Water Enginnering

Developing methods that can give a suitable prediction of hydrologic events is always interesting for both hydrologists and statisticians, because of its importance in designing hydraulic structures and water resource management. Because of the computer revolution in statistical computation and lack of robustness in at-site frequency analysis, since early 1990 the application of regional frequency analysis based on L-moments has been considered more for flood analysis. In this study, the above-mentioned method has been used for the selection of parent distributions to fit maximum monthly rainfall data of 18 sites in the Zayandehrood basin, Iran, and as a consequence the generalized extreme-value and Pearson type-III distributions have been selected and model parameters have been estimated. The obtained extreme rainfall values can be used for meteorological drought management in the arid zone.

Multiplicative seasonal autoregressive integrated moving average models are appropriate for the monthly stream flow of the Zayandehrud River in western Isfahan province, Iran, through Box and Jenkins time series modeling approach. Among the selected models interpreted from ACF and PACF, seasonal multiplicative ARIMA (1,1,0)×(0,1,1) satisfied all tests and showed the best performance. Seasonal moving average parameter in the model indicates periodicity and long memory and in the streamflow, while a nonseasonal autoregressive parameter indicates the linearity of the monthly streamflow. The model forecasted streamflow for 24 leading months showed the ability of the model to predict and forecast statistical properties of the streamflow.

Surge flow irrigation can reduce the irrigation water losses and improve irrigation performance. In this study attempts have being made to design, manufacture and evaluate an automatic surge flow irrigation system. The system includes an automatic surge valve, which can be programmed by user based on field conditions such as soil infiltration characteristics changes during the irrigation season. The surge valve is inexpensive, portable and wireless and its energy is supplied by a chargeable battery, which the battery can also be recharged by a solar panel for a long duration uses. To evaluate the performance of the system, the surge valve including constant head water delivery system to the furrows were installed in an furrow irrigation experimental farm and based on input data given to the system the furrows were irrigated automatically by surge method with cycled inflow of 10 min on and 10 min off. The results showed that the system is able to accurately and automatically irrigate the furrows by surge method based on information given to the system. For the same discharge and volume of water applied to the furrows the water advance along the furrows were faster for surge flow as compared to the continuous flow.

The aim of this study was to establish the relationships between wheat yield andmeteorological variables together with agrometeorological indices for prediction of wheatyield for different regions of Kordestan province, Iran. Wheat prediction was carried outusing different meteorological variables as well as agrometeorological indices in differentregions of Kordestan province, Iran including Sanandaj, Ghorveh and Bijar districts for theyears 2004-05 and 2005-06. On the basis of correlation coefficients, standard error of theestimates and relative deviations of the predicted yield from actual yield using differentstatistical models, the best subset of agrometeorological indices were selected including dailyminimum temperature (Trail), accumulated difference of maximum and minimumtemperatures (TD), accumulated Photothermal Units (PTU) and Sunshine Hours (SH). Theresults revealed that in Sanandaj district, yield prediction was performed two months inadvance before harvesting time which was coincide with end of the second active vegetativestage after dormancy stage of wheat (March 27th to May 31st). For Ghorveh district, yieldprediction was done one month before harvesting time which was at the end of reproductivestage of wheat (May 22nd to June 20th). In Bijar district, although there was a significantrelationship between yield and agrometeorological variables but a high relative deviationbetween predicted and actual yields exists. Therefore, none of the suggested models wasused for Bijar district. It can be concluded that in the final statistical models, 68% of wheatyield variability was accounted for variation in the above agrometeorological indices forSanandaj and Ghorveh districts.

The aim of present study is to generate the daily weather values for maximum and minimum airtemperatures and solar radiation. Two well known weather generators are evaluated here. Data from the fiveIranian synoptic stations having long-term weather records and dry climates have been used to compare theactual data sets with generated one. The accuracy of the different weather generators models was evaluatedby means of three widely used statistics : Correlation coefficient (R), Root Mean Square Error (RMSE) and MeanBias Error (MBE). For maximum and minimum temperatures, Bushehr's data show the lowest RMSE andEsfahan's data show the highest RMSE. For radiation data, RMSEs of all of the stations are very high, exceptfor Esfahan station. Ingenerai, the computed values of temperature are in good agreement with the data derivedby the observation, but the computed values for radiation do not indicate a good agreement with the measureddata.

