Search

Macro‐scale distributed hydrologic modeling advanced hydro‐system understandings and scientized relevant human activities. However, it faces challenges of hydrometeorological heterogeneities, parametric interactions, data uncertainty, computational expensiveness, and other complexities, especially over cold regions with intense climatic changes. As an effort to address them, a multifactorial principal‐monotonicity inference (MFPMI) method is developed through integrating, extending, or improving climate classification, hydrologic modeling, and sensitivity analysis. MFPMI is applied to an undammed macro‐scale high‐latitude cold‐region watershed, Athabasca River Basin (ARB) in Canada. MFPMI mitigates the underestimation of climatic impacts on streamflows in process‐based models, hydrologic classification, large‐scale hydroclimatic data deconstruction, parametric‐interaction neglection, and climatic homogenization; its superiority is particularly evident for highly heterogeneous climates. Dominant climatic impacts on ARB streamflows of various regimes increase from tributaries to the mainstem and decrease from up‐ through down‐ to mid‐stream catchments, possibly due to the offset effects of non‐climatic factors (e.g., vegetation and soil). The impacts also decline with streamflow magnitudes, and vary with seasons, spatial scales and lead months rather than temporal resolutions. Streamflow magnitudes, catchments and metrics differ compositions of the climate conditions explaining cross‐scale uppermost discharge variations. In spite of this, streamflow increases with temperature and precipitation, and headwater climates play part of dominant roles in forcing discharges throughout ARB. Accuracy metrics differentiate parameters and accordingly structures of MFPMI models, and hydroclimatic data uncertainty is high for high flows, fine temporal scales or low climatic impacts, increasing uncertainty in hydrologic simulations and climatic‐impact estimates. This study helps advance modeling and understandings of macro‐scale cold‐region hydrology. Key Points: Develop a method for macro‐scale hydrologic modeling under hydrometeorological heterogeneities, parametric interactions, data uncertainty, etcAvoid underestimates of dominant cold‐region climatic impacts in climate homogenization, process‐based modeling & hydrologic classificationReveal impacts' variabilities, metrics & data uncertainties, and climate conditions explaining cross‐scale uppermost discharge variations [ABSTRACT FROM AUTHOR]

A stochastic rainwater harvesting system modeling (StRaHaS) method is developed to enhance both accurateness and applicability of hydrologic modeling in guiding extensive applications of individual rainwater harvesting (RWH) systems (especially over large data‐scarce regions). The reliability of a RWH system is characterized as the fraction of time water demands being satisfied by a rainwater storage unit (RWSU). The variations of water balances, the post‐rainfall RWSU full‐storage probability, and the system reliability with random rainfall features, variable water demands, and the other RWH system characteristics are derived as analytical, accurate, and easily applied models through stochastic integration. StRaHaS is applied to three domestic RWH systems in Toronto, Canada. Due to high accurateness and low complexity in modeling, designing, assessing, and analyzing the RWH systems, StRaHaS outperforms existing methods (e.g., inaccurate water‐balance estimation, complicated continuous simulation, and simplified stochastic simulation). Based on StRaHaS, we quantitatively expound the increases in the reliability of the systems with higher rainwater supplies (corresponding to higher rainfall depths, and lower rainfall losses and first‐flush depths), lower water demands (in shorter wet and dry periods), and higher RWSU capacities. Long dry periods are lengthened by climate change and play dominant roles in low, spatially heterogeneous reliability of the systems. Synchronic changes and extremely high values of rainfall features do not significantly affect the reliability. Overall, StRaHaS is a promising method in modeling RWH systems, revealing RWH mechanisms, scientizing RWH applications, and facilitating the Sustainable Development Goals for addressing global water issues. Key Points: Accurate & simple models to simulate, design, assess, and analyze rainwater harvesting systems under random rainfalls and variable water demandsInsignificant changes of water‐supply reliability of the systems with removing synchronic changes or high extremes of rainfall featuresLong dry periods are lengthened by climate change, and dominate low, spatially heterogeneous reliability of the systems in Toronto, Canada [ABSTRACT FROM AUTHOR]

