Development of a Flood Model Based on Globally-Available Satellite Data

Examensarbete vid Institutionen för geovetenskaper Degree Project at the Department of Earth Sciences ISSN 1650-6553 Nr 325

Development of a Flood Model Based on Globally-Available Satellite Data for the Papaloapan River, Mexico Utvecklingen av en översvämningsmodell baserad på globalt tillgängliga satellitdata för floden Papaloapan, Mexiko

Janis Kreiselmeier

INSTITUTIONEN FÖR

GEOVETENSKAPER

DEPARTMENT OF EARTH SCIENCES

Examensarbete vid Institutionen för geovetenskaper Degree Project at the Department of Earth Sciences ISSN 1650-6553 Nr 325

Janis Kreiselmeier

ISSN 1650-6553

Development of a Flood Model Based on Globally-Available Satellite Data for the Papaloapan River, Mexico Janis Kreiselmeier

Flood inundation modelling is highly dependent on an accurate representation of floodplain topography. These remotely sensed accurate data are often not available or expensive, especially in developing countries. As an alternative, freely available Digital Elevation Models (DEMs), such as the near-global Shuttle Radar Topography Mission (SRTM) data, have come into the focus of flood modellers. To what extent these low-resolution data can be exploited for hydraulic modelling is still an open research question. This benchmarking study investigated the potentials and limitations of the SRTM data set for flood inundation modelling on the example of the Papaloapan River, Mexico. Furthermore the effects of vegetation signal removal from the SRTM DEM as in Baugh et al. (2010) were tested. A reference model based on a light detection and ranging (LiDAR) DEM was set up with the model code LISFLOOD-FP and run for two flood events. Test models based on SRTM DEMs were run and output flood extents compared to the reference model by applying a measure of fit. This measure of fit, which was based on binary wet/dry maps of both model outputs, gave information on how well the test models simulated the flood inundation extents compared to the reference model by giving a percentage of the model performance from theoretically 0 to 100 %. SRTM-based models could not reproduce the promising results of previous studies. Flood extents were mostly underestimated and commonly flooded areas were almost exclusively made up out of the main channel surface. One of the reasons for this likely was the much steeper slope of the SRTM DEM as opposed to the LiDAR DEM where water probably was conducted much faster though the main channel. Too high bank cells as well as generally more pronounced elevation differences of the SRTM DEM throughout the whole floodplain were another problem of the SRTM DEM preventing accurate flood inundation simulations. Vegetation signal removal was successful to a certain degree improving the fit by about 10 %. However, a realistic shape of flood extent could not be simulated due to too big pixel sizes of the used canopy height data set. Also, the conditioned models overestimated flooded areas with increasing vegetation signal removal, rendering some of the models useless for comparison, as water leaving the model domain could not be accounted for in the measure of fit. This study showed the limitations of SRTM data for flood inundation modeling where an accurate approximation of the river slope as well as accurately captured bank cells and floodplain topography are crucial for the simulated outcome. Vegetation signal removal has been shown to be potentially useful but should rather be applied on more densely covered catchments.

Keywords: Flood inundation modelling, shuttle radar topography mission (SRTM), light detection and ranging (LiDAR), LISFLOOD-FP, Remote sensing

Degree Project E1 in Earth Science, 1GV025, 30 credits Supervisors: Giuliano Di Baldassarre Department of Earth Sciences, Uppsala University, Villavägen 16, SE-752 36 Uppsala (www.geo.uu.se)

ISSN 1650-6553, Examensarbete vid Institutionen för geovetenskaper, No. 325, 2015

The whole document is available at www.diva-portal.org

Populärvetenskaplig sammanfattning

Utvecklingen av en översvämningsmodell baserad på globalt tillgängliga satellitdata för floden Papaloapan, Mexiko Janis Kreiselmeier

Översvämningar skapar stora problem världen över och fler och fler människor lever i områden som är utsatta för risk för att svämmas över. Dessutom förväntas översvämningar förekomma mer frekvent i många delar av världen i framtiden på grund av klimatförändringar. Skada orsakad av översvämningar kan överstiga flera miljarder US$. Men översvämningar orsakar också andra problem, förutom ekonomiska förluster. De senaste 10 åren har mer än 60 000 människor dött på grund av översvämningar. Ytterligare 900 000 000 människor har drabbats på något sätt. Därför är det viktigt att man vet vilka områden som är utsatta för hög risk. Ett av de verktyg för att avgöra översvämningsrisker är hydrauliska datormodeller som försöker förutse hur en bestämd översvämning breder ut sig. Modellerna är baserade på fysiska principer och topografisk information. Helst vill man ha topografisk information med hög kvalitet och upplösning. Ofta har man data från fjärranalyser, insamlade från flygplan. Ett exempel på det är LiDAR-data som är baserad på laser. Dock är det ofta dyrt eller inte tillgängligt med LiDAR i avlägsna områden och utvecklingsländer, där man behöver sådan data som mest. Därför har forskare försökt att använda globalt tillgängliga topografiska data av låg kvalitet för hydrauliska modeller. En sådan datauppsättning är det så kallade SRTM-datat från amerikanska NASA. SRTM samlas in med hjälp av radarstrålar från satelliter. I flera studier har man fått goda resultat inom översvämningsmodellering med SRTM. Dock måste man testa det vidare för fler avrinningsområden. I den här studien har man försökt att använda SRTM i en hydraulisk modell för den mexikanska floden Papaloapan. För att se hur bra (eller dålig) SRTM-modellen är för att simulera hur en översvämning sprids har man jämfört den med en modell baserad på högkvalitativ LiDAR-data. Båda modellerna simulerade samma översvämningar. Topografiska information från SRTM-data är oftast inkorrekt där det finns väldigt tät och hög vegetation, eftersom radarsignalen då inte räcker till marken och den uppskattade höjden är därför för hög i sådana områden. Av denna anledning ville man därför i denna studie även testa hur resultatet av SRTM-modellen skulle förbättras om man tog bort viss vegetation. Dessvärre var den utformade SRTM-modellen inte så bra för det här fallstudieområdet och SRTM- modellen förutspådde mycket mindre översvämningar än den förmodade mer korrekta LiDAR- modellen. Då vegetation avlägsnandes kunde man förbättra SRTM-modellen till viss mån, men det var fortfarande inte tillräckligt för det här området. Denna studie visar att det är viktigt att fortsätta undersöka hur passande och användbart SRTM är, eftersom det har visat sig att SRTM inte är lämpligt för att förutspå översvämningar i alla delar av världen.

Nyckelord: Översvämningsmodellering, shuttle radar topography mission (SRTM), light detection and ranging (LiDAR), LISFLOOD-FP, Fjärranalys

Examensarbete E1 i geovetenskap, 1GV025, 30 hp Handledare: Giuliano Di Baldassarre Institutionen för geovetenskaper, Uppsala universitet, Villavägen 16, 752 36 Uppsala (www.geo.uu.se)

ISSN 1650-6553, Examensarbete vid Institutionen för geovetenskaper, Nr 325, 2015

Hela publikationen finns tillgänglig på www.diva-portal.org

List of Figures

Figure 1 Overview map roughly displaying the Papaloapan catchment according to the HydroSHEDS database of the United States Geological Survey (USGS)...... 8 Figure 2 Hydrograph depicting hourly measured discharge values at the Papaloapan gauging station for 2011...... 9 Figure 3 Hydrograph depicting hourly measured discharge values at the Papaloapan gauging station from 01.01.1985 until 01.01.1992...... 10 Figure 4 Modelled river reach with the aggregated 90 m resolution LiDAR DEM as a basis...... 12 Figure 5 Modelled river reach with the resampled 90 m SRTM DEM as a basis...... 13 Figure 6 Vegetation map for the modelled river reach based on the global canopy height database (Simard et al. 2011)...... 14 Figure 7 Example profile extracted from the original 5x5 m LiDAR DEM close to the Chacaltianguis gauging station...... 17 Figure 8 Hydrographs and stages for the modelled September 2008 flood...... 19 Figure 9 Hydrographs and stages for the modelled October 2004 flood...... 20 Figure 10 Relative Elevation Differences between aggregated LiDAR DEM and resampled SRTM DEM...... 22 Figure 11 MAE of the simulated stages in comparison with the measured stages for different parameter sets...... 24 Figure 12 Measured and simulated stage at the Chacaltianguis gauging station for the September 2008 flood...... 24 Figure 13 Measured and simulated stage at the Chacaltianguis gauging station for October 2004...... 25 Figure 14 Flood extent of the 2008 flood for both the LiDAR and the SRTM model...... 27 Figure 15 SRTM simulations compared to the LiDAR simulation for the September 2008 event...... 27 Figure 16 Reclassified simulation results for the September 2008 flood event...... 28 Figure 17 Comparison of LiDAR and SRTM derived river profiles through subtraction of 4 m and the water surface of the September 2008 flood...... 29 Figure 18 Input hydrograph for the September 2008 flood and downstream outflow hydrograph for the LiDAR and the SRTM simulation respectively...... 30 Figure 19 Flood extent of the October 2004 flood for both the LiDAR and the SRTM model...... 31 Figure 20 SRTM simulations compared to the LiDAR simulation for the October 2004 event. .... 32 Figure 21 Reclassified simulation results for the October 2004 flood event...... 33 Figure 22 Input hydrograph for the October 2004 flood and downstream outflow hydrograph for the LiDAR and the SRTM simulation respectively...... 34 List of Figures (continued)

