scholarly journals Review of the paper « Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across a large sample of cathcments in Great Britain », by Rosanna A. Lane et al.

2019 ◽  
Author(s):  
Thibault Mathevet
Keyword(s):  
Great Britain ◽  
River Flow ◽  
Hydrological Models ◽  
Predictive Capability ◽  
Large Sample ◽  
Flood Peak

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 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 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 CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain

2020 ◽  
Vol 12 (4) ◽  
pp. 2459-2483 ◽  
Author(s):  
Gemma Coxon ◽  
Nans Addor ◽  
John P. Bloomfield ◽  
Jim Freer ◽  
Matt Fry ◽  
...  
Keyword(s):  
Time Series ◽  
Great Britain ◽  
River Flow ◽  
Potential Evapotranspiration ◽  
Large Sample ◽  
Uncertainty Estimates ◽  
Wide Range ◽  
Human Management ◽  
Temperature Radiation ◽  
The Uk

Abstract. We present the first large-sample catchment hydrology dataset for Great Britain, CAMELS-GB (Catchment Attributes and MEteorology for Large-sample Studies). CAMELS-GB collates river flows, catchment attributes and catchment boundaries from the UK National River Flow Archive together with a suite of new meteorological time series and catchment attributes. These data are provided for 671 catchments that cover a wide range of climatic, hydrological, landscape, and human management characteristics across Great Britain. Daily time series covering 1970–2015 (a period including several hydrological extreme events) are provided for a range of hydro-meteorological variables including rainfall, potential evapotranspiration, temperature, radiation, humidity, and river flow. A comprehensive set of catchment attributes is quantified including topography, climate, hydrology, land cover, soils, and hydrogeology. Importantly, we also derive human management attributes (including attributes summarising abstractions, returns, and reservoir capacity in each catchment), as well as attributes describing the quality of the flow data including the first set of discharge uncertainty estimates (provided at multiple flow quantiles) for Great Britain. CAMELS-GB (Coxon et al., 2020; available at https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9) is intended for the community as a publicly available, easily accessible dataset to use in a wide range of environmental and modelling analyses.

scholarly journals CAMELS-GB: Hydrometeorological time series and landscape attributes for 671 catchments in Great Britain

2020 ◽  
Author(s):  
Gemma Coxon ◽  
Nans Addor ◽  
John P. Bloomfield ◽  
Jim Freer ◽  
Matt Fry ◽  
...  
Keyword(s):  
Great Britain ◽  
River Flow ◽  
Potential Evapotranspiration ◽  
Large Sample ◽  
Uncertainty Estimates ◽  
Wide Range ◽  
Human Management ◽  
Temperature Radiation ◽  
Hydrological Extreme ◽  
The Uk

Abstract. We present the first large-sample catchment hydrology dataset for Great Britain, CAMELS-GB (Catchment Attributes and MEteorology for Large-sample Studies). CAMELS-GB collates river flows, catchment attributes and catchment boundaries from the UK National River Flow Archive together with a suite of new meteorological timeseries and catchment attributes. These data are provided for 671 catchments that cover a wide range of climatic, hydrological, landscape and human management characteristics across Great Britain. Daily timeseries covering 1970–2015 (a period including several hydrological extreme episodes) are provided for a range of hydro-meteorological variables including rainfall, potential evapotranspiration, temperature, radiation, humidity and river flow. A comprehensive set of catchment attributes are quantified including topography, climate, hydrology, land cover, soils and hydrogeology. Importantly, we also derive human management attributes (including attributes summarising abstractions, returns and reservoir capacity in each catchment), as well as attributes describing the quality of the flow data including the first set of discharge uncertainty estimates for Great Britain. CAMELS-GB (Coxon et al, 2020; available at https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9) is intended for the community as a publicly available, easily accessible dataset to use in a wide range of environmental and modelling analyses.

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.

Why is large sample hydrology important in hydrological forecasting? 

2021 ◽  
Author(s):  
Ilias Pechlivanidis ◽  
Louise Crochemore ◽  
Marc Girons Lopez
Keyword(s):  
River Flow ◽  
Forecast Skill ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
River Systems ◽  
Local Conditions ◽  
Large Sample ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow

