Initial calibration of a semi-distributed rainfall runoff model for the Congo River basin

2011 ◽  
Vol 36 (14-15) ◽  
pp. 761-774 ◽  
Author(s):  
R.M. Tshimanga ◽  
D.A. Hughes ◽  
E. Kapangaziwiri
Keyword(s):  
River Basin ◽  
Rainfall Runoff ◽  
Congo River ◽  
Congo River Basin ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals A Modified IHACRES Rainfall–Runoff Model for Predicting Hydrologic Response of a River Basin System with a Relevant Groundwater Component

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 24
Author(s):  
Iolanda Borzì ◽  
Brunella Bonaccorso ◽  
Aldo Fiori
Keyword(s):  
River Basin ◽  
Slow Component ◽  
Temperature Data ◽  
Continuous Series ◽  
Effective Rainfall ◽  
Rainfall Runoff ◽  
Aquifer Recharge ◽  
Daily Streamflow ◽  
Rainfall Runoff Model ◽  
Runoff Model

A flow regime can be broadly categorized as either perennial, intermittent, or ephemeral, depending on whether the streamflow is continuous all year round, or ceasing for weeks or months each year. Various conceptual models are needed to capture the behavior of these different flow regimes, which reflect differences in the stream–groundwater hydrologic connection. As the hydrologic connection becomes more transient and a catchment’s runoff response more nonlinear, such as for intermittent streams, the need for explicit representation of the groundwater increases. In the present study, we investigated the connection between the Northern Etna groundwater system and the Alcantara River basin in Sicily, which is intermittent in the upstream, and perennial since the midstream, due to groundwater resurgence. To this end, we apply a modified version of IHACRES rainfall–runoff model, whose input data are a continuous series of concurrent daily streamflow, rainfall and temperature data. The structure of the model includes three different modules: (1) a nonlinear loss module that transforms precipitation to effective rainfall by considering the influence of temperature; (2) a linear module based on the classical convolution between effective rainfall and the unit hydrograph which is able to simulate the quick component of the runoff; and (3) a second nonlinear module that simulates the slow component of the runoff and that feeds the groundwater storage. From the sum of the quick and slow components (except for groundwater losses, representing the aquifer recharge), the total streamflow is derived. This model structure is applied separately to sub-basins showing different hydrology and land use. The model is calibrated at Mojo cross-section, where daily streamflow data are available. Point rainfall and temperature data are spatially averaged with respect to the considered sub-basins. Model calibration and validation are carried out for the period 1984–1986 and 1987–1988 respectively.

scholarly journals Development of rainfall – runoff model for extreme storm events in the Bagmati River Basin, Nepal

2021 ◽  
Vol 1 (1) ◽  
pp. 158-173
Author(s):  
Nirajan Devkota ◽  
Narendra Man Shrestha
Keyword(s):  
River Basin ◽  
Monsoon Season ◽  
Capital City ◽  
Rainfall Runoff ◽  
Unit Hydrograph ◽  
Peak Maximum ◽  
Hourly Data ◽  
Rainfall Runoff Model ◽  
Bagmati River ◽  
Runoff Model

This study is based on the Bagmati river basin that flows along with the capital city, Kathmandu which is a small and topographically steep basin. Major flood occurring in 1993 and 2002 as stated in the report of DWIDP shows that the basin is subjected to water-induced disaster in monsoon season affecting people and property. This study focuses on the development of a rainfall-runoff model for Bagmati basin in HEC-HMS using the Synthetic Unit Hydrograph (SUH) with Khokana as the outlet. The coefficients for SUH like Lag time coefficient (Ct), peak discharge coefficient (Cp), unit hydrograph widths at 50% and 75% of peak and base time were determined calibrating the Synder’s equation where Ct varies from 0.244 to 1.016 and Cp varies from 0.439 to 0.410. The rainfall-runoff model in HEC-HMS has been calibrated from daily data of 1992-2013 and validated from hourly data for July 2011, August 2012, and July 2013. Furthermore, the model has been tested to compare the discharge for various return periods with the observed ones which are in close agreement. The determination of Peak Maximum Flood (PMF) using the calculated Peak Maximum Precipitation (PMP) is also another application of the model which can be used to design various hydraulic structures. Thus the values of coefficients, Ct and Cp can be used to construct unit hydrograph for the basin. Moreover, the satisfactory performance of the model during calibration and validation proves the applicability of the model in flood forecasting and early warning.