Figure A1-1 Originial LiDAR DEM in 5x5 m resolution...... 47 Figure A2-1 Location of profile transects for DEM comparison...... 48 Figure A2-2 Profiles for Transect 1. For transect location see Figure A2-1...... 48 Figure A2-3 Profiles for Transect 2. For transect location see Figure A2-1...... 49 Figure A2-4 Profiles for Transect 3. For transect location see Figure A2-1...... 49 Figure A2-5 Profiles for Transect 4. For transect location see Figure A2-1...... 50 Figure A3-1 Measured and simulated stages for the 2008 flood with different combinations of parameters (water depth, channel friction)...... 50 Figure A4-1 Reclassified results for the original SRTM models in both tested years...... 51 Figure A4-2 Flood extents for all models with 30 % vegetation signal removal in 2008...... 52 Figure A4-3 Flood extents for all models with 40 % vegetation signal removal in 2008...... 53 Figure A4-4 Flood extents for all models with 30 % vegetation signal removal in 2004...... 54 Figure A4-5 Flood extents for all models with 40 % vegetation signal removal in 2004...... 55 Table of Contents

1 Introduction ...... 1 2 Aims ...... 3 3 Background ...... 4 3.1 Previous Research ...... 4 3.1.1 Hydraulic Models in the Context of Flood Extent Simulations ...... 4 3.1.2 Advances in Remote Sensing and Applicability for Flood Extent Modelling .. 5 3.1.3 Calibration and Validation ...... 6 3.2 Study area: Papaloapan River, Mexico ...... 7 3.2.1 Site Description ...... 7 4 Methodology ...... 11 4.1 DEM Preparation ...... 11 4.1.1 Rescaling and Resampling of the DEMs ...... 11 4.1.2 SRTM Vegetation Signal Removal ...... 13 4.2 Hydraulic Model Setup and Calibration ...... 15 4.2.1 Main Channel Properties ...... 16 4.2.2 Upstream Boundary ...... 18 4.2.3 Model Parameters ...... 18 4.3 Modelling Floods ...... 18 4.3.1 September 2008 Flood ...... 18 4.3.2 October 2004 Flood ...... 19 4.4 Evaluation of Modelling Results ...... 20 5 Results ...... 22 5.1 Vertical Error in Original and Conditioned SRTM DEMs ...... 22 5.2 Calibration ...... 23 5.3 Simulation Results ...... 25 5.3.1 September 2008 Flood ...... 25 5.3.2 October 2004 Flood ...... 30 6 Discussion ...... 35 6.1 Vertical Error in Original and Conditioned SRTM DEMs ...... 35 6.2 Calibration ...... 35 6.3 Simulations ...... 36 7 Conclusions ...... 39 8 Acknowledgements ...... 41 9 References...... 42 Appendix 1: Original LiDAR DEM ...... 47 Appendix 2: DEM Comparison ...... 48 Appendix 3: Calibration Results ...... 50 Appendix 4: Model Results...... 51

1 Introduction Flood events and the associated inundation extent can be modelled and predicted locally via hydraulic modelling. These models require a number of input data, including Digital Elevation Models (DEMs) to describe the topography of rivers and floodplain areas. These DEMs can be derived from remotely sensed data and used as a basis for hydraulic model codes, such as LISFLOOD-FP, to calculate flood propagation with one-dimensional (1D) channel flow and two-dimensional (2D) floodplain flow (Bates & De Roo 2000). This requires high-accuracy as well as high-resolution elevation data (Charlton, Large & Fuller 2003; Ludwig & Schneider 2006; Walker & Willgoose 1999; Yan, Di Baldassarre & Solomatine 2013). However, global models as an alternative in data scarce areas typically work with a coarser spatial and to some extent temporal resolution (Winsemius et al. 2013). This results in additional uncertainties, but also bears potentials as globally available elevation data, such as NASA´s Shuttle Radar Topography Mission (SRTM), are free of costs or relatively cheap as opposed to, for example, often expensive and therefore scarcely available highly accurate airborne light detection and ranging (LiDAR) data. To what extent SRTM data can be exploited to support hydraulic modelling of floods is still an open research question (Di Baldassarre & Uhlenbrook 2012; Bates 2012). The need to develop and improve hydraulic models arises from the widespread damage that flood inundations cause globally. In the past ten years more than 60,000 deaths and around 900 million affected people were attributed to flooding (Guha-Sapir, Below & Hoyois 2015). Economically, flooding is the single most expensive natural disaster (Miller, Muir-Wood & Boissonnade 2008, pp. 225-247). A recent example is the event in Indonesia in 2013, where 100,000 houses in the area around Jakarta were either damaged or destroyed and 47 people died. Costs were as high as US$ 3 billion. The same year the province of Alberta, Canada, was hit by the most severe floods up to the present with economical damage amounting close to US$ 6 billion (Munich Re 2014). In a changing climate, an increased flood frequency is expected for many regions of the world (Hirabayashi et al. 2008). A modelling study projected an increase both in frequency and intensity of 100-year floods for vast parts of northern and north-western Europe (Lehner et al. 2006). More recently, Hirabayashi et al. (2013) projected an increase of flood frequency for most of Southeast Asia, eastern Africa and parts of India as well as the northern Andes. At the same time the occurrence of flood disasters could decrease in other parts of the world. Generally it can be assumed that under future atmospheric forcing, flooding patterns change and thus cause potentially more flood damage locally. However, climate change is not the only driver of increasing future flood risk. In Africa for instance, it was shown that there has been a growing risk even without a significant change in the frequency of floods due to uncontrolled, intensive settlements in areas prone to flooding (Di Baldassarre et al. 2010). Global population growth and increased urbanization in vulnerable areas add

to the growing flood risk (Jonkman 2005). Furthermore an ongoing global shift from grassland and forests to intensive agriculture and urbanizations (Foley et al. 2005; Goldewijk 2001) has the potential to alter stream flow in catchments (Zhang & Schilling 2006).

2 Aims This study tries to further explore the potential and limitations of globally available and remotely sensed data by setting up a coupled 1D/2D hydraulic model with both a high-resolution (LiDAR) and a low-resolution (SRTM) DEM for a reach of the Mexican river Papaloapan. This is done with the model code LISFLOOD-FP. Furthermore the potential of SRTM improvement through vegetation removal as in Baugh et al. (2013) is tested. The LiDAR model is calibrated with measured flood stage data and then used as a reference for comparison with different SRTM models, i.e. benchmarking. It is not the aim to build a complex model and try to perfectly simulate the flood extents at the test site but rather evaluate the relative performance of the model based on the coarse resolution topography (SRTM) with respect to the reference model based on high quality topography (LiDAR).

3 Background 3.1 Previous Research 3.1.1 Hydraulic Models in the Context of Flood Extent Simulations Over past decades various hydraulic models have been developed to simulate flood extent and its dynamics. This has been necessary especially for areas where there is only sparse coverage of remotely sensed inundation extent. Here hydraulic models can be used to detect flood-prone areas (Bates & De Roo 2000). The complexity of different types of hydraulic models varies greatly. A relatively simple approach is the linear interpolation of measured gauge levels. From this a flood level with a uniform slope is derived and projected upon a DEM. All points below the obtained plane are counted as being flooded. A big drawback of this method is that it does not account for water volumes of a flood and thus can result in unrealistically vast inundated areas, especially in low-lying floodplains without limiting topographic features (Apel et al. 2008). More sophisticated 1D numerical models such as HEC-RAS and Mike 11 simulate flood extent on the basis of the 1D Saint-Venant Equations (1D version of the Shallow Water Equations) or variations of them (Brunner 2010; Danish Hydraulic Institute 2009). The Saint-Venant Equations, originally derived by Barré de Saint Venant (1871), consist of the continuity (mass conservation) equation and the momentum conservation. They are used to calculate unsteady open-channel flow with a variation of depth and velocity solely in the longitudinal direction of a channel. Furthermore resistance parameters such as Manning´s coefficient can be applied to account for channel roughness. The channel slope is assumed to be small (Chow, Maidment & Mays 1988, pp. 272–281). Flow in the aforementioned models is routed one-dimensionally through cross-sections that intersect both the channel and floodplain perpendicular to the direction of flow. The governing equations are solved numerically and from this water depth and flow velocity can be derived at the location of the cross- sections. Flood extent can then be obtained by overlaying a DEM with the water depths from each cross-section or by linear interpolation of water depths at each cross-section (Bates & De Roo 2000). However, 1D models face several limitations such as lack of information on bathymetry which can affect flow processes, as well as limitations in the representation of complex river systems (Merwade, Cook & Coonrod 2008). Also, floodplain flow is simulated only 1D, perpendicular to the cross- sections and parallel to the channel. More complex situations with 2D flow and varying velocities across the floodplain are not accounted for respectively have less physical meaning for the simulated outcome (Néelz & Pender 2009). Nevertheless, 1D models are still being used and can provide satisfactory results for in-channel flow (Tayefi et al. 2007) as well as for relatively well defined and narrow floodplains (Horritt et al. 2007). For confined floodplains with artificial levees at both sides, 1D models such as HEC-RAS simulate flood extent reasonably well. However, there is a need to test

