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Towards a large-scale locally relevant flood inundation modeling framework using SWAT and LISFLOOD-FP
Journal Pre-proofs Research papers Towards a large-scale locally relevant flood inundation modeling framework using SWAT and LISFLOOD-FP Adnan Rajib, Zhu Liu, Venkatesh Merwade, Ahmad A. Tavakoly, Michael L. Follum PII: DOI: Reference:
S0022-1694(19)31141-2 https://doi.org/10.1016/j.jhydrol.2019.124406 HYDROL 124406
To appear in:
Journal of Hydrology
Received Date: Revised Date: Accepted Date:
16 October 2019 23 November 2019 25 November 2019
Please cite this article as: Rajib, A., Liu, Z., Merwade, V., Tavakoly, A.A., Follum, M.L., Towards a large-scale locally relevant flood inundation modeling framework using SWAT and LISFLOOD-FP, Journal of Hydrology (2019), doi: https://doi.org/10.1016/j.jhydrol.2019.124406
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2019 Published by Elsevier B.V.
Towards a large-scale locally relevant flood inundation modeling framework using SWAT and LISFLOOD-FP Adnan Rajib1*#, Zhu Liu2*, Venkatesh Merwade3, Ahmad A. Tavakoly4,5, and Michael L. Follum5 1Department 2Department 3Lyles 4Earth 5Coastal
of Environmental Engineering, Texas A&M University, Kingsville, Texas, USA
of Land, Air and Water Resource, University of California, Davis, California, USA
School of Civil Engineering, Purdue University, West Lafayette, Indiana, USA
System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
and Hydraulics Laboratory, US Army Engineer Research and Development Center, Vicksburg, Mississippi, USA
*Shared lead authorship #Corresponding
author: Frank H. Dotterweich College of Engineering, 700 University Blvd, MSC 213, Kingsville, TX 78363, USA; email: [email protected]
Highlights
Degree of local relevance is defined by the spatial resolution of stream network.
Simulation of real-life flood events across 26,000 streams in the Ohio River Basin.
Satellite images and another similar framework verify flood mapping accuracy.
Incorporating streamflow uncertainty in flood maps minimizes prediction bias.
Streamflow input in lower order streams is essential for accurate flood mapping.
ABSTRACT
1
Lack of geospecificity or local relevance is a major limitation in contemporary large-scale flood modeling frameworks. There is a little practical value for configuring a large-scale model if the model produces streamflow and/or inundation maps only along the large rivers while numerous lower order streams remain overlooked. This study fills the gap through a new flood prediction framework based on the loose coupling of a hydrologic model Soil and Water Assessment Tool (SWAT) and a 1D/2D hydrodynamic model LISFLOOD-FP (hence, SWAT-LISFP). The prototype SWAT-LISFP framework was configured with ~26,000 stream reaches across the ~500,000 km2 Ohio River Basin, United States. After being calibrated against 50 gauge stations across the basin, SWAT simulated streamflow outputs were fed as upstream boundary conditions in LISFLOOD-FP. The resultant flood inundation extents consistently captured 70-80% of the remotely sensed inundation, irrespective of the flood events or locations within the basin. This was also confirmed via cross-validation with an existing flood modeling framework AutoRAPID (Follum et al., 2017). Additional modeling experiments were conducted to facilitate two critical discussions – how simulated inundation extent is affected by the uncertainty in streamflow prediction and the density of streamflow boundary conditions. Taking into account the uncertainties in SWAT streamflow, LISFLOOD-FP showed a remarkable improvement with more than 95% of remotely sensed inundation captured within the simulated extent. While this approach produces a variable-area flood map (i.e., a range of areas likely to be inundated at a particular point of time), inundation in the lower order streams can still remain undetected. A solution to this problem was demonstrated by setting up streamflow boundary conditions across further lower order streams, which subsequently justified the need for high-resolution stream network, and hence, the essence of locally relevant flood inundation modeling. The new contributions of his study, particularly through introducing SWAT as a functional hydrologic alternative to supplement a
2
hydrodynamic model such as LISFLOOD-FP and the series of experiments to draw insights on addressing lack of accuracy and local relevance, will enhance the global flood modeling initiatives.
