Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem

The response of an ecosystem to external drivers may not always be gradual and reversible. Discontinuous and sometimes irreversible changes, called 'regime shifts' or 'Critical transitions', can occur. The likelihood of such shifts is expected to increase for a variety of ecosystems, and it is difficult to predict how close an ecosystem is to a critical transition. Recent modelling studies identified indicators of impending regime shifts that can be used to provide early warning signals of a critical transition. The identification of such transitions crucially depends on the ability to monitor key ecosystem variables, and their success may be limited by lack of appropriate data. Moreover, empirical demonstrations of the actual functioning of these indicators in real-world ecosystems are rare. This paper presents the first study which uses remote sensing data to identify a critical transition in a wetland ecosystem. In this study, we argue that a time series of remote sensing data can help to characterize and determine the timing of a critical transition. This can enhance our abilities to detect and anticipate them. We explored the potentials of remotely sensed vegetation (NDVI), water (MNDWI), and vegetation- water (VWR) indices, obtained from time series of MODIS satellite images to characterize the stability of a wetland ecosystem, Dorge Sangi, near the lake Urmia, Iran, that experienced a regime shift recently. In addition, as a control case, we applied the same methods to another wetland ecosystem in Lake Arpi, Armenia which did not experience a regime shift. We propose a new composite index (MVWR) based on combining vegetation and water indices, which can improve the ability to anticipate a critical transition in a wetland ecosystem. Our results revealed that MVWR in combination with autocorrelation at-lag-1 could successfully provide early warning signals for a critical transition in a wetland ecosystem, and showed a significantly improved performance compared to either vegetation (NDVI) or water (MNDWI) indices alone.Peer reviewe

Multitemporal Change Detection On Urmia Lake And Its Catchment Area using Remote Sensing And Geographical Information Systems

