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  • Source Publication: Environmental Research Communications, 4, 1, 015009, doi:10.1088/2515-7620/ac4bab Authors: Wu, L, Elshorbagy, A. and MS Alam Publication Date: Feb 2022

    Understanding the dynamics of water-energy-food (WEF) nexus interactions with climate change and human intervention helps inform policymaking. This study demonstrates the WEF nexus behavior under ensembles of climate change, transboundary inflows, and policy options, and evaluates the overall nexus performance using a previously developed system dynamics-based WEF nexus model—WEF-Sask. The climate scenarios include a baseline (1986–2014) and near-future climate projections (2021–2050). The approach is demonstrated through the case study of Saskatchewan, Canada. Results show that rising temperature with increased rainfall likely maintains reliable food and feed production. The climate scenarios characterized by a combination of moderate temperature increase and slightly less rainfall or higher temperature increase with slightly higher rainfall are easier to adapt to by irrigation expansion. However, such expansion uses a large amount of water resulting in reduced hydropower production. In contrast, higher temperature, combined with less rainfall, such as SSP370 (+2.4 °C, −6 mm), is difficult to adapt to by irrigation expansion. Renewable energy expansion, the most effective climate change mitigation option in Saskatchewan, leads to the best nexus performance during 2021–2050, reducing total water demand, groundwater demand, greenhouse gas (GHG) emissions, and potentially increasing water available for food&feed production. In this study, we recommend and use food&feed and power production targets and provide an approach to assessing the impacts of hydroclimate and policy options on the WEF nexus, along with suggestions for adapting the agriculture and energy sectors to climate change.

  • Source Publication: Progress in Oceanography, 198, 102659. doi:10.1016/j.pocean.2021.102659 Authors: Heneghan, R. F. et al. (T.C. Tai is 21st coauthor) Publication Date: Oct 2021

    Climate change is warming the ocean and impacting lower trophic level (LTL) organisms. Marine ecosystem models can provide estimates of how these changes will propagate to larger animals and impact societal services such as fisheries, but at present these estimates vary widely. A better understanding of what drives this inter-model variation will improve our ability to project fisheries and other ecosystem services into the future, while also helping to identify uncertainties in process understanding. Here, we explore the mechanisms that underlie the diversity of responses to changes in temperature and LTLs in eight global marine ecosystem models from the Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP). Temperature and LTL impacts on total consumer biomass and ecosystem structure (defined as the relative change of small and large organism biomass) were isolated using a comparative experimental protocol. Total model biomass varied between −35% to +3% in response to warming, and -17% to +15% in response to LTL changes. There was little consensus about the spatial redistribution of biomass or changes in the balance between small and large organisms (ecosystem structure) in response to warming, an LTL impacts on total consumer biomass varied depending on the choice of LTL forcing terms. Overall, climate change impacts on consumer biomass and ecosystem structure are well approximated by the sum of temperature and LTL impacts, indicating an absence of nonlinear interaction between the models’ drivers. Our results highlight a lack of theoretical clarity about how to represent fundamental ecological mechanisms, most importantly how temperature impacts scale from individual to ecosystem level, and the need to better understand the two-way coupling between LTL organisms and consumers. We finish by identifying future research needs to strengthen global marine ecosystem modelling and improve projections of climate change impacts.

  • Source Publication: Science Advances, 7, eabh0895 Authors: Cheung, W. W. L., T.L. Frölicher, V.W.Y. Lam, M. Oyinlola, G. Reygondeau, U.R. Sumaila, T.C. Tai, L.C.L Teh and C.C.C. Wabnitz Publication Date: Oct 2021

    Extreme temperature events have occurred in all ocean basins in the past two decades with detrimental impacts on marine biodiversity, ecosystem functions, and services. However, global impacts of temperature extremes on fish stocks, fisheries, and dependent people have not been quantified. Using an integrated climate-biodiversity-fisheries-economic impact model, we project that, on average, when an annual high temperature extreme occurs in an exclusive economic zone, 77% of exploited fishes and invertebrates therein will decrease in biomass while maximum catch potential will drop by 6%, adding to the decadal-scale mean impacts under climate change. The net negative impacts of high temperature extremes on fish stocks are projected to cause losses in fisheries revenues and livelihoods in most maritime countries, creating shocks to fisheries social-ecological systems particularly in climate-vulnerable areas. Our study highlights the need for rapid adaptation responses to extreme temperatures in addition to carbon mitigation to support sustainable ocean development.

