Change-point detection and trend analysis in monthly, seasonal and annual air temperature and precipitation series in Bartın province in the western Black Sea region of Turkey

Studies associated with climate change and variability are of great importance at both the global and local scale in the global climate crisis. In this study, change-point detection and trend analysis were carried out on mean, maximum, minimum air temperatures and total precipitation based on monthly, seasonal and annual scale in Bartın province located in the western Black Sea Region of Turkey. For this aim, 4-different homogenei-ty tests (von Neumann test, Pettitt test, Buishand range test and standard normal homogeneity test) for change-point detection, Modified Mann–Kendall test and Şen’s innovative trend test for trend analysis, and Sen’s slope test for the magnitude estimation of trends were used. According to the test results, the summer temperatures in particular show increasing trends at the 0.001 significance level. Mean maximum temperature in August, mean minimum temperature in June and August, and mean temperature in July and August are in increasing trend at the 0.001 significance level. Over a 51 year period (1965–2015) in Bartın province, the highest rate of change per decade in air temperatures is in August (0.55°C for Tmax, 0.46°C for Tmin and 0.43°C for Tmean) based on Sen’s slope. However, the study showed that apart from October precipitation, there is no significant trend in monthly, seasonal and annual precipitation in Bartın. Increasing trends in mentioned climate variables are also visually very clear and strong in Şen’s innovative trend method, and they comply with the statistical results. As a result, the study revealed some evidence that temperatures will increase in the future in Bartın and its environs

Assessment and Redesign of the Synoptic Water Quality Monitoring Network in the Great Smoky Mountains National Park

The purpose of this study was to assess and redesign an existing 83-site synoptic water quality monitoring network in the Great Smoky Mountains National Park. The study involved a spatial analysis of water quality data (pH, ANC, conductivity, chloride, nitrate, sulfate, sodium, and potassium), watershed characteristics (geology, morphology, and vegetation), and collocated site information to determine which sites were redundant and a temporal analysis to determine the effectiveness of the current sampling frequency to detect long-term trends. The spatial analysis employed a simulated annealing algorithm using the variable costs of the network and the results of multivariate data techniques to identify an optimized subset of the existing sampling sites based on a maximization of benefits. A second simulated annealing algorithm was created to identify optimum user-defined monitoring networks of n sites and to validate the results of the first simulated annealing program. The first simulated annealing program identified an optimized network consisting of 67 of the existing 83 sampling sites. The second simulated annealing algorithm bracketed the same 67 sites and also provided a basis for an ordered discontinuation of sampling sites by identifying the best ten-site monitoring network through the best 70-site monitoring network. The temporal analysis employed the “effective” sample method, Sen\u27s slope estimator, Mann-Kendall test for trend, and a boxplot analysis to determine the effectiveness and the power of the current sampling frequency to detect long-term trends. The results showed that the current sampling frequency of four samples per year presents a low statistical power for short historical records. However, increasing the v sampling frequency to more than 12 samples per year creates serial dependence between samples. By combining the results of the spatial and temporal analyses a new network is proposed by dividing the network into primary, secondary, and tertiary sites with sampling frequencies of six and 12 samples per year. Seventeen new sites are also proposed to collect additional data above 3000 feet MSL because the existing number of sampling sites is not proportional to park area in certain elevation ranges

A Benchmarking Framework for Sensitivity and Comparative Analysis of Energy Harvesting Strategies via Retractable Wind Energy Harvesters

