6/22/2023 0 Comments Hydrological processes runoff![]() ![]() Nonlinear dynamics in hydrological systems poses substantial challenges in catchment models (Beven 1995). This issue of equifinality plagues many contemporary catchment hydrology model applications (Klemeš 1986). ![]() 2017) however, if accuracy and robustness of model predictions are only evaluated based on hydrological response at catchment outlets, many combinations of land management effects could produce similar outcomes (Moore and Grayson 1991). Of course, many distributed models can incorporate complex patterns of land use (e.g., Heuvelmans et al. As such, it is difficult, if not impossible, to quantitatively assess the effects of spatially distributed land management practices or heterogeneous inputs of precipitation on runoff behaviour. While this practice may seem reasonable and is certainly appropriate for applications such as downstream flood assessment at a specific site, it does not ensure that internal processes within the catchment are accurately captured (Sidle 2006). One of the limitations of most catchment or river basin hydrology models is that they are tested based on runoff responses at their outlets. Clearly, for many applications, there is a need to develop ‘smarter’ hydrological models that are compatible with the available data, characterize processes across multiple scales, capture relevant water sources, are not excessively complex, and, importantly, address the relevant questions at hand. Furthermore, in many remote and developing regions of the world where data are sparse, huge assumptions are required to parameterize the spatial complexity of drainage basins, thus requiring extensive calibration to produce questionable spatially explicit results (Andersen et al. This begs the need to ensure hydrological models capture reasonably accurate processes at appropriate scales (Blöschl and Sivapalan 1995 Sidle 2006). As such, a critical observer may ask whether we are creating more complex hydrological models that focus on accuracy of spatially explicit model outputs for the wrong reasons. more complex), does not necessarily equate with an improved representation of hydrological processes, including how water and material transport (e.g., nutrients, pollutants, sediments) processes change with increasing spatial scale (Cammeraat 2002 Lane et al. ![]() Nevertheless, just because models now have more discrete spatial and temporal characterization of catchment attributes (i.e. 1991 Refsgaard and Knudsen 1996 Thanapakpawin et al. ![]() Precipitation–runoff processes have been simulated at scales ranging from hillslopes to catchments to large river basins to continents with varying degrees of specificity of flow paths that generally decrease at broader scales (e.g., Quinn et al. Hydrological models have evolved with greater complexity due to increased computational power and spatial–temporal data availability from satellites. 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. 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. Also important is consideration of the scale dependency of hydrological parameters to avoid scale mismatch between measured and modelled parameters. Incorporating concepts of hydrological connectivity into flexible model structures is a promising approach for improving flow path representation. 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. This discussion focuses mostly on conceptual and physical/process-based models where understanding the internal catchment processes and hydrologic pathways is important. 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. Hydrological models have proliferated in the past several decades prompting debates on the virtues and shortcomings of various modelling approaches. ![]()
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