State-of-the-art science is helping to improve operational forecasting of floods, drought conditions, and water resources, delivering more precise and accessible information to customers.

Most rainfall data is stored in formats that are not easy to analyze and mine. In these formats, the amount of data is enormous. In this paper, we propose techniques to summarize the raw rainfall data into a model that facilitates storm analysis and mining, and reduces the data size. The result is to convert raw rainfall data into meaningful storm-centric data, which is then stored in a relational database for easy analysis and mining. The size of the storm data is less than 1% of the size of the raw data. We can determine the spatio-temporal characteristics of a storm, such as how big a storm is, how many sites are covered, and what is its overall depth (precipitation) and duration. We present formal definitions for the storm-related concepts that are needed in our data conversion. Then we describe storm identification algorithms based on these concepts. Our storm identification algorithms analyze precipitation values of adjacent sites within the period of time that covers the whole storm and combines them together to identify the overall storm characteristics.

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The Moody Diagram is widely used to determine the friction factor for fluid flow in pipes. The diagram combines the effects of Reynolds number and relative roughness to determine the friction factor. The relationship is highly non-linear and appears to have a complex interaction between viscous and boundary roughness effects. The Moody Diagram is based on predictions from an equation developed by Colebrook in 1939. The relationship requires an iteration process to make predictions. While empirical relationships have been developed that provide good predictions without an iteration process, no one has fully explained the cause for the observed results. The objective of this paper is to present a logical development for prediction of the friction factor. An equation has been developed that models the summed effect of both the laminar sublayer and the boundary roughness on the fluid profile and the resulting friction factor for pipes. The new equation does not require an iteration procedure to obtain values for the friction factor. Predicted results match well with values generated from Colebrook's work that is expressed in the Moody Diagram. Predictions are within one percent of Colebrook values and generally less than 0.3 percent error from his values. The development provides insight to how processes operating at the boundary cause the friction factor to change.

Strategies for flood mitigation are compared within an urbanising watershed. An approach for modelling and evaluating placement of detention in a developing watershed is presented. Effect of regionalised detention upon required detention volume is compared with localised detention. The study compares the effect of detention basins placed within the completely urbanised watershed by generating models of both the overall watershed and detailed sub-basins. For planning within a developing watershed, this requires modelling the ultimate developed condition, with and without detention. From a watershed having 114 sub-basins, detailed models of five selected sub-basins are generated, representing the ultimate urbanised condition with and without local detention. Hydrologic Engineering Center -Hydrologic Modeling System optimisation is used to estimate parameters of Muskingum routing reaches resembling the effects of localised detention. Next, using Muskingum routing to replicate those effects in other sub-basins, detained hydrographs are generated for the other sub-basins throughout the watershed. Thus, a model of the entire watershed with localised detention is generated. The original watershed model is modified yet again, with detention applied only at selected regional sites. Regional basins are designed to reduce peak flow to values comparable with that achieved by localised detention. Regional versus localised detentions are evaluated by comparison of total required detention volume.