Modeling multidecadal surface water inundation dynamics and key drivers on large river basin scale using multiple time series of <scp>E</scp>arth‐observation and river flow data

Heimhuber, Valentin; Tulbure, Mirela G.; Broich, Mark

doi:10.1002/2016wr019858

Cited by 45 publications

(38 citation statements)

References 84 publications

(154 reference statements)

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“…The third one is that the long time series of hydrological data can enrich the relatively shorter time series of remotely sensed data through modeling. For example, Heimhuber et al () built a statistical model for surface water dynamics based on a 26‐year time series of Landsat‐derived surface water maps in combination with river flow data from 68 gauges, and spatial time series of rainfall, evaporation, and soil moisture. Figure shows the validation of modeled surface water extents (black bars) against the observed Landsat‐based surface water extents (green bars) in two example floodplain modeling units (Ex‐A: Lower Murray site; Ex‐C: Paroo site) with contrasting flooding regimes during the 2010/2011 La Nina floods.…”

Section: Progresses and Challengesmentioning

confidence: 99%

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Huang

Chen

Zhang

et al. 2018

Reviews of Geophysics

545

267

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Observation of surface water is a functional requirement for studying ecological and hydrological processes. Recent advances in satellite-based optical remote sensors have promoted the field of sensing surface water to a new era. This paper reviews the current status of detecting, extracting, and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field, including spatio-temporal scale issues, integration with in situ hydrological data and elevation data, obscuration caused by clouds and vegetation, and the growing need to map surface water at a global scale. Historically, sensors have exhibited a contradiction in resolutions. Techniques including pixel unmixing and reconstruction, and spatio-temporal fusion have been developed to alleviate this contradiction. Spatio-temporal dynamics of surface water have been modeled by combining remote sensing data with in situ river flow. Recent studies have also demonstrated that the river discharge can be estimated using only optical remote sensing imagery, providing valuable information for hydrological studies in ungauged areas. Another historical issue for optical sensors has been obscuration by clouds and vegetation. An effective approach of reducing this limitation is to combine with synthetic aperture radar data. Digital elevation model data have also been employed to eliminate cloud/terrain shadows. The development of big data and cloud computation techniques makes the increasing demand of monitoring global water dynamics at high resolutions easier to achieve. An integrated use of multisource data is the future direction for improved global and regional water monitoring.Plain Language Summary Observing surface water is essential for ecological and hydrological studies. This paper reviews the current status of detecting, extracting, and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field. For example, it was found that pixel unmixing and reconstruction, and spatio-temporal fusion are two common and low-cost approaches to enhance surface water monitoring. Remote sensing data have been integrated with in situ river flow to model spatio-temporal dynamics of surface water. Recent studies have also proved that the river discharge can be estimated using only optical remote sensing imagery. This will be a breakthrough for hydrological studies in ungauged areas. Optical sensors are also easily obscured by clouds and vegetation. This limitation can be reduced by integrating optical data with synthetic aperture radar data and digital elevation model data. There is increasing demand of monitoring global water dynamics at high resolutions. It is now easy to achieve with the development of big data and cloud computation techniques. Enhanced global or regional water monitoring in the future requires integrated use of multiple sources of remote sensing data.

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Section: Progresses and Challengesmentioning

confidence: 99%

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Huang

Chen

Zhang

et al. 2018

Reviews of Geophysics

545

267

View full text Add to dashboard Cite

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“…Moreover, event-based modeling and data assimilation in low and dry periods were envisaged as pragmatic solutions to improve floodplain inundation forecast skill. Conceptualization of the outcomes of this study can be readily applied to improve the accuracy of long-term flow modeling in the MDB, thus providing a powerful tool to (i) support flood forecast and risk analysis (e.g., Domeneghetti et al, 2018), (ii) complement remote sensing-based investigations of surface flow connectivity and its ecological implications (e.g., Bishop-Taylor et al, 2015;Bishop-Taylor et al, 2018;Heimhuber et al, 2016Heimhuber et al, , 2017Huang et al, 2014;Tulbure et al, 2016), and (iii) support investigation of groundwater recharge due to flooding (e.g., Doble et al, 2014).…”

Section: Significance Of the Study Limitations And Future Workmentioning

confidence: 99%

Challenges, Opportunities, and Pitfalls for Global Coupled Hydrologic‐Hydraulic Modeling of Floods

