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The estimate of rainfall using data from an operational dual-polarized C-band radar in convective storms in southeast United Kingdom is compared against a network of gauges. Four different rainfall estimators are considered: reflectivity-rain-rate (Z-R) relation, with and without correcting for rain attenuation; a composite estimator, based on (i) Z-R, (ii) R(Z, Z dr ), and (iii) R(K dp ); and exclusively R(K dp ). The various radar rain-rate estimators are developed using Joss disdrometer data from Chilbolton, United Kingdom. Hourly accumulations over radar pixels centered on the gauge locations are compared, with approximately 2500 samples available for gauge hourly accumulations . 0.2 mm. Overall, the composite estimator performed the ''best'' based on robust statistical measures such as mean absolute error, the Nash-Sutcliffe coefficient, and mean bias, at all rainfall thresholds (.0.2, 1, 3, or 6 mm) with improving measures at the higher thresholds of .3 and .6 mm (higher rain rates). Error variance separation is carried out by estimating the gauge representativeness error using 4 yr of gauge data from the Hydrological Radar Experiment. The proportion of variance of the radar-to-gauge differences that could be explained by the gauge representativeness errors ranged from 20% to 55% (for the composite rain-rate estimator). The radar error is found to decrease from approximately 70% at the lower rain rates to 20% at the higher rain rates. The composite rainrate estimator performed as well as can be expected from error variance analysis, at mean hourly rain rates of about 5 mm h 21 or larger with mean bias of ;10% (underestimate).

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Hydrological yearbooks, especially in developing countries, are full of gaps in flow data series. Filling missing records is needed to make feasibility studies, potential assessment, and real-time decision making. In this research project, it was tried to predict the missing data of gauging stations using data from neighboring sites and a relevant architecture of artificial neural networks (ANN) as well as adaptive neuro-fuzzy inference system (ANFIS). To be able to evaluate the results produced by these new techniques, two traditionally used methods including the normal ratio method and the correlation method were also employed. According to the results, although in some cases all four methods presented acceptable predictions, the ANFIS technique presented a superior ability to predict missing flow data especially in arid land stations with variable and heterogeneous data. Comparing the results, ANN was also found as an efficient method to predict the missing data in comparison to the traditional approaches.