Keywords: Flood mapping; floodplain; global flood hazards, hydrologic modeling; hydrodynamic modeling; Ohio River Basin
1. INTRODUCTION With frequently occurring high magnitude floods and associated socio-economic damages in many regions across the world, researchers and decision makers are actively seeking suitable approaches for flood inundation mapping. Traditionally, flood inundation maps are created by driving a hydrodynamic model with available stage or streamflow data (e.g., Cook and Merwade, 2009; Liu et al., 2019a; Liu and Merwade, 2019; Musser and Dyar, 2007). However, this approach cannot represent the propagation, retention, and attenuation of flooding across the entire stream network. This is because gauges that record flow and stage data are mostly installed along higher order streams, thereby leaving many lower order streams unmonitored. Even the data available at higher order streams are of too short length or too poor quality to accurately capture floodplain hydrodynamics (Di Baldassarre and Montanari, 2009). An effective way to address this information gap, at both gauged and ungauged streams, is through an integrated hydrologichydrodynamic modeling framework. In such a framework, one or more hydrologic models can simulate streamflow, which can provide upstream boundary conditions (or forcing data) to one or more hydrodynamic models for producing flood inundation maps. The coupling of hydrologic and hydrodynamic models is a well-recognized approach, but common only to small watersheds (e.g., Huang and Hattermann, 2018; Kim et al., 2012; Komi et
3
al., 2017; Nguyen et al., 2016). Development of “large-scale” hydrologic-hydrodynamic modeling frameworks covering the world’s major river basin(s) (e.g., Biancamaria et al., 2009; Hoch et al., 2017; Paiva et al., 2013; Paiva et al., 2011; Schuman et al., 2013) are not yet full-fledged. We identified three persistent limitations in the current state-of-the-art large-scale flood modeling initiatives: (i) lack of geospecificity or “local relevance” in predicted flood information, (ii) applications restricted only to identify a zone of potential food hazards (so-called floodplain maps), not the actual flooding, and (iii) acute inaccuracy problem. To justify the rationale of our study, these limitations are elaborately discussed below. Large-scale predictions of streamflow and inundation maps are immensely helpful for operational disaster information systems. What seriously impedes such potential is the lack of “local relevance”. Spatial resolution of topography data and computational mesh configuration in a hydrologic and/or hydrodynamic model are frequently used to define the local relevance of its outputs (e.g., Chen et al., 2017; Gallegos et al., 2009; Hartnett and Nash, 2017; Wood et al., 2011). We suggest that this definition is a misnomer for (fluvial) flood prediction and mitigation planning. For instance, there is little practical value for configuring a model with meter-scale computational mesh if the so-called high- or hyper-resolution model produces streamflow and/or inundation maps only along the main river channels (see, e.g., Biancamaria et al., 2009; Chen et al., 2017; Komi et al., 2017; Logah et al., 2017; Schuman et al., 2013; Wilson et al., 2007). On the contrary, it is physically more meaningful and thus practically informative to construct flood models so that the degree of local relevance is dictated by the resolution (spatial density) of stream network. Considering as many lower order streams as possible via a high-resolution stream network would ensure the maximum local relevance of streamflow and inundation maps across large spatial scales
4
– a fundamental aspect of flood prediction previously ignored in research and operational management. In line with efforts to build “large-scale locally relevant” flood prediction frameworks, global flood hazard models have received considerable attention in recent times (e.g., Alfieri et al., 2014; Dottori et al., 2016; Pappenberger et al., 2012; Sampson et al., 2015; Wing et al., 2017). However, these initiatives have only produced static maps with a specific probability of flood occurrence (e.g., 100-year return period). While these maps stimulate new-generation of spatially explicit floodplain ecohydrologic and socio-economic management (Kiedrzyńska et al., 2015; Smith et al., 2019), they cannot be considered as actual flood maps. Near real-time mapping of flood inundation extents (with corresponding streamflow data) is the main requirement for a decision-support system that can inform real-life flood management. To facilitate near-real time predictions, National Oceanic and Atmospheric Administration in the United States developed the National Water Model (NWM) framework (Maidment, 2017). NWM predicts streamflow and potential flood inundation extents across ~2.7 million streams in the continental US – an unprecedented combination considering both spatial scale and resolution of stream network (Maidment et al., 2016). Follum et al. (2017) introduced the AutoRAPID framework, demonstrating its capability for fast, parsimonious flood map simulation across millions of streams in the Midwest US and Mississippi Delta. There is a concomitant trend of similar initiatives for other parts of the world as well (e.g., GLOFRIM by Hoch et al., 2017). While these emergent frameworks offer increased flood information capacity in public domain, their accuracy during real-life flood events and means to efficiently address their prediction uncertainties have not been thoroughly investigated (see, e.g., Afshari et al., 2018; Salas et al., 2018; Zheng et al., 2018a). What is also being overlooked in the previous studies is the possibility 5
that two such frameworks, independently developed for a particular basin, may produce largely inconsistent flood information. Accordingly, there remains the urgency to continue developing robust hydrologic-hydrodynamic model combinations and ensure reliable flood information via real-life accuracy assessments and inter-framework comparisons. This study aims to fill these gaps with a newly developed large-scale locally relevant flood prediction framework. At its core, the framework loosely couples hydrologic model SWAT (Soil and Water Assessment Tool; Arnold et al., 2012; Neitsch et al., 2011) with 1D/2D hydrodynamic model LISFLOOD-FP (Bates and De Roo, 2000; Horritt and Bates, 2001; Bates et al., 2013; Neal et al., 2012). With a prototype daily-simulation setup involving ~26,000 stream reaches across the Ohio River Basin, a ~500,000 km2 region within the Mississippi River system (Figure 1), we addressed the following questions to infer a comprehensive understanding on the framework’s predictability and representativeness: 1. How accurate is the new framework during real-life flood events? Does the existing
framework(s) corroborate the new framework’s predicted flood inundation extents? 2. Does knowing the level of hydrologic uncertainty help increase the predictability of inundation
extents? 3. How does local relevance (i.e., spatial resolution of stream network) influence models’ ability to capture inundation extents over large spatial scales?
S0022-1694(19)31141-2 https://doi.org/10.1016/j.jhydrol.2019.124406 HYDROL 124406
To appear in:
Journal of Hydrology
Received Date: Revised Date: Accepted Date:
16 October 2019 23 November 2019 25 November 2019
Please cite this article as: Rajib, A., Liu, Z., Merwade, V., Tavakoly, A.A., Follum, M.L., Towards a large-scale locally relevant flood inundation modeling framework using SWAT and LISFLOOD-FP, Journal of Hydrology (2019), doi: https://doi.org/10.1016/j.jhydrol.2019.124406
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2019 Published by Elsevier B.V.
