scholarly journals HESS Opinions "The art of hydrology"*

2009 ◽  
Vol 13 (2) ◽  
pp. 157-161 ◽  
Author(s):  
H. H. G. Savenije
Keyword(s):  
Physical Theory ◽  
Hydrological Model ◽  
Hydrological Modelling ◽  
Hydrological Models ◽  
Top Down ◽  
Science And Engineering ◽  
Bottom Up ◽  
Site Specific ◽  
Flexible Models ◽  
Modelling Process

Abstract. Hydrological modelling is the same as developing and encoding a hydrological theory. A hydrological model is not a tool but a hypothesis. The whole discussion about the inadequacy of hydrological models we have witnessed of late, is related to the wrong concept of what a model is. Good models don't exist. Instead of looking for the "best" model, we should aim at developing better models. The process of modelling should be top-down, learning from the data while at the same time connection should be established with underlying physical theory (bottom-up). As a result of heterogeneity occurring at all scales in hydrology, there always remains a need for calibration of models. This implies that we need tailor-made and site-specific models. Only flexible models are fit for this modelling process, as opposed to most of the established software or "one-size-fits-all" models. The process of modelling requires imagination, inspiration, creativity, ingenuity, experience and skill. These are qualities that belong to the field of art. Hydrology is an art as much as it is science and engineering.

scholarly journals <i>HESS Opinions</i> "The art of hydrology"<sup>*</sup>

2008 ◽  
Vol 5 (6) ◽  
pp. 3157-3167 ◽  
Author(s):  
H. H. G. Savenije
Keyword(s):  
Hydrological Model ◽  
Hydrological Modelling ◽  
Hydrological Models ◽  
Top Down ◽  
Science And Engineering ◽  
Site Specific ◽  
Flexible Models ◽  
Modelling Process

Abstract. Hydrological modelling is the same as developing and encoding a hydrological theory. A hydrological model is not a tool but a theory. The whole discussion about the inadequacy of hydrological models we have witnessed of late, is related to the wrong concept of what a model is. Good models don't exist. Instead, hydrological research should focus on improving models and enhancing understanding. The process of modelling should be top-down, learning from the data. There is always a need for calibration, which implies that we need tailor-made and site-specific models. Only flexible models are fit for this modelling process, as opposed to most of the "established" models, "one-size-fits-all" models or "models of everywhere". The process of modelling requires imagination, inspiration, creativity, ingenuity, experience and skill. These are qualities that belong to the field of art. Hydrology is an art as much as it is science and engineering.

scholarly journals How can expert knowledge increase the realism of conceptual hydrological models? A case study based on the concept of dominant runoff process in the Swiss Pre-Alps

2018 ◽  
Vol 22 (8) ◽  
pp. 4425-4447 ◽  
Author(s):  
Manuel Antonetti ◽  
Massimiliano Zappa
Keyword(s):  
Spatial Distribution ◽  
Expert Knowledge ◽  
Hydrological Models ◽  
Top Down ◽  
Bottom Up ◽  
Process Maps ◽  
Uncertainty Sources ◽  
Simulation Results ◽  
Set Up ◽  
Better Than