SRTM data for different river scales and scenarios with 2D flood situations (Yan, Di Baldassarre & Solomatine 2013). To simulate these more complex floodplain flows 2D models (or coupled 1D/2D models) based on topographic information (such as DEMs derived from remotely sensed data) have been developed over the years (e.g. Bates & De Roo 2000; Nicholas & Mitchell 2003; Stewart et al. 1999). The model used in this study was based on the model code LISFLOOD-FP developed by Bates & De Roo (2000). LISFLOOD-FP solves the kinematic wave approximations to the 1D Saint-Venant Equations. This simulates flood wave propagation of a river channel reach. Later the possibility to simulate diffusive waves in the channel was added (Horritt & Bates 2001). When the water exceeds bankfull depth of the channel, 2D spreading of the flood is simulated with help of a storage cell concept and a Cartesian grid. This allows remotely sensed data to be integrated into the model easily. Floodplain flow is calculated with different solvers that have been developed and implemented into LISFLOOD-FP over the years. The original code by Bates & De Roo (2000) uses a fixed time-step and considers friction and water slope terms of the shallow water equations. Both convective and local acceleration are assumed to be negligible. A flow limiter was implemented in order to prevent upstream cells to empty completely in one time step which would lead to flow reversal and cause chequerboard-like oscillations. To overcome several limitations of the flow limited solver such as results that depend on a chosen time step or grid size and insensitivity of the model to floodplain friction, an adaptive solver was developed. It is based on the same assumptions as the flow limited solver, but its time step varies throughout the simulation making a flow limiter unnecessary (Hunter et al. 2005). However, the computational time can increase to a great extent. The so-called acceleration solver by Bates, Horritt and Fewtrell (2010) includes the local acceleration term in addition to the friction and water slope terms and calculates the time step based on the Courant-Friedrich-Levy Conditions which leads to improved computational times with regards to the flow limited solver and satisfying simulation outcomes for most flooding scenarios compared to the previous solver (De Almeida & Bates 2013). 3.1.2 Advances in Remote Sensing and Applicability for Flood Extent Modelling An important, if not the most important, input for hydraulic models is the topographic input, which can be derived from remote sensing data (Farr et al. 2007). Because of this the availability and quality of topographic data has been for long time a constraining factor for hydraulic models (Marks & Bates 2000). However, in most recent years both spaceborne and airborne remotely sensed topographic data such as LiDAR and SRTM have become more and more available (Bates 2004, 2012). LiDAR derived DEMs usually have a relatively high accuracy as well as a high price. Because of this, access to such data sets often is either limited or not available. This is true especially for floodplains and rivers in developing countries (Yan et al. 2015). In light of the expenses and sparse global coverage with highly accurate data, SRTM derived DEMs have come into the focus of flood modellers (e.g. Schumann et al. 2008; Yan, Di Baldassarre & Solomatine 2013). SRTM is the most complete freely available near-global topographic high-resolution dataset to date. The SRTM mission

took place in February 2000 for 10 days and covered the area between 60° north and 56° south. A resolution of 90 m is available to the public for most of the globe except North America, where a 30 m resolution is available. The global absolute vertical height error of SRTM data amounts to less than 16 m (Farr et al. 2007). It has been shown that a model on the basis of SRTM data can simulate design floods and their extents reasonably well for a medium- to large-scale river. The errors are within the accuracy range commonly associated with hydraulic investigations (Yan, Di Baldassarre & Solomatine 2013). One of the problems with SRTM, but also some LiDAR derived DEMs, is the lack of information on channel bathymetry as neither radar nor some laser beams do penetrate water surfaces. Often this information comes from ground studies where the profile of several river cross- sections are gathered for example by boat surveys. If water levels at the time of data collection (here February 2000) were relatively low, SRTM DEMs can be used without further manipulation of the channel bathymetry (Yan, Di Baldassarre & Solomatine 2013). Another cheaper possibility to obtain bathymetries could be the global river bankfull width and depth database by Andreadis, Schumann and Pavelsky (2013). Channel widths were derived from Landsat imagery and the HydroSHEDS hydrography data set (Lehner, Verdin & Jarvis 2008). Bankfull depths for rivers were obtained from relationships between width, discharge, drainage area and depth and a near-global database was made public. However, this database only provides estimates. A mean error of the bankfull depth in two field studies amounted to about 24 %. In September 2014 the United States Geological Survey (USGS) has started the release of the global 30 m SRTM data set in several phases until the end of 2015 (USGS & US Department of the Interior 2012) which could further improve the possibilities for global hydraulic models. Apart from these freely available or cheap but rather coarse data sets, it is expected that the recent and future advances in remote sensing will lead to high resolution and high accuracy also in the low-cost range. Nevertheless, for some time it is deemed necessary to further explore the potential of data sets like SRTM for hydraulic models. This applies especially for developing countries where the integration of remotely sensed data into hydraulic models often still is a constraining factor (Yan et al. 2015). 3.1.3 Calibration and Validation There are several different methods to calibrate hydraulic models depending on the available information. Satellite imagery of flood extents can serve as calibration data. These observations are for example obtained from synthetic aperture radar (SAR) sensors. Flood maps taken at the time of simulated flooding can be compared to the modelled outcome and the model can then be calibrated against the imagery so that simulation and observation align. In case of LISFLOOD-FP this adjustment is usually done by varying both Manning´s coefficient for the channel and the floodplain (Di Baldassarre, Schumann & Bates 2009a) but also through rating curve parameter variations explicitly accounting for inflow uncertainty (Mukolwe et al. 2015). Recently it has been tried to verify and recalibrate a hydraulic model in near real time while a flood occurs (Di Baldassarre, Schumann & Bates 2009b). A relatively low-resolution ENVISAT ASAR image (~ 100 m) was obtained from the

European Space Agency at the time of flooding. With this it was possible to recalibrate the model in a shorter time than the flood wave propagation. The model, which was calibrated on a high-magnitude flood, could not predict the flood extent for the low-magnitude flood accurately enough. The authors of this study stressed the need to readjust and critically question previously well working hydraulic models for different events as for example roughness parameters can vary with the magnitude of a flood. If no flood extent imagery is available high water marks as either remotely sensed or in-situ measured water stages can serve as calibration respectively verification data. Measured flood stages can be used to evaluate models by comparison with the simulated water levels and for example calculating the root mean squared error (RMSE) (Mukolwe et al. 2015). Similarly high water marks can be used for calibration and evaluation purposes (Schumann et al. 2008). Schumann et al. (2013) calibrated their hydraulic model of the Lower Zambezi River based on SRTM data and LISFLOOD- FP with remotely sensed water levels obtained from NASA´s ICESat Geoscience Laser Altimeter System (GLAS) data. The so calibrated model simulated channel water levels with a very high accuracy (RMSE about 27 cm). Comparing simulated flood extents with imagery obtained from Landsat showed that this model operated in an acceptable range. 3.2 Study area: Papaloapan River, Mexico 3.2.1 Site Description The Papaloapan River originates in the Mexican deserts of Puebla and Ixtlán and disembogues into the Gulf of Mexico near the city of Alvarado in the state of Veracruz, Mexico (Espinoza & Gonzalez 2014). Its catchment covers about 46,517 km². When it comes to discharge Papaloapan is the second largest Mexican river with an average annual discharge that amounts to about 44,662 million m³ per year at its mouth in the lagoon of Alvarado after the Grijalva-Usumacinta River with 115,536 million m³ per year. The reach actually called Papaloapan starts at the junction of the Valle Nacional River and the Santo Domingo River and is only 354 km long (Comisión Nacional del Agua, México 2012). The whole catchment includes a population of about 3.3 million spread out over 244 municipalities (Espinoza & Gonzalez 2014). Large parts of the lower basin are controlled by two major dams and reservoirs (Figure 1). The dam Miguel Alemán (also Temascal) impounds the Tonto River with a capacity to store 8,119 million m³. It is both used for flood protection and electricity production and was built in 1955. The dam Miguel de la Madrid (also Cerro de Oro) controls the Santo Domingo River with a capacity of 1,250 million m³. It was finished in 1988 and inaugurated in May 1989 and is mostly used for energy generation and irrigation purposes but also flood control in combination with the Temascal dam (Comisión Nacional del Agua, México 2008). Further measures that altered Papaloapan are straightening which lead to a 50 km shorter stream and embankments at both sides for large parts of the river (Espinoza & Gonzalez 2014).

According to a recent map of the Köppen-Geiger climate classification (Peel, Finlayson & McMahon 2007) the Papaloapan catchment encompasses Tropical Rainforest (Af), Tropical Monsoon (Am) and in some small parts Tropical Savannah (Aw) climate. This includes a temperature of the coldest month to be at least or above 18 °C with a precipitation of the driest month above 60 mm for the Af climate. The Am climate is marked by less than 60 mm precipitation in the driest month but more than 1/25 of the total annual precipitation. Aw climate is characterized by the driest month having less than 60 mm and less than 1/25 of the total annual precipitation. The here modelled lowland floodplain reach of the Papaloapan river mostly includes the Am and Aw climate classifications which indicates a seasonal pattern of rainfall. In some higher elevated upstream regions the Af climate dominates. Additionally some smaller upstream areas are engraved by the Aw climate with a pronounced dry season.

Figure 1 Overview map roughly displaying the Papaloapan catchment according to the HydroSHEDS database of the United States Geological Survey (USGS). As the catchment area was derived from relatively low resolution SRTM data the borders might not exactly correspond with the real borders; catchment data from the USGS HydroSHEDS dataset (Lehner, Verdin & Jarvis 2008), topography data from CGIAR-CSI (Jarvis et al. 2008).

