How to Tailor My Process‐Based Hydrological Model? Dynamic Identifiability Analysis of Flexible Model Structures

Introduction Motivation and BackgroundModel fidelity and accurate process representation at the scale of application have been the crux of [scientific] hydrological modeling. Additionally, the need to include ecological and/or anthropogenic factors pushes for a stronger synergy between the development of hydrological models and the growing volume of observational data. The advancement in process representation provides insights into the complex interactions of hydrological processes and potentially results in more reliable simulations and hence decisions. The special issue "Advancing process representation in hydrological models: Integrating new concepts, knowledge and data" in Water Resources Research is part of wider ongoing activities to reconcile the efforts for better process representation in models and in developing and applying hydrological models across scales and for multidisciplinary purposes. This special issue is a materialization of several years of well-attended sessions at the European Geosciences Union General Assembly (2016-2021) and American Geophysical Union Fall Meeting (2016) organized by the editors of the special issue. The editors were also involved in organizing the second modeling workshop of the initiative "Improving the Theoretical Underpinnings of Hydrological Models" in Sopron, Hungary in 2018, initiated by Clark et al. (2016).The special issue received contributions from September 2018 to June 2021. In this editorial, we discuss 29 contributions to the special issue. The authors' affiliations are spanning across 19 countries (Figure 1), and case studies in the submissions cover 11 countries. From the 29 contributions, 10 are openly accessible.We categorize the contributions to the special issue into four major topical themes presented in the following section. We conclude the editorial with our findings and reflection.

Section: Top Down and Bottom Up Both Needed In Model Structure Analysismentioning

confidence: 99%

“…Also Pilz et al. (2020) took the model as a starting point to formulate new hypotheses of system functioning. In their framework, they conducted an automatic identification of a flexible process‐based hydrological model.…”

Section: Topical Themesmentioning

confidence: 99%

Advancing Process Representation in Hydrological Models: Integrating New Concepts, Knowledge, and Data

Guse

Fatichi

Gharari

et al. 2021

“…Environmental modeling requires many competing descriptions for a specific process, and any differences in the formulation or parameterization of a process in the model structure will influence model's performance (Pilz et al., 2020). The main challenges associated with these differences are how to account for the ambiguities in model structures and how to choose appropriate model architecture (Clark et al., 2011).…”

Section: Introductionmentioning

confidence: 99%

Two Alternatives to the Two‐Source Energy Balance Evapotranspiration Model

Guo-xiao

Zhao

Tian

et al. 2023

The storage and flux of terrestrial water between reservoirs significantly affect nearly all land-atmosphere coupling processes. These reservoirs and fluxes are also vital elements of the Earth's surface energy balance. They are essential for scientific research and practical applications, including managing the availability and supply of freshwater for societies, determining ecological water requirements, and simulating global climate change. Evapotranspiration (ET) is the second-largest hydrologic flux in the terrestrial water cycle. It globally returns approximately 60%-65% of terrestrial precipitation directly to the atmosphere (Brutsaert, 2005) and also affects the weather and climate through vegetation-atmosphere interaction. Accurate estimates of the ET are necessary to form a theoretical understanding of vegetation-atmosphere interaction and also to simulate its influence on weather, climate, and water resource utilization and management.An environmental model is a simplified representation of a natural system, and several important assumptions are required to allow the actual system to be simulated as realistically as possible. However, these assumptions can yield biased model predictions and compromise system modeling. As the scientific community's understanding of the interplay between ecological, geological, and hydrometeorological systems improves, new physically realistic and accurate assumptions are constantly proposed, which can, in turn, be used to further improve environmental models. For the ET model, Penman (1948) originally developed the Penman equation for estimating the potential evaporation from a wet surface. Later, Monteith (1965) adopted a surface or stomatal resistance term to reflect the effect of partially dry surfaces on evaporation, which led to the formalization of the Penman-Monteith (PM) model (Monteith, 1965;Penman, 1948). The initial development of the Penman equation and PM model used an implicit surface energy equation for evaporation. In order to analytically solve the energy balance equation (EBE) in the Penman equation and PM model, Penman (1948) and Monteith (1965) both used a major assumption by linearizing the Clausius-Clapeyron relation (CCR) around air temperature, which produced an approximate solution. However, linearization of the CCR introduces both empirical and conceptual errors into ET estimates (McColl, 2020; Paw U & Gao, 1988). Vallis et al. (2019) proposed a new approach to approximate

“…The SUPERFLEX framework was also used in Fenicia, Kavetski, Savenije, and Pfister (2016) to tailor model structures for individual HRUs across multiple catchments. More recent studies in hydrology continue to deploy flexible frameworks and cite their importance in facilitating research related to model structures (Chadalawada et al., 2020; Pilz et al., 2020; J. O. Remmers et al., 2020). Such frameworks with multiple process options are standard in land‐surface modeling (Cuntz, Mai, Samaniego, et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Calibration of Hydrologic Model Structure and Parameters Using a Blended Model

Chlumsky

Mai

Craig

et al. 2021

Hydrologic models have been used for numerous applications in the last number of decades. These applications include streamflow prediction, flood forecasting, or reservoir level forecasting, or in a scientific capacity to advance our understanding of hydrologic systems. Whether used in a predictive or scientific capacity, models are an abstraction of the complex natural system being simulated, and necessarily simplify the treatment of hydrological processes occurring in a watershed, either to facilitate computational expediency or in recognition of the large degree of uncertainty regarding how the watershed functions. In most practical cases, model calibration is required in order to reconcile model output with historical observation; conventionally, this calibration process focuses on tuning only model parameters. However, hydrologic models are typically considered to have four main components that contribute to model uncertainty (e.g., Beven, 2005;Butts et al., 2004;Gupta, Clark, et al., 2012): (1) input forcing data, such as precipitation and temperature, (2) model structure, which includes the algorithmic functions used to simulate the hydrologic cycle, (3) model parameters related to the selected hydrologic process algorithms (often the only degree of freedom used to tune model performance), and (4) an error term, which represents any uncertainty or deviation from reality not captured in the other three components of model uncertainty.This study focuses upon the simultaneous calibration of model structure and model parameters. We refer to model structure here as a combination of (a) the number and type of stores represented in the model and (b) the collection of algorithms used to describe the relationships between fluxes and storage in each hydrological response unit (HRU). Model structure can be further extended to other modeling decisions such as spatial discretization, but this extended definition is not evaluated here. Historically, most hydrologic models have been designed with a fixed model structure while the input data and model parameters may vary from watershed to watershed. These fixed model structures were typically chosen because they (1) adequately represented the hydrologic response of one or more watersheds, (2) generally respected the physics of water flow and storage, (3) were consistent with conceptual models of the water cycle garnered from field investigation, and/or (4) convention (Addor & Melsen, 2019). In many cases, trial and error was used to determine final model structure (e.g., Perrin et al., 2003). However, there are many such model structures that can generally meet these criteria, and as such, there exist dozens of fixed structure models which used different algorithms to represent various components of the water cycle. The fixed model structure concept