FACULTEIT WETENSCHAPPENN Opleiding Master of Science in de geologieie
Reconstruction of the Southern Alaskan climate during the last 700 years, based on a multi-proxy analysis of annually laminated lake records
Evelien Boes
Academiejaar 2013-2014
Scriptie voorgelegd tot het behalen van de graad van Master of Science in de geologie
Promotor: Prof. Dr. Marc De Batist Begeleiders: Dr. Maarten Van Daele, Prof. Dr. Jasper Moernaut Leescommissie: Prof. Dr. Dirk Verschuren, Dr. Sébastien Bertrand
Picture cover sheet: Eklutna Lake, view towards Eklutna Glacier By Nore Praet, February 2014
ABSTRACT IN DUTCH
Gedurende de laatste decennia is de bestaande kennis omtrent klimaat en klimaatsverandering enorm gegroeid. Het toegenomen bewustzijn van een zekere antropogene invloed op de hedendaagse klimaatsomstandigheden heeft de maatschappij wakker geschud en aangezet tot het doorgronden van het aardse klimaatssysteem en al zijn facetten. Om gefundeerde voorspellingen voor de toekomst te kunnen doen, dient het verleden grondig bestudeerd en geïnterpreteerd te worden. Het verleden herbergt immers een schat aan gegevens omtrent de mechanismen die het klimaat sturen, welke rol deze spelen en hoe ze met elkaar interageren. Aangezien instrumentele data niet ver genoeg terugreiken in het verleden, moet men op zoek gaan naar andere bronnen van informatie. Deze bronnen worden ook wel eens paleoklimaatsarchieven genoemd en zijn onder andere vertegenwoordigd door ijskernen, veen-afzettingen, koraalriffen, boomringen, mariene sedimenten en meersedimenten. Laatstgenoemde vertonen dikwijls hoge-resolutie opnamen van in het verleden heersende milieucondities, dankzij hun capaciteit om hoge-frequentie (seizoenen, jaren) veranderingen in de omgeving te reflecteren. De oorzaak van dit fenomeen schuilt in de klimaatsafhankelijke sedimentatie in meren. Niet alleen de hoeveelheid sediment dat naar het meer getransporteerd en in het meer afgezet wordt, maar ook zijn samenstelling weerspiegelt het heersende klimaat.
Deze studie is gericht op het doorgronden van klimaatssignalen in sedimenten (boorkernen) uit drie verschillende proglaciale meren in Zuid-Alaska, op Kenai Peninsula (Kenai Lake en Skilak Lake) en in de omgeving van Anchorage (Eklutna Lake). In het geval van proglaciale meren wordt sediment-transport en -afzetting grotendeels gecontroleerd door de jaarlijkse cyclus van ijs- en sneeuwsmelt tijdens lente en zomer, gevolgd door een periode van sneeuwval en hernieuwde ijsvorming tijdens herfst en winter. Deze cyclische variaties in omstandigheden manifesteren zich in de vorming van jaarlijkse afzettingen, waarnaar verwezen wordt als glaciale of klastische ‘warven’. De kwaliteit van dit soort warven beperkt zich niet uitsluitend tot hun bijdrage in het uitvoeren van hoge-resolutie dateringen, maar schuilt ook in hun sedimentologische, geochemische en geofysische eigenschappen die men kan koppelen aan specifieke weersparameters (temperatuur, neerslag en sneeuwval). Op deze manier kunnen klimaatsproxies gedefinieerd worden na kalibratie met instrumentele data.
