scholarly journals Latest developments of the airGR rainfall-runoff modelling R-package: inclusion of an interception store in the hourly model

2020 ◽  
Author(s):  
Guillaume Thirel ◽  
Olivier Delaigue ◽  
Andrea Ficchi
Keyword(s):  
R Package ◽  
Snow Accumulation ◽  
Recent Model ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Environmental Modelling ◽  
Model Improvement ◽  
Improved Model ◽  
The Impact ◽  
Daily Time

<p>airGR (Coron et al., 2017, 2019) is an R package that offers the possibility to use the GR rainfall-runoff models developed in the Hydrology Research Group at INRAE (formerly at Irstea), including the daily GR4J model as well as hourly, monthly and annual models. Recent model developments are regularly introduced in airGR.</p><p>Recently, an hourly model including an interception store was implemented in airGR. The additional interception store, developed by Ficchi et al. (2019), aims at better representing the impact of vegetation on evaporation fluxes. This improved model showed a better consistency of model fluxes across time and enhanced performance.</p><p>In addition, the possibility to run the hourly GR models together with the CemaNeige snow accumulation and melt module was added to airGR.</p><p> </p><p>References:</p><p>Coron L., Thirel G., Delaigue O., Perrin C., Andréassian V. (2017). The Suite of Lumped GR Hydrological Models in an R package, Environmental Modelling & Software, 94, 166-171. DOI: 10.1016/j.envsoft.2017.05.002.</p><p>Coron, L., Delaigue, O., Thirel, G., Perrin, C. and Michel, C. (2019). airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling. R   package version 1.4.3.30. URL: https://CRAN.R-project.org/package=airGR.</p><p>Ficchì, A., Perrin, C., and Andréassian, V., 2019. Hydrological modelling at multiple sub-daily time steps: model improvement via flux-matching, Journal of Hydrology, 575, 1308-1327, https://doi.org/10.1016/j.jhydrol.2019.05.084.</p>

scholarly journals Evaluation of the appearing changes in water regime at Ráztoka and Červík basins in the Beskydy Mts.

Beskydy ◽  
2013 ◽  
Vol 6 (2) ◽  
pp. 135-148
Author(s):  
M. Tesař ◽  
J. Buchtele
Keyword(s):  
Time Series ◽  
Water Regime ◽  
Significant Contribution ◽  
River Basins ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Clear Cutting ◽  
Daily Time Series ◽  
Large Floods ◽  
Daily Time

The influence of vast salvage clear cutting in some hilly regions induced by acid rains is sometimes considered as a significant contribution to the disastrous character of the recent floods. Then the considerations having also partly emotional character, appeared, namely after large floods in the Morava and Odra Rivers in the July 1997 and in the Labe River basins in August 2002. The simulations of rainfall-runoff process for several experimental catchments have been carried out using daily time series up to 50 years long. The outputs of hydrological models SAC-SMA and BROOK´90 provide naturally the differences between observed and simulated discharge, which could show the tendencies in the runoff. They have been analysed and findings indicate the increases of runoff after deforestation. The differences between observed and simulated flows can be helpful also for the assessment of changes in evapotranspiration demands as the significant long-termed phenomenon.

New airGR developments: semi-distribution and data assimilation

2021 ◽  
Author(s):  
Guillaume Thirel ◽  
Olivier Delaigue ◽  
David Dorchies ◽  
Gaia Piazzi
Keyword(s):  
Data Assimilation ◽  
Water Resource Management ◽  
Transfer Functions ◽  
Initial Conditions ◽  
R Package ◽  
Snow Accumulation ◽  
Sequential Data ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Distributed Models

