scholarly journals How to make advances in hydrological modelling

2019 ◽  
Vol 50 (6) ◽  
pp. 1481-1494 ◽  
Author(s):  
Keith Beven
Keyword(s):  
Fundamental Problem ◽  
Hydrological Modelling ◽  
Major Advance ◽  
Observational Methods ◽  
Hydrological Models ◽  
Hydrological Data ◽  
Fit For Purpose ◽  
Real Progress

Abstract After some background about what I have learned from a career in hydrological modelling, I present some opinions about how we might make progress in improving hydrological models in future, including how to decide whether a model is fit for purpose; how to improve process representations in hydrological models; and how to take advantage of Models of Everywhere. Underlying all those issues, however, is the fundamental problem of improving the hydrological data available for both forcing and evaluating hydrological models. It would be a major advance if the hydrological community could come together to prioritise and commission the new observational methods that are required to make real progress.

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 The HOOPLA toolbox: a HydrOlOgical Prediction LAboratory to explore ensemble rainfall-runoff modeling

2020 ◽  
Author(s):  
Antoine Thiboult ◽  
Gregory Seiller ◽  
Carine Poncelet ◽  
François Anctil
Keyword(s):  
Data Assimilation ◽  
Potential Evapotranspiration ◽  
Hydrological Modelling ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Technical Report ◽  
Flexible Framework ◽  
Hydrological Prediction ◽  
Easy Integration

Abstract. This technical report introduces the HydrOlOgical Prediction LAboratory (HOOPLA) developed at Université Lavalfor ensemble lumped hydrological modelling. HOOPLA includes functionalities to perform calibration, simulation, and forecast for multiple hydrological models and various time steps. It includes a range of hydrometeorological tools such as calibration algorithms, data assimilation techniques, potential evapotranspiration formulas and a snow accounting routine. HOOPLA is a flexible framework coded in MATLAB that allows easy integration of user-defined hydrometeorological tools. This report also illustrates HOOPLA's functionalities using a set of 31 Canadian catchments.

scholarly journals How far can we go in distributed hydrological modelling?

2001 ◽  
Vol 5 (1) ◽  
pp. 1-12 ◽  
Author(s):  
K. Beven*
Keyword(s):  
Model Space ◽  
Hydrological Modelling ◽  
Space Mapping ◽  
Hydrological Models ◽  
Distributed Modelling ◽  
The Future ◽  
Distributed Hydrological Modelling ◽  
Distributed Hydrological Models ◽  
Physical Realism

Abstract. This paper considers distributed hydrological models in hydrology as an expression of a pragmatic realism. Some of the problems of distributed modelling are discussed including the problem of nonlinearity, the problem of scale, the problem of equifinality, the problem of uniqueness and the problem of uncertainty. A structure for the application of distributed modelling is suggested based on an uncertain or fuzzy landscape space to model space mapping. This is suggested as the basis for an Alternative Blueprint for distributed modelling in the form of an application methodology. This Alternative Blueprint is scientific in that it allows for the formulation of testable hypotheses. It focuses attention on the prior evaluation of models in terms of physical realism and on the value of data in model rejection. Finally, some unresolved questions that distributed modelling must address in the future are outlined, together with a vision for distributed modelling as a means of learning about places.

Towards Open and FAIR Hydrological Modelling with eWaterCycle

2021 ◽  
Author(s):  
Niels Drost ◽  
Jerom P.M. Aerts ◽  
Fakhereh Alidoost ◽  
Bouwe Andela ◽  
Jaro Camphuijsen ◽  
...  
Keyword(s):  
Ad Hoc ◽  
Hydrological Model ◽  
Climate Model ◽  
Hydrological Modelling ◽  
Analysis Tool ◽  
Hydrological Models ◽  
Software Environment ◽  
Processing Pipeline ◽  
Basic Model ◽  
Input Processing

