Long term simulations of global lakes using the Variable Infiltration Capacity modelHuilin Gao1, Theodore Bohn1, Michelle Vliet2, Elizabeth Clark1, and Dennis P. Lettenmaier1

1Department of Civil and Environmental Engineering, Box 352700, University of Washington, Seattle, WA 981952 Department of Earth System Science and Climate Change, Wageningen University and Research Centre, P.O. Box 47, 6700 AA Wageningen, The Netherlands

1 Objective

VIC dynamic lake/wetland module2

3 Case study of Lake Chad, Africa

This project is supported by NASA Grant NNX08AN40A – “Developing Consistent Earth System Data Records for the Global Terrestrial Water Cycle”

7 References

5 Summary and future work

4 Towards global lake simulations

Although lakes and reservoirs play a major role in the hydrology of the land

surface over substantial areas of the globe, coherent information about their

dynamics is largely lacking. The quality and completeness of information from

in situ sources varies tremendously for different countries and regions.

Recently, satellite data have provided some information about variations in

lake surface elevation (from satellite altimeters) and surface extent (from

visible and other sensors) for the largest lakes. Land surface models offer an

alternative means of gaining insights into lake dynamics. Here we use a recent

version of the Variable Infiltration Capacity (VIC) model which includes a

lake/wetland module, to simulate the 57-year (1950-2006) variations of lake

level and surface area of Lake Chad, Africa. The objectives of this study are

two-fold:

1. To test the VIC lake/wetland module, which was originally intended for

application to smaller sized lakes in high latitudes regions, over a large lake in

the tropics;

2. To gain insights into issues associated with simulating lakes and wetlands

globally using the modified version of the VIC model and ancillary data sets.

Figure 2. Schematic for the wetland algorithm: a) when the lake is at its maximum extent the soil column is saturated, b) as the lake shrinks runoff from the land surface enters the lake and c) evaporation from the land surface depletes soil moisture, d) as the lake grows, water from the lake recharges the wetland soil moisture (Bowling and Lettenmaier, 2009).

VIC lake algorithm VIC wetland algorithm

Major module characteristics:

• Multi-layer energy balance lake model of Hostetler et al. 2000 as modified

by Bowling and Lettenmaier (2009)

• Dynamic lake area allows seasonal inundation of adjacent wetlands

• Currently not part of channel network

• Lake/wetland parameters:

Lake depth-area profile;

Wfrac (Width of lake outlet, as a fraction of the lake perimeter);

Rpercent (Fraction of grid cell runoff that enters lake)

Figure 1. Schematic of the VIC lake algorithm. I: Evaporation from the lake is calculated via energy balance, II. Runoff enters the lake from the land surface, III: Runoff out of the lake is calculated based on the new stage, and IV: The stage is re-calculated (Bowling and Lettenmaier, 2009).

Figure 3 Geographic situation of the Lake Chad basin (figure cited from Coz et al., 2009)

I. Study area: the vanishing Lake Chad

Komadugu

Logone-Chari

(m)

Figure 4 Lake bathymetry from DEM.

Located in Central Africa with an area of 2,500,000 km2, the Lake Chad basin is the largest

endoreic basin in the world. Lake Chad is shared by four countries: Chad, Niger, Nigeria and

Cameroon. The hydrologically active part of the basin is mainly drained by the Chari–Logone

river system, and to a lesser extent, by the Komadugu River.

In the 1960s Lake Chad had an area of more than 26,000 km², making it the fourth largest lake

in Africa. By 2000 its extent had fallen to less than 1,500 km² due to a combination of severe

droughts and increased irrigation water usage.

II. Data and approach

III. Results

Figure 6 Modeled lake level (south part) and its comparison with observations .

Figure 5 VIC simulated discharge to the north part (a), and south part (b) of the lake; lake depth-area profile based on bathymetry of the north part (c) and south part (d).

Figure 7 Modeled lake level (north part) .

Figure 8 Modeled lake surface area and the comparisons with satellite images in selected days.

According to the bathymetry, Lake Chad features a relatively deep northern half and a very shallow southern half, with most

of its inflows from the Logone-Chari river system into the southern part. Therefore, we use two separate grid cells to

represent the lake. The southern cell is modeled first, with its forcings modified by the inflows from the southern part of the

Chad basin. The runoff from this cell is then partitioned to the inflow for the northern part and to the irrigation water usagefor the basin. For both grid

cells, the depth-area

relationship is from

topography, and rpercent = 1.

For the north wfrac = 0; for

the south wfrac is calibrated.

Forcings are from Sheffield

et al. (2006).

Results from the modeled lake level for the southern part (Fig. 6)

suggest two things. First, the lake level and its variations were

significantly reduced during the droughts in the 1970’s and 1980’s.

Second, the modeled results are

fairly consistent with

observations from satellite

altimetry. The lake level for the

northern part of the lake

indicates a disappearance in the

1970’s, with a decrease in the

lake size of more than 80% from

the mid 60s to the mid 70s. The

modeled lake water coverage

maps (based on lake level and

bathymetry) demonstrate a good

coherency with the available

satellite imagery, except for a

low bias in the southern part in

the 1963 comparison.

Global Lakes and Wetlands Database

small lakes big lakes

Aggregate within grid cell

Grid cell simulation

parameterization

Big lakes downstream?

NO YES

Routing to outlet Simulate the first downstream big lake

Modified routing network

Routing to the first downstream big lake

Figure 9 Flowchart of the global lake simulation plan.

We are in the process of simulating lakes and wetlands globally

following the procedure outlined in the flowchart below. Large lakes

and small lakes are separated using the Global Lakes and Wetlands

Database (GLWD). Only the largest lakes are represented in the model’s

river routing network, and small lakes are considered in aggregate as an

effective land cover class within each grid cell. Similar to the approach

used for Lake Chad, the large lakes are simulated using a constructed

grid cell containing the whole lake (in most cases) with its forcings

modified by the routed inflows.

Birkett, C.M., 2000, Synergistic remote sensing of Lake Chad: Variability of basin inundation. Remote Sensing of Environment, 72, 218-236.Bowling and Lettenmaier, 2009: Modeling the effects of lakes and wetlands on the water balance of Arctic environments Journal of Hydrometeorology (accepted). Coe, M. T., and Foley, J. A., 2001, Human and natural impacts on the water resources of the Lake Chad basin. Journal of Geophysical Research-Atmospheres, 106, 3349-3356.Le Coz, M., Delclaux, F., Genthon, P., and Favreau, G., 2009, Assessment of Digital Elevation Model (DEM) aggregation methods for hydrological modeling: Lake Chad basin, Africa. Computers & Geosciences, 35, 1661-1670.Lehner, B., and Doll, P., 2004, Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology, 296, 1-22.Sheffield, J., Goteti, G., and Wood, E. F., 2006, Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. Journal of Climate, 19, 3088-3111.

In this study we used a recent version of the VIC macroscale hydrology

model with a lake/wetland module, in combination with remotely sensed

altimetry data, to simulate and verify lake level and area variations in

Lake Chad, Africa. The 57-year (1950-2006) results are consistent with

both observations and known climate change in the area.

Further steps toward global simulations are being taken, as shown in a

strategy for global implementation of the model. Future work will focus

on the parameterizations of lakes globally, and modification of the river

networks to incorporate large lakes. Irrigation water usage will be a

critical term for the model to handle to achieve realistic results.

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