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  • Source Publication: Weather and Climate Extremes, 33, 100332, doi:10.1016/j.wace.2021.100332 Authors: Huang, W.K., A.H. Monahan and F.W. Zwiers Publication Date: Aug 2021

    Simultaneous concurrence of extreme values across multiple climate variables can result in large societal and environmental impacts. Therefore, there is growing interest in understanding these concurrent extremes. In many applications, not only the frequency but also the magnitude of concurrent extremes are of interest. One way to approach this problem is to study the distribution of one climate variable given that another is extreme. In this work we develop a statistical framework for estimating bivariate concurrent extremes via a conditional approach, where univariate extreme value modeling is combined with dependence modeling of the conditional tail distribution using techniques from quantile regression and extreme value analysis to quantify concurrent extremes. We focus on the distribution of daily wind speed conditioned on daily precipitation taking its seasonal maximum. The Canadian Regional Climate Model large ensemble is used to assess the performance of the proposed framework both via a simulation study with specified dependence structure and via an analysis of the climate model-simulated dependence structure.

  • Source Publication: Hydrological Processes, 35, 7, e14253, doi:10.1002/hyp.14253 Authors: Tsuruta, K. and M.A. Schnorbus Publication Date: Aug 2021

    The mountainous watersheds of western Canada are generally thought to be in a state of transition from snow-dominated to hybrid regimes. In stream networks that are regulated, the effects of this transition on streamflow can have compelling operational consequences. Seasonal magnitude changes may impact spill-risk management, while changes in the composition of summer runoff may increase its variability and reduce the forecasting capabilities of state variables like peak snow water equivalent. Though glacier loss can have a considerable impact on summer runoff, few studies explicitly model the ongoing glacier recession in conjunction with other primary hydrological processes. In this study, we incorporate glacier dynamics from a previous run of the Regional Glaciation Model into the University of British Columbia Watershed Model via the Raven modelling framework. We use this modelling system to explore potential changes under Representative Concentration Pathways 4.5 and 8.5 to the hydrology of the ∼20000km2 Mica Basin, a regulated watershed containing the headwaters of the Columbia River. Our results project statistically significant increases in spring flow in future eras, which may force lower reservoir drafting in late winter, creating potential for energy shortfalls in early spring. We project the coefficient of variation of summer runoff generally goes unchanged in future eras as does the summer runoff forecasting capability of April 1st SWE. Hence, despite modelled glacier loss and reduced snowmelt contribution, our study does not reject the null hypothesis that the predictability of the Mica Basin's summer runoff is unchanged in future eras. We explore these results in detail because they superficially appear to contrast the conventional conceptualization that reduced snowmelt negatively affects the predictive powers of snowpack and glacier loss increases the variability of runoff. We argue that our results' apparent discordance from convention displays the complexities inherent in isolating the effects of changes to a single water balance component when other components are also non-stationary and highlights the benefits of using modelling to more explicitly explore such implications.

  • Source Publication: Scientific Reports, 11, 13574, doi:10.1038/s41598-021-92920-7 Authors: Meshesha, T.W., J. Wang and N.D. Melaku Publication Date: Aug 2021

    Groundwater is a vital resource for human welfare. However, due to various factors, groundwater pollution is one of the main environmental concerns. Yet, it is challenging to simulate groundwater quality dynamics due to the insufficient representation of nutrient percolation processes in the soil and Water Assessment Tool model. The objectives of this study were extending the SWAT module to predict groundwater quality. The results proved a linear relationship between observed and calculated groundwater quality with coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS) values in the satisfied ranges. While the values of R2, NSE and PBIAS were 0.69, 0.65, and 2.68 during nitrate calibration, they were 0.85, 0.85 and 5.44, respectively during nitrate validation. Whereas the values of R2, NSE and PBIAS were 0.59, 0.37, and - 2.21 during total dissolved solid (TDS) calibration and they were 0.81, 0.80, 7.5 during the validation. The results showed that the nitrate and TDS concentrations in groundwater might change with varying surface water quality. This indicated the requirement for designing adaptive management scenarios. Hence, the extended SWAT model could be a powerful tool for future regional to global scale modelling of nutrient loads and effective surface and groundwater management.