Looking at the hydrograph of the Papaloapan gauging station the aforementioned seasonality of rainfall becomes visible (Figure 2). The highest discharge usually occurs in the end of summer to

autumn. This is followed by relatively low discharge and almost no variation in the winter months from November to January. From February to June variations in discharge are becoming bigger but usually stay below 1,000 m³/s. With the start-up of the Cerro de Oro dam in May 1989 changes in the hydrograph can be observed (Figure 3). High discharges in summer and autumn are generally more attenuated and less extreme flood peaks occur whereas runoff in spring and early summer increased slightly. Despite these flood control measures flooding in the lower Papaloapan catchment is still a persistent problem and relatively high flood peaks, as in October 1991, can still occur.

3500 Hydrograph for 2011 3000

2500

2000

1500 Discharge [m³/s] Discharge

1000

500

0 J F M A M J J A S O N D Months Figure 2 Hydrograph depicting hourly measured discharge values at the Papaloapan gauging station for 2011. The measurements contained some gaps that were linearly interpolated in this figure; based on data from the Mexican National Water Commission CONAGUA.

The mean annual precipitation within the catchment amounts to 1,878 mm and on average there is one extreme rainfall event each year with up to 280 mm precipitation per event in some places (Espinoza & Gonzalez 2014). The majority of the superficial water in the lower catchment is brought by seasonal rainfall originating in tropical cyclones that come from the Gulf of Mexico and the Pacific Ocean. These events bring great amounts of water into the catchment, whose north-eastern south- western orientation coincides with the direction of the trade winds which in turn favours precipitation (Vega & Pérez 2002). In the months from August to October of 2010 there were severe inundations in the lower catchment as a consequence of extreme rainfall and high discharges from the Temascal and Cerro de Oro dams. Hurricane Karl and the tropical storm Matthew favoured the large amounts of precipitation over the catchment. Peak discharge at the Papaloapan gauging station was as high as 4,483 m³/s in August 2010. Even before the building of the Cerro the Oro dam peak discharges rarely exceeded 3000 m³/s (see also Figure 3) making the 2010 flood an exceptional event. The Papaloapan River, together with its tributaries, inundated several municipalities and affected an estimated 300,000 people (Espinoza & Gonzalez 2014).

As for topography the lower catchment part including the whole main Papaloapan reach that also includes the here modelled domain encompasses a slope of more than 5 % only for less than 98 % of the covered area. Upstream of the two dams Temascal and Cerro de Oro the area with a slope greater than 5 % increases to more than 40 % (Cruz Gerón et al. 2014). Elevations in the catchment range from 0 m by the sea at the Lagoon of Alvarado to about 4,500 m in the western upstream parts of the catchment (Figure 1).

3500 Hydrograph from 1985 to 1992 3000

2500 Startup of the Cerro de Oro 2000 Dam

1500 Discharge [m³/s] Discharge 1000

500

0 Jan- 85 Jan- 86 Jan- 87 Jan- 88 Jan- 89 Jan- 90 Jan- 91 Jan- 92 Date

Figure 3 Hydrograph depicting hourly measured discharge values at the Papaloapan gauging station from 01.01.1985 until 01.01.1992. The measurements contained some gaps that were linearly interpolated in this figure; based on data from the Mexican National Water Commission CONAGUA.

4 Methodology To set up the model, the DEMs used as topographic input data had to be prepared by choosing the river reach to be modelled and defining a common resolution. To make the reference model (based on LiDAR) and test models (based on SRTM) comparable the cells of both models had to align exactly as wet and dry cells of both simulations were directly compared to each other by creating binary maps and from that calculating a measure of fit. After this several SRTM versions with 30 and 40 % vegetation height removal and different low-pass filters were prepared. This was done with a vegetation canopy height data set (Simard et al. 2011) of which a percentage of the canopy height was subtracted from the SRTM DEM. The reference model was calibrated according to measured stage data for a flood event in September 2008. Calibration was done by varying the channel roughness coefficient and the channel depth. A parameter set was chosen and simulations were run for all reference and test models and a second flood event in October 2004. Flood extents of reference and test models were compared by applying a measure of fit. In the following paragraphs these steps are going to be explained more detailed. 4.1 DEM Preparation Both LiDAR and SRTM derived DEMs were prepared for the model setup using ArcGIS. The LiDAR DEM with an original resolution of 5 m was provided by the National Water Commission of Mexico CONAGUA (Figure A1-1). SRTM data was downloaded from the SRTM 90m Digital Elevation Database v4.1 by the Consortium for Spatial Information of the Consultative Group on International Agricultural Research (CGIAR-CSI). This data set, which originates from NASA´s SRTM mission in 2000, was processed by Jarvis et al. (2008) in order to fill in no-data voids. Data voids in the SRTM mission mainly occurred in high-elevated areas and over water bodies. 4.1.1 Rescaling and Resampling of the DEMs In order to make both DEMs and their resulting simulated flood extents comparable, both DEMs had to be resampled to the same resolution. As for the LiDAR DEM the aggregate tool in ArcGIS was applied with a rescaling factor of 18 to obtain a 90 m resolution. The aggregate tool lowers the resolution according to the input rescaling factor by taking into account the neighbouring cells. A new value for the rescaled cell is calculated according to the specified aggregate type. Here it was chosen to use the "median" for noise reduction so that high or low outliers did not weigh too much in the calculation of the new grid. The SRTM DEM had to be resampled (resample tool in ArcGIS) in order to get the raster cells to align exactly with the LiDAR DEM and to get exact 90x90 m cells. This was done with the bilinear resampling type. As the data set was only in a geographic coordinate system (WGS84), a projection into the LiDAR DEMs projected coordinate system (WGS84 UTM Zone 15N) had to be made. After these preliminary steps, the river reach to be modelled was clipped in ArcGIS out of both the LiDAR and SRTM DEM (Figure 4 and 5 respectively). The upstream border was made up by the

Papaloapan gauging station. About 47 km downstream was the Chacaltianguis gauging station for calibration and validation purposes. The downstream border was made up by the sea at the lagoon of Alvarado. The so modelled reach was 137 km long from the Papaloapan gauging station to the river mouth at the sea. On both sides of the reach the limits of the provided LiDAR DEM were chosen. Finally both DEMs were saved as ASCII files in order for LISFLOOD-FP to be able to read the elevation information.

Figure 4 Modelled river reach with the aggregated 90 m resolution LiDAR DEM as a basis; original 5 m LiDAR obtained from the Mexican National Water Commission CONAGUA.

Figure 5 Modelled river reach with the resampled 90 m SRTM DEM as a basis; SRTM data from CGIAR-CSI (Jarvis et al. 2008).

4.1.2 SRTM Vegetation Signal Removal The interferometric synthetic aperture radar (INSAR) signal on which the SRTM dataset is based only penetrates vegetation cover to a certain degree resulting in decreased vertical accuracy. This is especially true for forested areas (Brown, Sarabandi & Pierce 2010). This becomes a problem in using these data to support hydraulic modelling, where accurate information on floodplain elevation is essential for the simulated outcomes (e.g. Sanders 2007). Recently, global canopy height datasets have been developed from moderate resolution imaging spectroradiometer (MODIS) and ICESat data (Lefsky 2010; Simard et al. 2011). Baugh et al. (2013) proposed an approach where globally estimated vegetation heights are used to remove the vegetation signal from a SRTM DEM. The removal of 50 to 60 % percent of the vegetation for a reach of the Amazon floodplain resulted in significant improvements of hydrodynamic modelling results. Here the same global vegetation height dataset (Simard et al. 2011) as used by Baugh et al. (2013) was applied to condition the SRTM DEM. This canopy height data set with a 1 km resolution was derived by Simard et al. (2011) from data of the ICESat satellite with the Geoscience Laser Altimeter System (GLAS) onboard. However, the Amazon

basin is covered with much denser and higher vegetation compared to the Papaloapan lower catchment. For this reason two lower percentages were tested here with 30 and 40 % vegetation height removal respectively. To do this the vegetation canopy height dataset was reduced to 30 and 40 % of its original height. The so obtained raster was then subtracted from the SRTM DEM. Figure 6 shows the original canopy height for the modelled river reach. Especially the downstream end of the reach shows relatively dense and high canopy coverage indicating a possible improvement of hydrodynamic modelling results through vegetation signal removal.

Figure 6 Vegetation map for the modelled river reach based on the global canopy height database (Simard et al. 2011).

To prepare the vegetation map as a tool for the DEM conditioning it had to be resampled and projected to the 90 m grid and the WGS84 UTM Zone 15N projection of the SRTM. Here the nearest neighbour interpolation was used for resampling to preserve the original height values at each

location. To reduce random noise caused by the vegetation removal and reduce the chequerboard like patterns caused by the relatively big pixel size (1x1 km) of the canopy height raster an averaging low- pass filter with different window sizes was applied so that nine hydrodynamic simulations of different SRTM models were carried out (Table 1) for each of two simulated flood events.

Table 1 Overview over the produced SRTM DEMs including amount of vegetation height removal and applied low-pass filter windows.

Low-pass filter SRTM SRTM -30 % SRTM -40 % window size No filter x x x 3x3 x x 5x5 x x 7x7 x x

Some of the formerly 1 km cells of the vegetation map were overlapping the river and some percentage of the canopy elevation was removed from parts of Papaloapan. To undo this undesired removal and the filtering a shapefile covering the water surface of Papaloapan was created manually. This was then converted into a raster and the elevation information was extracted from the SRTM DEM. This raster file was then used to burn the original SRTM water surfaces elevation information into the lowered DEMs after vegetation removal and filtering. To compare all SRTM DEMs in terms of vertical accuracy the relative differences and the RMSE between the LiDAR DEM and each SRTM DEM were calculated (Eq.(1)) under the assumption that the LiDAR DEM provided the best representation of the real elevation:

RMSE = (y x ) (1) 1 n 2 � n ∑ i=1 i − i

where n is the number of observations respectively cells, yi the aggregated LiDAR DEM and xi the respective conditioned SRTM DEM. 4.2 Hydraulic Model Setup and Calibration Hydraulic models were built by using the model code LISFLOOD-FP (version 5.9.6) provided by the University of Bristol. The required input data were organized in different text files. This section describes how the input text files were set up for this study. For more detailed information on model setup consult the LISFLOOD-FP user manual provided on the website of the University of Bristol (Bates et al. 2013).

