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The weather radar is an efficient alternative for measuring spatially varying rainfall covering a large area at a high temporal resolution. This paper studies the impact of rainfall gauge temporal resolution on optimal relationships between radar reflectivity (Z) and rainfall rate (R). Four datasets of radar reflectivity and corresponding rain gauge rainfall data from Sydney and Brisbane, Australia, and one dataset from Bangkok, Thailand, were used in the analysis. Climatological Z-R relationships were calibrated using rainfall aggregated over 1-24 h to investigate the evidence of temporal scaling in the Z-R calibrated parameters. This analysis points to an increase in the multiplicative term (the A parameter) of the Z-R relationship as temporal resolutions become finer. This pattern is repeated in all the datasets analyzed. Thereafter, a simple scaling hypothesis was proposed to develop transformations that could scale the A parameter in the Z-R relation across a range of temporal resolutions. This scaling relationship was found to be suitable, with the scaling exponent attaining values close to 0.055 across all the datasets analyzed. The proposed relationship has a significant role in radar rainfall estimation studies, especially in regions where subdaily gauge rainfall measurements are not readily available to ascertain optimal Z-R parameters.

ABSTRACT:Weather radar can potentially provide high-resolution spatial and temporal rainfall estimates bringing more accuracy to flood estimations as well as having some other applications in areas with insufficient rainfall stations like Thailand. Weather radar cannot be used to measure the rainfall depth directly; so an empirical relationship between the reflectivity (Z) and rainfall rate (R), called the Z-R relationship (Z = AR b ), is generally used to assess the rainfall depth using radar. In this study, an optimization approach was used to find a suitable climatological Z-R relationship for the upper Ping river basin, Northern Thailand. The reflectivity data between June and October in 2003 and 2004 at the Omkoi radar station located in Chiangmai Province, together with the daily rainfall depths at fifty rainfall stations located in and around the basin during the same periods were used. A climatological Z-R relationship in the form Z = 74R 1.6 shows acceptable statistical indicators, making it suitable for radar rainfall prediction for the upper Ping river basin.

Flood hydrographs are usually estimated from models on gauged catchments. Flood estimation on ungauged catchments requires relationships between model parameters and catchment characteristics. In this study, both the URBS model and Nedbor-Afstromings model (NAM) were shown to be successful in simulating flood behaviour in the upper Ping river basin, Northern Thailand. To formulate the relationships for applying to ungauged catchments, we chose the URBS model as it requires only 4 parameters whereas the NAM requires 6. The relationships between the URBS model parameters calibrated from 11 runoff stations and the corresponding catchment characteristics were adopted to estimate the URBS model parameters at 4 runoff stations in the target area as if the catchments were ungauged. The results of flood estimation obtained from the ungauged catchment approach were then compared with that gained from the gauged catchment approach. The results revealed that the proposed relationships between the URBS model parameters and catchment characteristics can be confidently applied for flood estimation of the ungauged catchments within the catchment area of the 11 stations used in the formulation process.

Abstract. The low density of conventional rain gauge networks is often a limiting factor for radar rainfall bias correction. Citizen rain gauges offer a promising opportunity to collect rainfall data at a higher spatial density. In this paper, hourly radar rainfall bias adjustment was applied using two different rain gauge networks: tipping buckets, measured by Thai Meteorological Department (TMD), and daily citizen rain gauges. The radar rainfall bias correction factor was sequentially updated based on TMD and citizen rain gauge data using a two-step Kalman filter to incorporate the two gauge datasets of contrasting quality. Radar reflectivity data from the Sattahip radar station, gauge rainfall data from the TMD, and data from citizen rain gauges located in the Tubma Basin, Thailand, were used in the analysis. Daily data from the citizen rain gauge network were downscaled to an hourly resolution based on temporal distribution patterns obtained from radar rainfall time series and the TMD gauge network. Results show that an improvement in radar rainfall estimates was achieved by including the downscaled citizen observations compared with bias correction based on the conventional rain gauge network alone. These outcomes emphasize the value of citizen rainfall observations for radar bias correction, in particular in regions where conventional rain gauge networks are sparse.