  • Source Publication: Frontiers in Marine Science, 8, 596644, doi: doi:10.3389/fmars.2021.596644 Authors: Tai T.C., U.R. Sumaila and W.W.L. Cheung, 2021 Publication Date: Aug 2021

    Elevated atmospheric carbon dioxide (CO2) is causing global ocean changes and drives changes in organism physiology, life-history traits, and population dynamics of natural marine resources. However, our knowledge of the mechanisms and consequences of ocean acidification (OA) – in combination with other climatic drivers (i.e., warming, deoxygenation) – on organisms and downstream effects on marine fisheries is limited. Here, we explored how the direct effects of multiple changes in ocean conditions on organism aerobic performance scales up to spatial impacts on fisheries catch of 210 commercially exploited marine invertebrates, known to be susceptible to OA. Under the highest CO2 trajectory, we show that global fisheries catch potential declines by as much as 12% by the year 2100 relative to present, of which 3.4% was attributed to OA. Moreover, OA effects are exacerbated in regions with greater changes in pH (e.g., West Arctic basin), but are reduced in tropical areas where the effects of ocean warming and deoxygenation are more pronounced (e.g., Indo-Pacific). Our results enhance our knowledge on multi-stressor effects on marine resources and how they can be scaled from physiology to population dynamics. Furthermore, it underscores variability of responses to OA and identifies vulnerable regions and species.

  • Source Publication: Geophysical Research Letters, 48, 9, e2021GL092831, doi:10.1029/2021GL092831 Authors: Wang, J., C. Li, F. Zwiers, X. Zhang, G. Li, Z. Jiang, P. Zhai, Y. Sun, Z. Li and Q. Yue Publication Date: Aug 2021

    Field significance tests have been widely used to detect climate change. In most cases, a local test is used to identify significant changes at individual locations, which is then followed by a field significance test that considers the number of locations in a region with locally significant changes. The choice of local test can affect the result, potentially leading to conflicting assessments of the impact of climate change on a region. We demonstrate that when considering changes in the annual extremes of daily precipitation, the simple Mann-Kendall trend test is preferred as the local test over more complex likelihood ratio tests that compare the fits of stationary and nonstationary generalized extreme value distributions. This lesson allows us to report, with enhanced confidence, that the intensification of annual extremes of daily precipitation in China since 1961 became field significant much earlier than previously reported.

  • Authors: Schoeneberg, A.T. and M.A. Schnorbus Publication Date: Jun 2021

    This PCIC report demonstrates an analysis of projected changes in three streamflow metrics that are of interest to decision makers. Changes in low, mean and high daily streamflow in the 2020s, 2050s and 2080s were analyzed in three select watersheds using PCIC’s CMIP5 hydrologic model results. This report was enabled with financial support from FLNRORD/ENV that is gratefully acknowledged, and draws on hydrologic modelling that PCIC has recently undertaken with support from BC Hydro, its own core resources, and Compute Canada. The report is a potential starting point for dialogue between PCIC and water managers that would allow both parties to learn more about each other’s needs and capabilities.

  • Source Publication: Journal of Climate, 34, 9, 3441-3460, doi:10.1175/JCLI-D-19-1013.1. Authors: Li, C., F. Zwiers, X. Zhang, G. Li, Y. Sun and M. Wehner Publication Date: May 2021

    This study presents an analysis of daily temperature and precipitation extremes with return periods ranging from 2 to 50 years in phase 6 of the Coupled Model Intercomparison Project (CMIP6) multimodel ensemble of simulations. Judged by similarity with reanalyses, the new-generation models simulate the present-day temperature and precipitation extremes reasonably well. In line with previous CMIP simulations, the new simulations continue to project a large-scale picture of more frequent and more intense hot temperature extremes and precipitation extremes and vanishing cold extremes under continued global warming. Changes in temperature extremes outpace changes in global annual mean surface air temperature (GSAT) over most landmasses, while changes in precipitation extremes follow changes in GSAT globally at roughly the Clausius–Clapeyron rate of ~7% °C−1. Changes in temperature and precipitation extremes normalized with respect to GSAT do not depend strongly on the choice of forcing scenario or model climate sensitivity, and do not vary strongly over time, but with notable regional variations. Over the majority of land regions, the projected intensity increases and relative frequency increases tend to be larger for more extreme hot temperature and precipitation events than for weaker events. To obtain robust estimates of these changes at local scales, large initial-condition ensemble simulations are needed. Appropriate spatial pooling of data from neighboring grid cells within individual simulations can, to some extent, reduce the needed ensemble size.