4.2.1 Main Channel Properties Information on the main channel properties is stored in the .river file. It provides information on the main channel location, its width, roughness coefficient and bed elevation. X and Y coordinates were extracted from the LiDAR DEM to define the vector of the main channel. For each point in the channel, information on channel width, friction and bed elevation had to be provided:

172428.507352 2011365.96188 210 0.036 2.25 QVAR upstream1 172521.115864 2011309.77842 300 0.036 2.2 ⁞ ⁞ ⁞ ⁞ ⁞ 172744.892471 2011229.17042 280 0.036 2.13 HFIX

Column one and two show X and Y coordinates respectively. QVAR signals the model to read a varying discharge input file (.bdy file). HFIX which is being put at the downstream end of the channel signals a fixed water level downstream. This can be the case for a river ending in a lake or the sea. As the downstream end of the modelled reach ended in the Gulf of Mexico a fixed downstream water level of 0 m.a.s.l. was assumed. For simplicity and lack of available data varying water levels because of for example tides were ignored. The third column shows the channel width. Widths for each point along the channel were measured manually in the original LiDAR DEM which provided the most accurate values. Between the points the widths were linearly interpolated by LISFLOOD-FP. Column four depicts the roughness coefficient, which can vary along the channel. However, to keep the model as parsimonious as possible and because of lack of more detailed information it was chosen to only have one roughness coefficient for the whole channel. Column five shows the bed elevation which can either be derived from field surveys or be extracted from remote sensing surveys. Another possibility could be the before mentioned global river bankfull width and depth dataset but due to rather larger errors in the depth estimations this was not considered here (Andreadis, Schumann & Pavelsky 2013). In this study elevations from the original LiDAR were extracted. As the LiDAR DEM did not show bed elevations but only the water surface elevation at the (unknown) time of the remote sensing survey, different assumed water depths were subtracted from these water surface elevations to obtain an approximation to the real channel profile (Figure 7). Several water depths between 2.50 m and 4 m were tested in several simulations. In combination with the main channel roughness coefficient this served as calibration parameters. The simulated water depths at the Chacaltianguis gauging station were then compared with the measured stages by calculating the Mean Absolute Error (MAE) and the parameter set with the lowest obtained value was chosen for further simulations with the SRTM models and a second flood event.

For the SRTM model the same water depth was subtracted from the channel water surface to obtain an approximation to the in-channel profile. However, the water surface in the SRTM likely did not represent the real absolute measured stage in m.a.s.l as rivers wider than 183 m were processed to continuously decrease in the SRTM data set in order to have a continuous slope for example for hydrologic calculations (Farr et al. 2007). Stages here were at 15 and 9 m.a.s.l. respectively at the location of the two gauging stations Papaloapan and Chacaltianguis. In the 10 days of the SRTM mission the measured stages varied between 7.58 and 8.73 m.a.s.l.. as well as between 2.42 and 3.85 m.a.s.l. respectively (Table 2). As the LiDAR DEM showed a water elevation of 6.21 m and 2.4 m for the two gauging stations, similar relative water elevations for the whole reach in both DEMs can be assumed with a possible error range of roughly 2.50 m.

Elevation [m.a.s.l.] 3

2 Lowered Profile 2 m

1 LiDAR Profile

0 0 100 200 300 400 500 600 700 Distance [m]

Figure 7 Example profile extracted from the original 5x5 m LiDAR DEM close to the Chacaltianguis gauging station with the dashed line indicating the subtraction of the (assumed) water depth to obtain the approximate channel bathymetry.

Table 2 Extracted water surfaces elevations from both DEMs at the location of the gauging stations and measured stages from the 10 day period of the SRTM mission.

Papaloapan Chacaltianguis LiDAR before (after) aggregation [m.a.s.l.] 6.21 (6.21) 2.4 (2.42) SRTM [m.a.s.l.] 15 9 Measured Stages [m.a.s.l.] 7.58 - 8.73 2.42 - 3.85

4.2.2 Upstream Boundary In the .bdy file (time varying boundary condition file) the upstream input discharge is defined. In this case measured values from the Papaloapan gauging station (see Figure 4 for location) were used as input data:

332.31 0 ⁞ ⁞ 446.61 6 444.06 7 438.94 8 438.94 10

Column one shows the discharge in m³/s. The second column shows the time (here in hours, can also be seconds) that has passed since beginning of the simulation. For the time in between the model interpolated the discharge values linearly. 4.2.3 Model Parameters The parameter file (.par) includes information for the model on which text files (DEM ASCII, .river, .bdy etc.) to read and which solvers to use. As a channel solver one can chose between the kinematic and the diffusive solver. Usually the kinematic solver is being used for rather steep mountain rivers (Bates et al. 2013). The here modelled 137 km long reach of Papaloapan had a rather small average slope of 5.3x10-5 for the LiDAR DEM and 1.09x10-4 for the SRTM DEM. Thus, the diffusive solver was chosen for the main channel. As there was no remotely sensed flood extent available for calibration the floodplain friction was not varied in this study and a fixed value of 0.05 was chosen. For computational efficiency the non-adaptive solver of Bates and De Roo (2000) was chosen with a fixed time step of 10 seconds. As this was only a comparative study where the same conditions except for the DEMs applied to both models the aforementioned shortcomings of this solver should not matter too much for the outcome of the simulations as long as no numerical problems occur and a small enough time step is chosen. The simulations were run on an Intel Core i5-2430M 2.4 GHz dual core processor platform. Water depth files were saved every 5 hours of simulated time. Additionally hourly information on water stages at the location of the Chacaltianguis gauging station was extracted. This was used for calibration of the model with the on-site stage measurements. 4.3 Modelling Floods 4.3.1 September 2008 Flood A first flood event was used for the calibration of the LiDAR-based model (i.e. reference model). It took place in September 2008 of which a time period of about 15 days was simulated from 16

September 7:00 am until 1 October 6 pm (Figure 8). This included about 10 days of simulation before the peak flow of 2,308 m³/s on 26 September at 7 am at the Papaloapan gauging station. Eight hours later the peak reached the 47 km downstream gauging station Chacaltianguis at 3 pm more attenuated with a discharge of 1,887 m³/s. Simulation of the recession curve after the peak was done long enough for high discharge to pass through the whole model domain. The hydrograph for Chacaltianguis shows some irregularities especially in the recession curve that seem to be faulty measurements. However, the for the calibration important stage curve does not show this pattern but rather a more attenuated form of the Papaloapan stage curve.

3000 12,00

2500 10,00

2000 8,00

1500 6,00 Stage [m.a.s.l] 1000 4,00 Discharge [m³/s] Discharge

500 2,00

0 0,00

Time Figure 8 Hydrographs (solid lines) and stages (dashed lines) for the modelled September 2008 flood with data from the upstream Papaloapan gauging station (black) and the further downstream Chacaltianguis gauging station (grey). Peak discharge in Papaloapan occurred on 26 September at 7:00 am and on 26 September at 3 pm in Chacaltianguis; data from the Mexican National Water Commission, CONAGUA.

4.3.2 October 2004 Flood The second modelled event was the flood of October 2004. A time span from the 2 October at 8:00 am to the 13 October at 8:00 am was simulated (Figure 9). On the 5 October at 3 pm the peak discharge of this event was measured at the Papaloapan gauging station with 1,197 m³/s. The next day at 4 am the flood wave passed through the Chacaltianguis gauging station with about 1,032 m³/s discharge. Compared to the September 2008 event this flood was more than 1,000 m³/s smaller when comparing the peak discharges. Flood wave propagation was somewhat slower with 11 hours travel time from Papaloapan to Chacaltianguis as opposed to 8 hours for 2008.

1400 10

9 1200 8

/s] 1000 3 7

6 800 5 600 4 Discharge [m Discharge Stage [m.a.s.l] 400 3 2 200 1

0 0

Time

Figure 9 Hydrographs (solid lines) and stages (dashed lines) for the modelled October 2004 flood with data from the upstream Papaloapan gauging station (black) and the further downstream Chacaltianguis gauging station (grey). Peak discharge in Papaloapan occurred on 5 October at 3:00 am and on 6 October at 4 pm in Chacaltianguis; data from the Mexican National Water Commission, CONAGUA. 4.4 Evaluation of Modelling Results To compare the different simulation outcomes the time of maximum flood extent in the LiDAR model was chosen. If necessary, a time where the maximum usable extent before water spread beyond the model domain was chosen. For evaluation purposes the obtained flood extent respectively water depth maps were reclassified as either flooded or dry. A cell with more than 10 cm of water depth was considered as being flooded whereas a water depth equal to or less than 10 cm was classified as dry. To make a comparison between simulations and observations using a contingency table (Table 3) for the LiDAR simulations (used as a reference) binary maps were created where a value of 5 was given to all flooded cells and a value of 3 for all dry cells. In the SRTM simulations (tested versus the reference) flooded cells were assigned the value 1 and dry cells the value 0.The raster of LiDAR and the respective SRTM binary maps were added together so that the output raster contained four different values (Table 3).

