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Open AccessArticle

Water Quality and Flow Management Scenarios in the Qu’Appelle River–Reservoir System Using Loosely Coupled WASP and CE-QUAL-W2 Models

by Julie Terry and Karl-Erich Lindenschmidt

Water 2023, 15(11), 2005; https://doi.org/10.3390/w15112005 - 25 May 2023

Cited by 12 | Viewed by 2804

Abstract

The water bodies of the Saskatchewan Prairies suffer multiple stressors, and demand for water is expected to increase. Water quality models can help evaluate water management strategies and risks such as climate change. This study assesses the impact of interbasin water transfers on the water quality of a strategic, eutrophic prairie reservoir that receives poor-quality watershed run-off. A one-dimensional WASP model was used to estimate nutrient transformations in the transfers along a 97 km river channel. The WASP model was then loosely coupled to a two-dimensional CE-QUAL-W2 model of the downstream receiving reservoir. Output from the WASP model was manually transformed into boundary conditions for the CE-QUAL-W2 reservoir model. This method improves on an earlier attempt to estimate nutrient transformations in the transfers using linear regression. Results from the loosely coupled models suggest Buffalo Pound Lake would respond well to the interbasin transfers. The number of threshold exceedances decreased for all modeled water quality variables. Nutrient concentrations were most influenced in the open water season following spring freshet. Any additional reduction of threshold exceedances during winter was minimal in comparison. These results are interesting from a management perspective as increased transfers under winter operations risk ice damage to the river channel. Full article

(This article belongs to the Special Issue Research on Nutrient Dynamics in Surface Water Using Water Quality Models and State-of-the-Art Monitoring)

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15 pages, 2493 KB

Open AccessArticle

A Machine Learning Approach for Prediction of the Quantity of Mine Waste Rock Drainage in Areas with Spring Freshet

by Can Zhang, Liang Ma and Wenying Liu

Minerals 2023, 13(3), 376; https://doi.org/10.3390/min13030376 - 8 Mar 2023

Cited by 6 | Viewed by 2757

Abstract

A new machine learning approach was developed to predict the quantity of mine waste rock drainage using weather data as the inputs. The novelty of the approach is that it includes spring freshet (melting of snow/ice in spring) as an input to the drainage flow rate model. Specifically, the machine learning approach integrates the decision tree algorithm to classify the occurrence or absence of spring freshet and a long short-term memory (LSTM) algorithm to predict the flow rate of mine waste rock drainage. The two algorithms are integrated by using the classification result of spring freshet as an input to the flow rate model. The machine learning approach developed was applied to predict the drainage flow rate at a case study mine in Canada. The model developed was trained with the local weather data as the inputs and the historical monitoring data of drainage flow rate as the target (output). The results show that the decision tree algorithm is able to classify the occurrence or absence of spring freshet with an accuracy of 91%. The inclusion of spring freshet as an input to the flow rate model significantly improves the performance of the flow rate model. The sensitivity tests show that changes in temperature and atmospheric precipitation influence the drainage flow rate. Full article

(This article belongs to the Special Issue Mobility of Potentially Toxic Elements: Environmental Hazards)

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