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Each year throughout the contiguous United States (CONUS), flood hazards cause damage amounting to billions of dollars in homeowner insurance claims. As climate change threatens to raise the frequency and severity of flooding in vulnerable areas, the ability to predict the number of property insurance claims resulting from flood events becomes increasingly important to flood resilience. Based on random forest, we develop a flood property Insurance Claims model (iClaim) by fusing records from the National Flood Insurance Program (NFIP), including building locations, topography, basin morphometry, and land cover, with data from multiple sources of hydrometeorological variables, including flood extent, precipitation, and operational river-stage and oceanic water-level measurements. The model utilizes two steps—damage level classification and claim number regression—and subsampling strategies designed accordingly to reduce overfitting and underfitting caused by the flood claim samples, which are unevenly distributed and widely ranged. We evaluate the model using 446,446 grid samples identified from 589 flood events occurring from 2016 to 2019 over CONUS, overlapping 258,159 claims out of a total of 287,439 NFIP records of the same period. Our rigorous validation yields acceptable performance at the grid/event, county/event, and event accumulative level, with R2 over 0.5, 0.9, and 0.95, respectively. We conclude that the iClaim model can be used in many application scenarios, including assessing flood impact and improving flood resilience.

Flooding affects more people than any other environmental hazard and hinders sustainable development1,2. Investing in flood adaptation strategies may reduce the loss of life and livelihood caused by floods3. Where and how floods occur and who is exposed are changing as a result of rapid urbanization4, flood mitigation infrastructure5 and increasing settlements in floodplains6. Previous estimates of the global flood-exposed population have been limited by a lack of observational data, relying instead on models, which have high uncertainty3,7–11. Here we use daily satellite imagery at 250-metre resolution to estimate flood extent and population exposure for 913 large flood events from 2000 to 2018. We determine a total inundation area of 2.23 million square kilometres, with 255–290 million people directly affected by floods. We estimate that the total population in locations with satellite-observed inundation grew by 58–86 million from 2000 to 2015. This represents an increase of 20 to 24 per cent in the proportion of the global population exposed to floods, ten times higher than previous estimates7. Climate change projections for 2030 indicate that the proportion of the population exposed to floods will increase further. The high spatial and temporal resolution of the satellite observations will improve our understanding of where floods are changing and how best to adapt. The global flood database generated from these observations will help to improve vulnerability assessments, the accuracy of global and local flood models, the efficacy of adaptation interventions and our understanding of the interactions between landcover change, climate and floods. Satellite imagery for the period 2000–2018 reveals that population growth was greater in flood-prone regions than elsewhere, thus exposing a greater proportion of the population to floods.

Every year riverine flooding affects millions of people in developing countries, due to the large population exposure in the floodplains and the lack of adequate flood protection. Preparedness and monitoring are effective ways to reduce flood risk. State-of-the-art technologies relying on satellite remote sensing as well as numerical hydrological and meteorological predictions can detect and monitor severe flood events at the global scale. This chapter describes the emerging role of the Global Flood Partnership (GFP), a global network of scientists, users, and private and public organizations active in global flood risk management. Currently, a number of GFP partner institutes regularly share results from their experimental products, developed to predict and monitor where and when flooding is taking place in near real time. Products of the GFP have already been used on several occasions by national environmental agencies and humanitarian organizations to support emergency operations and to reduce the overall socioeconomic impacts of disasters. The chapter includes a discussion on existing challenges and ways forward to improve rapid access to flood information and increase resilience to flooding.

A reliable flood event inventory that reflects the occurrence and evolution of past floods is important for studies of flood hazards and risks, hydroclimatic extremes, and future flood projections. However, currently available flood inventories are based on single-sourced data and often neglect underreported or less impactful flood events. Furthermore, traditional archives store flood events only at sparse geographic points, which significantly limits their further applicability. Also, few publicly available archives contain all-inclusive records of potential natural flooded area over time. To tackle these challenges, we construct two types of multisourced flood event inventories (MFI) for all river basins across the contiguous United States covering the period 1998–2013 on daily and subcatchment scales, which is publicly available at http://flood.umd.edu/download/CONUS/. These archives integrate flood information from in situ observations, remote sensing observations, hydrological model simulations, and five high-quality precipitation products. The first inventory (MFI-Actual) includes all actual floods that occurred in the presence of flood protection infrastructures, while the second, “natural (undefended)” inventory (MFI-Natural) reconstructs the possible “historical” floods without flood protection, which could be more directly influenced by climate variation. In the proposed two inventories, 2,755 and 4,661 flood events were estimated, respectively. MFI-Natural reconstructed 1,597 floods in ungauged basins, and recovered 608 extreme streamflow events in gauged subcatchments where floods would have happened if there were no flood protection. There is an average of four upstream dams located in these flood-recovered subcatchments, which indicates that modern flood defenses efficiently prevent significant flooding from extreme precipitation in many catchments over the country.