Wind power is well known for being variable. Our main insight is that one can take advantage of variability by appropriately building wind-energy harvesters that may be stowed/retracted when winds are calm. We refer to harvesters that can be deployed and retracted on command as retractable wind-energy harvesters (RWEHs). Among other advantages, stowed harvesters do not block views, do not constrain avian life, and do not make noise, and thus can increase the neighborliness of harvesting wind near or within a residential community. RWEH control algorithms help owners to achieve the neighborliness that might be required by an RWEH hosting community while helping RWEHs' efficiency. The stowing requirements, or operation limitation agreements (OLAs), specify conditions when the retractable harvesters should be stowed (e.g., when it is not windy). In this work, we contribute a suite of benchmarks to compare RWEH control algorithms, three families of control algorithms, and a simulator with which to run the algorithms. The benchmark suite provides workloads formed from the following workload components: 1. specifications of a harvester to be controlled, 2. a set of historical windspeeds from 30 weather stations, and 3. a variety of stowing requirements. We derived OLAs from a survey of 304 respondents in which survey-takers were asked whether they would support RWEHs viewable from where they live and when the RWEHs should be hidden or stowed

HYDROLOGICAL AND WATER QUALITY ASSESSMENT OF A RAPIDLY URBANIZING SOUTHEASTERN PIEDMONT WATERSHED

The purpose of this dissertation research was to assess the change in hydrological and watershed processes influencing water quality in a rapidly urbanizing SE Piedmont watershed. Specifically, this dissertation research assessed the effectiveness of engineered stormwater control measures (SCMs) and stream restoration projects in a rapidly urbanizing watershed to maintain the pre development hydrologic and water quality regime in compliance with local stormwater and water quality regulations. The hydrologic and water quality benefits of a network of the existing engineered SCMs and alternative engineered SCMs that included distributed backyard rain-gardens and additional offline bio-retention basins were simulated in the most developed sub- watershed of the study watershed using the Model of Urban Stormwater Improvement Conceptualization (MUSIC). Model simulation results indicated that the post- development simulation with existing engineered SCMs network in comparison to without-engineered SCMs network lowered the annual load of total suspended sediment (TSS), total phosphorus (TP), and total nitrogen (TN) by 56.7%, 50.7%, and 9.5%, respectively. Model simulations indicated that mandatory 85% and 70% TSS and TP annual load reductions, respectively could be obtained by diverting runoff from 70% and higher of the contributing drainage area of the existing engineered SCMs into additional offline bio-retention basins. The effectiveness of the existing engineered SCMs network in maintaining the predevelopment runoff hydrology of five developing sub-watersheds (10% to 54% suburban development) was evaluated with the unit hydrograph, unit impulse response, and Mann-Kendall trend test approaches. The measured reduction in peakflow discharge and increase in direct runoff coefficient and runoff duration is attributed to the engineered SCMs in the most developed sub-watersheds, whereas little difference in runoff response could be attributed to the stream restoration projects. The three approaches applied to assess the change in hydrologic responses from different BDC sub-watershed provided similar results. Finally, a residual mass balance approach was applied to assess the in stream transport and retention dynamics of sediment, nutrients, and organic carbon (OC) in two restored and two unaltered or “natural” stream reaches of the study watershed during different flow regimes. The restored stream reaches indicated a net retention of TSS, N (PN, TN, TDN, and DON), P (TP and PP), and OC during baseflow monitoring periods. Whereas, the restored stream reaches exhibited a net export of TSS, NO3-N, TP, PP, and POC during storm events. The predominately forested and unaltered stream reach exhibited a net retention of ortho-P and a decline in per unit flux of most of the other water quality constituents during baseflow and storm runoff events. The suburban unaltered stream reach with significant engineered SCMs indicated the downstream mobilization of most of the water quality constituents during baseflow and storm events. Overall, this dissertation provided a comprehensive assessment of the alterations of the hydrological and biogeochemical processes in an urbanizing SE Piedmont watershed and an assessment of the effectiveness of current Stormwater Control and Stream Restoration practices through stormwater modeling, analytical, and field based monitoring approaches

Assessment of climate change and development of data based prediction models of sediment yields in Upper Indus Basin

