models for predicting bed load transport; but there are many uncertainties in determination of bed load transport by ANFIS and ANN, especially for high sediment loads. Based on the predictions and confidence intervals, the superiority of ANFIS to those of ANN is proved.     

Non-parametric trend analysis of the aridity index for three large arid and semi-arid basins in Iran

Currently, an important scientific challenge that researchers are facing is to gain a better understanding of climate change at the regional scale, which can be especially challenging in an area with low and highly variable precipitation amounts such as Iran. Trend analysis of the medium-term change using ground station observations of meteorological variables can enhance our knowledge of the dominant processes in an area and contribute to the analysis of future climate projections. Generally, studies focus on the long-term variability of temperature and precipitation and to a lesser extent on other important parameters such as moisture indices. In this study the recent 50-year trends (1955–2005) of precipitation (P), potential evapotranspiration (PET), and aridity index (AI) in monthly time scale were studied over 14 synoptic stations in three large Iran basins using the Mann–Kendall non-parametric test. Additionally, an analysis of the monthly, seasonal and annual trend of each parameter was performed. Results showed no significant trends in the monthly time series. However, PET showed significant, mostly decreasing trends, for the seasonal values, which resulted in a significant negative trend in annual PET at five stations. Significant negative trends in seasonal P values were only found at a number of stations in spring and summer and no station showed significant negative trends in annual P. Due to the varied positive and negative trends in annual P and to a lesser extent PET, almost as many stations with negative as positive trends in annual AI were found, indicating that both drying and wetting trends occurred in Iran. Overall, the northern part of the study area showed an increasing trend in annual AI which meant that the region became wetter, while the south showed decreasing trends in AI.     

Regionalization of watersheds based on the concept of rough set

Natural Hazards - In this study, an algorithm inspired by some concepts of the rough set theory is proposed for regionalization of watersheds. The algorithm includes a clustering step and a...