Modeling the Effects of Lakes and Wetlands on the Water Balance of Arctic Environments

Bowling, L. C.; Lettenmaier, Dennis P.

doi:10.1175/2009jhm1084.1

Cited by 131 publications

(117 citation statements)

References 44 publications

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“…As an example of model performance compared with our observation-based approach, Gao et al [6] uses the Variable Infiltration Capacity (VIC) hydrological model [30] to estimate the total surface water area of Lake Chad from 1952 to 2006. From the late 1980s to 2006, they show a net rising trend in lake total surface water area, as does this study for the same period.…”

Section: Discussionmentioning

confidence: 99%

Lake Chad Total Surface Water Area as Derived from Land Surface Temperature and Radar Remote Sensing Data

et al. 2018

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Lake Chad, located in the middle of the African Sahel belt, underwent dramatic decreases in the 1970s and 1980s leaving less than ten percent of its 1960s surface water extent as open water. In this paper, we present an extended record (dry seasons 1988-2016) of the total surface water area of the lake (including both open water and flooded vegetation) derived using Land Surface Temperature (LST) data (dry seasons [2000][2001][2002][2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015][2016] from the NASA Terra MODIS sensor and EUMETSAT Meteosat-based LST measurements (dry seasons 1988-2001) from an earlier study. We also examine the total surface water area for Lake Chad using radar data (dry seasons 2015-2016) from the ESA Sentinel-1a mission. For the limited number of radar data sets available to us (18 data sets), we find on average a close match between the estimates from these data and the corresponding estimates from LST, though we find spatial differences in the estimates using the two types of data. We use these spatial differences to adjust the record (dry seasons 2000-2016) from MODIS LST. Then we use the adjusted record to remove the bias of the existing LST record (dry seasons 1988-2001) derived from Meteosat measurements and combine the two records. From this composite, extended record, we plot the total surface water area of the lake for the dry seasons of 1988-1989 through 2016-2017. We find for the dry seasons of 1988-1989 to 2016-2017 that the maximum total surface water area of the lake was approximately 16,800 sq. km (February and May, 2000), the minimum total surface water area of the lake was approximately 6400 sq. km (November, 1990), and the average was approximately 12,700 sq. km. Further, we find the total surface water area of the lake to be highly variable during this period, with an average rate of increase of approximately 143 km 2 per year.

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Section: Discussionmentioning

confidence: 99%

Lake Chad Total Surface Water Area as Derived from Land Surface Temperature and Radar Remote Sensing Data

et al. 2018

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“…Thermal properties of organic soil are also taken into account (Farouki, 1981). To account for dynamic surface water storage (lakes and seasonally flooded wetlands) UW-VIC's lake/wetland model was employed (Bowling and Lettenmaier, 2010). This feature allocates one land cover tile to contain a combination of a lake (representing all lakes in the grid cell) and its surrounding catchment.…”

Section: Uw-vicmentioning

confidence: 99%

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

Wania

Melton²,

Hodson³

et al. 2013

Geosci. Model Dev.

184

180

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Abstract. The Wetland and Wetland CH 4 Intercomparison of Models Project (WETCHIMP) was created to evaluate our present ability to simulate large-scale wetland characteristics and corresponding methane (CH 4 ) emissions. A multi-model comparison is essential to evaluate the key uncertainties in the mechanisms and parameters leading to methane emissions. Ten modelling groups joined WETCHIMP to run eight global and two regional models with a common experimental protocol using the same climate and atmospheric carbon dioxide (CO 2 ) forcing datasets. We reported the main conclusions from the intercomparison effort in a companion paper (Melton et al., 2013). Here we provide technical details for the six experiments, which included an equilibrium, a transient, and an optimized run plus three sensitivity experiments (temperature, precipitation, and atmospheric CO 2 concentration). The diversity of approaches used by the models is summarized through a series of conceptual figures, and is used to evaluate the wide range of wetland extent and CH 4 fluxes predicted by the models in the equilibrium run. We discuss relationships among the various approaches and patterns in consistencies of these model predictions. Within this group of models, there are three broad classes of methods used to estimate wetland extent: prescribed based on wetland distribution maps, prognostic relationships between hydrological states based on satellite observations, and explicit hydrological mass balances. A larger variety of approaches was used to estimate the net CH 4 fluxes from wetland systems. Even though modelling of wetland extent and CH 4 emissions has progressed significantly over recent decades, large uncertainties still exist when estimating CH 4 emissions: there is little consensus on model structure or complexity due to knowledge gaps, different aims of the models, and the range of temporal and spatial resolutions of the models.

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“…Alternatively, regional models allow for a higher resolution but then depend strongly on detailed soil property information (e.g. Bowling and Lettenmaier, 2010;Yu et al, 2006) or are calibrated for specific catchments (e.g. Bohn et al, 2007).…”

Section: T Stacke and S Hagemann: The Dynamical Wetland Schemementioning

confidence: 99%

Development and evaluation of a global dynamical wetlands extent scheme

Stacke

Hagemann

2012

Hydrol. Earth Syst. Sci.

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Abstract. In this study we present the development of the dynamical wetland extent scheme (DWES) and evaluate its skill to represent the global wetland distribution. The DWES is a simple, global scale hydrological scheme that solves the water balance of wetlands and estimates their extent dynamically. The extent depends on the balance of water flows in the wetlands and the slope distribution within the grid cells. In contrast to most models, the DWES is not directly calibrated against wetland extent observations. Instead, wetland affected river discharge data are used to optimise global parameters of the model. The DWES is not a complete hydrological model by itself but implemented into the Max Planck Institute -Hydrology Model (MPI-HM). However, it can be transferred into other models as well.For present climate, the model evaluation reveals a good agreement for the spatial distribution of simulated wetlands compared to different observations on the global scale. The best results are achieved for the Northern Hemisphere where not only the wetland distribution pattern but also their extent is simulated reasonably well by the DWES. However, the wetland fraction in the tropical parts of South America and Central Africa is strongly overestimated. The simulated extent dynamics correlate well with monthly inundation variations obtained from satellites for most locations. Also, the simulated river discharge is affected by wetlands resulting in a delay and mitigation of peak flows. Compared to simulations without wetlands, we find locally increased evaporation and decreased river flow into the oceans due to the implemented wetland processes.In summary, the evaluation demonstrates the DWES' ability to simulate the distribution of wetlands and their seasonal variations for most regions. Thus, the DWES can provide hydrological boundary conditions for wetland related studies.In future applications, the DWES may be implemented into an Earth system model to study feedbacks between wetlands and climate.

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Modeling the Effects of Lakes and Wetlands on the Water Balance of Arctic Environments

Cited by 131 publications

References 44 publications

Lake Chad Total Surface Water Area as Derived from Land Surface Temperature and Radar Remote Sensing Data

Lake Chad Total Surface Water Area as Derived from Land Surface Temperature and Radar Remote Sensing Data

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

Development and evaluation of a global dynamical wetlands extent scheme

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