Abstract. Both modellers and experimentalists agree that using expert knowledge can improve the realism of conceptual hydrological models. However, their use of expert knowledge differs for each step in the modelling procedure, which involves hydrologically mapping the dominant runoff processes (DRPs) occurring on a given catchment, parameterising these processes within a model, and allocating its parameters. Modellers generally use very simplified mapping approaches, applying their knowledge in constraining the model by defining parameter and process relational rules. In contrast, experimentalists usually prefer to invest all their detailed and qualitative knowledge about processes in obtaining as realistic spatial distribution of DRPs as possible, and in defining narrow value ranges for each model parameter.Runoff simulations are affected by equifinality and numerous other uncertainty sources, which challenge the assumption that the more expert knowledge is used, the better will be the results obtained. To test for the extent to which expert knowledge can improve simulation results under uncertainty, we therefore applied a total of 60 modelling chain combinations forced by five rainfall datasets of increasing accuracy to four nested catchments in the Swiss Pre-Alps. These datasets include hourly precipitation data from automatic stations interpolated with Thiessen polygons and with the inverse distance weighting (IDW) method, as well as different spatial aggregations of Combiprecip, a combination between ground measurements and radar quantitative estimations of precipitation. To map the spatial distribution of the DRPs, three mapping approaches with different levels of involvement of expert knowledge were used to derive so-called process maps. Finally, both a typical modellers' top-down set-up relying on parameter and process constraints and an experimentalists' set-up based on bottom-up thinking and on field expertise were implemented using a newly developed process-based runoff generation module (RGM-PRO). To quantify the uncertainty originating from forcing data, process maps, model parameterisation, and parameter allocation strategy, an analysis of variance (ANOVA) was performed.The simulation results showed that (i) the modelling chains based on the most complex process maps performed slightly better than those based on less expert knowledge; (ii) the bottom-up set-up performed better than the top-down one when simulating short-duration events, but similarly to the top-down set-up when simulating long-duration events; (iii) the differences in performance arising from the different forcing data were due to compensation effects; and (iv) the bottom-up set-up can help identify uncertainty sources, but is prone to overconfidence problems, whereas the top-down set-up seems to accommodate uncertainties in the input data best. Overall, modellers' and experimentalists' concept of model realism differ. This means that the level of detail a model should have to accurately reproduce the DRPs expected must be agreed in advance.

Site-specific effects on productivity of an upper trophic-level marine predator: Bottom-up, top-down, and mismatch effects on reproduction in a colonial seabird

2006 ◽  
Vol 68 (2-4) ◽  
pp. 303-328 ◽  
Author(s):  
Robert M. Suryan ◽  
David B. Irons ◽  
Evelyn D. Brown ◽  
Patrick G.R. Jodice ◽  
Daniel D. Roby
Keyword(s):  
Trophic Level ◽  
Top Down ◽  
Bottom Up ◽  
Site Specific ◽  
Marine Predator ◽  
Specific Effects ◽  
Colonial Seabird

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

&lt;p&gt;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/).&amp;#160;&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;

“Top-Down” or “Bottom-Up”

1998 ◽  
pp. 61-68
Author(s):  
Francisco Flores
Keyword(s):  
Physical Theory ◽  
Scientific Explanation ◽  
Theoretical Explanation ◽  
Top Down ◽  
Bottom Up ◽  
Close Relationship ◽  
Physical Laws ◽  
The Relationship ◽  
Principle Theory

Wesley Salmon has suggested that the two leading views of scientific explanation, the “bottom-up” view and the “top-down” view, describe distinct types of explanation. In this paper, I focus on theoretical explanations in physics, i.e., explanations of physical laws. Using explanations of E=mc2, I argue that the distinction between bottom-up explanations (BUEs) and top-down explanations (BUEs) is best understood as a manifestation of a deeper distinction, found originally in Newton’s work, between two levels of theory. I use Einstein’s distinction between ‘principle’ and ‘constructive’ theories to argue that only lower level theories, i.e., ‘constructive’ theories, can yield BUEs. These explanations, furthermore, depend on higher level laws that receive only TDEs from a ‘principle’ theory. Thus, I conclude that Salmon’s challenge to characterize the relationship between the two types of explanation can be met only by recognizing the close relationship between types of theoretical explanation and the structure of physical theory.

scholarly journals How can expert knowledge increase the realism of conceptual hydrological models? A case study in the Swiss Pre-Alps

2017 ◽  
Author(s):  
Manuel Antonetti ◽  
Massimiliano Zappa
Keyword(s):  
Spatial Distribution ◽  
Expert Knowledge ◽  
Hydrological Models ◽  
Runoff Generation ◽  
Top Down ◽  
Bottom Up ◽  
Process Maps ◽  
Uncertainty Sources ◽  
Simulation Results ◽  
Better Than