Towards a large-scale locally relevant flood inundation modeling framework using SWAT and LISFLOOD-FP Adnan Rajib1*#, Zhu Liu2*, Venkatesh Merwade3, Ahmad A. Tavakoly4,5, and Michael L. Follum5 1Department 2Department 3Lyles 4Earth 5Coastal
of Environmental Engineering, Texas A&M University, Kingsville, Texas, USA
of Land, Air and Water Resource, University of California, Davis, California, USA
School of Civil Engineering, Purdue University, West Lafayette, Indiana, USA
System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
and Hydraulics Laboratory, US Army Engineer Research and Development Center, Vicksburg, Mississippi, USA
*Shared lead authorship #Corresponding
author: Frank H. Dotterweich College of Engineering, 700 University Blvd, MSC 213, Kingsville, TX 78363, USA; email: [email protected]
Highlights
Degree of local relevance is defined by the spatial resolution of stream network.
Simulation of real-life flood events across 26,000 streams in the Ohio River Basin.
Satellite images and another similar framework verify flood mapping accuracy.
Incorporating streamflow uncertainty in flood maps minimizes prediction bias.
Streamflow input in lower order streams is essential for accurate flood mapping.
ABSTRACT
1
Lack of geospecificity or local relevance is a major limitation in contemporary large-scale flood modeling frameworks. There is a little practical value for configuring a large-scale model if the model produces streamflow and/or inundation maps only along the large rivers while numerous lower order streams remain overlooked. This study fills the gap through a new flood prediction framework based on the loose coupling of a hydrologic model Soil and Water Assessment Tool (SWAT) and a 1D/2D hydrodynamic model LISFLOOD-FP (hence, SWAT-LISFP). The prototype SWAT-LISFP framework was configured with ~26,000 stream reaches across the ~500,000 km2 Ohio River Basin, United States. After being calibrated against 50 gauge stations across the basin, SWAT simulated streamflow outputs were fed as upstream boundary conditions in LISFLOOD-FP. The resultant flood inundation extents consistently captured 70-80% of the remotely sensed inundation, irrespective of the flood events or locations within the basin. This was also confirmed via cross-validation with an existing flood modeling framework AutoRAPID (Follum et al., 2017). Additional modeling experiments were conducted to facilitate two critical discussions – how simulated inundation extent is affected by the uncertainty in streamflow prediction and the density of streamflow boundary conditions. Taking into account the uncertainties in SWAT streamflow, LISFLOOD-FP showed a remarkable improvement with more than 95% of remotely sensed inundation captured within the simulated extent. While this approach produces a variable-area flood map (i.e., a range of areas likely to be inundated at a particular point of time), inundation in the lower order streams can still remain undetected. A solution to this problem was demonstrated by setting up streamflow boundary conditions across further lower order streams, which subsequently justified the need for high-resolution stream network, and hence, the essence of locally relevant flood inundation modeling. The new contributions of his study, particularly through introducing SWAT as a functional hydrologic alternative to supplement a
2
hydrodynamic model such as LISFLOOD-FP and the series of experiments to draw insights on addressing lack of accuracy and local relevance, will enhance the global flood modeling initiatives.
Keywords: Flood mapping; floodplain; global flood hazards, hydrologic modeling; hydrodynamic modeling; Ohio River Basin
1. INTRODUCTION With frequently occurring high magnitude floods and associated socio-economic damages in many regions across the world, researchers and decision makers are actively seeking suitable approaches for flood inundation mapping. Traditionally, flood inundation maps are created by driving a hydrodynamic model with available stage or streamflow data (e.g., Cook and Merwade, 2009; Liu et al., 2019a; Liu and Merwade, 2019; Musser and Dyar, 2007). However, this approach cannot represent the propagation, retention, and attenuation of flooding across the entire stream network. This is because gauges that record flow and stage data are mostly installed along higher order streams, thereby leaving many lower order streams unmonitored. Even the data available at higher order streams are of too short length or too poor quality to accurately capture floodplain hydrodynamics (Di Baldassarre and Montanari, 2009). An effective way to address this information gap, at both gauged and ungauged streams, is through an integrated hydrologichydrodynamic modeling framework. In such a framework, one or more hydrologic models can simulate streamflow, which can provide upstream boundary conditions (or forcing data) to one or more hydrodynamic models for producing flood inundation maps. The coupling of hydrologic and hydrodynamic models is a well-recognized approach, but common only to small watersheds (e.g., Huang and Hattermann, 2018; Kim et al., 2012; Komi et
3