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2014Thesis (M.Sc.) -- İstanbul Technical University, Instıtute of Science and Technology, 2014Göllerde ve barajlarda bulunan su rezervleri ve bu rezervlerin izlenmesi uzun yıllardır, yerel ve küresel ölçekte en önemli çevresel konulardan biri olmuştur. Su kaynakları ve havzalarındaki değişimlerin izlenmesi, bu kaynakların yönetimi ve doğru kullanımı açısından gereklidir. Su kaynakları ve özellikle göller, küresel ısınma, kuraklık ve artan dünya nüfusunun beraberinde getirdiği insan gereksinimleri nedeniyle önem kazanmaktadır. İnsan gereksinimleri için (içme suyu gibi) kaynak sağlaması dışında, bir göldeki su rezervi, Urmiye Gölü örneğinde olduğu gibi, bir ülkenin ekonomisine katkı sağlayan önemli bir kaynak da olabilmektedir. Klasik yöntemler kullanılarak göllerde yapılan ölçümler genellikle noktasal bazlı olup, küçük çalışma alanları ile sınırlı kalmaktadır. Bu durum göz önünde bulundurulduğunda, uzaktan algılama teknikleri özellikle geniş alanlara yönelik farklı parametreler ile bilgi ve haritalar üretilmesine imkan sağladığı için su kaynaklarının izlenmesi gibi pek çok farklı çalışmada kullanılmaktadır. Bu projenin amacı; uydu görüntüleri, saha ölçümleri ve meteorolojik verileri kullanarak Uzaktan Algılama ve CBS yöntemleriyle Urmiye Gölü ve civarında olan zamansal değişimleri analiz etmektir. Buna ek olarak göldeki değişimlerin olası  nedenlerini incelemek, meteorolojik parametrelerin zamansal analizlerini yapmak ve gölün kurumasını engellemeye yönelik bilimsel öneriler ortaya koymaktır. Urmiye Gölü, İran’ın kuzey batısında, Batı Azerbaycan ve Doğu Azerbaycan arasında yer almaktadır (N 37.5° E 45.5°). İran’ın en büyük içgölü olan Urmiye Gölü dünyada Lut Gölü’nden sonra aşırı tuzluluk oranına sahip ikinci göldür. Aynı zamanda bu göl özel bir canlı türü olan Urmiye Artemia’ya da ev sahipliği yapmaktadır. Artemia dünya çapında bilinen ve tuz göllerinde bulunan bir zooplanktonik organizmadır. Bu çalışmada yapılan analizlere göre, Urmiye gölü, 1995 yılında yaklaşık 5982 km² yüzey alana sahipken 2013 yılında yaklaşık 1852 km² yüzey alana kadar düşen göl, deniz seviyesinden 1250 m yükseklikte ve en fazla 16-20 m derinliğe sahip olup ortalama derinliği ise 6 m’dir. 1995 yılında eni max. 60 km., boyu ise max. 150 km. olarak ölçülmüştür. Göl 15 km uzunluğunda toprak yol ile kuzey ve güney olmak üzere iki parçaya ayrılmıştır. Bu yolun ortası 1500 m uzunluğunda bir köprü ile bağlanmış, köprü altından, bu iki bölüm arasındaki su geçişi sağlanmıştır. Urmiye Gölü havzası 51876 km² alana sahiptir. Havzada 80.000 adetten fazla kuyunun bulunması, birçok barajın kurulmuş olması, sıcaklık ve yağmur değişimleri, ve kuraklık gibi nedenlerden dolayı tuzluluk oranı, son yıllarda göl havzasını tehdit edecek şekilde artmıştır. Yapılan bu çalışmada son otuz yıl içerisinde gölün yaklaşık %70’inin kurumuş olduğu tespit edilmiştir. Göl ve havzasında meydana gelen doğal ve yapay değişikliklerin etkilerini iki önemli nokta ile açıklamak mümkündür. İlk olarak, gölü besleyen akarsular üzerinde özellikle 2000 yılı sonrasında kurulmuş olan çok sayıda baraj, göle akarsular tarafından taşınan su miktarını azaltmıştır. Ayrıca, göl havzasında bulunan çok sayıda kuyu ve bu kuyulardan özellikle tarımsal sulama amaçlı çekilen sular, yeraltı su seviyesinde değişimlere neden olmuştur. Göl çevresindeki istasyonlardan elde edilen meteorolojik veriler incelendiğinde ise sıcaklık artışı ve yağış azalması gözlemlenmiştir. Bazı araştırmalarda bu değişimlerin küresel ısınmadan kaynaklı olduğu belirtilmiş olmasına rağmen, göl ve çevresindeki insan kaynaklı müdahalelerin de bu değişimler üzerinde etkisi olduğu gözardı edilmemelidir. Belirtilen nedenler dolayısıyla göldeki su seviyesi ve yüzey alanı azalmaktadır. Bu durum göl suyunun tuzluluk oranının artmasına neden olmaktadır. Değişen koşullar nedeni ile Urmiye gölü flamingo gibi binlerce göçmen kuşa ve Urmiye Artemia’si gibi özel türlere artık ev sahipliği yapamaz hale gelmektedir. İkinci olarak, Urmiye Gölü’nde oluşan kuraklık, İran başta olmak üzere göl çevresinde yer alan ülkelerde ekosistem ve insan hayatı için tehlike yaratmaktadır. Urmiye Gölü’nün kuruması ile oluşan iklim değişiklikleri, insan ve doğal hayat üzerinde hastalık ve göç gibi olumsuz olaylara neden olmaktadır. Benzer problem ile Aral Denizi de karşı karşıya kalmış olup bu göl için gerekli tedbirlerin alınmamış olması nedeniyle gölün büyük kısmı artık kullanılamaz haldedir. Bu durum Aral Deniz'i ve çevresindeki ülkeler için önemli bir çevresel sorun haline gelmiştir. Aral Denizi örneği dikkate alınarak benzer problemlerin yaşanmaması adına Urmiye Gölünün koruma altına alınması  son derece önemlidir. Bu çalışma esas olarak Uzaktan Algılama ve Coğrafi Bilgi Sistemlerinin (CBS) entegrasyonu ile Urmiye Gölü’ndeki 1984 ve 2014 yılları arasındaki otuz yıl içerisinde zamansal değişimleri belirlemeyi hedeflemektedir. Uydu görüntüleri, meteorolojik veriler, GPS ölçümleri, barajlar, yeralti su kaynakları, nüfus değişikliği ve arazi kullanım haritaları Urmiye Gölü’ndeki değişimleri tespit etmek amacı ile kullanılmıştır.  