scholarly journals Rainfall runoff estimation using GIS and SCS-CN method for awash river basin, Ethiopia

2021 ◽  
Vol 5 (1) ◽  
pp. 33-37
Author(s):  
Shimelis Sishah
Keyword(s):  
Simulation Model ◽  
Correlation Coefficient ◽  
River Basin ◽  
Annual Rainfall ◽  
Rainfall Runoff ◽  
Soil Water Retention ◽  
Runoff Simulation ◽  
Study Region ◽  
Awash River Basin ◽  
Scs Cn

Understanding hydrological behavior is an important part of effective watershed management and planning. Runoff resulted from rainfall is a component of hydrological behavior that is needed for efficient water resource planning. In this paper, GIS based SCS-CN runoff simulation model was applied to estimate rainfall runoff in Awash river basin. Global Curve Number (GCN250), Maximum Soil Water Retention (S) and Rainfall was used as an input for SCS-CN runoff simulation model. The final surface runoff values for the Awash river basin were generated on the basis of total annual rainfall and maximum soil water retention potential (S) of the year 2020. Accordingly, a runoff variation that range from 83.95 mm/year to a maximum of 1,416.75 mm/year were observed in the study region. Conversely, recently developed Global Curve Number (GCN250) data was tested with Pearson correlation coefficient to be used as an input for SCS-CN runoff simulation model. In doing so, predicted runoff generated in SCS-CN using GCN250 as a model input was validated with observed runoff obtained from station gauges in the study region. The results of validation show that, predicted runoff was well correlated with observed runoff with correlation coefficient of 0.9253. From this stand point, it is observed that the new GCN250 data can be used as an input for SCS-CN model to estimate rainfall runoff at basin level. Furthermore, correlation analysis was performed to explain the relationship between mean annual rainfall and surface runoff. The relationship between these two variables indicates a strong linear relationship with correlation coefficient of 0.9873.

Assessment of surface runoff depth changes in Sǎrǎţel River basin, Romania using GIS techniques

2014 ◽  
Vol 6 (3) ◽  
Author(s):  
Costache Romulus ◽  
Fontanine Iulia ◽  
Corodescu Ema
Keyword(s):  
River Basin ◽  
Surface Runoff ◽  
Land Use Changes ◽  
Curve Number ◽  
Annual Rainfall ◽  
Average Annual Rainfall ◽  
Runoff Depth ◽  
Gis Environment ◽  
Quantitative Changes ◽  
Scs Cn

AbstractSǎrǎţel River basin, which is located in Curvature Subcarpahian area, has been facing an obvious increase in frequency of hydrological risk phenomena, associated with torrential events, during the last years. This trend is highly related to the increase in frequency of the extreme climatic phenomena and to the land use changes. The present study is aimed to highlight the spatial and quantitative changes occurred in surface runoff depth in Sǎrǎţel catchment, between 1990–2006. This purpose was reached by estimating the surface runoff depth assignable to the average annual rainfall, by means of SCS-CN method, which was integrated into the GIS environment through the ArcCN-Runoff extension, for ArcGIS 10.1. In order to compute the surface runoff depth, by CN method, the land cover and the hydrological soil classes were introduced as vector (polygon data), while the curve number and the average annual rainfall were introduced as tables. After spatially modeling the surface runoff depth for the two years, the 1990 raster dataset was subtracted from the 2006 raster dataset, in order to highlight the changes in surface runoff depth.

Comparison of recorded rainfall with quantitative precipitation forecast in a rainfall-runoff simulation for the Langat River Basin, Malaysia

2011 ◽  
Vol 3 (3) ◽  
Author(s):  
Lawal Billa ◽  
Hamid Assilzadeh ◽  
Shattri Mansor ◽  
Ahmed Mahmud ◽  
Abdul Ghazali
Keyword(s):  
Response Time ◽  
River Basin ◽  
Flood Forecasting ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Noaa Avhrr ◽  
Quantitative Precipitation Forecast ◽  
Langat River