al., 2017; Nguyen et al., 2016). Development of “large-scale” hydrologic-hydrodynamic modeling frameworks covering the world’s major river basin(s) (e.g., Biancamaria et al., 2009; Hoch et al., 2017; Paiva et al., 2013; Paiva et al., 2011; Schuman et al., 2013) are not yet full-fledged. We identified three persistent limitations in the current state-of-the-art large-scale flood modeling initiatives: (i) lack of geospecificity or “local relevance” in predicted flood information, (ii) applications restricted only to identify a zone of potential food hazards (so-called floodplain maps), not the actual flooding, and (iii) acute inaccuracy problem. To justify the rationale of our study, these limitations are elaborately discussed below. Large-scale predictions of streamflow and inundation maps are immensely helpful for operational disaster information systems. What seriously impedes such potential is the lack of “local relevance”. Spatial resolution of topography data and computational mesh configuration in a hydrologic and/or hydrodynamic model are frequently used to define the local relevance of its outputs (e.g., Chen et al., 2017; Gallegos et al., 2009; Hartnett and Nash, 2017; Wood et al., 2011). We suggest that this definition is a misnomer for (fluvial) flood prediction and mitigation planning. For instance, there is little practical value for configuring a model with meter-scale computational mesh if the so-called high- or hyper-resolution model produces streamflow and/or inundation maps only along the main river channels (see, e.g., Biancamaria et al., 2009; Chen et al., 2017; Komi et al., 2017; Logah et al., 2017; Schuman et al., 2013; Wilson et al., 2007). On the contrary, it is physically more meaningful and thus practically informative to construct flood models so that the degree of local relevance is dictated by the resolution (spatial density) of stream network. Considering as many lower order streams as possible via a high-resolution stream network would ensure the maximum local relevance of streamflow and inundation maps across large spatial scales
4
– a fundamental aspect of flood prediction previously ignored in research and operational management. In line with efforts to build “large-scale locally relevant” flood prediction frameworks, global flood hazard models have received considerable attention in recent times (e.g., Alfieri et al., 2014; Dottori et al., 2016; Pappenberger et al., 2012; Sampson et al., 2015; Wing et al., 2017). However, these initiatives have only produced static maps with a specific probability of flood occurrence (e.g., 100-year return period). While these maps stimulate new-generation of spatially explicit floodplain ecohydrologic and socio-economic management (Kiedrzyńska et al., 2015; Smith et al., 2019), they cannot be considered as actual flood maps. Near real-time mapping of flood inundation extents (with corresponding streamflow data) is the main requirement for a decision-support system that can inform real-life flood management. To facilitate near-real time predictions, National Oceanic and Atmospheric Administration in the United States developed the National Water Model (NWM) framework (Maidment, 2017). NWM predicts streamflow and potential flood inundation extents across ~2.7 million streams in the continental US – an unprecedented combination considering both spatial scale and resolution of stream network (Maidment et al., 2016). Follum et al. (2017) introduced the AutoRAPID framework, demonstrating its capability for fast, parsimonious flood map simulation across millions of streams in the Midwest US and Mississippi Delta. There is a concomitant trend of similar initiatives for other parts of the world as well (e.g., GLOFRIM by Hoch et al., 2017). While these emergent frameworks offer increased flood information capacity in public domain, their accuracy during real-life flood events and means to efficiently address their prediction uncertainties have not been thoroughly investigated (see, e.g., Afshari et al., 2018; Salas et al., 2018; Zheng et al., 2018a). What is also being overlooked in the previous studies is the possibility 5
that two such frameworks, independently developed for a particular basin, may produce largely inconsistent flood information. Accordingly, there remains the urgency to continue developing robust hydrologic-hydrodynamic model combinations and ensure reliable flood information via real-life accuracy assessments and inter-framework comparisons. This study aims to fill these gaps with a newly developed large-scale locally relevant flood prediction framework. At its core, the framework loosely couples hydrologic model SWAT (Soil and Water Assessment Tool; Arnold et al., 2012; Neitsch et al., 2011) with 1D/2D hydrodynamic model LISFLOOD-FP (Bates and De Roo, 2000; Horritt and Bates, 2001; Bates et al., 2013; Neal et al., 2012). With a prototype daily-simulation setup involving ~26,000 stream reaches across the Ohio River Basin, a ~500,000 km2 region within the Mississippi River system (Figure 1), we addressed the following questions to infer a comprehensive understanding on the framework’s predictability and representativeness: 1. How accurate is the new framework during real-life flood events? Does the existing
framework(s) corroborate the new framework’s predicted flood inundation extents? 2. Does knowing the level of hydrologic uncertainty help increase the predictability of inundation
extents? 3. How does local relevance (i.e., spatial resolution of stream network) influence models’ ability to capture inundation extents over large spatial scales?