Çalışmada 1984 yılı ve 2014 yılı arasında elde edilen toplam 95 uydu görüntüsüyle Urmiye Gölü yakınlarında kurulmuş olan 20 sinoptik meteorolojik istasyonun kaydettiği sıcaklık, yağış, ve nem gibi farklı meteorolojik veriler temin edilerek kullanılmıştır. Bu verilere ek olarak, Batı Azerbaycan ve Doğu Azerbaycan bölgelerine ait arazi kullanım haritaları aracılığıyla, nüfus, yeraltı su kaynakları ve barajlar gölün durumunun genel değerlendirilmesi için kullanılmıştır. Çalışmanın ilk aşamasında uydu görüntüsü olarak kullanılan veri seti oluşturulmuştur. USGS arşivindeki Landsat-4, -5 TM ve Landsat-8 uydularına ait farklı yılların aynı aylarında ve mevsimlerinde elde edilmiş, düşük bulut etkisi gözlenen en iyi verilerin olduğu görüntüler seçilmiştir. Daha sonra1984-yaz, 1987-bahar, 1987-yaz, 1990-yaz, 1995-yaz, 1998-bahar, 1998-yaz, 2000-yaz, 2006-yaz, 2007-bahar, 2007-yaz, 2009-yaz, 2010-yaz, ve 2011-yaz görüntülerini içeren Landsat-5 TM uydu verileri ile 2013-bahar, 2013-yaz, ve 2014-kış Landsat-8 verileri ve 2011-bahar, ve 2012-yaz mevsimlerini içeren DMC verileri seçilerek veri seti oluşturulmuştur. Sonuç olarak, toplamda 1984-2014 yılları arasında 95 adet uydu görüntüsü ile çalışılmıştır. Görüntü ön işlemenin ilk aşamasında Landsat-5 TM uydusunun 1, 2, 3, 4, 5, 7 bantları ve Landsat-8 uydusunun 1, 2, 3, 4, 5, 6, 7 bantları birleştirerek görüntü oluşturulmuştur. İkinci aşamada görüntülerde piksellerin parlaklık değerlerinde meydana gelen hatalar ve atmosferik koşullardan meydana gelen bulut etkisini düşürmek için radyometrik ve atmosferik düzeltmeler yapılmıştır.  Görüntü ön işlemesi bittikten sonra, çalışma alanını kapsamak için 6 görüntü mozaiklenmiş ve alanı kapsayan tek bir görüntü oluşturulmuştur. Bu çalışmadaki amaçlardan bir tanesi Urmiye gölünün yüzey alanında meydana gelen değişikliklerin belirlenmesi için en uygun ve en doğru yöntemi ortaya koymaktır. Bu amaçla, görüntüler kontrollü ve kontrolsüz sınıflandırma yöntemleri kullanılarak sınıfandırılmış ve son 30 yıllık periyotta göl ve çevresinde meydana gelen değişimler karşılaştırılmıştır. Yapılan Doğruluk analizlerine göre kontrollü sınıflandırma ile daha iyi sonuçlar elde edilmiştir. Bu nedenle gölün yüzeyinde meydana gelen değişimlerin tespiti için kontrollü sınıflandırma sonuçları kullanılmıştır. Bu sonuçlara göre gölün su yüzey alanı 1995 yılında yaklaşık 5982 km² iken 2013 yılında yaklaşık 1852 km² olarak hesaplanmıştır. Aynı zamanda bu çalışmada su yüzey alanını, kıyı boyunca su kütlelerini ve sulu olmayan kütleleri ayırmak için NDVI (Normalized Difference Vegetation Index) ve MNDWI (Modified Normalized Difference Water Index) kullanılmıştır ve bu indislerden elde edilen sonuçlar kıyaslanmıştır. Gölün su yüzey alanı 1995 ve 2006 yıllar arasında yaklaşık 2000 km² azalmıştır, bu %32 oranında bir kurumanın meydana geldiğini göstermektedir. Bu tarihten sonra, 2006 ve 2013 yıllar arasında da gölün su yüzey alanı 2000 km² azalmıştır ve kontrollü sınıflandırma sonuçlarına gore gölün su yüzey alanı 2013 yılında 1853 km² bulunmuştur. Urmiye gölünün havzasında olan değişimleri tespit etmek için NDVI (Normalize Difference Vegetation Index), NDWI (Normalized Difference Water Index), NDSI (Normalized Differential Salinity Index), SI (Salinity Index) ve NDDI (Normalized Difference Drought Index) kullanılmıştır. Bulunan Sonuçlara göre 2006 yılı, 30 yıllık periyotta yüksek toprak tuzluluğu, en az NDVI, en az NDWI ve en şiddetli kuraklığa sahip olan yıldır.  2006 yılının tersine 1987 yılı düşük toprak tuzluluğu, yüksek NDVI, yüksek NDWI ve az kuraklığa sahip bir yıl olmuştur.  Meteorolojik verilerin analizine göre 2006 ve 2010 yılları, son yıllarin en sıcak yılları olmasına rağmen, bu yıllara ait olan yağış grafiklerine bakıldığında, son yıllara göre yüksek miktarda yağış artışı gözükmektedir. Jeoistatistik analizi ve SPI (Standard Preicitation Index) sonuçlarını dikkate alındığında 1999 ve 2010 yılları arasında kuraklık gözlemlenmekte fakat bu kuraklık yılların hepsini kapsamamaktadır. Örnek olarak 2003, 2004 ve 2007 yıllarında kuraklık tespit edilmemiştir.  Gölün havzasında bulunan su kaynaklarına göre , Kuzey ve Güney Azerbeycan da toplam 103 tane baraj bulunmaktadır. Bu barajlardan 56 tanesi Urmiye Gölü havzasında yer almaktadır. Bu barajların 14 tanesi 1970-1990, ve 10 tanesi 1990-2000 ve 32 tanesi ise 2000-2014 yılları arasında inşa edilmiştir. Bu barajlar, Urmiye gölü havzasında tarım alanlarının geliştirilmesinde önemli bir rol oynamaktadır.  1999 yılında 102966 hektar tarım alanı varken 2013 yılında tarım alanları 192648 hektara kadar ulaşmıştır. 2013 istatistiklerine göre Urmiye Gölü havzasındaki barajların yıllık taşıdığı toplam su miktarı 2060.30 milyon metreküp olup bunların 1320.28 milyon metreküpü yalnızca tarım faaliyetleri için kullanılmaktadır. 