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 Uncertainties on mean areal precipitation: assessment and impact on streamflow simulations

2009 ◽  
Vol 13 (2) ◽  
pp. 99-114 ◽  
Author(s):  
L. Moulin ◽  
E. Gaume ◽  
C. Obled
Keyword(s):  
Flood Forecasting ◽  
Error Model ◽  
Rainfall Runoff ◽  
Rainfall Estimation ◽  
Estimation Errors ◽  
Rain Gauges ◽  
Areal Precipitation ◽  
Areal Rainfall ◽  
Streamflow Simulation

Abstract. This paper investigates the influence of mean areal rainfall estimation errors on a specific case study: the use of lumped conceptual rainfall-runoff models to simulate the flood hydrographs of three small to medium-sized catchments of the upper Loire river. This area (3200 km2) is densely covered by an operational network of stream and rain gauges. It is frequently exposed to flash floods and the improvement of flood forecasting models is then a crucial concern. Particular attention has been drawn to the development of an error model for rainfall estimation consistent with data in order to produce realistic streamflow simulation uncertainty ranges. The proposed error model combines geostatistical tools based on kriging and an autoregressive model to account for temporal dependence of errors. It has been calibrated and partly validated for hourly mean areal precipitation rates. Simulated error scenarios were propagated into two calibrated rainfall-runoff models using Monte Carlo simulations. Three catchments with areas ranging from 60 to 3200 km2 were tested to reveal any possible links between the sensitivity of the model outputs to rainfall estimation errors and the size of the catchment. The results show that a large part of the rainfall-runoff (RR) modelling errors can be explained by the uncertainties on rainfall estimates, especially in the case of smaller catchments. These errors are a major factor limiting accuracy and sharpness of rainfall-runoff simulations, and thus their operational use for flood forecasting.

scholarly journals Uncertainties on mean areal precipitation: assessment and impact on streamflow simulations

2008 ◽  
Vol 5 (4) ◽  
pp. 2067-2110 ◽  
Author(s):  
L. Moulin ◽  
E. Gaume ◽  
C. Obled
Keyword(s):  
Flood Forecasting ◽  
Error Model ◽  
Rainfall Runoff ◽  
Rainfall Estimation ◽  
Estimation Errors ◽  
Rain Gauges ◽  
Areal Precipitation ◽  
Areal Rainfall ◽  
Streamflow Simulation

Abstract. This paper investigates the influence of mean areal rainfall estimation errors on a specific case study: the use of lumped conceptual rainfall-runoff models to simulate the flood hydrographs of three small to medium-sized catchments of the upper Loire river. This area (3200 km2) is densely covered by an operational network of stream and rain gauges. It is frequently exposed to flash floods and the improvement of flood forecasting models is then a crucial concern. Particular attention has been drawn to the development of an error model for rainfall estimation consistent with data in order to produce realistic streamflow simulation uncertainty ranges. The proposed error model combines geostatistical tools based on kriging and an autoregressive model to account for temporal dependence of errors. It has been calibrated and partly validated for hourly mean areal precipitation rates. Simulated error scenarios were propagated into two calibrated rainfall-runoff models using Monte Carlo simulations. Three catchments with areas ranging from 60 to 3200 km2 were tested to reveal any possible links between the sensitivity of the model outputs to rainfall estimation errors and the size of the catchment. The results show that a large part of the rainfall-runoff (RR) modelling errors can be explained by the uncertainties on rainfall estimates, especially in the case of smaller catchments. These errors are a major factor limiting accuracy and sharpness of rainfall-runoff simulations, and thus their operational use for flood forecasting.

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 Coupling meteorological and hydrological models for flood forecasting

2005 ◽  
Vol 9 (4) ◽  
pp. 333-346 ◽  
Author(s):  
◽  
Keyword(s):  
Extreme Event ◽  
Coupled Model ◽  
Flood Forecasting ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Rainfall Runoff ◽  
Quantitative Precipitation Forecasts ◽  
Meteorological Models

Abstract. This paper deals with the problem of analysing the coupling of meteorological meso-scale quantitative precipitation forecasts with distributed rainfall-runoff models to extend the forecasting horizon. Traditionally, semi-distributed rainfall-runoff models have been used for real time flood forecasting. More recently, increased computer capabilities allow the utilisation of distributed hydrological models with mesh sizes from tenths of metres to a few kilometres. On the other hand, meteorological models, providing the quantitative precipitation forecast, tend to produce average values on meshes ranging from slightly less than 10 to 200 kilometres. Therefore, to improve the quality of flood forecasts, the effects of coupling the meteorological and the hydrological models at different scales were analysed. A distributed hydrological model (TOPKAPI) was developed and calibrated using a 1x1 km mesh for the case of the river Po closed at Ponte Spessa (catchment area c. 37000 km2). The model was then coupled with several other European meteorological models ranging from the Limited Area Models (provided by DMI and DWD) with resolutions from 0.0625° * 0.0625°, to the ECMWF ensemble predictions with a resolution of 1.85° * 1.85°. Interesting results, describing the coupled model behaviour, are available for a meteorological extreme event in Northern Italy (Nov. 1994). The results demonstrate the poor reliability of the quantitative precipitation forecasts produced by meteorological models presently available; this is not resolved using the Ensemble Forecasting technique, when compared with results obtainable with measured rainfall.

