Theme 3

LEADER:

Dr. Peter Rasmussen - In Memoriam (University of Manitoba)
Email:  -

Dr. Tricia Stadnyk

Email: tricia.stadnyk@ucalgary.ca

CO-INVESTIGATORS:

Dr. Thian Gan
Email:  tgan@ualberta.ca

Dr. Paulin Coulibaly
Email:  couliba@mcmaster.ca

Objective: Review flood forecasting systems currently implemented by Canadian provinces and evaluate their performance in meeting their intended purpose.

Significance of research: In order to propose better methods for flood forecasting in Canada, it is imperative to start with a thorough evaluation and comparison of existing methods. In this project, we will evaluate adopted and alternative methods through a number of selected case studies.

Outcomes: The proposed research will result in recommendations for the type of tools and data that are most suitable for flood forecasting under particular circumstances. This information will provide input to other projects and guide some of the research efforts in FloodNet, specifically Project 3-4.

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Gizaw, M.S. & T.Y. Gan (2016) Regional Flood Frequency Analysis using Support Vector Regression under historical and future climate. Journal of Hydrology, 538, 387-398.

Jha, S., Shrestha, D., Stadnyk, T. & P. Coulibaly (2018) Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment. Hydrology and Earth System Sciences, 22(3), 1957–1969.

Muhammad, A., Stadnyk, T.A., Unduche, F. & P. Coulibaly (2018) Multi-model approaches for improving seasonal ensemble streamflow prediction scheme with various statistical post-processing techniques in the Canadian Prairie Region. Water, 10(11), 1604.

Muhammad, A., Evenson, G.R., Stadnyk, T.A., Boluwade, A., Jha, S.K. & P. Coulibaly (2018) Assessing the importance of potholes in the Canadian Prairie Region under future climate change scenarios. Water, 10(11), 1657.

Muhammad, A., Evenson, G.R., Unduche, F. & T.A. Stadnyk (2020) Climate Change Impacts on Reservoir Inflow in the Prairie Pothole Region: A Watershed Model Analysis. Water, 12(1), 1-18.

Muhammad, A., Evenson, G.R., Stadnyk, T.A., Boluwade, A., Jha, S.K. & P. Coulibaly (2019) Impact of model structure on the accuracy of hydrological modeling of a Canadian Prairie watershed. Journal of Hydrology: Regional Studies, 21, 40-56.

Zahmatkesh, Z., Jha, S. K., Coulibaly, P. & T. Stadnyk (2019) An overview of river flood forecasting procedures in Canadian watersheds. Canadian Water Resources Journal, 44(3), 213-229.

LEADER:

Dr. Aaron Berg
Email:  aberg@uoguelph.ca

CO-INVESTIGATORS:

Dr. Paulin Coulibaly
Email:  couliba@mcmaster.ca  

Dr. Thian Gan
Email:  tgan@ualberta.ca

Objective 1: Assess whether the monitoring network and available data can meet enhanced flood forecasting requirements and use other sources of information (e.g., remote sensing, radar, gridded/reanalysis datasets) along with regionalization techniques to address data limitations.

Objective 2: Use available satellite derived soil moisture products to derive and assess near real time, spatially distributed, estimates of the status of soil water and snow water equivalence. Investigate bias correction techniques for estimating rainfall from the next generation of radar (NEXRAD) data.

Significance: This subproject will address the critical issue of the paucity of monitoring data. The limitation of Canadian hydrometric networks is well established. A general framework is needed to consistently address data limitation. In recent years satellite derived hydrological products have emerged that are of interest for the improvement of initial state estimation for hydrologic models used in flood forecasting. Similarly, merging radar rainfall with rain gauge data allows the generation of distributed rainfall fields needed for improved flood forecasting.

Outcomes: A robust tool for data estimation will be developed to address the common problem of data limitation due to inadequate monitoring networks.This subproject will generate practical tools for deriving distributed products (rainfall, soil moisture, snow water equivalent) from radar and satellite data for inclusion in flood forecasting systems. In addition, these products can have various applications including climate model initialization, reservoir operation planning, and crop yield prediction.

