Geomorphology 396 (2022) 108000 Contents lists available at ScienceDirect Geomorphology j ourna l homepage: www.e lsev ie r .com/ locate /geomorph Improving regional flood risk assessment using flood frequency and dendrogeomorphic analyses in mountain catchments impacted by tropical cyclones Adolfo Quesada-Román a,b,⁎, Juan Antonio Ballesteros-Cánovas a,c, Sebastián Granados-Bolaños b, Christian Birkel b, Markus Stoffel a,c,d a Climatic Change and Climate Impacts, Institute for Environmental Sciences, University of Geneva, Boulevard Carl-Vogt 66, CH-1205 Geneva, Switzerland b Department of Geography and Water and Global Change Observatory, University of Costa Rica, 2060 San José, Costa Rica c Dendrolab.ch, Department of Earth Sciences, University of Geneva, Boulevard Carl-Vogt 66, CH-1205 Geneva, Switzerland d Department F.-A. Forel for Environmental and Aquatic Sciences, University of Geneva, Geneva, Switzerland ⁎ Corresponding author at: Department of Geography Observatory, University of Costa Rica, 2060 San José, Costa E-mail address: adolfo.quesada@gmail.com (A. Quesad https://doi.org/10.1016/j.geomorph.2021.108000 0169-555X/© 2021 The Author(s). Published by Elsevier B a b s t r a c t a r t i c l e i n f o Article history: Received 13 July 2021 Received in revised form 28 September 2021 Accepted 12 October 2021 Available online 23 October 2021 River floods frequently occur when tropical cyclones hit land. Nonetheless, systematic, long-term discharge data remain rather scarce inmany tropical countries, which prevent proper analysis of peak discharges occurring dur- ing floods. The Térraba catchment is the biggest andmost dynamic catchment in Costa Rica. In this study, we de- veloped regional flood-frequency analyses combining tree-ring based estimation and measurement of peak discharge atmonitoring stations during tropical cyclones to derivefloodquartiles. Floodquartileswere combined with the TopographicWetness Index (TWI) to determine regional flood hazards along floodplains. The flood risk assessment was based on a high-resolutionmapping of infrastructure, population density (as ameasure of expo- sure), and a social development index (to represent vulnerability). We show that peak discharge of cyclone- induced floods can be assessed accurately with flood-frequency analyses including dendrogeomorphic recon- structions and systematic discharge measurements. We also show that regional flood risk assessments can be performed in large-scale catchments if both coarse and detailed inputs are used. The results of this study will be useful for the development of flood risk schemes promoting resilience of local populations. © 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Disaster risk reduction Dendrogeomorphology Bayesian MCMC UAV Tropics Térraba River Costa Rica 1. Introduction Tropical regions have faced substantial land-use changes, especially over the course of the twentieth century. These changes have increased vulnerability of settlements to severe weather-related hazards (Lawrence and Vandecar, 2015; Carabella et al., 2020) as they increase sediment yields and riverscape variations (Wohl, 2006; Piacentini et al., 2020). The problem is particularly acute in regions where tropical cyclones commonly occur, resulting in intense floods with widespread, negative socio-economic consequences for local populations (Syvitski et al., 2014; Rodríguez-Morata et al., 2018). Tropical cyclones have re- peatedly provoked intense devastation, and damage has often been ag- gravated by widespread urbanization and the lack of flood hazard assessments (Raymond et al., 2020). and Water and Global Change Rica. a-Román). .V. This is an open access article und Furthermore, proper flood hazard assessments depend on reliable data on the spatiotemporal distribution of rainfall events and discharge along rivers (Baker, 2008; IPCC, 2014; UNDRR –United Nations Disaster Risk Reduction, 2019; Guerriero et al., 2020). In tropical countries, this information is commonly limited or of poor quality, which in turn ren- ders flood forecasts even more difficult (Wohl et al., 2012). In data- limited regions, different techniques can be applied to adequately esti- mate peak discharges and return periods of floods (Baker, 2008; Bodoque et al., 2015; Wilhelm et al., 2019; Bodoque et al., 2020). Flood marks left in rivers can be used to determine spatial pat- terns and magnitudes of floods even months after an event (Borga et al., 2008). Botanical indicators provide relevant information to an- alyze, and date floods a posteriori to determine their magnitude (Ballesteros-Cánovas et al., 2015a). Botanical evidence of previous floods comprises injured stems and branches, tilted trunks, and ex- posed roots of trees growing alongside fluvial reaches (Ballesteros- Cánovas et al., 2020a, 2020b; Bodoque et al., 2020; Stoffel et al., 2012). In addition, scars in trees can be used as paleostage indicators (PSI) that can be accurately dated (Ballesteros-Cánovas et al., 2011a, 2011b), thereby allowing the extension of flood records back in time er the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). http://crossmark.crossref.org/dialog/?doi=10.1016/j.geomorph.2021.108000&domain=pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ https://doi.org/10.1016/j.geomorph.2021.108000 mailto:adolfo.quesada@gmail.com https://doi.org/10.1016/j.geomorph.2021.108000 http://creativecommons.org/licenses/by-nc-nd/4.0/ http://www.sciencedirect.com/science/journal/ www.elsevier.com/locate/geomorph A. Quesada-Román, J.A. Ballesteros-Cánovas, S. Granados-Bolaños et al. Geomorphology 396 (2022) 108000 (Ballesteros-Cánovas et al., 2015b) and to derive flood hazard zona- tion (Ballesteros-Cánovas et al., 2013; Brooks and George, 2015; Garrote et al., 2019; St. George et al., 2020). For engineering projects and flood risk management (Nguyen et al., 2014; Díez-Herrero and Garrote, 2020), flood-frequency analyses are normally derived to define the relation between flood magnitude and the exceedance probability to