The Mekong Basin’s rapidly growing population and changing water infrastructure (e.g., dams and canals) requires major improvements in observations of river discharge and reservoir storage changes. Floods and droughts can affect food supplies, requiring frequent and long-term observations for evaluation. We use satellite passive microwave radiometry (PMR) to monitor rivers and reservoirs, and compare performance at different frequencies and polarization combinations. PMR from TRMM, AMSR-E, AMSR2, GPM, SMOS, and SMAP sensitively monitors water surface area change at selected satellite gauging reaches (SGRs). These reaches can be measured globally at daily or near-daily intervals from 1998 to present. Rating curves that translate PMR signal to stage and discharge units can be obtained from nearby gauging stations (even if now discontinued) or from hydrologic modeling. We demonstrate the PMR capability to measure river stage/discharge/runoff and lake/reservoir water level as verified with in-situ gauging data for selected locations in the Lower Mekong Basin.

River flow is a fundamental observable in hydrology but there is no consistent global ground measurement network. Various types of orbital remote sensing are therefore well positioned to meet an important observational need, including for hydrological modeling and for understanding trends through time. In previous studies, we showed that passive microwave radiometry (PMR) can measure streamflow over selected locations around the globe with a high correlation to co-located in situ discharge observations. This paper demonstrates the potential of low-frequency, L-band NASA Soil Moisture Active and Passive (SMAP) satellite observations for streamflow measurement: an unanticipated but exceptionally valuable use of this sensor. Utilizing the fully polarimetric capability of SMAP with full Stokes parameters, we optimize the polarization combinations of the observations to retrieve accurate river hydrographs from space. Flow measurements over 150 satellite gauging reaches (SGR) are retrieved over different continents, and 14 SGRs provide comparisons to available in situ river gauging data. Results from linear correlation calculations provide coefficients of determination r2 of approximately 0.75 for SMAP-based discharge measurements when compared to in situ streamflow observations. SMAP river observations thereby improve river gauging results compared to ESA’s Soil Moisture Ocean Salinity (SMOS) satellite L-band PMR as the analysis indicates typically lower r2 values of approximately 0.68 for SMOS.

The global water cycle is accelerating in a changing climate. A key element of the hydrology cycle is surface streamflow, which lacks a global river gauging network with open data shared among international stakeholders. As an alternative, rivers have been monitored from space with multiple orbital sensors that continue providing river measurements worldwide for the past several decades and into the future. With its all-weather and day-and-night capabilities, passive microwave satellite sensors provide a unique data source for near-daily global streamflow monitoring.

Flooding is one of the deadliest and costliest natural disasters. Climate change-induced flooding events are increasing worldwide, disproportionately impacting low-income and developing communities. While early warning systems save lives, satellite-based observation systems are vital for the disaster relief and recovery phases. Current satellite-based operational flood products are largely based on either optical remote-sensing methods, which exhibit a limited ability to detect water through clouds and vegetation, or microwave remote sensing, which provides relatively low spatial and temporal resolution. New small satellite constellations using radar or GNSS reflectometry (GNSS-R) have been shown to enhance our ability to overcome these deficiencies. In this work, we quantify the performance of using GNSS-R measurements from the NASA Cyclone Global Navigation Satellite Systems (CYGNSS) satellite constellation to map surface water in South Sudan and the Sudd wetland in comparison with a set of representative operational products. We make quantitative comparisons of our results with operational flood products based on Visible Infrared Imaging Radiometer Suite (VIIRS) and MODIS and with C-band Sentinel-1 synthetic aperture radar. We find that our method detects 35.4% more surface water than Sentinel-1, while the VIIRS- and MODIS-based products underestimate by 4.8% and 83.7%, respectively. We use several metrics commonly used to evaluate classification performance: precision, true positive rate (TPR), true negative rate (TNR), F2-score, and the Matthews correlation coefficient (MCC) and assess the comparisons in this statistical framework. We discuss the consequences of our findings, including ways CYGNSS data may enhance current flood products and assist decision-makers and emergency managers.