<p>The scientific community has made significant progress towards improving the skill of hydrological forecasts; however, most investigations have normally been conducted at single or in a limited number of catchments. Such an approach is indeed valuable for detailed process investigation and therefore to understand the local conditions that affect forecast skill, but it is limited when it comes to scaling up the underlying hydrometeorological hypotheses. To advance knowledge on the drivers that control the quality and skill of hydrological forecasts, much can be gained by comparative analyses and from the availability of statistically significant samples. Large-scale modelling (at national, continental or global scales) can complement the in-depth knowledge from single catchment modelling by encompassing many river systems that represent a breadth of physiographic and climatic conditions. In addition to large sample sizes which cover a gradient in terms of climatology, scale and hydrological regime, the use of machine learning techniques can contribute to the identification of emerging spatiotemporal patterns leading to forecast skill attribution to different regional physiographic characteristics.</p><p>Here, we draw on two seasonal hydrological forecast skill investigations that were conducted at the national and continental scales, providing results for more than 36,000 basins in Sweden and Europe. Due to the large generated samples, we are capable of demonstrating that the quality of seasonal streamflow forecasts can be clustered and regionalized, based on a priori knowledge of the local hydroclimatic conditions. We show that the quality of seasonal streamflow forecasts is linked to physiographic and hydroclimatic descriptors, and that the relative importance of these descriptors varies with initialization month and lead time. In our samples, hydrological similarity, temperature, precipitation, evaporative index, and precipitation forecast biases are strongly linked to the quality of streamflow forecasts. This way, while seasonal river flow can generally be well predicted in river systems with slow hydrological responses, predictability tends to be poor in cold and semiarid climates in which river systems respond immediately to precipitation signals.</p>

scholarly journals Climate change impact on river flow extremes in the Upper Blue Nile River basin

2018 ◽  
Vol 10 (4) ◽  
pp. 759-781 ◽  
Author(s):  
Hadush K. Meresa ◽  
Mulusew T. Gatachew
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Change Impact ◽  
River Flow ◽  
Nile River ◽  
Hydrological Models ◽  
Hydrological Extremes ◽  
Dry Spell ◽  
Change Impact ◽  
Blue Nile River Basin

Abstract This paper aims to study climate change impact on the hydrological extremes and projected precipitation extremes in far future (2071–2100) period in the Upper Blue Nile River basin (UBNRB). The changes in precipitation extremes were derived from the most recent AFROCORDEX climate data base projection scenarios compared to the reference period (1971–2000). The climate change impacts on the hydrological extremes were evaluated using three conceptual hydrological models: GR4 J, HBV, and HMETS; and two objective functions: NSE and LogNSE. These hydrological models are calibrated and validated in the periods 1971–2000 and 2001–2010, respectively. The results indicate that the wet/dry spell will significantly decrease/increase due to climate change in some sites of the region, while in others, there is increase/decrease in wet/dry spell but not significantly, respectively. The extreme river flow will be less attenuated and more variable in terms of magnitude, and more irregular in terms of seasonal occurrence than at present. Low flows are projected to increase most prominently for lowland sites, due to the combined effects of projected decreases in Belg and Bega precipitation, and projected increases in evapotranspiration that will reduce residual soil moisture in Bega and Belg seasons.

scholarly journals Calibration of hydrological models for ecologically relevant streamflow predictions: a trade-off between fitting well to data and estimating consistent parameter sets?

2020 ◽  
Vol 24 (3) ◽  
pp. 1031-1054 ◽  
Author(s):  
Thibault Hallouin ◽  
Michael Bruen ◽  
Fiachra E. O'Loughlin
Keyword(s):  
Flow Regime ◽  
River Flow ◽  
Freshwater Ecosystems ◽  
Living Environment ◽  
Rate Of Change ◽  
Low Flow ◽  
Hydrological Models ◽  
Flow Conditions ◽  
Local Flow ◽  
Trade Off

Abstract. The ecological integrity of freshwater ecosystems is intimately linked to natural fluctuations in the river flow regime. In catchments with little human-induced alterations of the flow regime (e.g. abstractions and regulations), existing hydrological models can be used to predict changes in the local flow regime to assess any changes in its rivers' living environment for endemic species. However, hydrological models are traditionally calibrated to give a good general fit to observed hydrographs, e.g. using criteria such as the Nash–Sutcliffe efficiency (NSE) or the Kling–Gupta efficiency (KGE). Much ecological research has shown that aquatic species respond to a range of specific characteristics of the hydrograph, including magnitude, frequency, duration, timing, and the rate of change of flow events. This study investigates the performance of specially developed and tailored criteria formed from combinations of those specific streamflow characteristics (SFCs) found to be ecologically relevant in previous ecohydrological studies. These are compared with the more traditional Kling–Gupta criterion for 33 Irish catchments. A split-sample test with a rolling window is applied to reduce the influence on the conclusions of differences between the calibration and evaluation periods. These tailored criteria are shown to be marginally better suited to predicting the targeted streamflow characteristics; however, traditional criteria are more robust and produce more consistent behavioural parameter sets, suggesting a trade-off between model performance and model parameter consistency when predicting specific streamflow characteristics. Analysis of the fitting to each of 165 streamflow characteristics revealed a general lack of versatility for criteria with a strong focus on low-flow conditions, especially in predicting high-flow conditions. On the other hand, the Kling–Gupta efficiency applied to the square root of flow values performs as well as two sets of tailored criteria across the 165 streamflow characteristics. These findings suggest that traditional composite criteria such as the Kling–Gupta efficiency may still be preferable over tailored criteria for the prediction of streamflow characteristics, when robustness and consistency are important.

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