Most existing inundation inventories are based on surveys, news, or passive remote sensing imagery. Affected by spatiotemporal resolution or weather conditions, these inventories are limited in spatial details or coverage. Satellite synthetic aperture radar (SAR) data have recently enabled flood mapping at unprecedented spatiotemporal resolution. However, the bottleneck in producing SAR-based flood maps is the requirement of expert manual processing to maintain acceptable accuracy by most SAR-driven mapping techniques. To fill the vacancy, we generate a high-resolution (10 m) flood inundation dataset over the contiguous United States (CONUS) from nearly the entire Sentinel-1 SAR archive (from January 2016 to the present), using a recently developed automated Radar Produced Inundation Diary (RAPID) system. RAPID uses U.S. Geological Survey (USGS) water watch system and accumulated precipitation to identify SAR images that potentially overlap a flood event. The dataset include inundation events with the temporal scale from several days to months. Concluded from all 559 overlapping images in the period from 2016 to the first half of 2019, the comparison of the proposed dataset against the USGS Dynamic Surface Water Extent (DSWE) product yields an overall, user, producer agreements, and critical success index of 99.06%, 87.63%, 91.76%, and 81.23%, respectively, demonstrating the high accuracy of the proposed dataset and the robustness of the automated system. We anticipate this archive to facilitate many applications, including large-scale flood loss and risk assessment, and inundation model calibration and validation.

Permafrost degradation by ongoing climate warming has expanded the erodible thermokarst landscapes, enhanced the thermal erosion, and altered the sediment transport processes in cryosphere basins. Thermal-activated sediment sources and enhanced sediment export due to developed hillslope-channel connectivity can increase the annual sediment flux and accelerate the sediment response to hydroclimatic disturbances, thus complicating suspended sediment concentration (SSC) and discharge (Q) relationships and forming various hysteretic patterns. Yet, the commonly used sediment rating curve (SSC=a×Qb with a and b as static fitting parameters) is unable to capture the SSC-Q hysteretic patterns and most single-event-scale hysteresis models mainly emphasize the pluvially enhanced sediment transport (e.g. rainstorms), but overlook the thermally-erosional processes.To rebuild dynamic SSC-Q relationships and hysteresis in sediment transport in cryosphere basins, we propose a Sediment-Availability-Transport (SAT) model by extending traditional rating curves to incorporate the time-varying sediment availability regulated by thermal-fluvial processes and long-term storage exhaustion. In the SAT-model, increased thermal erosion is represented by basin temperature; enhanced fluvial erosion is represented by runoff increase; sediment transport capacity is represented by total runoff. Specifically, thawing permafrost as temperature rising can enhance sediment generation by forming active layer detachment, retrogressive thaw slump, and thermal erosion gully from hillslopes, and fluvio-thermal erosion along the riverbank, associated with a time-lag in the sediment response due to the time for temperature accumulation to melt cryosphere and long-travel distance from thermal-activated sediment sources to the basin outlet. A surge in basin water supply during intense rainfall and excessive melting with a certain time-lag can increase sediment availability and fluvial erosion by flushing the erodible slope and scouring the river channel. Moreover, sediment storage is assumed to be continuously depleted throughout a hydrological year and leads to sediment exhaustion.With the support of multi-decadal daily SSC and Q in-situ observations (1985-2017), the SAT-model can be parameterized and validated in the permafrost-dominated Tuotuohe basin on Tibetan Plateau. In Tuotuohe, thermal erosion processes are found to be best captured by an eight-day average temperature, associated with an exponential amplification of SSC. Fluvial erosion is best captured by a two-day runoff increase and shows a linear amplification of SSC. Moreover, the warming-wetting climate over the past decades has expanded the thermokarst landscapes and boosted the slope-channel connectivity by thermal gullies, which leads to the significant inter/intra-annual variation in SSC-Q relationships and reduces the performance of the sediment rating curve. Yet, the SAT-model can robustly reproduce the long-term evolution, seasonality, and various event-scale hysteresis of SSC, including clockwise, counter-clockwise, figure-eight, counter-figure-eight, and more complex hysteresis loops. Overall, the SAT-model can explain over 75% of long-term SSC variance, outperforming the sediment rating curve approach by 20%, with stable performance under an abrupt hydroclimate change.Part of the results is also published in Water Resources Research: Zhang et al., 2021. Constraining dynamic sediment-discharge relationships in cold environments: The sediment-availability-transport (SAT) model.;. Li et al., 2021. Air temperature regulates erodible landscape, water, and sediment fluxes in the permafrost dominated catchment on the Tibetan Plateau.

Computational modelling occupies a unique niche in Earth and environmental sciences. Models serve not just as scientific technology and infrastructure, but also as digital containers of the scientific community's understanding of the natural world. As this understanding improves, so too must the associated software. This dual nature–models as both infrastructure and hypotheses–means that modelling software must be designed to evolve continually as geoscientific knowledge itself evolves. Here we describe design principles, protocols, and tools developed by the Community Surface Dynamics Modeling System (CSDMS) to promote a flexible, interoperable, and ever-improving research software ecosystem. These include a community repository for model sharing and metadata, interface and ontology standards for model interoperability, language bridging tools, a modular programming library for model construction, modular software components for data access, and a Python-based execution and model-coupling framework. Methods of community support and engagement that help create a community-centered software ecosystem are also discussed.