In this study, regional climates of Iran were identified based on the properties of the monthly rainfall time series models of 28 main cities of Iran. The autocorrelation (ACF) and partial autocorrelation (PACF) of selected series revealed the seasonal behavior of the monthly rainfall. After the parameters of the models were estimated and the residuals of the models analysed to be time independent and the normality was checked using Portmanteau lack of fit and nonparametric tests, the multiplicative ARIMA model was fitted to monthly rainfall time series of the stations. To determine regional climates, a hierarchical cluster analysis was applied on autocorrelation coefficients at different lags and three main climatic groups were found based on the time series models, namely, simple, moderate and complex climates. The results of the time series modeling showed a high variation of the temporal pattern of the monthly rainfall over Iran except for the margins of the Caspian Sea and the Persian Gulf. The study also shows that the correlation between the seasonal autocorrelation coefficient of the rainfall time series and the rainfall coefficient of variation and elevation of the stations is significant while lag-one autocorrelation coefficient does not correlate to rainfall coefficient of variation and the elevation of the stations. Different models also imply the high variation in the spatial rainfall producing mechanism and different stationarity and periodicity characteristics of the rainfall temporal pattern over Iran. A nomenclature of the abbreviation is given at the end of the paper.

To gain a process-based understanding of river hydrology, daily time-series of discharge are superior to monthly or annual discharge means, as their higher temporal resolution allows fine-scale phenomena to be resolved. In particular, the timing and magnitude of flow extremes, as well as dynamic structures (e.g. flood waves, recession flows) are captured. Identifying extremes within some arbitrary time step yields a derivative time-series (typically yearly, quarterly, or monthly) of high or low flows, which is then used by hydrologists to construct probability density functions for flood and/or drought risk assessment. The main objective of the low flow investigations is a better understanding of low flows in order to improve water projects with respect to regional management, effluent dilution, water withdrawals and hydroecology. The following research issues might be considered: analysis of the natural variability of low flows in relation to soil type, hydrogeology and climate; examination of the impact of anthropogenic activities on low flows.

There are generally no measurements in the parts of river that the low flow estimates are required. For overcoming this problem, the regional analysis has been often used. In this paper, using the mean daily flow statistics from 41 hydrometric stations in Karkheh basin, Iran after checking the region homogeneity by cluster method and Andrew’s curves, low flow analysis has been performed by several models namely: multivariate regression for determining the relations between low flow values and hydrologic characteristics of basin (MRLF), index low flow method (ILFM), regionalization model of frequency formula parameters (RFFP, determining regression equation between mean and standard deviation of low flows and hydrologic characteristics of basin) and hybrid low flow model (HLFM). The error tests of regional models for four mentioned methods in comparison with point analysis in the basis of mean relative error (MRE) and root of mean square error (RMSE) criteria show that for 5, 10, 20, 25, 50 and 100 years return periods, MRLF and ILFM have more accuracy in comparing with RFFP and HLFM. In the basis of MRE, ILFM and also in the basis of RMSE, MRLF are suitable methods for low flow modeling in this basin. Also the results show that MRLF and ILFM for the low and moderate return periods (about 50 years) and RFFP and HLFM for the high return periods (higher than 50 years) do not have a significant difference by 5 percent of confidence level. Finally for comparing HLFM with the other three methods, a relative error criterion has been determined. The results show that HLFM for 5, 10, 20, 50 and 100 years return periods displays the MRE of 86.6, 7.5, 15.8, 60.11, 73.20 and 257.5 percent and also the RMSE of 2.5, 2.75, 2.52, 1.99, 1.75 and 1.56, respectively. On this basis, HLFM is determined as the third rank of importance after the two models, MRLF and ILFM. Also, it indicates more accuracy while comparing with RFFP.