AbstractObserved rainfall is used for runoff modeling in flood forecasting where possible, however in cases where the response time of the watershed is too short for flood warning activities, a deterministic quantitative precipitation forecast (QPF) can be used. This is based on a limited-area meteorological model and can provide a forecasting horizon in the order of six hours or less. This study applies the results of a previously developed QPF based on a 1D cloud model using hourly NOAA-AVHRR (Advanced Very High Resolution Radiometer) and GMS (Geostationary Meteorological Satellite) datasets. Rainfall intensity values in the range of 3–12 mm/hr were extracted from these datasets based on the relation between cloud top temperature (CTT), cloud reflectance (CTR) and cloud height (CTH) using defined thresholds. The QPF, prepared for the rainstorm event of 27 September to 8 October 2000 was tested for rainfall runoff on the Langat River Basin, Malaysia, using a suitable NAM rainfall-runoff model. The response of the basin both to the rainfall-runoff simulation using the QPF estimate and the recorded observed rainfall is compared here, based on their corresponding discharge hydrographs. The comparison of the QPF and recorded rainfall showed R2 = 0.9028 for the entire basin. The runoff hydrograph for the recorded rainfall in the Kajang sub-catchment showed R2 = 0.9263 between the observed and the simulated, while that of the QPF rainfall was R2 = 0.819. This similarity in runoff suggests there is a high level of accuracy shown in the improved QPF, and that significant improvement of flood forecasting can be achieved through ‘Nowcasting’, thus increasing the response time for flood early warnings.

scholarly journals Model Calibration Parameter Using Optimization Trial in HEC-HMS for Unda Watershed

2021 ◽  
Vol 930 (1) ◽  
pp. 012040
Author(s):  
G A P Eryani ◽  
I M S Amerta ◽  
M W Jayantari
Keyword(s):  
Simulation Model ◽  
Water Availability ◽  
Resource Planning ◽  
Rainfall Simulation ◽  
Calibration Model ◽  
Hydrological Process ◽  
Rainfall Runoff ◽  
Initial Model ◽  
Runoff Simulation ◽  
Resources Management

Abstract In water resource planning, information on water availability is needed. Nowadays, data on water availability is still difficult to obtain. With technology in the form of a rainfall-runoff simulation model that can predict water availability in the Unda watershed. It can add information about the potential for water in the Unda watershed. It can be used to prepare water resources management in the Unda watershed so that the existing potential can be used sustainably. Based on the rainfall simulation model results in the Unda watershed, it can be concluded that after running the initial model and calibration. The results are obtained R2 value was 0.68 and increased by 9.81% to 0.754. Both the initial model and the calibration model show an efficient R2 value, NASH value increases by 49.93% to 0.713, which includes satisfactory criteria, RMSE value of 1.135 and decreased by 49.47% to 0.758, and the PBIAS value was 44.70% which was classified as unsatisfactory and decreased from 80.24% to 24.80% at the time of calibration which was classified as satisfactory. In general, the overall simulation results are quite good for representing the watershed’s efficient hydrological process.

scholarly journals EVALUATION OF RAINFALL-RUNOFF MODELS FOR MEDITERRANEAN SUBCATCHMENTS

2016 ◽  
Vol XLI-B8 ◽  
pp. 321-325 ◽  
Author(s):  
A. Cilek ◽  
S. Berberoglu ◽  
C. Donmez
Keyword(s):  
Mediterranean Basin ◽  
Eastern Mediterranean ◽  
Ground Cover ◽  
Annual Rainfall ◽  
Hydrological Process ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Data Set ◽  
Erosion Risk ◽  
Rainfall Runoff Model

The development and the application of rainfall-runoff models have been a corner-stone of hydrological research for many decades. The amount of rainfall and its intensity and variability control the generation of runoff and the erosional processes operating at different scales. These interactions can be greatly variable in Mediterranean catchments with marked hydrological fluctuations. <br><br> The aim of the study was to evaluate the performance of rainfall-runoff model, for rainfall-runoff simulation in a Mediterranean subcatchment. The Pan-European Soil Erosion Risk Assessment (PESERA), a simplified hydrological process-based approach, was used in this study to combine hydrological surface runoff factors. In total 128 input layers derived from data set includes; climate, topography, land use, crop type, planting date, and soil characteristics, are required to run the model. Initial ground cover was estimated from the Landsat ETM data provided by ESA. <br><br> This hydrological model was evaluated in terms of their performance in Goksu River Watershed, Turkey. It is located at the Central Eastern Mediterranean Basin of Turkey. The area is approximately 2000 km<sup>2</sup>. The landscape is dominated by bare ground, agricultural and forests. The average annual rainfall is 636.4mm. This study has a significant importance to evaluate different model performances in a complex Mediterranean basin. The results provided comprehensive insight including advantages and limitations of modelling approaches in the Mediterranean environment.

scholarly journals Hydro-Climatic Variability: A Characterisation and Trend Study of the Awash River Basin, Ethiopia