2013 yılında içme suyu tüketimi ise 389.04 milyon metreküptür. Bölgede 1985 yılından 2010’a yılına kadar nüfus 1.800.000 artış göstermiş ve buna bağlı olarak İrandaki su tüketimi dünya standartlarına oranla 2 kat artmıştır. Yeraltı suları, tarım arazileri için diğer temel su kaynağıdır. Yer altı sularının çekilme miktarı, 1984-1985 yılları arasında 1534 milyon metreküpken, 2011-2012 yılları arasında 2156 milyon metreküptür. Örnek olarak yalnızca 1998-1999 yılları arasında yer altı suları çekilmesi 400 milyon metreküp artmıştır. Ulaşılabilir kaynaklar doğrultusunda 2012 yılında Urmiye Gölü havzasında toplam 74336 adet orta-derin kuyu ve 8047 adet derin kuyu bulunmaktadır. Sonuç olarak, gölün giriş suyunu temin eden kaynaklara baktığımızda, gölün havzasında olan Cığatı (Zarrinerood), Tatau (Siminerood), Soyuk Bulak Çay (Mahabad), Gadar Çay, Baranduz Çay, Şehir Çay, Roze Çay, Nazlı Çay, Zola Çay, Tesuc Çay, Acı Çay, ve Sufi Çay Urmiye Gölü’nün yaklaşık %75 giriş suyunu sağlamaktadır. Kalan %25 giriş suyu yağış, yeraltı suları ve diğer kaynaklara bağlıdır. Urmiye Gölü havzasında nüfusun artması, çok sayıda barajın yapılması, yeraltı sularının çekilmesi ve tarımsal arazının çoğalması göz önüne alındığında bölgede meydana gelen değişikliklerde iklim etkisinden daha çok insan etkisi olduğu tespit edilmiştir. Urmiye Gölü ve havzasında değişimlerin takibi için başarılı bir izleme sistemi kurulması noktasında Uzaktan algılama ve CBS entegrasyonu büyük bir önem taşımaktadır.  Bu çalışmada DMC uydu görüntüleri, İstanbul Teknik Üniversitesi (İTÜ BAP: 37016) tarafından sağlanmıştır. Landsat görüntüleri Amerika Birleşik Devletleri Jeolojik Araştırmalar sitesinin veritabanından indirilmiştir. Ayrıca meteorolojik veriler Batı Azerbaycan Meteoroloji Dairesi ve Doğu Azerbaycan Meteoroloji Dairesi’nden temin edilirken arazi haritaları da Ulusal Kartoğrafya Merkezi'nden alınmıştır. Bu çalışmada ERDAS IMAGINE 2011 ve 2013, Arcgis 10 ve 10.1,  Envi 5 ve SPI_SL_6.exe programları kullanılmıştır.Different types of environmental sources, especially water bodies play a crucial role in human life and economy. Nowadays, the significance of water bodies, especially fresh water sources like lakes is increasing since these sources are being threatened due to global warming, drought and human needs. In addition to serving as supply for human needs such as irrigation and drinking water, a water reserve in a lake and its catchment area can also be important source contributing to country’s economy and policy like the case of Urmia Lake in Iran. Urmia Lake is located in the northwest of Iran between West Azerbaijan and East Azerbaijan provinces (N 37.5° E 45.5°). Its catchment area is about 51876 km² and it is the largest inland lake of Iran and the second largest hypersaline lake in the world after Dead Sea and the habitat of Artemia Urmiana which is a unique bisexual Artemia Species. The brine shrimp Artemia is a zooplanktonic organism found in hypersaline habitats such as inland salt lakes, coastal salt pans and manmade saltworks worldwide.  Urmia Lake is divided into 2 parts including north and south parts separated by a causeway which has about 1500 m bridge allows a little water exchange between 2 parts. Due to the establishment of different dams on contrary rivers which supply Urmia Lake’s water, establishment of more than 80,000 wells in Urmia Lake’s catchment area, increased demands for irrigation in the Lake’s basin, temperature and precipitation changes, and drought, the salinity of the lake has risen remarkable during recent years, and about 70% of the lake’s area is drought. There are two important points that should be emphasized for the temperature and precipitation changes impacts on Urmia Lake and its vicinity.  Firstly, the annual amount of water the lake receives has significantly decreased as a result of establishment of dams, wells, and drought. This in turn has increased the salinity of the lake’s water, lowering the lake viability as home to thousands of migratory birds including the large flamingo populations and diminishing other assets especially Artemia Urmiana.  Secondly, it is also important to consider the results of drying Urmia Lake and its risks on human life and ecosystem in Iran and neighbor countries of Urmia Lake. Drying of Urmia Lake will impact the local and regional climate of the area and this will have severe impacts on human and environment. Hotter temperature values and water shortage as a result of complete drying of Urmia Lake may even cause diseases and migration of local people. A similar example to Urmia Lake case is Aral Sea and its vicinity, therefore lessons learned from the Aral Sea case should be taken into account for the protection of Urmia Lake.  This study focuses mainly on multi-temporal change detection on Urmia Lake and its catchment area by integration of remote sensing and geographical information systems for a thirty year period from 1984 to 2014. In addition to satellite images, meteorological data, GPS measurements, landuse maps and ground photographs were analyzed to investigate the changes on Urmia Lake and understand the causes of this environmental problem including the role and effects of  human and global warming. A total number of 95 Landsat-5 TM, Landsat-8, and DMC images obtained between 1984 and 2014 were used in this study. Also, different meteorological variables like temperature, precipitation, humidity which has been measured at 20 synoptic stations around Urmia Lake were used to interpret meteorological impacts during last years. Moreover, data collected from different sources like Landuse maps of West Azerbaijan and East Azerbaijan provinces, control points, population, dams, underground water resources were used in this study to analyze the human and climate induced impacts on drying of Urmia Lake. After preprocessing steps, 6 frames, which have taken between 1984 and 2014 are mosaiced to output study area including Urmia Lake and its catchment area. Then, Unsupervised classification and supervised classifications