Towards a large-scale locally relevant flood inundation modeling framework using SWAT and LISFLOOD-FP

Rajib, Adnan; Liu, Zhu; Merwade, Venkatesh; Tavakoly, Ahmad A.; Follum, Michael L.

doi:10.1016/j.jhydrol.2019.124406

Cited by 101 publications

(76 citation statements)

References 86 publications

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“…The utility of geoBAM vis a vis its accuracy is therefore interesting in light of other data intensive, higher skill approaches for discharge estimation. This “local relevance” of global hydrology has been explored from multiple perspectives (e.g., Benstead & Leigh, 2012; Bierkens et al, 2015; Brown et al, 2015; Fleischmann et al, 2019; Rajib et al, 2020). Specifically, Fleischmann et al (2019) suggest that for a global hydrodynamic model to be “locally relevant,” discharge estimates need to have an NSE > 0.90.…”

Section: Discussionmentioning

confidence: 99%

Constraining Remote River Discharge Estimation Using Reach‐Scale Geomorphology

Brinkerhoff

Gleason

Feng

et al. 2020

Water Resources Research

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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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Section: Discussionmentioning

confidence: 99%

Constraining Remote River Discharge Estimation Using Reach‐Scale Geomorphology

Brinkerhoff

Gleason

Feng

et al. 2020

Water Resources Research

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“…Increased research interest in the hydrological, biophysical, and biogeochemical functions of surface depressions has led to a concomitant trend of depression-integrated SWAT applications in small-or meso-scale watersheds (e.g., Almendinger et al, 2014;Evenson et al, 2016;Evenson et al, 2018,b;Liu et al, 2008;Nasab et al, 2017;Wang et al, 2008). Because SWAT is also frequently used for modeling processes within major river basins worldwide (e.g., Abbaspour et al, 2015;Du et al, 2018;Rajib et al, 2020;Schuol et al, 2008), it is an ideal tool to explore how surface depressions influence basin-scale hydrologic predictions. Using SMAP data for the entire simulation period (2008-2017) was not feasible because of data availability since 31 March 2015.…”

Section: The Hydrologic Modelmentioning

confidence: 99%

Surface Depression and Wetland Water Storage Improves Major River Basin Hydrologic Predictions

Rajib

Golden

Lane

et al. 2020

Water Resources Research

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Surface water storage in small yet abundant landscape depressions—including wetlands and other small waterbodies—is largely disregarded in conventional hydrologic modeling practices. No quantitative evidence exists of how their exclusion may lead to potentially inaccurate model projections and understanding of hydrologic dynamics across the world's major river basins. To fill this knowledge gap, we developed the first‐ever major river basin‐scale modeling approach integrating surface depressions and focusing on the 450,000‐km2 Upper Mississippi River Basin (UMRB) in the United States. We applied a novel topography‐based algorithm to estimate areas and volumes of ~455,000 surface depressions (>1 ha) across the UMRB (in addition to lakes and reservoirs) and subsequently aggregated their effects per subbasin. Compared to a “no depression” conventional model, our depression‐integrated model (a) improved streamflow simulation accuracy with increasing upstream abundance of depression storage, (b) significantly altered the spatial patterns and magnitudes of water yields across 315,000 km2 (70%) of the basin area, and (c) provided realistic spatial distributions of rootzone wetness conditions corresponding to satellite‐based data. Results further suggest that storage capacity (i.e., volume) alone does not fully explain depressions' cumulative effects on landscape hydrologic responses. Local (i.e., subbasin level) climatic and geophysical drivers and downstream flowpath‐regulating structures (e.g., reservoirs and dams) influence the extent to which depression storage volume in a subbasin causes hydrologic effects. With these new insights, our study supports the integration of surface depression storage and thereby catalyzes a reassessment of current hydrological modeling and management practices for basin‐scale studies.

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“…Predicting the hurricane-level extreme flood events requires a compromise between accuracy and efficiency. To accommodate the required lead time, the large-scale river forecasting systems usually adopt one-dimensional kinematic-wave or diffusive-wave equations to represent channel flows [13,14], which may or may not take account of adequate flow regimes on a local scale, especially when the two-way interactions between the channel flow and overland lateral inflow exist. For example, some non-physics-based simplified models could not be used for rapidly varying flow simulations [15].…”

Section: Introductionmentioning

confidence: 99%

An Assessment of the Influence of Uncertainty in Temporally Evolving Streamflow Forecasts on Riverine Inundation Modeling

Feng

Judi

Rakowski

2020

Water

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Continental-scale river forecasting platforms forecast streamflow at reaches that can be used as boundary conditions to drive a local-scale flood inundation model. Uncertainty accumulated during this process stems not only from any part of the forecasting chain but can also be caused by the daily variations in weather forcing that keeps evolving as the event advances. This work aims to examine the influence of the evolving forecast streamflow on predicting the maximum inundation for extreme floods. A diagnostic case study was made on the basis of a hindcast of Hurricane Matthew striking the eastern U.S. in 2016. The U.S. National Water Model was one-way coupled to a hydrodynamic inundation model through a developed automated workflow. Although the river forcing has significantly mismatched hydrographs versus observations, the simulated peak water surface elevations and maximum extents were validated to be comparable with the observations, which indicates that the inundation model may not be sensitive to the inherited uncertainty from the weather forcing. Moreover, the uncertainty of the forecast streamflow time series caused only one order of magnitude fewer variations in inundation prediction; this dampening effect may become clearer for extreme events with large areas inundated. In addition, the forecast total volume of stream discharge appears to be an important metric for assessing the performance of river forcing for inundation mapping, as a linear correlation between the total volume and the accuracy of the predicted peak water surface elevation and maximum extent was found, with the coefficients of determination all above 0.8. Extra best-practice experience of running similar operational tasks demonstrated the tradeoff between the cost and accuracy gain.

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Towards a large-scale locally relevant flood inundation modeling framework using SWAT and LISFLOOD-FP

Cited by 101 publications

References 86 publications

Constraining Remote River Discharge Estimation Using Reach‐Scale Geomorphology

Constraining Remote River Discharge Estimation Using Reach‐Scale Geomorphology

Surface Depression and Wetland Water Storage Improves Major River Basin Hydrologic Predictions

An Assessment of the Influence of Uncertainty in Temporally Evolving Streamflow Forecasts on Riverine Inundation Modeling

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