  • Source Publication: Climatic Change 165, 14, doi: 10.1007/s10584-021-03037-9 Authors: Mahmoudi, M.H., M.R. Najafi, H. Singh and M. Schnorbus Publication Date: Mar 2021

    Increases in the intensity and frequency of hydroclimatic extremes associated with climate change can cause significant socioeconomic problems. Assessments of projected extremes using only a limited number of general circulation model (GCM) simulations can undermine the capacity to differentiate and communicate the contribution of internal climate variability (ICV) and external forcing and result in an underestimation of associated risks. In this study, we assess the impacts of climate change on extreme temperature and precipitation and quantify the contribution of internal variability over the Columbia, Fraser, Peace and Campbell River basins in northwestern North America (NWNA). Seven GCMs that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and a large ensemble of CanESM2 model simulations (50 members) are downscaled to 1/16° spatial resolution using Bias Correction Constructed Analogues with Quantile mapping reordering version 2 (BCCAQ2). Spatial and temporal changes of climate extreme indices, representing the frequency and intensity of extreme temperature and precipitation, are assessed over the historical (1981–2010) and future (2060–2089) periods under the Representative Concentration Pathway (RCP) 8.5. The influence of ICV on the estimated trends of extreme indices is characterised. Overall, both the frequency and intensity of extreme temperature and precipitation events are projected to increase in NWNA indicating more severe dry days and wet conditions in the future. High-elevation Rocky and the Coast Mountains are at larger risks of extreme precipitation, while the Columbia basin, which already faces drought issues, is expected to experience severe dry conditions. Internal climate variability plays a significant role, particularly in the trends of precipitation-related indices. The signal to internal noise ratio analyses suggest that higher elevations experience stronger forcing signals for precipitation-based indices compared to the other regions.

  • Source Publication: Weather and Climate Extremes,30, 100290, doi:10.1016/j.wace.2020.100290 Authors: Ben Alaya, M.A., F.W. Zwiers and X. Zhang Publication Date: Dec 2020

    We describe in this paper a semi-parametric bivariate extreme value approach for studying rare extreme precipitation events considered as events that result from a combination of extreme precipitable water (PW) in the atmospheric column above the location where the event occurred and extreme precipitation efficiency, described as the ratio between precipitation and PW. An application of this framework to historical 6-h precipitation accumulations simulated by the Canadian Regional Climate Model CanRCM4 shows that uncertainties and biases of very long-period return level estimates can be substantially reduced relative to the standard univariate approach that fits Generalized Extreme Value distributions to samples of annual maxima of extreme precipitation even when using modest amounts of data.

  • Source Publication: Journal of Climate, advanced online view, doi: 10.1175/JCLI-D-19-0892.1. Authors: Sun, Q., X. Zhang, F. W. Zwiers, S. Westra, and L.V. Alexander Publication Date: Sep 2020