Remote sensing analysis is routinely used to map flooding extent either retrospectively or in near-real-time. For flood emergency response, remote sensing-based flood mapping is highly valuable as it can offer continued observational information about the flood extent over large geographical domains. Information about the floodwater depth across the inundated domain is important for damage assessment, rescue, and to prioritize relief resource allocation, but cannot be readily estimated from remote sensing analysis. The Floodwater Depth Estimation Tool (FwDET) was developed to augment remote sensing analysis by calculating water depth based solely on an inundation map with an associated Digital Elevation Model (DEM). The tool was shown to be accurate and was used in flood response activations by the Global Flood Partnership. Here we present a new version of the tool, FwDET v2.0, which enables water depth estimation for coastal flooding. FwDET v2.0 features a new flood boundary identification scheme which accounts for the lack of confinement of coastal flood domains at the shoreline. A new algorithm is used to calculate the local floodwater elevation for each cell, which improves the tool's runtime by a factor 15 and alleviates inaccurate local boundary assignment across permanent water bodies. FwDET v2.0 is evaluated against physically-based hydrodynamic simulations in both riverine and coastal case studies. The results show good correspondence, with an average difference of 0.18 m and 0.31 m for the coastal (using a 1-m DEM) and riverine (using a 10-m DEM) case studies respectively. A FwDET v2.0 application of using remote sensing derived flood maps is presented for three case studies. These case studies showcase FwDET v2.0 ability to efficiently provide a synoptic assessment of floodwater. Limitations include challenges in obtaining high-resolution DEMs and increases in uncertainty when applied for highly fragmented flood inundation domains.

In order to investigate human impact in the Changjiang – East China Sea source to sink conveying system, we analyze the siltation and erosion state of the estuary-shelf deposition systems, and discuss the response intensity and timing of the depositional system of the Changjiang subaqueous delta and the related Changjiang distal mud. Results show that after the Three Gorges Dam was put into operation in 2003, the abrupt reduction of the sediment discharge to the estuary disturbed the equilibrium between the estuary-coast-shelf deposition systems, modifying the siltation-erosion balance and seabed sediment types. From the Changjiang subaqueous delta to its distal mud areas, the degree of response to catchment changes diminishes, with a larger response lag from north to south of the distal muds. Influenced by dam emplacements, the change of the Changjiang – East China Sea source to sink conveying system can be divided into three stages, i.e., pre-1950, 1950–2002 and post-2003. Before 1950, the source to sink system remained relatively stable. Between 1950 and 2002, man-made fluvial impoundments created an important sink for terrigenous sediment of the upstream regions, but without profound system changes. In contrast, after 2003, the morphodynamic patterns for the entire mid-lower reaches of the Changjiang River, the subaqueous delta and the distal muds shifted rapidly from siltation to erosion within a short period of time. Furthermore, the upper reaches of the river became disconnected from the source to sink conveying system, and the mid-lower reaches of the river, together with its subaqueous delta and northern distal mud area were converted into net sediment sources. As such, the sink area has shrunk while the source area expanded for the Changjiang – East China Sea source to sink conveying system.

Fluvial floodplain evolution is poorly understood despite its importance in sediment deposition, the carbon cycle, and the unique ecosystems it represents. Here we present how different magnitude and frequency of river floods contribute to floodplain evolution by analyzing three profiles of the middle and lower reaches of the Yangtze River. To investigate the formation of the Yangtze flood deposits we determine the percentage of flood events with different magnitude that can be identified from sediment cores and analyze their contribution to floodplain deposition. Our study demonstrates that magnitude of flood events strongly controls the identifiability of flood event deposition, and therefore the evolution of floodplains. Analyses show that detectability of flood layers resulted from large floods is remarkably influenced by sedimentation rates within the floodplain. High occurrence frequency of large to extreme flood events tend to correspond to high sedimentation rates, regardless of decreasing suspended sediment loads over the wet season. This implies that frequent large to extreme floods can counterbalance the decrease in suspended sediment load to maintain a high sedimentation rate in the floodplain. High flood stage is a principal factor that accounts for acceleration of floodplain sedimentation induced by large to extreme floods in the middle and lower Yangtze River. However, acceleration of sedimentation is limited by the quantity of sediment supply.

In the event of a flood disaster, first response agencies need inundation maps produced in near real time (NRT). Such maps can be generated using satellite-based information. In this study, we developed mapping techniques that rely on synthetic aperture radar (SAR) on-board earth-orbiting platforms. SAR provides valid ground surface measurements through cloud cover with high resolution and sampling frequency that has recently increased through multiple missions. Despite numerous efforts, automatic processing of SAR data to derive accurate inundation maps still poses challenges. To address them, we have developed an NRT system named RAdar-Produced Inundation Diary (RAPID). RAPID integrates four processing steps: classification based on statistics, morphological processing, multi-threshold-based compensation, and machine-learning correction. Besides SAR data, the system integrates multisource remote-sensing data products, including land cover classification, water occurrence, hydrographical, water type, and river width products. In comparison to expert handmade flood maps, the fully-automated RAPID system exhibited “overall,” “producer,” and “user” accuracies of 93%, 77%, and 75%, respectively. RAPID accommodates commonly encountered over- and under-detections caused by noise-like speckle, water-like radar response areas, strong scatterers, and isolated inundation areas—errors that are in common practice to ignore, mask out, or be filtered out by coarsening the effective resolution. RAPID can serve as the kernel algorithm to derive flood inundation products from satellites—both existing and to be launched—equipped with high-resolution SAR sensors, including Envisat, Radarsat, NISAR, Advanced Land Observation Satellite (ALOS)-1/2, Sentinel-1, and TerraSAR-X.

