Modelling yearly flooding extent of the Waza-Logone floodplain in northern Cameroon based on MODIS and rainfall data

Westra, Toon; Wulf, Robert R. De

doi:10.1080/01431160802672872

Cited by 23 publications

(21 citation statements)

References 12 publications

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“…While these products have very high spatial resolution, they have very low temporal resolution, with up to two weeks between observations. Although with lower spatial resolution, and frequent contamination due to cloud cover, the products from MODIS [37][38][39][40] and the Advanced Very High Resolution Radiometer (AVHRR) are also used to classify flood areas [2,[41][42][43][44]. Validation of MODIS-derived inundation extent mapping is done through comparison with either other remotely sensed data such as Landsat [2,3] or stream gauge measurements associated with flood peak detection [4,21].…”

Section: Data Opportunities and Constraintsmentioning

confidence: 99%

MODIS Inundation Estimate Assimilation into Soil Moisture Accounting Hydrologic Model: A Case Study in Southeast Asia

2014

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Flash Flood Guidance consists of indices that estimate the amount of rain of a certain duration that is needed over a given small basin in order to cause minor flooding. Backwater catchment inundation from swollen rivers or regional groundwater inputs are not significant over the spatial and temporal scales for the majority of upland flash flood prone basins, as such, these effects are not considered. However, some lowland areas and flat terrain near large rivers experience standing water long after local precipitation has ceased. NASA is producing an experimental product from the MODIS that detects standing water. These observations were assimilated into the hydrologic model in order to more accurately represent soil moisture conditions within basins, from sources of water from outside of the basin. Based on the upper soil water content, relations are used to derive an error estimate for the modeled soil saturation fraction; whereby, the soil saturation fraction model state can be updated given the availability of satellite observed inundation. Model error estimates were used in a Monte Carlo ensemble forecast of soil water and flash flood potential. Numerical experiments with six months of data -December 2011 showed that MODIS inundation data, when assimilated to correct soil moisture estimates, increased the likelihood that bankfull flow would occur, over non-assimilated modeling, at catchment outlets for approximately 44% of basin-days during the study time period. While this is a much more realistic representation of conditions, no actual events occurred allowing for validation during the time period.

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Section: Data Opportunities and Constraintsmentioning

confidence: 99%

MODIS Inundation Estimate Assimilation into Soil Moisture Accounting Hydrologic Model: A Case Study in Southeast Asia

2014

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“…We adopted the MNDWI according to Hui et al (2008) but with a different selection of bands due to the improvement in MODIS multi-band ratio given by Westra and De Wulf (2009). The MNDWI for MODIS is computed as follows:…”

Section: Altimetry and Radiometrymentioning

confidence: 99%

“…Active remote-sensing or geodetic sensors including satellite altimetry, global positioning systems (GPS), interferometric synthetic aperture radar (InSAR), as well as infrared/passive-microwave sensors, i.e. the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Very High Resolution Radiometer (AVHRR) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) have been innovatively used as monitoring tools for providing data from water bodies, where in situ gauge data are insufficient for hydrologic studies Westra and De Wulf, 2009;Zhang et al, 2010;Jung et al, 2011;Lee et al, 2011). The MODIS instrument onboard NASA's Terra and Aqua satellites is particularly useful for the studies embarked on here, as it not only monitors the atmospheric composition and thermal variation , but also surveys estuarine water colour and ecosystems thanks to its multi-bands proportional indices (Hu et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Study of the variation of schistosomiasis risk in Lake Poyang in the People’s Republic of China using multiple space-borne sensors for monitoring and modelling