2. MODEL SELECTION Many 1D/2D hydrodynamic models have been developed as stand-alone software packages to support reach- or watershed-scale simulations (Nelson et al., 2003; Musser and Dyar,
6
2007; Vanderkimpen et al., 2009; Ballesteros et al., 2011; Nguyen et al. 2016; USACE, 2016). For a large-scale flood modeling framework involving numerous streams, choice of a hydrodynamic model depends primarily on its easy interoperability with a driver hydrologic model, opportunities for cyber-enabled high performance computation, and flexibility for data assimilation (Kauffeldt et al., 2016; Merwade et al., 2018). This is why “low-complexity” parsimonious inundation mapping tools relying on topography, roughness parameters, and simple hydraulic equations, such as those used in the NWM or AutoRAPID frameworks (Follum et al., 2017; Zheng et al., 2016, 2018a,b), have gained great community attention. However, because these tools lack physical process descriptions on floodplain mechanisms, their application involves a trade-off between fast computation power and accuracy. As such, they may underperform compared to 1D/2D hydrodynamic models (Afshari et al., 2018). To this end, LISFLOOD-FP has emerged as a suitable hydrodynamic model. Although nearly all of the large-scale frameworks using LISFLOOD-FP are designed for mapping a static areal extent with a specific probability of inundation (e.g., the “floodplain maps” by Alfieri et al., 2014; Dottori et al., 2016; Sampson et al., 2015; Wing et al., 2017), it is possible to configure LISFLOOD-FP for near real-time prediction of potential flood events (Wing et al., 2019). Hoch et al. (2017) and Schuman et al. (2013) demonstrated this by coupling LISFLOOD-FP respectively with Variable Infiltration Capacity (VIC; Liang et al., 2003) and PCRaster GLOBal Water Balance (PCR-GLOBWB; van Beek and Bierkens, 2009) models, however, using coarse-resolution stream networks. Therefore, how LISFLOOD-FP would perform in a much complex setup (e.g., 26,000 stream reaches across the 500,000 km2 Ohio River Basin; Figure 1) is still an outstanding question. For large-scale simulation of streamflow across high-resolution stream networks, coupled atmospheric-land surface-river routing models are being increasingly used in recent times (e.g.,
7
Lin et al., 2019, 2018a,b; Tavakoly et al., 2017). For instance, the NOAH-MP model, under the auspices of WRF-Hydro modeling system, is adopted in the NWM flood prediction framework (Salas et al., 2018). There have been several large-scale applications of VIC and PCR-GLOBWB as well, but these applications often involve coarse-resolution stream network (e.g., Hoch et al., 2017; Schuman et al., 2013; as noted above) or monthly time-scale simulation (e.g., Oubeidillah et al., 2014; Safeeq et al., 2014; van Beek et al., 2011). SWAT is another process-based model frequently applied on the world’s major river basins (e.g., Abbaspour et al., 2015; Daggupati et al., 2015; Du et al., 2018; Pervez and Henebry, 2015; Rajib and Merwade, 2017; Schuol et al., 2008). In addition to the well-documented evaluations that approve SWAT’s effectiveness (Arnold et al., 2012), there are web-based platforms such as SWATShare that can be used to publish and share SWAT models with a broader community, and perform extensive calibration using high performance computational resource (Rajib et al., 2016a). The companion tools of SWATShare such as HydroGlobe can assimilate multi-source earth observations (e.g., Rajib et al., 2018a,b) and SWATFlow can perform dynamic visualization of hydrographs and flood inundation extents. These are the emergent opportunities which led to SWAT’s selection for our flood prediction framework. To the best of our knowledge, this is the first study to show the application of SWAT model as the hydrologic component of a large-scale flood prediction framework. Our framework also involves the most complex assembly of LISFLOOD-FP till date, specifically considering the combined overburden of large basin area, high-resolution stream network and topography, and continuous daily simulation. While this study demonstrates a prototype framework using recent flood events in the Ohio River Basin as test cases, efforts are underway to extend the models over a much larger domain and perform near real-time predictions.
8
3. METHODOLOGY 3.1 Ohio River Basin as the flood modeling testbed The ~500,000 km2 Ohio River Basin (ORB) (Figure 1) is the largest sub-basin of the Mississippi River system in terms of water volume, thus carrying a great socio-economic and ecological significance for the North American continent. An intriguing factor to consider ORB as a flood modeling testbed is the increasing frequency of flood events over the past decades (Holmes et al., 2010; Holmes and Dinicola, 2012; Mallakpour and Villarini, 2015). A historical record by US National Weather Service (NWS, 2018) suggests that the Ohio River at Cincinnati (located in the most downstream portion of the basin) came near or above the designated flood stage 108 times within a span of 160 years (1858-2018, i.e., nearly 1 potential extreme event in every 1.5-year period). Previous studies analyzing land management and/or climate change effects also confirmed the increasing flood frequency in ORB (e.g., Du et al., 2018; Kunkel et al., 2013; Tayyebi et al., 2015). Despite this rising threats of flood hazard, flood modeling studies specifically focusing on ORB do not exist in literature. 3.2 Hydrologic modeling 3.2.1 SWAT model setup Setting up a SWAT model in the GIS interface (ArcSWAT) requires three geospatial datasets: digital elevation model (DEM), land use, and soil texture. These datasets were obtained from the following sources: 30-m DEM from the US Geological Survey (USGS) National Elevation Dataset (NED) (USGS, 2013a), 30-m land use data from the 2011-National Land Cover Database (NLCD) (USGS, 2013b), and 1:250,000 scale State Soil Geographic Data (STATSGO) built-in with SWAT geodatabase. To reduce the computational burden, all topographic analyses
9