Table 3 Contingency table used for the evaluation of the simulated outcome of LiDAR and SRTM models. The number in brackets stands for the value obtained after reclassifying the simulated water depth in the raster cells as either flooded or dry respectively after adding the reclassified LiDAR and SRTM raster together.

SRTM flooded (1) dry (0)

flooded (5) A (6) B (5) dry (3)

LiDAR C (4) D(3)

A measure of fit (Eq.(2)), similar as in Horritt (2006), was then applied as a performance measure of the SRTM model compared to the LiDAR model:

#A F= (2) #A+#B+#C

where #A stands for the number of cells that were both flooded in the LiDAR and the SRTM simulations. #B depicts the number of cells that were flooded in the LiDAR but dry in the SRTM simulations (underestimation) and #C accounts for the number of cells that were dry in the LiDAR but flooded in the SRTM (overestimation). #D as the number of dry cells coinciding in both simulations were ignored, which allowed a fair comparison (i.e. independent from the subjective dimension of the model domain). The so obtained value between 0 and 1 gave information on the fit between the flood extent simulated by the SRTM-based models and the one simulated by the LiDAR-based model, which as mentioned was used as a reference. Note that if flood extent areas coincide, the SRTM-based model would not simulate flooded areas that are dry in the LiDAR (#C = 0) and vice versa (#B = 0), which would give an F value of 1, corresponding to a perfect fit of the two models (100 %). A fit of 0 % was not possible here as the main channel was being accounted for in the measure of fit. Thus there were always commonly flooded areas.

5 Results 5.1 Vertical Error in Original and Conditioned SRTM DEMs Elevation differences between LiDAR and SRTM DEMs were relatively high. In some places the resampled SRTM DEM was about 21 m higher than the aggregated LiDAR DEM (Figure 10). Elevations in the modelled reach ranged from -1.17 m.a.s.l to 87.51 m.a.s.l. in the LiDAR DEM and from -6 m.a.s.l. to 132 m.a.s.l. (see also Figure 4 and 5 respectively). Extreme differences between the two DEMs occurred especially close to the river mouth and in some upstream areas flanking the main channel. Furthermore a clear systematic error could be seen in the upstream quarter of the domain. Here the SRTM differed from the LiDAR with about 4 m and higher. Downstream this border differences mostly were below 4 m except for the mentioned areas close to the river and at the mouth (Figure 10).

Figure 10 Relative Elevation Differences between aggregated LiDAR DEM and resampled SRTM DEM. Negative values = SRTM elevation > LiDAR elevation and vice versa.

For the whole reach a RMSE of 6.14 m was determined (Table 4). After vegetation removal and the application of the different low pass filters the RMSE decreased by about 1.5 m with the bigger window sizes causing a lower RMSE. All in all the difference in RMSE was not very big between different filter window sizes. A 40 % vegetation removal also resulted in a relatively lower RMSE than the 30 % removal. For profiles through the catchment of LiDAR, SRTM and SRTM 40 % DEMs see Figures A2-1 to A2-5.

Table 4 RMSE for the different SRTM DEMs compared to the LiDAR DEM.

DEM original 30 % vegetation signal removal 40 % vegetation signal removal

no filter no filter 3x3 5x5 7x7 no filter 3x3 5x5 7x7 RMSE [m]

6.14 4.85 4.72 4.67 4.65 4.6 4.44 4.37 4.34

5.2 Calibration Calibration of the LiDAR models with different channel depths that were removed from the DEM and different roughness coefficients resulted in a minimum MAE between simulated and measured stages of 0.219 m for the September 2008 flood event (Figure 11). This best fit was achieved with the subtraction of 4 m elevation in the correspondence of the main channel from the LiDAR DEM and a roughness coefficient of 0.036. The MAE had to be taken after a warm-up period from 19 September 7 am onwards as the main channel had to be filled with water and stages were underestimated or in one case overestimated by far for the first two simulated days (Figure A3-1). Generally the MAE converged to 0.21 m and 0.25 m with a lower value being difficult to achieve. These values are anyhow similar to the vertical accuracy of LiDAR DEMs. Simulated and measured stages aligned relatively well (Figure 12). However, the rise of the water level before the peak came delayed for the simulated stages and the recession curve after the peak had a more convex form as opposed to a more common concave form of the measured stages. Generally the simulated stages were more attenuated and did not show small scale variations in the simulations. Applying the same chosen parameter set to the October 2004 flood led to an increase in MAE by 0.21 m (Figure 13). The MAE was taken after 48 hours of simulation. Stages were underestimated until the peak had passed through the Chacaltianguis gauging station. Then simulated stages were about 0.5 m higher than the measured water levels.

0,45 MAE [m] 0,40

0,35

0,30

0,25

0,20

0,15

Mean Absolute Error [m] Error Absolute Mean 0,10

0,05

0,00 4 m 4 m 4 m 3.9 m 3.9 m 3.8 m 2.5 m 2.5 m 0.035 0.036 0.038 0.03 0.032 0.028 0.019 0.02 Parameter sets [water depth, channel friction] Figure 11 MAE of the simulated stages in comparison with the measured stages for different parameter sets. A subtracted water depth of 4 m in combination with a main channel friction of 0.036 resulted in the lowest MAE of the tested parameter sets with a value of 0.219 m.

3 Water depth 4 m, Channel friction 0.036 Stage [m.a.s.l] 2 Measured Stage

Absolute Differences 1

Time Figure 12 Measured and simulated stage at the Chacaltianguis gauging station for the September 2008 flood. The parameter set with 4 m water depth and 0.036 channel friction was chosen as it produced the lowest MAE. The vertical dotted line marks the end of the warm-up period.

3 Water depth 4 m, Channel friction 0.036 Stage [m.a.s.l] 2 Measured Stage

1 Absolute Differences

Time

Figure 13 Measured and simulated stage at the Chacaltianguis gauging station for October 2004. MAE taken 2 days after beginning of the simulations was 0.43 m. The vertical dotted line marks the end of the warm-up period. 5.3 Simulation Results 5.3.1 September 2008 Flood Simulation of the 15 days of the September 2008 flood event with the chosen parameter set took 70 minutes and resulted in an inundation extending beyond the LiDAR domain. To get comparable results a time step shortly before the water went beyond the model domain was chosen. This was 255 hours after the start of the simulation and only a few hours after the peak had passed the Chacaltianguis gauging station. In the LiDAR model the flood extent was most pronounced in the downstream half of the domain (Figure 14, see Figure A4-1 for reclassified results). Here the water depth mostly ranged from about 0.9 m close to the main channel to around 0.1 m at the margins of the extent. Further upstream the inundations became more scattered with some deeper water depths up to 4 m in old river arms. Looking at the SRTM model one can see much less extensive flooding. Especially downstream almost no water left the main channel. Some areas in the middle of the domain were flooded which were dry in the LiDAR model. The fit of the original SRTM model reached 0.2 or 20 % respectively with 4.1 %% of the cells being flooded in both reference and test model. 14.6 % of the cells were only flooded in the reference model and 1.4 % of the cells were exclusively flooded in the test model (Figure 15). Roughly 80 % of the cells were not flooded in both models. Removing 30 % and 40 % of the vegetation canopy height resulted in a general increase of the fit by about 0.1 respectively 10 %. 40 % removal caused a somewhat better fit as did the increase of the filter window size. Correspondingly the number of

commonly flooded cells increased with the percentage of vegetation signal removal and filter window size as did the number of cells only flooded in the respective SRTM models. With this the number of cells only flooded in the reference model decreased. The best fit with 32.3 % was obtained for a vegetation signal removal of 40 % and a 7x7 low-pass filter. However, here the water of the SRTM model extended beyond the model domain (Figure 16; see Figures A4-2 and A4-3 for only the flood extents). The same was true for all models with a 5x5 and a 7x7 filter window. Because of this the fit was not representative as the number of cells only flooded in the SRTM model likely would have been higher. Excluding these simulations left the 40 % model without any filtering and a fit of 32.8 % followed by the same model smoothed with the 3x3 filter. The respective 30 % models both achieved a fit of 28 %. Figure 17 displays the channel profile for both the LiDAR and SRTM model. Water surfaces represent the simulated water depth at the chosen time step of maximum comparable flood extent. This graph illustrates the slope differences of both models and the more detailed LiDAR profile. The stepwise elevation decrease in the SRTM profile stems from the before mentioned processing of rivers wider than 183 m (Farr et al. 2007). Generally the water surface of both models was relatively similarly shaped. On average water depths were 0.73 m deeper in the LiDAR model. Due to the elevation differences of both DEMs the water surfaces only reached the same elevation above sea level at the downstream boundary where by definition the water surface elevation was set to be 0 m.a.s.l. and also the DEMs almost reached an equal elevation. Looking at the outflow at the downstream end of the model domain the LiDAR model shows a much more attenuated hydrograph compared to the input discharge as well as the outflow of the SRTM model (Figure 18). The peak of the flood wave of the LiDAR model reached the upstream end 344 hours after beginning of the simulations. In the SRTM model it took the peak of the flood wave 50 hours less to pass through the whole reach. The total travel time of the peak flow from the upstream to the downstream end was 91 and 41 hours respectively.

Figure 14 Flood extent of the 2008 flood for both the LiDAR and the SRTM model after 255 hours after the start of the simulation.