Grimaldi

Schumann

Shokri

et al. 2019

Water Resources Research

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Flood modeling at the regional to global scale is a key requirement for equitable emergency and land management. Coupled hydrological‐hydraulic models are at the core of flood forecasting and risk assessment models. Nevertheless, each model is subject to uncertainties from different sources (e.g., model structure, parameters, and inputs). Understanding how uncertainties propagate through the modeling cascade is essential to invest in data collection, increase flood modeling accuracy, and comprehensively communicate modeling results to end users. This study used a numerical experiment to quantify the propagation of errors when coupling hydrological and hydraulic models for multiyear flood event modeling in a large basin, with large morphological and hydrological variability. A coupled modeling chain consisting of the hydrological model Hydrologiska Byråns Vattenbalansavdelning and the hydraulic model LISFLOOD‐FP was used for the prediction of floodplain inundation in the Murray Darling Basin (Australia), from 2006 to 2012. The impacts of discrepancies between simulated and measured flow hydrographs on the predicted inundation patterns were analyzed by moving from small upstream catchments to large lowland catchments. The numerical experiment was able to identify areas requiring tailored modeling solutions or data collection. Moreover, this study highlighted the high sensitivity of inundation volume and extent prediction to uncertainties in flood peak values and explored challenges in time‐continuous modeling. Accurate flood peak predictions, knowledge of critical morphological features, and an event‐based modeling approach were outlined as pragmatic solutions for more accurate prediction of large‐scale spatiotemporal patterns of flood dynamics, particularly in the presence of low‐accuracy elevation data.

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“…Wulder et al [81] surveyed Landsat-based change detection applications including, for example, forestland change, phenology, wetlands, land fragmentation, and urban impervious surface change. In an application that is more related to flood prevention and planning, Heimhuber et al [82] and Heimhuber et al [83] modeled surface water extent dynamics using statistically validated long-term time series consisting of more than 25 000 Landsat images available for the period 1986-2011, in combination with streamflow, rainfall, evaporation, and soil moisture data, for Australia's Murray-Darling Basin. Zou et al [84] analyzed open-surface water bodies using Landsat 5, 7, and 8 images (∼370 000 images, >200 TB) of the contiguous US in the period 1984-2016.…”

Section: Ewm Big Data Applications 341 Problems Big Data Have Tackledmentioning

confidence: 99%

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

Sun

Scanlon

2019

Environ. Res. Lett.

340

129

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Big Data and machine learning (ML) technologies have the potential to impact many facets of environment and water management (EWM). Big Data are information assets characterized by high volume, velocity, variety, and veracity. Fast advances in high-resolution remote sensing techniques, smart information and communication technologies, and social media have contributed to the proliferation of Big Data in many EWM fields, such as weather forecasting, disaster management, smart water and energy management systems, and remote sensing. Big Data brings about new opportunities for data-driven discovery in EWM, but it also requires new forms of information processing, storage, retrieval, as well as analytics. ML, a subdomain of artificial intelligence (AI), refers broadly to computer algorithms that can automatically learn from data. ML may help unlock the power of Big Data if properly integrated with data analytics. Recent breakthroughs in AI and computing infrastructure have led to the fast development of powerful deep learning (DL) algorithms that can extract hierarchical features from data, with better predictive performance and less human intervention. Collectively Big Data and ML techniques have shown great potential for data-driven decision making, scientific discovery, and process optimization. These technological advances may greatly benefit EWM, especially because (1) many EWM applications (e.g. early flood warning) require the capability to extract useful information from a large amount of data in autonomous manner and in real time, (2) EWM researches have become highly multidisciplinary, and handling the ever increasing data volume/types using the traditional workflow is simply not an option, and last but not least, (3) the current theoretical knowledge about many EWM processes is still incomplete, but which may now be complemented through data-driven discovery. A large number of applications on Big Data and ML have already appeared in the EWM literature in recent years. The purposes of this survey are to (1) examine the potential and benefits of data-driven research in EWM, (2) give a synopsis of key concepts and approaches in Big Data and ML, (3) provide a systematic review of current applications, and finally (4) discuss major issues and challenges, and recommend future research directions. EWM includes a broad range of research topics. Instead of attempting to survey each individual area, this review focuses on areas of nexus in EWM, with an emphasis on elucidating the potential benefits of increased data availability and predictive analytics to improving the EWM research.

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Modeling multidecadal surface water inundation dynamics and key drivers on large river basin scale using multiple time series of Earth‐observation and river flow data

Cited by 45 publications

References 84 publications

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Challenges, Opportunities, and Pitfalls for Global Coupled Hydrologic‐Hydraulic Modeling of Floods

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

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