Flooding Patterns of the Okavango Wetland in Botswana between 1972 and 2000

McCarthy, Jenny; Gumbricht, Thomas; McCarthy, T. S.; Frost, Philip; Wessels, Konrad J; Seidel, Frank

doi:10.1639/0044-7447(2003)032[0453:fpotow]2.0.co;2

Cited by 28 publications

(38 citation statements)

References 7 publications

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“…Thereafter, the flood wave reaches the lower end of the delta during the dry season, around July , 1991). A similar study has previously been undertaken by McCarthy et al (2003) using the coarse NOAA AVHRR satellite images. Applying the described methodology to this example would enable the flood wave to be traced across the delta at a time when there should be minimal cloud coverage.…”

Section: The Use Of Remote-sensing Imagerymentioning

confidence: 87%

The spatial and temporal characterisation of flooding within the floodplain wetland of the Nyl River, Limpopo Province, South Africa

McCarthy

Thompson

Nomnganga

et al. 2011

WSA

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The inundated area of a wetland is characterised by annual and interannual variability. This paper presents remotely-sensed imagery in order to better understand the spatial and temporal patterns of flooding within the floodplain wetland of the Nyl River, Limpopo Province. A detailed understanding of the hydrological characteristics of these flood events is essential in order to develop sustainable ecological and hydrological management plans for the area. From the results, flooding is shown to occur in 2 distinct phases. The initial phase is characterised by water ponding on the floodplain. The later phase is characterised by the input of water from tributaries to the north (e.g. Andriesspruit and Tobiasspriut) and southwest (e.g. Klein Nyl and Groot Nyl). This distinction may relate to the increasingly widespread practice of agricultural irrigation within adjacent tributary catchments. The methodology described in this study could yield valuable results when applied to other wetland systems in southern Africa.

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Section: The Use Of Remote-sensing Imagerymentioning

confidence: 87%

The spatial and temporal characterisation of flooding within the floodplain wetland of the Nyl River, Limpopo Province, South Africa

McCarthy

Thompson

Nomnganga

et al. 2011

WSA

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“…The area also is extremely smooth and flat. Maximum local relief is generally less than 2 or 3 m (McCarthy et al, 2003;Gumbricht et al, 2001). As a result, like many wetlands, flow direction and inundation area is determined by very slight differences in elevation.…”

Section: Study Sitementioning

confidence: 99%

“…The objective functions for calibration were the inundation extents and outflow from the Boro River at Maun. The estimation of inundation extents was derived from satellite imagery available from McCarthy et al (2003). Outflow from the Boro River was gauged in the river.…”

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confidence: 99%

Reducing uncertainty based on model fitness: Application to a reservoir model

Linhoss¹,

Muñoz‐Carpena²,

Kiker³

et al. 2015

WSA

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Sensitivity and uncertainty analysis are important tools in the modelling process: they assign confidence to model results, can aid in focusing monitoring and preservation efforts, and can be used in model simplification. A weakness of global sensitivity and uncertainty analysis methodologies is the often subjective definition of prior parameter probability distributions, especially in data-poor areas. We apply Monte Carlo filtering in conjunction with quantitative variancebased global sensitivity and uncertainty analysis techniques to address this weakness and define parameter probability distributions in the absence of measured data. This general methodology is applied to a reservoir model of the Okavango Delta, Botswana. In addition to providing a methodology for setting prior parameter distributions, results show that the use of Monte Carlo filtering reduces model uncertainty and produces simulations that better represent the calibrated ranges. Thus, Monte Carlo filtering increases the accuracy and precision of parametric model uncertainty. Results also show that the most important parameters in our model are the volume thresholds, the reservoir area/volume coefficient, floodplain porosity, and the island extinction coefficient. The reservoir representing the central part of the wetland, where flood waters separate into several independent distributaries, is a keystone area within the model. These results identify critical areas and parameters for monitoring and managing, refine and reduce input/output uncertainty, and present a transferable methodology for developing parameter probability distribution functions, especially when using empirical models in datascarce areas.

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“…EO data enable analyses of changes in the availability and distribution of surface water at continental or sub-continental scales based on comparison of snapshots of the state of the system at two (Baker et al, 2007;Teferi et al, 2010) or multiple points in time (Huang et al, 2014b;Zhao et al, 2011). The opening of archives of continuous optical satellite data, such as Landsat and MODIS imagery, further increased the potential of performing time series analysis of remotely sensed surface water extent (SWE) and inundation dynamics over large areas and long periods of time (Klein et al, 2014;Kuenzer et al, 2015;Mccarthy et al, 2003;Sakamoto et al, 2007;Tulbure and Broich, 2013;Tulbure et al, 2016). Such EO-based analyses of SWE dynamics are to be distinguished from EO-based flood mapping, which focuses on flooding of areas that are not frequently inundated and large-scale damage assessment of floods .…”

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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Flooding Patterns of the Okavango Wetland in Botswana between 1972 and 2000

Cited by 28 publications

References 7 publications

The spatial and temporal characterisation of flooding within the floodplain wetland of the Nyl River, Limpopo Province, South Africa

The spatial and temporal characterisation of flooding within the floodplain wetland of the Nyl River, Limpopo Province, South Africa

Reducing uncertainty based on model fitness: Application to a reservoir model

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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