40000 35

35000 30

30000 25 25000 20 20000

15 Fit [%] 15000

Number ofNumber Cells 10 A (6) 10000 B (5) 5 5000 C (4)

0 0 Fit

Simulations Figure 15 SRTM simulations compared to the LiDAR simulation for the September 2008 event. LiDAR - SRTM: A (6) flooded - flooded, B (5) flooded - dry, C (4) dry - flooded. The fit describes the performance of each SRTM model in terms of flood extents compared to the LiDAR model.

Figure 16 Reclassified simulation results for the September 2008 flood event. LiDAR - SRTM: flooded - flooded = A (6), flooded - dry = B (5); dry - flooded = C (4); dry - dry = D (3).

SRTM Bathymetry 18

LiDAR Bathymetry 15 SRTM Water Surface Elevation

12 LiDAR Water Surface Elevation

6 Elevation [m.a.s.l.] 3

-3

-6 0 20000 40000 60000 80000 100000 120000

Distance from upstream model boundary [m] Figure 17 Comparison of LiDAR and SRTM derived river profiles through subtraction of 4 m and the water surface of the September 2008 flood after 255 hours simulated time. Overall slope LiDAR: 5.3x10-5; SRTM: 1.09x10-4.

3000

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1500

1000

500

0 Qin Discharge [m³/s] Discharge -500

-1000 Qout LiDAR -1500 Qout SRTM -2000 0 50 100 150 200 250 300 350 Time passed since start of simulation [hours]

Figure 18 Input hydrograph for the September 2008 flood and downstream outflow hydrograph for the LiDAR and the SRTM simulation respectively.

5.3.2 October 2004 Flood Simulation of the 11 days of the October 2004 flood took 37 minutes for the reference model. Again the inundations extended beyond the model domain so that a flood extent before the end of the simulations had to be chosen. The here used extent was taken 160 hours after the beginning of the simulations and almost three days after the flood wave had passed the Chacaltianguis gauging station. The stage MAE for the LiDAR model taken after 48 hours of simulation was with 0.43 m about 20 cm higher than the calibrated stages in 2008. Generally the stages were overestimated by the model after some 90 hours. For this event the flood almost exclusively spread in the downstream third of the reach in the LiDAR model (Figure 19, see Figure A4-1 for the reclassified result). Further upstream the water did not exceed bankfull depth. For this lower part the model almost showed the same pattern as for the September 2008 event (Figure 14). The SRTM model did almost nowhere exceed bankfull depth and only flooded some minor areas that were dry in the reference model. Main channel water depths were on average 1.25 m deeper in the LiDAR model than in the SRTM. The fit of this model was with 14 % lower than for the 2008 flood. 1.6 % of the cells were inundated in both models and 9.8 % cells were under water only in the LiDAR model (Figure 20). The number of cells flooded exclusively in the test model amounted to 0.6 %. A remainder of 88% of the cells were not flooded in both models. After removal of some of the vegetation canopy height the fit increased by up to 14 %. The best fit of 28 % was obtained with the 40 % removal without any filtering. Again the models that were based on a filtered DEM with a window size of 5x5 and 7x7 simulated the water to flow beyond the lateral margins of the model domain rendering these models

useless for comparison (Figure 21, see Figure A4-4 and A4-5 for only the flood extents). For both events water left the model domain at the same location. In the LiDAR model these cells were not affected by flooding. Generally the number of equally predicted flooded cells increased to a greater extent for the 40 % removal and for bigger window sizes (Figure 20). Accordingly the cells only flooded by the reference model decreased. Compared to the 2008 event areas only flooded by the SRTM were more abundant especially for the models based on 40 % vegetation signal removal. Again, the outflow hydrograph of the LiDAR model was much more attenuated than the one of the SRTM model (Figure 22). Accordingly the travel time of the peak from the upstream to the downstream end amounted to 149 and 48 hours respectively. The differences of floodwave propagation time between LiDAR and SRTM model was 101 hours longer than what was simulated in the 2008 flood.

Figure 19 Flood extent of the October 2004 flood for both the LiDAR and the SRTM model after 160 hours after the start of the simulation.

30000 30

25000 25

20000 20

15000 15 Fit [%]

10000 10 Number ofNumber Cells A (6) 5000 5 B (5) C (4) 0 0 Fit

Simulations Figure 20 SRTM simulations compared to the LiDAR simulation for the October 2004 event. LiDAR - SRTM: A (6) flooded - flooded, B (5) flooded - dry, C (4) dry - flooded. The fit describes the performance of each model compared to the LiDAR model.

Figure 21 Reclassified simulation results for the October 2004 flood event. LiDAR - SRTM: flooded - flooded = A (6), flooded - dry = B (5); dry - flooded = C (4); dry - dry = D (3)

1400

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800

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400

200 Discharge [m³/s] Discharge 0 Qin -200 Qout LiDAR -400 Qout SRTM -600 0 50 100 150 200 250 Time passed since start of simulation [hours]

Figure 22 Input hydrograph for the October 2004 flood and downstream outflow hydrograph for the LiDAR and the SRTM simulation respectively.

6 Discussion 6.1 Vertical Error in Original and Conditioned SRTM DEMs With a RMSE of 6.14 m the original SRTM DEM differed considerably from the LiDAR DEM (Table 4). For almost all areas the SRTM DEM was higher than the LiDAR DEM. This is not surprising considering the reported absolute height error for North America of about 9 m due to various errors of the SRTM data (Rodríguez, Morris & Belz 2006), although errors are much lower in floodplain areas (Schumann et al. 2013; Yan et al. 2015). Parts of these errors could be attributed to the inability of the radar beam to completely penetrate vegetation canopy (Brown, Sarabandi & Pierce 2010). Removal of 30 and 40 % of the canopy height from the SRTM resulted in an improvement in vertical accuracy with a reduced RMSE for all conditioned DEMs. However, the conditioned DEMs showed a pronounced chequerboard effect due to the relatively big pixel size of the used canopy height data set by Simard et al. (2011) compared to the SRTM DEM. Some areas did not have any vegetation coverage according to the canopy height data set which contributed to this chequerboard effect. The applied low-pass filters were able to smooth some of these artefacts away but the window sizes 5x5 and 7x7 cells likely took away too many of the floodplain elevation details. A problem with the here applied technique was the initially large pixel size of the vegetation map that lead to too much height removal for some areas from the much smaller 90 m cells of the SRTM. Vegetation removal affected areas where elevation differences between LiDAR and SRTM were not as big as the amount of removed vegetation. Better results with the vegetation removal are probably achieved when applying it to a model with a bigger cell size, as was the case in the study of Baugh et al. (2013) were a 270x270 m resolution was applied. Also, this method as used here should rather be applied on floodplains completely covered with forest which reduces the chequerboard effect in the DEM and the resulting simulated flood extent. Other ways to account for these effects could be filling sinks operations in a GIS which could reduce these effects to some degree. Apart from these height differences there was also a pronounced difference in slope between the LiDAR and SRTM DEMs which is likely to have had a bigger effect on simulation outcomes than the vegetation signal (Figure 17). The slope of the SRTM DEM was by a factor of 2 bigger than the slope of the LiDAR DEM. Elevations along the river profile only levelled out close to the sea. Further upstream the SRTM DEM generally was more elevated and thus the stream had a steeper slope over the length of the main channel. This might have caused the water in the SRTM model to flow faster through the main channel, thus significantly altering output flood extents. While many previous studies pointed to the vertical inaccuracy of SRTM, this work shows issues also in determining the slope, which is critical for hydraulic modelling. 6.2 Calibration The choice of flood events initially proved to be relatively difficult. Bigger events such as the aforementioned 2010 flood with more than 4000 m³/s discharge, but also smaller events rapidly

flooded vast parts of the LiDAR domain when calibrated against the measured stage data from Chacaltianguis. Thus, smaller events had to be chosen where it was possible to take a flood extent after most parts of the peak flood wave had passed the reach. For a comparative benchmarking study such as this one, artificial levees at the sides of the DEMs could be included to prevent water from leaving the domain. This could be achieved by raising the cells at the margins of both LiDAR and SRTM DEM. With these dams the simulations could be run until the maximum flood extent is reached and the peak has passed the downstream model boundary. Calibration of the LiDAR model with the chosen 2008 event was fairly accurate with an MAE of 0.219 m (Figure 12). The simulated hydrograph captured the measured stages quite well, except for minor variations. This could be due to the failure to capture bathymetric details by subtracting the same water depth of 4 m along the whole reach. Simulation of the second flood event with the same chosen parameter set lead to an increase in MAE almost double that of the calibration MAE (Figure 13). Although being still rather small, this difference in MAE indicates the need to be careful and critical when applying well working models to flood events of a different magnitude as pointed out for example by Di Baldassarre, Schumann and Bates (2009b). Another issue here could have been a too short warm-up period which did not leave enough time for the whole channel to fill with water (Figure 13). How much of an effect this difference would have had on the simulation of accurate flood extents cannot be said as no satellite data of an inundation was available to compare the model output to. If the intention is to have more than a comparative model high water marks or remotely sensed inundation extents would be needed. The chosen calibration method could be flawed in a way that, for example, the bathymetry at the gauging station was relatively deep compared to other parts of the channel so that a relatively high roughness coefficient was chosen to achieve the same elevation as the measured stages. This in turn could cause other parts of the main channel that are relatively shallow to be overestimated which would lead to too much flooding locally. However, as in this comparative study the same conditions applied to both the reference and the test models, the specific calibration is not considered to be a major limitation. 6.3 Simulations Models based on the original SRTM DEM only produced a fit of 20 and 14 % for 2008 and 2004 respectively. Floods for both events were underestimated by far. The fit consisted mainly of commonly flooded cells in the main channel. This was especially true for the lower magnitude flood of 2004 (Figure 19). For further studies these channel cells should be excluded from the measure of fit. If applied here, this exclusion would have resulted in an even lower fit of both SRTM models. In light of previous studies with SRTM based hydraulic models this is very low. Yan, Di Baldassarre and Solomatine (2013) conducted a study with a LiDAR and SRTM model based on the model code HEC- RAS for the Po River in Italy. Although being significant, differences between LiDAR and SRTM simulations were within an acceptable range of accuracy. Schumann et al. (2013) set up a coupled