scholarly journals Comment on “Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?” by Mazzoleni et al. (2017)

2018 ◽  
Vol 22 (1) ◽  
pp. 171-177 ◽  
Author(s):  
Daniele P. Viero
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Data Uncertainty ◽  
Model Structure ◽  
Hydrological Models ◽  
Flood Prediction ◽  
Irregular Frequency ◽  
Crowdsourced Data ◽  
Internal States ◽  
The Impact

Abstract. Citizen science and crowdsourcing are gaining increasing attention among hydrologists. In a recent contribution, Mazzoleni et al. (2017) investigated the integration of crowdsourced data (CSD) into hydrological models to improve the accuracy of real-time flood forecasts. The authors used synthetic CSD (i.e. not actually measured), because real CSD were not available at the time of the study. In their work, which is a proof-of-concept study, Mazzoleni et al. (2017) showed that assimilation of CSD improves the overall model performance; the impact of irregular frequency of available CSD, and that of data uncertainty, were also deeply assessed. However, the use of synthetic CSD in conjunction with (semi-)distributed hydrological models deserves further discussion. As a result of equifinality, poor model identifiability, and deficiencies in model structure, internal states of (semi-)distributed models can hardly mimic the actual states of complex systems away from calibration points. Accordingly, the use of synthetic CSD that are drawn from model internal states under best-fit conditions can lead to overestimation of the effectiveness of CSD assimilation in improving flood prediction. Operational flood forecasting, which results in decisions of high societal value, requires robust knowledge of the model behaviour and an in-depth assessment of both model structure and forcing data. Additional guidelines are given that are useful for the a priori evaluation of CSD for real-time flood forecasting and, hopefully, for planning apt design strategies for both model calibration and collection of CSD.

scholarly journals Technical note: Hydrology modelling R packages – a unified analysis of models and practicalities from a user perspective

2021 ◽  
Vol 25 (7) ◽  
pp. 3937-3973
Author(s):  
Paul C. Astagneau ◽  
Guillaume Thirel ◽  
Olivier Delaigue ◽  
Joseph H. A. Guillaume ◽  
Juraj Parajka ◽  
...  
Keyword(s):  
Technical Note ◽  
Ease Of Use ◽  
Data Availability ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
User Perspective ◽  
R Packages ◽  
Spatial Discretisation ◽  
Technical Features ◽  
Selection Of

Abstract. Following the rise of R as a scientific programming language, the increasing requirement for more transferable research and the growth of data availability in hydrology, R packages containing hydrological models are becoming more and more available as an open-source resource to hydrologists. Corresponding to the core of the hydrological studies workflow, their value is increasingly meaningful regarding the reliability of methods and results. Despite package and model distinctiveness, no study has ever provided a comparison of R packages for conceptual rainfall–runoff modelling from a user perspective by contrasting their philosophy, model characteristics and ease of use. We have selected eight packages based on our ability to consistently run their models on simple hydrology modelling examples. We have uniformly analysed the exact structure of seven of the hydrological models integrated into these R packages in terms of conceptual storages and fluxes, spatial discretisation, data requirements and output provided. The analysis showed that very different modelling choices are associated with these packages, which emphasises various hydrological concepts. These specificities are not always sufficiently well explained by the package documentation. Therefore a synthesis of the package functionalities was performed from a user perspective. This synthesis helps to inform the selection of which packages could/should be used depending on the problem at hand. In this regard, the technical features, documentation, R implementations and computational times were investigated. Moreover, by providing a framework for package comparison, this study is a step forward towards supporting more transferable and reusable methods and results for hydrological modelling in R.

scholarly journals Mapping land use suitability for development of recharge wells in the Ciliwung watershed, Indonesia

2017 ◽  
Vol 12 (1) ◽  
pp. 166-178
Author(s):  
Nyoman Suwartha ◽  
Ikhwan Maulani ◽  
Cindy Rianti Priadi ◽  
Elzavira Felaza ◽  
Tri Tjahjono ◽  
...  
Keyword(s):  
Land Use ◽  
Land Suitability ◽  
Rainfall Runoff ◽  
Runoff Reduction ◽  
Mathematical Simulations ◽  
Average Rainfall ◽  
Weighted Data ◽  
The Impact ◽  
Technical Recommendations ◽  
Selection Of

Rapid population growth and the need to mitigate the impact of rainfall-runoff has made groundwater conservation a significant environmental issue in Indonesia's Ciliwung Watershed. The availability of recharge wells in developed areas is essential for groundwater conservation and runoff reduction. Selection of suitable locations for the construction of recharge wells depends on a combination of factors such as topography, soil layers, land use, and climatology. This study of land suitability for recharge well development in the Ciliwung Watershed, an area of heterogenous land use, employed GPS-based weighted data on technical geology, soil type, soil hydrology group, groundwater level, slope, average rainfall, and land use. Mathematical simulations were performed to develop a land suitability map. The findings indicate that only 2% of the total area (in Cisarua, Bogor) is ideal for the construction of recharge wells, and that 48% of existing recharge wells in the Jakarta area are situated in a suitable zone. The results provide a basis for technical recommendations for future construction of recharge wells in the Ciliwung Watershed.

