The ability of the seven CMIP5 models to simulate extreme precipitation events over Iran was evaluated using the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN‐CDR) data set. The criterion used to select the CMIP5 models was the availability of historical daily precipitation data (PERSIANN‐CDR) for the retrospective period 1983–2005, as well as future projections for the three representative concentration pathways emission scenarios (RCP2.6, RCP4.5, and RCP8.5) and spatial resolution higher than 2 × 2°. This is the first study to focus on extreme precipitation climate model simulations over Iran that includes high topography and different climates. The results show that CCSM4 has the highest correlation coefficients (CC = 0.85) and lowest root‐mean‐square error (RMSE = 73.6 mm) compared to PERSIANN‐CDR for the mean annual precipitation. However, HadGEM2‐ES shows the best (highest CCs between 0.67–0.79 and almost the lowest root‐mean‐square errors [RMSEs] compared to PERSIANN‐CDR) performance for intensity indices; MIROC5 ranked seventh (least CCs and almost the highest RMSEs) among the selected models. The results show that BCC‐CSM1‐1‐M captures maximum consecutive dry days (CDD) better than the other models. The probability matching method (PMM) is used to bias‐correct daily precipitation events from CMIP5 models with respect to the PERSIANN‐CDR estimations. All the model performances designed to capture the mean annual precipitation, as well as extreme intensity indices, improved after correction. The ensemble, constructed from the bias‐corrected model simulations using multiple linear regression (MLR), has the best performance for simulating the mean annual precipitation and extreme indices (CCs between 0.82 for consecutive wet days [CWD] and 0.93 for the mean annual precipitation) compared to the PERSIANN‐CDR estimations. Among the seven selected models, CCSM4 has the highest ranking (CCs between 0.70 for CWD to 0.91 for mean annual precipitation) after bias correction.

Purpose This study aims to determine the optimal value of concession period length in combination with capital structure in build–operate–transfer (BOT) contracts, based on direct negotiation procurement and considering the conflicting financial interests of different parties involved in the project. Design/methodology/approach The financial model of a BOT project is developed considering all the influencing factors. Then, fuzzy set theory is used to take into account the existing risks and uncertainties. Bilateral bargaining game based on alternating-offers protocol is applied between the government and the sponsor to divide project financial benefit considering the lender’s requirements. Finally, concession period and equity level will be determined simultaneously according to the sponsor’s and government’s share of project financial benefit and the lender’s requirements. Findings The proposed model is implemented on a real case study, and a fair and efficient agreement on concession period length and capital structure is achieved between the government and the sponsor considering the lender’s requirements. It is revealed that being the first proposer in the bargaining process will affect the concession period length; however, it will not affect the equity level. Moreover, it is shown that considering income tax as a part of government’s financial benefit increases the length of concession period. Research limitations/implications The presented model concentrates on direct negotiation procurement in BOT projects where the sponsor and government bargain on dividing financial benefits of project. It is assumed that the product/service price is determined before according to market analysis or users’ affordability. All the revenue of project during concession period is assumed to belong to the sponsor. Practical implications The proposed model provides a practical tool to aid BOT participants to reach a fair and efficient agreement on concession period and capital structure. This could prevent failing or prolonging the negotiation and costly renegotiation. Originality/value By investigation of previous studies, it is revealed that none of them can determine the optimal value of concession period length and capital structure simultaneously considering the BOT negotiation process and different financial interests of parties involved in the project. The proposed model presents a new approach to determine the financial variables considering the conflicting interests of involved parties. The other novelty aspects of the presented model are as follows: introducing a new approach for calculating the sponsor and the government’s share of project financial benefit that will affect the determination of the concession period length and considering the effect of existing risks and uncertainties on final agreement between the involved parties using fuzzy set theory.

High-resolution real-time satellite-based precipitation estimation datasets can play a more essential role in flood forecasting and risk analysis of infrastructures. This is particularly true for extended deserts or mountainous areas with sparse rain gauges like Iran. However, there are discrepancies between these satellite-based estimations and ground measurements, and it is necessary to apply adjustment methods to reduce systematic bias in these products. In this study, we apply a quantile mapping method with gauge information to reduce the systematic error of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Due to the availability and quality of the ground-based measurements, we divide Iran into seven climate regions to increase the sample size for generating cumulative probability distributions within each region. The cumulative distribution functions (CDFs) are then employed with a quantile mapping 0.6° × 0.6° filter to adjust the values of PERSIANN-CCS. We use eight years (2009–2016) of historical data to calibrate our method, generating nonparametric cumulative distribution functions of ground-based measurements and satellite estimations for each climate region, as well as two years (2017–2018) of additional data to validate our approach. The results show that the bias correction approach improves PERSIANN-CCS data at aggregated to monthly, seasonal and annual scales for both the calibration and validation periods. The areal average of the annual bias and annual root mean square errors are reduced by 98% and 56% during the calibration and validation periods, respectively. Furthermore, the averages of the bias and root mean square error of the monthly time series decrease by 96% and 26% during the calibration and validation periods, respectively. There are some limitations in bias correction in the Southern region of the Caspian Sea because of shortcomings of the satellite-based products in recognizing orographic clouds.

In this paper a blind test methodology is described and applied for inter-comparison of hydrological models using publicly available global data sets. The model inter-comparison involves making "blind" prediction of selected hydrological responses in the Mae Chaem basin (3853km 2 ) which is located in the North West of Thailand. Moreover the value of the publicly available data sets (e.g. various satellite-based precipitations) is investigated to determine their suitability for Prediction in Ungauged Basin (PUB). The distributed hydrological model BTOPMC is applied for runoff simulation. Transfer parameters from proxy catchment and a limited measurement method are used for model parameters estimation. Though each of the satellite-based precipitations underestimated the flow, GPCP was found best among them and it showed that the application of satellite-based data in PUB is encouraging. The study also prepared a base for model inter-comparison in the Mae Chaem basin.