hydrologic-hydraulic model based on SRTM data and LISFLOOD-FP for the Lower Zambezi River and achieved correctly predicted inundation extents up to about 86 %. They concluded that satisfying results can be achieved with grid resolutions up to several km². However, the SRTM DEM for this floodplain area had a mean vertical error of only 1.95 m. Also, the slope of the river in this study was well approximated by the SRTM data. Figures A2-1 to A2-5 show much more pronounced elevation differences for the SRTM DEM used in this study. This was the case for most of the modelled reach which becomes a problem when bank cells are relatively too high and bankfull depth is only reached with much higher discharges than in the LiDAR model. Also the floodplain flow is significantly hindered through these elevation differences. As with the vegetation signal removal fill operations could have helped to even out some of these differences and bring the SRTM DEM closer to the LiDAR DEM. Another major issue with the SRTM DEM was the significant difference in the average slope between the two DEMs. Water in the main channel of the SRTM model with its steeper slope was likely being conducted faster through the reach and thus elevations in the main channel were lower. For 2008 water depths were on average 0.73 m deeper in the LiDAR model compared to the original SRTM model. In 2004 this average difference was even bigger with 1.25 m. Accordingly the peak flow reached the lower model boundary faster in both years for the original SRTM model supporting the probable effect of a steeper slope (Figure 18 and 22). This difference in flood wave travel time could also arise from the fact that more water was leaving the channel in the LiDAR model which caused the peak to be delayed downstream. The highest achieved fits for both floods were 32.8 % in 2008 and 28 % in 2004. For both years this was the case for a conditioned SRTM DEM. Vegetation signal removal thus was able to improve the fit. The so treated SRTM models were able to simulate larger parts of the downstream flooding in both scenarios (Figure 16 and 21). This was due to lowering of the bank cells which allowed water to leave the main channel easier. In 2008 also some more upstream areas were predicted quite accurately (Figure 16). However, similar upstream areas were also flooded in the conditioned 2004 SRTM models where the LiDAR model did not predict any flooding (Figure 21). Here the bank cells were probably lowered too much so that with even low discharges bankfull depth was reached. Further consequences of the vegetation signal removal was the creation of a corridor with increased smoothing that lead water out of the upstream model domain rendering the models useless for comparison (Figure 16 and 21) as flooded areas outside the model domain could not be accounted for in the measure of fit. This was the case in both years. Another issue was the influence the bigger pixel size of the vegetation map had on the shape of the flood extent. Smoothing did attenuate this chequerboard effect slightly but caused the aforementioned problems of too much flooding. Generally the recommendation is to only apply the vegetation signal removal on lower model resolutions, e.g. 270x270 m as was the case in Baugh et al. (2013) and more densely covered forest areas without too many gaps without vegetation in between.

Another difficulty and source of uncertainty was that the water surface had to be protected from lowering and smoothing through overlaying vegetation cells. This had to be done manually and could result in vegetated bank pixels that were not being lowered which could in turn alter simulated flood extents by not allowing water to leave the main channel due to too high bank cells.

7 Conclusions SRTM data have been applied relatively successfully in previous hydraulic studies with promising results for further applications (e.g. Mukolwe et al. 2015; Schumann et al. 2013; Yan, Di Baldassarre & Solomatine 2013). However, under the assumption that the LiDAR data used here are not only precise, but also accurate, this case study for the Papaloapan River did not produce satisfying results. SRTM-based model simulations resulted in a poor fit compared to the reference LiDAR-based model. 20 % and 14 % fit of the original SRTM models could mainly be attributed to commonly flooded main channel cells which prevented the possibility of a 0 % fit. For future studies this should be accounted for by excluding the channel cells from the binary map created to calculate the fit. If applied here, an even lower fit of the SRTM models would have been obtained. Bankfull depth was almost nowhere reached in both years. The main cause was likely the higher bank cells in the SRTM DEM as well as its inability to capture the river slope, which is crucial for hydraulic modeling. River banks were too high which prevented flooding in the SRTM. These elevation differences not only occurred close to the river but throughout the whole floodplain and would have prevented accurate floodplain flow simulations even if the bank cells would have been low enough. The higher slope caused water to flow faster through the domain and in turn caused a lower water depth in the main channel. These differences in slope and elevation could be characteristic for this very flat and low-lying floodplain where errors in SRTM data and vegetation cover can weigh quite much and influence simulation outcomes to a substantial extent. Vegetation signal removal was able to improve the model performance by 12 and 14 % in 2008 and 2004 respectively. This was the case for 40 % removal of the canopy height from the SRTM DEM. Larger flooded areas were predicted correctly. However, cell size of the vegetation data set was too big for the here applied model resolution causing an unrealistic shape of flood extent. Furthermore, vast areas were flooded in the SRTM model, which in the LiDAR model were dry. Nevertheless, the method developed by Baugh et al. (2013) has great potential in improving hydraulic modelling with SRTM data in densely vegetated study areas when applied in models with a lower resolution and a significant amount of area-wide vegetation coverage. Ideally the whole modelled river reach should be covered with vegetation, which was not the case in this study. To overcome some of the problems with this technique fill operations could be applied to reduce the chequerboard effect caused by the removal. Also it should be considered that the vegetation canopy height data set used here was based on data from 2005 (Simard et al. 2011). If major landuse changes have occurred between the capturing of the SRTM in 2000 and 2005 this dataset might not be feasible to use for this kind of study. This study has shown the limitations of SRTM data, which have been proven not necessarily applicable for all cases of hydraulic floodplain modelling. To verify the results obtained here a reference LiDAR-based model calibrated with remotely sensed flood extent could be set up if such data is available. Also it should be tried to reduce random noise in the SRTM DEM by applying

different GIS operations such as filling sinks. Further, case studies with SRTM data should be done to critically evaluate different types of floodplains and their respective performance.

8 Acknowledgements First and foremost I would like to thank my supervisor Giuliano Di Baldassarre for giving me the opportunity to take part in his research and his guidance throughout the last half year. I would also like to thank Kun Yan from UNESCO-IHE for his valuable input and thoughts on the model setup. Further I am also grateful to my subject reviewer Thomas Grabs for his positive and very helpful comments on the thesis. Thank you also to my opponent Sara Doverfelt for her comments and questions during the presentation and on the written thesis. Special thanks go to Åsa Horgby for the constant exchange of knowledge during the modelling and writing process as well as for the help with writing the Swedish popular scientific article.

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Appendix 1: Original LiDAR DEM

Figure A1-1 Originial LiDAR DEM in 5x5 m resolution; DEM obtained from the Mexican National Water Commission CONAGUA.

Appendix 2: DEM Comparison

Figure A2-1 Location of profile transects for DEM comparison.

55 Transect 1 LiDAR 45

SRTM 35

SRTM 40% 25 Papaloapan water surface 15

Elevation [m.a.s.l] B1 C1 5 A1

-5 0 5000 10000 15000 20000 Distance from A1 [m]

Figure A2-2 Profiles for Transect 1. For transect location see Figure A2-1.

14 Transect 2 12

8 B2 A2 6

2 Elevation [m.a.s.l]

-2

-4 0 5000 10000 15000 20000

Distance from A2 [m]

Figure A2-3 Profiles for Transect 2. For transect location see Figure A2-1.

25 B3 Transect 3

A3 D3 15 C3

10 Elevation [m.a.s.l]

0 0 5000 10000 15000 20000 Distance from A3 [m]

Figure A2-4 Profiles for Transect 3. For transect location see Figure A2-1.

25 Transect 4 A4 20

B4 15

10 Elevation [m.a.s.l] 5

0 0 5000 10000 15000 20000

Distance from A4 [m]

Figure A2-5 Profiles for Transect 4. For transect location see Figure A2-1. Appendix 3: Calibration Results

4 2.5 m, 0.019

Stage [m.a.s.l] 3 2.5 m, 0.02 3.8 m, 0.028 3.9 m, 0.032 2 3.9 m, 0.03 4 m, 0.038 1 4 m, 0.035 4 m, 0.036 Measured Stage 0

Time Figure A3-1 Measured and simulated stages for the 2008 flood with different combinations of parameters (water depth, channel friction). The red line marks the chosen set for further simulations.

Appendix 4: Model Results

Figure A4-1 Reclassified results for the original SRTM models in both tested years. LiDAR - SRTM: flooded - flooded = A (6), flooded - dry = B (5); dry - flooded = C (4); dry - dry = D (3).

Figure A4-2 Flood extents for all models with 30 % vegetation signal removal in 2008.

Figure A4-3 Flood extents for all models with 40 % vegetation signal removal in 2008.

Figure A4-4 Flood extents for all models with 30 % vegetation signal removal in 2004.

Figure A4-5 Flood extents for all models with 40 % vegetation signal removal in 2004.

Examensarbete vid Institutionen för geovetenskaper ISSN 1650-6553