Abstract: Urmia Salt Lake (USL) is a hypersaline lake in the northwest of Iran. It contains halite as main dissolved and precipitated mineral and the major mineral mixed with lake bed sediments. Other detrital minerals such as calcite, aragonite, dolomite, quartz, feldspars, augite are forming lake sediments. This study examined the impact of weathering of this sediments collected from 1.5 meters depth and augite placers. The study indicated that weathering of tephritic and adakite rocks of the Islamic Island at the immediate boundary of the lake play a main control of lake bed sediments and has produced a large volume of augite placer along the lake bank. Weathering increases from south to toward north with increasing distance from Islamic Island. Geochemistry of lake sediments demonstrated the enrichment of MgO, CaO, Sr with an elevated anomaly of Eu, possibly due to surface absorbance of Mn and Fe associated Sr elevation originating from adakite volcanic rocks in the vicinity of the lake basin. The study shows the local geology is the major factor in origin of lake sediments than chemical and biochemical produced mineral during diagenetic processes.
Abstract: Any variation in environmental characteristics of
geomorphosites would lead to destabilisation of their geotouristic
values all around the planet. The Urmia lake, with an area of
approximately 5,500 km2 and a catchment area of 51,876 km2, and to
which various reasons over time, especially in the last fifty years
have seen a sharp decline and have decreased by about 93 % in two
recent decades. These variations are not only driving significant
changes in the morphology and ecology of the present lake
landscape, but at the same time are shaping newly formed
morphologies, which vanished some valuable geomorphosites or
develop into smaller geomorphosites with significant value from a
scientific and cultural point of view. This paper analyses and
discusses features and evolution in several representative coastal and
island geomorphosites. For this purpose, a total of 23 geomorphosites
were studied in two data series (1963 and 2015) and the respective
data were compared and analysed. The results showed, the total loss
in geomorphosites area in a half century amounted to a loss of more
than 90% of the valuable geomorphosites. Moreover, the comparison
between the mean yearly value of coastal area lost over the entire
period and the yearly average calculated for the shorter period (1998-
2014) clearly indicates a pattern of acceleration. This acceleration in
the rate of reduction in lake area was seen in most of the southern
half of the lake. In the region as well, the general water-level falling
is not only causing the loss of a significant water resource, which is
followed by major impact on regional ecosystems, but is also driving
the most marked recent (last century) changes in the geotouristic
landscapes. In fact, the disappearance of geomorphosites means the
loss of tourism phenomenon. In this context attention must be paid to
the question of conservation. The action needed to safeguard
geomorphosites includes: 1) Preventive action, 2) Corrective action,
and 3) Sharing knowledge.
Abstract: Water level forecasting using records of past time series is of importance in water resources engineering and management. For example, water level affects groundwater tables in low-lying coastal areas, as well as hydrological regimes of some coastal rivers. Then, a reliable prediction of sea-level variations is required in coastal engineering and hydrologic studies. During the past two decades, the approaches based on the Genetic Programming (GP) and Artificial Neural Networks (ANN) were developed. In the present study, the GP is used to forecast daily water level variations for a set of time intervals using observed water levels. The measurements from a single tide gauge at Urmia Lake, Northwest Iran, were used to train and validate the GP approach for the period from January 1997 to July 2008. Statistics, the root mean square error and correlation coefficient, are used to verify model by comparing with a corresponding outputs from Artificial Neural Network model. The results show that both these artificial intelligence methodologies are satisfactory and can be considered as alternatives to the conventional harmonic analysis.
Abstract: Nowadays, biometrical characterizations of Artemia
cysts are used as one of the most important factors in the study of
Artemia populations and intraspecific particularity; meanwhile these
characters can be used as economical indices. For example, typically
high hatching efficiency is possible due to the small diameter of
cysts (high number per gram); therefore small diameter of cysts
show someway high quality of cysts. This study was performed
during a ten year period, including two different ecological
conditions: rainy and drought. It is important from two different
aspects because it covers alteration of A. urmiana during ten years
also its variation in the best and worst environmental situations in
which salinity increased from 173.8 ppt in 1994 to 280.8 ppt in
2003/4. In this study the biometrical raw data of Artemia urmiana
cysts at seven stations from the Urmia Lake in 1994 and their seven
identical locations at 26 studied stations in 2003/4 were reanalyzed
again and compared together. Biometrical comparison of untreated
and decapsulated cysts in each of the seven similar stations showed a
highly significant variation between 1994 and 2003/4. Based on this
study, in whole stations the untreated and decapsulated cysts from
1994 were larger than cysts of 2003/4 without any exception. But
there was no logical relationship between salinity and chorion
thickness in the Urmia Lake. With regard to PCA analyses the
stations of two different studied years certainly have been separated
with factor 1 from each other. In conclusion, the interaction between
genetic and environmental factors can determine and explain
variation in the range of cysts diameter in Artemia.