Artificial intelligence (AI) techniques have been successfully adopted in predictive modeling to capture the nonlinearity of natural systems. The high seasonal variability of rivers in cold weather regions poses a challenge to river flow forecasting, which tends to be complex and data demanding. This study proposes a novel technique to forecast flows that use a single-input sequential adaptive neuro-fuzzy inference system (ANFIS) along the Athabasca River in Alberta, Canada. After estimating the optimal lead time between four hydrometric stations, gauging data measured near the source were used to predict river flow near the mouth, over approximately 1000 km. The performance of this technique was compared to nonsequential and multi-input ANFISs, which use gauging data measured at each of the four hydrometric stations. The results show that a sequential ANFIS can accurately predict river flow (r² = 0.99, Nash–Sutcliffe = 0.98) with a longer lead time (6 days) by using a single input, compared to nonsequential and multi-input ANFIS (2 days). This method provides accurate predictions over large distances, allowing for flow forecasts over longer periods of time. Therefore, governmental agencies and community planners could utilize this technique to improve flood prevention and planning, operations, maintenance, and the administration of water resource systems.

Catchments located in cold weather regions are highly influenced by the natural seasonality that dictates all hydrological processes. This represents a challenge in the development of river flow forecasting models, which often require complex software that use multiple explanatory variables and a large amount of data to forecast such seasonality. The Athabasca River Basin (ARB) in Alberta, Canada, receives no or very little rainfall and snowmelt during the winter and an abundant rainfall–runoff and snowmelt during the spring/summer. Using the ARB as a case study, this paper proposes a novel simplistic method for short-term (i.e., 6 days) river flow forecasting in cold regions and compares existing hydrological modelling techniques to demonstrate that it is possible to achieve a good level of accuracy using simple modelling. In particular, the performance of a regression model (RM), base difference model (BDM), and the newly developed flow difference model (FDM) were evaluated and compared. The results showed that the FDM could accurately forecast river flow (E_NS = 0.95) using limited data inputs and calibration parameters. Moreover, the newly proposed FDM had similar performance to artificial intelligence (AI) techniques, demonstrating the capability of simplistic methods to forecast river flow while bypassing the fundamental processes that govern the natural annual river cycle.

The Athabasca River flows through the Lower Athabasca Region (LAR) in Alberta, Canada, which is characterized by variable inter-annual weather, long winters and short summers. LAR is important for the extraction of energy resources and industrial activities that lead to environmental concerns, including river pollution and exploitation. This study attempts to forecast the Athabasca River at Fort McMurray and understand the suitability of HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) in cold weather regions, characterized by poorly gauged streams. Daily temperature and precipitation records (1971–2014) were employed in two calibration–validation schemes: (1) a temporally dependent partition (1971–2000 for calibration; 2001–2014 for validation) and (2) a temporally independent partition (alternating years assigned to calibration and validation). The temporally independent approach achieved superior performance, with a Nash–Sutcliffe efficiency of 0.88, outperforming previously developed regional models. HEC-HMS successfully reproduced hydrologic dynamics and peak discharge events under conditions of sparse hydroclimatic data and limited computational inputs, underscoring its robustness for operational forecasting in data-scarce, cold-climate catchments. However, long-term projections may be subject to uncertainty due to the exclusion of anticipated changes in land use and climate forcing. These results substantiate the applicability of HEC-HMS as a cost-effective and reliable tool for hydrological modeling and flow forecasting in support of water resource management, particularly in regions subject to industrial pressures and associated environmental impacts.