Ben Linhoff

GIS in Water Resources

Hydrogeology of Mongolian Soda Lakes and Correlating the Wetness Index with Strontium Isotopes

Introduction:

The goals of my project were to understand the chemical evolution of groundwater and soda lakes in the semi arid region of eastern Mongolia using strontium isotopes and GIS. The eastern Mongolian landscape is characterized by rolling grassland steppe dotted with small terminal soda lakes.  The soda lakes are small, shallow (<1Km2,<1m), and have variable salinity.  Five lakes, six wells and one stream were selected for sampling in eastern Mongolia.  All of the ground waters sampled were shallow (2-3m) and in an unconfined aquifer.

While it is likely that these lakes originated from evaporating meteoric water mixed with an influx of groundwater, it is unknown the degree of water rock interactions the ground water underwent before input into the lakes.  The chemical evolution of the lakes and groundwater is function of both the area of land draining to a lake or groundwater point as well as the amount of time the water took to reach that spot.  The time in transit is important when considering water rock interactions which increase with the time a water spends with a particular rock type and distance a water travels.

Strontium isotopes are a useful parameter for examining water rock interactions, distinguishing aquifers, and understanding mixing dynamics in groundwater.  By comparing the strontium isotope values of ground water and lake water, it can be possible to determine the source water of a lake.  At all of the eleven sample sites, water was collected for strontium isotope analysis.  Strontium isotopes are the typical tool used to understand water rock interactions and gauge the degree of interactions.

By using Arc GIS it was possible to approximate the amount of time a water had been in contact with rocks as well as the distance ground water had traveled to reach a particular point.  This was done by using a wetness index layer of my field site in eastern Mongolia.  This information was then compared with strontium isotopes to determine if it was possible to correlate the two independent indicators water rock interactions.

The wetness index layer is a function of both a flow accumulation layer as well as a slope layer.  In flow accumulation layer (Figure 1), values are assigned to each cell according to how many cells are draining into a particular cell. In places where there are more upstream cells a darker color is assigned to the cell.  This creates a map which clearly shows drainages and rivers across a landscape.  The slope layer (figure 2) describes the maximum change in elevation between each cell and its eight neighboring cells.  Each cell is given a value between 0-90 according to the slope. The slope layer used was created using ArcINFO’s slope function.  The wetness index layer (Figure 3) was created using the slope layer and the flow accumulation layer.  Each cell is given a value which incorporates the flow accumulation layer and the slope layer through the natural log function:

Wetness Index = ln (flow accumulation / tan(slope))

By this method, both the amount of upstream cells and the time the water spent in transit (related to the slope) are incorporated into the cell value.  In a wetness index layer, darker cells are given higher values and represent where water has traveled farther and slower while lighter cells represent where water traveled quickly and over a short distance. The purpose of this study was to determine whether this wetness index could be correlated to strontium isotopes, and therefore be an indicator of water rock interactions

Methods:

Data used in ArcGIS was downloaded from USGS website Hydro1K Asia data source:

http://edc.usgs.gov/products/elevation/gtopo30/hydro/asia.html

At each site, I sampled and subsequently analyzed for a variety of geochemical constituents including strontium and uranium isotopes, cations and trace metals, anions, alkalinity, and unstable field parameters (temperature, disoloved oxygen and redox potential-ORP).

Results and interpretations:

Strontium isotopes correlated surprisingly well with the wetness index values at all well locations (Figure 4).  Because rainwater varies dramatically between geographical regions in its strontium isotopic composition, it can also be said that rainwater in eastern Mongolia has a lower strontium isotope signature being similar to well sites with a lower wetness index value.  This is assuming that the sites with lower wetness index indeed do represent fresher ground water and therefore are chemically more similar to rainwater.

In lake samples, the opposite correlation was found between the wetness index and the strontium isotopic composition (Figure 5).  This is likely due to the chemical nature of the soda lakes in that they increase in pH with more evaporation.  Smaller lakes with lower wetness index values represented lakes with smaller catchment areas and therefore more evaporation and higher pH values.  The higher pH in the smaller lakes is probably much more important in determining the degree of water rock interactions as high pH (pH averages around 10 in the smaller lakes) is very effective at breaking down rocks and chemically reacting water with rocks. When the wetness index is plotted with bicarbonate (which increases with pH) (Figure 6), it can be seen that the smaller lakes with lower wetness index values indeed have much more bicarbonate than the larger lakes with higher wetness index values.  When bicarbonate is plotted against strontium isotopes (Figure 7) it is clear that the lakes with the most bicarbonate and therefore greatest potential for water rock interactions have a more radiogenic strontium isotope value.

Because variation in strontium isotopes are a function of the degrees of water rock interactions and in well samples it correlated strongly with the wetness index, it can be suggested that wetness index is a good proxy for accessing water rock interactions in shallow ground water.  As water rock interactions determine the amount of dissolved constituents in a watershed including many naturally derived contaminants such as arsenic and antimony, this method could be applied to a variety of regions and related to other chemical indicators of water rock interactions such as chloride content and total dissolved solids. The opposite correlation found in the soda lakes implies the limits of this conclusion and the need to understand the chemistry of a system before assuming wetness index is a good proxy for determining the extent of water rock interactions.

Figure 1.  Flow accumulation layer with sample points plotted.

Figure 2.  Slope layer with sample points plotted.  Darker cells represent steeper slopes.  The lack of color variation shows how flat the region is.

Figure 3.

This wetness index map was created by combining a digital elevation map, a flow accumulation map, and a slope layer map. Darker cells are given higher values and represent where water has traveled farther and slower while lighter cells represent where water traveled quickly and over a short distance.

Figure 4.  Good correlation is seen between the wetness index and strontium isotopes measured at well sites.

Figure 5.  Strontium isotopes versus wetness index values at each lake site.  The opposite correlation from the well sites is because of the soda lake chemical nature of the lakes.

Figure 6.  Bicarbonate (HCO3) plotted against the wetness index in lake sites.  This shows that lakes with smaller catchment areas (lower wetness index values) evaporate more and therefore produce more HCO3 and have higher pH.

Figure 7.

Strontium isotopes and bicarbonate plotted for lake sites.  The smaller lakes with more bicarbonate and higher pH have more water rock interactions and therefore a more radiogenic strontium isotope signature.