Zuhal Akyürek

Middle East Technical University

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Semih Kuter

Çankırı Karatekin University

AÜ

A. Ünal Şorman

Middle East Technical University

Ali Arda Şorman

Eskisehir Technical University

AŞ

Aynur Şensoy

Eskisehir Technical University

Ahmet Emre Tekeli

Çankırı Karatekin University

ÇH

Çağrı Hasan Karaman

Middle East Technical University

S. Sürer

Middle East Technical University

Gerhard‐Wilhelm Weber

University of Vienna

Mustafa Berkay AKPINAR

Middle East Technical University

Juraj Párajka

TU Wien

Cooperative Institutions

Middle East Technical University

Çankırı Karatekin University

Forschungszentrum Jülich

Hacettepe University

Helmholtz Centre for Environmental Research

University of Potsdam

Institut de Ciències del Mar

Norsk Hydro (Germany)

Universitat Politècnica de València

University of Arizona

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Modelling the temporal variation in snow-covered area derived from satellite images for simulating

Hydrological Sciences Journal (2005)

Ahmet Emre Tekeli Zuhal Akyürek Aynur Şensoy Ali Arda Şorman A. Ünal Şorman

Monitoring the change of snow-covered area (SCA) in a basin is vitally important for optimum operation of water resources, where the main contribution comes from snowmelt. A methodology for obtaining the depletion pattern of SCA, which is based on satellite image observations where mean daily air temperature is used, is applied for the 1997 water year and tested for the 1998 water year. The study is performed at the Upper Euphrates River basin in Turkey (10 216 km2). The major melting period in this basin starts in early April. The cumulated mean daily air temperature (CMAT) is correlated to the depletion of snow-covered area with the start of melting. The analysis revealed that SCA values obtained from NOAA-AVHRR satellite images are exponentially correlated to CMAT for the whole basin in a lumped manner, where R 2 values of 0.98 and 0.99 were obtained for the water years 1997 and 1998, respectively. The applied methodology enables the interpolation between the SCA observations and extrapolation. Such a procedure reduces the number of satellite images required for analysis and provides solution for the cloud-obscured images. Based on the image availability, the effect of the number of images on the quality of snowmelt runoff simulations is also discussed. In deriving the depletion curve for SCA, if the number of images is reduced, the timing of image analysis within the snowmelt period is found very important. Analysis of the timing of satellite images indicated that images from the early and middle parts of the melt period are more important.

Snowmelt

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A machine learning-based accuracy enhancement on EUMETSAT H-SAF H35 effective snow-covered area product

Remote Sensing of Environment (2022)

Semih Kuter Kenan Bolat Zuhal Akyürek

10.1016/j.rse.2022.112947

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Snow Cover Detection Over Forested and Mountainous Regions from Sentinel-2 Imagery Using Deep Learning Techniques

Sadettin Özen Semih Kuter Zuhal Akyürek

Monitoring the amount of snow in mountainous and forested areas is crucial for many ecological, environmental, and climatic research projects. Due to their poor coverage and accuracy, traditional techniques for detecting snow cover frequently encounter difficulties in these difficult terrains. Deep learning methods have been making waves in recent years, revolutionizing remote sensing applications and opening up a promising new path for precise and effective snow cover detection. This research uses Sentinel-2 multispectral imagery to provide a thorough analysis of the use of deep learning techniques for snow cover detection over mountainous and forested areas. The study processes multi-band satellite imagery using cutting-edge deep learning techniques. Our findings demonstrate how deep learning models are performing better. Notably, our model scores 0.805 on a dice set with acquisition time variability, demonstrating its resilience in the face of difficult circumstances. Furthermore, on an independent test set, our model obtains a dice score of 0.928, indicating its efficacy in precisely defining the extent of snow cover. In addition, this article discusses transfer learning strategies that minimize the requirement for a large amount of labeled data by fine-tuning pre-trained deep learning models on large-scale datasets for snow cover detection. To sum up, the use of Sentinel-2 imagery and customized band combinations in conjunction with deep learning techniques to detect snow cover over mountainous and forested areas is an improvement over previous remote sensing methods.