were done on output information and compare the changes which have been occurring during the last 30 years. According to the results of the accuracy assessment process, the overall classification accuracy and overall Kappa statistics using the supervised classification method were shown to be better than the unsupervised classification for every time period except the summer of 2011. Therefore, to analyze the water surface area of Urmia Lake using supervised classification was determined to be better than unsupervised classification. The minimum and maximum water surface areas are about 1852 (2013) and 5982 (1995) km². The water surface area of Urmia Lake decreased nearly 2000 km²  from 5982 km² in 1995 to 4058 km²  in 2006. In other words,  32% of Urmia Lake dried up during the period of 1995 until 2006. It then decreased another 2000 km² from 4058 km² in 2006 to 1852 km² in 2013.  To analyze Urmia Lake’s catchment area and change detection in Urmia Lake’s vicinity, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Differential Salinity Index (NDSI), Salinity Index (SI), and Normalized Difference Drought Index (NDDI) are used. According to the results of these indexes, 2006 can be considered as a year with the  highest soil salinity value, least NDVI, least NDWI, and most severe drought conditions. 1987 can be considered as the year with the lowest soil salinity value, highest NDVI, highest NDWI, and least drought condition. The salinity of soil and water bodies has been  increased in all parts of the study area during recent years especially in south and east parts of Urmia Lake. The air temperatures in 2006 and 2010 were the warmest while following years cooled down. In 2006 and 2010 the high temperatures were also years of increased precipitation compared to other years. By considering the results of geostatistical analysis and Standard Precipitation Index (SPI), the meteorological analysis showed changes toward a dry climatic condition from 1999 to 2010 but these changes were not regular and some years like 2003, 2004 and 2007 had normal climatic condition.  There are, in total, 103 dams in the West Azerbaijan and East Azerbaijan provinces. Of these dams 56 are located in Urmia Lake’s catchment area. 14 dams were established between 1970 and 1990, and 10 dams were made from 1990 to 2000, and 32 dams were built from 2000 to 2014. Moreover, there are additional dams which are under construction or in the study stage. These dams play a critical role in developing agriculture areas in Urmia Lake’s catchment area, but it also means an increase in irrigation and water usage. The total cultivation area using dams supplied water was about 102966 Hectare in 1999 and it increased to 192648 Hectare in 2013. Annual adjustable water volumes of all dams in Urmia lake’s catchment area was about 2060.30 million m³ in 2013 while the annual agricultural water consumption was about 1320.28 million m³. According to these statistics, cultivation areas using water supplied from dams doubled from the periods of 1970-1999 until 1999-2013. Underground water sources which include deep wells, semi deep wells, aqueducts, and water fountains are another source that provides needed water for irrigation and agricultural developing.  Discharge water from underground water sources was 1534 million m³ during 1984 to 1985 with an increase to 2156 million m³ during 2011 to 2012. Moreover, discharge water from underground water sources increased by 400 million m³ alone from 1998 to 1999. According to the available statistics from underground water sources between 1972 and 2012, there are totally 74336 semi deep wells and 8047 deep wells in Urmia Lake’s catchment area in 2012.  By considering that rivers Jighati (Zarrinerood), Tatau (Siminerood), Soyugh Bulagh chay (Mahabad), Gadar chay, Baranduz chay, Shahar chay, Roze chay, Nazlu chay, Zola chay, Tasuj chay, Aji chay, and Sufi Chay rivers provide 75% of the inflow water to Urmia Lake while underground water sources, precipitation, and flood water provide 25% of the water inflow. When comparing this climate and nature controlled inflow sources to population and agricultural activities during recent years, it seems more probable that the primary reason of the drying of Urmia Lake must be human activities such as improper water and agricultural management in the catchment area.  A good water and agricultural monitoring and management program should be designed for Urmia Lake’s catchment area to rescue and recover the Urmia Lake. Remotely sensed data in conjunction with field survey would be a valuable asset for such monitoring program. In addition, GIS technology could be effectively used to conduct spatial and temporal analysis within the lake and its catchment in order to support the decision making process. The DMC satellite images used in this study were provided by Istanbul Technical University (ITU, BAP: 37016) and Landsat images were downloaded from United States Geological Survey website. Meteorological data were also provided by West Azerbaijan Meteorological and East Azerbaijan Meteorological Offices. Landuse maps are provided by National Cartographic Center of Iran. ERDAS IMAGINE 2011, ERDAS IMAGINE 2013, ArcGIS10, ArcGIS 10.1, Envi 5, and SPI_SL_6.exe programs were used in this study.Yüksek LisansM.Sc

