Abstract of the Thesis

Lake Urmia was the second largest permanent hyper-saline lake in the World and the biggest in Middle East. The alerting situation of the lake pushed the authority such that the United Nations declared it as the wetland of international importance and World’s biosphere reserve. In late 1990’s water level in the lake started to decline with a sharp trend such that a wasteland of salty desert was conceptualized for the lake’s future. Many researchers around the world tried to model, suggest or decode the fact behind the lake’s atrophy mostly accused by miss-management, dam construction, development of agricultural zones, climate change etc. as the main reason for the lake atrophy.

In this study, a lake water budget approach using hydro-meteorological variables; precipitation, evaporation, runoff and groundwater, was considered. For this aim, data from meteorological stations, stream-flow gauging stations and groundwater wells were gathered. Data were analyzed and a data inventory was obtained. The data inventory consists of 253 meteorological stations, 156 stream-flow gauging stations, 593 groundwater wells and 1 lake water level station all scattered over the Lake Urmia basin. Precipitation and evaporation were taken from meteorological stations. In this study, 7 meteorological stations, 18 stream-flow gauging stations and 9 groundwater wells were considered together with the lake water level station. The selected stations and groundwater wells are close to the lake and scatter around it. 

Data in the selected stations and groundwater wells were checked against any missing data periods. Most of the stations were found with missing data. Groundwater wells have particularly long period of time with no data. For getting a common period for the analysis, missing data were reconstructed by a frequency domain analysis using decomposition. With this method, each time series of each station and groundwater well were decomposed into its components; trend, cycle, seasonality and randomness. An additive decomposition method was chosen. Observed time series was divided into calibration and validation parts. The decomposition method was used to fit a model to the calibration time series and to validate it then on the validation time series. Once validated, the model was run to reconstruct the missing data. This procedure was applied on all time series of precipitation, evaporation, runoff and groundwater. The observed and reconstructed precipitation, evaporation, runoff and groundwater time series were used to calculate lake water level. The calculated lake water level was compared with the observed lake water level. They were found in a very good agreement. This has been considered as a further validation of the reconstructed missing data. 

Observed and reconstructed hydro-meteorological data were used together to develop models for forecasting lake water level. In the model, lake water depth was considered instead of lake water level. For this aim, two methods were combined. First, lake water depth was regressed on independent variables; precipitation, evaporation, runoff and groundwater. The second step is the development of stochastic model for each variable. Auto-regressive integrated moving average (ARIMA) models were used. A number of models were tested and finally the best models were determined based on performance criteria for each variable. The number of parameters was kept at minimum for the sake of parsimony. As the final step in the modeling, not hydro-meteorological variables (precipitation, evaporation, runoff and groundwater) but their selected stochastic models were inserted into the regression model developed at the very beginning step. This is defined as regressive-stochastic depth model.

Alternatively, difference in the lake water levels of two subsequent months was taken into account instead of the lake water depth when the regression model is developed in the first step. Because lake water depth can mask change in the lake water level as they have different orders of magnitudes.

From this study it is seen that Lake Urmia is under a serious atrophy problem that should be studied in a long-term interdisciplinary approach. Lake Urmia has a considerably well documented data although record periods without data may become problematic. The frequency domain analysis can be a tool to satisfactorily reconstruct the missing data in the hydro-meteorological time series. Lake water level models can be developed based on either lake water depth or the difference in the lake water level between subsequent months. Due to the order of magnitude difference between the depth and the depth difference, it is clear that depth models can mask the effect of each input variable; precipitation, evaporation, runoff and groundwater, on the lake water level. Therefore, depth difference models should be preferred for the sake of understanding the physical process in the lake water level precisely. Regressive-stochastic models were found successful in calculating the lake water level. In the proposed regressive-stochastic models, only previously observed hydro-meteorological data are needed. This is a good opportunity for one to be able to estimate the next month lake water level. This will help us decision makers to act in advance.

As a future suggestion, the lake and its watershed should be investigated through an interdisciplinary approach. As the change is a continuous process it is suggested that any model proposed should be revised every several years and/or after any major change happens in the basin.

Lithology of groundwater in Lake Urmia

Hi everyone,
since my last post I have a major break through in my thesis. So I will try to share some of them with you in near future. No I am going through for clearing some issues about groundwater flow.
Now I am sharing a map which can be helpful for those whom want to get an idea about the Lithology of lake Urmia.

Groundwater time series

Hey guys

After being done with Runoff time series, it is inevitable challenge the groundwater time series. As I mentioned before (1, 2, 3), I think coastal aquifers have very significant affect on the lake itself. Their flux to the lake or even the salinity intrusion from lake to the aquifer are the consequences of this interaction.

Most of the researchers used to handle this interaction with Darcy's law (Darcy, 1856), while others try to control it by the chemical components that transfers between lake and aquifer. Such methods are useless for me cause I don't have any observation data on chemical components and/or hydraulic conductivity between lake and aquifers. Thus, I tried to solve it by simplifying the problem.