10.2139/ssrn.4804007

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Mesocosm Design and Implementation of Two Synchronized Case Study Experiments to Determine the Impacts of Salinization and Climate Change on the Structure and Functioning of Shallow Lakes

Water (2023)

Korhan Özkan Mustafa Korkmaz Cihelio Alves Amorim Gültekin Yılmaz Meltem Koru

Salinization of freshwater ecosystems is one of the major challenges imposed largely by climate change and excessive water abstraction for irrigated crop farming. Understanding how aquatic ecosystems respond to salinization is essential for mitigation and adaptation to the changing climate, especially in arid landscapes. Field observations provide invaluable data for this purpose, but they rarely include sufficient spatial and temporal domains; however, experimental approaches are the key to elucidating complex ecosystem responses to salinization. We established similar experimental mesocosm facilities in two different climate zones in Turkey, specifically designed to simulate the effects of salinization and climate change on shallow lake ecosystems. These facilities were used for two case-study experiments: (1) a salinity gradient experiment consisting of 16 salinity levels (range: 0–50 g/L); and (2) a heatwave experiment where two different temperature regimes (no heatwave and +6 °C for two weeks) were crossed with two salinity levels (4 and 40 g/L) with four replicates in each treatment. The experiments lasted 8 and 2 months, respectively, and the experimental mesocosms were monitored frequently. Both experiments demonstrated a significant role of salinization modulated by climate on the structure and function of lake ecosystems. Here, we present the design of the mesocosm facilities, show the basic results for both experiments and provide recommendations for the best practices for mesocosm experiments conducted under saline/hypersaline conditions.

Mesocosm

Freshwater ecosystem

10.3390/w15142611

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Anadolu Platosundan Amerika Ovalarına: EUMETSAT H SAF'ın Yeni Nesil Kar Suyu Eşdeğeri Ürününün Türkiye ve ABD Üzerindeki Kıtalararası Değerlendirmesi

Anadolu Orman Araştırmaları Dergisi (2023)

Semih Kuter Çağrı Hasan Karaman Mustafa Berkay AKPINAR Zuhal Akyürek

The main frame of this paper is to present the first validation results of the new generation hemi-spherical daily snow water equivalent (SWE) product of the EMUTSAT H SAF, namely, H65. It utilizes data from passive microwave radiometry sensors to estimate SWE. Operating at a suitable spatial scale, it offers insights into snow accumulation and melting dynamics, advancing satellite-based snow monitoring across diverse regions. The validation period covers the 2021 snow year, from January to March 2023, during which the dry snow conditions prevail. The validation is conducted in two distinct geographic regions, Turkey, and the conterminous U.S. For Turkey, the in-situ snow depth measurements provided by the Turkish State Meteorological Service are employed. On the other hand, the 1-km gridded SWE dataset of NOAA National Ice and Snow Data Center is used in the validation over the U.S. The validation results over Turkey yields an overall RMSE of 39.27 mm, whereas it reads 15.19 mm for the U.S. These results indicate that the H65 SWE product complies with the product requirement thresholds for both flat (40 mm) and mountainous (45 mm) areas.

Water equivalent

10.53516/ajfr.1347158

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A multi-technique approach to determine temporal and spatial variability of groundwater-stream water exchange

Hydrological Processes (2020)

