Streamflow Modelling: A Primer on Applications, Approaches and Challenges

Abstract

This article examines the current practice of streamflow modelling, a field under development for over a century. A sample of the wide range of assessment and planning applications of streamflow models is presented. The diversity in the use of these models is mirrored in the diversity of model complexity, and modelling approaches ranging from empirical to physically based and from lumped to fully distributed are described with examples. Predictions derived from hydrological models are subject to many sources of error; these are discussed along with methods for error minimization or anticipation. Model error is generally quantified using an ensemble of forecasts meant to sample the range of predictive uncertainty. This ensemble can be used to generate reliable probabilistic forecasts of hydrological quantities if all sources of error are accounted for. To date, applications of ensemble methods in streamflow forecasting have typically focused on only one or two error sources. A challenge will be to develop ensemble streamflow forecasts that sample a wider range of predictive uncertainty.

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

“…In many cases, the complicated interaction in the hydroclimatic system cannot be characterized by linear models. The Artificial Intelligence (AI) (or machine learning, soft computing) models, including Artificial Neural Network (ANN), Fuzzy Logic (FL), Support Vector Regression (SVR) or Support Vector Machine (SVM), Genetic Algorithm (GA) or Genetic Programming (GP), and wavelet transformation, can be used to model complex interactions of hydroclimatic variables for a variety of applications (Bourdin et al, 2012;Fahimi et al, 2016;Nourani et al, 2014;Rhee & Im, 2017;Wang, Chau, et al, 2009;Yaseen et al, 2015). Several AI models, including the ANN (Mishra & Desai, 2006;Mishra et al, 2007;Morid et al, 2007), SVM (Ganguli & Reddy, 2014), and wavelet transformation (Maity et al, 2016;Özger et al, 2011), have been used to model complicated and nonlinear interactions between drought indicators and influencing factors for drought prediction.…”

Impacts of DEM resolution, source, and resampling technique on SWAT-simulated streamflow

“…Digital Elevation Models (DEMs) are one of the most important spatial input data sets in hydrological modelling (Bourdin, Fleming, & Stull, 2012;Wu, Li, & Huang, 2008). DEMs constitute a key spatial layer for estimating a watershed's channel networks, slope gradients, flow direction and accumulations, and several other controls of the water movements in landscapes (Moore, Grayson, & Ladson, 1991;Wechsler, 2007).…”

Climate and water availability indicators in Canada: Challenges and a way forward. Part III – Future scenarios

“…Results showed that statistical downscaling was better able to assess climate change effects on spring peak and summer low flows because of improved prediction of precipitation. Bourdin et al (2012) indicated that statistical downscaling approaches have been more popular because of Table 3). Adapted and updated from Bush et al (2014).…”

“…A hydrologic model inter-comparison might yield some useful insights, particularly considering the effects of model structural uncertainty which have led to differences in the representation of evaporation and snowmelt processes (Jiménez Cisneros et al 2014). Scenarios should include both climate and land changes (Bourdin et al 2012).…”

“…Concurrent land cover/use change scenarios would also be relevant for scenario-based climate change impact studies, but these are rarely included (Bourdin et al 2012). In the case study of Trepanier watershed, Harma et al (2012) did include a scenario of forest cover reductions due to potential MPB infestation.…”