<p>The eWaterCycle platform (https://www.ewatercycle.org/) is a fully Open Source system designed explicitly to advance the state of Open and FAIR Hydrological modelling. While working with Hydrologists to create a fully Open and FAIR comparison study, we noticed that many ad-hoc tools and scripts are used to create input (forcing, parameters) for a hydrological model from the source datasets such as climate reanalysis and land-use data. To make this part of the modelling process better reproducible and more transparent we have created a common forcing input processing pipeline based on an existing climate model analysis tool: ESMValTool (https://www.esmvaltool.org/). </p><p>Using ESMValTool, the eWaterCycle platform can perform commonly required preprocessing steps such as cropping, re-gridding, and variable derivation in a standardized manner. If needed, it also allows for custom steps for a hydrological model. Our pre-processing pipeline directly supports commonly used datasets such as ERA-5, ERA-Interim, and CMIP climate model data, and creates ready-to-run forcing data for a number of Hydrological models.</p><p>Besides creating forcing data, the eWaterCycle platform allows scientists to run Hydrological models in a standardized way using Jupyter notebooks, wrapping the models inside a container environment, and interfacing to these using BMI, the Basic Model Interface (https://bmi.readthedocs.io/). The container environment (based on Docker) stores the entire software stack, including the operating system and libraries, in such a way that a model run can be reproduced using an identical software environment on any other computer.</p><p>The reproducible processing of forcing and a reproducible software environment are important steps towards our goal of fully reproducible, Open, and FAIR Hydrological modelling. Ultimately, we hope to make it possible to fully reproduce a hydrological model experiment from data pre-processing to analysis, using only a few clicks.</p>

scholarly journals Fully distributed hydrological modelling for catchment-wide hydrological data verification

2019 ◽  
Vol 50 (6) ◽  
pp. 1520-1534 ◽  
Author(s):  
D. Ocio ◽  
T. Beskeen ◽  
K. Smart
Keyword(s):  
Potential Evapotranspiration ◽  
Data Availability ◽  
Hydrological Models ◽  
Limited Data ◽  
Runoff Generation ◽  
Data Scarcity ◽  
Hydrological Data ◽  
Distributed Modelling ◽  
Spatial Dimensions ◽  
Distributed Hydrological Models

Abstract Hydrological data scarcity and uncertainty is a fundamental challenge in hydrology, particularly in places with weak or declining investment in hydrometric networks. It is well established that fully distributed hydrological models can provide robust estimation of flows at ungauged locations, through local calibration and regionalisation using spatial datasets of physical properties. Even in situations where data are abundant, the existence of inconsistent information is not uncommon. The measurement, estimation or interpolation of rainfall, potential evapotranspiration and flow as well as the difficulty in monitoring artificial influences are all sources of potential inconsistency. Less studied but as important, distributed hydrological models, given their capability of capturing both the temporal and spatial dimensions of the water balance and runoff generation, are suitable tools to identify potential deficiencies in, and reliability of, input data. Three heavily modified catchments in the East of England such as the Ely Ouse, the Witham, and the Black Sluice have been considered, all of which have issues of data scarcity and uncertainty. This paper demonstrates not only the benefits of fully distributed modelling in addressing data availability issues but also in its use as a catchment-wide data validation tool that serves to maximise the potential of limited data and contributes to improved basin representation.

scholarly journals Comparing hydrological modelling, linear and multilevel regression approaches for predicting baseflow index for 596 catchments across Australia

2017 ◽  
Author(s):  
Junlong Zhang ◽  
Yongqiang Zhang ◽  
Jinxi Song ◽  
Lei Cheng ◽  
Rong Gan ◽  
...  
Keyword(s):  
Linear Regression ◽  
Hydrological Modelling ◽  
Ensemble Average ◽  
Spatial Proximity ◽  
Hydrological Models ◽  
Multilevel Regression ◽  
Large Spatial Scale ◽  
Climate Zones ◽  
Australian Continent ◽  
Regression Approach

Abstract. Estimating baseflow at a large spatial scale is critical for water balance budget, water resources management, and environmental evaluation. To predict baseflow index (BFI, the ratio of baseflow to total streamflow), this study introduces a multilevel regression approach, which is compared to two traditional approaches: hydrological modelling (SIMHYD, a simplified version of the HYDROLOG model, and Xinanjiang models) and classic linear regression. All of the three approaches were evaluated against ensemble average estimates from four well-parameterised baseflow separation methods (Lyne–Hollick, UKIH (United Kingdom Institute of Hydrology), Chapman–Maxwell and Eckhardt) at 596 widely spread Australian catchments in 1975–2012. The two hydrological models obtain BFI from three modes: calibration and two regionalisation schemes (spatial proximity and integrated similarity). The classic linear regression estimates BFI using linear regressions established between catchment attributes and the ensemble average estimates in four climate zones (arid, tropics, equiseasonal and winter rainfall). The multilevel regression approach not only groups the catchments into the four climate zones, but also considers variances both within all catchments and catchments in each climate zone. The two calibrated and regionalised hydrological models perform similarly poorly in predicting BFI with a Nash–Sutcliffe Efficiency (NSE) of −8.44 ~ −2.58 and an absolute percenrate bias (Bias) of 81 146; the classic linear regression is intermediate with the NSE of 0.57 and bias of 25; the multilevel regression approach is best with the NSE of 0.75 and bias of 19. Our study indicates the multilevel regression approach should be used for predicting large-scale baseflow index such as Australian continent where sufficient catchment predictors are available.

