Rainfall threshold" is considered as one of the evolving flood forecasting approaches. When the cumulative rainfall depth for a given initial soil moisture condition intersects the corresponding moisture curve, the peak discharge is expected to be equal or greater than the threshold discharge for flooding at the target site. Besides the total rainfall depth, spatial and temporal distribution of rainfall can influence the peak discharge and the time to peak. In the few past studies on the extraction of rainfall threshold curves for flood forecasting, the rainfall assumed to be uniform in space whereas the temporal distribution was subjected to certain assumptions. In the present study, the spatial distribution of rainfall was simulated with the Monte Carlo (MC) method and the mean Huff pattern for all rainfall durations was imposed for the temporal distribution. For each of the MC run, the random weight assigned to every sub-watershed follows the pdf of weights in historical rainfall events. The HEC-HMS model with two different infiltration methods namely SCS-CN and Green-Ampt and Muskingum river routing were adopted as the hydrologic model. After the calibration and validation of the model for Madarsoo watershed in Golestan province in Northeastern Iran, the MC simulations were performed for 1, 2, 6 and 12 h durations. The outputs from the SCS-CN method exhibit only a slight increase in threshold values with respect to duration and was not in the range of our expectations from watershed response, i.e. the rainfalls with greater durations should be greater in depth to produce a specific peak discharge. For the GreenAmpt infiltration method, the rainfall thresholds with 50% probability associated S. Golian et al. with the critical discharge under dry soil moisture condition were 44.5, 49.0, 64.2 and 94.6 mm for 1, 2, 6 and 12 h durations, respectively. Results for July 2001 flooding revealed that the cumulative rainfall intersected all 10%, 50% and 90% rainfall threshold curves but for July 2005 flooding the 10% curve was only intersected by the cumulative rainfall curve. The advantage of MC-derived rainfall threshold curves in real-time operations is that decision-makers have the flexibility to adopt a curve more consistent with flood observations in the region.
High-resolution satellite-retrieved precipitation products are useful input data for hydrologic predictions and water resources management, especially in developing countries where the availability of ground-based rainfall measurements with high spatial coverage is very limited. In this study, four widely used satellite rainfall estimates (TMPA-3B42V7, TMPA-3B42RT, PERSIANN, and CMORPH) are evaluated with a dense raingauge network over six regions with various physiographic and climate conditions in Iran. Assessments are implemented at daily scale for different seasons during the five years period from 2003 to 2008. Overall, the results show that 3B42V7 leads to better performance than the other three products over different terrains. According to the value of relative bias (RBias) as one of the verification metric used in this study, 3B42V7 with an average value of 13.43% over all the regions matches best with the raingauge observations, while both PERSIANN and 3B42RT overestimate precipitation by 78.13% and 31%,respectively. On the other hand, CMORPH with RBias of -17.6% tends to underestimate the rainfall amount. Furthermore, the evaluations over different seasons indicate that the best performance for PERSIANN and both TMPA products is during the winter, while for CMORPH is during the autumn season. With respect to the critical success index (CSI) in order to assess the rain detecting skill of satellite products, one can conclude that PERSIANN leads to better estimations during the winter and summer, 3B42RT during the spring, and CMORPH during the autumn season. Generally, the implemented analyses in this research provide quantitative information of error characteristics associated with satellite precipitation products over different parts of Iran and thus will offer hydrologic users a better understanding of satellite rainfall estimates applicability in this area.
Precipitation is a critical variable to monitor and predict meteorological drought. The WMO recommended standardized precipitation index (SPI) is calculated from gauge (i.e. GPCC), satellitegauge (GPCP, CHIRPS), reanalysis (i.e. ERA-Interim, and MERRA-2), and satellite-gauge-reanalysis ( i.e. MSWEP) over the global domain. Measured differences among the precipitation datasets include metrics such as percent area under drought, number of drought events, spread and correlation in the number of drought events, and critical success index in capturing moderate and severe-exceptional droughts. As precipitation products are available at different lengths and spatial resolutions, sensitivity of drought metrics to record-length and spatial resolution were explored. The results suggest that precipitation-based drought metrics can vary significantly with the choice of precipitation product, its record-lengths, and spatial resolution. These relationships also vary with the severity of drought events with more severe drought events being more sensitive to the differences in resolution and record length. The quantified variation among the products has to be recognized in the interpretation of drought events when a single or a subset of products used.
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