The Congo River is the world's second largest river in terms of drainage area and water discharge. Monitored for decades, a large dataset is available, onshore for both the hydrology and sediment load, and offshore by many paleo-environmental proxies compiled at the Late-Quaternary time-scale. These numerous data allow for accurate calibration of numerical modeling. In this study, we aim to numerically quantify the evolution of sediment supply leaving the tropical Congo watershed during the last 155 ka and to decipher the forcing parameters that control this sediment supply over glacial/interglacial stages. For this, a modified version of the model HydroTrend, that besides morphologic, climatic, hydrologic, lithologic, land cover and anthropogenic factors now also considers sediment deposition on the floodplain, is used. In addition, a method to quantify the impact of natural vegetation changes is developed. Simulations match well the present-day observed data. They indicate that a significant portion of suspended sediments is trapped on the floodplain. Long-term simulations indicate that environmental changes between glacial and interglacial stages account for a 30% maximum variation of sediment supply. Climatic changes - precipitation and temperature, account for a maximum decrease in sediment supply of 20% during cold periods while conversely, induced land cover changes (loss of forest during colder and dryer stages) lead to enhanced sediment supply up to 30%. Over a longer period, the average sediment supply remained almost constant during glacial and interglacial periods, while peaks may have occurred during a warming period, just before forests had time to recover the catchment, i.e. during post-glacial periods. These moderate changes in sediment export, despite major changes in climate and vegetation cover, can be explained by the efficiency of sediment trapping of large tropical catchments that buffer fluvial fluxes towards the ocean.

Water supports life, however it does come with hazards. Floods area amongst the most impactful environmental disasters. Accurate flood forecasting and early warning are critical for disaster risk management. Detecting and forecasting floods at an early stage is certainly relevant for Mali, hence crucial in order to prevent a hazard from turning into a disaster. Remotely sensed river monitoring can be an effective, systematic and time-efficient technique to detect and forecast extreme floods. Conventional flood forecasting systems require extensive data inputs and software to model floods. Moreover, most models rely on discharge data, which is not always available and is less accurate in a overbank flow situations. There is a need for an alternative method which detects riverine inundation, while making use of the available state-of-the-art.This research investigates the use of passive microwave remote sensing with different spatial resolutions for the detection and forecasting of flooding. Brightness temperatures from two different downscaled spatial resolutions (1 x 1 km and 10 x 10 km) are extracted from passive microwave remote sensing sensors and are converted into discharge estimators: a dry CM ratio and a wet CMc ratio. Surface water has a low emission, thus let the CM ratio increase as the surface water percentage in the pixel increases. Sharp increases are observed for over-bank flow conditions.Overall, we compared the passive microwave remote sensing model results of the different spatial resolutions to the results of a conventional global runoff model GloFAS. The passive microwave remote sensing model does not require extensive input data when used as an Early Warning System (EWS), as many smaller-scale EWS do, we suggest that when improved, the passive microwave remote sensing method is implemented as part of an integrative EWS solution, including a passive microwave remote sensing model and various other models. This would allow for early warnings in data-scarce regions and at a variety of lead times. In order for this to be effective, we suggest that more research be done on correctly setting the trigger threshold, and into the potential spatial interpretation of CMc.

Historical and current information regarding river discharge is essential, not only from a water management, energy, or global change perspective but also to better analyze, control and forecast flooding. However, globally the number of ground-based gauging stations declines, and data that is measured by ground-based gauging stations is often not, or shared with a considerable delay.It has been demonstrated that existing satellite sensors can be utilized for useful discharge measurements without requiring ground-based information. The DFO – Flood Observatory uses the Advanced Microwave Scanning Radiometer band at 36.5 GHz (e.g. TRMM, AMSR‐E, AMSR2, GMP), pre-processed by the Joint Research Center (JRC) to estimate discharges. With a nearly-daily repeat interval, this microwave signal has been successfully applied to measure water discharge at a global scale, where the calibration of the microwave discharge signal to discharge units is accomplished by comparison to results from a global hydrological numerical model, the Water Balance Model (WBM), for a calibration period. Once calibrated, daily discharge can be back-calculated to January 1998, providing a daily discharge record for more than 20 years.Here we present the methods used to utilize remote sensing to measure discharge. We indicate the challenges and how to overcome these when using a multiple sensor approach to capture daily discharges for over a 20-year period. And we show an example for the Amazon river, comparing the remote sensed discharge data with ground observations for multiple locations. Additionally, applications are shown on how this discharge can be combined with flood extent maps to analyze flood frequency.

Climate-related natural disasters have increased significantly in the last few decades. However, conventional water monitoring systems such as discharge guaging stations, even if data are widely shared, have limitations for monitoring, understanding, and predicting these disasters. This chapter provides an overview of water-related products that the DFO (Dartmouth Flood Observatory)—Flood Observatory, in collaboration with other agencies and initiatives, has developed and shares. Most of these products are experimental and semi-operational and are based on freely-available satellite measurements and images. DFO preserves for public use a digital map record of the Earth’s changing surface water, conducts remote sensing-based water measurement, and mapping in “near real time” for humanitarian purposes, and supports and encourages operational uses of remote sensing-based surface water information. It is our vision that these products can support the larger hydrological community in an effort to reduce the impact of flooding and related natural disasters.

Flooding is the most common natural hazard worldwide, affecting over a billion people and costing $100 billion every year. This will likely increase in the future due to increasing population and assets in the flood-prone zones and climate change. Earth observation data have been utilized to map flooding on a global scale. This was mostly done by utilizing optical bands, as for these satellites the return period is relatively short; often daily. However, the usage of optical data can be restrictive due to e.g. cloud cover and nighttime. With synthetic aperture radar data most of these limitations can be overcome. As such, many entities now provide remotely sensed flood products, such that it becomes difficult for users to determine the best available flood data source. Here we describe the long-time development and implementation of a semi operational ‘one-stop-shop’ portal that contains globally scoped, flood prediction, monitoring capabilities and risk evaluations by leveraging on efforts of the entire flood community.

