Ali Salajegheh

Spatiotemporal patterns of stable isotopes and hydrochemistry in springs and river flow of the upper Karkheh River Basin, Iran

Karst springs of the Zagros Mountains contribute a significant amount to agricultural and human water demands of western and south-western Iran. For an adequate management of available water resources in semi-arid and arid regions, sufficient hydrological monitoring is needed, and hydro-chemical and isotope hydrological data provide important additional information. About 350 water samples were collected from precipitation, river water, and karst springs of the upper part of the Karkheh River Basin (20,895 km2) located between 33°35′ and 34°55′ North and 46°22′ and 49°10′ East with elevations ranging from 928 to 3563 m above sea level. Sampling was conducted in monthly time resolution from August 2011 to July 2012. All samples were analysed for hydro-chemical parameters (pH, electrical conductivity, and major ions) and stable isotopes (deuterium, oxygen-18). Isotope values of precipitation indicate a local meteoric water line (Zagros MWL δ2H=6.8 δ18O+10.1; R2=0.99) situated between the Mediterranean MWL and Global MWL. Spring and river water isotope values vary between−7.1 and−4.1 ‰, and−38 and−25 ‰ for δ18O and δ2H, respectively, responding to winter snowmelt and evaporation. This work implements stable isotopes and hydro-chemical information of springs and river water to understand hydrological and hydro-geological interrelations in karstic semi-arid areas and helps to improve the current water resources management practices of western Iran.

Comparative evaluation with the analytical hierarchy procedure of the Kalinske, Brooks, and Toffaleti equations used to estimate suspended–sediment discharges in rivers

This paper presents a comparative analysis of the application of the analytical hierarchy process (AHP) to determine the reliability of three equations used estimate suspended–sediment discharges at the Armand River in Iran. The AHP procedure was utilised to compare the discharges of suspended sediments measured at a station on the Armand River with results calculated with the equations derived by Kalinske (1942), Brooks (1963) and Toffaleti (1969). The equations were then used with the applicable coefficients from the literature to estimate sediment loads for the same periods as those calculated from the field data. A weighted coefficient was calculated from the estimated results from each equation and compared to each other and the field data. The results of the analysis with the AHP model provided an objective screening and ranking procedure with respect to accuracy, economic, and numbers of input parameters criteria.

Application of joint probability approach in derivation of rainfall temporal patterns: case study: Kan Watershed, Iran

Joint probability approach is a superior method of design flood estimation which considers the probabilistic nature of the inputs such as rainfall temporal pattern in the rainfall-runoff modelling. Design flood estimation is one of the major steps in the design and sizing of hydraulic structures and installations. Under the conditions of significant storage capacity or long return periods, use of mathematical models is a common choice for changing of design rainfall to design flood. One of characteristics of design rainfall that cause design flood is distribution of rainfall during the rainy period which is the so-called design rainfall temporal pattern. Design rainfall temporal pattern is a major source of uncertainty in rainfall-based design flood estimation methods. Despite high variability of temporal patterns from storm to storm, an average temporal pattern is used in the design event approach, the recommended rainfall-based design flood estimation method. This paper examines the variability of temporal patterns using joint probability approach. In this study, Monte Carlo simulation technique based on joint probability approach, that considers probability-distributed inputs, model parameters and their correlations, applied to find the probability-distributed rainfall temporal pattern in the study area.