Technical note: assessment of observation quality for data assimilation in flood models

The assimilation of satellite-based water level observations (WLOs) into 2D hydrodynamic models can keep flood forecasts on track or be used for reanalysis to obtain improved assessments of previous flood footprints. In either case, satellites provide spatially dense observation fields, but with spatially correlated errors. To date, assimilation methods in flood forecasting either incorrectly neglect the spatial correlation in the observation errors or, in the best of cases, deal with it by thinning methods. These thinning methods result in a sparse set of observations whose error correlations are assumed to be negligible. Here, with a case study, we show that the assimilation diagnostics that make use of statistical averages of observation-minus-background and observation-minus-analysis residuals are useful to estimate error correlations in WLOs. The average estimated correlation length scale of 7km is longer than the expected value of 250m. Furthermore, the correlations do not decrease monotonically; this unexpected behaviour is shown to be the result of assimilating some anomalous observations. Accurate estimates of the observation error statistics can be used to support quality control protocols and provide insight into which observations it is most beneficial to assimilate. Therefore, the understanding gained in this paper will contribute towards the correct assimilation of denser datasets

Sentinel-1 InSAR coherence to detect floodwater in urban areas: Houston and hurricane harvey as a test case

This paper presents an automatic algorithm for mapping floods. Its main characteristic is that it can detect not only inundated bare soils, but also floodwater in urban areas. The synthetic aperture radar (SAR) observations of the flood that hit the city of Houston (Texas) following the landfall of Hurricane Harvey in 2017 are used to apply and validate the algorithm. The latter consists of a two-step approach that first uses the SAR data to identify buildings and then takes advantage of the Interferometric SAR coherence feature to detect the presence of floodwater in urbanized areas. The preliminary detection of buildings is a pre-requisite for focusing the analysis on the most risk-prone areas. Data provided by the Sentinel-1 mission acquired in both Strip Map and Interferometric Wide Swath modes were used, with a geometric resolution of 5 m and 20 m, respectively. Furthermore, the coherence-based algorithm takes full advantage of the Sentinel-1 mission's six-day repeat cycle, thereby providing an unprecedented possibility to develop an automatic, high-frequency algorithm for detecting floodwater in urban areas. The results for the Houston case study have been qualitatively evaluated through very-high-resolution optical images acquired almost simultaneously with SAR, crowdsourcing points derived by photointerpretation from Digital Globe and Federal Emergency Management Agency's (FEMA) inundation model over the area. For the first time the comparison with independent data shows that the proposed approach can map flooded urban areas with high accuracy using SAR data from the Sentinel-1 satellite mission

Estimating global warming potential for agricultural landscapes with minimal field data and cost

Greenhouse gas (GHG) emissions from agriculture comprise 10-12% of anthropocentric global emissions; and 76% of the agricultural emissions are generated in the developing world. Landscape GHG accounting is an effective way to efficiently develop baseline emissions and appropriate mitigation approaches. In a 9,736-hectare case study area dominated by rice and wheat in the Karnal district of Haryana state, India, the authors used a low-cost landscape agricultural GHG accounting method with limited fieldwork, remote sensing, and biogeochemical modeling. We used the DeNitrification-DeComposition (DNDC) model software to simulate crop growth and carbon and nitrogen cycling to estimate net GHG emissions, with information based on the mapping of cropping patterns over time using multi- resolution and multi-temporal optical remote sensing imagery. We estimated a mean net emission of 78,620 tCO2e/yr (tons of carbon dioxide equivalents per year) with a 95% confidence interval of 51,212-106,028 tCO2e/yr based on uncertainties in our crop mapping and soil data. A modeling sensitivity analysis showed soil clay fraction, soil organic carbon fraction, soil density, and nitrogen amendments to be among the most sensitive factors, and therefore critical to capture in field surveys. We recommend a multi-phase approach to increase efficiency and reduce cost in GHG accounting. Field campaigns and aspects of remote sensing image characteristics can be optimized for targeted landscapes through solid background research. An appropriate modeling approach can be selected based on crop and soil characteristics. Soil data in developing world landscapes remain a significant source of uncertainty for studies like these and should remain a key research and data development effort

Assessing the utility of geospatial technologies to investigate environmental change within lake systems

Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future

Fuzzy set approach to calibrating distributed flood inundation models using remote sensing observations

The paper presents a methodology for the estimation of uncertainty of inundation extent, which takes account of the uncertainty in the observed spatially distributed information and implements a fuzzy evaluation methodology. The Generalised Likelihood Uncertainty Estimation (GLUE) technique and the 2-D LISFLOOD-FP model were applied to derive the set of uncertain inundation realisations and resulting flood inundation maps. Conditioning of the inundation maps on fuzzified Synthetic Aperture Radar (SAR) images results in much more realistic inundation risk maps which can better depict the variable pattern of inundation extent than previously used methods. It has been shown that the evaluation methodology compares well to traditional approaches and can produce flood hazard maps that reflect the uncertainties in model evaluation