Hydrology ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 35 ◽  
Author(s):  
Mahtsente Tibebe Tadese ◽  
Lalit Kumar ◽  
Richard Koech ◽  
Birhanu Zemadim
Keyword(s):  
River Basin ◽  
Climatic Variability ◽  
Management Strategies ◽  
Annual Rainfall ◽  
Sustainable Water Management ◽  
Mk Test ◽  
Awash River Basin ◽  
Research Findings ◽  
Droughts And Floods ◽  
The Impact

The objective of this study was to characterize, quantify and validate the variability and trends of hydro-climatic variables in the Awash River Basin (ARB) in Ethiopia using graphical and statistical methods. The rainfall and streamflow trends and their relationships were evaluated using the regression method, Mann–Kendall (MK) test and correlation analysis. The analysis focused on rainfall and streamflow collected from 28 and 18 stations, respectively. About 85.7% and 75.3% of the rainfall stations exhibited normal to moderate variability in annual and June to September rainfall, respectively, whereas 96.43% of rainfall stations showed high variability in March to May. The MK test showed that most of the significant trends in annual rainfall were decreasing except in two stations. These research findings provide valuable information on the characteristics, variability, and trend of rainfall and streamflow necessary for the design of sustainable water management strategies and to reduce the impact of droughts and floods in the ARB.

scholarly journals Review on the HEC-HMS Rainfall-Runoff Simulation Model

2020 ◽  
Vol 10 (4) ◽  
pp. 183-190
Author(s):  
Salil Sahu, et al., Salil Sahu, et al., ◽  
Keyword(s):  
Simulation Model ◽  
Rainfall Runoff ◽  
Runoff Simulation

scholarly journals Modeling of Rainfall-Runoff Correlations Using Artificial Neural Network-A Case Study of Dharoi Watershed of a Sabarmati River Basin, India

2017 ◽  
Vol 3 (2) ◽  
pp. 78-87 ◽  
Author(s):  
Ajaykumar Bhagubhai Patel ◽  
Geeta S. Joshi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
River Basin ◽  
Annual Rainfall ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Feed Forward Back Propagation ◽  
Sabarmati River ◽  
Artificial Neural

The use of an Artificial Neural Network (ANN) is becoming common due to its ability to analyse complex nonlinear events. An ANN has a flexible, convenient and easy mathematical structure to identify the nonlinear relationships between input and output data sets. This capability could efficiently be employed for the different hydrological models such as rainfall-runoff models, which are inherently nonlinear in nature. Artificial Neural Networks (ANN) can be used in cases where the available data is limited. The present work involves the development of an ANN model using Feed-Forward Back Propagation algorithm for establishing monthly and annual rainfall runoff correlations. The hydrologic variables used were monthly and annual rainfall and runoff for monthly and annual time period of monsoon season. The ANN model developed in this study is applied to Dharoi reservoir watersheds of Sabarmati river basin of India. The hydrologic data were available for twenty-nine years at Dharoi station at Dharoi dam project. The model results yielding into the least error is recommended for simulating the rainfall-runoff characteristics of the watersheds. The obtained results can help the water resource managers to operate the reservoir properly in the case of extreme events such as flooding and drought.

scholarly journals Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi)

2014 ◽  
Vol 11 (1) ◽  
pp. 1169-1201 ◽  
Author(s):  
D. Kneis ◽  
C. Chatterjee ◽  
R. Singh
Keyword(s):  
Real Time ◽  
River Basin ◽  
Stream Flow ◽  
Remotely Sensed ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Rain Gage ◽  
Precipitation Estimates ◽  
Rainfall Estimates

Abstract. The paper examines the quality of satellite-based precipitation estimates for the Lower Mahanadi River Basin (Eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gage-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gage data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analyzing their performance in the context of rainfall-runoff simulation. At sub-basin level (4000 to 16 000 km2) the satellite-based areal precipitation estimates were found to be moderately correlated with the gage-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high intensity levels. The rainfall depth derived from rain gage data is often not reflected by the TRMM estimates (hit rate < 0.6 for ground-based intensities > 80 mm day−1). At the same time, the remotely sensed rainfall rates frequently exceed the gage-based equivalents (false alarm ratios of 0.2–0.6). In addition, the real time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalization of rain gage data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gage data were used as model input (Nash–Sutcliffe Index of 0.76–0.88 at gages not affected by reservoir operation). This compares to the values of 0.71–0.78 for the gage-adjusted TRMM 3B42 data and 0.65–0.77 for the 3B42-RT real-time data. Whether the 3B42-RT data are useful in the context of operational runoff prediction in spite of the identified problems remains a question for further research.

Sign in / Sign up

Export Citation Format

Share Document