Flexible watershed simulation with the Raven hydrological modelling framework

Craig, James R.; Brown, Genevieve; Chlumsky, Robert; Jenkinson, R. Wayne; Jost, G.; Lee, Konhee; Mai, Juliane; Serrer, Martin; Sgro, Nicholas; Shafii, Mahyar; Snowdon, A. P.; Tolson, Bryan A.

doi:10.1016/j.envsoft.2020.104728

Cited by 85 publications

(109 citation statements)

References 57 publications

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“…The AMSI framework proposed in this paper combines the modular hydrological modeling framework RAVEN (Craig et al., 2020) and the heuristic, global DDS algorithm (Tolson & Shoemaker, 2007; Tolson et al., 2009) in order to optimize the model construction process. However, AMSI could potentially be any approach that simultaneously optimizes hydrologic model structure and model parameters against one or more performance metrics used as an objective function.…”

Section: Methodsmentioning

confidence: 99%

Automatic Model Structure Identification for Conceptual Hydrologic Models

Spieler

Mai

Craig

et al. 2020

Water Resources Research

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Choosing (an) adequate model structure(s) for a given purpose, catchment, and data situation is a critical task in the modeling chain. However, despite model intercomparison studies, hypothesis testing approaches with modular modeling frameworks, and continuous efforts in model development and improvement, there are still no clear guidelines for identifying a preferred model structure. By introducing a framework for Automatic Model Structure Identification (AMSI), we support the process of identifying (a) suitable model structure(s) for a given task. The proposed AMSI framework employs a combination of the modular hydrological model RAVEN and the heuristic global optimization algorithm dynamically dimensioned search (DDS). It is the first demonstration of a mixed-integer optimization algorithm applied to simultaneously optimize model structure choices (integer decision variables) and parameter values (continuous decision variables) in hydrological modeling. The AMSI framework is thus able to sift through a vast number of model structure and parameter choices for identifying the most adequate model structure(s) for representing the rainfall-runoff behavior of a catchment. We demonstrate the feasibility of the approach by reidentifying given model structures that produced a specific hydrograph and show the limits of the current setup via a real-world application of AMSI on 12 MOPEX catchments. Results show that the AMSI framework is capable of inferring feasible model structure(s) reproducing the rainfall-runoff behavior of a given catchment. However, it is a complex optimization problem to identify model structure and parameters simultaneously. The variance in the identified structures is high due to near equivalent diagnostic measures for multiple model structures, reflecting substantial model equifinality. Future work with AMSI should consider the use of hydrologic signatures, case studies with multiple types of observation data, and the use of mixed-integer multiobjective optimization algorithms.

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Section: Methodsmentioning

confidence: 99%

Automatic Model Structure Identification for Conceptual Hydrologic Models

Spieler

Mai

Craig

et al. 2020

Water Resources Research

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“…CC BY 4.0 License. et al, 2008), SUPERFLEX (Fenicia et al, 2011;Kavetski and Fenicia, 2011), SUMMA (Clark et al, 2015a(Clark et al, , 2015b, and RAVEN (Craig et al, 2020) are some widely used flexible modelling frameworks. The high degree of transferability in flexible modelling frameworks is an aiding factor in proceeding in the direction of a unified hydrological theory at a watershed level.…”

Section: Fixed Models Vs Flexible Modelsmentioning

confidence: 99%

Hydrologically Informed Machine Learning for Rainfall-Runoff Modelling: Towards Distributed Modelling

Herath¹,

Chadalawada²,

Babovic³

2020

Preprint

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Abstract. Despite showing a great success of applications in many commercial fields, machine learning and data science models in general, show a limited use in scientific fields including hydrology. The approach is often criticized for lack of interpretability and physical consistency. This has led to the emergence of new paradigms, such as Theory Guided Data Science (TGDS) and physics informed machine learning. The motivation behind such approaches is to improve the physical meaningfulness of machine learning models by blending existing scientific knowledge with learning algorithms. Following the same principles, in our prior work (Chadalawada et al., 2020), a new model induction framework was founded on Genetic Programming (GP) namely Machine Learning Rainfall-Runoff Model Induction Toolkit (ML-RR-MI). ML-RR-MI is cable of developing fully-fledged lumped conceptual rainfall-runoff models for a watershed of interest using the building blocks of two flexible rainfall-runoff modelling frameworks (FUSE and SUPERFLEX). In this study, we extend ML-RR-MI towards inducing semi-distributed rainfall-runoff models. This effort is motivated by the desire to address the decreasing meaningfulness of lumped models which tend to particularly deteriorate within large catchments where the spatial heterogeneity of forcing variables and watershed properties are significant. Henceforth, our machine learning approach for rainfall-runoff modelling titled Machine Induction Knowledge-Augmented System Hydrologique Asiatique (MIKA-SHA) captures spatial variabilities and automatically induces rainfall-runoff models for the catchment of interest without any subjectivity in model selection. Currently, MIKA-SHA learns models utilizing the model building components of FUSE and SUPERFLEX. However, the proposed framework can be coupled with any internally coherent collection of building blocks. MIKA-SHA’s model induction capabilities have been tested on the Red Creek catchment near Vestry, Mississippi, United States. The resulted model architectures through MIKA-SHA are compatible with previously reported research findings and fieldwork insights of the watershed and are readily interpretable by hydrologists.