Figure 1 shows the potential elevation of the aquifer in comparison with lake water level. As a potential depth of influx/drainage it can be used as the potential depth which would affect the lake surface at most correlated wells near the shore line or from underneath.

Fig.1. Schematic of potential influx/outflow between lake and coastal aquifers

For this, in the first place an iso-correlation map between lake and coastal aquifer will be generated. In the next stage an iso-correlation map with different lag times (Cross-correlation) will be investigated also. Then, proper wells (i.e. most correlated ones) will be selected and iso-height maps of water level in aquifers will be recognized. Then the potential depth between lake and aquifer will be calculated by subtracting the Lake water level from wells' water level.

This values could be positive or negative which defines the availability of GW in flux/draining the lake, which would be very handy in my calculation. In addition, I will not loose site of depth while using a semi-water budget or mass balance equation while all variables have dimensions in length.

Please do not hesitate to share your point of view with me

Babak

Interaction of coastal aquifer and lake water level

Hi,

Tomorrow is my 3rd thesis report day. I am now finished with the presentation and I just started to do analysis on groundwater.

In the first place, I started with Pearson correlation coefficient. Result are very interesting, as I mentioned before, there is a strong evidence that groundwater and lake have strong relations. Accounting for a probable cross-correlation lag between lake and ground water, it sounds like interaction between these two are inevitable. AS you can see in Fig.1 Pearson correlation coefficient in West bank of the Lake is negative while positive correlations are observed in the East bank of the lake.

Fig.1. Iso-correlation map of the Lake Urmia, defining the negative correlation coefficient for the West (Left) and positive correlation for the East (Right) bank

This values some how make sense while, the incline of the terrain in the West bank is more rapid and sharp. Fig. 2 shows a schematic representation of geological features in the Lake Urmia. In the West bank of the lake, groundwater elevation changes in reverse order with the lake and decreases when the lake water level increase. On the other hand, in East bank, lake water level and ground water elevation changes in the same way. This could be an evidence to recognize that, salinity intrusion is more severe in the East bank of the lake (East Azerbaijan) while the West bank plays more active role in interaction.

Fig. 2. Schematic of lake Urmia and the possible interaction between lake and coastal aquifer

Please share your comments with me

Thankfully

Babak

Interaction of Coastal aquifer and Lake Urmia

Hey guys.

As I discussed previously, I am a true believer of  existence of interaction between coastal aquifer and Lake Urmia water level. Many authorities and politicians refuse to accept the theory and there are some research articles based on rejection of existence of such an interaction.

Recently I used to publish a conference paper (ASCE, EWRI 2015) about the interaction of water level in some random coastal aquifers in West coast of the Lake Urmia basin and water level in the Lake itself. I used a soft computational method named "Decision Tree" to manipulate my model. It is based on Entropy and probability. Evidence and results of this model are in agreement with a theory of existence of such interaction in Coastal aquifer.

Fig. 1 shows the schematic relation between lake and coastal aquifer which I believe that exist in the hydrological process. In general in closed basin lakes, such interaction is one of the main hydrological variables that should be considered and studied carefully.

Fig. 1. Schematic of interaction of coastal aquifer and Lake Urmia in balance

So I used to select some random wells just near to the west coast of the lake. You can find the position of this wells in Fig. 2. Data in east coast is not ready for use for now and I will try to manipulate them a.s.a.p. Followingly, a Pearson correlation coefficient test between Lake water level and water level in wells is done and interesting results are shown in Fig. 3 with a radar chart including the direction of such relations.

Fig. 3. Correlaogram radar chart

Fig. 2. Position of wells in west coast of the Lake

It is obvious that, there is strong linear relationship specially in North and South of the basin all with negative values. Same analysis on probability distribution function of lake water and water level in wells showed strong similarities in shape and moments of distribution. I have done some investigations on the structure of cross-correlations in time and space between lake and coastal aquifer. Two samples of such investigation are shown in Fig. 4. You can see seasonality and strong interaction between lake and coastal aquifer. As shown in Fig. 3 and 4,  these two stations (Station 1 and 6) have the most impact on the interaction.

Fig. 4. Cross-correlation between lake water level and water level in wells of station 1 and 6

I though a model may reveal more detailed structure of the relation, so I used to select a probabilistic one. As entropy concept is very popular now a days I used DT for manipulation of data and calibrated my tree. Here is the scatter plot of my model in Fig. 5. As you can see these are strong estimation result and I personally satisfied with the results.

Fig. 5. Scatter lot of DT model

That is all I was eager to share for now!

So I think I proved my theory at least to some extent. You may find out my paper's abstract in Related page in my weblog and/or download the whole article from ASCE library.

Please share your points of view with me.

Thank you