Flooding affects more people than any other environmental hazard and hinders sustainable development1–3. Investing in flood adaptation can reduce loss of life and livelihoods 4,5. However, where and how floods occur, and who is exposed, is changing due to rapid urbanization 6, new flood mitigation infrastructure 7,8, and increasing human settlements in floodplains9. Previous studies estimating population exposed to floods are limited by lack of observational data, relying instead on global models with high uncertainty4,10–14. Here we use nearly two decades of daily satellite imagery at 250 meter resolution (MODIS) to develop new estimates of flood extent and population exposure for 913 large flood events from 2000-2018, estimating 2.23 million km2 of flooded area and 290 million people directly affected. We find the at least 86 million people newly settled in a location where satellites observed inundation in the past 20 years. This represents a 20% increase in the proportion of population exposed to floods, ten times higher than previous estimates10. Near-future climate change projections (2030), indicate the proportion of population exposed to floods will continue to expand. Our findings can aid prioritization of global flood adaptation investment and inform adaptation strategies, from insurance to managed retreat, at local scales15–17. As satellite coverage and resolution increase, we anticipate observation of flood events to aid understanding where floods change and how to adapt. This flood event database will contribute to improved accuracy of global and local flood models, vulnerability assessments, efficacy of adaptation intervention, and understanding interactions of land change, climate, and floods.

Integrated geographic modelling and simulation is a computational means to improve understanding of the environment. With the development of Service Oriented Architecture (SOA) and web technologies, it is possible to conduct open, extensible integrated geographic modelling across a network in which resources can be accessed and integrated, and further distributed geographic simulations can be performed. This open web-distributed modelling and simulation approach is likely to enhance the use of existing resources and can attract diverse participants. With this approach, participants from different physical locations or domains of expertise can perform comprehensive modelling and simulation tasks collaboratively. This paper reviews past integrated modelling and simulation systems, highlighting the associated development challenges when moving to an open web-distributed system. A conceptual framework is proposed to introduce a roadmap from a system design perspective, with potential use cases provided. The four components of this conceptual framework - a set of standards, a resource sharing environment, a collaborative integrated modelling environment, and a distributed simulation environment - are also discussed in detail with the goal of advancing this emerging field.

The demand for timely and accurate flood information is well understood and more urgent than ever as flooding has become the most common natural hazard worldwide, impacting people of all continents in both developed and less developed countries. Population and total exposed assets by river flooding are certain to increase in the coming century making the need for flood information even more pressing. Unlike the World Meteorological Organization (WMO), the hydrological community hasn’t been very successful in establishing a global hydrological network of observations through which model simulations and measurements and novel measurement technologies could be exploited. Countries that can afford have departments in place that are tasked to develop flood risk maps and are involved in flood forecasting and relief efforts. However, the majority of countries do not or cannot allocate sufficient funds to support such efforts, nor has there been a global initiative to identify and determine global flood risk areas.Due to the lack of objective knowledge of the impact of flooding during or after an event, first relief agency assistance is often constrained and therefore less effective. These humanitarian catastrophes could be reduced with better transformation of existing observational and modeling technologies into information useful to local populations and decision makers.Here I present new efforts to produce a state-of-the-art, globally-scoped, flood prediction, monitoring capabilities and risk evaluations platform that is interactive and includes high resolution flood information to better serve local needs. The platform builds upon already operational or quasi-operational NASA-supported global flood systems, including the DFO - Flood Observatory satellite-based hydrological gauging stations, UMD Global Flood Monitoring System (GFMS) and have these integrated with the European Commission’s GloFAS, and SAR-based high-resolution flood mapping. This with the intension to have these data layers (flood forecasting, flood extent, and flood history) available to everybody.

Accurate determination of past flooding characteristics is necessary to effectively predict future flood disaster risk and the dominant controls. However, understanding the role of environmental forcing on past flooding frequency and magnitude is difficult due to the deficiency of observations and too short measurement time series. Here, a numerical model HydroTrend, that generates synthetic time series of daily water discharge at a river outlet, is applied to Yalu River to: (1) reconstruct annual peak discharges over the past 1000 years and estimate flood annual exceedance probabilities; (2) identify and quantify the impacts of climate change and human activity (runoff yield induced by deforestation and dam retention) on the flooding frequency and magnitude. Climate data obtained from meteorological stations and ECHO-G climate model output, morphological characteristics (hypsometry, drainage area, River length, slope and Lapse rate) and hydrological properties (groundwater properties, canopy interception effects, cascade reservoirs retention effect and saturated hydraulic conductivity) are form the significant reliable model inputs. Monitored for decades and some proxies on ancient floods allow for accurate calibration and validation of numerical modeling. Simulations match well present-day monitored data (1958–2012) and historical flood events literature records (1000–1958). They indicate that flood frequencies of Yalu River increased during AD 1000–1940, followed by a decrease until the present day. Frequency trends were strongly modulated by climate variability, particularly by intensity and frequency of rainfall events. The magnitudes of larger floods, events with a return period of 50 to 100 years, increased by 19.1 and 13.9 %, respectively, due to climate variability over the last millennium. Anthropogenic processes were found to either enhance or reduce flooding, depending on the type of the human activities. Deforestation increased the magnitude of larger floods by 19.2–20.3 %, but the construction of cascade reservoirs in AD 1940 significantly reduced their magnitude by 36.7 to 41.7 %. We conclude that under intensified climate change and human activity in the future, effective river engineering should be considered, particularly for small and medium-sized mountainous river systems, which are at higher risk of flood disasters due to their relatively poor capacity for hydrological regulation.