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Ambadan, J.T., Berg, A., Merryfield, W.J. & W-S. Lee (2018) Influence of snowmelt on soil moisture and on near surface air temperature during winter–spring transition season. Climate Dynamics, 51(4), 1295-1309.

Bhuiyan, HA.K.M., McNairn, H., Powers, J., Friesen, M., Pacheco, A., Jackson, T.J., Cosh, M.H., Colliander, A., Berg, A., Rowlandson, T., Bullock, P., & R. Magagi (2018) Assessing SMAP Soil Moisture Scaling and Retrieval in the Carman (Canada) Study Site. Vadose Zone Journal, 17(1), 1-14.

Bindlish, R., Cosh, M., Jackson, T., Koike, T., Fujii, T., Chan, S., Asanuma, J., Berg, A., Bosch, D., Caldwell, T., Holifield, C., McNarin, H., Martinez-Fernández, J., Rowlandson, T., Seyfried, M., Starks, P., Thibeault, M., van der Velde, R., Walker, J. & E.J. Coopersmith (2018) GCOM-W AMSR2 soil moisture product validation using core validation sites. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 11(1), 209-219.

Champagne, C., Rowlandson, T., Berg, A., Burns, T., L’Heureux, J., Tetlock, E., Adams, J.R., McNairn, H., Toth, B. & D. Itenfisu (2016) Satellite surface soil moisture from SMOS and Aquarius: Assessment for applications in agricultural landscapes. International Journal of Applied Earth Observation and Geoinformation 45B, 143-154.

Champagne, C., White, J., Berg, A., Bélair, S., & M. Carrera (2019) Impact of soil moisture data characteristics on the sensitivity to crop yields under drought and excess moisture conditions. Remote Sensing, 11(4), 372 (19 pages).

Chan, S., Bindlish, R., O’Neill, P., Jackson, T., Dunbar, S, Chaubell, J., et al. (2017) Development and validation of the SMAP enhanced passive soil moisture product. International Geoscience and Remote Sensing Symposium (IGARSS), 2017–July (October 2017), 2539–2542.

Chan, S., Bindlish, R., O’Neill, P., Jackson, T., Njoku, E., Dunbar, S., Chaubell, J., et al. (2018) Development and assessment of the SMAP enhanced passive soil moisture product. Remote Sensing of Environment 204, 931-941.

Gherboudj, I., Magagi, R., Berg, A. & B. Toth (2017) Characterization of the spatial variability of in-situ soil moisture measurements for upscaling at the spatial resolution of RADARSAT-2. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(5), 1813-1823.

Hachborn, E., Berg, A., Levison, J. & J. Ambadan (2017) Sensitivity of GRACE-derived estimates of groundwater-level changes in southern Ontario, Canada. Hydrogeology Journal 25(8), 2391-2402.

Lyu, H., McColl, K., Li, X., Derksen, C., Berg, A., Black, A., Euskirchen, E., et al. (2018) Validation of the SMAP freeze/thaw product using categorical triple collocation. Remote Sensing of Environment, 205, 329-337.

Rowlandson, T.L., Berg, A., Roy, A., Kim, E., Pardo Lara, R., Powers, J., Lewis, K., Houser, P., McDonald, K., Toose, P., Wu, A., De Marco, E., Derksen, C., Entin, J., Colliander, A., Xu, X. & A. Mavrovic (2018) Capturing agricultural soil freeze/thaw state through remote sensing and ground observations: A soil freeze/thaw validation campaign. Remote Sensing of Environment, 211, 59-70.

Roy, A., Toose, P., Derksen, C., Rowlandson, T., Berg, A., Lemmetyinen, J., Royer, A., et al. (2017) Spatial variability of L-Band brightness temperature during freeze/thaw events over a prairie environment. Remote Sensing, 9(9), 894, 1-16.