    This paper provides an updated analysis of observed changes in extreme precipitation using high quality station data up to 2018. We examine changes in extreme precipitation represented by annual maxima of one day (Rx1day) and five-day (Rx5day) precipitation accumulations at different spatial scales and attempt to address whether the signal in extreme precipitation has strengthened with several years of additional observations. Extreme precipitation has increased at about two thirds of stations and the percentage of stations with significantly increasing trends is significantly larger than that can be expected by chance for the globe, continents including Asia, Europe, and North America, and regions including C. North-America, E. North-America, N. Central-America, N. Europe, Russian-Far-East, E.C. Asia, and E. Asia. The percentage of stations with significantly decreasing trends is not different from that expected by chance. Fitting extreme precipitation to generalized extreme value distributions with global mean surface temperature (GMST) as a co-variate reaffirms the statistically significant connections between extreme precipitation and temperature. The global median sensitivity, percent change in extreme precipitation per Kelvin increase in GMST is 6.6% (5.1 to 8.2%, 5–95% confidence interval) for Rx1day and is slightly smaller at 5.7% (5.0 to 8.0%) for Rx5day. The comparison of results based on observations ending in 2018 with those from data ending in 2000–2009 shows a consistent median rate of increase, but a larger percentage of stations with statistically significant increasing trends, indicating an increase in the detectability of extreme precipitation intensification, likely due to the use of longer records.

  • Source Publication: Journal of Climate, 33, 16, 6957–6970, doi:10.1175/JCLI-D-19-0011.1 Authors: Ben Alaya, M.A., F.W. Zwiers and X. Zhang Publication Date: Aug 2020

    The recurring devastation caused by extreme events underscores the need for reliable estimates of their intensity and frequency. Operational frequency and intensity estimates are very often obtained from generalized extreme value (GEV) distributions fitted to samples of annual maxima. GEV distributed random variables are “max-stable,” meaning that the maximum of a sample of several values drawn from a given GEV distribution is again GEV distributed with the same shape parameter. Long-period return value estimation relies on this property of the distribution. The data to which the models are fitted may not, however, be max-stable. Observational records are generally too short to assess whether max-stability holds in the upper tail of the observations. Large ensemble climate simulations, from which we can obtain very large samples of annual extremes, provide an opportunity to assess whether max-stability holds in a model-simulated climate and to quantify the impact of the lack of max-stability on very long period return-level estimates. We use a recent large ensemble simulation of the North American climate for this purpose. We find that the annual maxima of short-duration precipitation extremes tend not to be max-stable in the simulated climate, as indicated by systematic variation in the estimated shape parameter as block length is increased from 1 to 20 years. We explore how the lack of max-stability affects the estimation of very long period return levels and discuss reasons why short-duration precipitation extremes may not be max-stable.

  • Source Publication: Science of The Total Environment, 728, 138808, doi:0.1016/j.scitotenv.2020.138808 Authors: Brubacher, J., D.M. Allen, S.J. Déry, M.W. Parkes, B. Chhetri, S. Mak, S. Sobie and T.K. Takaro Publication Date: Aug 2020

    Background 
    Food- and water-borne pathogens exhibit spatial heterogeneity, but attribution to specific environmental processes is lacking while anthropogenic climate change alters these processes. The goal of this study was to investigate ecology, land-use and health associations of these pathogens and to make future disease projections. 

    Methods
    The rates of five acute gastrointestinal illnesses (AGIs) (campylobacteriosis, Verotoxin- producing Escherichia coli, salmonellosis, giardiasis and cryptosporidiosis) from 2000 to 2013 in British Columbia, Canada, were calculated across three environmental variables: ecological zone, land use, and aquifer type. A correlation analysis investigated relationships between 19 climatic factors and AGI. Mean annual temperature at the ecological zone scale was used in a univariate regression model to calculate annual relative AGI risk per 1 °C increase. Future cases attributable to climate change were estimated into the 2080s.

    Findings
    Each of the bacterial AGI rates was correlated with several annual temperature-related factors while the protozoan AGIs were not. In the regression model, combined relative risk for the three bacterial AGIs was 1.1 [95% CI: 1.02–1.21] for every 1 °C in mean annual temperature. Campylobacteriosis, salmonellosis and giardiasis rates were significantly higher (p < 0.05) in the urban land use class than in the rural one. In rural areas, bacteria and protozoan AGIs had significantly higher rates in the unconsolidated aquifers. Verotoxin-producing Escherichia coli rates were significantly higher in watersheds with more agricultural land, while rates of campylobacteriosis, salmonellosis and giardiasis were significantly lower in agricultural watersheds. Ecological zones with higher bacterial AGI rates were generally projected to expand in range by the 2080s.

