scholarly journals COMPARISON OF SCS UH AND CLARK METHODS FOR RAINFALL–RUNOFF MODEL IN DELUWANG WATERSHED

2018 ◽  
Vol 2 (01) ◽  
pp. 77
Author(s):  
Muhammad Arifin ◽  
Entin Hidayah ◽  
Wiwik Yunarni Widiarti
Keyword(s):  
Hydrological Modeling ◽  
Daily Rainfall ◽  
Water Source ◽  
Rainfall Data ◽  
Rainfall Runoff ◽  
Unit Hydrograph ◽  
Direct Runoff ◽  
Daily Rainfall Data ◽  
Rainfall Runoff Model ◽  
Better Than

Deluwang River water source is widely used for the needs of irrigation, plantation, and the fulfillment of domestic life. Given the importance of the role of water in Deluwang watershed, then he had to do the management of watershed. The proper management of watershed hydrological modeling requires accurate. Rainfall-runoff using HEC-HMS applications. This research aims tocomparison 2 methods in direct runoff. Therefore this study uses two methods, namely SCS Unit Hydrograph method and method of Clark Unit Hydrograph. On the calibration process using daily rainfall data and daily debit year 2006, whereas in the validation process using daily rainfall data and daily debit years 2007 to 2012. The results of the calibration using Clark Unit Hydrograph method better than using SCS Unit Hydrograph method with Nash's value 0,700 than 0,539. While the results of the validation of modeling using Clark Unit Hydrograph method is better than using SCSUnit Hydrograph method with a value of Nash 0,541 than 0,368. Sungai Deluwang sumber airnya banyak dimanfaatkan untuk kebutuhan irigasi, perkebunan, serta pemenuhan kehidupan rumah tangga. Mengingat pentingnya peranan air pada DAS Deluwang, maka perlu  dilakukannya pengelolaan DAS. Pengelolaan DAS yang tepat membutuhkan pemodelan  hidrologi yang akurat. Pemodelan hujan aliran menggunakan aplikasi HEC-HMS. Penelitian ini bertujuan membandingkan 2 metode yang terdapat pada direct runoff. Oleh karena itu penelitian ini menggunakan dua metode, yaitu metode SCS Unit Hydrograph dan metode Clark Unit Hydrograph. Pada proses kalibrasi menggunakan data curah hujan harian dan debit harian tahun 2006, sedangkan pada proses validasi menggunakan data curah hujan harian dan debit harian tahun 2007 sampai 2012. Hasil kalibrasi menggunakan  metode Clark Unit Hydrograph lebih bagus dibandingkan menggunakan metode SCS Unit Hydrograph dengan nilai Nash 0,700 berbanding 0,539. Sedangkan hasil validasi pemodelan menggunakan  metode Clark Unit Hydrograph lebih bagus dibandingkan menggunakan metode SCS Unit Hydrograph dengan nilai Nash 0,541 berbanding 0,368.

scholarly journals Comparison of Normal Ratio Method and Distance Power Method for Estimating Missing Rainfall Data with Three Neighboring Stations

2021 ◽  
pp. 1-9
Author(s):  
Celeste A. De Asis
Keyword(s):  
Correlation Coefficient ◽  
Daily Rainfall ◽  
T Test ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Ratio Method ◽  
Power Method ◽  
Daily Rainfall Data ◽  
Normal Ratio ◽  
Better Than

This study compared the performances of Normal Ratio Method and Distance Power Method as a tool for estimating missing rainfall data. The data utilized are the rainfall data of the three neighboring station of Catarman, Northern Samar, Philippines. These stations are Catbalogan Station (Samar Province), Legazpi (Bicol Province) and Masbate (Masbate Province). The observed daily rainfall data for the Catarman (Northern Samar), Catbalogan, Legazpi, and Masbate were obtained from the Philippine Atmospheric Geographical Astronomical Services Administration. The monthly rainfall were computed for the three (3) neighboring stations (Catbalogan, Legazpi, Masbate). The evaluation used the T-test for correlated samples and the Pearson’s Correlation Coefficient for the monthly rainfall data computed of the three neighboring Station of Catarman, Northern Samar with the three neighboring stations. Based from the results, Normal Ratio Method performs better than Distance Power Method as applied to three neighboring stations.

scholarly journals Hydrological Validation of a Radar-Based Nowcasting Technique

2005 ◽  
Vol 6 (4) ◽  
pp. 532-549 ◽  
Author(s):  
Marc Berenguer ◽  
Carles Corral ◽  
Rafael Sánchez-Diezma ◽  
Daniel Sempere-Torres
Keyword(s):  
Point Of View ◽  
Motion Field ◽  
Warning Systems ◽  
Rainfall Runoff ◽  
Weather Radars ◽  
Radar Echoes ◽  
Radar Measurements ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Better Than