Het studiegebied bevindt zich bovenop een belangrijke, destructieve plaatrand, de Alaskan- Aleutian Subduction Zone (AASZ). De oceanische Pacifische plaat duikt hier onder de continentale Noord-Amerikaanse plaat, hetgeen een voortdurende tektonische en vulkanische activiteit oplevert langsheen de Zuid-Alaskaanse kust. Gedurende de laatste eeuw was de AASZ bron voor tal van significante (Mw>7) tot extreem zware (Mw>8) aardbevingen, dikwijls gecombineerd met trans-Pacifische tsunami’s, waarvan de hevigste (Mw 9.2) plaatsgreep in 1964 A.D. ten noorden van Prince William Sound. Naast seismiciteit vormt ook subductie-gerelateerd vulkanisme een belangrijk component van de AASZ. Zowel aardbevingen als erupties zijn in staat karakteristieke afzettingen in meren te genereren. Wanneer deze afzettingen op basis van hun eigenschappen gecorreleerd kunnen worden
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met gerapporteerde, historische events, vormen ze een bijkomende absolute datering om warven-ouderdomsmodellen te verifiëren en eventueel te verbeteren.
Boorlocaties in de drie bestudeerde meren zijn gebaseerd op bestaande bathymetrische kaarten en resultaten van verkennende reflectie-seismiek. Meerbodems werden op verschillende locaties bemonsterd met een bob-corer, waarbij laterale hellingen en andere instabiele omgevingen zoals delta’s, werden vermeden. Boorkernen werden vervolgens geopend en gefotografeerd aan het RCMG (Universiteit Gent). Gedetailleerde macroscopische beschrijvingen van het sedimentoppervlak laten een eerste onderverdeling toe in event-afzettingen en gelaagde/gelamineerde achtergrond-sequenties, waarbij de aandacht vooral uitgaat naar observaties betreffende sedimentkleur, korrelgrootte, textuur en structuur. Bijkomende hoge-resolutie kleuranalyses (CIE L*a*b*) werden met gespecialiseerde software (Strati-Signal) uitgevoerd op onbewerkte kernfoto’s. Stapsgewijze passage doorheen een Multi-Sensor Core Logger (MSCL) levert geofysische eigenschappen (densiteit, magnetische susceptibiliteit en spectrofotometrische parameters) van het sediment op. Studie van smear slides en instrumentele korrelgrootte-analyses maken het mogelijk om microscopische kenmerken van individuele lagen en laminaties te onderzoeken. Door XRF-scans van enkele boorkernen te nemen, kunnen subtiele variaties in chemische element-concentraties achterhaald worden. Stroomgebieden van ieder meer en specifieke drainagenetwerken werden afgelijnd, zodat de bemonsterde, lacustriene afzettingen in verband konden gebracht worden met hun brongebieden in de nabijheid van de meren.
Om het vermoedelijk jaarlijkse karakter van de aanwezige achtergrond-laminaties en -laagjes te testen, werden radionuclide-dateringen (210Pb and 137Cs) uitgevoerd op de bovenste secties van boorkernen uit Eklutna Lake en Skilak Lake. In beide meren kan de aanwezigheid van warven bevestigd worden, terwijl confirmatie van deze jaarlijkse eenheden in Kenai Lake moet wachten op identificatie van historische event-afzettingen, die als gidslagen functioneren. Echter, omwille van het gelijkaardige uiterlijk van sequenties in dit laatste meer aan deze van de andere twee meren, kan men de aanwezigheid van warven in Kenai Lake voorlopig veronderstellen. Deze veronderstelling is nodig bij het opstellen van hoge- resolutie ouderdomsmodellen, waarbij warven over de volledige lengte van de meest representatieve kernen van ieder meer (EK12-01, KE12-07, SK12-10 en SK12-03) gemeten en geteld worden. Het aflijnen van deze laminaties en laagjes kan vergemakkelijkt worden door gebruik te maken van CT-beelden, gemaakt met behulp van een medische scanner, waarbij variaties in densiteit en samenstelling van het sediment gereflecteerd worden in grijswaarden van individuele voxels. Attenuatie van X-stralen loopt sterker op ter hoogte van het grovere warven-basis-sediment (silt) dan in de fijnkorreligere toppen (klei). Desondanks kunnen onzekerheden bij het tellen niet vermeden worden, aangezien verscheidene stratigrafische intervallen onduidelijke structuren vertonen. Na aan iedere afgebakende eenheid een ouderdom toegekend te hebben, blijkt de inhoud van EK12-01 (Eklutna Lake) terug te reiken tot 1856 A.D., KE12-07 (Kenai Lake) tot 1833 A.D., SK12-03 (Skilak Lake) tot 1646 A.D. en SK12-10 tot 1340 A.D. Daarnaast kan voor elke geanalyseerde kern een verloop van jaarlijkse sediment-accumulatie doorheen de tijd opgesteld worden.
