Watershed Modeling and its Applications: A State-of-the-Art Review

Abstract

Advances in the understanding of physical, chemical, and biological processes influencing water quality, coupled with improvements in the collection and analysis of hydrologic data, provide opportunities for significant innovations in the manner and level with which watershed-scale processes may be explored and modeled. This paper provides a review of current trends in watershed modeling, including use of stochastic-based methods, distributed versus lumped parameter techniques, influence of data resolution and scalar issues, and the utilization of artificial intelligence (AI) as part of a data-driven approach to assist in watershed modeling efforts. Important findings and observed trends from this work include (i) use of AI techniques artificial neural networks (ANN), fuzzy logic (FL), and genetic algorithms (GA) to improve upon or replace traditional physically-based techniques which tend to be computationally expensive; (ii) limitations in scale-up of hydrological processes for watershed modeling; and (iii) the impacts of data resolution on watershed modeling capabilities. In addition, detailed discussions of individual watershed models and modeling systems with their features, limitations, and example applications are presented to demonstrate the wide variety of systems currently available for watershed management at multiple scales. A summary of these discussions is presented in tabular format for use by water resource managers and decision makers as a screening tool for selecting a watershed model for a specific purpose.

Sensitivity analysis and implications for surface processes from a hydrological modelling approach in the Gunt catchment, high Pamir Mountains

“…Daniel (2011) gives a good comparison of different frequently used models but the number of different models is simply too large to be covered entirely. The integration of raster data sets has been implemented in different models such as the MIKE SHE (Cooper et al, 2006;Liu et al, 2012) or CREST (Khan et al, 2011) models, and several more examples of raster data input are available (Merritt et al, 2006;Stahl et al, 2008;Wood et al, 2004).…”

Scale effect challenges in urban hydrology highlighted with a distributed hydrological model

“…A notable illustration is the ongoing urbanization process that changes land cover and strongly influences the hydrological behaviour of urban catchments. Urban hydrological models were developed over the years and used to simulate the portion of the water cycle in urban environments (Refsgaard and Knudsen, 1996; Tech University of Darmstadt and Ostrowski, 2002;Salvadore et al, 2015;Hromadka, 1987;Daniel et al, 2011;Elliott and Trowsdale, 2007;Sarma et al, 1973;Blöschl and Sivapalan, 1995). They can be classified according to either the nature of the employed algorithms (empirical, conceptual or physically based; Salvadore et al, 2015), or their spatial resolution and how they represent the complexity of urban hydrology proPublished by Copernicus Publications on behalf of the European Geosciences Union.…”

Dinâmica das áreas de várzea do município de Belém/PA e a influência da precipitação pluviométrica na formação de pontos alagamentos / Dynamics of lowland areas in the city of Belém/PA and the influence of rainfall in the flooding points formation

“…Os elementos comumente analisados são: os morfométricos (BARBOSA e FURRIER, 2012); e os hidrológicos (DANIEL et al, 2011). Dentre os componentes do ciclo hidrológico a precipitação pluviométrica destaca-se em espaços urbanizados por ser condicionante de vários aspectos de risco ambiental, dentre eles os efeitos das inundações.…”