Abstract Nowcasting precipitation is a key element in the anticipation of floods in warning systems. In this framework, weather radars are very useful because of the high resolution of their measurements both in time and space. The aim of this study is to assess the performance of a recently proposed nowcasting technique (S-PROG) from a hydrological point of view in a Mediterranean environment. S-PROG is based on the advection of weather radar fields according to the motion field derived with an algorithm based on tracking radar echoes by correlation (TREC), and it has the ability of filtering out the most unpredictable scales of these fields as the forecasting time increases. Validation of this nowcasting technique was done from two different perspectives: (i) comparing forecasted precipitation fields against radar measurements, and (ii) by means of a distributed rainfall runoff model, comparing hydrographs simulated with a hydrological model using rainfall fields forecasted by S-PROG against hydrographs generated with the model using the entire series of radar measurements. In both cases, results obtained by a simpler nowcasting technique are used as a reference to evaluate improvements. Validation showed that precipitation fields forecasted with S-PROG seem to be better than fields forecasted using simpler techniques. Additionally, hydrological validation led the authors to point out that the use of radar-based nowcasting techniques allows the anticipation window in which flow estimates are forecasted with enough quality to be sensibly extended.

The effect of weather, drainage efficiency and duration of spring cultivations on barley yields in England

1972 ◽  
Vol 7 (2) ◽  
pp. 79-83 ◽  
Author(s):  
L P Smith
Keyword(s):  
Yield Loss ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Work Schedule ◽  
Average Yield ◽  
Daily Rainfall Data ◽  
Arable Farming ◽  
Work Distribution

Daily rainfall data for twenty years in arable farming areas are analysed with respect to four standards of drainage and for three lengths of schedule of spring work. Distribution and frequency in time of available work days are interpreted in terms of lateness of sowing and of barley yield. Formulae are established to calculate average yield loss in terms of drainage standard and work schedule, enabling estimates to be made of the effect of planned improvements.

scholarly journals Bayesian Time Series Modelling of the Italian Daily Rainfall Data using Mixed Distribution

2019 ◽  
Vol 8 (4) ◽  
pp. 2279-2288
Keyword(s):  
Time Series ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Seasonal Effects ◽  
Model Parameters ◽  
Daily Rainfall Data ◽  
Mixed Distribution ◽  
Time Series Modelling ◽  
Bayesian Time Series ◽  
The Ideal

A combination of continuous and discrete elements is referred to as a mixed distribution. For example, daily rainfall data consist of zero and positive values. We aim to develop a Bayesian time series model that captures the evolution of the daily rainfall data in Italy, focussing on directly linking the amount and occurrence of rainfall. Two gamma (G1 and G2) distributions with different parameterisations and lognormal distribution were investigated to identify the ideal distribution representing the amount process. Truncated Fourier series was used to incorporate the seasonal effects which captures the variability in daily rainfall amounts throughout the year. A first-order Markov chain was used to model rainfall occurrence conditional on the presence or absence of rainfall on the previous day. We also built a hierarchical prior structure to represent our subjective beliefs and capture the initial uncertainties of the unknown model parameters for both amount and occurrence processes. The daily rainfall data from Urbino rain gauge station in Italy were then used to demonstrate the applicability of our proposed methods. Residual analysis and posterior predictive checking method were utilised to assess the adequacy of model fit. In conclusion, we clearly found that our proposed method satisfactorily and accurately fits the Italian daily rainfall data. The gamma distribution was found to be the ideal probability density function to represent the amount of daily rainfall.

scholarly journals Automatic Prediction of High-Resolution Daily Rainfall Fields for Multiple Extents: The Potential of Operational Radar

2007 ◽  
Vol 8 (6) ◽  
pp. 1204-1224 ◽  
Author(s):  
J. M. Schuurmans ◽  
M. F. P. Bierkens ◽  
E. J. Pebesma ◽  
R. Uijlenhoet
Keyword(s):  
High Resolution ◽  
Hydrological Modeling ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Added Value ◽  
Rain Gauge Data ◽  
Geostatistical Methods ◽  
Gauge Data ◽  
Event Based ◽  
Better Than

