<p>Nowadays, most of the urban cities and their surrounding ambiances are facing increasing flooding issues. Many times, the cause of urban flooding is improper drainage under increasing rainfall intensity. To properly monitor and manage the drainage system in urban areas, high-resolution rainfall data is required to model the flooding scenarios a priori. However, the high-resolution rainfall data in urban regions to address the urban flooding issues are rarely available, especially in developing countries. To overcome this problem, many studies suggest the use of hourly scale IMERG-FR (Integrated Multi-satellitE Retrievals for GPM-Final Run) data which exhibits good agreement with the ground-truth rainfall measurements. Therefore, this study attempts to utilize area-averaged IMERG-FR hourly data over Bhubaneswar, a data-scarce urban area of eastern India as a benchmark for assessing the performance of six parametric (Bartlett-Lewis Model, BL) and a nonparametric (Method of Fragments, MOF) approaches disaggregating daily scale IMD (India Meteorological Department) rainfall data into hourly scale data. The performance of the considered approaches is evaluated by disaggregating the monsoon months (June-October) rainfall timeseries data for the period 2001-2015 by adopting performance criteria such as root mean square error (RMSE) and percent bias (PBIAS). The rainfall time series data from 2001-2010 and 2011-2015 were used for calibration and validation of the proposed approaches, respectively.</p><p>The obtained RMSE values in the case of the BL approach during calibration and validation period were 2.53 mm and 2.04 mm, respectively. Similarly, RMSE values in the case of the MOF approach during the calibration and validation period were 2.5 mm and 1.87 mm, respectively. This comparison suggests the both of these approaches exhibit nearly the same performance during the calibration period whereas the MOF approach was slightly better than BL during the validation period. The PBIAS estimates for the MOF approach were around -6.6% and 17.3% during the calibration and validation period, respectively, whereas the PBIAS estimates for the BL approach were around 11.25% for calibration and -11.25% for the validation period. From the present evaluation, it could be concluded that though the MOF approach exhibits slightly better performance in terms of RMSE, the BL approach can provide a more balanced performance in terms of PBIAS. As the MOF is a non-parametric approach, it can be applied to a lesser length of daily rainfall time series for disaggregation whereas the BL approach can perform well when its parameters are derived using a good length of rainfall series. Conclusively, this study summarizes the applicability of the BL and MOF approaches for disaggregating course resolution daily scale rainfall to hourly rainfall for the monsoon months in Bhubaneswar using IMERG-FR hourly rainfall data as a benchmark.</p><p><strong>Keywords: </strong>Rainfall; Rainfall disaggregation; Bartlett-Lewis Model (BL); Method of Fragments (MOF); IMERG-FR; IMD.</p>
The development of water resource projects of a river often requires information on stage and discharge to design hydraulic control structures at desired locations across the river. However, the rivers of many countries are often ungauged or poorly gauged due to inadequate streamflow monitoring networks. Even with the available limited monitoring stations, only a few of them monitor both stage and discharge data. To arrive at this information at the desired locations along a river, one may estimate this information by transferring the related information from nearby monitored gauging stations.Generally, a river reach is termed ungauged if the discharge measurements are unavailable, even if the associated stage information is available (Hrachowitz et al., 2013). As a limited number of stations are gauged along the rivers, conventionally, the downstream stage and discharge hydrographs are estimated by the forward routing models with the known upstream stage or discharge hydrographs. There are many river basins worldwide
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