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 The second component of the IHACRES model is a linear unit hydrograph which has been replaced by the classic and well-known Muskingum method in the ERM model.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 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 Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) Method

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3137
Author(s):  
Santiago Zazo ◽  
José-Luis Molina ◽  
Verónica Ruiz-Ortiz ◽  
Mercedes Vélez-Nicolás ◽  
Santiago García-López
Keyword(s):  
Hydrological Modeling ◽  
Arma Model ◽  
Causal Modeling ◽  
Change Processes ◽  
Rainfall Runoff ◽  
Insufficient Amount ◽  
Runoff Series ◽  
Rainfall Runoff Model ◽  
The One ◽  
Runoff Model

The uncertainty in traditional hydrological modeling is a challenge that has not yet been overcome. This research aimed to provide a new method called the hybrid causal–hydrological (HCH) method, which consists of the combination of traditional rainfall–runoff models with novel hydrological approaches based on artificial intelligence, called Bayesian causal modeling (BCM). This was implemented by building nine causal models for three sub-basins of the Barbate River Basin (SW Spain). The models were populated by gauging (observing) short runoff series and from long and short hydrological runoff series obtained from the Témez rainfall–runoff model (T-RRM). To enrich the data, all series were synthetically replicated using an ARMA model. Regarding the results, on the one hand differences in the dependence intensities between the long and short series were displayed in the dependence mitigation graphs (DMGs), which were attributable to the insufficient amount of data available from the hydrological records and to climate change processes. The similarities in the temporal dependence propagation (basin memory) and in the symmetry of DMGs validate the reliability of the hybrid methodology, as well as the results generated in this study. Consequently, water planning and management can be substantially improved with this approach.

Snow-Hydrological modeling using remote sensing data in Vilcanota basin, Peru

2020 ◽  
Author(s):  
Eber Risco ◽  
Waldo Lavado ◽  
Pedro Rau
Keyword(s):  
Hydrological Modeling ◽  
Remote Sensing Data ◽  
Time Consistency ◽  
Rain Gauge ◽  
Rainfall Runoff ◽  
Direct Evaluation ◽  
Daily Discharge ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Trmm 3B42

<p>Water resources availability in the southern Andes of Peru is being affected by glacier and snow retreat. This problem is already perceived in the Vilcanota river basin, where hydro-climatological information is scarce. In this particular mountain context, any water plan represents a great challenge. To cope with these limitations, we propose to assess the space-time consistency of 10 satellite-based precipitation products (CMORPH–CRT v.1, CMORPH–BLD v.1, CHIRP v.2, CHIRPS v.2, GSMaP v.6, GSMaP correction, MSWEP v.2.1, PERSIANN, PERSIANN–CDR, TRMM 3B42) with 25 rain gauge stations in order to select the best product that represents the variability in the Vilcanota basin. For this purpose, through a direct evaluation of sensitivity analysis via the GR4J parsimonious hydrological model over the basin. GSMap v.6, TRMM 3B42 and CHIRPS were selected to represent rainfall spatial variability according with different statistical criteria, such as correlation coefficient (CC), standard deviation (SD), percentage of bias (%B) and centered mean square error (CRMSE). To facilitate the interpretation of statistical results, Taylor's diagram was used to represent the CC statistics, normalized values of SD and CRMSE.</p><p>A distributed degree-day model was chosen to analyse the sensitivity of snow cover simulations and hydrological contribution. The GR4J rainfall-runoff model was calibrated (using global optimization) and applied to simulate the daily discharge and compared with the Distributed Hydrology and Vegetation Model with Glacier Dynamics (DHSVM-GDM) over the 2001-2018 period. Furthermore, the simulated streamflow was evaluated through comparisons with observations at the hydrological stations using Nash–Sutcliffe efficiency and Kling Gupta Efficiency (KGE). The results show that the snow-runoff have increased in recent years, so new water management and planning strategies should be developed in the basin. This research is part of the multidisciplinary collaboration between British and Peruvian scientists (Newton Fund, Newton-Paulet) through RAHU project.</p>

scholarly journals Regionalization of a Rainfall-Runoff Model: Limitations and Potentials

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2257 ◽  
Author(s):  
Jung-Hun Song ◽  
Younggu Her ◽  
Kyo Suh ◽  
Moon-Seong Kang ◽  
Hakkwan Kim
Keyword(s):  
Hydrological Modeling ◽  
Rainfall Runoff ◽  
Tank Model ◽  
Statistical Relationships ◽  
Function Selection ◽  
Rainfall Runoff Model ◽  
Parameter Values ◽  
Runoff Model ◽  
Selection Of