Hohe Raten von Sedimentflüssen und ihre Schätzungen in Flusseinzugsgebieten erfordern die Auswahl effizienter Quantifizierungsansätze mit einem besseren Verständnis der dominierten Faktoren, die den Erosionsprozess auf zeitlicher und räumlicher Ebene steuern. Die vorherige Bewertung von Einflussfaktoren wie Abflussvariation, Klima, Landschaft und Fließprozess ist hilfreich, um den geeigneten Modellierungsansatz zur Quantifizierung der Sedimenterträge zu entwickeln. Einer der schwächsten Aspekte bei der Quantifizierung der Sedimentfracht ist die Verwendung traditioneller Beziehung zwischen Strömungsgeschwindigkeit und Bodensatzlöschung (SRC), bei denen die hydrometeorologischen Schwankungen, Abflusserzeugungsprozesse wie Schneedecke, Schneeschmelzen, Eisschmelzen usw. nicht berücksichtigt werden können. In vielen Fällen führt die empirische Q-SSC Beziehung daher zu ungenauen Prognosen. Heute können datenbasierte Modelle mit künstlicher Intelligenz die Sedimentfracht präziser abschätzen. Die datenbasierten Modelle lernen aus den eingespeisten Datensätzen, indem sie bei komplexen Phänomenen wie dem Sedimenttransport die geeignete funktionale Beziehung zwischen dem Output und seinen Input-Variablen herstellen. In diesem Zusammenhang wurden die datenbasierten Modellierungsalgorithmen in der vorliegenden Forschungsarbeit am Lehrstuhl für Wasser- und Flussgebietsmanagement des Karlsruher Instituts für Technologie in Karlsruhe entwickelt, die zur Vorhersage von Sedimenten in oberen unteren Einzugsgebieten des oberen Indusbeckens von Pakistan (UIB) verwendet wurden. Die dieser Arbeit zugrunde liegende Methodik gliedert sich in vier Bearbeitungsschritte: (1) Vergleichende Bewertung der räumlichen Variabilität und der Trends von Abflüssen und Sedimentfrachten unter dem Einfluss des Klimawandels im oberen Indus-Becken (2) Anwendung von Soft-Computing-Modellen mit Eingabevektoren der schneedeckten Fläche zusätzlich zu hydro-klimatischen Daten zur Vorhersage der Sedimentfracht (3) Vorhersage der Sedimentfracht unter Verwendung der NDVI-Datensätze (Hydroclimate and Normalized Difference Vegetation Index) mit Soft-Computing-Modellen (4) Klimasignalisierung bei suspendierten Sedimentausträge aus Gletscher und Schnee dominierten Teileinzugsgebeiten im oberen Indus-Becken (UIB). Diese im UIB durchgeführte Analyse hat es ermöglicht, die dominiertenden Parameter wie Schneedecke und hydrologischen Prozesses besser zu und in eine verbesserte Prognose der Sedimentfrachten einfließen zu lassen. Die Analyse der Bewertung des Klimawandels von Flüssen und Sedimenten in schnee- und gletscherdominierten UIB von 13 Messstationen zeigt, dass sich die jährlichen Flüsse und suspendierten Sedimente am Hauptindus in Besham Qila stromaufwärts des Tarbela-Reservoirs im ausgeglichenen Zustand befinden. Jedoch, die jährlichen Konzentrationen suspendierter Sedimente (SSC) wurden signifikant gesenkt und lagen zwischen 18,56% und 28,20% pro Jahrzehnt in Gilgit an der Alam Bridge (von Schnee und Gletschern dominiertes Becken), Indus in Kachura und Brandu in Daggar (von weniger Niederschlag dominiertes Becken). Während der Sommerperiode war der SSC signifikant reduziert und lag zwischen 18,63% und 27,79% pro Jahrzehnt, zusammen mit den Flüssen in den Regionen Hindukush und West-Karakorum aufgrund von Anomalien des Klimawandels und im unteren Unterbecken mit Regen aufgrund der Niederschlagsreduzierung. Die SSC während der Wintersaison waren jedoch aufgrund der signifikanten Erwärmung der durchschnittlichen Lufttemperatur signifikant erhöht und lagen zwischen 20,08% und 40,72% pro Jahrzehnt. Die datenbasierte Modellierung im schnee und gletscherdominierten Gilgit Teilbecken unter Verwendung eines künstlichen neuronalen Netzwerks (ANN), eines adaptiven Neuro-Fuzzy-Logik-Inferenzsystems mit Gitterpartition (ANFIS-GP) und eines adaptiven Neuro-Fuzzy-Logik-Inferenzsystems mit subtraktivem Clustering (ANFIS) -SC), ein adaptives Neuro-Fuzzy-Logik- Inferenzsystem mit Fuzzy-C-Mittel-Clustering, multiplen adaptiven Regressionssplines (MARS) und Sedimentbewertungskurven (SRC) durchgeführt. Die