Spatio-temporal modeling of groundwater quality deterioration and resource depletion

In Hydrogeology, the analysis of groundwater features is based on multiple data related to correlated variables recorded over a spatio-temporal domain. Thus, multivariate geostatistical tools are fundamental for assessment of the data variability in space and time, as well as for parametric and nonparametric modeling. In this work, three key hydrological indicators of the quality of groundwater-sodium adsorption ratio, chloride and electrical conductivity-as well as the phreatic level, in the unconfined aquifer of the central area of Veneto Region (Italy) are investigated and modeled for prediction purposes. By using a new geostatistical approach, probability maps of groundwater resource deterioration are computed, and some areas where the aquifer needs strong attention are identified in the north-east part of the study region. The proposed analytical methodology and the findings can support policy makers in planning actions aimed at sustainable water management, which should enable better monitoring of groundwater used for drinking and also ensure high quality of water for irrigation purposes

Water Resource Variability and Climate Change

Climate change affects global and regional water cycling, as well as surficial and subsurface water availability. These changes have increased the vulnerabilities of ecosystems and of human society. Understanding how climate change has affected water resource variability in the past and how climate change is leading to rapid changes in contemporary systems is of critical importance for sustainable development in different parts of the world. This Special Issue focuses on “Water Resource Variability and Climate Change” and aims to present a collection of articles addressing various aspects of water resource variability as well as how such variabilities are affected by changing climates. Potential topics include the reconstruction of historic moisture fluctuations, based on various proxies (such as tree rings, sediment cores, and landform features), the empirical monitoring of water variability based on field survey and remote sensing techniques, and the projection of future water cycling using numerical model simulations

