Participatory Ecosystem Management Planning at Tuzla Lake (Turkey) Using Fuzzy Cognitive Mapping

A participatory environmental management plan was prepared for Tuzla Lake, Turkey. Fuzzy cognitive mapping approach was used to obtain stakeholder views and desires. Cognitive maps were prepared with 44 stakeholders (villagers, local decisionmakers, government and non-government organization (NGO) officials). Graph theory indices, statistical methods and "What-if" simulations were used in the analysis. The most mentioned variables were livelihood, agriculture and animal husbandry. The most central variable was agriculture for local people (villagers and local decisionmakers) and education for NGO & Government officials. All the stakeholders agreed that livelihood was increased by agriculture and animal husbandry while hunting decreased birds and wildlife. Although local people focused on their livelihoods, NGO & Government officials focused on conservation of Tuzla Lake and education of local people. Stakeholders indicated that the conservation status of Tuzla Lake should be strengthened to conserve the ecosystem and biodiversity, which may be negatively impacted by agriculture and irrigation. Stakeholders mentioned salt extraction, ecotourism, and carpet weaving as alternative economic activities. Cognitive mapping provided an effective tool for the inclusion of the stakeholders' views and ensured initial participation in environmental planning and policy making.Comment: 43 pages, 4 figure

A neural network model for simulation of water levels at the Sultan Marshes wetland in Turkey

An artificial neural network (ANN) model was developed for simulating water levels at the Sultan Marshes in Turkey. Sultan Marshes is a closed basin wetland located in the semi-arid Central Anatolia region of Turkey. It is one of the thirteen Ramsar sites of Turkey and a national park. Water levels at the Sultan Marshes showed strong fluctuations in recent decades due to the changes in climatic and hydrologic conditions. In this study, monthly average water levels were simulated using a multi-layer perceptron type ANN model. The model inputs consisted of climatic data (precipitation, air temperature, evapotranspiration) and hydrologic data (ground water levels, spring flow rates, and previous month water levels) available from 1993 to 2002. 70 % of the data were used for model training and remaining 30 % were used for model testing. Model training was accomplished by using a scaled conjugate gradient backpropagation algorithm. The performance of the model was evaluated by calculating the root mean square error (RMSE) and the coefficient of determination (R (2)) between observed and simulated water levels. The sensitivity of the model to input parameters was determined by evaluating the model performance when a single input variable was excluded. It was found that the ANN model can successfully be used for simulating water levels at the Sultan Marshes. The model developed using all input variables provided the best results with two neurons in the hidden layer. The RMSE and R (2) of the simulated water levels were 4.0 cm and 96 %, respectively. The sensitivity analysis showed that the model was most sensitive to previous month water levels and ground water levels

Modelling surface water-groundwater interactions at the Palas Basin (Turkey) using FREEWAT

Palas Basin is a semi-arid closed basin located in the Central Anatolia region of Turkey. The major economic activity in the basin is agriculture; therefore, both surface water and groundwater are used for irrigation. However, intensive use of water resources threatens the hydrologic sustainability of an ecologically important lake ecosystem (Tuzla Lake) located in the basin, as it is hydrologically dependent on surface and groundwater flows from the basin. In this study, we analyze the relationships between agricultural water uses in the Palas Basin and water flows in to the Tuzla Lake using groundwater flow model developed with the FREEWAT platform. The model grid with 250 m x 250 m resolution was created based on the entire watershed, where two hydrostratigraphic units were identified. The source terms defined to the model were rainfall recharge and the sink terms were evapotranspiration and groundwater abstraction from wells. The model was run for one year at steady-state conditions. The model successfully simulated the direction of groundwater flow and groundwater levels in the basin. Annual groundwater recharge was simulated as 5.27 million m(3). Groundwater losses were due to pumping (1.49 million m(3)/yr), leakance to Degirmen River (2.25 million m(3)/yr) and seepage to Tuzla Lake (1.53 million m(3)/yr). Three scenarios were simulated to understand the effect of groundwater use on the lake hydrology. The first scenario assumed that there was no groundwater abstraction. As the second and third water management scenarios, the model was run with 50% less and 50% more groundwater abstraction than that of the current conditions. Water flows to Tuzla Lake were significantly related to groundwater abstraction rates. Increasing groundwater pumping rates reduces groundwater flows to Tuzla Lake and lowers lake water level. No groundwater abstraction and reduction in groundwater pumping rates increase water flows to Tuzla Lake and allow higher lake water levels. This analysis showed that protection of hydrologic characteristics of Tuzla Lake is only possible with more control on groundwater abstraction

