scholarly journals Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain

2019 ◽  
Vol 23 (10) ◽  
pp. 4011-4032 ◽  
Author(s):  
Rosanna A. Lane ◽  
Gemma Coxon ◽  
Jim E. Freer ◽  
Thorsten Wagener ◽  
Penny J. Johnes ◽  
...  
Keyword(s):  
Great Britain ◽  
Water Balance ◽  
River Flow ◽  
Performance Metrics ◽  
Model Performance ◽  
Flow Simulation ◽  
Hydrological Models ◽  
Peak Flows ◽  
Flood Peak ◽  
Model Structures

Abstract. Benchmarking model performance across large samples of catchments is useful to guide model selection and future model development. Given uncertainties in the observational data we use to drive and evaluate hydrological models, and uncertainties in the structure and parameterisation of models we use to produce hydrological simulations and predictions, it is essential that model evaluation is undertaken within an uncertainty analysis framework. Here, we benchmark the capability of several lumped hydrological models across Great Britain by focusing on daily flow and peak flow simulation. Four hydrological model structures from the Framework for Understanding Structural Errors (FUSE) were applied to over 1000 catchments in England, Wales and Scotland. Model performance was then evaluated using standard performance metrics for daily flows and novel performance metrics for peak flows considering parameter uncertainty. Our results show that lumped hydrological models were able to produce adequate simulations across most of Great Britain, with each model producing simulations exceeding a 0.5 Nash–Sutcliffe efficiency for at least 80 % of catchments. All four models showed a similar spatial pattern of performance, producing better simulations in the wetter catchments to the west and poor model performance in central Scotland and south-eastern England. Poor model performance was often linked to the catchment water balance, with models unable to capture the catchment hydrology where the water balance did not close. Overall, performance was similar between model structures, but different models performed better for different catchment characteristics and metrics, as well as for assessing daily or peak flows, leading to the ensemble of model structures outperforming any single structure, thus demonstrating the value of using multi-model structures across a large sample of different catchment behaviours. This research evaluates what conceptual lumped models can achieve as a performance benchmark and provides interesting insights into where and why these simple models may fail. The large number of river catchments included in this study makes it an appropriate benchmark for any future developments of a national model of Great Britain.

scholarly journals Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across a large-sample of catchments in Great Britain

2019 ◽  
Author(s):  
Rosanna A. Lane ◽  
Gemma Coxon ◽  
Jim E. Freer ◽  
Thorsten Wagener ◽  
Penny J. Johnes ◽  
...  
Keyword(s):  
Great Britain ◽  
Water Balance ◽  
River Flow ◽  
Performance Metrics ◽  
Model Performance ◽  
Flow Simulation ◽  
Hydrological Models ◽  
Peak Flows ◽  
Flood Peak ◽  
Model Structures

Abstract. Benchmarking model performance across large samples of catchments is useful to guide future model development. Given uncertainties in the observational data we use to drive and evaluate hydrological models, and uncertainties in the structure and parameterisation of models we use to produce hydrological simulations and predictions, it is essential that model evaluation is undertaken within an uncertainty analysis framework. Here, we benchmark the capability of multiple, lumped hydrological models across Great Britain, by focusing on daily flow and peak flow simulation. Four hydrological model structures from the Framework for Understanding Structural Errors (FUSE) were applied to over 1100 catchments. Model performance was then evaluated using a standard performance metric for daily flows, and more novel performance metrics for peak flows considering parameter uncertainty. Our results show that simple, lumped hydrological models were able to produce adequate simulations across most of Great Britain, with median Nash–Sutcliffe efficiency scores of 0.72–0.78 across all catchments. All four models showed a similar spatial pattern of performance, producing better simulations in the wetter catchments to the west, and poor model performance in Scotland and southeast England. Poor model performance was often linked to the catchment water balance, with models unable to capture the catchment hydrology where the water balance did not close. Overall, performance was similar between model structures, but different models performed better for different catchment characteristics and for assessing daily or peak flows, demonstrating the value of using an ensemble of model structures. This research demonstrates what conceptual lumped models can achieve as a performance benchmark, as well as providing interesting insights into where and why these simple models may fail. The large number of river catchments included in this study makes it an appropriate benchmark for any future developments of a national model of Great Britain.