Abstract. Both modellers and experimentalists agree that using expert knowledge can improve the realism of conceptual hydrological models. However, their use of expert knowledge differs for each step in the modelling procedure, which involves hydrologically mapping the dominant runoff processes (DRPs) occurring on a given catchment, parameterising these processes within a model, and allocating its parameters. Modellers generally use very simplified mapping approaches, applying their knowledge in constraining the model by defining parameter and process relational rules. In contrast, experimentalists usually prefer to invest all their detailed and qualitative knowledge about processes in obtaining as realistic spatial distribution of DRPs as possible, and in defining narrow value ranges for each model parameter. Runoff simulations are affected by equifinality and numerous other uncertainty sources, which challenge the assumption that the more expert knowledge is used, the better will be the results obtained. To test to which extent expert knowledge can improve simulation results under uncertainty, we therefore applied a total of 60 modelling chain combinations forced by five rainfall datasets of increasing accuracy to four nested catchments in the Swiss Pre-Alps. These datasets include hourly precipitation data from automatic stations interpolated with Thiessen polygons and with the Inverse Distance Weighting (IDW) method, as well as different spatial aggregations of Combiprecip, a combination between ground measurements and radar quantitative estimations of precipitation. To map the spatial distribution of the DRPs, three mapping approaches with different levels of involvement of expert knowledge were used to derive so-called process maps. Finally, both a typical modellers' top-down setup relying on parameter and process constraints, and an experimentalists' setup based on bottom-up thinking and on field expertise were implemented using a newly developed process-based runoff generation module (RGM-PRO). To quantify the uncertainty originating from forcing data, process maps, model parameterisation, and parameter allocation strategy, an analysis of variance (ANOVA) was performed. The simulation results showed that: (i) the modelling chains based on the most complex process maps performed slightly better than those based on less expert knowledge; (ii) the bottom-up setup performed better than the top-down one when simulating short-duration events, but similarly to the top-down setup when simulating long-duration events; (iii) the differences in performance arising from the different forcing data were due to compensation effects; and (iv) the bottom-up setup can help identify uncertainty sources, but is prone to overconfidence problems, whereas the top-down setup seems to accommodate uncertainties in the input data best. Overall, modellers' and experimentalists' concept of "model realism" differ. This means that the level of detail a model should have to accurately reproduce the DRPs expected must be agreed in advance.

scholarly journals HESS Opinions: The complementary merits of top-down and bottom-up modelling philosophies in hydrology

2017 ◽  
Author(s):  
Markus Hrachowitz ◽  
Martyn Clark
Keyword(s):  
Conservation Laws ◽  
Functional Similarity ◽  
Hydrological Models ◽  
Top Down ◽  
Bottom Up ◽  
Micro Scale ◽  
Macro Scale ◽  
Modelling Strategies ◽  
Complementary Value ◽  
Process Based Models

Abstract. In hydrology, the two somewhat competing modelling philosophies of bottom-up and top-down approaches are the basis of most process-based models. Differing mostly (1) in their respective degree of detail in resolving the modelling domain and (2) in their respective degree of explicitly treating conservation laws, these two philosophies suffer from similar limitations. Nevertheless, a better understanding of their respective basis (i.e. micro-scale vs. macro-scale) as well as their respective short comings bears the potential of identifying the complementary value of the two philosophies for improving our models. In this manuscript we analyse several frequently communicated beliefs and assumptions to identify, discuss and emphasize the functional similarity of the two modelling philosophies. We argue that deficiencies in model applications largely do not depend on the modelling philosophy but rather on the way a model is implemented. Based on the premises that top-down models can be implemented at any desired degree of detail and that any type of model remains to some degree conceptual we argue that a convergence of the two modelling strategies may hold some value for progressing the development of hydrological models.

