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This paper presents management of groundwater resource using a Bayesian Decision Network (BDN). The Kordkooy region in North East of Iran has been selected as study area. The region has been sub-divided into three zones based on transmissivity (T) and electrical conductivity (EC) values. The BDN parameters: prior probabilities and Conditional Probability Tables -CPTs) have been identified for each of the three zones. Three groups of management scenarios have been developed based on the two decision variables including "Crop pattern" and "Domestic water demand" across the three zones of the study area: 1) status quo management for all three zones represent current conditions; 2) the effect of change in cropping pattern on management endpoints and 3) the effect of future increased domestic water demand on management endpoints. The outcomes arising from implementing each scenario have been predicted by use of the constructed BDN for each of the zones. Results reveal that probability of drawdown in groundwater levels of southern areas is relatively high compared with other zones.Groundwater withdrawal from northern and northwestern areas of the study area should be limited due to the groundwater quality problems associated with shallow groundwater of these two zones. The ability of the Bayesian Decision Network to take into account key uncertainties in natural resources and perform meaningful analysis in cases where there is not a vast amount of information and observed data available -and opportunities for enabling inputs for the analysis based partly on expert elicitation, emphasizes key advantages of this approach for groundwater management and addressing the groundwater related problems in a data-scarce area.

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Estimation of sediment volume in the reservoirs is an important management criterion in water use. Many methods are used for this purpose, including hydrography, remote sensing, hydrometry and mathematical and computer models. The high cost of field methods such as hydrography required other methods to be investigated more seriously. In the present research, the hydrometry method was used to estimate the sediment volume in Karaj Dam Reservoir, located on the southern slope of Mount Alborz of Iran. The estimation is based on evaluation of both suspended and bed-load sediments. Although the sediment rating curve method is not common in general, using corrected models based on effective factors of sediment transfer, such as time of measurement, have increased the model efficiency. For this purpose, the daily and annual suspended loads were estimated in two hydrometric stations of Seera and Beylaghan (inlet and outlet hydrometric stations of Karaj Dam) using daily water flow rates and monthly sediment rating equations. Because the empirical methods of bed load sediment did not give acceptable results, the Karaushev curve (which has suitable compatibility with Iranian rivers) was used and the ratio of bed load to suspended load was obtained based on the river slope at hydrometric stations. By using total sediment load and average sediment density, the volumes of sediment were calculated for dam inlet and outlet hydrometric stations. Subtraction of the two volumes gave the stored annual sediment in reservoir of about 406,000 m 3 . The sediment volume resulting from the hydrography method (from dam primary and secondary area-volume curves) was 416,000 m 3 , which gave 97% collation, and the trapping efficiency of the Karaj Dam was calculated to be 80%.

Accurate estimation of unsaturated hydraulic conductivity (HC) is one of the most challenging problems in soil science. Here, we propose a novel approach to model HC using percolation theory. Transient behavior of water transport phenomena at low moisture contents requires additional physical process representation, beside capillary conductivity, to ensure accurate prediction of unsaturated HC. We augment the capillary model from percolation theory with two additional components, namely, (1) film flow, which is the product of volumetric flow rate per perimeter by specific perimeter of solid particles, and (2) isothermal vapor HC, derived from the Fick's law of vapor diffusion and relative humidity. The fractal characteristics of last fractal regime are used to model tortuosity and ultimately HC of vapor flow. Since the typical pressure head range of universal scaling from percolation theory is analogous to the range of vapor flow, we demonstrate that the universal scaling presented in previous studies is not sufficient to model HC for water contents below a crossover point. We also, by analyzing the scaled water retention properties, demonstrate that most studied soils exhibit three fractal regimes. Therefore, a piecewise HC function of capillary flow is developed to account for three fractal regimes, providing more flexibility for soils with multimodal characteristics. The proposed joint HC function is more accurate compared to the model of Peters‐Durner‐Iden and predecessor percolation theory models.