assess the likelihood that a given flood discharge will occur in the future (Wilhelm et al., 2019). A suite of ap- proaches can be used to decrease the uncertainties of in situ flood- frequency analyses to produce more accurate flood magnitude esti- mates based on larger sample sizes (Gaál et al., 2010). These approaches can extend these analyses spatially (e.g., Hosking andWallis, 1997) and temporally by using information provided by paleofloods or historical floods (e.g., Reis and Stedinger, 2005). Bayesian Markov Chain Monte Carlo (MCMC) approaches provide a precise statistical tool to perform flood-frequency analyses (Gaume, 2018). In a context where flow re- cords are limited or incomplete, historical evidence can help to reduce uncertainties in flood magnitude, even if they are not as accurate as di- rect measurements (Baker, 2008; Benito et al., 2015). Risk management strategies primarily aim at reducing risks and losses (UNDRR – United Nations Disaster Risk Reduction, 2019). Flood riskmanagement can be achievedby implementing legal, economic, po- litical, educational, structural, technological, cultural, social, health, en- vironmental, and institutional measures to reduce and prevent hazard, vulnerability, and exposure (IPCC, 2014; UNISDR, 2015). This involves a holistic and comprehensive understanding of hazards at the local level, including flood risk (Pinto Santos et al., 2020). Like discharge data, tropical regions also lack information on economic losses caused by floods, which limits the potential to perform risk analyses (de Ruiter et al., 2020). In Costa Rica, more than 90% of disasters are hydrometeorological (LA RED, 2018). Therefore, work on flood processes has been extensive in Costa Rica; whereas the resolution of hazard maps that combine in- formation on exposure, vulnerability, and risk has remained largely un- explored (Quesada-Román and Mata-Cambronero, 2020). In the Térraba catchment, where this study was conducted, the ongoing changes in land cover from forests to extensive croplands have substan- tially intensified rates of erosion and sediment yield (Krishnaswamy et al., 2001b). As a result, intense floods now occur in the Térraba catch- ment during the passage of tropical cyclones, at roughly decadal inter- vals (Quesada-Román and Zamorano-Orozco, 2019a). Floods affect different parts of the catchment where they claim lives and cause dam- age to agriculture and communication infrastructure, particularly roads and bridges. Recent major hurricanes that led to severe floods in the Térraba catchment were Joan (1988) and Cesar (1996), as well as trop- ical storms Alma (2008) and Nate (2017) (Quesada-Román et al., 2020b). These tropical cyclones claimed dozens of lives, affected the property of hundreds of people, and caused substantial economic losses (Table 1). A clear need exists to adapt to strong tropical rainfall events (including, but not restricted to tropical cyclones) because these events are expected to become more frequent as climate warming continues (Saunders and Lea, 2008). In this perspective, the creation of a regional flood-frequency analysis and a risk assessment could help land-use planning and disaster risk reduction. This paper, therefore, aims at (i) developing a regional flood-frequency analysis employing dendrogeomorphic records, and (ii) providing a risk assessment of floods occurring during tropical cyclones in the Térraba catchment (Costa Rica). 2. Materials and methods Fig. 1 presents an overview of the approaches used in this study: (1) study site selection based on historical / flow gauge records, (2) ac- quisition of topographic and geomorphic data, (3) tree-ring based flood reconstruction, (4) hydraulic and statistical modeling, and (5) flood risk assessment based on exposure and vulnerability data. 2 2.1. Geographic setting The Térraba catchment (4765 km2) is in the southeast of Costa Rica from 8.7° to 9.5° N and 82.7° to 83.8° W (Fig. 2). The Térraba catchment and its sub-catchments have a strong tectonic control by local faults with a NE-SW and NW-SE alignment. Lithology is composed both of Mio-Pliocene (volcanic) and Oligocene-Miocene (sedimentary) rocks with very regular grain sizes among boulders and coarse sands in all sub-catchments (Denyer and Alvarado, 2007; Alvarado et al., 2017; Gardner et al., 2013). The Mio-Pliocene volcanic rocks are impermeable intrusive stocks and batholiths composed mostly of gabbros and extru- sive volcanic rocks such as andesitic-basaltic rocks formed by lavaswith prismatic fractures. These volcanic intrusive rocks rest beneath the Oligocene-Miocene sedimentary rocks, comprising permeable breccias, sandstones, and shales with textures from decimeters to meters. The highest peak in the country, Cerro Chirripó (3820 m asl), marks the origin of the headwaters (Veas-Ayala et al., 2018; Quesada-Román et al., 2019, 2020c, 2021) from where it crosses the General-Coto Brus Valley and the Térraba-Sierpe deltaic mangrove wetlands to flow into the Pacific Ocean (Acuña-Piedra and Quesada-Román, 2016, 2021; Quesada-Román and Zamorano-Orozco, 2019b). The postglacial alluvial fans and floodplains (Camacho et al., 2020) along the General-Coto Brus Valley are used currently for intense agriculture and former forests have been turned into croplands, especially after the 1950s – the same area is now affected by high erosion rates (Krishnaswamy et al., 2001a). The catchment outlet is in Palmar where the river becomes a delta covered mainly by mangrove. Roughly 256,000 people live in the Térraba catch- ment (in 2020), of which 143,000 inhabitants live in Pérez Zeledón, followed by Buenos Aires (53,000 inhabitants), Coto Brus (44,000), and Osa (16,000). The municipalities of Buenos Aires and Coto Brus are also the home of roughly 30% and 10% of indigenous people, respec- tively (INEC - Instituto Nacional de Estadística y Censos, 2020). 