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1. Compound Effects of Flood Drivers and Wetland Elevation Correction on Coastal Flood Hazard Assessment

   https://doi.org/10.1029/2020WR027544  

   Muñoz, D. F. ; Moftakhari, H. ; Moradkhani, H. ( July 2020 , Water Resources Research)

   Compound flooding frequently threatens life and assets of people who live in low‐lying coastal regions. Co‐occurrence or sequence of extremes (e.g., high river discharge and extreme coastal water level) is of paramount importance as it may result in flood hazards with potential impacts larger than each extreme in isolation. Here, we use a coupled approach, that is, bivariate statistical analysis linked to hydrodynamic modeling, to quantify compounding effects of flood drivers and generate flood hazard maps near Savannah, Georgia. Also, we integrate wetland elevation correction in digital elevation models to improve hydrodynamic simulations of compound events and hence the accuracy of flood hazard (inundation and velocity) maps. Using statistical measures, we analyze compounding effects of terrestrial/coastal flood drivers and wetland elevation correction on maximum floodwater height (MFH) and velocity (MFV) for 50‐year return period scenarios. In addition, we compare our results to MFH and MFV patterns of Hurricane Matthew that hit the West Atlantic Coasts on October 2016. The statistical measures indicate significant differences among the scenarios, partly explained by wetland elevation correction. Inundation and velocity maps suggest that a proposed composite, that is, synthesis of marginalQ, marginalH, and “AND” scenarios, can lead to the lowest average underestimation of MFH (−0.35 m) and overestimation of MFV (0.20 m/s) within wetland areas. We conclude that a thorough compound flooding assessment should leverage statistical analysis and hydrodynamic modeling of extremes including corrections of coastal digital elevation models.

    

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2. Snow Phenology and Hydrologic Timing in the Yukon River Basin, AK, USA

   https://doi.org/10.3390/rs13122284  

   Pan, Caleb G. ; Kirchner, Peter B. ; Kimball, John S. ; Du, Jinyang ; Rawlins, Michael A. ( June 2021 , Remote Sensing)

   The Yukon River basin encompasses over 832,000 km2 of boreal Arctic Alaska and northwest Canada, providing a major transportation corridor and multiple natural resources to regional communities. The river seasonal hydrology is defined by a long winter frozen season and a snowmelt-driven spring flood pulse. Capabilities for accurate monitoring and forecasting of the annual spring freshet and river ice breakup (RIB) in the Yukon and other northern rivers is limited, but critical for understanding hydrologic processes related to snow, and for assessing flood-related risks to regional communities. We developed a regional snow phenology record using satellite passive microwave remote sensing to elucidate interactions between the timing of upland snowmelt and the downstream spring flood pulse and RIB in the Yukon. The seasonal snow metrics included annual Main Melt Onset Date (MMOD), Snowoff (SO) and Snowmelt Duration (SMD) derived from multifrequency (18.7 and 36.5 GHz) daily brightness temperatures and a physically-based Gradient Ratio Polarization (GRP) retrieval algorithm. The resulting snow phenology record extends over a 29-year period (1988–2016) with 6.25 km grid resolution. The MMOD retrievals showed good agreement with similar snow metrics derived from in situ weather station measurements of snowpack water equivalence (r = 0.48, bias = −3.63 days) and surface air temperatures (r = 0.69, bias = 1 day). The MMOD and SO impact on the spring freshet was investigated by comparing areal quantiles of the remotely sensed snow metrics with measured streamflow quantiles over selected sub-basins. The SO 50% quantile showed the strongest (p < 0.1) correspondence with the measured spring flood pulse at Stevens Village (r = 0.71) and Pilot (r = 0.63) river gaging stations, representing two major Yukon sub-basins. MMOD quantiles indicating 20% and 50% of a catchment under active snowmelt corresponded favorably with downstream RIB (r = 0.61) from 19 river observation stations spanning a range of Yukon sub-basins; these results also revealed a 14–27 day lag between MMOD and subsequent RIB. Together, the satellite based MMOD and SO metrics show potential value for regional monitoring and forecasting of the spring flood pulse and RIB timing in the Yukon and other boreal Arctic basins. 

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3. High Resolution Modeling of River‐Floodplain‐Reservoir Inundation Dynamics in the Mekong River Basin

   https://doi.org/10.1029/2019WR026449  

   Shin, Sanghoon ; Pokhrel, Yadu ; Yamazaki, Dai ; Huang, Xiaodong ; Torbick, Nathan ; Qi, Jiaguo ; Pattanakiat, Sura ; Ngo‐Duc, Thanh ; Nguyen, Tuan Duc ( April 2020 , Water Resources Research)