Abstract This study investigates the added value of operational radar with respect to rain gauges in obtaining high-resolution daily rainfall fields as required in distributed hydrological modeling. To this end data from the Netherlands operational national rain gauge network (330 gauges nationwide) is combined with an experimental network (30 gauges within 225 km2). Based on 74 selected rainfall events (March–October 2004) the spatial variability of daily rainfall is investigated at three spatial extents: small (225 km2), medium (10 000 km2), and large (82 875 km2). From this analysis it is shown that semivariograms show no clear dependence on season. Predictions of point rainfall are performed for all three extents using three different geostatistical methods: (i) ordinary kriging (OK; rain gauge data only), (ii) kriging with external drift (KED), and (iii) ordinary collocated cokriging (OCCK), with the latter two using both rain gauge data and range-corrected daily radar composites—a standard operational radar product from the Royal Netherlands Meteorological Institute (KNMI). The focus here is on automatic prediction. For the small extent, rain gauge data alone perform better than radar, while for larger extents with lower gauge densities, radar performs overall better than rain gauge data alone (OK). Methods using both radar and rain gauge data (KED and OCCK) prove to be more accurate than using either rain gauge data alone (OK) or radar, in particular, for larger extents. The added value of radar is positively related to the correlation between radar and rain gauge data. Using a pooled semivariogram is almost as good as using event-based semivariograms, which is convenient if the prediction is to be automated. An interesting result is that the pooled semivariograms perform better in terms of estimating the prediction error (kriging variance) especially for the small and medium extent, where the number of data points to estimate semivariograms is small and event-based semivariograms are rather unstable.

scholarly journals Structural break or long memory: an empirical survey on daily rainfall data sets across Malaysia

2013 ◽  
Vol 17 (4) ◽  
pp. 1311-1318 ◽  
Author(s):  
F. Yusof ◽  
I. L. Kane ◽  
Z. Yusop
Keyword(s):  
Long Memory ◽  
Structural Breaks ◽  
Structural Break ◽  
Daily Rainfall ◽  
Least Square ◽  
Rainfall Data ◽  
Data Sets ◽  
Original Series ◽  
Empirical Survey ◽  
Daily Rainfall Data

Abstract. A short memory process that encounters occasional structural breaks in mean can show a slower rate of decay in the autocorrelation function and other properties of fractional integrated I (d) processes. In this paper we employed a procedure for estimating the fractional differencing parameter in semiparametric contexts proposed by Geweke and Porter-Hudak (1983) to analyse nine daily rainfall data sets across Malaysia. The results indicate that all the data sets exhibit long memory. Furthermore, an empirical fluctuation process using the ordinary least square (OLS)-based cumulative sum (CUSUM) test for the break date was applied. Break dates were detected in all data sets. The data sets were partitioned according to their respective break date, and a further test for long memory was applied for all subseries. Results show that all subseries follows the same pattern as the original series. The estimate of the fractional parameters d1 and d2 on the subseries obtained by splitting the original series at the break date confirms that there is a long memory in the data generating process (DGP). Therefore this evidence shows a true long memory not due to structural break.

scholarly journals Application of NEXRAD Radar-Based Quantitative Precipitation Estimations for Hydrologic Simulation Using ArcPy and HEC Software

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 273
Author(s):  
Younghyun Cho
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
Performance Test ◽  
Hydrological Modeling ◽  
Rainfall Data ◽  
Storm Event ◽  
Rainfall Runoff ◽  
Hydrologic Simulation ◽  
Great Opportunity ◽  
Spatially Distributed

Recent availability of various spatial data, especially for gridded rainfall amounts, provide a great opportunity in hydrological modeling of spatially distributed rainfall–runoff analysis. In order to support this advantage using gridded precipitation in hydrological application, (1) two main Python script programs for the following three steps of radar-based rainfall data processing were developed for Next Generation Weather Radar (NEXRAD) Stage III products: conversion of the XMRG format (binary to ASCII) files, geo-referencing (re-projection) with ASCII file in ArcGIS, and DSS file generation using HEC-GridUtil (existing program); (2) eight Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) models of ModClark and SCS Unit Hydrograph transform methods for rainfall–runoff flow simulations using both spatially distributed radar-based and basin-averaged lumped gauged rainfall were respectively developed; and (3) three storm event simulations including a model performance test, calibration, and validation were conducted. For the results, both models have relatively high statistical evaluation values (Nash–Sutcliffe efficiency—ENS 0.55–0.98 for ModClark and 0.65–0.93 for SCS UH), but it was found that the spatially distributed rainfall data-based model (ModClark) gives a better fit regarding observed streamflow for the two study basins (Cedar Creek and South Fork) in the USA, showing less requirements to calibrate the model with initial parameter values. Thus, the programs and methods developed in this research possibly reduce the difficulties of radar-based rainfall data processing (not only NEXRAD but also other gridded precipitation datasets—i.e., satellite-based data, etc.) and provide efficiency for HEC-HMS hydrologic process application in spatially distributed rainfall–runoff simulations.

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