2.2. Climate characteristics and tropical cyclones Local climate is dominated by the movement of the Intertropical Convergence Zone, cold fronts, the northeast trade winds, the Atlantic Multidecadal Oscillations (AMO), the El Niño-Southern Oscillation (ENSO), and the seasonal influence of tropical cyclones originating in the Caribbean (Hidalgo et al., 2015; Durán-Quesada et al., 2020; Quesada-Román et al., 2020a). This setting results in two rainfall max- ima producing between 1500 (lowlands and confluences) and 6000 mm (mountain tributaries) annually, the first one in May and the second one in October. These maxima are disconnected from each other by the Midsummer Drought, which lasts from July to August (Maldonado et al., 2016). The dry season extends from December to April. Annualmean temperatures range from 8 to 28 °C,mostly depend- ing on altitude (IMN - Instituto Meteorológico Nacional, 2008). Floods are triggered by strong local convection, but intense floods are normally related to the passage of tropical cyclones (Durán-Quesada et al., 2020). Devastating tropical cyclones over the Térraba River catchment occur every ten years on average (Table 1). 2.3. Reconstruction of recent flood event We chose seven sampling sites along the Térraba catchment to de- termine the peak discharge reconstruction employing botanical evi- dence, in particular scars in trees (see Quesada-Román et al., 2020b for methodological details). In the field, during January of both 2018 and 2019, we sampled treeswith flood scars that could be clearly attrib- uted to Tropical Storm Nate in 2017 (Sigafoos, 1964; Ballesteros- Cánovas et al., 2011a). The position of scarred trees was documented with a Global Positioning System (GPS; accuracy of <1 m) and scar heights were measured from the base of the tree to the central height of the injury (Ballesteros-Cánovas et al., 2011b, 2015a, b). The two- dimensional (2D) hydrodynamic model IBER (www.iberaula.es) was http://www.iberaula.es Table 1 Tropical cyclones that affected the Térraba catchment between 1970 and 2018 (LA RED, 2018). Date Municipalities affected Tropical cyclone Impacts 9/19/1971 Pérez Zeledón, Osa Tropical Storm Irene 1 death, 18 victims, 1 home destroyed and 8 affected 10/22/1988 Osa Hurricane Joan 1200 victims 7/25/1996 Pérez Zeledón, Buenos Aires, Osa Hurricane Cesar 13 deaths, thousands of victims, 449 houses destroyed 10/22/1998 Pérez Zeledón, Buenos Aires, Coto Brus, Osa Hurricane Mitch 954 victims, 18 houses destroyed, 592 houses affected 5/29/2008 Pérez Zeledón, Buenos Aires, Osa Tropical Storm Alma 900 affected 10/5/2017 Pérez Zeledón, Buenos Aires, Coto Brus, Osa Tropical Storm Nate 640 victims, 160 houses affected, $15 US million in economic losses Fig. 1. Conceptual diagram summarizing the regional flood-frequency analysis approach employed at the Térraba catchment. A. Quesada-Román, J.A. Ballesteros-Cánovas, S. Granados-Bolaños et al. Geomorphology 396 (2022) 108000 employed tomodel water depth for the flood of 2017 in all the sampling sites of the Térraba catchment (see Fig. 1 for details). IBER replicates unsteady surface flows, turbulent-free, and environ- mental processes in riverscapes by resolving depth-averaged 2D Fig. 2. Location of the Térraba catchment in Central America (A) and within Costa Rica (B). Tre acronyms see Table 2. (For interpretation of the references to colour in this figure legend, the 3 shallow water equations (2D Saint-Venant) using a finite volume approach with a second-order roe arrangement (Cea et al., 2019). The model is suitable for turbulent mountain streams where discontinuities are common. Even in intermittent regimes, the method is conservative. e-ring sampling sites are shown with green dots, hydrological stations with blue dots. For reader is referred to the web version of this article.) A. Quesada-Román, J.A. Ballesteros-Cánovas, S. Granados-Bolaños et al. Geomorphology 396 (2022) 108000 The model operates in a non-structured mesh comprising triangles or quadrilateral features. We used an Unmanned Aerial Vehicule (UAV; DJI Phantom 4 Pro V2) to acquire digital imagery. Using a Structure from Motion (SfM) approach we geo-rectified ortho-mosaics and ob- tained digital elevation models (Turner et al., 2012). Agisoft Photoscan 1.4.0 was used to reconstruct riverscapes and to produce elevation models with a 0.5 m resolution (Langhammer and Vacková, 2018). SfM derived from aerial digital imagery can produce both digital sur- face models and bare terrain models if the densified point cloud is clas- sified into different categories to interpolate only the desired data, which in this case was terrain and channel characteristics. These data can be used for hydraulic modeling of floods and stream behavior as shown by Quesada-Román (2020, 2021) and Langhammer and Vacková (2018). We are aware that aerial digital imagery cannot per- formbathymetric analysis ofwater bodies. Unfortunately, during the re- search, we did not have LIDAR or bathymetric equipment that allowed us to perform accurate measurements of the riverbed. Nevertheless, we consider the data on channel characteristics obtained by the UAV to be adequate for the hydraulic modeling of floods. UAV surveying is a robust tool to obtain channel data and geometry for hydraulic model- ing and fluvial geomorphology purposes (Granados-Bolaños et al., 2021; Vélez-Nicolás et al., 2021). We used Manning's n roughness coefficient for homogenous rough- ness units to evaluate bed friction in the field (Chow, 1959). For the main channel we used n = 0.075, 0.16 for forests and 0.08 for areas with sparse vegetation (Barnes, 1967; Arcement and Schneider, 1989). Consecutive input discharges (i.e., steady flow regime) were modeled based on historical extremes (applying steps from 100 to 1500 m3/s). The 2017 flood peak discharge was modeled with an iterative step- backwater method, and it entailed an (i) estimate of water stages from modeled peak discharges, and (ii) a fitting of resulting modeled water surfaces with PSI heights recognized at the sampling sites (Webb and Jarrett, 2002). We then determined the mean squared error (MSE) of each modeled discharge against the height each scar. Themagnitude of the flood in each river sectionwas then characterized as the peak discharge for which the MSE between the model and the scar heights was smaller (Fig. S1; Quesada-Román et al., 2020b). 