Reservoir interception has significantly affected the fluvial sediment budget as well as the sedimentary processes of the entire Changjiang catchment. To evaluate the impact of reservoirs, we analyze the combined effects of 1037 large and medium-sized reservoirs on the fluvial flux in general, and more specifically on the sedimentary processes in the middle and lower reaches. Results indicate that reservoir emplacement in the Changjiang catchment currently reduces the sediment load towards the East China Sea by 453 Mt y−1. Estimates at Yichang station show that the sediment discharge would exceed 555 Mt y−1, if there were no reservoirs involved. It is expected that in the near future, more dams will be constructed. The entire reach of the Changjiang River can be divided at Yichang station into two distinctly characterised reaches with regard to sedimentation, where the upper reach exhibits mostly siltation (over 589 Mt y−1 of sediment deposition), and the lower reach is affected by erosion (sediment loss, including sand extraction, exceeding 112 Mt y−1). As a consequence, the sediment flux to the sea will further decrease to 100 Mt y−1. Due to human interference, the upstream sediment load reduced and caused significant changes in the erosion/deposition pattern of the middle and lower reaches, which together altered the terrestrial sediment input to the sea. Before 2003, the upstream reaches were the dominant sediment source. After 2003, the sediment contribution of the middle and lower reaches became more important, and its sediment contribution will further increase to 78% of the total sediment load reaching the sea, after completion of the cascade reservoirs at the Jinsha Tributary. Hence, the middle and lower reaches are converting from a sediment sink to a major sediment source.

Every year riverine flooding affects millions of people in developing countries, due to the large population exposure in the floodplains and the lack of adequate flood protection measures. Preparedness and monitoring are effective ways to reduce flood risk. State-of-the-art technologies relying on satellite remote sensing as well as numerical hydrological and weather predictions can detect and monitor severe flood events at a global scale. This paper describes the emerging role of the Global Flood Partnership (GFP), a global network of scientists, users, private and public organizations active in global flood risk management. Currently, a number of GFP member institutes regularly share results from their experimental products, developed to predict and monitor where and when flooding is taking place in near real-time. GFP flood products have already been used on several occasions by national environmental agencies and humanitarian organizations to support emergency operations and to reduce the overall socio-economic impacts of disasters. This paper describes a range of global flood products developed by GFP partners, and how these provide complementary information to support and improve current global flood risk management for large scale catastrophes. We also discuss existing challenges and ways forward to turn current experimental products into an integrated flood risk management platform to improve rapid access to flood information and increase resilience to flood events at global scale.

Owing to the heterogeneity of geo-analysis models, many scholars and researchers have designed and promulgated standards in an attempt to address this. However, models based on different standards still cannot be shared and reused easily among different model frameworks. For example, models based on the OpenMI, BMI and OpenGMS-IS standards have heterogeneous development styles and formats, so they cannot interoperate. This article analyses the challenges faced when sharing and reusing models across different standards and provides a solution for model interoperation among them. By mapping fields, converting functions, and reorganizing components, our “interoperability engine” allows models that use one standard to be operated within a framework that supports a different standard. This article discusses the developed interoperability method and provides case studies (using e.g. SWMM, FDS, and the Permamodel Frost Number component) to successfully demonstrate model interoperation.

Remote sensing analysis is routinely used to map flood inundation during flooding events or retrospectively for planning and research activities. Quantification of the depth of floodwater is important for emergency response, relief operations, damage assessment etc. The Floodwater Depth Estimation Tool (FwDET) calculates water depth based on topographic analysis using standard GIS tools within a Python script. FwDET’s low input requirements (DEM and inundation polygon) and high computational efficiency lend it as a useful tool for emergency response and large-scale applications. Operational use of FwDET is described herein as part of emergency response activation of the Global Flood Partnership (GFP) during the 2017 USA Hurricane Season and May 2018 flooding in Sri Lanka. Use of FwDET during Hurricanes Harvey (Texas and Louisiana), Irma (Florida) and Maria (Puerto Rico) demonstrated its utility by producing large-scale water depth products at near-real-time at relatively high spatial resolution. Despite FwDET’s success, limitations of the tool stemmed from bureaucratic disallowance of non-governmental remote sensing products by U.S. federal emergency response agencies, misclassified remotely sensed floodwaters and challenges obtaining global high resolution DEMs specifically for the aforementioned Sri Lankian flooding. While global-scale DEM products at 30m resolution are freely available, these datasets are of integer precision and thus have limited vertical resolution. This limitation is significant primarily in flat (e.g. coastal) locations and flooded domains comprised of relatively small patches of water.