scholarly journals Quantile-Based Hydrological Modelling

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3420
Author(s):  
Hristos Tyralis ◽  
Georgia Papacharalampous
Keyword(s):  
Hydrological Model ◽  
Methodological Approach ◽  
Predictive Performance ◽  
Predictive Distribution ◽  
Hydrological Modelling ◽  
Hydrological Models ◽  
Proof Of Concept ◽  
Post Processing ◽  
Predictive Uncertainty ◽  
Quantile Loss

Predictive uncertainty in hydrological modelling is quantified by using post-processing or Bayesian-based methods. The former methods are not straightforward and the latter ones are not distribution-free (i.e., assumptions on the probability distribution of the hydrological model’s output are necessary). To alleviate possible limitations related to these specific attributes, in this work we propose the calibration of the hydrological model by using the quantile loss function. By following this methodological approach, one can directly simulate pre-specified quantiles of the predictive distribution of streamflow. As a proof of concept, we apply our method in the frameworks of three hydrological models to 511 river basins in the contiguous US. We illustrate the predictive quantiles and show how an honest assessment of the predictive performance of the hydrological models can be made by using proper scoring rules. We believe that our method can help towards advancing the field of hydrological uncertainty.

Comparing the impact for hydrology of the new ERA5 reanalyses dataset over ERA-Interim for 8 hydrological models in 6 catchments using the eWaterCycle community modelling environment.

2020 ◽  
Author(s):  
Rolf Hut ◽  
Niels Drost ◽  
Jerom Aerts ◽  
Laurene Bouaziz ◽  
Willem van Verseveld ◽  
...  
Keyword(s):  
Data Science ◽  
Conceptual Models ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Global Climate ◽  
Hydrological Modelling ◽  
Additional Parameter ◽  
Hydrological Models ◽  
Comparison Study ◽  
The Impact

&lt;p&gt;The release of the European Centre for Medium-Range Weather Forecasts (ECMWF)&amp;#8217;s Re-Analysis 5 (ERA-5) global climate forcing dataset is expected to greatly improve the quality of hydrological modeling. Following this release there is great interest in assessing the improvements of ERA-5 relative to its predecessor ERA-Interim for hydrological modeling and predictions.&lt;/p&gt;&lt;p&gt;In this study we compare streamflow predictions when using ERA-interim vs ERA-5 as forcing data for a suite of hydrological models from different research groups that capture the variation in modelling strategies within the hydrological modelling community. We check whether physically based models, defined as those that do not require additional parameter calibration, would lead to different conclusions in comparison to conceptual models, defined as those that require calibration. Based on the hydrological model structure we expect that conceptual models that need calibration show less difference in predicting discharge (skill) between ERA-5 and ERA-Interim, where-as the physical based (non-calibrated) models most likely will benefit from the improved accuracy of the ERA-5 input. This assessment will provide the HEPEX community with answers on how the ERA-5 dataset will improve hydrological predictions based on different hydrological modelling concepts.&lt;/p&gt;&lt;p&gt;An additional key objective while conducting this study is compliance to the FAIR principles of data science. To achieve this we held a workshop in Leiden, the Netherlands, where multiple hydrological models were integrated into the eWatercycle II system. eWatercycle II is a hydrological model platform containing a growing number of hydrological models. The platform facilitates research and cohesivity within the hydrological community by providing an Open-Source platform built specifically to advance the state of FAIR and Open Science in Hydrological Modeling. We also use this study to demonstrate the feasibility of eWatercycle II as a platform for FAIR hydrological models.&lt;/p&gt;&lt;p&gt;Preliminairy results from this comparison study were presented at the AGU Fall Meeting 2019. Here we will present the full results of the comparison study.&lt;/p&gt;

Psychology and Culture: Top Down Versus Bottom Up?

PsycCRITIQUES ◽  
2005 ◽  
Vol 50 (19) ◽  
Author(s):  
Michael Cole
Keyword(s):  
Top Down ◽  
Bottom Up
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