(e.g., sub-basin delineation) were performed by resampling the DEM to a 90-m spatial resolution. Following several other large-scale SWAT applications (e.g., Schuol et al., 2008; Faramarzi et al., 2017; Du et al., 2018), a sub-basin was treated as the smallest spatial unit of hydrologic simulations. Elevation difference across the basin was represented via three slope classes (0-4%, 4-10%, and >10%). To enable comparison with similar flood modeling initiatives in the US (prevously noted, e.g., Follum et al., 2017; Maidment, 2017; Tavakoly et al., 2017), the NHDPlus stream network (National Hydrography Dataset Plus; McKay et al., 2012) was superimposed (burned-in) on the 90-m DEM, along with a 100 km2 threshold area for headwater stream identification. This 100 km2 threshold was selected via multiple iterations to spatially match the DEM-generated stream network with NHDPlus dataset. The model could also use NHDPlus stream network directly; however, such an option would require a significant amount of GIS pre-processing and quality assessment tasks to ensure continuous connectivity of streams (see, e.g., David et al., 2013). Nonetheless, our DEM-based network delineation resulted into ~26,000 individual stream reaches (and sub-basins) (Figure 1). For every stream reach, hydraulic geometry, i.e., bank-full width (wc) and depth (dc) were defined by relating upstream drainage area (DA) with semi-empirical power law relationships (wc = 1.29DA0.6 and dc = 0.13DA0.4 where DA is in km2 and w is in m; Her et al., 2017). This approach, similar to other large-scale hydrologic/river routing models (Hoch et al., 2017; Li et al., 2013; Paiva et al., 2013), allowed w values in lower order streams to be smaller than the DEM’s spatial resolution. For instance, the minimum wc in the ORB model was 2-m, whereas the underlying DEM cell-size was 90-m. The above-bank water was simply routed through a hypothetical floodplain with a constant 5wc width (Neitsch et al., 2011). 10
Weather forcing data were obtained from the ~12 km North American Data Assimilation System (NLDAS-phase II; NASA, 2019) and National Centers for Environmental Prediction's (NCEP) ~38 km Climate Forecast System Reanalysis (CFSR; Saha et al., 2010). Specifically, total daily precipitation were obtained from NLDAS, whereas other energy-related inputs including minimum/maximum daily temperature, solar radiation, relative humidity and wind speed were obtained from CFSR. Potential evapotranspiration (PET) was estimated using SWAT’s built-in Penman-Monteith approach. Nearly 30% of ORB has artificial subsurface drainage (tile-drainage) (Du et al., 2018). Therefore, tile-drainage routine was activated for all the sub-basins having poorly-drained soils (e.g., Du et al., 2018; Green et al., 2006). Reservoir storage-discharge could not be explicitly considered in the current ORB setup because of limited access to dam operation data, although this limitation was implicitly addressed via rigorous streamflow calibration across a large number of gauge stations. Processes related to infiltration/ surface runoff generation and channel routing were simulated using the Curve Number and Variable Storage methods, respectively (Neitsch et al., 2011). 3.2.2 Calibration and validation of SWAT The ORB SWAT model was first initialized for 1-year (2009) and then calibrated for a 2year period (2010-2011) using daily streamflow observations at 50 USGS gauge stations (Figure 2a and supplementary information S1). The simultaneous multi-site calibration involved 18 parameters related to surface, subsurface, snow accumulation, and in-stream hydrologic responses (supplementary information S2). Du et al. (2018) previously calibrated a coarse-resolution ORB SWAT model using 80 years (1935-2014) of streamflow data at 9 gauge stations. For calibrating the current ORB SWAT model, initial parameter ranges were defined based on the optimization 11
results of Du et al. (2018). The parameter optimization was conducted with Sequential Uncertainty Fitting algorithm-version 2 (SUFI-2; Abbaspour, 2015) using a weighted Kling-Gupta Efficiency (KGE) (Gupta et al., 2009; Rajib et al., 2016b, 2018b) as the objective function. KGE was maximized to obtain the most optimal parameter set which produces the best possible agreement between simulated and observed streamflow hydrographs at target locations (KGE =1 means 100% agreement). Relying on a single streamflow output time-series (the so-called best hydrograph) may not be reasonable because of the uncertainties in extreme flow predictions – a problem common to any large-scale hydrologic model (Abbaspour et al., 2015; Faramarzi et al., 2017). Therefore, a “band” hydrograph representing the 95% prediction uncertainty (Abbaspour, 2015) was produced for every reach in addition to the “best” hydrograph representing the most optimal streamflow (corresponding the most optimal parameter set). To allow an independent posterior evaluation, the model was validated over a separate 2-year period (2012-2013), and at 5 separate gauge stations across the basin targeting different upstream drainage areas, topography, land use, and climatic conditions (Figure 2a). < Figure 2> 3.3 Hydrodynamic modeling 3.3.1 Coupling SWAT and LISFLOOD-FP Simulated streamflow outputs from SWAT were fed as the upstream boundary conditions in LISFLOOD-FP. This was a “loose coupling” prototype architecture where both SWAT and LISFLOO-FP were executed in stand-alone mode, and an intermediate relational data model (e.g., Hoch et al., 2017; Peckham et al., 2013) allowed the one-way transfer of information. We 12
performed three separate sets of simulations (M1–M3) by varying the boundary conditions of LISFLOOD-FP (Table 1). Irrespective of their differences, the basic setup and parameterization procedure in LISFLOOD-FP were kept identical across all the configurations. Throughout the following discussion, we name the framework SWAT-LISFP. 3.3.2 LISFLOOD-FP setup and calibration The geospatial datasets (topography, land use, and stream network) required to set up LISFLOOD-FP were the same as in the ORB SWAT model. In all the configurations (M1–M3), a sub-grid scale hydrodynamic scheme (Neal et al., 2012) was applied to solve the momentum and continuity equations for channel flow and floodplain flow, respectively (Eq.1-3).