scholarly journals Hydraulic Evaluation of the Levee System Evolution on the Kurobe Alluvial Fan in the 18th and 19th Centuries

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4406
Author(s):  
Tadaharu Ishikawa ◽  
Hiroshi Senoo
Keyword(s):  
Channel Capacity ◽  
Flood Control ◽  
River Flow ◽  
Global Climate ◽  
Flow Simulation ◽  
Japan Sea ◽  
Main Channel ◽  
Alluvial Fan ◽  
Flood Peak ◽  
Levee System

The development process and flood control effects of the open-levee system, which was constructed from the mid-18th to the mid-19th centuries, on the Kurobe Alluvial Fan—a large alluvial fan located on the Japan Sea Coast of Japan’s main island—was evaluated using numerical flow simulation. The topography for the numerical simulation was determined from an old pictorial map in the 18th century and various maps after the 19th century, and the return period of the flood hydrograph was determined to be 10 years judging from the level of civil engineering of those days. The numerical results suggested the followings: The levees at the first stage were made to block the dominant divergent streams to gather the river flows together efficiently; by the completed open-levee system, excess river flow over the main channel capacity was discharged through upstream levee openings to old stream courses which were used as temporary floodways, and after the flood peak, a part of the flooded water returned to the main channel through the downstream levee openings. It is considered that the ideas of civil engineers of those days to control the floods exceeding river channel capacity, embodied in their levee arrangement, will give us hints on how to control the extraordinary floods that we should face in the near future when the scale of storms will increase due to the global climate change.

scholarly journals Quantifying location error to define uncertainty in volcanic mass flow hazard simulations

2021 ◽  
Vol 21 (8) ◽  
pp. 2447-2460
Author(s):  
Stuart R. Mead ◽  
Jonathan Procter ◽  
Gabor Kereszturi
Keyword(s):  
Mass Flow ◽  
Performance Metrics ◽  
Numerical Models ◽  
Model Performance ◽  
Flow Simulation ◽  
Model Complexity ◽  
Model Parameters ◽  
Spatial Covariance ◽  
Trade Offs ◽  
Pixel Pair

Abstract. The use of mass flow simulations in volcanic hazard zonation and mapping is often limited by model complexity (i.e. uncertainty in correct values of model parameters), a lack of model uncertainty quantification, and limited approaches to incorporate this uncertainty into hazard maps. When quantified, mass flow simulation errors are typically evaluated on a pixel-pair basis, using the difference between simulated and observed (“actual”) map-cell values to evaluate the performance of a model. However, these comparisons conflate location and quantification errors, neglecting possible spatial autocorrelation of evaluated errors. As a result, model performance assessments typically yield moderate accuracy values. In this paper, similarly moderate accuracy values were found in a performance assessment of three depth-averaged numerical models using the 2012 debris avalanche from the Upper Te Maari crater, Tongariro Volcano, as a benchmark. To provide a fairer assessment of performance and evaluate spatial covariance of errors, we use a fuzzy set approach to indicate the proximity of similarly valued map cells. This “fuzzification” of simulated results yields improvements in targeted performance metrics relative to a length scale parameter at the expense of decreases in opposing metrics (e.g. fewer false negatives result in more false positives) and a reduction in resolution. The use of this approach to generate hazard zones incorporating the identified uncertainty and associated trade-offs is demonstrated and indicates a potential use for informed stakeholders by reducing the complexity of uncertainty estimation and supporting decision-making from simulated data.

scholarly journals Quantifying location error to define uncertainty in volcanic mass flow hazard simulations

2021 ◽  
Author(s):  
Stuart R. Mead ◽  
Jonathan Procter ◽  
Gabor Kereszturi
Keyword(s):  
Mass Flow ◽  
Performance Metrics ◽  
Numerical Models ◽  
Model Performance ◽  
Flow Simulation ◽  
Model Complexity ◽  
Model Parameters ◽  
Spatial Covariance ◽  
Trade Offs ◽  
Pixel Pair