2.4. Regional flood-frequency analysis Reconstructed peak discharge values and related uncertainty were included as a range of values to the systematic records of annual maxi- mum discharge from eight hydrological stations from 1962 to 2019 (Table 2; Table S2; ICE - Instituto Costarricense de Electricidad, 2019). We then utilized the available data to implement a regional flood- frequency analysis using Bayesian Markov Monte Carlo Chain (MCMC) Table 2 Hydraulicmodel estimated peak discharge at sites 1–7 and observed peak flows at hydro- logical stations 8–15 used for the regional flood-frequency analysis in the Térraba catch- ment. Number Code Name Area (km2) Maximum peak discharge (m3/s) Year 1 SA Site A (Pueblo Nuevo) 101 636 2017 2 SB Site B (Canaán) 164 455 2017 3 SC Site C (Miraflores) 206 1249 2017 4 SP San Pedro 68 146 2017 5 RV Río Volcán 66 143 2017 6 RC Río Coto Brus 852 336 2017 7 RS Río Sábalo 54 212 2017 8 RI Rivas 317 947 2017 9 LJ Las Juntas 823 2007 2005 10 LC La Cuesta 843 2776 2017 11 RE Remolino 1071 5250 1996 12 PE Pejibaye 129 1373 1993 13 BR El Brujo 2399 8809 1996 14 CA Caracucho 1135 3366 1996 15 PA Palmar 4766 13,500 1996 4 algorithm (Gaál et al., 2010; Gaume, 2018). Moreover, a Generalized Extreme Value distribution (GEV) was applied to calculate flood quantiles. Uniformity of the existing systematic flow series was verified using the Hosking and Wallis (1987) algorithm (Table S1), which highlights the difference among sites in samples Lcv (coefficient of L- variation) for the evaluated sectors (Fig. S2). A regional flood-frequency analysis thus permits the addition offlood quantile estimations at distinct catchment positions by flow-index regionalization (Figs. S3, S4). This ap- proach is based on the distribution of a flow discharge from dissimilar catchments of a larger and single basin (Fig. S5). This analysis was com- pleted using the R package nsRFA (Viglione, 2013). The strength of this method has been tested previously in other hydrological contexts (Reis and Stedinger, 2005; Gaume et al., 2010; Ballesteros-Cánovas et al., 2015b, 2016, 2017). Finally, we added historical peak discharges from Tropical StormNate 2017obtainedwith the dendrogeomorphic approach as they are expected to improve flood quantiles and reduce uncertainties at each reach analyzed within the catchment (Fig. 3; Bodoque et al., 2020).We then calculated the occurrence of floodswith different exceed- ance probabilities per catchment surface unit (1 km2) (Table S3). 2.5. Regional flood risk assessment To identify flood-prone areas within the Térraba catchment and sub- sequently define the flood hazard, we performed a hydrogeomorphic floodplain mapping (GFPLAIN) using the algorithm developed by Nardi et al. (2008, 2019) and parameters provided by the 10-m DEM following Annis et al. (2019). To separate differences in flood hazard within the identified floodplains, we used the TopographicWetness Index (TWI) be- cause it merges local upslope contributing area and slope, i.e., variables frequently applied to quantify topographic control in hydrological pro- cesses (Sörensen et al., 2006). Moreover, the TWI was multiplied by the flood magnitude for a return period T = 10 yr (Table S3), which corre- sponds, on average, to the frequency of historical observations of tropical storms affecting the catchment. The exposure to floods was defined with the population density and infrastructure product of WorldPop (Tatem, 2017) as it provides the best resolved estimate based onmachine learning approaches for the number of people residing in 100 × 100 m grid cells. For the assessment of flood vulnerability, we utilized the social develop- ment index (MIDEPLAN - Ministerio de Planificación Nacional y Política Económica, 2017) developed by the Costa Rican Ministry of National Planning and Economic Policy (MIDEPLAN). This index gathers and eval- uates educational, security, economic, public health, and civic participa- tion variables for all districts of the Térraba catchment (MIDEPLAN - Ministerio de Planificación Nacional y Política Económica, 2017). All vari- ables were normalized from 0 to 1 (Fig. S5). Flood risk gives the likelihood for the occurrence of a flood that has the potential to produce impacts to infrastructure, people, and property (Pinto Santos et al., 2020). The risk of flooding at the catchment level implies a direct probabilistic relation between the physical processes of flooding and given exposed and vulnerable elements (Pinto Santos et al., 2019). Flood risk (FR) is thus a dimensionless and equivalent cal- culation at the catchment level and is the result of hazard (H), exposure (E), and vulnerability (V): FR ¼ H 1 3 � E 1 3 � V 1 3 ð1Þ This expression of flood risk considering the scale, risk components, and input data differences is based on the INFORM risk index (De Groeve et al., 2014). When compared to the simple product of H, E, and V, used without the exponentiation, one can observe a dispersal and increase in the range of final flood risk scores. Finally, we produced a point map with the flood risk categorized as high, medium, and low using Jenks natural breaks classification method for practical applica- tions (Jiang, 2013; Allen et al., 2018). In our analysis, because of the lim- ited information available, we did not include and/or value economic losses. Fig. 3. Flood frequency distribution derived from systematicflow-gauge series and the reconstructed paleodischarges at the level of the gauge stationswith the smallest (a) and the largest (b) drainage areas in the Térraba catchment. A. Quesada-Román, J.A. Ballesteros-Cánovas, S. Granados-Bolaños et al. Geomorphology 396 (2022) 108000 3. Results 3.1. Discharge reconstruction of a flood triggered by Tropical storm Nate We analyzed scars on 148 trees impacted by sediment and wood carried during the flood generated by Tropical Storm Nate (Table 3). The mean scar height in these trees was 1.74 m with an average stan- dard deviation of 0.63 m. We found the highest scar heights at Site B with an average of 2.57 ± 0.73 m over the channel bed whereas the lowest scar heights were found in San Pedro with an average of 1.03 ± 0.66 m. The average of the estimated mean square error (MSE) between observed scar heights and simulated water depths was 1.39m for all the sampling sites. The hydraulic model indicates that dis- charge at the seven sites during the 2017 flood ranged from143 to 1249 m3/s (Table 2). 