6
2007; Vanderkimpen et al., 2009; Ballesteros et al., 2011; Nguyen et al. 2016; USACE, 2016). For a large-scale flood modeling framework involving numerous streams, choice of a hydrodynamic model depends primarily on its easy interoperability with a driver hydrologic model, opportunities for cyber-enabled high performance computation, and flexibility for data assimilation (Kauffeldt et al., 2016; Merwade et al., 2018). This is why “low-complexity” parsimonious inundation mapping tools relying on topography, roughness parameters, and simple hydraulic equations, such as those used in the NWM or AutoRAPID frameworks (Follum et al., 2017; Zheng et al., 2016, 2018a,b), have gained great community attention. However, because these tools lack physical process descriptions on floodplain mechanisms, their application involves a trade-off between fast computation power and accuracy. As such, they may underperform compared to 1D/2D hydrodynamic models (Afshari et al., 2018). To this end, LISFLOOD-FP has emerged as a suitable hydrodynamic model. Although nearly all of the large-scale frameworks using LISFLOOD-FP are designed for mapping a static areal extent with a specific probability of inundation (e.g., the “floodplain maps” by Alfieri et al., 2014; Dottori et al., 2016; Sampson et al., 2015; Wing et al., 2017), it is possible to configure LISFLOOD-FP for near real-time prediction of potential flood events (Wing et al., 2019). Hoch et al. (2017) and Schuman et al. (2013) demonstrated this by coupling LISFLOOD-FP respectively with Variable Infiltration Capacity (VIC; Liang et al., 2003) and PCRaster GLOBal Water Balance (PCR-GLOBWB; van Beek and Bierkens, 2009) models, however, using coarse-resolution stream networks. Therefore, how LISFLOOD-FP would perform in a much complex setup (e.g., 26,000 stream reaches across the 500,000 km2 Ohio River Basin; Figure 1) is still an outstanding question. For large-scale simulation of streamflow across high-resolution stream networks, coupled atmospheric-land surface-river routing models are being increasingly used in recent times (e.g.,
7
Lin et al., 2019, 2018a,b; Tavakoly et al., 2017). For instance, the NOAH-MP model, under the auspices of WRF-Hydro modeling system, is adopted in the NWM flood prediction framework (Salas et al., 2018). There have been several large-scale applications of VIC and PCR-GLOBWB as well, but these applications often involve coarse-resolution stream network (e.g., Hoch et al., 2017; Schuman et al., 2013; as noted above) or monthly time-scale simulation (e.g., Oubeidillah et al., 2014; Safeeq et al., 2014; van Beek et al., 2011). SWAT is another process-based model frequently applied on the world’s major river basins (e.g., Abbaspour et al., 2015; Daggupati et al., 2015; Du et al., 2018; Pervez and Henebry, 2015; Rajib and Merwade, 2017; Schuol et al., 2008). In addition to the well-documented evaluations that approve SWAT’s effectiveness (Arnold et al., 2012), there are web-based platforms such as SWATShare that can be used to publish and share SWAT models with a broader community, and perform extensive calibration using high performance computational resource (Rajib et al., 2016a). The companion tools of SWATShare such as HydroGlobe can assimilate multi-source earth observations (e.g., Rajib et al., 2018a,b) and SWATFlow can perform dynamic visualization of hydrographs and flood inundation extents. These are the emergent opportunities which led to SWAT’s selection for our flood prediction framework. To the best of our knowledge, this is the first study to show the application of SWAT model as the hydrologic component of a large-scale flood prediction framework. Our framework also involves the most complex assembly of LISFLOOD-FP till date, specifically considering the combined overburden of large basin area, high-resolution stream network and topography, and continuous daily simulation. While this study demonstrates a prototype framework using recent flood events in the Ohio River Basin as test cases, efforts are underway to extend the models over a much larger domain and perform near real-time predictions.
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3. METHODOLOGY 3.1 Ohio River Basin as the flood modeling testbed The ~500,000 km2 Ohio River Basin (ORB) (Figure 1) is the largest sub-basin of the Mississippi River system in terms of water volume, thus carrying a great socio-economic and ecological significance for the North American continent. An intriguing factor to consider ORB as a flood modeling testbed is the increasing frequency of flood events over the past decades (Holmes et al., 2010; Holmes and Dinicola, 2012; Mallakpour and Villarini, 2015). A historical record by US National Weather Service (NWS, 2018) suggests that the Ohio River at Cincinnati (located in the most downstream portion of the basin) came near or above the designated flood stage 108 times within a span of 160 years (1858-2018, i.e., nearly 1 potential extreme event in every 1.5-year period). Previous studies analyzing land management and/or climate change effects also confirmed the increasing flood frequency in ORB (e.g., Du et al., 2018; Kunkel et al., 2013; Tayyebi et al., 2015). Despite this rising threats of flood hazard, flood modeling studies specifically focusing on ORB do not exist in literature. 3.2 Hydrologic modeling 3.2.1 SWAT model setup Setting up a SWAT model in the GIS interface (ArcSWAT) requires three geospatial datasets: digital elevation model (DEM), land use, and soil texture. These datasets were obtained from the following sources: 30-m DEM from the US Geological Survey (USGS) National Elevation Dataset (NED) (USGS, 2013a), 30-m land use data from the 2011-National Land Cover Database (NLCD) (USGS, 2013b), and 1:250,000 scale State Soil Geographic Data (STATSGO) built-in with SWAT geodatabase. To reduce the computational burden, all topographic analyses
9