    Interpretation
    These findings suggest that risk of AGI can vary across ecosystem, land use and aquifer type, and that warming temperatures may be associated with an increased risk of food-borne AGI. In addition, spatial patterns of these diseases are projected to shift under climate change.

  • Source Publication: Journal of Hydrology, 587, 124939, doi:10.1016/j.jhydrol.2020.124939 Authors: Melaku, N.D., J. Wang and T.W. Meshesha Publication Date: Aug 2020

    Peatlands cover only about 3% of the Earth’s surface and store 15–30% of the Global soil carbon as a peat. However, human intervention and climate change threatens the stability of peatlands, owing to deforest, wildfire, mining, drainage, glacial retreat, and permafrost. In our study, we modified the SWAT model to couple snow, soil temperature and carbon dioxide emission. Then the modified SWAT was used for predicting snow depth, soil temperature at different depths and carbon dioxide emission from peatlands and other land uses at Athabasca river basin, Canada. The results of the study indicated that SWAT model estimated the daily snow depth with R2, NSE, RMSE and PBIAS values of 0.83, 0.76, 0.52 and −2.3 in the calibration period (2006–2007) and 0.79, 0.71, 0.97 and −3.6 for the validation period (2008–2009), respectively. The SWAT model also predicted soil temperature very well at three depths (5 cm, 10 cm and 30 cm). The simulation model results also confirmed that the modified SWAT model estimates the CO2 emission at Athabasca river basin with good model fit during calibration (R2 = 0.71, NSE = 0.67, RMSE = 2.6 and PBIAS = 3.2) and during validation (R2 = 0.63, NSE = 0.58, RMSE = 3.1 and PBIAS = 9.3). Overall, our result confirmed that SWAT model performed well in representing the dynamics of snow depth, soil temperature and CO2 emissions in the peatlands at the Athabasca river basin.

  • Source Publication: Journal of Hydrology, 587, 124952, doi:10.1016/j.jhydrol.2020.124952 Authors: Meshesha, T.W., J. Wang and N.D. Melaku Publication Date: Aug 2020

    Cold climate regions offer various ecosystem services. The water quality parameters such as dissolved oxygen (DO), water temperature (Tw), and dissolved organic carbon (DOC) have considerable impacts on the aquatic ecosystem species. Any impairments in water quality such as elevated water temperature, and low DO concentrations can limit the survival of aquatic ecosystems and its species, such as walleye, northern pike and salmon. Therefore, a good understanding of the aquatic ecosystem of rivers is essential for effective and sustainable river basin and watershed management of fisheries and aquatic resources. The objectives of this study is to improve a watershed scale module of water quality (DO, DOC and Fecal coliforms (FC) in the SWAT model to examine the spatiotemporal patterns and their impacts on aquatic ecosystem and water quality processes in the Athabasca River Basin (ARB), Alberta, Canada. The calibration and validation results of DO, DOC and FC show that the improved Soil and Water Assessment Tool (SWAT) model achieved successfully with a varied range (satisfactory to vey-good) of accuracy at the daily temporal scales. The results showed that concentrations of DO for the selected stations (spring and summer) reduced far below the thresholds for ecosystems survival. In concurrent reduction with DO, the FC concentration considerably varied in the different monitoring stations of ARB. These results highlight that DO, DOC and FC variability in the ARB may drive changes in water quality and ecosystem services that have to be understood on the specific research scale for designing adaptive management scenarios. This study reveals that the new SWAT model can be applied to other similar regions of the worlds.

  • Authors: RDH Building Science Publication Date: Jul 2020

    The primary objective of this study is to assess the implications of increasing outdoor air
    temperatures due to climate change on the thermal comfort of multifamily residential
    buildings in the Lower Mainland, and to identify cost-effective design measures that will
    maintain thermal comfort under future climate conditions.

    A variety of climate adaptation and mitigation measures (CAMMs) suitable for both new
    and existing, high and low rise multifamily residential buildings are explored using future
    climate projections. Ideally, solutions are identified that improve thermal comfort without
    sacrificing parallel societal objectives to reduce energy consumption and greenhouse gas
    emissions. It is also desirable that identified solutions improve the resiliency of buildings
    to maintain comfort during increasingly common extreme weather events such as
    unusually high temperatures, wildfire-induced poor air quality, or power outages.