Kasımcan Koruk Koray K. Yılmaz Zuhal Akyürek Andrew Binley

Characterizing the spatio-temporal distribution of groundwater–surface water (GW–SW) exchange fluxes is of paramount importance in understanding catchment behavior. A wide range of field-based techniques are available for such characterization. The objective of this study is to quantify the spatio-temporal distribution of the exchange fluxes along the Çakıt stream (Niğde, Turkey) through coupling a set of geophysical techniques and in-stream measurements in a hierarchical manner. First, geological and water quality information were combined at the catchment scale to determine key areas for reach-scale focus. Second, electromagnetic induction (EMI) surveys were conducted along the reach to pinpoint potential groundwater upwelling locations. EMI anomalies guided our focus to a 665 m-long reach of the stream. Along this selected reach, a fibre-optic distributed temperature sensing (FO-DTS) system was utilized to investigate streambed temperature profiles at fine spatial and temporal scales. Furthermore, vertical hydraulic gradients and exchange fluxes were investigated using nested piezometers and vertical temperature profiles, respectively, at two potential upwelling locations and a potential downwelling location identified by previous surveys. The results of the study reveal heterogeneity of vertical water-flow components with seasonal variability. The EMI survey was successful in identifying a localized groundwater upwelling location. FO-DTS measurements revealed a warm temperature anomaly during cold air temperature and low streamflow conditions at the same upwelling site. Our point-based methods, namely vertical temperature profiles and vertical hydraulic gradient estimates, however, did not always provide consistent results with each other and with EMI and FO-DTS measurements. This study, therefore, highlights the opportunities and challenges in incorporating multi-scale observations in a hierarchical manner in characterization of the GW–SW exchange processes that are known to be highly heterogeneous in time and space. Overall, a combination of different methods helps to overcome the limitations of each single method and increases confidence in the obtained results.

Downwelling

Piezometer

10.1002/hyp.13754

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Citations (7)

FRACTIONAL SNOW COVER MAPPING BY ARTIFICIAL NEURAL NETWORKS AND SUPPORT VECTOR MACHINES

ISPRS annals of the photogrammetry, remote sensing and spatial information sciences (2017)

Bahar Çiftçi Semih Kuter Zuhal Akyürek Gerhard‐Wilhelm Weber

Abstract. Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes. Hence, snow cover mapping with high accuracy is necessary to have a real understanding for present and future climate, water cycle, and ecological changes. This study aims to investigate and compare the design and use of artificial neural networks (ANNs) and support vector machines (SVMs) algorithms for fractional snow cover (FSC) mapping from satellite data. ANN and SVM models with different model building settings are trained by using Moderate Resolution Imaging Spectroradiometer surface reflectance values of bands 1–7, normalized difference snow index and normalized difference vegetation index as predictor variables. Reference FSC maps are generated from higher spatial resolution Landsat ETM+ binary snow cover maps. Results on the independent test data set indicate that the developed ANN model with hyperbolic tangent transfer function in the output layer and the SVM model with radial basis function kernel produce high FSC mapping accuracies with the corresponding values of R = 0.93 and R = 0.92, respectively.

Land Cover

Moderate-resolution imaging spectroradiometer

Kernel (algebra)

10.5194/isprs-annals-iv-4-w4-179-2017

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Citations (15)

Validation of the operational MSG-SEVIRI snow cover product over Austria

S. Sürer Juraj Párajka Zuhal Akyürek

Abstract. The objective of this study is to evaluate the mapping accuracy of the MSG-SEVIRI operational snow cover product over Austria. The SEVIRI instrument is on board of the geostationary Meteosat Second Generation (MSG) satellite. The snow cover product provides 32 images per day with a relatively low spatial resolution of 5 km over Austria. The mapping accuracy is examined at 178 stations with daily snow depth observations and compared with the daily MODIS combined (Terra + Aqua) snow cover product in the period April 2008–June 2012. The results show that the 15 min temporal sampling allows a significant reduction of clouds in the snow cover product. The mean annual cloud coverage is less than 30% in Austria, as compared to 52% for the combined MODIS product. The mapping accuracy for cloud-free days is 89% as compared to 94% for MODIS. The largest mapping errors are found in regions with large topographical variability. The errors are noticeably larger at stations with elevations that differ much from those of the mean MSG-SEVIRI pixel elevations. The median of mapping accuracy for stations with absolute elevation difference less than 50 m and more than 500 m is 98.9% and 78.2%, respectively. A comparison between the MSG-SEVIRI and MODIS products indicates an 83% overall agreement. The largest disagreements are found in alpine valleys and flatland areas in the spring and winter months, respectively.

Elevation (ballistics)

10.5194/hessd-10-12153-2013

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Citations (2)

Using Cosmic-ray Neutron Probes in validating the Satellite Soil Moisture Products and Land Surface Models

EGU General Assembly Conference Abstracts (2019)

Mustafa Berk Duygu Zuhal Akyürek

Neutron monitor

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Evaluating The Utility Of The ANSA Blended Snow Cover Product In Obtaining Snow Cover Area Depletion Curves