scholarly journals Physiographic Analysis of Tehri Dam Catchment and Development of GIUH Based Nash Model for Ungauged Rivers

2019 ◽  
Vol 14 (2) ◽  
pp. 215-230
Author(s):  
Niraj Kumar Agrawal ◽  
Anil Kumar Lohani ◽  
N. K. Goel
Keyword(s):  
Network Model ◽  
Monsoon Season ◽  
Small Rivers ◽  
Forecasting Model ◽  
Rainfall Runoff ◽  
Inflow Forecasting ◽  
Forecasting System ◽  
Rainfall Runoff Model ◽  
Tehri Dam ◽  
Runoff Model

Advanced information about incoming flows is required for operation of a variety of hydraulic structures including multipurpose storage hydropower projects. Inflow forecasts are used for optimum power generation during non -monsoon season and operation of gates and spillways during the flood season. In order to develop an inflow forecasting system for a reservoir, it has been observed that many a times number of ungauged rivers directly falling into the reservoirs are not accounted for. Such is the case for the Tehri Reservoir, where 16 small rivers/tributaries which are directly contributing to Tehri reservoir are ungauged. In the present study an attempt has been made to carry out physiographic objective Tehri catchment and to develop Geomorphological Instantaneous Unit Hydrograph (GIUH) for ungauged rivers/tributaries directly falling into the reservoir. GIUH developed for the ungauged rivers can be used to simulate the runoff from all the 16 ungauged rivers. Combining these GIUH models with a hydrological model of the other gauged rivers of the Tehri Catchment in the form of a network model provides a complete rainfall-runoff model. Thus, this study provides a useful input for the development of inflow forecasting model for the Tehri Dam as the network model can be used as flood forecasting model.

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 Assessment of an ensemble seasonal streamflow forecasting system for Australia

2017 ◽  
Vol 21 (12) ◽  
pp. 6007-6030 ◽  
Author(s):  
James C. Bennett ◽  
Quan J. Wang ◽  
David E. Robertson ◽  
Andrew Schepen ◽  
Ming Li ◽  
...  
Keyword(s):  
Error Model ◽  
Forecast Skill ◽  
Lead Times ◽  
Rainfall Runoff ◽  
Australian Continent ◽  
Wide Range ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. Despite an increasing availability of skilful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skilful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean–land–atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall–runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skilful forecasts at shorter lead times ( <  4 months), and transits to climatology-like forecasts at longer lead times. Forecasts are generally reliable and unbiased. However, FoGSS does not perform well in very dry catchments (catchments that experience zero flows more than half the time in some months), sometimes producing strongly negative forecast skill and poor reliability. We attempt to improve forecasts through the use of (i) ESP rainfall forcings, (ii) different rainfall–runoff models, and (iii) a Bayesian prior to encourage the error model to return climatology forecasts in months when the rainfall–runoff model performs poorly. Of these, the use of the prior offers the clearest benefit in very dry catchments, where it moderates strongly negative forecast skill and reduces bias in some instances. However, the prior does not remedy poor reliability in very dry catchments. Overall, FoGSS is an attractive alternative to historical inflow sequences in all but the driest catchments. We discuss ways in which forecast reliability in very dry catchments could be improved in future work.

scholarly journals Using pruning algorithms and genetic algorithms to optimise network architectures and forecasting inputs in a neural network rainfall-runoff model

1999 ◽  
Vol 1 (2) ◽  
pp. 103-114 ◽  
Author(s):  
Robert J. Abrahart ◽  
Linda See ◽  
Pauline E. Kneale
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Design Tool ◽  
Forecasting Model ◽  
Network Architectures ◽  
Rainfall Runoff ◽  
Small Catchment ◽  
Pruning Algorithms ◽  
Rainfall Runoff Model ◽  
Runoff Model

Four design tool procedures are examined to create improved neural network architectures for forecasting runoff from a small catchment. Different algorithms are used to remove nodes and connections so as to produce an optimised forecasting model, thereby reducing computational expense without loss in performance. The results also highlight issues in selecting analytical methods to compare outputs from different forecasting procedures.

scholarly journals From aquifers’ and rivers’ memory to a better low-flow forecasting model

2020 ◽  
Author(s):  
Antoine Pelletier ◽  
Vazken Andréassian
Keyword(s):  
Monitoring Network ◽  
Low Flow ◽  
Rainfall Runoff ◽  
Surface Models ◽  
Forecasting System ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Prior Analysis ◽  
Selection Of ◽  
Made In

&lt;p&gt;The 2019 major drought in northern France highlighted the necessity to design an efficient and reliable low-flow forecasting system. Most forecasting tools, based on rainfall-runoff surface models, could benefit from an utilization of piezometric data, broadly available over the French metropolitan territory: obviously, surface water/groundwater interaction are a key process to explain low-flow dynamics.&lt;/p&gt;&lt;p&gt;Indeed, aquifers carry most of the hydroclimatic memory of a catchment, which determines the intensity and duration of droughts: a catchment beginning summer with empty aquifers will not have the same trajectory as the same catchment with higher than average piezometric levels. However, the piezometric data itself is not straightforward to use in a hydrological model, since aquifer-river connexions are often equivocal. Thus, a prior analysis of available data is necessary.&lt;/p&gt;&lt;p&gt;In this work, using 100 catchments of the national French hydroclimatic database and available piezometric data from the national aquifer monitoring network, we performed a comparative memory analysis of piezometry and streamflow, through a simple convolution function. The results were then compared to the behaviour of GR6J, a conceptual lumped rainfall-runoff model. For each catchment of the dataset, a selection of relevant piezometers was made, in the perspective of developing a model incorporating their levels as input data.&lt;/p&gt;

scholarly journals Effects of streamflow isotope sampling strategies on the calibration of a tracer‐aided rainfall‐runoff model

2021 ◽  
Author(s):  
Jamie Lee Stevenson ◽  
Christian Birkel ◽  
Aaron J. Neill ◽  
Doerthe Tetzlaff ◽  
Chris Soulsby
Keyword(s):  
Sampling Strategies ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1226
Author(s):  
Pakorn Ditthakit ◽  
Sirimon Pinthong ◽  
Nureehan Salaeh ◽  
Fadilah Binnui ◽  
Laksanara Khwanchum ◽  
...  
Keyword(s):  
Exchange Rate ◽  
Rainfall Runoff ◽  
Management Planning ◽  
Runoff Variation ◽  
Groundwater Exchange ◽  
Monthly Runoff ◽  
Rainfall Runoff Model ◽  
Two Parameters ◽  
Runoff Model

Accurate monthly runoff estimation is crucial in water resources management, planning, and development, preventing and reducing water-related problems, such as flooding and droughts. This article evaluates the monthly hydrological rainfall-runoff model’s performance, the GR2M model, in Thailand’s southern basins. The GR2M model requires only two parameters: production store (X1) and groundwater exchange rate (X2). Moreover, no prior research has been reported on its application in this region. The 37 runoff stations, which are located in three sub-watersheds of Thailand’s southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. The Thornthwaite method was utilized for the determination of evapotranspiration. The model’s performance was conducted using three statistical indices: Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The model’s calibration results for 37 runoff stations gave the average NSE, r, and OI of 0.657, 0.825, and 0.757, respectively. Moreover, the NSE, r, and OI values for the model’s verification were 0.472, 0.750, and 0.639, respectively. Hence, the GR2M model was qualified and reliable to apply for determining monthly runoff variation in this region. The spatial distribution of production store (X1) and groundwater exchange rate (X2) values was conducted using the IDW method. It was susceptible to the X1, and X2 values of approximately more than 0.90, gave the higher model’s performance.

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