Lake Catchment Interaction Analysis by Using Remote Sensing and GIS Techniques – the case study of Kolleru Lake, South India

Wetlands belong to the most productive ecosystem on Earth. They provide many essential services to humans. They play an important role and possess ecosystem services, for example, in biodiversity conservation, for the hydrologic cycle, to buffer regional climate change, and for human health. Among the different types of wetlands, lakes (lacustrine wetlands) play a crucial role in maintaining global and regional water balances, natural and socio-economic resources, and habitats. Over the last decades, the lakes have gone through enormous changes derived from both natural processes and anthropogenic activities. Particularly, freshwater lakes are endangered through point and non-point pollutions, and such impacts are coming from agricultural runoff and industrial pollution, domestic waste, through municipal sewage, which may deteriorate the water quality and their ecological integrity. The Kolleru Lake wetland ecosystem in South India has been taken here as a case study, based on a comprehensive data analysis and modeling of Spatio-temporal variability of the pollutant loads, to achieve a better understanding of the man-environmental problems of the lake and its surrounding catchment. This is a necessary requirement for both better management of the agricultural, industrial, and water resources in the whole area and better lake protection and conservation. Kolleru Lake is the largest freshwater lake in India. It is a huge natural flood balancing reservoir and also a wildlife sanctuary. In 2002, the Ramsar Convention recognized the lake as a wetland of international importance. The lake is predominately fed by rivers. Among them, Budameru and Tammileru rivers are contributing to the lake influx substantially, plus supported by 68 minor irrigation (drainage) canals. The Kolleru Lake covers a total area of more than 90,100 hectares and holding approximately 1,350 cubic miles of freshwater. Additionally, Kolleru Lake provides drinking water to the inhabitants of the surrounded villages. The lake area up to 3' ft contour is consistent with water, while the 5' ft contour level of the Kolleru Lake belongs to the wildlife sanctuary. Further, it is mostly occupied by aquacultures followed by paddy cultivation, weed infests, and marshy land. There are many small scales to large scale industries growing steadily in order to support successful aquaculture. Before the 1970s, the lake area up to 5' ft contour was not occupied by any type of economic activity; however, the lake is saturated with water during the rainy season, and it remains dry during summer. Furthermore, it was completely free from contamination by aquaculture and agricultural activities before the 1970s. After the 1970s, the State Government had distributed the Kolleru Lake up to 5'ft contour area the poor people, migrant workers, and local inhabitants in the promise of whenever the government again needs the lake area, and they can take it back by paying compensation to them. Then farmers have started paddy cultivation in and around the lake. All bed villages in the lake region are frequently severely affected by massive flooding in connection with the submersion of paddy fields. Despite the fact that the state Government had encouraged the farmers to convert the paddy fields into fishponds by providing loans in order to overcome the floods. However, the maximum of lake area up to wildlife sanctuary is practiced by the aquaculture in the 1990s. Since 1970 until the current situation, the lake has been facing some severe environmental threats, such as degraded water quality, deteriorated aqua species and birds, and habitat losses, induced by human activities and accelerated by climate change. A major cause of the environmental problems was identified within the lake by the construction of fishponds resulted in pollution by using pesticides and waste food (exposed to bacterial diseases and infection) to enrich the fish growth. As a result, it causes biological magnification diseases, fertility, and respiratory problems to the animals, birds, and humans who live near to the lake. Thereby the ecosystem will become an inhospitable environment for those aqua species and birds. The fish ponds occupied approximately 42% of the lake area while aquaculture had encroached another 8.5%, together covering 50% of the lake region. If the human-induced debasement of the lake will continue, the lake will no longer cease to exist, and the wildlife species soon will disappear. Apart from the aquaculture tradition, the Kolleru Lake catchment is known for its intensive paddy cultivation. However, the massive application of pesticides and chemical fertilizers to agricultural lands across the catchment area is one reason for the eutrophication in Kolleru Lake. In addition to the several factors that influence the lake ecosystem, industrial pollution causes deteriorating water quality and makes them unfit for drinking water for the inhabitants of the villages around the Kolleru Lake. Both point and non-point sources issued threatens to the lake area becomes more sensitive by anthropogenic activities. The main focus of the present research was to analyze the problems related to the lake catchment and give recommendations to the government about the insight view of the land use cover and enlighten the public perception towards the lake degradation. However, sedimentation in a lake is a natural consequence of the inflow of respected tributaries, rivers, and streams. In addition to the natural influence, man-made activities like land use and others are also responsible for erosion in the catchment and the sediment transport and accumulation of the sediments in both the lower sections of the catchment and the lake basin itself, as discussed in the first research objective. Extensive use of land and the indiscriminate rise of embankments for the construction of fishponds as well as agricultural functions has resulted in widespread soil erosion in the catchment and sedimentation over the deltaic part of the Kolleru Lake catchment. In addition, the perennial rivers of Krishna and Godavari drift down to the lake about 68,000 tons/yr of sediments that coming from the whole catchments after passage from the river banks and river beds. The objective of this part was to analyze both the average annual soil loss rate and its change from the catchment and the sediment yields by using the RUSLE model both for the terrestrial part and the semi-aquatic deltaic part of the Kolleru Lake catchment for the years 1972 and 2012. The results indicated that the average annual soil loss was estimated with 13.6 t/ha/yr, classifying the Kolleru Lake Basin under a very high erosion rate category. Whereas, the average annual sediment yield was determined with 7.61 t/ha/yr. The resultant difference of the sediment balance is temporally interbedded within the terrestrial sites and within the river banks and river beds. However, this study has found that tributaries and streamlines of the catchment carry high sediment loads to the lake. This research has proved how intensive agricultural activities in wetland catchments interact with the pollution levels of the lake, causing a deteriorated water quality. Agricultural runoff (runoff from catchment areas dominated by agricultural use) is the main driving factor of accumulated non-point source pollution of the lake water, with side-effects on sediments and silts near the downstream areas of the Kolleru Lake catchment. It primarily caused eutrophication in the lake subsequently that led to proliferating the weeds. However, the second objective of the research was to estimate the tributaries' sub-basin loads and to highlight the diffuse critical sources against the village communities. For this purpose, the Soil and Water Assessment Tool (SWAT) was used to model the diffuse sources in the catchment. The spatial distribution of nitrate-nitrogen (NO3-N) and total phosphorus (TP) emissions were quantified. Some sub-basins contribute more pollutant load to the lake. Alternately, the first and second BMPs (Best Management Practices) level priority areas were identified. Further, suggestions for the implementation of agricultural management practices have been provided for the crucial protection of the lake ecosystem. Consequently, the Kolleru Lake wetland ecosystem is known for its both abundant water availability as well as water scarcity. The river and streams water diverted into the agricultural lands, and still, there is a dire need for groundwater too. When the monsoon rain was weak, and after rainless summer periods, the lake falls more or less dry. Therefore there is a high demand for groundwater, which is continuously increasing. An effective way to analyze groundwater recharge and groundwater availability is a remote sensing and GIS based mapping. The theoretical concepts are involved in this objective is more useful for t further research of the link between surface emission and groundwater contamination. That is why the present research has been investigated as the third objective, the potential groundwater resources in the catchment. A simple mathematical equation was derived from the catchment hydrologic characteristics. The catchment characteristics were analyzed and based on the previous literature sources, and the thematic weight was assigned to evaluate potential groundwater zones. About 13% of the catchment area falls under poor conditions, 38% of the area falls under moderate conditions, 42% of the area falls under good conditions, and about 7% of the area is under excellent condition. These results are a contribution to future groundwater management projects and artificial recharge plans of the Kolleru Lake catchment to maintain sufficient groundwater levels. Due to the still existing lack of observed data of the tributaries, i.e., runoff, sediment, water quality parameters, nutrient load, the used methods are limited and suitable just for an estimation. Sufficient calibration and validation of the results were also limited because the access to the study area and to an onside research institute was not allowed for the Ph.D. candidate, because of its status as a Ph.D. student from Germany. Field investigations on the interaction of pollutant loads with the runoff would be advantageous for a better calculation of the pollutant load and its dynamic. Because of the limited funding capacity, it is challenging to do a field survey to control every remote sensing and GIS result of this research. That is why, without a few exceptions, this study was conducted dominantly based on remote sensing data and accessible weather and soil data. From the research results emphasized that the Kolleru Lake water level and water quality are highly degraded, respectively polluted with metals, agricultural contaminants, which makes the lake water not advisable for human consumption. The erosion and sedimentation loads are also high, and the priority management practices should be targeted already in the middle catchment region. These results give a general understanding of the pollutant levels in the lake, which should be useful for government management plans.

