In situ rainfall data observed by gauges is the most important data in water resources management. However, these data have some limitations both spatially and temporally. With the advancements in satellite rainfall products, it is now possible to evaluate whether these products can capture the climatology of known rainfall characteristics. In this study, five satellite rainfall estimates (SREs) were evaluated against gauge data based on different rainfall regimes over Iran. The evaluated SREs are Climate Prediction Center Morphing Technique, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Tropical Rainfall Measuring Mission (TRMM), PERSIANN Climate Data Record (PERSIANN-CDR) and the most recently available Multi-Source Weighted-Ensemble Precipitation (MSWEP) data. The performance of these five SREs is evaluated with respect to gauge data (total: 958 stations) in eight different climatic zones at daily, monthly, and wet/dry spells during a ten-year period (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). Performance of SREs was evaluated using metrics of comparison based on correlation coefficient (CC), root mean square error, and relative error. The study shows that MSWEP has the highest CC (0.72) followed by TRMM (0.46) and PERSIANN-CDR (0.43) at daily time scale. The performance of SREs varies with respect to climatic regimes, for example, the best correlation was observed in the south, the shore of Persian Gulf with 'very hot and humid' climate with CC values of 0.72, 0.70, and 0.82 for MSWEP, TRMM and PERSIANN-CDR, respectively. Further, the performance of SREs was evaluated using the categorical statistics to capture the rainfall pattern based on different groups (e.g. light, moderate and heavy rainfall events). Results show that MSWEP, PERSIANN-CDR, and TRMM performed well to distinguish rain from no-rain condition, whereas for higher rainfall rates, PERSIANN-CDR outperforms the other SREs.

The main objective when managing inventories in blood supply chains is to establish an efficient balance between the wastage and shortage of blood units. The uncertain demand and the perishable nature of blood units can result in over-or under-stocking and increase wastage and shortage costs. In this study, we analyze how a proactive transshipment policy can avoid future shortages in addition to mitigate wastage. We consider a network of hospitals with uncertain demand in which each hospital makes decisions on the quantity to order from a central blood bank and to transship to other hospitals in each review period. We formulate the problem as a two-stage stochastic programming model. To generate scenarios, the Quasi-Monte Carlo sampling approach is employed and the optimal number of scenarios is determined by conducting stability tests. We performed numerical experiments to evaluate the performance of the proposed model and investigate its potential benefits of the outlined proactive transshipment. The developed model is used to compare the optimized policy with the current practice in some hospitals in Australia and with a no-transshipment policy. The numerical results indicate significant potential cost savings in comparison with the current policy in use and the no-transshipment policy.

No abstract

No abstract

S U M M A R YThe area of Neyshabour, a small historical city located in Northeast Iran, is subject to land subsidence. To monitor the temporal evolution of the subsidence, the small baseline subset (SBAS) algorithm is used for interferometric synthetic aperture radar (SAR) time-series analysis. To limit the spatial and temporal decorrelation phenomena, the interferograms produced from the raw ENVISAT ASAR data are characterized by small spatial and temporal baselines. Accordingly, four independent SAR acquisition data sets separated by large spatial and temporal baselines are used in the time-series analysis. To link the separate data sets, a smoothing constraint that minimizes the curvature of the subsidence temporal evolution is added to the least-squares method. The optimum smoothing factor estimated in the smoothed time-series analysis reduces the atmospheric noise, unwrapping and orbital errors whereas it preserves the non-linear seasonal deformation features. The time-series results show an incremental lowering of the ground surface, accompanied by small seasonal effects. The mean LOS deformation velocity map computed from the time-series analysis demonstrates a considerable subsidence rate of up to 19 cm yr -1 . Comparison between the InSAR time-series and continuous GPS measurements verifies the accuracy of the obtained results.Moreover, the quantitative integration of the InSAR-derived displacement measurements with observations of the hydraulic head fluctuations causing these displacements yields information about the compressibility and storage properties of the aquifer system.