    The results of this study will support development of design guidelines, policies and
    standards that ensure new building provide residents with thermally comfortable
    environments, as well as programs that improve the thermal comfort of existing
    residential buildings. This study will also guide best practises for incorporating
    projections of warmer future climate conditions into building energy modelling and
    design.

  • Source Publication: Climatic Change, doi:10.1007/s10584-020-02788-1 Authors: Sobie, S. Publication Date: Jul 2020

    Landslide hazards in British Columbia are mainly caused by precipitation and can result in significant damage and fatalities. Anthropogenic climate change is expected to increase precipitation frequency and intensity in the winter, spring, and fall in British Columbia (BC), potentially resulting in increased frequency of landslide hazard. Quantifying the effect of changing precipitation on future landslide hazard across the varying topographic and climatic conditions in BC requires detailed projections of future precipitation. Here, the operational Landslide Hazard Assessment for Situational Awareness (LHASA) model is used with high-resolution, statistically downscaled daily precipitation to generate detailed simulations of landslide hazard in BC over the twenty-first century. Historical evaluation of the LHASA model is performed using a station-based, gridded observational precipitation dataset. Classification of observed landslide dates and locations as hazard events occurs as successfully as, or slightly better than, when LHASA is applied globally with satellite precipitation. Using the LHASA model with precipitation projections from 12 downscaled global climate models following RCP8.5 indicates that future landslide hazard frequency will increase from 16 days per year to 21 days per year (32%) on average by the 2050s for landslide susceptible regions in the province. Areas of the province currently with the most frequent landslide hazards (18 to 21 days per year), including the west coast and northern Rocky Mountains, are expected to see between 8 and 11 additional hazardous days (49 to 61% increases) per year. Most of the increased hazard frequency occurs during winter and fall, reflecting those seasons with the largest projected increases in single and multi-day precipitation. Risk assessments for regions in British Columbia vulnerable to landslides will need to account for increasing hazard due to climate change altered precipitation.

  • Source Publication: Earth System Science Data, 12, 1561–1623, doi:10.5194/essd-12-1561-2020 Authors: Saunois, M. et al. Publication Date: Jul 2020

    Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations).

    For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4 yr−1 (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4 yr−1 or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4 yr−1 or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is 29 Tg CH4 yr−1 larger than our estimate for the previous decade (2000–2009), and 24 Tg CH4 yr−1 larger than the one reported in the previous budget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH4 emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH4 yr−1, range 594–881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (∼ 65 % of the global budget,

    Some of our global source estimates are smaller than those in previously published budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH4 yr−1 lower due to improved partition wetlands and other inland waters. Emissions from geological sources and wild animals are also found to be smaller by 7 Tg CH4 yr−1 by 8 Tg CH4 yr−1, respectively. However, the overall discrepancy between bottom-up and top-down estimates has been reduced by only 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methane budget include (i) a global, high-resolution map of water-saturated soils and inundated areas emitting methane based on a robust classification of different types of emitting habitats; (ii) further development of process-based models for inland-water emissions; (iii) intensification of methane observations at local scales (e.g., FLUXNET-CH4 measurements) and urban-scale monitoring to constrain bottom-up land surface models, and at regional scales (surface networks and satellites) to constrain atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/or co-emitted species such as ethane to improve source partitioning.

    The data presented here can be downloaded from https://doi.org/10.18160/GCP-CH4-2019 (Saunois et al., 2020) and from the Global Carbon Project.