Ergebnisse von Algorithmen für maschinelles Lernen zeigen, dass die Eingabekombination aus täglichen Abflüssen (Qt), Schneedeckenfläche (SCAt), Temperatur (Tt-1) und Evapotranspiration (Evapt-1) die Leistung der Sedimentvorhersagemodelle verbesserne. Nach dem Vergleich der Gesamtleistung der Modelle schnitt das ANN-Modell besser ab als die übrigen Modelle. Bei der Vorhersage der Sedimentfrachten in Spitzenzeiten lag die Vorhersage der ANN-, ANIS-FCM- und MARS-Modelle näher an den gemessenen Sedimentbelastungen. Das ANIS-FCM-Modell mit einem absoluten Gesamtfehler von 81,31% schnitt bei der Vorhersage der Spitzensedimente besser ab als ANN und MARS mit einem absoluten Gesamtfehler von 80,17% bzw. 80,16%. Die datenbasierte Modellierung der Sedimentfrachten im von Regen dominierten Brandu-Teilbecken wurde unter Verwendung von Datensätzen für Hydroklima und biophysikalische Eingaben durchgeführt, die aus Strömungen, Niederschlag, mittlerer Lufttemperatur und normalisiertem Differenzvegetationsindex (NDVI) bestehen. Die Ergebnisse von vier ANNs (Artificial Neural Networks) und drei ANFIS-Algorithmen (Adaptive Neuro-Fuzzy Logic Inference System) für das Brandu Teilnbecken haben gezeigt, dass der mittels Fernerkundung bestimmte NDVI als biophysikalische Parameter zusätzlich zu den Hydroklima-Parametern die Leistung das Modell nicht verbessert. Der ANFIS-GP schnitt in der Testphase besser ab als andere Modelle mit einer Eingangskombination aus Durchfluss und Niederschlag. ANN, eingebettet in Levenberg-Marquardt (ANN-LM) für den Zeitraum 1981-2010, schnitt jedoch am besten mit Eingabekombinationen aus Strömungen, Niederschlag und mittleren Lufttemperaturen ab. Die Ergebnisgenauigkeit R2 unter Verwendung des ANN-LM-Algorithmus verbesserte sich im Vergleich zur Sedimentbewertungskurve (SRC) um bis zu 28%. Es wurde gezeigt, dass für den unteren Teil der UIB-Flüsse Niederschlag und mittlere Lufttemperatur dominierende Faktoren für die Vorhersage von Sedimenterträgen sind und biophysikalische Parameter (NDVI) eine untergeordnete Rolle spielen. Die Modellierung zur Bewertung der Änderungen des SSC in schnee- und gletschergespeiste Gilgit- und Astore-Teilbecken wurde unter Verwendung des Temp-Index degree day modell durchgeführt. Die Ergebnisse des Mann-Kendall-Trendtests in den Flüssen Gilgit und Astore zeigten, dass der Anstieg des SSC während der Wintersaison auf die Erwärmung der mittleren Lufttemperatur, die Zunahme der Winterniederschläge und die Zunahme der Schneeschmelzen im Winter zurückzuführen ist. Während der Frühjahrssaison haben die Niederschlags- und Schneedeckenanteile im Gilgit-Unterbecken zugenommen, im Gegensatz zu seiner Verringerung im Astore-Unterbecken. Im Gilgit-Unterbecken war der SSC im Sommer aufgrund des kombinierten Effekts der Karakorum-Klimaanomalie und der vergrößerten Schneedecke signifikant reduziert. Die Reduzierung des Sommer-SSC im Gilgit Fluss ist auf die Abkühlung der Sommertemperatur und die Bedeckung der exponierten proglazialen Landschaft zurückzuführen, die auf erhöhten Schnee, verringerte Trümmerflüsse Trümmerflüsse und verringerte Schneeschmelzen von Trümmergletschern zurückzuführen sind. Im Gegensatz zum Gilgit River sind die SSC im Astore River im Sommer erhöht. Der Anstieg des SSC im Astore-Unterbecken ist auf die Verringerung des Frühlingsniederschlags und der Schneedecke, die Erwärmung der mittleren Sommerlufttemperatur und den Anstieg des effektiven Niederschlags zurückzuführen. Die Ergebnisse zeigen ferner eine Verschiebung der Dominanz von Gletscherschmelzen zu Schneeschmelzen im Gilgit-Unterbecken und von Schnee zu Niederschlägen im Astore-Unterbecken bei Sedimenteden Sedimentfrachten in UIB. Die vorliegende Forschungsarbeit zur Bewertung der klimabedingten Veränderungen des SSC und seiner Vorhersage sowohl in den oberen als auch in den unteren Teilbecken des UIB wird nützlich sein, um den Sedimenttransportprozess besser zu verstehen und aufbauen auf dem verbessertenProzessverständnis ein angepasstes Sedimentmanagement und angepasste Planungen der zukünftigen Wasserinfrastrukturen im UIB ableiten zu können

