Hydrology Tutorial on Flood

Floods refer to the overflow of water onto normally dry land, often caused by heavy rainfall, melting snow, or dam failure. They can cause significant damage to infrastructure, agriculture, and human settlements. Studying floods is crucial as it helps us understand their causes, patterns, and impacts on both the natural environment and human societies. This knowledge enables us to develop effective strategies for flood prevention, mitigation, and response, ultimately saving lives and minimizing economic losses. Click here to see the tutorial.

Below is the brief outline :

Outline :

1. Definition

2. Empirical method of Flood Peak Estimation

Rational Equation

Unit hydrograph technique

Flood frequency studies

3. Rational Method

Time of Concentration

Rainfall Intensity

4. Characteristics of Empirical Formulae

5. Dickens Formula

6. Ryves Formula

7. Inglis Formula

8. Fullers Formula

9. Baird and Mcillwraith(1951)

10. Estimation of Flood from Frequency Analysis

11. Probable density function (PDF)

Gumbel distribution

Normal Distribution

Log-Normal Distribution

Normal distribution of the Logarithm of Variate.

Pearson Type III Distribution

Log-Pearson Distribution

Pearson distribution of the Logarithm of the Variates.

12. Plotting Position Probability(PPP)

Bulletin 17B PPP

Weibull PPP for Uniformly Distributed Dataset

Hazen PPP for Uniformly Distributed Dataset

Cunnane PPP for Uniformly Distributed Dataset

13. Determination of the Flood Magnitude

Hydrologic Frequency Analysis

14. Adjustment for Urbanization

Rational Equation(modified for urbanization)

15. Urban Adjustment Factor(Leopold,1968)

16. Probable Maximum Precipitation

Hershfield Method

Frequency Factor Calculation

Homogeneity of Dataset

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1 “Flood peaks in Mid-Atlantic U.S. watersheds show a V-shaped response to urbanization: they decrease at low urban development (below 10% PDAW), then rise sharply as urbanization increases further due to complex climate-landscape interactions. A neural network model confirmed this nonlinear behavior, underscoring the need to consider more than just urban area when predicting flood risk.”…..Hua et.al.(2025). Click here to know more. 2 “Artificial neural network (ANN) models using climate indices outperformed ARIMA models for long-term streamflow forecasting at six Victorian stations, with the best ANN achieving Pearson R = 0.90, RMSE = 0.04, and MAE = 2.50 for six-month advance prediction. ANN’s superior accuracy highlights its suitability for operational streamflow forecasts in this region.ANN models using lagged climate indices significantly outperformed ARIMA models for forecasting streamflow at Victoria’s stations, especially at Acheron where the ANN achieved Pearson R = 0.90,...

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In recent years, advanced soft computation techniques such as Multi-Criteria Decision Making and Geographical Information Systems have been widely used to solve water resource development problems. Hydroinformatics (Hydro means Hydrus or water, and informatics means information science) is a branch of science concerned with the application of advanced soft computation techniques and information and communication technologies (ICTs) to water-related problems. The following are five hydro informatics project topics that can be applied to groundwater problems. Complete Article at HydroGeek Thanks, @Mrinmoy's Page @data_hydrology , @Merchandise or @ @products_sustainability Add to Listy /