The aim of this study is estimation of monthly pan evaporation using artificial neural networks and support vector machines. In the current study, the meteorological variables including air temperature, solar radiation, wind speed, relative humidity and precipitation were considered monthly. The R2 of ANNs and SVMs models were obtained 0.940 and 0.936, respectively; whereas the Mean Square Error values (MSE) were 1265.22 and 40.98, respectively. Both ANNs and SVMs approaches work well for the data set used in this region, but the SVMs technique works better than the ANNs model.

This investigation develops a new method, called the "Region of Influence" (RI) approach, to improve at-site estimates of extreme flow quantiles based on statistical and physical characteristics of the surrounding stream gauging stations. One main advantage of this approach over the traditional regional methods is that geographically dispersed sites in a "Region of Influence" of the reference site, are more likely to be independent of each other. Such "regions" are more hydrologiclly homogeneous than the traditionally defined regions. Statistical properties of flood data are needed for inclusion in the similarity distance algorithm and for estimating flood quantiles. Using the partial flood series, a number of distributions were tested by three fitting tests with data sets from 61 stream gauging stations located in south-eastern New South Wales (NSW), Australia. The successful distributions were the Log Pearson Type 3, the Wakeby, and the Pearson Type 3 for specific ranges of log skew. The criteria for choice of a set of the statistical and physical attributes of catchments and the effect of interaction between attributes on the defined regions are determined. The choice of the threshold value of the similarity distance and the associated sensitivity are discussed. The identification of regional outlier sites and their effects on standardization process of the attributes are also investigated. The effect of the combination of physical attributes with statistical one on the defined regions are evaluated. The associated flood frequency curves with the RI approach based on different combination of attributes are compared with observed at-site estimates. The steps for application of the RI approach to the gauged and ungauged sites are detailed. As a consequence, a physically-statistically based and a physically based RI approach has been recommended for improvement or estimation of flood quantiles of the gauged and ungauged sites in south-eastern NSW, respectively. Region of Influence Approach / Australia / Flood Frequency / Rainfall / Runoff / Catchments Djokic, Dean, Supervisor University of New South Wales, School of Civil Engineering, Dept. of Water Engineering, Sydney, NSW, Australia University of New South Wales, School of Civil Engineering, Dept. of Water Engineering, Sydney, NSW, Australia

The major objective of this study is to investigate the trend of some hydrological data in Karkhe basin where located in west of Iran. Thirty-six year records of annual rainfall depth and annual discharge data during 1966 to 2002 from five hydrometric and meteorological stations were used. Trend and persistence analyses of the data are performed using the Mann-Kendall Test, Regression Analysis, the Rank Test Statistic, the Cumulative Deviation, the Autocorrelation Coefficient, the Turning Point Test, and the Difference-Sign Test. The results indicate that rainfall and discharge data have not significant long-term trends and persistence in this watershed. Generally, there is no significant connection between these hydrological data and climate change phenomena. Existence a large dam in this basin (Karkhe Dam) probably adjusts the climate in microclimatic scale such that effect of climate change could not be clearly visible.

Ecological research is certainly entering a new era of integration and collaboration, building on a firm base of advancements in our ecological knowledge achieved in recent years, as we meet the challenge of understanding the great complexity of biological systems. Ecological sub-disciplines are rapidly combining and incorporating other biological, physical, mathematical, and sociological disciplines. The burgeoning base of theoretical and empirical work, made possible by new methods, technologies, and funding opportunities, is providing the opportunity to reach robust answers to major ecological questions. The United States National Science Foundation (1999) convened a white paper committee to evaluate what we know and do not know about important ecological processes, what hurdles currently hamper our progress, and what intellectual and conceptual interfaces need to be encouraged. The committee distilled the discussion into four frontiers in research on the ecological structure of the earth’s biological diversity and the ways in which ecological processes continuously shape that structure. Environmental scientists are also increasingly concerned not only with explaining the present, but with anticipating the future. An understanding of the past is vital to both concerns.
Paleo-ecology is playing an increasingly prominent role in environmental forecasting. As an example, paleo-fire records could be used to test the mechanistic models required for the prediction of future variations in fire. This paper highlights the discussions of those frontiers and explains why they are crucial to our understanding.