Spatial and temporal changes at Tuzla (Palas) Lake in Turkey

Saline lakes are widespread throughout the arid and semi-arid regions of the world and have considerable ecological importance. They are also very vulnerable to climatic changes or changes in their hydrological regimes. Most saline lakes of Turkey are close to the verge of extinction due to natural and anthropogenic impacts. This study analyzes the spatial and temporal changes at a relatively pristine saline lake (Tuzla (Palas) Lake) in Kayseri, Turkey, from 1987 to 2011 using satellite imagery techniques. Landsat Thematic Mapper images acquired in 1987, 2000, 2003, and 2011 were used in the analysis. The images were geometrically corrected by registering them to ground control points. The study area on each image was classified into seven information classes, i.e., water, watery ground, dry lake, mud/vegetated flats, salt flats, shrubs/sedges, and agriculture. The accuracies of the classifications were evaluated using a standard error matrix and kappa statistics. The analysis showed that the surface area of Tuzla Lake was highly variable during the 1987-2011 period. Lake surface area was the largest in 1987 and the smallest in 2003. Analysis of the climatic conditions for 4 years showed that the surface area of the lake is highly vulnerable to changes in precipitation and air temperatures

Trends in reference evapotranspiration in Turkey: 1975-2006

This study examines the trends in reference evapotranspiration (ETo) in Turkey by analysing data from 77 weather stations for a 32-year period (1975-2006). ETo values were calculated using the Penman-Monteith method using air temperature, wind speed, relative humidity, and sunshine hours data. Trends in annual and monthly ETo were determined using the Mann-Kendall trend test with the trend-free prewhitening procedure. The magnitude of trends was estimated by calculating the Sen's slope. The collective or field significance of the trends was evaluated using Walker test. The possible causes of changes in ETo were discussed by analysing the trends in air temperature, wind speed, relative humidity, and solar radiation data collected at the same stations. The implications of ETo trends for crop water requirements were evaluated. The analyses showed that the majority of stations (88%) in Turkey had annual ETo between 750 and 1200 mm during the 32-year period and ETo decreased gradually from south to north. From 1975 to 2006, 58% of stations had upward trends in annual ETo. Upward trends were statistically significant at the 0.05 level for 32% of stations. The rates of changes in annual ETo were on average 1.20 mm year(-2). The trends detected in monthly ETo were mostly upward with an average magnitude between -0.01 and 0.14 mm month year(-1). Trends detected at the annual timescale and for the majority of the months provided the field significance at the 0.05 level. Analysis of other climatic data showed that upward trends in air temperatures, downward trends in wind speeds, and downward trends in relative humidity were widespread over Turkey for the same time period. Changes in these three parameters could explain the majority of the changes in ETo rates. The ETo changes affect crop water requirements and increase the demand for irrigation

Discovery of hydrometeorological patterns

Hydrometeorological patterns can be defined as meaningful and nontrivial associations between hydrological and meteorological parameters over a region. Discovering hydrometeorological patterns is important for many applications, including forecasting hydrometeorological hazards (floods and droughts), predicting the hydrological responses of ungauged basins, and filling in missing hydrological or meteorological records. However, discovering these patterns is challenging due to the special characteristics of hydrological and meteorological data, and is computationally complex due to the archival history of the datasets. Moreover, defining monotonic interest measures to quantify these patterns is difficult. In this study, we propose a new monotonic interest measure, called the hydrometeorological prevalence index, and a novel algorithm for mining hydrometeorological patterns (HMP-Miner) out of large hydrological and meteorological datasets. Experimental evaluations using real datasets show that our proposed algorithm outperforms the naive alternative in discovering hydrometeorological patterns efficiently