3.2. Ungauged floods and regional flood-frequency analysis We applied the Hosking and Wallis test to eight flow gauge records (1962–2019) covering catchment areas ranging from 129 to 4765 km2. The test yielded a H1 value of −0.40, showing that the dataset used in this study can be assumed homogeneous (i.e., H1 ≤ 1; see Supplemen- tary Information; Table S1). Based on the eight available flow gauge re- cords and the sevenmodeled (historical) peak discharges, we identified the resulting flood frequency for a runoff surface of 1 km2 during which a flow surpassed the 90th percentile for the record length (Table S3). Fig. 3 provides an example of thefit of the distribution functionwith un- certainties at the 90% confidence level, amid the obtained regional flood frequencies and extrapolated estimates based on flow gauge data from the Pejibaye and Palmar stations. All station records are provided in 5 Table S2. Inclusion of the reconstructed flood discharges suggests that the flood hazard has been underestimated by <10.7% using systematic records alone and uncertainties ranging between the 5th and 95th per- centiles (Table 4). Large uncertainties (56.2% and 80.3%) are found in smaller catchments RI (317 km2) and LJ (823 km2) where local faults are found with a NE-SW orientation at RI, and a tectonic depression axis along the valley bottom at LJ. In the larger sub-catchments uncer- tainties are much smaller (e.g., <12.6% at PA). Variability in the uncer- tainties is presumably related to differences in catchment area between the different stations. Other key factors are their orientation (with RI, CA, and PA having a NE–SW, and LJ, LC, RE, PE, and BR a NW–SE bearing), tectonic control by regional or local faults, as well as differences in lithology (RI and CA mostly have volcanic substrates, PE is a sedimentary catchment while the rest are composed of both volca- nic and sedimentary bedrock). Stations with large (BR and PA) and small (RI, PE, and LJ) catchment areas produced intermediate and higher differences between observed (i.e.,measured) and reconstructed values, respectively. In contrast, the stations of intermediate size (RE, LC, and CA) had the smallest uncertainties between systematic and nonsys- tematic records. 3.3. Flood risk assessment To adequately represent flood hazard (H), we determined the hy- drogeomorphic floodplain of the Térraba catchment and combined the 10-yr return period with the TWI. Using the spatial distribution of the hazard as obtained with the Jenks natural breaks classification method, we obtained a graded differentiation between the northern andwestern sub-catchments (i.e., General, Pacuar, Unión, Volcán, and Ceibo) where low andmedium flood hazards are found, and high and moderate flood Table 3 Dendrogeomorphic attributes of the trees used as paleostage indicators (PSI) for each study reach. Study reach Number of scars in trees Mean scar height (m) MSE Drainage area (km2) Calculated peak discharge (m3/s) Site A (Pueblo Nuevo) 29 2.25 ± 0.70 1.46 101 636 Site B (Canaán) 31 2.57 ± 0.73 1.64 164 455 Site C (Miraflores) 31 2.15 ± 0.78 2.1 206 1249 San Pedro 15 1.03 ± 0.66 1.05 68 146 Río Volcán 9 1.63 ± 0.38 0.88 66 143 Río Coto Brus 17 1.10 ± 0.47 1.13 851 336 Río Sábalo 16 1.46 ± 0.70 1.49 54 212 Table 4 Comparison of flood magnitude for the 10 yr return period estimates in the Térraba catchment before and after inclusion of the reconstructed peak discharges. ML – mean values, X5, X95–5% and 95% uncertainties, respectively. Code Drainage area (km2) ONLY SYST RI = 10 yr SYST + NONSYST RI = 10 yr OBSERVED CHANGES (%) ML X5 X95 ML X5 X95 ML X5 X95 RI 317 393 340 537 710 709 886 80.3 −98.9 −31.9 LJ 823 970 837 1252 1516 1450 1750 56.2 −68.4 −46.9 LC 842 1534 1285 2057 1566 1457 1751 2.1 −56.9 −65.4 RE 1070 1915 1501 2715 1899 1731 2099 −0.87 −59.2 −74.7 PE 129 714 535 1372 358 341 446 −49.8 −80.3 −73.5 BR 2399 3899 3417 4141 3527 3177 3885 −9.5 −19.6 63.2 CA 1134 2109 1759 2681 1993 1808 2189 −5.5 −44.4 −63.6 PA 4765 5491 4617 6121 6184 5266 6896 12.6 −6.6 0.13 A. Quesada-Román, J.A. Ballesteros-Cánovas, S. Granados-Bolaños et al. Geomorphology 396 (2022) 108000 hazards in the medium-sized, south, and southeast-oriented sub- catchments (including Pejibaye, Cabagra, Coto Brus, Limón, and Chánguena) (Fig. 4). Exposure (E) behavesmostly in the oppositewaywhen compared to flood hazard except in urban centers. In fact, the General, Pacuar, and Fig. 4. Flood hazardmap of the Térraba catchment, Costa Rica. The inset histogram shows the lo to colour in this figure legend, the reader is referred to the web version of this article.) 6 Ceibo sub-catchments are influenced by important urban centers (such as San Isidro del General). In addition, urbanized surroundings are found to the northwest as well as at Buenos Aires at the center of the Térraba catchment (Fig. 5). These areas therefore exhibit intermedi- ate to high exposure values because of their high population and wer (red line) and upper (blue line) threshold values. (For interpretation of the references Fig. 5. Exposuremap of the Térraba catchment, Costa Rica. The inset histogram shows the lower (red line) and upper (blue line) threshold values. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) A. Quesada-Román, J.A. Ballesteros-Cánovas, S. Granados-Bolaños et al. Geomorphology 396 (2022) 108000 infrastructure density. The remainder of the catchment is primarily rural with very low population and infrastructure densities and thus produce low exposure values. Spatial differences in vulnerability (V) follow a dissimilar pattern that the exposure with a graded differentiation between the north- western to the south-southeast parts of the sub-catchments (Fig. 6). The major cities of San Isidro del General and San Vito have lower vul- nerability values. Higher vulnerability values are, in contrast, strongly linked to rural areas with amajority of extensive or subsistence agricul- tural activities and the indigenous territories within the municipalities of Buenos Aires and Coto Brus. In terms of risk (Fig. 7), the highest values can be localized in several areas along the Térraba catchment, sometimes in isolated spots, but mainly within the General, Unión, Pejibaye, Ceibo, and Limón sub- catchments, where population densities are high, income is low, and/ or found within indigenous territories. We obtained medium and low risk values especially in the least densely inhabited catchmentswith ag- ricultural landscapes such as Pacuar, parts of General, Volcán, and Coto Brus. In total, approximately 6000 inhabitants are settled in the flood- prone areas of Térraba catchment. 4. Discussion 4.1. Flood-frequency analysis based on dendrogeomorphic reconstructions and hydraulic modeling This study shows how peak discharge of a flood triggered by a trop- ical cyclone can be reconstructed retrospectivelywith a flood-frequency analysis using dendrogeomorphic reconstruction and flow gauge records. The information obtained can yield valuable information about past floods in an environment where data is scarce and new in- sights on flood frequency and magnitude can advise decision makers and residents. The flood frequency analysis carried out in several sub- 7 catchments of the Térraba catchment employed a classical approach that has been used widely for flood hazard cartography (Stephens and Bledsoe, 2020). Paleoflood andhistorical information can expand the re- cord length and provide data extremes often missing in gauge records (Baker, 2008). Likewise, regional flood-frequency assessments have been used to combine nonsystematic and systematic records in a flow-index regionalization (Gaál et al., 2010; Gaume et al., 2010; Nguyen et al., 2014). In that sense, dendrochronology allows coupling of nonsystematic records with flow gauge records to reconstruct floods (Meko et al., 2012; Ballesteros-Cánovas et al., 2019). The flood occurred during Tropical Storm Nate (Tables 1 and 2; ICE - Instituto Costarricense de Electricidad, 2019). Tropical cyclones provoke a regionalization of the rainfall distribution and often results in hetero- geneous peak discharges. The complex hydrological regime of this tropical mountain catchment is enhanced by the energy and moisture available in the system, the high inter- and intra-annual variability (ENSO) of precipitation and the occurrence of high magnitude, yet infrequent extreme events (Krishnaswamy et al., 2001a). In addition, the catchment suffers from rainfall erosion caused by land uses that damages the soil and higher sediment delivery ratios with increasing catchment area (Krishnaswamy et al., 2001b; Krishnaswamy et al., 2018). Our results are consistent with previous works: Ruiz-Villanueva et al. (2013) showed that uncertainty diminishes if historical informa- tion is included in small ungauged or poorly gauged mountain catch- ments. Ballesteros-Cánovas et al. (2013) demonstrated that non- systematic data obtained from dendrogeomorphic approaches of ripar- ian trees can be included in flood frequency analysis and integrated flood risk assessments. In the Tatra Mountains (Poland), Ballesteros- Cánovas et al. (2016) showed that the addition of non-systematic paleohydrological data derived from tree-ring series can have a critical impact on the resulting flood-frequency analysis. Ballesteros-Cánovas et al. (2017) also found in a study carried out in the Indian Himalaya Fig. 6. Vulnerability map of the Térraba catchment, Costa Rica. The inset histogram shows the lower (red line) and upper (blue line) threshold values. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Fig. 7. Flood risk map of the Térraba catchment, Costa Rica. The inset histogram shows the lower (red line) and upper (blue line) threshold values. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) A. Quesada-Román, J.A. Ballesteros-Cánovas, S. Granados-Bolaños et al. Geomorphology 396 (2022) 108000 8 A. Quesada-Román, J.A. Ballesteros-Cánovas, S. Granados-Bolaños et al. Geomorphology 396 (2022) 108000 that the insertion of non-systematic hydrological data from tree-ring se- ries can have a significant influence on both the estimation of flood quantiles and uncertainties derived from flood-frequency analysis. In our study, we applied this approach and sampled trees in the vicinity of the Rivas, San Pedro, Sábalo, and Coto Brus gauging stations. Finally, flood hazard assessments at regional or large scales along the main river valleys prove useful to detect areas that are highly susceptible to future floods (Allen et al., 2018). Limitations of regional flood-frequency assessments merging non- systematic and systematic records can be ascribed to the uncertainties in large catchments whose flow gauge data is less representative (Ruiz-Villanueva et al., 2013; Ballesteros-Cánovas et al., 2017). The un- certainties found are similar to those reported in previous studies (Quesada-Román et al., 2020b), especially in the lower sections of the Térraba catchment. Bigger sub-catchments (BR and PA) had intermedi- ate observed changes, while small sub-catchments (RI, LJ and PE) showed the largest uncertainties. Mid-sized sub-catchments (LC, RE and CA) had lower uncertainties between systematic and non- systematic (dendrogeomorphic) records. The catchments with smaller uncertainties are normally larger in size and have clear tectonic or lith- ological controls. These results are consistent with a study that showed that tectonics, and not