Use of Radar Quantitative Precipitation Estimates (QPEs) for Improved Hydrological Model Calibration and Flood Forecasting

2021 ◽  
Author(s):  
Dayal Wijayarathne ◽  
Paulin Coulibaly ◽  
Sudesh Boodoo ◽  
David Sills
Keyword(s):  
Real Time ◽  
Model Calibration ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Hydrological Models ◽  
Radar Rainfall ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates ◽  
Streamflow Simulation ◽  
Rainfall Estimates

AbstractFlood forecasting is essential to minimize the impacts and costs of floods, especially in urbanized watersheds. Radar rainfall estimates are becoming increasingly popular in flood forecasting because they provide the much-needed real-time spatially distributed precipitation information. The current study evaluates the use of radar Quantitative Precipitation Estimates (QPEs) in hydrological model calibration for streamflow simulation and flood mapping in an urban setting. Firstly, S-band and C-band radar QPEs were integrated into event-based hydrological models to improve the calibration of model parameters. Then, rain gauge and radar precipitation estimates’ performances were compared for hydrological modeling in an urban watershed to assess radar QPE's effects on streamflow simulation accuracy. Finally, flood extent maps were produced using coupled hydrological-hydraulic models integrated within the Hydrologic Engineering Center- Real-Time Simulation (HEC-RTS) framework. It is shown that the bias correction of radar QPEs can enhance the hydrological model calibration. The radar-gauge merging obtained a KGE, MPFC, NSE, and VE improvement of about + 0.42, + 0.12, + 0.78, and − 0.23, respectively for S-band and + 0.64, + 0.36, + 1.12, and − 0.34, respectively for C-band radar QPEs. Merged radar QPEs are also helpful in running hydrological models calibrated using gauge data. The HEC-RTS framework can be used to produce flood forecast maps using the bias-corrected radar QPEs. Therefore, radar rainfall estimates could be efficiently used to forecast floods in urbanized areas for effective flood management and mitigation. Canadian flood forecasting systems could be efficiently updated by integrating bias-corrected radar QPEs to simulate streamflow and produce flood inundation maps.

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 Rainfall disaggregation for hydrological modeling: is there a need for spatial consistence?

2018 ◽  
Vol 22 (10) ◽  
pp. 5259-5280 ◽  
Author(s):  
Hannes Müller-Thomy ◽  
Markus Wallner ◽  
Kristian Förster
Keyword(s):  
Time Series ◽  
Hydrological Modeling ◽  
Extreme Values ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Rainfall Time Series ◽  
Rainfall Runoff ◽  
Lower Saxony ◽  
Rainfall Characteristics ◽  
Areal Rainfall

Abstract. In this study, the influence of disaggregated rainfall products with different degrees of spatial consistence on rainfall–runoff modeling results is analyzed for three mesoscale catchments in Lower Saxony, Germany. For the disaggregation of daily rainfall time series into hourly values, a multiplicative random cascade model is applied. The disaggregation is applied on a station by station basis without consideration of surrounding stations; hence subsequent steps are then required to implement spatial consistence. Spatial consistence is represented here by three bivariate spatial rainfall characteristics that complement each other. A resampling algorithm and a parallelization approach are evaluated against the disaggregated time series without any subsequent steps. With respect to rainfall, clear differences between these three approaches can be identified regarding bivariate spatial rainfall characteristics, areal rainfall intensities and extreme values. The resampled time series lead to the best agreement with the observed ones. Using these different rainfall products as input to hydrological modeling, we hypothesize that derived runoff statistics – with emphasis on seasonal extreme values – are subject to similar differences as well. However, an impact on the extreme values' statistics of the hydrological simulations forced by different rainfall approaches cannot be detected. Several modifications of the study design using rainfall–runoff models with and without parameter calibration or using different rain gauge densities lead to similar results in runoff statistics. Only if the spatially highly resolved rainfall–runoff WaSiM model is applied instead of the semi-distributed HBV-IWW model can slight differences regarding the seasonal peak flows be identified. Hence, the hypothesis formulated before is rejected in this case study. These findings suggest that (i) simple model structures might compensate for deficiencies in spatial representativeness through parameterization and (ii) highly resolved hydrological models benefit from improved spatial modeling of rainfall.

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