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De op warven-tellingen gebaseerde tijdskaders kunnen gecontroleerd worden, of in het geval van Kenai Lake voor het eerst bevestigd worden, door opmerkelijke event-afzettingen te verbinden met historische aardbevingen, erupties of floods. In elk van de drie meren bevinden zich sporen van de zware 1964 A.D. Prince William Sound aardbeving in de vorm van turbidieten en/of mass-transport deposits (MTD’s), hetgeen mogelijkheden biedt tot het opstellen van een inter-meer correlatie. Ook enkele (crypto)tefra-lagen, zoals deze van de erupties van Redoubt in 1989 A.D. en Katmai-Novarupta in 1912 A.D. kunnen in meer dan één meer aangetroffen worden. Op seismische profielen zijn afzettingen van de 1964 A.D. aardbeving zichtbaar als chaotische reflecties en worden voorafgegaan door gelijkaardige lichamen op grotere stratigrafische dieptes. Verder zijn nog enkele andere, lokale event- afzettingen aanwezig in de archieven van Eklutna Lake, Kenai Lake en Skilak Lake. Zo bevat Eklutna Lake een relatief fijnkorrelige turbidiet, veroorzaakt door het gedeeltelijke falen van een dam-constructie ten gevolge van een flood in 1929 A.D., terwijl in Skilak Lake afzettingen terug te vinden zijn van een aardbeving in 1954 A.D. (Mw 6.7) waarvan het hypocentrum gelegen was onder noordelijk Kenai Peninsula. Ook verschillende tefra-lagen hebben een zuiver lokaal karakter, zoals deze van de 1883 A.D. Augustine eruptie in Eklutna Lake of van de 2005 A.D. Augustine eruptie in Kenai Lake. Al deze geïdentificeerde event- afzettingen bevestigen de vooropgestelde ouderdomsmodellen die gebaseerd zijn op warven-tellingen en radionuclide-dateringen.
Naast het valideren van ouderdomsmodellen staan event-afzettingen eveneens toe een benaderende schatting te maken van eventuele warven-telfouten. Een andere methode die het mogelijk maakt telfouten te evalueren, is het tunen van warven-diktes aan de hand van instrumentele klimaatsdata. Deze data werden opgevraagd voor verschillende stations in de nabijheid van Eklutna Lake, Kenai Lake en Skilak Lake. Tijdens het tuning-proces wordt uitgegaan van een positieve beïnvloeding van jaarlijkse sediment-accumulaties door extreme hoeveelheden neerslag, hoge temperaturen of de combinatie van een sneeuwrijke winter, gevolgd door een zomer met gemiddelde tot hoge temperaturen. Al deze factoren zijn immers in staat een toegenomen runoff en sediment-transport in de richting van de meerbekkens te veroorzaken. Vooraleer jaarlijkse pieken en dalen in sedimentatiesnelheid in relatie kunnen gebracht worden met pieken en dalen in klimaatsparameters, dienen zowel klimaatscurves als warven-dikte-curves ontdaan te worden van hun lage-frequentie trends (detrending), die niet aan jaarlijkse maar aan decadale klimaatsvariabiliteit gelinkt zijn. Als gevolg van het overwogen verplaatsen van warven-grenzen, neemt de lineaire correlatie (Pearsons correlatiecoëfficiënt) tussen individuele klimaatsparameters en jaarlijkse sedimentatiesnelheden toe. Door alle klimaatsparameters met elkaar te combineren en de gewichten van iedere parameter aan te passen, is het mogelijk de correlatie met warven- diktes nog verder op te drijven, hetgeen wijst op een bestaande relatie tussen klimaat en lacustriene sedimentatie. Bovendien kan men eventuele foutieve begrenzingen van warven inschatten aan de hand van tuning. Het extrapoleren van foutenmarges tot op de basis van de geanalyseerde kernen, levert voor EK12-01 een ouderdom op van 1856 A.D. ±7 jaar, voor KE12- 07, 1833 A.D. ±6 jaar, voor SK12-03, 1646 A.D. ±15 jaar en voor SK12-10, 1340 A.D. ±33 jaar.
