Calibration of a rainfall–runoff model at regional scale by optimising river discharge statistics: Performance analysis for the average/low flow regime

2012 ◽  
Vol 42-44 ◽  
pp. 77-84 ◽  
Author(s):  
Laura Lombardi ◽  
Elena Toth ◽  
Attilio Castellarin ◽  
Alberto Montanari ◽  
Armando Brath
Keyword(s):  
Performance Analysis ◽  
Flow Regime ◽  
River Discharge ◽  
Regional Scale ◽  
Low Flow ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals Analysis of low flow indices under varying climatic conditions in Poland

2017 ◽  
Vol 49 (2) ◽  
pp. 373-389 ◽  
Author(s):  
Marzena Osuch ◽  
Renata Romanowicz ◽  
Wai K. Wong
Keyword(s):  
Climate Models ◽  
Climatic Conditions ◽  
Low Flow ◽  
Test Results ◽  
Rainfall Runoff ◽  
Objective Functions ◽  
Rcp 8.5 ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Simulated Time

Abstract Changes in low flow indices under future climates are estimated for eight catchments in Poland. A simulation approach is used to derive daily flows under changing climatic conditions, following RCP 4.5 and RCP 8.5 emission scenarios. The HBV rainfall–runoff model is used to simulate low flows. The model is calibrated and validated using streamflow observations from periods 1971–2000 and 2001–2010. Two objective functions are used for calibration: Nash–Sutcliffe and log transformed Nash–Sutcliffe. Finally, the models are run using the bias-corrected precipitation and temperature data simulated by GCM/RCM models for the periods 2021–2050 and 2071–2100. We estimate low flow indices for the simulated time series, including annual minima of 7-day mean river flows and number, severity and duration of low flow events. We quantify the biases of low flow indices by N-way analysis of variance (ANOVA) analysis and Tukey test. Results indicate a large effect of climate models, as well as objective functions, on the low flow indices obtained. A comparison of indices from the two future periods with the reference period 1971–2000 confirms the trends obtained in previous studies, in the form of a projected decrease in the frequency and intensity of low flow events.

scholarly journals Sensitivity Analysis to Investigate the Reliability of the Grid-Based Rainfall-Runoff Model

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1839 ◽  
Author(s):  
Mun-Ju Shin ◽  
Yun Choi
Keyword(s):  
Water Movement ◽  
Parameter Sensitivity ◽  
Low Flow ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Rainfall Events ◽  
Flow Calculation ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Grid Based

This study aimed to assess the suitability of the parameters of a physically based, distributed, grid-based rainfall-runoff model. We analyzed parameter sensitivity with a dataset of eight rainfall events that occurred in two catchments of South Korea, using the Sobol’ method. Parameters identified as sensitive responded adequately to the scale of the rainfall events and the objective functions employed. Parameter sensitivity varied depending on rainfall scale, even in the same catchment. Interestingly, for a rainfall event causing considerable runoff, parameters related to initial soil saturation and soil water movement played a significant role in low flow calculation and high flow calculation, respectively. The larger and steeper catchment exhibited a greater difference in parameter sensitivity between rainfall events. Finally, we found that setting an incorrect parameter range that is physically impossible can have a large impact on runoff simulation, leading to substantial uncertainty in the simulation results. The proposed analysis method and the results from our study can help researchers using a distributed rainfall-runoff model produce more reliable analysis results.

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.

scholarly journals Commercial microwave links as rainfall input data in urban hydrological modelling

2021 ◽  
Author(s):  
Greta Cazzaniga ◽  
Carlo De Michele ◽  
Cristina Deidda ◽  
Michele D'Amico ◽  
Antonio Ghezzi ◽  
...  
Keyword(s):  
River Discharge ◽  
Hydrological Model ◽  
Warning System ◽  
Signal Propagation ◽  
Rainfall Data ◽  
Data Availability ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Commercial Microwave Links

<p>Many studies in literature have showed that hydrological models are highly sensitive to spatial variability of the rainfall field. Limited and inaccurate rainfall observations can negatively affect flood forecasting and the decision-making processes based on warning system. This problem becomes much more evident in urban catchments which usually covers huge areas and where the runoff process is faster, due to the highly impervious surfaces. Given this, it is a priority to develop always new operational instruments which can improve rainfall data availability and accurately quantify rainfall variability in space. To face this challenge, in the recent years, it has been investigated the use of commercial microwave links (CML) as opportunistic rainfall sensors which could be integrated with traditional rainfall observations in areas lacking sensors. The technique relies on the well-established relationship between CML's signal attenuation and rainfall intensity across the signal propagation path. Here, we assess the operational potential of a CML network, located in the northern area of Lambro river (Lombardia region, Italy). This urbanized region is of great hydrological interest, since it is often subjected to flash floods, hence it requires a robust and accurate warning system. We considered a set of about 80 CMLs distributed quite uniformly over the entire study area and we assessed if and how rainfall data collected by them can improve river discharge predictions. To this aim, we implemented a semi-distributed rainfall-runoff model, which reproduces the river flow at the outlet section in Lesmo (Monza e Brianza), and we fed the hydrological model with CML rainfall data. We tested the use of CML rainfall data as input to the hydrological model. In particular, we used path-averaged rainfall intensities, calculated from CML path attenuation, as point measurements with a weight inversely proportional to CML length. To check the suitability of CML data as input to our urban rainfall-runoff model, we compared the observed river discharge with the predicted one, obtained using different rainfall data layouts. Indeed, we tested CML data but also rain gauges measurements and a combination of CML and rain gauge observations.</p>

