Strategies for smarter catchment hydrology models: incorporating scaling and better process representation

Abstract

Hydrological models have proliferated in the past several decades prompting debates on the virtues and shortcomings of various modelling approaches. Rather than critiquing individual models or modelling approaches, the objective here is to address the critical issues of scaling and hydrological process representation in various types of models with suggestions for improving these attributes in a parsimonious manner that captures and explains their functionality as simply as possible. This discussion focuses mostly on conceptual and physical/process-based models where understanding the internal catchment processes and hydrologic pathways is important. Such hydrological models can be improved by using data from advanced remote sensing (both spatial and temporal) and derivatives, applications of machine learning, flexible structures, and informing models through nested catchment studies in which internal catchment processes are elucidated. Incorporating concepts of hydrological connectivity into flexible model structures is a promising approach for improving flow path representation. Also important is consideration of the scale dependency of hydrological parameters to avoid scale mismatch between measured and modelled parameters. Examples are presented from remote high-elevation regions where water sources and pathways differ from temperate and tropical environments where more attention has been focused. The challenge of incorporating spatially and temporally variable water inputs, hydrologically pathways, climate, and land use into hydrological models requires modellers to collaborate with catchment hydrologists to include important processes at relevant scales—i.e. develop smarter hydrological models.

Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment

“…The model calibration itself consisted of building the model (which means underground geometries and their associated parameters) only from observations. Building a model from observations was done to enhance our ability to understand the physical processes from hydrological modeling (Sidle, 2006(Sidle, , 2021. However, we did not have spatial observations for each pixel.…”

Comparison of artificial neural networks and reservoir models for simulating karst spring discharge on five test sites in the Alpine and Mediterranean regions

“…• Internal/external system dynamics: Karst systems are inherently complex media. Internal dynamics are not necessarily captured in hydrological models (Sidle, 2006(Sidle, , 2021Hartmann et al, 2017) and can be related to numerous processes in karst media, e.g. the saturation state of the system, surface water exchanges, temporary storage of water, incoming or outgoing flows from/to another aquifer, change of physical properties beyond a certain level, or karst features such as poljes or sinkholes.…”

Comparing model complexity for glacio-hydrological simulation in the data-scarce Peruvian Andes

“…Future studies should also continue with systematic data collection in the region, exploring improvements in understanding hydrological processes, as well as, the adaptation or design of particular model structures that represent the relevant catchment behavior in the glacierized tropical Andes. The use of remote sensing data seems promising to fill data gaps but modelers should be aware of the limitations and combine the advantages of comprehensive remote sensing assessments with ground-truth validation (Sidle, 2021;Taylor et al, 2021).…”