Uit plots van jaarlijkse sedimentaccumulatie doorheen de tijd blijkt dat dikkere warven meestal overeenkomen met specifieke sedimentaire architecturen, die geïnterpreteerd
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kunnen worden als flood-eenheden. Deze eenheden reflecteren afzetting ten tijde van verhoogde rivier-debieten, op hun beurt veroorzaakt door bepaalde klimaats- omstandigheden. Vier verschillende flood-categorieën kunnen onderscheiden worden. Een eerste categorie omvat midden-zomer floods, die het resultaat zijn van toegenomen precipitatie of ijs-/sneeuwsmelt en waarvan de omvang groter is dan deze van de eerste lente/zomer smelt-puls. De tweede categorie bestaat uit jaarlijkse eenheden met meerdere pulsen van hetzelfde karakter, die naar alle waarschijnlijkheid gevormd zijn door een opeenvolging van verschillende smelt-pulsen in plaats van pieken in precipitatie. Eenheden van de derde categorie bezitten per definitie slechts één enkele puls en onderscheiden zich van normale achtergrond-laminaties door hun afwijkende kleur, grofkorreligere basis en grotere dikte. De vierde en laatste categorie bevat architecturen van de drie voorgaande klassen, maar in extreme proporties, ‘mega-floods’ genaamd. Eenheden van deze vier categorieën zijn afgezet door krachtige en minder krachtige turbiditeitsstromingen, afhankelijk van de positie in het meer ten opzichte van de bron van sedimenttoevoer (rivierdelta’s) en waterdiepte.
Door curves van jaarlijkse sedimentaccumulatie te vergelijken met bestaande instrumentele klimaatsdata, is het mogelijk om warven-diktes te kalibreren en dus als klimaatsproxies te beschouwen. Deze kalibratie kan zowel op langere tijdsschaal als op kortere tijdsschaal (jaarlijks) toegepast worden. In Eklutna Lake blijken langere-termijn variaties in warven-dikte gerelateerd te zijn aan veranderingen in temperatuur die zich op dezelfde tijdsschaal afspelen. Ook in Skilak Lake bestaat er een opvallende relatie tussen temperatuur en sedimentatiesnelheid. De aard van deze relaties is eerder contra-intuïtief, gezien periodes met koudere temperaturen (1945-1976 A.D. negatieve PDO-fase en verschillende LIA-pulsen) vertegenwoordigd worden door toegenomen warven-diktes. Een verklaring voor dit verschijnsel kan gezocht worden in de verhoogde erosieve activiteit van uitgroeiende gletsjers tijdens langdurige koude perioden, gecombineerd met het effect van een veranderend substraat waarop de gletsjertermini zich bevinden, gaande van massief gesteente naar smeltwatervlakte met een grote hoeveelheid aan los, gemakkelijk erodeerbaar materiaal. Zowel het stroomgebied van Eklutna Lake als dat van Skilak Lake wordt gedomineerd door uitgebreide gletsjers, Eklutna Glacier en Skilak Glacier respectievelijk. De belangrijke invloed van temperatuur op lage-frequentie variaties in sedimentaccumulatie in deze meren is dus niet geheel onverwachts, aangezien schommelingen in gletsjerbalans zich op dezelfde tijdsschaal afspelen. De huidige Skilak Glacier wordt stroomafwaarts begrensd door een klein meertje dat smeltwater opvangt vooraleer dit verder naar Skilak Lake kan stromen en dat dus fungeert als een soort bufferzone, die het onmiddellijke effect van smelt-pulsen op sediment-transport uitmiddelt. Tijdens koudere periodes in het verleden reikte de terminus van Skilak Glacier naar alle waarschijnlijkheid verder dan de positie van het huidige meertje, waardoor de invloed van het jaarlijkse lente/zomer-smelten op glacio-lacustriene sedimentatie veel groter moet geweest zijn. Het stroomgebied van Kenai Lake omvat enkele kleinere gletsjers, maar ook een aantal gletsjerloze valleien, wat de sedimentatierespons in het meerbekken enigszins compliceert. Hierbij komt nog eens kijken dat het grootste deel van smelt- en regenwater en getransporteerd sediment opgevangen wordt in verschillende kleinere meertjes (e.g. Ptarmigan Lake, Grant Lake) in het stroomgebied van Kenai Lake. Bovendien zijn lange- termijn evoluties in precipitatie en sneeuwval in de nabijheid van Kenai Lake sterker
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uitgesproken dan deze in de omgeving van Anchorage, wat eveneens bijdraagt tot een gemengd klimaatssignaal, dat niet overheerst wordt door temperatuur zoals in het geval van Skilak Lake en Kenai Lake.
