scholarly journals Hydrological modeling using Effective Rainfall routed by the Muskingum method (ERM)

2013 ◽  
Vol 15 (4) ◽  
pp. 1437-1455 ◽  
Author(s):  
M. Baymani-Nezhad ◽  
D. Han
Keyword(s):  
Hydrological Modeling ◽  
Effective Rainfall ◽  
Rainfall Runoff ◽  
Optimization Scheme ◽  
Calibration And Validation ◽  
Wide Range ◽  
Muskingum Method ◽  
Rainfall Runoff Model ◽  
Main Components ◽  
Runoff Model

This paper introduces a new rainfall runoff model called ERM (Effective Rainfall routed by Muskingum method), which has been developed based on the popular IHACRES model. The IHACRES model consists of two main components to transfer rainfall to effective rainfall and then to streamflow. The second component of the IHACRES model is a linear unit hydrograph which has been replaced by the classic and well-known Muskingum method in the ERM model. With the effective rainfall by the first component of the IHACRES model, the Muskingum method is used to estimate the quick flow and slow flow separately. Two different sets of input data (temperature or evapotranspiration, rainfall and observed streamflow) and genetic algorithm (GA) as an optimization scheme have been selected to compare the performance of IHACRES and ERM models in calibration and validation. By testing the models in three different catchments, it is found that the ERM model has better performance over the IHACRES model across all three catchments in both calibration and validation. Further studies are needed to apply the ERM on a wide range of catchments to find its strengths and weaknesses.

scholarly journals Neural networks and non-parametric methods for improving real-time flood forecasting through conceptual hydrological models

2002 ◽  
Vol 6 (4) ◽  
pp. 627-639 ◽  
Author(s):  
A. Brath ◽  
A. Montanari ◽  
E. Toth
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Analysis ◽  
Real Time ◽  
Rainfall Runoff ◽  
Rainfall Forecasting ◽  
Short Term ◽  
Analysis Techniques ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. Time-series analysis techniques for improving the real-time flood forecasts issued by a deterministic lumped rainfall-runoff model are presented. Such techniques are applied for forecasting the short-term future rainfall to be used as real-time input in a rainfall-runoff model and for updating the discharge predictions provided by the model. Along with traditional linear stochastic models, both stationary (ARMA) and non-stationary (ARIMA), the application of non-linear time-series models is proposed such as Artificial Neural Networks (ANNs) and the ‘nearest-neighbours’ method, which is a non-parametric regression methodology. For both rainfall forecasting and discharge updating, the implementation of each time-series technique is investigated and the forecasting schemes which perform best are identified. The performances of the models are then compared and the improvement in the efficiency of the discharge forecasts achievable is demonstrated when i) short-term rainfall forecasting is performed, ii) the discharge is updated and iii) both rainfall forecasting and discharge updating are performed in cascade. The proposed techniques, especially those based on ANNs, allow a remarkable improvement in the discharge forecast, compared with the use of heuristic rainfall prediction approaches or the not-updated discharge forecasts given by the deterministic rainfall-runoff model alone. Keywords: real-time flood forecasting, precipitation prediction, discharge updating, time-series analysis techniques

Macropore domination in runoff generation process: A case study by hydrological modelling in Hilly Watersheds of Koshi River Basin, Himalaya

2021 ◽  
Author(s):  
Suman Kumar Padhee ◽  
Subashisa Dutta
Keyword(s):  
River Basin ◽  
Hydrological Process ◽  
Generation Process ◽  
Watershed Scale ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Calibration And Validation ◽  
Koshi River Basin ◽  
Rainfall Runoff Model ◽  
Runoff Model

<p>A recent initiative by the hydrologic community identified processes that control hillslope-riparian-stream-groundwater interactions as one of the major unsolved scientific problems in Hydrology. It is a long-time argument among hydrologists whether to eliminate the minor details from field-based costing a lot of time, effort, and resources to understand the hydrological process in watershed scale. The modelling approaches are helpful is these cases by focusing on the dominant controllers and might/might'nt bypassing the implications from minor details. In this work, a conceptual semi-distributed rainfall-runoff model for hilly watersheds is used with satellite-based hydrometeorological inputs to parameterize, and thus understand by calibration and validation, at Koshi River Basin, a partly hilly watershed in Himalaya. The semi-distributed model is operated by dividing the river basin into small grids of around 1km<sup>2</sup>, each representing a micro-watershed. Majority of the model concept is drawn from fill and spill approach from previous literature, observations from plot-scale hillslope experiments, and macropore characterization from dye-tracer experiments, which are upscaled at micro-watershed scale. The parameterization in the rainfall-runoff model includes the daily average variables namely, threshold for runoff generation (<em>T</em>), gradient of runoff generation rate (<em>S</em>), saturated hydraulic conductivity for hillslope aquifers (<em>Ksat</em>), and aquifer thickness limit (<em>D</em>). Variable ranges of these parameters were simulated to find the best values (<em>T</em> = 1±0.25cm; <em>S</em> = 0.6 – 0.1; <em>Ksat</em> ≈ 10<sup>5</sup> – 10<sup>10</sup> times original Ksat; and <em>D </em>= 1m). These ranges resulted in over (NSE = 0.6; R<sup>2</sup> = 0.65) during calibration and validation for daily flow volume at the outlet. In these simulations, the <em>Ksat </em>multiplied with factors at several orders higher scale and producing good NSE values shows domination of preferential pathways in runoff generation process. This might represent a flow similar to that of overland flow affecting the surface runoff volume at river basin scale. This model could be used for water budgeting studies in hilly watersheds where several hillslopes dominated by macropores are present.</p>

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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