Abstract. The use of mass flow simulations in volcanic hazard zonation and mapping is often limited by model complexity (i.e. uncertainty in correct values of model parameters), a lack of model uncertainty quantification, and limited approaches to incorporate this uncertainty into hazard maps. When quantified, mass flow simulation errors are typically evaluated on a pixel-pair basis, using the difference between simulated and observed (actual) map-cell values to evaluate the performance of a model. However, these comparisons conflate location and quantification errors, neglecting possible spatial autocorrelation of evaluated errors. As a result, model performance assessments typically yield moderate accuracy values. In this paper, similarly moderate accuracy values were found in a performance assessment of three depth-averaged numerical models using the 2012 debris avalanche from the Upper Te Maari crater, Tongariro Volcano as a benchmark. To provide a fairer assessment of performance and evaluate spatial covariance of errors, we use a fuzzy set approach to indicate the proximity of similarly valued map cells. This fuzzification of simulated results yields improvements in targeted performance metrics relative to a length scale parameter, at the expense of decreases in opposing metrics (e.g. less false negatives results in more false positives) and a reduction in resolution. The use of this approach to generate hazard zones incorporating the identified uncertainty and associated trade-offs is demonstrated, and indicates a potential use for informed stakeholders by reducing the complexity of uncertainty estimation and supporting decision making from simulated data.

scholarly journals Future Flows Hydrology: an ensemble of daily river flow and monthly groundwater levels for use for climate change impact assessment across Great Britain

2012 ◽  
Vol 5 (2) ◽  
pp. 1159-1178 ◽  
Author(s):  
C. Prudhomme ◽  
T. Haxton ◽  
S. Crooks ◽  
C. Jackson ◽  
A. Barkwith ◽  
...  
Keyword(s):  
Climate Change ◽  
Great Britain ◽  
River Flow ◽  
Hydrological Modelling ◽  
Time Slice ◽  
Practical Recommendation ◽  
Hydrological Models ◽  
Groundwater Levels ◽  
Modelling Uncertainty ◽  
The Impact

Abstract. The dataset Future Flows Hydrology was developed as part of the project "Future Flows and Groundwater Levels" to provide a consistent set of transient daily river flow and monthly groundwater levels projections across England, Wales and Scotland to enable the investigation of the role of climate variability on river flow and groundwater levels nationally and how this may change in the future. Future Flows Hydrology is derived from Future Flows Climate, a national ensemble projection derived from the Hadley Centre's ensemble projection HadRM3-PPE to provide a consistent set of climate change projections for the whole of Great Britain at both space and time resolutions appropriate for hydrological applications. Three hydrological models and one groundwater level model were used to derive Future Flows Hydrology, with 30 river sites simulated by two hydrological models to enable assessment of hydrological modelling uncertainty in studying the impact of climate change on the hydrology. Future Flows Hydrology contains an 11-member ensemble of transient projections from January 1951 to December 2098, each associated with a single realisation from a different variant of HadRM3 and a single hydrological model. Daily river flows are provided for 281 river catchments and monthly groundwater levels at 24 boreholes as .csv files containing all 11 ensemble members. When separate simulations are done with two hydrological models, two separate .csv files are provided. Because of potential biases in the climate-hydrology modelling chain, catchment fact sheets are associated with each ensemble. These contain information on the uncertainty associated with the hydrological modelling when driven using observed climate and Future Flows Climate for a period representative of the reference time slice 1961–1990 as described by key hydrological statistics. Graphs of projected changes for selected hydrological indicators are also provided for the 2050s time slice. Limitations associated with the dataset are provided, along with practical recommendation of use. Future Flows Hydrology is freely available for non-commercial use under certain licensing conditions. For each study site, catchment averages of daily precipitation and monthly potential evapotranspiration, used to drive the hydrological models, are made available, so that hydrological modelling uncertainty under climate change conditions can be explored further. doi:10.5285/f3723162-4fed-4d9d-92c6-dd17412fa37b.

scholarly journals Glacio-hydrological melt and runoff modelling: a limits of acceptability framework for model selection