Development of a distributed water quality model using advanced hydrologic simulation

Cypress Creek is an urbanizing watershed in the Gulf Coast region of Texas that contributes the largest inflow of urban runoff containing suspended solids to Lake Houston, the primary source of drinking water for the City of Houston. Historical water quality data was statistically analyzed to characterize the watershed and its pollutant sources. It was determined that the current sampling program provides limited information on the complex behaviors of pollutant sources in both dry weather and rainfall events. In order to further investigate the dynamics of pollutant export from Cypress Creek to Lake Houston, fully distributed hydrologic and water quality models were developed and employed to simulate high frequency small storms. A fully distributed hydrologic model, Vflo(TM) , was used to model streamflow during small storm events in Cypress Creek. Accurately modeling small rainfall events, which have traditionally been difficult to model, is necessary for investigation and design of watershed management since small storms occur more frequently. An assessment of the model for multiple storms shows that using radar rainfall input produces results well matched to the observed streamflow for both volume and peak streamflow. Building on the accuracy and utility of distributed hydrologic modeling, a water quality model was developed to simulate buildup, washoff, and advective transport of a conservative pollutant. Coupled with the physically based Vflo(TM) hydrologic model, the pollutant transport model was used to simulate the washoff and transport of total suspended solids for multiple small storm events in Cypress Creek Watershed. The output of this distributed buildup and washoff model was compared to storm water quality sampling in order to assess the performance of the model and to further temporally and spatially characterize the storm events. This effort was the first step towards developing a fully distributed water quality model that can be widely applied to a wide variety of watersheds. It provides the framework for future incorporation of more sophisticated pollutant dynamics and spatially explicit evaluation of best management practices and land use dynamics. This provides an important tool and decision aid for watershed and resource management and thus efficient protection of the sources waters

The Drought Risk Analysis, Forecasting, and Assessment under Climate Change

This Special Issue is a platform to fill the gaps in drought risk analysis with field experience and expertise. It covers (1) robust index development for effective drought monitoring; (2) risk analysis framework development and early warning systems; (3) impact investigations on hydrological and agricultural sectors; (4) environmental change impact analyses. The articles in the Special Issue cover a wide geographic range, across China, Taiwan, Korea, and the Indo-China peninsula, which covers many contrasting climate conditions. Hence, the results have global implications: the data, analysis/modeling, methodologies, and conclusions lay a solid foundation for enhancing our scientific knowledge of drought mechanisms and relationships to various environmental conditions