scholarly journals Investigation of satellite rainfall-driven rainfall–runoff model using deep learning approaches in two different catchments in India

2021 ◽  
Author(s):  
Pavan Kumar Yeditha ◽  
Maheswaran Rathinasamy ◽  
Sai Sumanth Neelamsetty ◽  
Biswa Bhattacharya ◽  
Ankit Agarwal
Keyword(s):  
Deep Learning ◽  
River Basin ◽  
Flood Forecasting ◽  
Precipitation Data ◽  
Rainfall Runoff ◽  
Data Set ◽  
Satellite Precipitation ◽  
Satellite Rainfall ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-driven rainfall–runoff model is emerging and requires careful investigation. In this work, two satellite rainfall data sets, namely Global Precipitation Measurement-Integrated Multi-Satellite Retrieval Product V6 (GPM-IMERG) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS), are evaluated for the development of rainfall–runoff models and the prediction of 1-day ahead streamflow. The accuracy of the data from the SPPs is compared to the India Meteorological Department (IMD)-gridded precipitation data set. Detection metrics showed that for light rainfall (1–10 mm), the probability of detection (POD) value ranges between 0.67 and 0.75 and with an increasing rainfall range, i.e., medium and heavy rainfall (10–50 mm and >50 mm), the POD values ranged from 0.24 to 0.45. These results indicate that the satellite precipitation performs satisfactorily with reference to the IMD-gridded data set. Using the daily precipitation data of nearly two decades (2000–2018) over two river basins in India's Eastern part, artificial neural network, extreme learning machine (ELM), and long short-time memory (LSTM) models are developed for rainfall–runoff modelling. One-day ahead runoff prediction using the developed rainfall–runoff modelling confirmed that both the SPPs are sufficient to drive the rainfall–runoff models with a reasonable accuracy estimated using the Nash–Sutcliffe Efficiency coefficient, correlation coefficient, and the root-mean-squared error. In particular, the 1-day streamflow forecasts for the Vamsadhara river basin (VRB) using LSTM with GPM-IMERG inputs resulted in NSC values of 0.68 and 0.67, while ELM models for Mahanadhi river basin (MRB) with the same input resulted in NSC values of 0.86 and 0.87, respectively, during training and validation stages. At the same time, the LSTM model with CHIRPS inputs for the VRB resulted in NSC values of 0.68 and 0.65, and the ELM model with CHIRPS inputs for the MRB resulted in NSC values of 0.89 and 0.88, respectively, in training and validation stages. These results indicated that both the SPPs could reliably be used with LSTM and ELM models for rainfall–runoff modelling and streamflow prediction. This paper highlights that deep learning models, such as ELM and LSTM, with the GPM-IMERG products can lead to a new horizon to provide flood forecasting in flood-prone catchments.

scholarly journals A Modified IHACRES Rainfall-Runoff Model for Predicting the Hydrologic Response of a River Basin Connected with a Deep Groundwater Aquifer

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2031 ◽  
Author(s):  
Iolanda Borzì ◽  
Brunella Bonaccorso ◽  
Aldo Fiori
Keyword(s):  
Flow Regime ◽  
River Basin ◽  
Cross Section ◽  
Slow Component ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Daily Streamflow ◽  
Groundwater Aquifer ◽  
Rainfall Runoff Model ◽  
Runoff Model

A flow regime is influenced by the degree of hydrologic connection between surface water and groundwater. As this connection becomes more transient and the basin’s runoff response more non-linear, such as for intermittent streams, the need for explicit representation of the groundwater component increases. The present study investigates the connection between Northern Etna groundwater system and the Alcantara river basin in Sicily (Italy). In particular, the upstream part of the basin, whose flow regime is essentially intermittent, is modeled through a modified version of the IHACRES rainfall-runoff model. The structure of the model includes a routing module formulated as a two-store model, with the upper store simulating the quick component of the runoff and recharging the lower store which, in turn, describes the slow component of the runoff and the groundwater extraction and losses. Both stores are conceptualized as simple linear reservoirs, with the lower one that maintains a continuous water balance account of groundwater storage volumes for the upstream basin area with respect to a control cross-section, assumed to be the stream gauging station. The model is calibrated at Moio Alcantara cross-section, where daily streamflow data are available. Model calibration and validation are carried out for the period 1980–1984 and 1986–1988, respectively. A first-order analysis is also performed to assess the sensitivity of model parameters. The adopted configuration is shown to improve model performance with respect to the original IHACRES model, with the proposed formulation able to better capture the interactions between the aquifer and the river.

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