As climate change-induced global flooding increases in both frequency and magnitude, having accurate and timely flood maps becomes essential for humanitarian and future flood mitigation efforts. The Dartmouth Flood Observatory (DFO) aids humanitarian organizations and inundation observation research efforts through its archive of historical flood events extending back through 1985, as well as by providing current daily flood maps derived from a combination of observations, and also precipitation-based modeling products. Both could benefit from the addition of microwave observations that penetrate through clouds, rain, and vegetation, such as GNSS-R data now becoming available on a daily basis. In this work, we discuss a current collaboration to combine CYGNSS data with operational MODIS flood maps and evaluate the expected benefits for an example scenario over the recent anomalous flooding in South Sudan.

The Floodwater Depth Estimation Tool (FwDET) provides rapid-response floodwater depth estimations during time-sensitive flood events. Recently, modern cloud-computing advancements and platforms, such as Google Earth Engine (GEE), have further enabled the streamlining and scalability of large-scale geoprocessing. This letter presents a FwDET implementation in GEE (FwDET-GEE) that is open access, utilizes cloud-stored elevation data, and performs geospatial analytics on the fly. This tool offers an innovative solution for producing timely floodwater data during flood activations that require emergency response and post-flood assessment. Accuracy metrics were generated to validate the comparability of FwDET-GEE to FwDETv2.0, which used Python in ArcGIS. To demonstrate the geographic scalability of the model, both local- and region-scale flood events/systems were evaluated. This letter also highlights a use case wherein flooded areas are overlain with building footprint data to identify infrastructure at risk of damage.

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.

An innovative flood-prediction framework is developed using Tropical Rainfall Measuring Mission precipitation forcing and a proxy for river discharge from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) onboard the National Aeronautics and Space Administration's Aqua satellite. The AMSR-E-detected water surface signal was correlated with in situ measurements of streamflow in the Okavango Basin in Southern Africa as indicated by a Pearson correlation coefficient of 0.90. A distributed hydrologic model, with structural data sets derived from remote-sensing data, was calibrated to yield simulations matching the flood frequencies from the AMSR-E-detected water surface signal. Model performance during a validation period yielded a Nash-Sutcliffe efficiency of 0.84. We concluded that remote-sensing data from microwave sensors could be used to supplement stream gauges in large sparsely gauged or ungauged basins to calibrate hydrologic models. Given the global availability of all required data sets, this approach can be potentially expanded to improve flood monitoring and prediction in sparsely gauged basins throughout the world.

Floods are among the most catastrophic natural disasters around the globe impacting human lives and infrastructure. Implementation of a flood prediction system can potentially help mitigate flood-induced hazards. Such a system typically requires implementation and calibration of a hydrologic model using in situ observations (i.e., rain and stream gauges). Recently, satellite remote sensing data have emerged as a viable alternative or supplement to in situ observations due to their availability over vast ungauged regions. The focus of this study is to integrate the best available satellite products within a distributed hydrologic model to characterize the spatial extent of flooding and associated hazards over sparsely gauged or ungauged basins. We present a methodology based entirely on satellite remote sensing data to set up and calibrate a hydrologic model, simulate the spatial extent of flooding, and evaluate the probability of detecting inundated areas. A raster-based distributed hydrologic model, Coupled Routing and Excess STorage (CREST), was implemented for the Nzoia basin, a subbasin of Lake Victoria in Africa. Moderate Resolution Imaging Spectroradiometer Terra-based and Advanced Spaceborne Thermal Emission and Reflection Radiometer-based flood inundation maps were produced over the region and used to benchmark the distributed hydrologic model simulations of inundation areas. The analysis showed the value of integrating satellite data such as precipitation, land cover type, topography, and other products along with space-based flood inundation extents as inputs to the distributed hydrologic model. We conclude that the quantification of flooding spatial extent through optical sensors can help to calibrate and evaluate hydrologic models and, hence, potentially improve hydrologic prediction and flood management strategies in ungauged catchments.

We describe a network of sensors linked by software and the Internet to an autonomous satellite observation response capability. This system of systems is designed with a flexible, modular, architecture to facilitate expansion in sensors, customization of trigger conditions, and customization of responses. This system has been used to implement a global surveillance program of science phenomena including: volcanoes, flooding, cryosphere events, and atmospheric phenomena. In this paper we describe the importance of the earth observing sensorweb application as well as overall architecture for the system of systems.