Wave power is essential to carbon neutrality and other energy strategies. Its estimation and analyses at macroscales are enabled by extensive satellite altimetric data, but several challenges or questions exist. Thus, this study develops an advanced macroscale altimetry-based wave-power estimation and analysis (WPEA) method through integrating regionalized ensemble estimates of wave heights, wind speeds and wave periods, models of wave energy flux, indices of flux variabilities and wave power potentials, and statistical analyses of associations between wave power and selected externalities (e.g., offshore distances, water depths, ice-free days, and wind speeds). The method is applied to all Canadian waters, which reveals a series of findings. For instance, Pacific Ocean, Atlantic Ocean, St. Lawrence, and Davis Strait in Canada are suitable for harvesting wave energy, because of considerable fluxes and low variabilities of wave energy at various timescales. Annual total wave power (approximately 2.04 PWh) largely exceeds national electricity consumption (249 TWh in 2021). The regionalized ensemble estimation helps WPEA enhance wave-power estimation accuracy under significant effects of data uncertainty. At the national scale, high wave power tends to be produced over remote, deep, windy, eastern waters and, without the consideration of technological applicability, over ice-free, southern waters. Potential associations of wave power with the externalities present seasonalities and heterogeneities under intensification effects of power-externality covariations and tech-applicability restrictions. Besides advancing wave-power estimation, analyses and modeling at macroscales (including the globe), this study provides data and scientific supports for studies and practices of local, regional and national wave energy development over Canada. • Develop a macroscale altimetry-based wave-power estimation & analysis method. • Total wave power (2.04 PWh) largely exceeds electricity consumption of Canada. • High wave power tends to be produced over remote, deep, windy, eastern waters. • Associations of wave power with externalities are seasonal & heterogeneous. • Provide data & scientific supports for wave energy development over Canada. [ABSTRACT FROM AUTHOR]

As a maritime nation, Canada is the home to high-quality offshore wind resources. The assessment of Canada's offshore wind resources has a strategic significance for realizing its low-carbon future. This study focused on high-resolution and high-accuracy estimates of offshore wind resources over Canada using satellite-derived wind maps, e.g., CCMP (Cross-Calibrated Multi-Platform) images from 1988 to 2017. The CCMP derived wind speed and density were bias-corrected by ocean surface wind measurements collected from 63 stations located along the seas/lakes in Canada from 1987 to 2018. A series of consistent offshore wind resource maps were produced and analyzed. The results generated from CCMP were then compared with previous assessment (i.e., Environment and Climate Change Canada (ECCC)'s Wind Atlas) and proved to perform better in simulating wind speeds. We estimated the technically available offshore wind area, gross power capacity and gross potential over Canada. For example, a gross potential production of 20.348, 20.441, and 20.376 PWh/year were calculated using three commercial offshore wind turbines, i.e. GE 3.6s, REpower 5 M, and V164–8.0 over the calculated total gross offshore area of 1.053 million km2. It far exceeds the Canada's electricity consumption (i.e., 0.641 PWh) in 2018. In addition, most of the potential power productions are distributed in the Canadian Atlantic and Pacific Oceans, Hudson Bay and St Lawrence, with the mean wind speeds increasing from 1988 to 2017. This study can help to assess offshore wind resources and provide decision-makers with some scientific evidences for developing offshore wind in Canada. • Canada's offshore wind energy is assessed using buoy-measurement-based bias-corrected CCMP. • The assessment provides wind speed, power density, and potential at high resolution & accuracy. • Offshore wind power production far exceeds Canada's power consumption (0.64 PWh in 2018). • The production is the highest in Atlantic & Pacific Oceans, Hudson Bay, and St Lawrence. • We build one basis for Canadian energy systems planning (e.g., renewables for carbon neutrality). [ABSTRACT FROM AUTHOR]

