Radar hydrology: rainfall estimation

Radar observations of rainfall and their use in hydrologic research provide the focus for the paper. Radar-rainfall products are crucial for input to runoff and flood prediction models, validation of satellite remote sensing algorithms, and for statistical characterization of extreme rainfall frequency. In this context we discuss the issues of radar-rainfall product development, and the theoretical and practical requirements of validating radar-rainfall maps and new radar technologies. We discuss a framework for reflectivity based rainfall estimation, including estimation of uncertainty of radar-rainfall estimates. Validation of radar-rainfall products is a major challenge for broad utilization of these products in hydrologic applications. In the discussion of radar-rainfall prediction we focus on orographically induced extreme rainfall and flooding, discuss the issues of detection, statistical sample size, and scale effects. We conclude the paper with a set of recommendations for research priorities and experimental requirements to address them. [1]

Rainfall energy and its relationship to soil loss

A relatively simple procedure is presented for computation of kinetic energy of a rainstorm from information on a recording‐raingage chart. An equation is developed describing rainfall energy as a function of rainfall intensity. The effects of rainfall energy and its interaction with other variables are evaluated in multiple regression analyses based on data representing four soil types. Application of this information to separate the effects of rainfall from those of physical and management characteristics in plot data is discussed briefly. [2]

An analytical model of rainfall interception by forests

The description of the evaporation of rainfall intercepted by forests in terms of a regression of evaporation loss on incident rainfall is discussed and some of the assumptions implicit in that method are re‐examined. The two major factors which control the evaporation of intercepted rainfall are identified. These are: (i) the amount of time that the canopy spends saturated during rainfall and the evaporation rate applicable under these conditions; and (ii) the canopy saturation capacity and the number of times this store is emptied, by drying out after the cessation of rainfall. A model is then constructed which is conceptually similar to the Rutter model, but which replaces that model’s numerical approach with an analysis by storm events. [3]

Spatio-temporal Variability and Trends of Rainfall and Temperature Over Gamo Gofa Zone, Ethiopia

This study examined spatiotemporal climate variability and trends of observed rainfall and temperature data from 1991-2010 over Gamo Gofa zone, southern Ethiopia. Normalized anomaly and coefficient of variation were analyzed to observe the variability of climate. Linear regression was used to analyze trends; Mann Kendall trend test was used for testing the significance and ArcGIS was used to map trend magnitudes and the statistical significance. [4]

Hourly Real-Time Rainfall Estimation for Improved Smart Irrigation System Using Nearby Automated Weather Station

Smart irrigation is done by extracting climatic data such as historical data, off-site data, weather station, moisture sensor, wireless sensor network and web-based forecast. In existing sensorbased smart irrigation schedule, the decision-making of current irrigation depends on the current climatic data. Irrigation control decision making systems can be improved by using neighborhood real-time rainfall for approximate local rainfall estimation. This method can result in better water saving techniques. This paper shows the development of low-cost smart irrigation system which consists of Automatic Weather Station (AWS), Central Irrigation Control Server, wireless modules, soil moisture sensors and solenoid values. For improved decision making an artificial neural network with back-propagation algorithm is implemented to estimate real-time hourly rainfall by using nearby AWS. Depending on the estimated rainfall input, the irrigation decision can be immediate irrigation if no rainfall or reschedule of irrigation for next cycle if expecting sufficient amount of rainfall or may be partial irrigation for insufficient rainfall. This method can utilize rainfall [5]


[1] Krajewski, W.F. and Smith, J.A., 2002. Radar hydrology: rainfall estimation. Advances in water resources25(8-12), pp.1387-1394.

[2] Wischmeier, W.H. and Smith, D.D., 1958. Rainfall energy and its relationship to soil loss. Eos, Transactions American Geophysical Union39(2), pp.285-291.

[3] Gash, J.H.C., 1979. An analytical model of rainfall interception by forests. Quarterly Journal of the Royal Meteorological Society105(443), pp.43-55.

[4] Teyso, T.A. and Anjulo, A., 2016. Spatio-temporal Variability and Trends of Rainfall and Temperature over Gamo Gofa Zone, Ethiopia. Journal of Scientific Research and Reports, pp.1-11.

[5] Hema, N. and Kant, K., 2016. Hourly Real-Time Rainfall Estimation for Improved Smart Irrigation System Using Nearby Automated Weather Station. Current Journal of Applied Science and Technology, pp.1-13.

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