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“…A key purpose of model sensitivity analysis is to inform model calibration or model uncertainty analysis so as to focus either of these analyses on only the model inputs/model structural choices the model outputs are most sensitive to. While the literature on sensitivity analysis for model parameters is rich (Morris, 1991;Sobol', 1993;Demaria et al, 2007;Foglia et al, 2009;Campolongo et al, 2011;Rakovec et al, 2014;Pianosi and Wagener, 2015;Cuntz et al, 2015Cuntz et al, , 2016Razavi and Gupta, 2016a, b;Borgonovo et al, 2017;Haghnegahdar et al, 2017), and there has likewise been a good deal of research into the influence of model input uncertainty (Baroni and Tarantola, 2014;Abily et al, 2016;Schürz et al, 2019), sensitivity to model structural choice has received far less attention (McMillan et al, 2010;Clark et al, 2011). With model structure we refer to various process conceptualizations within a model rather than, for example, model discretization.…”

Section: Introductionmentioning

confidence: 99%

“…One major reason for difficulties in addressing sensitivity to model structural choice, or model structural uncertainty, is due, in part, to the historical inflexibility of environmental and hydrological models which readily allow the user to perturb parameters or input forcings via input files, but are often constrained to a hard-coded model structure or a generally fixed model structure with a relatively small number of options. However, the advent of flexible hydrological modeling frameworks such as FUSE (Clark et al, 2008), SUPERFLEX (Fenicia et al, 2011), SUMMA (Clark et al, 2015), or Raven (Craig et al, 2020) enables manipulation of model structure in addition to parameters and inputs. They afford a sufficient number of degrees of freedom in model structure to start to explore model sensitivity to structural choices, and the interplay between model structures.…”

Section: Introductionmentioning

confidence: 99%

“…To our knowledge, the method of grouping parameters to derive sensitivities of parameters, process options, and processes without the explicit necessity for averaging parameter sensitivities after deriving them for individual models (referred to as conventional/traditional sensitivity analysis) has not yet been applied. The xSSA method is made uniquely possible due to a special property of the Raven hydrologic modeling framework (Craig et al, 2020), whereby hydrologic fluxes (e.g., infiltration, runoff, or baseflow) may be calculated via the weighted average of simulated fluxes generated by individual process algorithms; other flexible models may be revised to accommodate such analysis. The weighted averaging means that at each time step each option chosen for a process would derive an estimate for the flux, in mm d −1 , and the weighted average of these estimates would be used for the next step.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneously determining global sensitivities of model parameters and model structure

Mai

Craig

Tolson

2020

Hydrol. Earth Syst. Sci.

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Abstract. Model structure uncertainty is known to be one of the three main sources of hydrologic model uncertainty along with input and parameter uncertainty. Some recent hydrological modeling frameworks address model structure uncertainty by supporting multiple options for representing hydrological processes. It is, however, still unclear how best to analyze structural sensitivity using these frameworks. In this work, we apply the extended Sobol' sensitivity analysis (xSSA) method that operates on grouped parameters rather than individual parameters. The method can estimate not only traditional model parameter sensitivities but is also able to provide measures of the sensitivities of process options (e.g., linear vs. non-linear storage) and sensitivities of model processes (e.g., infiltration vs. baseflow) with respect to a model output. Key to the xSSA method's applicability to process option and process sensitivity is the novel introduction of process option weights in the Raven hydrological modeling framework. The method is applied to both artificial benchmark models and a watershed model built with the Raven framework. The results show that (1) the xSSA method provides sensitivity estimates consistent with those derived analytically for individual as well as grouped parameters linked to model structure. (2) The xSSA method with process weighting is computationally less expensive than the alternative aggregate sensitivity analysis approach performed for the exhaustive set of structural model configurations, with savings of 81.9 % for the benchmark model and 98.6 % for the watershed case study. (3) The xSSA method applied to the hydrologic case study analyzing simulated streamflow showed that model parameters adjusting forcing functions were responsible for 42.1 % of the overall model variability, while surface processes cause 38.5 % of the overall model variability in a mountainous catchment; such information may readily inform model calibration and uncertainty analysis. (4) The analysis of time-dependent process sensitivities regarding simulated streamflow is a helpful tool for understanding model internal dynamics over the course of the year.

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Flexible watershed simulation with the Raven hydrological modelling framework

Cited by 85 publications

References 57 publications

Automatic Model Structure Identification for Conceptual Hydrologic Models

Automatic Model Structure Identification for Conceptual Hydrologic Models

Hydrologically Informed Machine Learning for Rainfall-Runoff Modelling: Towards Distributed Modelling

Simultaneously determining global sensitivities of model parameters and model structure

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