  • Source Publication: Weather and Climate Extremes, 34, 100388, doi:10.1016/j.wace.2021.100388. Authors: Ben Alaya, M.A., F.W. Zwiers and X. Zhang Publication Date: Oct 2021

    The uniform risk engineering practices that are increasingly being adopted for structural design require estimates of the extreme wind loads with very low annual probabilities of exceedance, corresponding to return periods of up to 3000-years in some cases. These estimates are necessarily based on observational wind data that typically spans only a few decades. The estimates are therefore affected by both large sampling uncertainty and, potentially, non-negligible biases. Design practices that aim to meet mandated structural reliability criteria take the sampling uncertainty of long period wind speed or wind pressure estimates into account, but reliability could be compromised if estimates are also biased. In many circumstances, estimates are obtained by fitting an extreme value distribution to annual maximum wind speed observed over a few decades. A key assumption implicit in doing so is that wind speed annual maxima are max-stable. Departures from max-stability can exacerbate the uncertainty of long-period return level estimates by inducing systematic estimation bias as well. Observational records, however, are generally too short to assess max-stability. We therefore use wind speed data from a large (50-member) ensemble of CanRCM4 historical simulations over North America to assess whether wind speed annual maxima are max-stable. While results are generally reassuring at the continental scale, disquieting evidence of a lack of max-stability is often found in the central and southern parts of the continent. Results show that when annual maximum wind speeds are not max-stable, long period return level extreme wind speeds tend to be underestimated, which would compromise reliability if used to design infrastructure such as tall buildings and towers.

  • Source Publication: Ecology Letters, doi:10.1111/ele.13866 Authors: Florko, K. R. N., T.C. Tai, W.W.L. Cheung, S.H. Ferguson, U.R. Sumaila, D.J. Yurkowski and M. Auger-Méthé Publication Date: Oct 2021

    Arctic sea ice loss has direct consequences for predators. Climate-driven distribution shifts of native and invasive prey species may exacerbate these consequences. We assessed potential changes by modelling the prey base of a widely distributed Arctic predator (ringed seal; Pusa hispida) in a sentinel area for change (Hudson Bay) under high- and low-greenhouse gas emission scenarios from 1950 to 2100. All changes were relatively negligible under the low-emission scenario, but under the high-emission scenario, we projected a 50% decline in the abundance of the well-distributed, ice-adapted and energy-rich Arctic cod (Boreogadus saida) and an increase in the abundance of smaller temperate-associated fish in southern and coastal areas. Furthermore, our model predicted that all fish species declined in mean body size, but a 29% increase in total prey biomass. Declines in energy-rich prey and restrictions in their spatial range are likely to have cascading effects on Arctic predators.

  • 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.

  • Source Publication: Frontiers in Marine Science, 8, 1–12. doi:10.3389/fmars.2021.596644 Authors: Tai, T. C., U.R. Sumaila, and W.W.L. Cheung Publication Date: Jul 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.

  • 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.

  • Authors: Schoeneberg, A.T., Q. Sun and M.A. Schnorbus Publication Date: Mar 2021

    Pilot study for the development of stream flow design value projections and a prototype online tool. The BC Ministry of Transportation and Infrastructure supported PCIC in a pilot project to quantify design flood values (2-, 20-, 50-, 100- and 200-year events) for historical and future periods and make them accessible as a gridded product via PCIC’s Climate Explorer tool. As part of this work, PCIC has also been asked to calculate and supply the Melton Ratio as a gridded product. This study focuses on the Upper Fraser, a 34,200 km2 region upstream of Prince George, BC, with primarily snow-dominated watersheds. This report was prepared for the Engineering Services Branch of the Engineering Systems Department of the Highway Services Department, Ministry of Transportation and Infrastructure, Government of British Columbia.

  • 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

    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. 

    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.

    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.

    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.