• Innovate a BaPMI method for macro-scale distributed hydrologic modeling. • Apply it to a typical large cold-region watershed, Athabasca River Basin in Canada. • Verify feasibility & advantages in characterizing macro-scale cold-region hydrology. • Quantify parametric & hyperparametric sensitivities for facilitating its applications. • Reveal variabilities of climate-driven cross-scale uppermost hydro-variational effects. At macroscales (e.g., large river basins, nations, continents, or the globe), hydro-hazard mitigation under climate change requires accurate, applicable hydrologic models, especially over cold regions of complicated hydroclimatic relations. As an effort to address such a challenge, we innovate a macroscale distributed hydro-modeling method, Bayesian principal-monotonicity inference (BaPMI), based on climate classification, representative-grids selection, statistical hydrology, surrogates-based Bayesian optimization, and variance-based sensitivity analyses. The method is applied to a typical large cold-region watershed, i.e., Athabasca River Basin in Canada, and reveals a series of findings such as the following representatives. BaPMI shows promising skills in quantitatively characterizing macroscale cold-region hydrology under overall impacts of heterogeneous climates. Bayesian optimization of which hyperparameters are insensitive decreases hydro-modeling computational time by up to 96.3% and, together with climate classification, largely enhances BaPMI applicability while ensuring modeling accuracies. Individual BaPMI hydro-model parameters can explain over ¾ of the variation of hydro-modeling accuracies and, due to their interaction, joint calibration is required for avoiding underestimation of the impacts. Climatic conditions dominating cross-scale uppermost hydrologic variations consist of night temperature (40%), day temperature (38%), and precipitation (22%) for all catchments; all river flows along the mainstem are associated with upstream climatic events such as glacier melts. Climatic impacts decline from upstream to downstream and from summer, spring, winter to autumn; this may relate to tempo-spatial heterogeneity of soil, vegetation, geology, human interference and other non-climatic factors. Without this study, macroscale distributed hydrologic modeling would lack one advanced method (i.e., BaPMI) that can immunize against effects of data uncertainty, subjective judgement and climatic collinearity, adapt to complicated hydroclimatic relations and diverse hydro-variable(s) distributions, reveal cross-scale uppermost hydrologic variations and, through inexpensive computations, enhance hydro-modeling accuracies to avoid climatic-impact underestimation. The findings of this study could facilitate BaPMI applications and advance macroscale (cold-region) hydrology. [ABSTRACT FROM AUTHOR]

• Advance macroscale distributed climate-informed hydrologic projections. • Reveal changes in magnitudes & variabilities of cold-region river discharges. • Decompose individual & interactive variations of the changes with various factors. A macroscale composite principal-monotonicity distributed hydro-projection system is developed through hybridizing advanced statistical hydroclimatic systems analysis methods of verified superiorities. The system can enhance accuracies and feasibilities of climate-informed hydrologic projections, quantitatively reveal climate-induced hydro-magnitude and -variability changes, and decompose variations of the changes with factors such as time, space, scales and greenhouse gas (GHG) emissions. The system of high adaptability for worldwide watersheds is applied to a representative large cold-region watershed, Athabasca River Basin in Canada. Incremental ratios of river discharges with climate change are projected to enlarge at fine timescales, e.g., from 3.4% (octo-decadal) to 7.0% (monthly). According to mixed-level multifactorial analyses, variations of the ratios are dominated by catchments (59.2%) and GHG emissions (19.6%) at annual and longer timescales, and the dominance increases with timescales. At intra-annual timescales, the dominator becomes interactive tempo-spatial variations (62.6%) of the ratios. GHG emissions and their interaction with catchments pose significant or even leading effects on trends of discharge changes. Ratios of changes (i.e., decreases mostly) in tempo-spatial variabilities of discharges are generally the most significant for interannual variabilities, in warm seasons, at downstream catchments, under high GHG emissions, and by the late 21st century. Their variation is dominated by individual factorial effects (78.0%), e.g., catchments (50.3%) and GHG emissions (16.2%); the dominance declines from interannual (89.0%), temporal (79.4%), intra-annual (75.6%) to spatial (67.9%) variabilities. Interactions of the factors in explaining variations of spatial and interannual variabilities are non-negligible in warm and cool seasons, respectively. These findings can help advance macroscale distributed climate-informed hydrologic projections and, accordingly, scientize assessment, adaptation and mitigation of hydrologic hazards over climate-sensitive cold regions. [ABSTRACT FROM AUTHOR]