The distinctive plan-view shape of the Ebro Delta coast reveals a rich morphologic history. The degree to which the form and depositional history of the Ebro and other deltas represent autogenic (internal) dynamics or allogenic (external) forcing remains a prominent challenge for paleo-environmental reconstructions. Here we use simple coastal and fluvial morphodynamic models to quantify paleo-environmental changes affecting the Ebro Delta over the late Holocene. Our findings show that these models are able to broadly reproduce the Ebro Delta morphology, with simple fluvial and wave climate histories. Based on numerical model experiments and the preserved and modern shape of the Ebro Delta plain, we estimate that a phase of rapid shoreline progradation began approximately 2100 years BP, requiring approximately a doubling in coarse-grained fluvial sediment supply to the delta. River profile simulations suggest that an instantaneous and sustained increase in coarse-grained sediment supply to the delta requires a combined increase in both flood discharge and sediment supply from the drainage basin. The persistence of rapid delta progradation throughout the last 2100 years suggests an anthropogenic control on sediment supply and flood intensity. Using proxy records of the North Atlantic Oscillation, we do not find evidence that changes in wave climate aided this delta expansion. Our findings highlight how scenario-based investigations of deltaic systems using simple models can assist first-order quantitative paleo-environmental reconstructions, elucidating the effects of past human influence and climate change, and allowing a better understanding of the future of deltaic landforms.

The Surface Water and Ocean Topography (SWOT) mission will measure water surface elevations and inundation extents of rivers of the world but with limited temporal sampling. By comparing flood location and duration of 4,664 past flood events recorded by the Dartmouth Flood Observatory to SWOT's orbit ephemeris, we estimate that SWOT would have seen 55% of these, with higher probabilities associated with more extreme events and with those that displaced more than 10,000 people. However, SWOT measurements will exhibit uneven temporal sampling and may require a combination of data obtained at different times to accurately characterize large events. This is illustrated using recent flooding in the United States, in eastern Iowa and in Houston and surrounding areas from Hurricane Harvey. SWOT data have significant potential to improve flood forecasting models by offering data needed to enhance flow routing modeling, provided that users can overcome the potential hurdles associated with its temporal and spatial sampling characteristics.

Downstream flow in rivers is repeatedly delayed by hydrologic exchange with off-channel storage zones where biogeochemical processing occurs. We present a dimensionless metric that quantifies river connectivity as the balance between downstream flow and the exchange of water with the bed, banks, and floodplains. The degree of connectivity directly influences downstream water quality - too little connectivity limits the amount of river water exchanged and leads to biogeochemically inactive water storage, while too much connectivity limits the contact time with sediments for reactions to proceed. Using a metric of reaction significance based on river connectivity, we provide evidence that intermediate levels of connectivity, rather than the highest or lowest levels, are the most efficient in removing nitrogen from Northeastern United States' rivers. Intermediate connectivity balances the frequency, residence time, and contact volume with reactive sediments, which can maximize the reactive processing of dissolved contaminants and the protection of downstream water quality. Our simulations suggest denitrification dominantly occurs in riverbed hyporheic zones of streams and small rivers, whereas vertical turbulent mixing in contact with sediments dominates in mid-size to large rivers. The metrics of connectivity and reaction significance presented here can facilitate scientifically based prioritizations of river management strategies to protect the values and functions of river corridors. U.S. Geological SurveyUnited States Geological Survey; National Science Foundation Hydrologic Sciences ProgramNational Science Foundation (NSF)NSF - Directorate for Geosciences (GEO); USGS National Water Quality Program; DOE Office of Biological and Environmental Research (BER) in the Subsurface Biogeochemistry Program (SBR) as part of SBR's Scientific Focus Area at the Pacific Northwest National Laboratory (PNNL) The work is a product of the John Wesley Powell Center River Corridor Synthesis Group, supported by U.S. Geological Survey and National Science Foundation Hydrologic Sciences Program. USGS authors received additional support from the USGS National Water Quality Program. Gomez-Velez received additional support from the DOE Office of Biological and Environmental Research (BER) in the Subsurface Biogeochemistry Program (SBR) as part of SBR's Scientific Focus Area at the Pacific Northwest National Laboratory (PNNL). The synthesis is based entirely on analysis of published information and publicly available data sources. Any use of trade, firm, or product is for descriptive purposes only and does not imply endorsement by the U.S. Government. Public domain – authored by a U.S. government employee

Flood Risk Workshop; Boulder, Colorado, 1–3 October 2018

River discharge data and magnitudes of floods are often not readily available for decision makers of many developing nations, including Colombia. And this while flooding for these regions is often devastating, causing many fatalities and insurmountable damage to the most vulnerable communities. During the wet season, in strong La Nina years, infrastructural damages of over $US 7.2 billion have occurred. Mitigation of such natural disasters lacks data-supported scientific approaches for evaluating river response to extreme climate events. Here, we propose a satellite-based technique to measure river discharge at selected sites for the main northern Andean River, the Magdalena. This method has the advantage of back calculating daily river discharges over a period of two decades, and thus making it possible to calculate return intervals of significant flood events. The study shows that satellite based river discharges well capture a) the inter-annual variability of river discharge; b) the natural seasonality of water discharge along the floodplains; and c) peak discharges that were observed during La Nina conditions between 2008 and 2011. The last is likely more accurate compared to ground-based gauging stations, as ground-based stations tend to overflow during large flood events and as such are hampered to accurately monitor peak discharges. Furthermore, we show that these derived discharges can form the base to study river-floodplain connectivity, providing environmental decision makers with a technique that makes it possible to better monitor river and ecosystem processes.