2018 ◽  
Author(s):  
Jonathan D. Mackay ◽  
Nicholas E. Barrand ◽  
David M. Hannah ◽  
Stefan Krause ◽  
Christopher R. Jackson ◽  
...  
Keyword(s):  
Model Selection ◽  
River Discharge ◽  
River Flow ◽  
Performance Metrics ◽  
Assessment Tool ◽  
Full Range ◽  
Model Complexity ◽  
Future Climate Change ◽  
Model Structures ◽  
Runoff Routing

Abstract. Glacio-hydrological models (GHMs) underpin our understanding how future climate change will affect river flow regimes in glaciated watersheds. A variety of simplified GHM structures and parameterisations exist, yet the performance of these are rarely quantified at the process-level or with metrics beyond global summary statistics. A fuller understanding of the deficiencies in competing model structures and parameterisations and the ability of models to simulate physical processes require performance metrics utilising the full range of uncertainty information within input observations. Here, the glacio-hydrological characteristics of the Virkisá river basin in southern Iceland are characterised using 33 signatures derived from observations of ice melt, snow coverage and river discharge. The uncertainty of each set of observations are harnessed to define limits of acceptability (LOA), a set of criteria used to objectively evaluate the acceptability of different GHM structures and parameterisations. This framework is used to compare and diagnose deficiencies in three melt and three runoff-routing model structures. Increased model complexity is shown to improve acceptability when evaluated against specific signatures, but does not always result in better consistency across all signatures, emphasising the difficulty in appropriate model selection and the need for multi-model prediction approaches to account for model selection uncertainty. Melt and runoff-routing structures demonstrate a hierarchy of influence on river discharge signatures with melt model structure having the most influence on discharge hydrograph seasonality and runoff-routing structure on shorter-timescale discharge events. None of the tested GHM structural configurations returned acceptable simulations across the full population of signatures. The framework outlined here provides a comprehensive and rigorous assessment tool for evaluating the acceptability of different GHM process hypotheses. Future melt and runoff model forecasts should seek to diagnose structural model deficiencies and evaluate diagnostic signatures of system behaviour using the LOA framework.

scholarly journals Future Flows Hydrology: an ensemble of daily river flow and monthly groundwater levels for use for climate change impact assessment across Great Britain

2013 ◽  
Vol 5 (1) ◽  
pp. 101-107 ◽  
Author(s):  
C. Prudhomme ◽  
T. Haxton ◽  
S. Crooks ◽  
C. Jackson ◽  
A. Barkwith ◽  
...  
Keyword(s):  
Climate Change ◽  
Great Britain ◽  
Groundwater Level ◽  
River Flow ◽  
Hydrological Modelling ◽  
Time Slice ◽  
Practical Recommendation ◽  
Hydrological Models ◽  
Groundwater Levels ◽  
Modelling Uncertainty

Abstract. The dataset Future Flows Hydrology was developed as part of the project "Future Flows and Groundwater Levels'' to provide a consistent set of transient daily river flow and monthly groundwater level projections across England, Wales and Scotland to enable the investigation of the role of climate variability on river flow and groundwater levels nationally and how this may change in the future. Future Flows Hydrology is derived from Future Flows Climate, a national ensemble projection derived from the Hadley Centre's ensemble projection HadRM3-PPE to provide a consistent set of climate change projections for the whole of Great Britain at both space and time resolutions appropriate for hydrological applications. Three hydrological models and one groundwater level model were used to derive Future Flows Hydrology, with 30 river sites simulated by two hydrological models to enable assessment of hydrological modelling uncertainty in studying the impact of climate change on the hydrology. Future Flows Hydrology contains an 11-member ensemble of transient projections from January 1951 to December 2098, each associated with a single realisation from a different variant of HadRM3 and a single hydrological model. Daily river flows are provided for 281 river catchments and monthly groundwater levels at 24 boreholes as .csv files containing all 11 ensemble members. When separate simulations are done with two hydrological models, two separate .csv files are provided. Because of potential biases in the climate–hydrology modelling chain, catchment fact sheets are associated with each ensemble. These contain information on the uncertainty associated with the hydrological modelling when driven using observed climate and Future Flows Climate for a period representative of the reference time slice 1961–1990 as described by key hydrological statistics. Graphs of projected changes for selected hydrological indicators are also provided for the 2050s time slice. Limitations associated with the dataset are provided, along with practical recommendation of use. Future Flows Hydrology is freely available for non-commercial use under certain licensing conditions. For each study site, catchment averages of daily precipitation and monthly potential evapotranspiration, used to drive the hydrological models, are made available, so that hydrological modelling uncertainty under climate change conditions can be explored further. doi:10.5285/f3723162-4fed-4d9d-92c6-dd17412fa37b

scholarly journals Calibration of a Distributed Hydrological Model in a Data-Scarce Basin Based on GLEAM Datasets