<p>airGR (Coron et al., 2017, 2020) is an R package that offers the possibility to use the GR rainfall-runoff models developed in the Hydrology Research Group at INRAE (formerly at Irstea). It allows running seven hydrological models (including GR4J) dedicated to different time steps (hourly to annual) that can be combined to a snow accumulation and melt model (CemaNeige).</p><p>Thanks to the success of the airGR package, that was downloaded 45,000 times so far among 50 countries in the world and was used in dozen of publications since its release[1], its development team carries on its efforts to offer new features and improve the computer codes. This is how after offering a first add-on, the airGRteaching package, expressly developed for educational purposes, the team now offers tools dedicated to semi-distribution and data assimilation.</p><p>Using (semi-)distributed models is often necessary to explicitly represent spatial climatic and physiographic heterogeneities and to allow an analysis of their impact on the watershed response. Consequently, in the latest version of the airGR package, we introduced the semi-distribution of GR models, which are traditionally lumped, on a sub-basin basis. This development will also ultimately enable possibilities of implementing on a modular way different transfer functions as well as integrated water resource management (see package airGRiwrm in Abstract EGU21-2190).</p><p>In addition, a new package, called airGRdatassim, was recently proposed (Piazzi et al., 2021a, b) as an add-on to the airGR package. airGRdatassim enables the user to assimilate discharge observations via both Ensemble Kalman filter (EnKF) and particle filter (PF) schemes. Besides improving the simulations of GR models, this new package extends the potential applications of airGR to forecasting purposes by allowing for a reliable assessment of the initial conditions of streamflow forecasts. </p><p> </p><p>References:</p><p>Coron L., Thirel G., Delaigue O., Perrin C., Andréassian V. (2017). The Suite of Lumped GR Hydrological Models in an R package, Environmental Modelling & Software, 94, 166-171. DOI: 10.1016/j.envsoft.2017.05.002.</p><p>Coron, L., Delaigue, O., Thirel, G., Perrin, C. and Michel, C. (2020). airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling. R package version 1.4.3.65. URL: https://CRAN.R-project.org/package=airGR.</p><p>Piazzi, G., Delaigue, O. (2021a). airGRdatassim: Suite of Tools to Perform Ensemble-Based Data Assimilation in GR Hydrological Models. R package version 0.0.3.13. URL: https://gitlab.irstea.fr/HYCAR-Hydro/airgrdatassim.</p><p>Piazzi, G., Thirel, G., Perrin, C., Delaigue, O. (2021b, accepted). Sequential data assimilation for streamflow forecasting: assessing the sensitivity to uncertainties and updated variables of a conceptual hydrological model. Water Resources Research.</p><div><br><div> <p>[1] https://hydrogr.github.io/airGR/page_publications.html</p> </div> </div>

Investigating global changes in snow dynamics and the impact on water resources

2020 ◽  
Author(s):  
Adrià Fontrodona Bach ◽  
Joshua Larsen ◽  
Ross Woods ◽  
Bettina Schaefli ◽  
Ryan Teuling
Keyword(s):  
Water Resources ◽  
Hydrological Cycle ◽  
Snow Accumulation ◽  
Global Changes ◽  
Hydrological Models ◽  
Winter Snow ◽  
Long Term Trends ◽  
And Shifts ◽  
The Impact

<p>Snow is a key component of the hydrological cycle in many regions of the world, providing a natural storage of water by accumulating snow in winter and releasing it in spring. Many ecosystems, societies and economies rely on this mechanism as a water resource. There is strong evidence in the literature that global warming leads to decreasing snowfall and snow accumulation and shifts the onset of the melt season to earlier in the year. However, little is known about how rising temperatures affect snowmelt rates and timing, and how these can have an impact on water resources for instance by changing the time and magnitude of streamflow. Some studies predict slower snowmelt rates in a warmer world, due to the onset of melt being earlier when there is less energy available for melt, but there is not yet an observation-based study showing such trends. As a first step, here we present preliminary results of observed long term trends in snowmelt rates from different climates. We use a dataset that has already shown strong decreasing signals for winter snow accumulation. Here we also present potential avenues to investigate the sensitivity of snowpacks and snowmelt regimes in different climatic settings to further rising temperatures using modeled snow dynamics. A few possibilities on how to link the snowpack dynamics to impacts in water resources are also discussed, for instance by comparing modelled dynamics to hydrological models and observations.</p>

scholarly journals Insights from a new methodology to optimize rain gauge weighting for rainfall-runoff models

2019 ◽  
Author(s):  
Ashley J. Wright ◽  
David E. Robertson ◽  
Jeffrey P. Walker ◽  
Valentijn R. N. Pauwels
Keyword(s):  
Conceptual Understanding ◽  
Flood Forecasting ◽  
Rain Gauge ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Model Dynamics ◽  
Areal Rainfall ◽  
The Impact ◽  
Streamflow Simulation ◽  
Selection Of

Abstract. Floods continue to devastate societies and their economies. Resilient societies commonly incorporate flood forecasting into their strategy to mitigate the impact of floods. Hydrological models which simulate the rainfall-runoff process are at the core of flood forecasts. To date operational flood forecasting models use areal rainfall estimates that are based on geographical features. This paper introduces a new methodology to optimally blend the weighting of gauges for the purpose of obtaining superior flood forecasts. For a selection of 7 Australian catchments this methodology was able to yield improvements of 15.3 % and 7.1 % in optimization and evaluation periods respectively. Catchments with a low gauge density, or an overwhelming majority of gauges with a low proportion of observations available, are not well suited to this new methodology. Models which close the water balance and demonstrate internal model dynamics that are consistent with a conceptual understanding of the rainfall-runoff process yielded consistent improvement in streamflow simulation skill.