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 Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?

2017 ◽  
Vol 21 (2) ◽  
pp. 839-861 ◽  
Author(s):  
Maurizio Mazzoleni ◽  
Martin Verlaan ◽  
Leonardo Alfonso ◽  
Martina Monego ◽  
Daniele Norbiato ◽  
...  
Keyword(s):  
Case Studies ◽  
Low Cost ◽  
Hydrological Modelling ◽  
Data Availability ◽  
Hydrological Models ◽  
Flood Prediction ◽  
Technological Advances ◽  
Crowdsourced Data ◽  
Streamflow Data ◽  
Synthetic Time Series

Abstract. Monitoring stations have been used for decades to properly measure hydrological variables and better predict floods. To this end, methods to incorporate these observations into mathematical water models have also been developed. Besides, in recent years, the continued technological advances, in combination with the growing inclusion of citizens in participatory processes related to water resources management, have encouraged the increase of citizen science projects around the globe. In turn, this has stimulated the spread of low-cost sensors to allow citizens to participate in the collection of hydrological data in a more distributed way than the classic static physical sensors do. However, two main disadvantages of such crowdsourced data are the irregular availability and variable accuracy from sensor to sensor, which makes them challenging to use in hydrological modelling. This study aims to demonstrate that streamflow data, derived from crowdsourced water level observations, can improve flood prediction if integrated in hydrological models. Two different hydrological models, applied to four case studies, are considered. Realistic (albeit synthetic) time series are used to represent crowdsourced data in all case studies. In this study, it is found that the data accuracies have much more influence on the model results than the irregular frequencies of data availability at which the streamflow data are assimilated. This study demonstrates that data collected by citizens, characterized by being asynchronous and inaccurate, can still complement traditional networks formed by few accurate, static sensors and improve the accuracy of flood forecasts.

scholarly journals Bacterial Cell Enlargement Requires Control of Cell Wall Stiffness Mediated by Peptidoglycan Hydrolases

mBio ◽  
2015 ◽  
Vol 6 (4) ◽  
Author(s):  
Richard Wheeler ◽  
Robert D. Turner ◽  
Richard G. Bailey ◽  
Bartłomiej Salamaga ◽  
Stéphane Mesnage ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Cell Wall ◽  
Fundamental Problem ◽  
Biochemical Properties ◽  
Major Advance ◽  
Bacterial Cells ◽  
Cell Enlargement ◽  
Content Type ◽  
Wall Stiffness ◽  
Peptidoglycan Hydrolases

ABSTRACTMost bacterial cells are enclosed in a single macromolecule of the cell wall polymer, peptidoglycan, which is required for shape determination and maintenance of viability, while peptidoglycan biosynthesis is an important antibiotic target. It is hypothesized that cellular enlargement requires regional expansion of the cell wall through coordinated insertion and hydrolysis of peptidoglycan. Here, a group of (apparent glucosaminidase) peptidoglycan hydrolases are identified that are together required for cell enlargement and correct cellular morphology ofStaphylococcus aureus, demonstrating the overall importance of this enzyme activity. These are Atl, SagA, ScaH, and SagB. The major advance here is the explanation of the observed morphological defects in terms of the mechanical and biochemical properties of peptidoglycan. It was shown that cells lacking groups of these hydrolases have increased surface stiffness and, in the absence of SagB, substantially increased glycan chain length. This indicates that, beyond their established roles (for example in cell separation), some hydrolases enable cellular enlargement by making peptidoglycan easier to stretch, providing the first direct evidence demonstrating that cellular enlargement occurs via modulation of the mechanical properties of peptidoglycan.IMPORTANCEUnderstanding bacterial growth and division is a fundamental problem, and knowledge in this area underlies the treatment of many infectious diseases. Almost all bacteria are surrounded by a macromolecule of peptidoglycan that encloses the cell and maintains shape, and bacterial cells must increase the size of this molecule in order to enlarge themselves. This requires not only the insertion of new peptidoglycan monomers, a process targeted by antibiotics, including penicillin, but also breakage of existing bonds, a potentially hazardous activity for the cell. UsingStaphylococcus aureus, we have identified a set of enzymes that are critical for cellular enlargement. We show that these enzymes are required for normal growth and define the mechanism through which cellular enlargement is accomplished, i.e., by breaking bonds in the peptidoglycan, which reduces the stiffness of the cell wall, enabling it to stretch and expand, a process that is likely to be fundamental to many bacteria.

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