climate, exerted the dominant control on the shape of river longitudinal profiles globally and eventually in its uncer- tainties (Seybold et al., 2021). We concentrated our analysis in the mountain headwater catch- ments, where systematic and non-systematic data was available, and reported peak discharges were consistent with our results (Quesada- Román, 2020). Although tree-rings have been used to improve flood risk, given the inherent difficulties to work in tropical environments, here such improvements are circumscribed to the characterization of the recent events, having a less relevant impact in the flood hazard as- sessment. We suggest the use of isotope analyses to distinguish the or- igin of tropical precipitation events and overcome with statistical assumptions in future studies (Sánchez-Murillo et al., 2019, 2020). We consider that the approach we employed can be implemented in other catchments that are commonly affected by tropical cyclones, phenom- ena which will become more intense and frequent in decades to come (Alvarado and Alfaro, 2003; Saunders and Lea, 2008; Walsh et al., 2016; Tennille and Ellis, 2017; Bhatia et al., 2019). 4.2. Improved regional flood risk assessment and potential applications This study shows that regional flood risk assessments within large- scale geomorphic units – at scales of 103–104 km2 (Dramis et al., 2011) – are feasible using an assemblage of coarse (hydrological sta- tions data, social indexes) and detailed inputs (UAV data, hydraulic modeling, high-resolution population density spatio-temporal informa- tion). The hydrogeomorphic floodplains prove useful for the demarca- tion of flood-prone areas along the catchment, as previously reported by Annis et al. (2019) and Nardi et al. (2019). The combination of the hydrogeomorphic floodplains, TWI (Pourali et al., 2016), and the 10-yr return periods of floods during tropical cyclones allowed a regional flood hazard zonation. Whereas previous local studies have identified distinct flood hazard levels using geomorphic mapping techniques (Quesada-Román, 2016, 2017), most of them have associated tropical cyclones every 10 yr as themain triggers that relate to our results, espe- cially in Upper General River catchment where high hazard and risk values were determined (Quesada-Román and Zamorano-Orozco, 2018, 2019a, 2019b). At larger scales (i.e., at the state to district level), direct quantifica- tion of exposure is complex, and surveys must rely on proxy indicators (such as inhabitants or housingdensity) inmany instances to give an es- timated value of the exposure level (Allen et al., 2018). We obtained very realistic results for exposure using WorldPop (https://www. worldpop.org/) in the Térraba catchment when compared to available census data from 2011. As an open access, high-resolution source of 9 information, these spatial demographic datasets were widely used in the past to support development and disaster response applications. For example, Phongsapan et al. (2019) used this database to determine a flood risk index at the national scale inMyanmar. The use ofWorldPop for vulnerability determination inworldwide flood riskmapping assess- ments have been proved in the past (Tatem, 2017; Glas et al., 2019). Moreover, the use of social indexes such as the IDS 2017 (MIDEPLAN - Ministerio de Planificación Nacional y Política Económica, 2017) to de- termine the vulnerability shows the economic, social participation, health, educative, and security conditions of political-administrative units. Applying a social development index to calculate vulnerability also shows that other indexes (i.e., human development index at mu- nicipal scales) can be useful for risk assessments. For all the steps of analyses, however, it is important to consider the remaining uncer- tainties, resilience, and sensitivities of the social vulnerability indexes that are known to depend chiefly on outputs scale (Tate, 2012; Rufat et al., 2015; Spielman et al., 2020). In the Térraba catchment, nearly 60% of the inhabitants live in rural settings and 40% of the workforce is associated with agriculture (INEC - Instituto Nacional de Estadística y Censos, 2020). Resilience in rural areas is drivenprimarily by community capital and there is considerable spatial variability in the components of disaster resilience (Cutter et al., 2016). Settlements closer to cities have more capacity to deal with floods (Jamshed et al., 2020) whereas rural areas in developing coun- tries mostly remain disproportionately vulnerable to disasters. Their vulnerability to disaster often results in high migration rates, diffused benefit from social protection schemes, and scarce or non-existent sav- ings to smooth the impacts (Deria et al., 2020). Furthermore, the Térraba catchment comprises several indigenous territories that should be assessed using the knowledge and cultural appropriation of risk management strategies by the indigenous population (Kelman et al., 2012). Consequently, rural and indigenous incomes depend on fewer livelihood assets, and they are more likely to live in vulnerable ecosys- tems (UNDRR – United Nations Disaster Risk Reduction, 2019). There- fore, the impact of disasters in the Térraba catchment will likely affect lower income households in rural settings the most (Jakobsen, 2012; Arouri et al., 2015). In this sense, policy instruments such as land-use management, poverty reduction, and environmental management can help manage disaster risks (Lavell and Maskrey, 2014). During the first week of November 2020, Hurricane Eta (category 4) impacted Central America. Preliminary reports indicate economic losses that would exceed 5 US billion dollars. Hurricane Eta will be re- membered as the worst tropical cyclone in decades in Central America, comparable to Hurricane Mitch in 1998. Indirect effects of tropical cyclones normally affect the Pacific slope of Costa Rica (Hidalgo et al., 2020), as what occurred during Eta in November 2020. Interestingly, one of the most affected regions by this event within