scholarly journals Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithm

2020 ◽  
Vol 24 (6) ◽  
pp. 3189-3209
Author(s):  
Céline Monteil ◽  
Fabrice Zaoui ◽  
Nicolas Le Moine ◽  
Frédéric Hendrickx
Keyword(s):  
R Package ◽  
Calibration Procedure ◽  
Automatic Calibration ◽  
Hydrological Models ◽  
Environmental Modelling ◽  
Nsga Ii ◽  
Multi Objective ◽  
Constrained Problem ◽  
Stochastic Rules

Abstract. Environmental modelling is complex, and models often require the calibration of several parameters that are not able to be directly evaluated from a physical quantity or field measurement. Multi-objective calibration has many advantages such as adding constraints in a poorly constrained problem or finding a compromise between different objectives by defining a set of optimal parameters. The caRamel optimizer has been developed to meet the requirement for an automatic calibration procedure that delivers not just one but a family of parameter sets that are optimal with regard to a multi-objective target. The idea behind caRamel is to rely on stochastic rules while also allowing more “local” mechanisms, such as the extrapolation along vectors in the parameter space. The caRamel algorithm is a hybrid of the multi-objective evolutionary annealing simplex (MEAS) method and the non-dominated sorting genetic algorithm II (ε-NSGA-II). It was initially developed for calibrating hydrological models but can be used for any environmental model. The caRamel algorithm is well adapted to complex modelling. The comparison with other optimizers in hydrological case studies (i.e. NSGA-II and MEAS) confirms the quality of the algorithm. An R package, caRamel, has been designed to easily implement this multi-objective algorithm optimizer in the R environment.

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 Reproducing different types of changes in hydrological indicators with rainfall-runoff models

2020 ◽  
Vol 51 (2) ◽  
pp. 238-256
Author(s):  
Carolina Massmann
Keyword(s):  
Objective Function ◽  
Model Structure ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Anthropogenic Changes ◽  
Riverine Ecosystems ◽  
Different Types ◽  
Future Value ◽  
The Impact ◽  
Flow Reversals

Abstract Hydrological indicators support analyses about the impact of climate and anthropogenic changes on riverine ecosystems. As these studies often rely on hydrological models for estimating the future value of the indicators, it is important to investigate how well, and under which conditions, we can replicate changes in the indicators. This study looks at these questions by investigating the performance that can be achieved depending on the objective function for calibrating the model, the direction of the change in the indicator, the magnitude of this change and the properties of the catchments. The results indicate that, in general, indicators describing the magnitude of discharge (monthly and annual) can be adequately estimated with hydrological models, but that there are difficulties when estimating the characteristics of flow pulses, flow reversals and timing variables. For some of these indicators, it is not even possible to correctly estimate the direction of large changes. The analysis showed further that these problems cannot be resolved by adjusting the calibrated parameters, but that the model structure is unsuitable for modelling these indicators.

airGRiwrm: an extension of the airGR R-package for handling Integrated Water Resources Management modeling

2021 ◽  
Author(s):  
David Dorchies ◽  
Olivier Delaigue ◽  
Guillaume Thirel
Keyword(s):  
Water Resources ◽  
River Basin ◽  
Water Resource Management ◽  
R Package ◽  
Hydrological Models ◽  
Environmental Modelling ◽  
Seine River ◽  
Local Flow ◽  
Distributed Models ◽  
Seine River Basin

<p>IWRM modeling aims at representing interactions between humans and their environment (Badham et al. 2019), which can involve hydrological, surface-hydraulic, and groundwater models. Semi-distributed models implementing a simplified hydraulic propagation between sub-catchments are often used as IWRM model (Ficchi et al. 2014, Dorchies et al. 2016) because of the good trade-off they offer between simplification and result relevancy.<br><br>The R-package <strong>airGR</strong> (Coron et al., 2017, 2020) is widely used in the R language hydrology community and its recent development with semi-distributive (see Abstract EGU21-1371) capabilities allows to use it for IWRM modeling. The R-package <strong>airGRiwrm</strong> has been developed for multiple purposes linked to IWRM. First, it proposes a simplified network description for building semi-distributed models containing several sub-basins with diverse connections, which greatly simplifies the calibration and modeling steps. Then, it allows to easily integrate predefined flows (feedforward control) into the model, namely local flow injections or withdrawals. Finally, it integrates controllers that apply user-defined decision algorithms given model outputs during simulation (feedback control). The controllers allows for example to apply withdrawal restriction in case of drought, or to simulate a reservoir behaviour with complex management rules.</p><p>In this presentation, we will introduce the <strong>airGRiwrm</strong> possibilities and we will demonstrate its use on the case of the Seine River basin in France. </p><p> </p><p><strong>References:</strong></p><p>Badham, J., et al., 2019. Effective modeling for Integrated Water Resource Management: A guide to contextual practices by phases and steps and future opportunities. Environmental Modelling & Software 116, 40–56. https://doi.org/10.1016/j.envsoft.2019.02.013</p><p>Coron, L., Delaigue, O., Thirel, G., Perrin, C., Michel, C., 2020. airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling. R package version 1.4.3.65. https://doi.org/10.15454/EX11NA</p><p>Coron, L., Thirel, G., Delaigue, O., Perrin, C., Andréassian, V., 2017. The suite of lumped GR hydrological models in an R package. Environmental Modelling & Software 94, 166–171. https://doi.org/10.1016/j.envsoft.2017.05.002</p><p>Dorchies, D., Thirel, G., Perrin, C., Bader, J.-C., Thepot, R., Rizzoli, J.-L., Jost, C., Demerliac, S., 2016. Climate change impacts on water resources and reservoir management in the Seine river basin (France). La Houille Blanche 32–37. https://doi.org/10.1051/lhb/2016047<br>Ficchi, A., Raso, L., Malaterre, P.-O., Dorchies, D., Jay-Allemand, M., 2014. Short Term Reservoirs Operation On The Seine River: Performance Analysis Of Tree-Based Model Predictive Control. Presented at the International Conference on Hydroinformatics, New York.</p>