ASSOCIATIONS BETWEEN STREAM FLOW AND CLIMATIC VARIABLES AT KIZILIRMAK RIVER BASIN IN TURKEY

This study aims to demonstrate the use of association analysis for discovering the relationships between stream flow and climatic variables in the Kizilirmak River Basin in Turkey. Association analysis is a data mining technique that aims to discover rules in the form of A -> B that may occur in large datasets with frequency above a given threshold. A and B can be defined as events of a certain type, with the rule if A occurs then B occurs. In this study, A refers to climatic variable(s) (i.e., precipitation, temperature, wind speed, relative humidity) of certain magnitude, and B refers to the magnitude of stream flow. The interesting rules were quantified using support and confidence measures. Stream-flow data from three gauging stations in the Kizilirmak River Basin and climate data from three weather stations in the same basin were included in the analyses. All data were first segregated into three groups that were named as low, medium, and high. Low and high ranges of stream-flow data were further divided into three to increase our focus on extreme events. The analyses were conducted at the annual and seasonal timescales. The analyses indicated that the relationships between precipitation and temperature and stream flow are most prevalent but, relative humidity and wind speed are also important determinants of stream flow in the Kizilirmak River Basin

Impact of Natural Organic Matter Competition on the Adsorptive Removal of Acetochlor and Metolachlor from Low-Specific UV Absorbance Surface Waters

Although activated carbon adsorption is a very promising process for the removal of organic compounds from surface waters, the removal performance for nonionic pesticides could be adversely affected by co-occurring natural organic matter. Natural organic matter can compete with pesticides during the adsorption process, and the size of natural organic matter affects the removal of pesticides, as low-molecular-weight organics directly compete for adsorbent sites with pesticides. This study aims to investigate the competitive impact of low-molecular-weight organics on the adsorptive removal of acetochlor and metolachlor by four commercial powdered activated carbons. The adsorption features of selected powdered activated carbons were evaluated in surface water samples collected from the influent stream of the filtration process having 2.75 mg/L organic matter and 0.87 L/mg-m specific UV absorbance. The adsorption kinetics and capacities were examined by employing pseudo-first-order, pseudo-second-order, and intraparticle diffusion kinetic models and modified Freundlich and Langmuir isotherm models to the experimental data. The competitive removal of acetochlor and metolachlor in the presence of natural organic matter was evaluated for varied powdered activated carbon dosages on the basis of UV and specific UV absorbance values of adsorbed organic matter. The adsorption data were well represented by the modified Freundlich isotherm, as well as pseudo-second-order kinetics. The maximum organic matter adsorption capacities of the modified Freundlich isotherm were observed to be 120.6 and 127.2 mg/g by Norit SX Ultra and 99.5 and 100.6 mg/g by AC Puriss for acetochlor- and metolachlor-containing water samples, respectively. Among the four powdered activated carbons, Norit SX Ultra and AC Puriss provided the highest natural organic matter removal performances with 76 and 72% and 71 and 65% for acetochlor- and metolachlor-containing samples, respectively. Similarly, Norit SX Ultra and AC Puriss were very effective for adsorbing aromatic organics with higher than 80% specific UV absorbance removal efficiency. Metolachlor was almost completely removed by higher than 98% by Norit SX Ultra, Norit SX F Cat, and AC Puriss, even at low adsorbent dosages. However, an adsorbent dose of 100 mg/L and above should be added for all powdered activated carbons, except for Norit SX F Cat, for achieving an acetochlor removal performance of higher than 98%. The competition between low-molecular-weight organics (low-specific UV absorbance) and acetochlor and metolachlor was more apparent at low adsorbent dosages (10-75 mg/L)