Evaluation of the Global Precipitation Measurement (<scp>GPM</scp>) Satellite Rainfall Products over the Lower Colorado River Basin, Texas

Omranian, Ehsan; Sharif, Hatim O.

doi:10.1111/1752-1688.12610

Cited by 70 publications

(66 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work in Muster et al [52], which examines water body mapping at different resolutions, adds that the decrease of resolution not only leads to the omission of small water bodies and misclassification of border pixels, but also may result in local overestimation of water surface area when clusters of small water bodies are merged into single larger water bodies. Accuracy fluctuations between satellite products of varying spatial resolution are also indicated in the evaluation study of Omnarian et al [53]. Except for the resolution difference, an additional factor that may cause partial disagreement between S2 and Landsat maps is the possible classification errors in the Landsat maps.…”

Section: Discussionmentioning

confidence: 84%

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

et al. 2018

View full text Add to dashboard Cite

Satellite data offer the opportunity for monitoring the temporal flooding dynamics of seasonal wetlands, a parameter that is essential for the ecosystem services these areas provide. This study introduces an unsupervised approach to estimate the extent of flooded areas in a satellite image relying on the physics of light interaction with water, vegetation and their combination. The approach detects automatically thresholds on the Short-Wave Infrared (SWIR) band and on a Modified-Normalized Difference Vegetation Index (MNDVI), derived from radiometrically-corrected Sentinel-2 data. Then, it combines them in a meaningful way based on a knowledge base coming out of an iterative trial and error process. Classes of interest concern water and non-water areas. The water class is comprised of the open-water and water-vegetation subclasses. In parallel, a supervised approach is implemented mainly for performance comparison reasons. The latter approach performs a random forest classification on a set of bands and indices extracted from Sentinel-2 data. The approaches are able to discriminate the water class in different types of wetlands (marshland, rice-paddies and temporary ponds) existing in the Doñana Biosphere Reserve study area, located in southwest Spain. Both unsupervised and supervised approaches are examined against validation data derived from Landsat satellite inundation time series maps, generated by the local administration and offered as an online service since 1983. Accuracy assessment metrics show that both approaches have similarly high classification performance (e.g., the combined kappa coefficient of the unsupervised and the supervised approach is 0.8827 and 0.9477, and the combined overall accuracy is 97.71% and 98.95, respectively). The unsupervised approach can be used by non-trained personnel with a potential for transferability to sites of, at least, similar characteristics.

show abstract

Section: Discussionmentioning

confidence: 84%

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The final paper in this featured collection (Omranian and Sharif ) evaluates the accuracy of different satellite‐derived precipitation products over the Lower Colorado River in Texas. The accuracy of any hydrological model is dependent upon the robustness of its precipitation input.…”

Section: Overviewmentioning

confidence: 99%

“…In forecasting modeling frameworks (e.g., NWM), spatial and temporal scaling of recent and forecasted precipitation data add a layer of complexity which is challenging to evaluate. Omranian and Sharif () evaluated the impact of spatial and temporal downscaling of the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement mission (IMERG) products by comparing them to in situ observations at 214 rain gauges. They found IMERG products better correspond to observed precipitation when downscaled, but more improvement is needed.…”

Section: Overviewmentioning

confidence: 99%

Featured Collection Introduction: National Water Model

Cohen

Praskievicz

Maidment

2018

J American Water Resour Assoc

View full text Add to dashboard Cite

“…In the United States, based on a U.S. National Weather Service (NWS) report, the average annual property/human losses are estimated to be more than $8 billion [1][2][3] (Federal Emergency Management Agency (FEMA), 2013). Flood inundation maps help to detect flood-prone regions and can prevent major catastrophe by providing reliable information to the public about the flood-risk [4,5].…”

Section: Introductionmentioning

confidence: 99%

Merging Real-Time Channel Sensor Networks with Continental-Scale Hydrologic Models: A Data Assimilation Approach for Improving Accuracy in Flood Depth Predictions

et al. 2018

Self Cite

View full text Add to dashboard Cite

This study proposes a framework that (i) uses data assimilation as a post processing technique to increase the accuracy of water depth prediction, (ii) updates streamflow generated by the National Water Model (NWM), and (iii) proposes a scope for updating the initial condition of continental-scale hydrologic models. Predicted flows by the NWM for each stream were converted to the water depth using the Height Above Nearest Drainage (HAND) method. The water level measurements from the Iowa Flood Inundation System (a test bed sensor network in this study) were converted to water depths and then assimilated into the HAND model using the ensemble Kalman filter (EnKF). The results showed that after assimilating the water depth using the EnKF, for a flood event during 2015, the normalized root mean square error was reduced by 0.50 m (51%) for training tributaries. Comparison of the updated modeled water stage values with observations at testing locations showed that the proposed methodology was also effective on the tributaries with no observations. The overall error reduced from 0.89 m to 0.44 m for testing tributaries. The updated depths were then converted to streamflow using rating curves generated by the HAND model. The error between updated flows and observations at United States Geological Survey (USGS) station at Squaw Creek decreased by 35%. For future work, updated streamflows could also be used to dynamically update initial conditions in the continental-scale National Water Model.

show abstract

Evaluation of the Global Precipitation Measurement (GPM) Satellite Rainfall Products over the Lower Colorado River Basin, Texas

Cited by 70 publications

References 39 publications

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

Featured Collection Introduction: National Water Model

Merging Real-Time Channel Sensor Networks with Continental-Scale Hydrologic Models: A Data Assimilation Approach for Improving Accuracy in Flood Depth Predictions

Contact Info

Product

Resources

About