The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub‐daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state‐of‐the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model‐data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge‐guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision‐relevant predictions of riverine water quality.

Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate model, misspecification, or the presence of random fluctuations in the solar irradiance series can result in this approach underperforming. This paper proposes a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance. For each unseen test pattern, HetDS chooses the most suitable forecasting model based on a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation was performed with four data sets of hourly solar irradiance measurements in Brazil. The proposed model attained an overall accuracy that is superior to the single models in terms of five well-known error metrics.

Stream temperature ($T_s$) is an important water quality parameter that affects ecosystem health and human water use for beneficial purposes. Accurate $T_s$ predictions at different spatial and temporal scales can inform water management decisions that account for the effects of changing climate and extreme events. In particular, widespread predictions of $T_s$ in unmonitored stream reaches can enable decision makers to be responsive to changes caused by unforeseen disturbances. In this study, we demonstrate the use of classical machine learning (ML) models, support vector regression and gradient boosted trees (XGBoost), for monthly $T_s$ predictions in 78 pristine and human-impacted catchments of the Mid-Atlantic and Pacific Northwest hydrologic regions spanning different geologies, climate, and land use. The ML models were trained using long-term monitoring data from 1980–2020 for three scenarios: (1) temporal predictions at a single site, (2) temporal predictions for multiple sites within a region, and (3) spatiotemporal predictions in unmonitored basins (PUB). In the first two scenarios, the ML models predicted $T_s$ with median root mean squared errors (RMSE) of 0.69–0.84 °C and 0.92–1.02 °C across different model types for the temporal predictions at single and multiple sites respectively. For the PUB scenario, we used a bootstrap aggregation approach using models trained with different subsets of data, for which an ensemble XGBoost implementation outperformed all other modeling configurations (median RMSE 0.62 °C).The ML models improved median monthly $T_s$ estimates compared to baseline statistical multi-linear regression models by 15–48% depending on the site and scenario. Air temperature was found to be the primary driver of monthly $T_s$ for all sites, with secondary influence of month of the year (seasonality) and solar radiation, while discharge was a significant predictor at only 10 sites. The predictive performance of the ML models was robust to configuration changes in model setup and inputs, but was influenced by the distance to the nearest dam with RMSE <1 °C at sites situated greater than 16 and 44 km from a dam for the temporal single site and regional scenarios, and over 1.4 km from a dam for the PUB scenario. Our results show that classical ML models with solely meteorological inputs can be used for spatial and temporal predictions of monthly $T_s$ in pristine and managed basins with reasonable (<1 °C) accuracy for most locations.

The effect of climate change on the hydro-climatology, particularly the streamflow, of six major Canadian rivers (Mackenzie, Yukon, Columbia, Fraser, Nelson, and St. Lawrence) is investigated by analyzing results from the historical and future simulations (RCP 4.5 and 8.5 scenarios) performed with the Canadian regional climate model (CanRCM4). Streamflow is obtained by routing runoff using river networks at 0.5° resolution. Of these six rivers, the Nelson and St. Lawrence are the most regulated. As a result, the streamflow at the mouth of these rivers shows very little seasonality. Additionally, the Great Lakes significantly dampen the seasonality of streamflow for the St. Lawrence River. Mean annual precipitation (P), evaporation (E), runoff (R), and temperature increase for all six river basins in both future scenarios considered here, and the increases are higher for the more fossil-fuel-intensive RCP 8.5 scenario. The only exception is the Nelson River basin, for which the simulated runoff increases are extremely small. The hydrological response of these rivers to climate warming is characterized by their existing climate states. The northerly Mackenzie and Yukon River basins show a decrease in the evaporation ratio ($E/P$ ) and an increase in the runoff ratio ($R/P$ ) since the increase in precipitation is more than enough to offset the increase in evaporation associated with increasing temperature. For the southerly Fraser and Columbia River basins, the $E/P$ ratio increases despite an increase in precipitation, and the $R/P$ ratio decreases due to an already milder climate in the northwestern Pacific region. The seasonality of simulated monthly streamflow is also more affected for the southerly Fraser and Columbia rivers than for the northerly Mackenzie and Yukon rivers as snow amounts decrease and snowmelt occurs earlier. The streamflow seasonality for the Mackenzie and Yukon rivers is still dominated by snowmelt at the end of the century, even in the RCP 8.5 scenario. The simulated streamflow regime for the Fraser and Columbia rivers shifts from a snow-dominated to a hybrid or rainfall-dominated regime towards the end of this century in the RCP 8.5 scenario. While we expect the climate change signal from CanRCM4 to be higher than that from other climate models, owing to the higher-than-average climate sensitivity of its parent global climate model, the results presented here provide a consistent overview of hydrological changes across six major Canadian river basins in response to a warmer climate.

The worldwide appeal has increased for the development of new technologies that allow the use of green energy. In this category, photovoltaic energy (PV) stands out, especially with regard to the presentation of forecasting methods of solar irradiance or solar power from photovoltaic generators. The development of battery energy storage systems (BESSs) has been investigated to overcome difficulties in electric grid operation, such as using energy in the peaks of load or economic dispatch. These technologies are often applied in the sense that solar irradiance is used to charge the battery. We present a review of solar forecasting methods used together with a PV-BESS. Despite the hundreds of papers investigating solar irradiation forecasting, only a few present discussions on its use on the PV-BESS set. Therefore, we evaluated 49 papers from scientific databases published over the last six years. We performed a quantitative analysis and reported important aspects found in the papers, such as the error metrics addressed, granularity, and where the data are obtained from. We also describe applications of the BESS, present a critical analysis of the current perspectives, and point out promising future research directions on forecasting approaches in conjunction with PV-BESS.

Large-scale hydrologic models (LHMs) are increasingly relied upon for assessing climate-driven hydrologic changes from watershed to global scales. However, their ability to provide robust projections for a range of hydrologic variables remains unclear. Here, we evaluate the historical performance and future projections from the Community Water Model (CWatM) LHM against the Variable Infiltration Capacity (VIC) watershed hydrologic model for the Liard River basin (drainage area of ∼ 275 000 km²) in subarctic Canada. The model setups have key differences in terms of model configuration (CWatM is set up with two subbasins, and VIC is set up with 28 subbasins) and model structure (e.g., snowmelt and frozen soil representation). We drive both models with an ensemble of eight global climate models from the Coupled Model Intercomparison Project Phase 6, downscaled and bias-corrected with a multivariate method. We analyze a range of hydrologic projections at 1.5 to 4.0 °C global warming levels (GWLs) above the preindustrial period. The historical performance benchmarking shows reasonable goodness-of-fit metrics for both models, with a slightly better performance for VIC. Projected hydrologic responses from CWatM are generally consistent with VIC in terms of annual water balance, as well as monthly snow water equivalent and flow changes, suggesting the robustness of the projections. Both models project coherent hydrologic changes, including progressively higher annual evapotranspiration; increased annual, winter, spring, and maximum flows; increased frequency of extreme flow; and earlier timing of maximum flow, with higher GWLs. However, the magnitudes of maximum flow and late-summer flow diverge between the two models, which can be explained by structural uncertainties associated with the representation of frozen soil and groundwater processes. Thus, our study provides insights into the robustness of hydrologic projections from an LHM and offers a basis for model improvements.