Roy, A., Toose, P., Williamson, M., Rowlandson, T., Derksen, C., Royer, A., Berg, A., Lemmetyinen, J. & L. Arnold (2017) Response of L-Band brightness temperatures to freeze/thaw and snow dynamics in a prairie environment from ground-based radiometer measurements. Remote Sensing of Environment 191, 67-80.

Tetlock, E., Toth, B., Berg, A., Rowlandson, T. & J. Ambadan-Thomas (2018) An 11-yr (2007–2017) soil moisture and precipitation dataset from the Kenaston Network in the Brightwater Creek basin, Saskatchewan, Canada.  Earth System Science Data Discussions, 1-16.

Williamson, M., Adams, J., Berg, A., Derksen, C., Toose, P. & A. Walker (2018) Plot-scale assessment of soil freeze/thaw detection and variability with impedance probes: implications for remote sensing validation networks. Hydrology Research, 49(1), 1-16.

Williamson, M., Rowlandson, T., Berg, A., Roy, A., Toose, P., Derksen, C., Arnold, L., et al. (2018) L-band radiometry freeze/ thaw validation using air temperature and ground measurements. Remote Sensing Letters, 9(4), 403-410.

Zahmatkesh, Z., Tapsoba, D., Leach, J. & P. Coulibaly (2019) Evaluation and bias correction of SNODAS snow water equivalent (SWE) for streamflow simulation in eastern Canadian basins. Hydrological Sciences Journal, 64(13), 1541-1555.

Zwieback, S., Colliander, A., Cosh, M. H., Martínez-Fernández, J., McNairn, H., Starks, P. J., Thibeault, M., & A. Berg (2018) Estimating time-dependent vegetation biases in the SMAP soil moisture product. Hydrology and Earth System Sciences, 22, 4473-4489.

Zwieback, S., & A. Berg (2019) Fine-scale SAR soil moisture estimation in the subarctic tundra. IEEE Transactions on Geoscience and Remote Sensing, 1-15.

Zwieback, S., Westermann, S., Langer, M., Boike, J., Marsh, P. & A. Berg (2019) Improving permafrost modeling by assimilating remotely sensed soil moisture. Water Resources Research, 1-19.

LEADER:

Dr. Weihua Zhuang (University of Waterloo)
Email:  wzhuang@uwaterloo.ca  

CO-INVESTIGATORS:

Dr. Wei Song (University of New Brunswick)
Email:  wsong@unb.ca

Dr. Paulin Coulibaly (McMaster University)
Email:  couliba@mcmaster.ca

Objective:  Investigate and develop effective and efficient wireless networking strategies to reliably transmit FloodNet sensing data to the information processing center and disseminate flood warning messages to the general public in a timely manner.

Significance:  Due to potentially devastating damages from floods to lives and properties, enhanced information collection and communication is essential to assist the FloodNet framework in preventing costly impacts on the economy and casualties caused by disasters.

Outcomes: We will develop new integrated systematic approaches for reliable transmission of spatially distributed data collected from various sources to an information processing center. We will also develop effective and efficient information dissemination strategies (algorithms and protocols) to deliver timely alerts of the flood forecasting system to the general public. The outcomes from Project 3-3 will feed directly into CAFFEWS.

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Li, P., Miyazaki, T., Wang, K., Guo, S. & W. Zhuang (2017) Vehicle-Assist Resilient Information and Network System for Disaster Management. IEEE Transactions on Emerging Topics in Computing, 5(3), 438-448.

Moussa, H.G., & W. Zhuang (2019) RACH Performance Analysis for Large-Scale Cellular IoT Applications. IEEE Internet of Things Journal, 6(2), 3364-3372.

Moussa, H.G., & W. Zhuang (2019) Energy and Delay Aware Two-hop NOMA-Enabled Massive Cellular IoT Communications. IEEE Internet of Things Journal, 1-11.

Rahimi Malekshan, K. & W. Zhuang (2017) Joint scheduling and transmission power control in wireless ad hoc networks. IEEE Transactions on Wireless Communications, 16(9), 5982–5993.