Research and Improvement of the Keep-Right-Except-to-Pass Rule

2014 ◽  
Vol 915-916 ◽  
pp. 459-463
Author(s):  
He Quan Zhang
Keyword(s):  
Fluid Mechanics ◽  
Traffic Flow ◽  
Influence Factors ◽  
Traffic Model ◽  
Light Traffic ◽  
The Road ◽  
Model Based ◽  
Light Passing ◽  
Improved Model ◽  
The Impact

In order to deal with the impact on traffic flow of the rule, we compare the influence factors of traffic flow (passing, etc.) into viscous resistance of fluid mechanics, and establish a traffic model based on fluid mechanics. First, in heavy and light traffic, we respectively use this model to simulate the actual segment of the road and find that when the traffic is heavy, the rule hinder the further increase in traffic. For this reason, we make further improvements to the model to obtain a fluid traffic model based on no passing and find that the improved model makes traffic flow increase significantly. Then, the improved model is applied to the light traffic, we find there are no significant changes in traffic flow .In this regard we propose a new rule: when the traffic is light, passing is allowed, but when the traffic is heavy, passing is not allowed.

Communicating water-related climate change hazards to local stakeholders

2021 ◽  
Author(s):  
Laura Müller ◽  
Petra Döll
Keyword(s):  
Climate Change ◽  
Water Management ◽  
Climate Change Impact ◽  
Biosphere Reserve ◽  
Hydrological Models ◽  
Model Ensemble ◽  
Hydrological Hazards ◽  
Local Stakeholders ◽  
Change Impact ◽  
The Impact

<p>Due to climate change, the water cycle is changing which requires to adapt water management in many regions. The transdisciplinary project KlimaRhön aims at assessing water-related risks and developing adaptation measures in water management in the UNESCO Biosphere Reserve Rhön in Central Germany. One of the challenges is to inform local stakeholders about hydrological hazards in in the biosphere reserve, which has an area of only 2433 km² and for which no regional hydrological simulations are available. To overcome the lack of local simulations of the impact of climate change on water resources, existing simulations by a number of global hydrological models (GHMs) were evaluated for the study area. While the coarse model resolution of 0.5°x0.5° (55 km x 55 km at the equator) is certainly problematic for the small study area, the advantage is that both the uncertainty of climate simulations and hydrological models can be taken into account to provide a best estimate of future hazards and their (large) uncertainties. This is different from most local hydrological climate change impact assessments, where only one hydrological model is used, which leads to an underestimation of future uncertainty as different hydrological models translate climatic changes differently into hydrological changes and, for example, mostly do not take into account the effect of changing atmospheric CO<sub>2</sub> on evapotranspiration and thus runoff.   </p><p>The global climate change impact simulations were performed in a consistent manner by various international modeling groups following a protocol developed by ISIMIP (ISIMIP 2b, www.isimip.org); the simulation results are freely available for download. We processed, analyzed and visualized the results of the multi-model ensemble, which consists of eight GHMs driven by the bias-adjusted output of four general circulation models. The ensemble of potential changes of total runoff and groundwater recharge were calculated for two 30-year future periods relative to a reference period, analyzing annual and seasonal means as well as interannual variability. Moreover, the two representative concentration pathways RCP 2.6 and 8.5 were chosen to inform stakeholders about two possible courses of anthropogenic emissions.</p><p>To communicate the results to local stakeholders effectively, the way to present modeling results and their uncertainty is crucial. The visualization and textual/oral presentation should not be overwhelming but comprehensive, comprehensible and engaging. It should help the stakeholder to understand the likelihood of particular hazards that can be derived from multi-model ensemble projections. In this contribution, we present the communication approach we applied during a stakeholder workshop as well as its evaluation by the stakeholders.</p>

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