River sediment fluxes to the sea contribute to the morphological evolution as well as environmental and biological conditions of estuaries and their adjacent coasts. Especially for river basins that are dominated by a monsoonal climate, extreme high discharges can occur due to intense rainfall during the flood season, which will considerably affect the amount of sediment that is transported to the sea. In this paper, 4 major floods of the Yangtze River (2 extreme floods, one affecting most of the basin in 1954, and one more pronounced in the upper reach in 1981; and 2 large floods, one impacting mainly the middle reach in 1983, and one affecting most of the basin in 1998) are analyzed as these provide a unique opportunity to investigate fluvial sediment characteristics to the sea affected by major flooding. Results reveal that more sediments were delivered to the sea during the flood season when regional-sized major floods occurred, and this was even more pronounced when major floods occurred in the upper reach of the Yangtze River. Sediment retention in the middle reach of the Yangtze River during the major floods significantly reduced the amount of sediments reaching the sea. As for the flood season of 1981, the sediment size distribution to the sea was not significant different than that of the perennial average. However, coarser-grained sediments were delivered to the sea during July 1983 due to the remarkably high water discharges at that period. These findings can provide enlightenment regarding the timing of wetland restoration and interpretation of previous flood deposits in the deltaic zones of the Yangtze River.

Monitoring changes in flood extent is critical for flood control and mitigation purposes in areas where flooding affects many people and dense infrastructure and properties. Remote sensing can be an effective technique to detect changes in surface water extent and its dynamics. Compared to optical remote sensing, microwave information is suitable for working in any weather condition without severe cloud interference. Usually passive microwave data has a high temporal but a rather coarse spatial resolution, whereas for active microwave data this is reversed and only with ideal satellite constellation observations it can reach high sampling rates. To overcome these spatial and temporal restrictions, we proposed an integrated methodology to combine the passive and active microwave remote sensing and thus provide flood information more frequently at a high resolution. In this paper, a demonstration of the methodology is presented for a flood event occurring in Wuhan, Hubei Province of China in July 2016. The major inundation occurred along the Jushui River, part of the Middle Yangtze River basin. Using the brightness temperature data from a special data set (MEaSUREs), a daily passive microwave signal at the resolution of 3.125 km is used as an indicator to monitor flood occurrence and obtain the flood duration over time. Synthetic Aperture Radar (SAR) imagery (12-day revisit, 10-m spatial resolution) from Sentinel-1 was processed to estimate high resolution flood extents within the time span of the flood based on a threshold-based method together with the High Above Nearest Drainage (HAND) index post-processor. Surface water fraction data generated from SAR images presents a strong correlation with the passive microwave signal (R2 = 0.84) when using a quadratic polynomial fit. The average bias between surface water fraction computed by the passive microwave signals and SAR observations for water pixels within the Jushui River basin for the validation dates is 0.34%, indicating that this relationship can be applied to interpolate surface water fraction for each pixel along the river and other permanent water bodies for days when SAR observations are not available. This integrated method takes advantage of both passive and active microwave remote sensing and enriches temporally sparse flood data. Given the global coverage of the datasets used in this study, it can be utilized to estimate flood status for other flood-prone areas and thus contribute towards societal flood preparedness and response.

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.

Geoprocesses, Geohazards—CSDMS 2018: A CSDMS hosted Workshop; Boulder, Colorado, 22–24 May 2018

The Yellow River, China, experienced >1000 levee breaches during the last 3000 years. A reduced-complexity model is developed in this study to explore the effects of climate change and human activity on flood levels, levee breaches, and river avulsions. The model integrates yearly morphological change along a channel belt with daily river fluxes and hourly evolution of levee breaches. Model sensitivity analysis reveals that under natural conditions, superelevation of the channel belt dominates flood frequency. When there is significant human-accelerated basin erosion and breach repair, the dominant factors shift to a combination of mean annual precipitation, superelevation, critical shear stress of weak channel banks, and the time interval between breach initiation and its repair. The effect of precipitation on flood frequency is amplified by land use changes in the hinterland, particularly in the erodible Loess Plateau. Uncertainty analysis estimates the most likely values of the dominant factors for six historical periods between 850 B.C. and A.D. 1839, which are used to quantitatively reconstruct flood dynamics. During 850 B.C. to A.D. 1839, when the sediment load increased fourfold, the breach recurrence interval was shortened from more than 500 years to less than 6 years, and the breach outflow rate increased ~27 times. River management practices during A.D. 1579 to A.D. 1839 focused on levees and triggered a severe positive feedback of increased levee heights and flood hazard exacerbation. Raising the levee heights proved to be ineffective for sustainable flood management.

Human induced soil erosion reduces soil productivity; compromises freshwater ecosystem services, and drives geomorphic and ecological change in rivers and their floodplains. The Andes of Colombia have witnessed severe changes in land-cover and forest loss during the last three decades with the period 2000 and 2010 being the highest on record. We address the following: (1) what are the cumulative impacts of tropical forest loss on soil erosion? and (2) what effects has deforestation had on sediment production, availability, and the transport capacity of Andean rivers? Models and observations are combined to estimate the amount of sediment liberated from the landscape by deforestation within a major Andean basin, the Magdalena. We use a scaling model BQART that combines natural and human forces, like basin area, relief, temperature, runoff, lithology, and sediment trapping and soil erosion induced by humans. Model adjustments in terms of land cover change were used to establish the anthropogenic-deforestation factor for each of the sub-basins. Deforestation patterns across 1980–2010 were obtained from satellite imagery. Models were employed to simulate scenarios with and without human impacts. We estimate that, 9% of the sediment load in the Magdalena River basin is due to deforestation; 482 Mt of sediments was produced due to forest clearance over the last three decades. Erosion rates within the Magdalena drainage basin have increased 33% between 1972 and 2010; increasing the river’s sediment load by 44 Mt y −1 . Much of the river catchment (79%) is under severe erosional conditions due in part to the clearance of more than 70% natural forest between 1980 and 2010.