Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration

Self Cite

Abstract. Many environmental systems models, such as conceptual rainfall-runoff models, rely on model calibration for parameter identification. For this, an observed output time series (such as runoff) is needed, but frequently not available (e.g., when making predictions in ungauged basins). In this study, we provide an alternative approach for parameter identification using constraints based on two types of restrictions derived from prior (or expert) knowledge. The first, called parameter constraints, restricts the solution space based on realistic relationships that must hold between the different model parameters while the second, called process constraints requires that additional realism relationships between the fluxes and state variables must be satisfied. Specifically, we propose a search algorithm for finding parameter sets that simultaneously satisfy such constraints, based on stepwise sampling of the parameter space. Such parameter sets have the desirable property of being consistent with the modeler's intuition of how the catchment functions, and can (if necessary) serve as prior information for further investigations by reducing the prior uncertainties associated with both calibration and prediction.

Section: Constraints In Environmental Modelsmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

A constraint-based search algorithm for parameter identification of environmental models

Gharari

Shafiei

Hrachowitz

et al. 2014

Self Cite

“…Overall, by incorporating an additional source of external information in a sensible manner (here by structural modification), the need for calibration can be reduced (note that the model was not calibrated against GAET); see the extensive discussion by Gharari et al (2014) and Bahremand (2016) on this topic. Nevertheless, given the simplistic nature of the hydrologic models and the large uncertainties that exist therein, some degree of calibration will generally remain important and relevant.…”

Section: Discussionmentioning

confidence: 99%

Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model

Roy

Gupta

Serrat-Capdevila

et al. 2017

Abstract. Daily, quasi-global (50 • N-S and 180 • W-E), satellite-based estimates of actual evapotranspiration at 0.25 • spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchmentscale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making "diagnostic structural improvements" to hydrologic models whenever possible.

“…Some examples of studies applying the topdown approach include Young (1998Young ( , 2003, Jothityangkoon et al (2001), Son and Sivapalan (2007), Fenicia et al (2008aFenicia et al ( , 2016, Kavetski and Fenicia (2011), Gharari et al (2014a), Hrachowitz et al (2014), Willems (2014) or more recently Garavaglia et al (2017).…”

Section: Top-down Modelsmentioning

confidence: 99%

HESS Opinions: The complementary merits of competing modelling philosophies in hydrology

Hrachowitz

Clark

2017

Self Cite

162

129

Abstract. In hydrology, two somewhat competing philosophies form the basis of most process-based models. At one endpoint of this continuum are detailed, high-resolution descriptions of small-scale processes that are numerically integrated to larger scales (e.g. catchments). At the other endpoint of the continuum are spatially lumped representations of the system that express the hydrological response via, in the extreme case, a single linear transfer function. Many other models, developed starting from these two contrasting endpoints, plot along this continuum with different degrees of spatial resolutions and process complexities. A better understanding of the respective basis as well as the respective shortcomings of different modelling philosophies has the potential to improve our models. In this paper we analyse several frequently communicated beliefs and assumptions to identify, discuss and emphasize the functional similarity of the seemingly competing modelling philosophies. We argue that deficiencies in model applications largely do not depend on the modelling philosophy, although some models may be more suitable for specific applications than others and vice versa, but rather on the way a model is implemented. Based on the premises that any model 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 modelling strategies may hold some value for advancing the development of hydrological models.