Regionalized lumped rainfall-runoff (RR) models have been widely employed as a means of predicting the streamflow of an ungauged watershed because of their simple yet effective simulation strategies. Parameter regionalization techniques relate the parameter values of a model calibrated to the observations of gauged watersheds to the geohydrological characteristics of the watersheds. Thus, the accuracy of regionalized models is dependent on the calibration processes, as well as the structure of the model used and the quality of the measurements. In this study, we have discussed the potentials and limitations of hydrological model parameter regionalization to provide practical guidance for hydrological modeling of ungauged watersheds. This study used a Tank model as an example model and calibrated its parameters to streamflow observed at the outlets of 39 gauged watersheds. Multiple regression analysis identified the statistical relationships between calibrated parameter values and nine watershed characteristics. The newly developed regional models provided acceptable accuracy in predicting streamflow, demonstrating the potential of the parameter regionalization method. However, uncertainty associated with parameter calibration processes was found to be large enough to affect the accuracy of regionalization. This study demonstrated the importance of objective function selection of the RR model regionalization.

scholarly journals Inferring the flood frequency distribution for an ungauged basin using a spatially distributed rainfall-runoff model

2008 ◽  
Vol 5 (1) ◽  
pp. 1-26 ◽  
Author(s):  
G. Moretti ◽  
A. Montanari
Keyword(s):  
Frequency Distribution ◽  
River Flow ◽  
Spatial Scales ◽  
Flood Frequency ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Wide Range ◽  
Spatially Distributed ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. The estimation of the peak river flow for ungauged river sections is a topical issue in applied hydrology. Spatially distributed rainfall-runoff models can be a useful tool to this end, since they are potentially able to simulate the river flow at any location of the watershed drainage network. However, it is not fully clear to what extent these models can provide reliable simulations over a wide range of spatial scales. This issue is investigated here by applying a spatially distributed, continuous simulation rainfall-runoff model to infer the flood frequency distribution of the Riarbero Torrent. This is an ungauged mountain creek located in northern Italy, whose drainage area is 17 km2. The results were checked by using estimates of the peak river flow obtained by applying a classical procedure based on hydrological similarity principles. The analysis highlights interesting perspectives for the application of spatially distributed models to ungauged catchments.

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 The Impact of the Variability of Precipitation and Temperatures on the Efficiency of a Conceptual Rainfall-Runoff Model

2016 ◽  
Vol 24 (4) ◽  
pp. 1-7 ◽  
Author(s):  
P. Sleziak ◽  
J. Szolgay ◽  
K. Hlavčová ◽  
J. Parajka
Keyword(s):  
Hydrological Model ◽  
Differential Evolution Algorithm ◽  
Rainfall Runoff ◽  
University Of Technology ◽  
Wide Range ◽  
Evolution Algorithm ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
The Relationship ◽  
Runoff Model

AbstractThe main objective of the paper is to understand how the model’s efficiency and the selected climatic indicators are related. The hydrological model applied in this study is a conceptual rainfall-runoff model (the TUW model), which was developed at the Vienna University of Technology. This model was calibrated over three different periods between 1981-2010 in three groups of Austrian catchments (snow, runoff, and soil catchments), which represent a wide range of the hydroclimatic conditions of Austria. The model’s calibration was performed using a differential evolution algorithm (Deoptim). As an objective function, we used a combination of the Nash-Sutcliffe coefficient (NSE) and the logarithmic Nash-Sutcliffe coefficient (logNSE). The model’s efficiency was evaluated by Volume error (VE). Subsequently, we evaluated the relationship between the model’s efficiency (VE) and changes in the climatic indicators (precipitation ΔP, air temperature ΔT). The implications of findings are discussed in the conclusion.

scholarly journals Component Combination Test to Investigate Improvement of the IHACRES and GR4J Rainfall–Runoff Models

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2126
Author(s):  
Mun-Ju Shin ◽  
Chung-Soo Kim
Keyword(s):  
Flow Simulation ◽  
High Flow ◽  
Low Flow ◽  
Effective Rainfall ◽  
Rainfall Runoff ◽  
Flow Simulations ◽  
New Models ◽  
Pros And Cons ◽  
Rainfall Runoff Model ◽  
Runoff Model

Rainfall–runoff models are not perfect, and the suitability of a model structure depends on catchment characteristics and data. It is important to investigate the pros and cons of a rainfall–runoff model to improve both its high- and low-flow simulation. The production and routing components of the GR4J and IHACRES models were combined to create two new models. Specifically, the GR_IH model is the combination of the production store of the GR4J model and the routing store of the IHACRES model (vice versa in the IH_GR model). The performances of the new models were compared to those of the GR4J and IHACRES models to determine components improving the performance of the two original models. The suitability of the parameters was investigated with sensitivity analysis using 40 years’ worth of spatiotemporally different data for five catchments in Australia. These five catchments consist of two wet catchments, one intermediate catchment, and two dry catchments. As a result, the effective rainfall production and routing components of the IHACRES model were most suitable for high-flow simulation of wet catchments, and the routing component improved the low-flow simulation of intermediate and one dry catchments. Both effective rainfall production and routing components of the GR4J model were suitable for low-flow simulation of one dry catchment. The routing component of the GR4J model improved the low- and high-flow simulation of wet and dry catchments, respectively, and the effective rainfall production component improved both the high- and low-flow simulations of the intermediate catchment relative to the IHACRES model. This study provides useful information for the improvement of the two models.

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