Rahimi Malekshan, K., Zhuang, W. & Y. Lostanlen (2016) Coordination-based Medium Access Control with Space-reservation for Wireless Ad Hoc Networks. IEEE Transactions on Wireless Communications, 15(2), 1617-1628.

Song, W., Zhao, Y. & W. Zhuang (2018) Stable device pairing for collaborative data dissemination with device-to-device communications. IEEE Internet of Things Journal. 5(2), 1251-1264.

Song, W. & W. Zhuang (2016) Packet assignment under resource constraints with D2D communications. IEEE Network 30(5), 54-60.

Ye, Q., Zhuang, W., Zhang, S., Jin, A.L., Shen, X. & X. Li (2018) Dynamic radio resource slicing for a two-tier heterogeneous wireless network. IEEE Transactions on Vehicular Technology, 67(10), 9896-9910.

Ye, Q.  & W. Zhuang (2017) Distributed and adaptive medium access control for Internet-of-Things-enabled mobile networks. IEEE Internet of Things Journal 4(2), 446-460.

Ye, Q., & W. Zhuang (2017) Token-based adaptive mac for a two-hop internet-of-things enabled MANET. IEEE Internet of Things Journal, 4(5), 1739–1753.

Zhou, Y. & W. Zhuang (2017) Opportunistic cooperation in wireless ad hoc networks with interference correlation. Peer-to-Peer Networking and Applications 10(1), 238-252.

Zhou, Y. & W. Zhuang (2016) Performance analysis of cooperative communication in decentralized wireless networks with unsaturated traffic. IEEE Transactions on Wireless Communications 15(5), 3518–3530.

Zhou, Y. & W. Zhuang (2015) Throughput Analysis of Cooperative Communication in Wireless Ad Hoc Networks With Frequency Reuse. IEEE Transactions on Wireless Communications, 14(1), 205-218.

LEADER:

Dr. Paulin Coulibaly (McMaster University)
Email:  couliba@mcmaster.ca

CO-INVESTIGATORS:

Dr. Amin Elshorbagy (University of Saskatchewan)
Email:  amin.elshorbagy@usask.ca

Dr. Aaron Berg (University of Guelph)
Email:  aberg@uoguelph.ca

Dr. Bryan Tolson (University of Guelph)
Email:  btolson@uwaterloo.ca

Dr. François Anctil (Université Laval)
Email:  francois.anctil@gci.ulaval.ca

Dr. Peter Rasmussen - In Memoriam (University of Manitoba)
Email:  -

Objective: Develop a forward-looking flood forecasting tool (i.e., CAFFEWS) that includes adaptive structures for assimilating new technology and new data/information as they become available, and which can be adapted to different regions of Canada for enhanced flood forecasting.

Significance: Accurate and reliable flood forecasts are fundamental to enhanced flood mitigation and can be obtained by developing an advanced flood forecasting system. The development of CAFFEWS will contribute significantly to enhancing the national capacity for flood forecasting, which is one of the key targets of FloodNet.

Outcomes: To deliver an adaptive flood forecasting and early warning system for Canada.

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Awol, F. Coulibaly, P., Tsanis, I. & F. Unduche (2019). Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed. Water, 11(11), 1-27.

Awol, F., Coulibaly, P. & B. Tolson (2018) Event-based model calibration approaches for selecting representative distributed parameters in semi-urban watersheds. Advances in Water Resources 118, 12–27.

Darbandsari, P. & P. Coulibaly (2019) Inter-comparison of different bayesian model averaging modifications in streamflow simulation. Water 11(8), 1707.

Han, S. & P. Coulibaly (2019) Probabilistic flood forecasting using hydrologic uncertainty processor with ensemble weather forecasts. Journal of Hydrometeorology, 20(7), 1379-1398.

Han, S. Coulibaly, P. & D. Biondi (2019). Assessing Hydrologic Uncertainty Processor Performance for Flood Forecasting in a Semiurban Watershed. Journal of Hydrologic Engineering, 24(9), 05019025.