et al. 2014

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Abstract. The dynamics of the Poyang Lake in Jiangxi province, People's Republic of China has been monitored to demonstrate the association of various variables with the distribution of schistosomiasis transmission with particular reference to the annual variation of the habitats for the Oncomelania snail, the intermediate host of Schistosoma japonicum. This was studied with multiple space-borne sensors, including the ENVISAT radar altimeter (RA-2) and MODIS/Terra radiometry data products such as the 16-day enhanced vegetation index, the 8-day sun reflectance, and the derived modified normalized difference water index. The measurements of physical properties were in good accordance with previous reports based on in situ gauge data, spectroradiometry and other optical methods, which encouraged us to build a predictive model based on reported geospatial constraints to assess the limits of potential variation of the snail habitat areas. The simulated results correspond fairly well with surveys conducted by local authorities showing a correlation coefficient of 0.82 between highpotential habitat areas and local estimates in a 9-year (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) analysis. Taken together, these data indicate that spaceborne observations and in situ measurements can be integrated and used as a first step of a monitoring system for control and analysis of the potential of schistosomiasis dissemination. Since the true range and intensity of transmission in the study region remain elusive at present, a long-term survey around the lake is warranted to build a robust, parametric model.

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“…Even though some of the larger study sites are divided into smaller sub-regions for modeling (Table 1), only two studies accounted for lag times between discharge recorded at the gauge (Westra and De Wulf, 2009) or water surface elevation at key points (Jung et al, 2011) and the correlated SWE in different areas of the sub-region. The overall aim of this study was to develop a holistic and data-driven methodology for modeling SWE and its drivers through periods of flooding and drying across a large and heterogeneous river basin.…”

Section: Introductionmentioning

confidence: 99%

“…Empirical models of SWE on floodplains derived from optical satellite data as a function of discharge or water height in the adjacent river (Table 1) have been previously developed, with case studies including the Okavango Delta (∼ 15 000 km 2 ) (Gumbricht et al, 2004), the Waza-Logone floodplain in Cameron (∼ 3000 km 2 ) (Jung et al, 2011;Westra and De Wulf, 2009), the Tana River delta in Kenya (∼ 1300 km 2 , Leauthaud et al, 2013), and various floodplains across the Murray-Darling Basin (MDB) in Australia (Table 1, study no. 4, 5, 6, 7, 8).…”

Section: Introductionmentioning

confidence: 99%

Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of Earth observation data

Heimhuber

Tulbure

Broich

2016

Hydrol. Earth Syst. Sci.

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Abstract. The usage of time series of Earth observation (EO) data for analyzing and modeling surface water extent (SWE) dynamics across broad geographic regions provides important information for sustainable management and restoration of terrestrial surface water resources, which suffered alarming declines and deterioration globally. The main objective of this research was to model SWE dynamics from a unique, statistically validated Landsat-based time series continuously through cycles of flooding and drying across a large and heterogeneous river basin, the Murray-Darling Basin (MDB) in Australia. We used dynamic linear regression to model remotely sensed SWE as a function of river flow and spatially explicit time series of soil moisture (SM), evapotranspiration (ET), and rainfall (P ). To enable a consistent modeling approach across space, we modeled SWE dynamics separately for hydrologically distinct floodplain, floodplain-lake, and non-floodplain areas within ecohydrological zones and 10 km × 10 km grid cells. We applied this spatial modeling framework to three sub-regions of the MDB, for which we quantified independently validated lag times between river gauges and each individual grid cell and identified the local combinations of variables that drive SWE dynamics. Based on these automatically quantified flow lag times and variable combinations, SWE dynamics on 233 (64 %) out of 363 floodplain grid cells were modeled with a coefficient of determination (r 2 ) greater than 0.6. The contribution of P , ET, and SM to the predictive performance of models differed among the three sub-regions, with the highest contributions in the least regulated and most arid sub-region. The spatial modeling framework presented here is suitable for modeling SWE dynamics on finer spatial entities compared to most existing studies and applicable to other large and heterogeneous river basins across the world.

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Modelling yearly flooding extent of the Waza-Logone floodplain in northern Cameroon based on MODIS and rainfall data

Cited by 23 publications

References 12 publications

MODIS Inundation Estimate Assimilation into Soil Moisture Accounting Hydrologic Model: A Case Study in Southeast Asia

MODIS Inundation Estimate Assimilation into Soil Moisture Accounting Hydrologic Model: A Case Study in Southeast Asia

Study of the variation of schistosomiasis risk in Lake Poyang in the People’s Republic of China using multiple space-borne sensors for monitoring and modelling

Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of Earth observation data

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