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.

scholarly journals Estimation of River Discharge from Satellite Observations

2021 ◽  
Vol 12 (4) ◽  
pp. 1072-1083
Author(s):  
Dhanendra Bahekar, Et. al.
Keyword(s):  
River Discharge ◽  
Structure Design ◽  
Flood Routing ◽  
Rainfall Runoff ◽  
Daily Streamflow ◽  
Rainfall Runoff Model ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Gauged Basins ◽  
Runoff Model

The role of streamflow is very important in any type of hydrologic. For very effective flood routing and hydraulic structure design, it is important to have a large dataset of past years. We now have a conceptual rainfall-runoff model that can predict streamflow based on pre-existing datasets. Because there is no or very little observed data in un-gauged basins, calibrating these models to predict daily streamflow becomes difficult. Nowadays, parameters for example river width can be observed using satellite images, and some studies show a promising associated relation between discharge and river width. The suggested study demonstrates a method for calculating streamflow from river width extracted with the help of satellite imagery. To predict streamflow, hydrological models are calibrated using river width instead of in site observed streamflow, and for estimating uncertainty Generalized Likelihood Uncertainty Estimation (GLUE) is used. For validation, the suggested method is implemented in the Kharun river basin situated in the Chhattisgarh state of India. The obtained Nash-Sutcliffe efficiency is 92.6 % for simulated river discharge in 2019-2020 at the 50% quantile, which is promising.

scholarly journals Impact of the Mean Areal Rainfall Calculation on a Modular Rainfall-Runoff Model

2020 ◽  
Vol 8 (12) ◽  
pp. 980
Author(s):  
Jose Valles ◽  
Gerald Corzo ◽  
Dimitri Solomatine
Keyword(s):  
Divide And Conquer ◽  
Low Flow ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
The Mean ◽  
Areal Rainfall ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
The Relationship ◽  
Runoff Model

Hydrological models are based on the relationship between rainfall and discharge, which means that a poor representation of rainfall produces a poor streamflow result. Typically, a poor representation of rainfall input is produced by a gauge network that is not able to capture the rainfall event. The main objective of this study is to evaluate the impact of the mean areal rainfall on a modular rainfall-runoff model. These types of models are based on the divide-and-conquer approach and two specialized hydrological models for high and low regimes were built and then combined to form a committee of model that takes the strengths of both specialized models. The results show that the committee of models produces a reasonable reproduction of the observed flow for high and low flow regimes. Furthermore, a sensitivity analysis reveals that Ilopango and Jerusalem rainfall gauges are the most beneficial for discharge calculation since they appear in most of the rainfall subset that produces low Root Mean Square Error (RMSE) values. Conversely, the Puente Viejo and Panchimalco rainfall gauges are the least beneficial for the rainfall-runoff model since these gauges appear in most of the rainfall subset that produces high RMSE value.

scholarly journals Constraining a lumped rainfall-runoff model with piezometry to improve low-flow simulation

2021 ◽  
Author(s):  
Antoine Pelletier ◽  
Vakzen Andréassian
Keyword(s):  
Flow Simulation ◽  
Daily Rainfall ◽  
Climatic Conditions ◽  
Low Flow ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Low Flows ◽  
Sample Test ◽  
Rainfall Runoff Model ◽  
Runoff Model

<p>Most lumped hydrological models are focused on the rainfall-runoff relationship, since climatic conditions are the driving force of the hydrological behaviour of a catchment. Many hydrological models, like the ones used by the French national PREMHYCE platform, only take climatic variables as inputs – daily rainfall and potential evaporation – to simulate and forecast low-flows. Yet, a hydrological drought is generally a medium- to long-term phenomenon, which is the consequence of long records of dry climatic conditions. Daily lumped hydrological models often struggle to integrate these records to reproduce catchment memory.</p><p>In many French catchments, it was observed that this memory of past hydroclimatic conditions is well represented in piezometric signals that are broadly available over the national territory. Indeed, aquifers, especially the large ones, do store water on the long, feeding rivers during droughts: aquifers are not only <em>water carriers</em> – the etymology for the word <em>aquifer </em>– they are also <em>memory carriers</em>. A dataset of 108 catchments, each of them being associated with one or several piezometers, was used to investigate whether the GR6J daily lumped rainfall-runoff model could be constrained by piezometric time series to improve low-flow simulations. We found that a particular state of the model, the exponential store, is particularly well correlated with piezometry in most studied catchments.</p><p>In order to get a univocal relationship between the exponential store and piezometry, a multi-objective calibration approach was implemented, optimising both (i) flow simulation with a criterion focused on low-flows and (ii) affine correspondence between the exponential store level and piezometry. For that purpose, a new parameter was added to the model. The modified calibration was then evaluated through a split-sample test and the performance in simulating particular drought events. The calibrated store-piezometry relationship can now be used for data assimilation to improve low-flow forecasting.</p>

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