Han, S. & P. Coulibaly (2017) Bayesian flood forecasting methods: A review. Journal of Hydrology 551, 340-351.

Keum, J., Awol, F., Ursulak, J. & P. Coulibaly (2019) Introducing the ensemble-based dual entropy and multiobjective optimization for hydrometric network design problems: EnDEMO. Entropy, 21(10), 947.

Keum, J., Coulibaly, P., Razavi, T., Tapsoba, D., Gobena, A., Weber, F. & A. Pietroniro (2018) Application of SNODAS and hydrologic models to enhance entropy-based snow monitoring network design. Journal of Hydrology, 561, 688–701.

Keum, J. & P. Coulibaly (2017) Sensitivity of entropy method to time series length in hydrometric network design. ASCE Journal of Hydrologic Engineering 22(7), 04017009-1 - 04017009-13.

Keum, J. & P. Coulibaly (2017) Information theory-based decision support system for integrated design of multivariable hydrometric networks. Water Resources Research 53(7), 6239–6259.

Keum, J., Kornelsen, K.C., Leach, J.M. & P. Coulibaly (2017) Entropy Applications to Water Monitoring Network Design: A Review. Entropy, 19(11), 613.

Leach, J.M. & P. Coulibaly (2019) An extension of data assimilation into the short-term hydrologic forecast for improved prediction reliability. Advances in Water Resources, 134(October), 103443.

Leach, J.M., Kornelsen, K.C. & P. Coulibaly (2018) Assimilation of near-real time data products into models of an urban basin. Journal of Hydrology, 563, 51–64.

Razavi, T. & P. Coulibaly (2017) An evaluation of regionalization and watershed classification schemes for continuous daily streamflow prediction in ungauged watersheds. Canadian Water Resources Journal, 42(1), 2-20.

Razavi, T., Switzman, H., Arain, A. & P. Coulibaly (2016) Regional climate change trends and uncertainty analysis using extreme indices: A case study of Hamilton, Canada. Climate Risk Management 13, 43-63.

Switzman, H., Razavi, T., Traore, S., Coulibaly, P., Burn, D.H., Henderson, J., Fausto, E. & R. Ness (2017) Variability of future extreme rainfall statistics: Comparison of multiple IDF projections. ASCE Journal of Hydrologic Engineering 22(10), 04017046-1 -  04017046-20.

Werstuck, C.A. & P. Coulibaly (2017) Hydrometric network design using dual entropy multi-objective optimization in the Ottawa River Basin. Hydrology Research 48(6), 1639-1651.

Werstuck, C.A. & P. Coulibaly (2018) Assessing spatial scale effects on hydrometric network design using entropy and multi-objective methods. Journal of the American Water Resources Association 54(1), 276-286.

Wijayarathne, D.B. & P. Coulibaly (2020) Identification of hydrological models for operational flood forecasting in St. John’s, Newfoundland, Canada. Journal of Hydrology: Regional Studies, 27, 100646.

LEADER:

Dr. Donald Burn (University of Waterloo)
Email:  dhburn@uwaterloo.ca

CO-INVESTIGATORS:

Dr. Amin Elshorbagy (University of Saskatchewan)
Email:  amin.elshorbagy@usask.ca

Dr. Paulin Coulibaly (McMaster University)
Email:  couliba@mcmaster.ca

Objective: To provide an impartial and thorough evaluation of CAFFEWS in a variety of forecast settings for conditions typical of Canadian forecast situations.

Significance:  The efficacy of CAFFEWS for forecasting in a Canadian environment needs to be evaluated to determine under which conditions the new system can provide improved forecasts and to quantify the range of expected improvements in forecasting capability.  This work will lead to implementation recommendations that our partners can use to revise their current flood forecasting approaches.

Outcomes:  The outcomes from this project will include: i) a rigorous evaluation of CAFFEWS for a variety of differing data availability conditions and forecasting environments; ii) a summary of the conditions for which CAFFEWS is anticipated to result in improved forecasts; and iii) an estimate of the expected magnitude of improvement in the various forecast performance metrics.

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