Representing local dynamics within water resource systems through a data-driven emulation approach

Growing population and socio-economic activities along with looming effects of climate change have led to enormous pressures on water resource systems. To diagnose and quantify potential vulnerabilities, effective tools are required to represent the interactions between limited water availability and competing water demands across a range of spatial and temporal scales. Despite significant progresses in integrated modeling of water resource systems, the majority of existing models are still unable to fully describe the contemplating dynamics within and between elements of water resource systems across all relevant scales and/or variables. Here, a data-driven approach is suggested to represent local details of a water resource system through emulating an existing water resource system model, in which these details have been missed. This is through advising a set of interconnected functional mappings, i.e. integrated emulators, parameterized using the simulation results of the existing model at a common scale and/or variable but can support process representation with finer resolution and/or details. The proposed approach is applied to a complex water resource system in Southern Alberta, Canada, to provide a detailed understanding of the system’s dynamics at the Oldman Reservoir, which is the key to provision of effective water resource management in this semi-arid and already stressed cold region. By proposing a rigorous setup/falsification procedure, a set of alternative hypotheses for emulators describing the local dynamics of local irrigation demand and withdrawals along with reservoir release and evaporation is developed. Findings show that emulators formed using Artificial Neural Networks mainly outperform simpler emulators developed for the variables considered. The non-falsified emulators are then coupled to represent the local dynamics of the water resource system at the reservoir location, considering the underlying interplays with hydro-climatological conditions and human decision on the irrigation area. It is found that emulators with input variables identified through expert knowledge can outperform fully data-driven emulators in which proxies were selected based on an input variable selection method. The top non-falsified coupled models are able to capture the dynamic of lake evaporation, water withdrawal, irrigation demand, reservoir release and storage with coefficient of determination of 0.80 to 0.82, 0.45 to 0.55, 0.52 to 0.59, 0.98 to 0.99 and 0.72 to 0.88, respectively. The practical utility of the proposed approach is demonstrated through an impact assessment study by analysing four performance criteria, corresponding to reservoir’s storage, local irrigation demand, number of spill events and median reservoir release, in three stress-tests. These stress tests asses the local sensitivity of water resource system at the Oldman reservoir at three different levels, corresponding to (1) changing incoming streamflow to the basin in a bottom-up approach; (2) joint scenario of changing streamflow and warming climate, using a coupled bottom-up/top-down approach; and (3) specific changes in incoming streamflow, climate and irrigation area in a heuristic approach. For the first experimentation, weekly realizations for possible water availability are stochastically reconstructed and fed into the top non-falsified integrated emulator. By defining warm/dry, historical and cold/wet flow conditions, we found through alteration from dry to wet regime condition, the expected number of low storage duration is not changed, and expected annual water deficit is declined. Moreover, the expected number of spill events increases whereas median reservoir release increases. In the next impact assessment study, different scenarios of warming climate obtained from NASA-NEX downscaled global climate projections and the joint impact of changing streamflow and temperature on the system’s behaviour is evaluated. This assessment demonstrated that in warmer climate, the expected number of low storage duration in dry condition increases whereas in historical and wet conditions, the low storage duration does not change. In addition, the expected annual water deficit increases while the expected number of spill events decreases in the three flow regime conditions. Moreover, the expected median reservoir release increases in the dry, historical and wet regime conditions. In the final level of assessment, vulnerability of the system under changing streamflow, climate including temperature and precipitation and changing irrigation area is assessed. Results show that increasing irrigation area combined with declining inflow can considerably increase the duration of low reservoir storage in the Oldman Reservoir. Increasing temperature can lead to decline in both reservoir storage and outflow. In addition, when combined with declining inflow, increasing temperature can severely increase the annual water deficit for irrigation sector. Furthermore, it is noted that although the performance of unfalsified models are identical in representing the dynamics of the Oldman Reservoir under the historical data, but assessment can be slightly to moderately different depending on the defined scenarios of change. This is due to the choice of model configuration and can address the uncertainty regarding the system’s behaviour. Our study shows the promise of data-driven emulation approach as a tool for developing more enhanced water resource system models to face emerging management problems in the era of change

Remote Sensing of the Aquatic Environments

The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet

Geo-Environmental Approaches for the Analysis and Assessment of Groundwater Resources at Catchment-Scale

This book focuses on the tools and methods used for tackling the complexity of the different hydrological and hydrogeological set-ups, the hydrodynamic patterns, the site specifications, and the wide variability of internal and external factors and/or processes on the catchment-scale level that impose the need for combined integrated approaches of robust methods. This Special Issue aims to provide successful applications or new insights on the stand-alone or joint considerations of groundwater resources assessment and characterization methods and explore new state-of-the-art methodological concepts in light of a rapidly changing environment

Agro-hydrological modelling of regional irrigation water demand

The irrigation sector accounts for over 70% of the total freshwater consumption in the world. Therefore, e cient management of irrigation water is essential to ensure water, food, energy and environmental securities in a sustainable manner; these securities are grand challenges of the 21st century. The main objective of this research is to evaluate the simulation of irrigation water demand at the catchment scale in order to develop improved tools for conducting quantitative planning and climate change studies. Irrigation water demand is mostly driven by soil moisture. It is a state variable which is used to trigger the irrigation in hydrological models. In this study, a hydrolgical model (Soil and Water Assessment Tool, SWAT) is evaluated for reliably simulating the spatial and temporal patterns of soil moisture at a catchment scale. The SWAT simulated soil moisture was compared with the indirect estimates of soil moisture from Landsat and Time-domain re ectometry (TDR). The results showed that the SWAT simulated soil moisture was comparable with the soil moisture estimated from Landsat and TDR. Secondly, the applicability of the SWAT model was tested for simulating stream ow, evapotranspiration (ET) and irrigation water demand for four di erent agro-climatic zones (Mediterranean, Subtropical monsoon, Humid, and Tropical). Two di erent irrigation scheduling techniques were used to simulate irrigation namely, soil water de cit and plant water demand. It was seen from the results that the SWAT simulated irrigation amounts under soil moisture irrigation scheduling technique were close to the irrigation statistics provided by the state. However, the irrigation amounts simulated under the plant water demand irrigation scheduling technique were underestimated. Additionally, the two reanalysis data were also used to check the data uncertainty in simulating irrigation water demand. SWAT model code was modi ed by incorporating modi ed root density distribution function and dynamic stress factor. The modi ed model was used to simulate irrigation and crop yield. It was tested against the irrigation and crop yield simulated by Soil Water Atmosphere Plant (SWAP) model and eld data (Hamerstorf, Lower Saxony, Germany). It was then validated for di erent catchments (Germany, India and Vietnam). The results showed that the SWAT simulated irrigation water demand in case of plant water demand is comparable with the amount simulated by the model under soil water de cit irrigation scheduling technique. This dissertation not only bridges the gap between the scales of soil moisture determination but also establishes a close connection with the actual observations and modelled soil moisture and irrigation amounts at the eld, regional and global studies in agricultural water management. Additionally, the studies about simulating irrigation water requirement in data-scarce areas must address data uncertainty when using reanalysis data. It was found that rainfall is not always the dominant variable in irrigation simulation. Therefore, it is worth checking and bias correct the other climate variables