Sensor-based Nonlinear and Nonstationary Dynaimc Analysis of Online Structural Health Monitoring

This dissertation focuses on robust online Structural Health Monitoring (SHM) framework for civil engineering structures. The proposed framework improves the diagnostic and prognostic schemes for damage-state awareness and structural life prediction in civil engineering structures. The underlying research achieves three main objectives, namely, (1) sensor placement optimization using partial differential equation modeling and Fisher information matrix, (2) structural damage detection using quasi-recursive correlation dimension (QRCD), and (3) structural damage prediction using online empirical mode decomposition (EMD).The research methodology includes three research tasks: Firstly, to formulate the optimal criteria for the sensor placement optimization damage detection problem based upon a partial differential equation (PDE) analytical model. The PDE model is derived and then validated through experimental results using correlation analysis. Secondly, to develop a novel quasi-recursive correlation dimension method for structural damage detection. The QRCD algorithm is integrated with an attractor analysis and overlapping windowing technique. Thirdly, to design an online structural damage prediction method based on empirical mode decomposition. The proposed SHM prediction scheme consists of two steps: prediction based change point detection using Hilbert instantaneous phase, and damage severity prediction using the energy index of the most representative intrinsic mode function (IMF).Study results show that; (1) the proposed optimal sensor placement method leads to an optimal spatial location for a collection of sensors, which are sensitive to structural damage, (2) the proposed damage detection algorithm can significantly alleviate the complexity of computation for correlation dimension to approximate O(N), making the online monitoring of nonlinear/nonstationary processes more applicable and efficient; and (3) the proposed empirical mode decomposition method for online damage prediction overcomes the boundary effects of the sifting process, and it has significant prediction accuracy improvement (greater than 30%) over other commonly used prediction techniques.Industrial Engineering & Managemen

Linearization Methods in Time Series Analysis

In this dissertation, we propose a set of computationally efficient methods based on approximating/representing nonlinear processes by linear ones, so-called linearization. Firstly, a linearization method is introduced for estimating the multiple frequencies in sinusoidal processes. It utilizes a regularized autoregressive (AR) approximation, which can be regarded as a "large p - small n" approach in a time series context. An appealing property of regularized AR is that it avoids a model selection step and allows for an efficient updating of the frequency estimates whenever new observations are obtained. The theoretical analysis shows that the regularized AR frequency estimates are consistent and asymptotically normally distributed. Secondly, a sieve bootstrap scheme is proposed using the linear representation of generalized autoregressive conditional heteroscedastic (GARCH) models to construct prediction intervals (PIs) for the returns and volatilities. Our method is simple, fast and distribution-free, while providing sharp and well-calibrated PIs. A similar linear bootstrap scheme can also be used for diagnostic testing. Thirdly, we introduce a robust lagrange multiplier (LM) test, which utilizes either the bootstrap or permutation procedure to obtain critical values, for detecting GARCH effects. We justify that both bootstrap and permutation LM tests are consistent. Intensive numerical studies indicate that the proposed resampling algorithms significantly improve the size and power of the LM test in both skewed and heavy-tailed processes. Moreover, fourthly, we introduce a nonparametric trend test in the presence of GARCH effects (NT-GARCH) based on heteroscedastic ANOVA. Our empirical evidence show that NT-GARCH can effectively detect non-monotonic trends under GARCH, especially in the presence of irregular seasonal components. We suggest to apply the bootstrap procedure for both selecting the window length and finding critical values. The newly proposed methods are illustrated by applications to astronomical data, to foreign currency exchange rates as well as to water and air pollution data. Finally, the dissertation is concluded by an outlook on further extensions of linearization methods, e.g., in model order selection and change point detection