(e.g., sub-basin delineation) were performed by resampling the DEM to a 90-m spatial resolution. Following several other large-scale SWAT applications (e.g., Schuol et al., 2008; Faramarzi et al., 2017; Du et al., 2018), a sub-basin was treated as the smallest spatial unit of hydrologic simulations. Elevation difference across the basin was represented via three slope classes (0-4%, 4-10%, and >10%). To enable comparison with similar flood modeling initiatives in the US (prevously noted, e.g., Follum et al., 2017; Maidment, 2017; Tavakoly et al., 2017), the NHDPlus stream network (National Hydrography Dataset Plus; McKay et al., 2012) was superimposed (burned-in) on the 90-m DEM, along with a 100 km2 threshold area for headwater stream identification. This 100 km2 threshold was selected via multiple iterations to spatially match the DEM-generated stream network with NHDPlus dataset. The model could also use NHDPlus stream network directly; however, such an option would require a significant amount of GIS pre-processing and quality assessment tasks to ensure continuous connectivity of streams (see, e.g., David et al., 2013). Nonetheless, our DEM-based network delineation resulted into ~26,000 individual stream reaches (and sub-basins) (Figure 1). For every stream reach, hydraulic geometry, i.e., bank-full width (wc) and depth (dc) were defined by relating upstream drainage area (DA) with semi-empirical power law relationships (wc = 1.29DA0.6 and dc = 0.13DA0.4 where DA is in km2 and w is in m; Her et al., 2017). This approach, similar to other large-scale hydrologic/river routing models (Hoch et al., 2017; Li et al., 2013; Paiva et al., 2013), allowed w values in lower order streams to be smaller than the DEM’s spatial resolution. For instance, the minimum wc in the ORB model was 2-m, whereas the underlying DEM cell-size was 90-m. The above-bank water was simply routed through a hypothetical floodplain with a constant 5wc width (Neitsch et al., 2011). 10
Weather forcing data were obtained from the ~12 km North American Data Assimilation System (NLDAS-phase II; NASA, 2019) and National Centers for Environmental Prediction's (NCEP) ~38 km Climate Forecast System Reanalysis (CFSR; Saha et al., 2010). Specifically, total daily precipitation were obtained from NLDAS, whereas other energy-related inputs including minimum/maximum daily temperature, solar radiation, relative humidity and wind speed were obtained from CFSR. Potential evapotranspiration (PET) was estimated using SWAT’s built-in Penman-Monteith approach. Nearly 30% of ORB has artificial subsurface drainage (tile-drainage) (Du et al., 2018). Therefore, tile-drainage routine was activated for all the sub-basins having poorly-drained soils (e.g., Du et al., 2018; Green et al., 2006). Reservoir storage-discharge could not be explicitly considered in the current ORB setup because of limited access to dam operation data, although this limitation was implicitly addressed via rigorous streamflow calibration across a large number of gauge stations. Processes related to infiltration/ surface runoff generation and channel routing were simulated using the Curve Number and Variable Storage methods, respectively (Neitsch et al., 2011). 3.2.2 Calibration and validation of SWAT The ORB SWAT model was first initialized for 1-year (2009) and then calibrated for a 2year period (2010-2011) using daily streamflow observations at 50 USGS gauge stations (Figure 2a and supplementary information S1). The simultaneous multi-site calibration involved 18 parameters related to surface, subsurface, snow accumulation, and in-stream hydrologic responses (supplementary information S2). Du et al. (2018) previously calibrated a coarse-resolution ORB SWAT model using 80 years (1935-2014) of streamflow data at 9 gauge stations. For calibrating the current ORB SWAT model, initial parameter ranges were defined based on the optimization 11
results of Du et al. (2018). The parameter optimization was conducted with Sequential Uncertainty Fitting algorithm-version 2 (SUFI-2; Abbaspour, 2015) using a weighted Kling-Gupta Efficiency (KGE) (Gupta et al., 2009; Rajib et al., 2016b, 2018b) as the objective function. KGE was maximized to obtain the most optimal parameter set which produces the best possible agreement between simulated and observed streamflow hydrographs at target locations (KGE =1 means 100% agreement). Relying on a single streamflow output time-series (the so-called best hydrograph) may not be reasonable because of the uncertainties in extreme flow predictions – a problem common to any large-scale hydrologic model (Abbaspour et al., 2015; Faramarzi et al., 2017). Therefore, a “band” hydrograph representing the 95% prediction uncertainty (Abbaspour, 2015) was produced for every reach in addition to the “best” hydrograph representing the most optimal streamflow (corresponding the most optimal parameter set). To allow an independent posterior evaluation, the model was validated over a separate 2-year period (2012-2013), and at 5 separate gauge stations across the basin targeting different upstream drainage areas, topography, land use, and climatic conditions (Figure 2a). < Figure 2> 3.3 Hydrodynamic modeling 3.3.1 Coupling SWAT and LISFLOOD-FP Simulated streamflow outputs from SWAT were fed as the upstream boundary conditions in LISFLOOD-FP. This was a “loose coupling” prototype architecture where both SWAT and LISFLOO-FP were executed in stand-alone mode, and an intermediate relational data model (e.g., Hoch et al., 2017; Peckham et al., 2013) allowed the one-way transfer of information. We 12
performed three separate sets of simulations (M1–M3) by varying the boundary conditions of LISFLOOD-FP (Table 1). Irrespective of their differences, the basic setup and parameterization procedure in LISFLOOD-FP were kept identical across all the configurations. Throughout the following discussion, we name the framework SWAT-LISFP.