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 897 ◽  
Author(s):  
Xin Jin ◽  
Yanxiang Jin
Keyword(s):  
Water Balance ◽  
Land Surface ◽  
Assessment Tool ◽  
Swat Model ◽  
Model Performance ◽  
Hydrological Processes ◽  
Model Parameters ◽  
Great Effort ◽  
Observation Data ◽  
Hydrological Models

The calibration of hydrological models is often complex in regions with scarce data, and generally only uses site-based streamflow data. However, this approach will yield highly generalised values for all model parameters and hydrological processes. It is therefore necessary to obtain more spatially heterogeneous observation data (e.g., satellite-based evapotranspiration (ET)) to calibrate such hydrological models. Here, soil and water assessment tool (SWAT) models were built to evaluate the advantages of using ET data derived from the Global Land surface Evaporation Amsterdam Methodology (GLEAM) to calibrate the models for the Bayinhe River basin in northwest China, which is a typical data-scarce basin. The result revealed the following: (1) A great effort was required to calibrate the SWAT models for the study area to obtain an improved model performance. (2) The SWAT model performance for simulating the streamflow and water balance was reliable when calibrated with streamflow only, but this method of calibration grouped the hydrological processes together and caused an equifinality issue. (3) The combination of the streamflow and GLEAM-based ET data for calibrating the SWAT model improved the model performance for simulating the streamflow and water balance. However, the equifinality issue remained at the hydrologic response unit (HRU) level.

scholarly journals Using observed river flow data to improve the hydrological functioning of the JULES land surface model (vn4.3) used for regional coupled modelling in Great Britain (UKC2)

2019 ◽  
Vol 12 (2) ◽  
pp. 765-784 ◽  
Author(s):  
Alberto Martínez-de la Torre ◽  
Eleanor M. Blyth ◽  
Graham P. Weedon
Keyword(s):  
Great Britain ◽  
Surface Runoff ◽  
Land Surface ◽  
River Flow ◽  
Model Performance ◽  
Soil Conditions ◽  
Surface Model ◽  
Climate Modelling ◽  
Prediction System ◽  
Runoff Production

Abstract. Land surface models (LSMs) represent terrestrial hydrology in weather and climate modelling operational systems and research studies. We aim to improve hydrological performance in the Joint UK Land Environment Simulator (JULES) LSM that is used for distributed hydrological modelling within the new land–atmosphere–ocean coupled prediction system UKC2 (UK regional Coupled environmental prediction system 2). Using river flow observations from gauge stations, we study the capability of JULES to simulate river flow at 1 km2 spatial resolution within 13 catchments in Great Britain that exhibit a variety of climatic and topographic characteristics. Tests designed to identify where the model results are sensitive to the scheme and parameters chosen for runoff production indicate that different catchments require different parameters and even different runoff schemes for optimal results. We introduce a new parameterisation of topographic variation that produces the best daily river flow results (in terms of Nash–Sutcliffe efficiency and mean bias) for all 13 catchments. The new parameterisation introduces a dependency on terrain slope, constraining surface runoff production to wet soil conditions over flatter regions, whereas over steeper regions the model produces surface runoff for every rainfall event regardless of the soil wetness state. This new parameterisation improves the model performance across Great Britain. As an example, in the Thames catchment, which has extensive areas of flat terrain, the Nash–Sutcliffe efficiency exceeds 0.8 using the new parameterisation. We use cross-spectral analysis to evaluate the amplitude and phase of the modelled versus observed river flows over timescales of 2 days to 10 years. This demonstrates that the model performance is modified by changing the parameterisation by different amounts over annual, weekly-to-monthly and multi-day timescales in different catchments, providing insights into model deficiencies on particular timescales, but it reinforces the newly developed parameterisation.

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