   Numerous studies have examined the changes in streamflow in the Mekong River Basin (MRB) using observations and hydrological modeling; however, there is a lack of integrated modeling studies that explicitly simulate the natural and human‐induced changes in flood dynamics over the entire basin. Here we simulate the river‐floodplain‐reservoir inundation dynamics over the MRB for 1979–2016 period using a newly integrated, high‐resolution (~5 km) river hydrodynamics‐reservoir operation model. The framework is based on the river‐floodplain hydrodynamic model CaMa‐Flood in which a new reservoir operation scheme is incorporated by including 86 existing MRB dams. The simulated flood extent is downscaled to a higher resolution (~90 m) to investigate fine‐scale inundation dynamics, and results are validated with ground‐ and satellite‐based observations. It is found that the historical variations in surface water storage have been governed primarily by climate variability; the impacts of dams on river‐floodplain hydrodynamics were marginal until 2009. However, results indicate that the dam impacts increased noticeably in 2010 when the basin‐wide storage capacity doubled due to the construction of new mega dams. Further, results suggest that the future flood dynamics in the MRB would be considerably different than in the past even without climate change and additional dams. However, it is also found that the impacts of dams can largely vary depending on reservoir operation strategies. This study is expected to provide the basis for high‐resolution river‐floodplain‐reservoir modeling for a holistic assessment of the impacts of dams and climate change on the floodpulse‐dependent hydro‐ecological systems in the MRB and other global regions.

    

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4. Constraining Remote River Discharge Estimation Using Reach‐Scale Geomorphology

   https://doi.org/10.1029/2020WR027949  

   Brinkerhoff, C. B. ; Gleason, C. J. ; Feng, D. ; Lin, P. ( November 2020 , Water Resources Research)

   Recent advances in remote sensing and the upcoming launch of the joint NASA/CNES/CSA/UKSA Surface Water and Ocean Topography (SWOT) satellite point toward improved river discharge estimates in ungauged basins. Existing discharge methods rely on “prior river knowledge” to infer parameters not directly measured from space. Here, we show that discharge estimation is improved by classifying and parameterizing rivers based on their unique geomorphology and hydraulics. Using over 370,000 in situ hydraulic observations as training data, we test unsupervised learning and an “expert” method to assign these hydraulics and geomorphology to rivers via remote sensing. This intervention, along with updates to model physics, constitutes a new method we term “geoBAM,” an update of the Bayesian At‐many‐stations hydraulic geometry‐Manning's (BAM) algorithm. We tested geoBAM on Landsat imagery over more than 7,500 rivers (108 are gauged) in Canada's Mackenzie River basin and on simulated hydraulic data for 19 rivers that mimic SWOT observations without measurement error. geoBAM yielded considerable improvement over BAM, improving the median Nash‐Sutcliffe efficiency (NSE) for the Mackenzie River from −0.05 to 0.26 and from 0.16 to 0.46 for the SWOT rivers. Further, NSE improved by at least 0.10 in 78/108 gauged Mackenzie rivers and 8/19 SWOT rivers. We attribute geoBAM improvement to parameterizing rivers by type rather than globally, but prediction accuracy worsens if parameters are misassigned. This method is easily mapped to rivers at the global scale and paves the way for improving future discharge estimates, especially when coupled with hydrologic models.

    

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5. Developing a Modeling Framework to Simulate Compound Flooding: When Storm Surge Interacts With Riverine Flow

   https://doi.org/10.3389/fclim.2020.609610  

   Loveland, Mark ; Kiaghadi, Amin ; Dawson, Clint N. ; Rifai, Hanadi S. ; Misra, Shubhra ; Mosser, Helena ; Parola, Alessandro ( February 2021 , Frontiers in Climate)

   null (Ed.)

   Compound flooding is a physical phenomenon that has become more destructive in recent years. Moreover, compound flooding is a broad term that envelops many different physical processes that can range from preconditioned, to multivariate, to temporally compounding, or spatially compounding. This research aims to analyze a specific case of compound flooding related to tropical cyclones where the compounding effect is on coastal flooding due to a combination of storm surge and river discharge. In recent years, such compound flood events have increased in frequency and magnitude, due to a number of factors such as sea-level rise from warming oceans. Therefore, the ability to model such events is of increasing urgency. At present, there is no holistic, integrated modeling system capable of simulating or forecasting compound flooding on a large regional or global scale, leading to the need to couple various existing models. More specifically, two more challenges in such a modeling effort are determining the primary model and accounting for the effect of adjacent watersheds that discharge to the same receiving water body in amplifying the impact of compound flooding from riverine discharge with storm surge when the scale of the model includes an entire coastal line. In this study, we investigated the possibility of using the Advanced Circulation (ADCIRC) model as the primary model to simulate the compounding effects of fluvial flooding and storm surge via loose one-way coupling with gage data through internal time-dependent flux boundary conditions. The performance of the ADCIRC model was compared with the Hydrologic Engineering Center- River Analysis System (HEC-RAS) model both at the watershed and global scales. Furthermore, the importance of including riverine discharges and the interactions among adjacent watersheds were quantified. Results showed that the ADCIRC model could reliably be used to model compound flooding on both a watershed scale and a regional scale. Moreover, accounting for the interaction of river discharge from multiple watersheds is critical in accurately predicting flood patterns when high amounts of riverine flow occur in conjunction with storm surge. Particularly, with storms such as Hurricane Harvey (2017), where river flows were near record levels, inundation patterns and water surface elevations were highly dependent on the incorporation of the discharge input from multiple watersheds. Such an effect caused extra and longer inundations in some areas during Hurricane Harvey. Comparisons with real gauge data show that adding internal flow boundary conditions into ADCIRC to account for river discharge from multiple watersheds significantly improves accuracy in predictions of water surface elevations during coastal flooding events. 

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