Costa Rica was the Térraba catchment. A preliminary qualitative cross validation of our results against the national authorities' field surveys (CNE – Comisión Nacional de Prevención de Riesgos y Atención de Emergencias, 2021) indicates that the sub-catchments in the mountain areas of the Térraba catch- ment for which we calculated the highest risk (i.e., the areas close to San Isidro, Buenos Aires, and San Vito) were also those who were af- fected heavily by the flooding from Hurricane Eta. Moreover, economic impacts were reported to be 87 US million dollars in all the municipali- ties integrating the catchment. The most affected economic sectors were streets (41%), bridges (32%), housing (15%), and river rehabilita- tion (8%; CNE – Comisión Nacional de Prevención de Riesgos y Atención de Emergencias, 2021). Despite the legitimate scientific and technical production of flood studies in Costa Rica, regional high-resolution flood risk assessments in large-scale catchments, such as Térraba, are still critically missing (Quesada-Román et al., 2020d). The approach presented in this study proves critical for mountain tropical and low-latitude regions as the ex- pected economic losses and social developments will make these https://www.worldpop.org/ https://www.worldpop.org/ A. Quesada-Román, J.A. Ballesteros-Cánovas, S. Granados-Bolaños et al. Geomorphology 396 (2022) 108000 countries more vulnerable to tropical cyclones and floods (Shi and Karsperson, 2015). Many urban centers in tropical mountain regions are affected by uncontrolled growth or urban sprawl, which will favor disaster occurrence (Zhou et al., 2019). Therefore, an integration of risk management and climate change scenarios is critically needed in urban planning processes (Park and Lee, 2019; Pinos et al., 2020).With- out a clear land-use planning and the enforcement of regulatory plans, disaster risk will continue to increase. In cases for which baseline infor- mation is lacking, innovative and practical approaches must be applied to assist disaster risk assessment effectively (Quesada-Román and Villalobos-Chacón, 2020). 5. Conclusions We performed a regional flood-frequency analysis along with a risk assessment that includes hazard, exposure, and vulnerability mapping to address the impacts associated with floods triggered by tropical cy- clones in the Térraba catchment, Costa Rica. We used a 10-y return pe- riod as a reliable time window for the recurrence of tropical cyclones and as it allows determination of the flood risk in a large catchment with observed and not extrapolated data. Peak discharges occurring after the passage of tropical cyclones were determined using flood- frequency and dendrogeomorphic records. The combination of ap- proaches has proven suitable in the data-limited Térraba catchment where approximately 6000 inhabitants are located in flood-prone areas. Our results illustrate spatial variations of risk and coincide with the areas that have been hit by the floods triggered by Hurricane Eta in November 2020, causing millions of US dollars in economic impacts, mainly in streets, bridges, housing and the rehabilitation of rivers. This approach can thus be considered a useful input for land-use planning and disaster risk reduction, increasing the resilience of residents within the Térraba catchment in Costa Rica. Furthermore, this innovative and practicalmethodmay be successfully applied in developing and tropical countries for which hydrological data is often scarce or missing. Declaration of competing interest We the undersigned declare that this manuscript is original, has not been published before and is not currently being considered for publica- tion elsewhere. We confirm that the manuscript has been read and ap- proved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further con- firm that the order of authors listed in the manuscript has been ap- proved by all of us. We understand that the Corresponding Author is the sole contact for the Editorial process. He is responsible for commu- nicatingwith the other authors about progress, submissions of revisions and final approval of proofs. Acknowledgments We greatly acknowledge to Berny Fallas and the Hydrology Area of the Basic Studies Services Center of the Costa Rican Electricity Institute for the peak discharge information of hydrological stations along the catchment. Special thanks to Soll Kracher for her accurate corrections of English grammar. This work is part of a PhD project of AQR, funded by the Swiss Federal Commission for Scholarships (ESKAS-Nr 2017.1072), Ministry of Science, Technology and Communications of Costa Rica (No MICITT-PINN-CON-2-1-4-17-1-002), and the University of Costa Rica (OAICE-187-2017). Finally, thanks to the anonymous re- viewers and Scott A. Lecce for their valuable collaboration, suggestions and corrections that highly improved the manuscript. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.geomorph.2021.108000. 10 References Acuña-Piedra, J.F., Quesada-Román, A., 2016. Evolución geomorfológica entre 1948 y 2012 del delta Térraba–Sierpe, Costa Rica. Cuat. Geomorfol. 30 (3-4), 49–73. https://doi. org/10.17735/cyg.v30i3-4.53055. Acuña-Piedra, J.F., Quesada-Román, A., 2021. Multidecadal biogeomorphic dynamics of a deltaic mangrove forest in Costa Rica. 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Introduction 2. Materials and methods 2.1. Geographic setting 2.2. Climate characteristics and tropical cyclones 2.3. Reconstruction of recent flood event 2.4. Regional flood-frequency analysis 2.5. Regional flood risk assessment 3. Results 3.1. Discharge reconstruction of a flood triggered by Tropical storm Nate 3.2. Ungauged floods and regional flood-frequency analysis 3.3. Flood risk assessment 4. Discussion 4.1. Flood-frequency analysis based on dendrogeomorphic reconstructions and hydraulic modeling 4.2. Improved regional flood risk assessment and potential applications 5. Conclusions Declaration of competing interest Acknowledgments Appendix A. Supplementary data References