  • Source Publication: Mine Water Environ., doi:10.1007/s10230-020-00695-6. Authors: Alam, M.S., et al. Publication Date: Jun 2020

    The oil sands industry in Canada uses soil–vegetation–atmosphere-transfer (SVAT) water balance models, calibrated against short-term (less than 10 years) field monitoring data, to evaluate long-term (≈60 years) reclamation cover design performance. These evaluations use long-term historical climate data; however, the effects of climate change should also be incorporated in these analyses. Although statistical downscaling of global climate change projections is commonly used to obtain local, site-specific climate, high resolution dynamical downscaling can also be used. The value of this latter approach to obtain local site-specific projections for mine reclamation covers has not been evaluated previously. This study explored the differences in key water balance components of three reclamation covers and three natural sites in northern Alberta, Canada, under future, site-specific, statistical, and dynamical climate change projections. Historical meteorological records were used to establish baseline periods. Temperature datasets were used to calculate potential evapotranspiration (PET) using the Hargreaves–Samani method. Statistical downscaling uses the Long Ashton Research Station Weather Generator (LARS-WG) and global circulation model (GCM) projections of temperature and precipitation. Dynamical climate change projections were generated on a 4 km grid using the weather research and forecasting (WRF) model. These climate projections were applied to a physically-based water balance model (i.e. Hydrus-1D) to simulate actual evapotranspiration (AET) and net percolation (NP) for the baseline and future periods. The key findings were: (a) LARS-WG outperformed WRF in simulating baseline temperatures and precipitation; (b) both downscaling methods showed similar directional shifts in the future temperatures and precipitation; (c) this, in turn, created similar directional shifts in future growing season median AET and NP, although the increase in future NP for LARS-WG was higher than that for WRF. The relative increases in future NP were much higher than the relative increases in future AET, particularly for the reclamation covers.

  • Source Publication: Geophysical Research Letters, 47, 12, e2019GL086875, doi:10.1029/2019GL086875 Authors: Paik, S., S.K. Min, X. Zhang, M.G. Donat, A.D. King and Q. Sun Publication Date: May 2020

    Human influences have been identified in the observed intensification of extreme precipitation at global and continental scales, but quantifying the contribution of greenhouse gas increases remains challenging. Here, we isolate anthropogenic greenhouse gas impacts on the observed intensification of extreme precipitation during 1951–2015 by comparing observations with CMIP6 individual forcing experiments. Results show that greenhouse gas influences are detected over the global land, Northern Hemisphere extratropics, western and eastern Eurasia, and global “dry” and “wet” regions, which are separable from other external forcings such as solar and volcanic activities and anthropogenic aerosols. The human‐induced greenhouse gas increases are also found to explain most of the observed changes in extreme precipitation intensity, which are consistent with the increased moisture availability with warming. Our results provide the first quantitative evidence for the dominant influence of human‐made greenhouse gases on extreme precipitation increase.

  • Source Publication: Journal of Hydrology, 582, 124513, doi:10.1016/j.jhydrol.2019.124513 Authors: Meshesha, T.W., J. Wang and N. Demelash Melaku Publication Date: Mar 2020

    Quantifying bacteria fluxes and contaminants from the point and nonpoint sources in a watershed are important for the management of water quality and safeguard public health. Therefore, the appropriate characterization of bacteria from different sources is necessary for understanding of fate and transport of bacteria in watersheds. However, it is challenging to simulate the effects of pH on bacteria, such as Escherichia coli (E. coli) in the original version of Soil and Water Assessment Tool (SWAT). This study aimed to augment SWAT-bacteria module to evaluate the potential effect of pH on E. coli concentrations. We modified SWAT-bacteria module to incorporate pH factor and to check E. coli observations from four sites of Athabasca River Basin. The modified SWAT-bacteria model demonstrated a linear relationship between observed and simulated daily E. coli data with R2 values found between 0.70 and 0.80; NSE: 0.59 and 0.68; PBIAS: 7.94% and 17.85% during calibration for all monitoring sites (2010–2012). While during the validation (2013–2014) the performance statistics found to be: R2: 0.59–0.72; NSE: 0.55–0.66; PBIAS: 10–22%. The results of the sensitivity analysis confirmed that pH is one of the most significant fate factors of E. coli. The modified SWAT-bacteria module provides an improved estimate of E. coli concentration from the river basin. This study contributes new insight to E. coli modelling. Therefore, the modified SWAT-bacteria model could be a powerful tool for the future regional to global scale model of E. coli concentrations thus significantly contribute for the application of effective river basin management.

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