De invloed van klimaatsparameters op een jaarlijkse tijdsschaal kan achterhaald worden door correlatiecoëfficiënten te berekenen tussen gedetrende data van temperatuur, neerslag en sneeuwval, enerzijds, en warven-diktes, anderzijds. In elk van de drie meren geven deze coëfficiënten aan dat hoge-frequentie variaties in sedimentaanvoer beïnvloed worden door een combinatie aan klimaatsfactoren. In Eklutna Lake en Kenai Lake blijkt precipitatie dominant te zijn, terwijl dit in Skilak Lake sneeuwval is. Deze contrasterende dominantie is mogelijks te wijten aan het verschil in hoogteligging tussen de drainagegebieden van Eklutna Lake en Kenai Lake (Chugach en Kenai Mountains), en Skilak Lake (Kenai Lowland), waardoor de productie van smeltwater in de directe omgeving van het laatste meer eerder gelimiteerd is door de hoeveelheid gevallen wintersneeuw dan door temperatuur.
Uit het kalibreren van warven-diktes kan geconcludeerd worden dat sediment-accumulatie in de bestudeerde meren wel degelijk verbanden vertoont met overheersende klimatologische omstandigheden. Deze omstandigheden worden op hun beurt gestuurd door tal van verschillende atmosferische en oceanische oscillaties. Spectraalanalyses op warven-diktes tonen aan dat zowel de pseudo-periodisch fluctuerende Pacific Decadal Oscillation (PDO) als El Niño Southern Oscillation (ENSO) en mogelijks de Arctic Oscillation (AO) een invloed hebben op lacustriene sedimentatie in Zuid-Alaska. De exacte werking van deze oscillaties en hun complexe interacties met elkaar zijn echter niet volledig doorgrond en vergen bijkomende aandacht in eventuele verdere studies.
Korrelgrootte en chemische samenstelling van het bemonsterde sediment blijken gerelateerd te zijn aan het voorkomen van flood-afzettingen, hun overeenkomstige sedimentaire architecturen en dus ook warven-diktes. Bijgevolg kunnen deze twee eigenschappen eveneens aanzien worden als proxies voor paleoklimaatsomstandigheden. Flood-afzettingen zijn immers het resultaat van het extreme smelten van sneeuw en ijs en/of intense precipitatie op een relatief kort tijdsinterval, en dus sterk verhoogde debieten in drainerende rivieren binnen corresponderende stroomgebieden. De toegenomen capaciteit van deze stromen om sediment te transporteren, reflecteert zich niet uitsluitend in dikkere, maar ook in grofkorreligere warven. Bovendien blijken deze korrelgrootte-fluctuaties gebonden te zijn aan subtiele veranderingen in mineralogie en chemische element- concentraties. Op deze manier wordt een dominantere silt-fractie weerspiegeld in hogere waarden voor Ti en lagere waarden voor K en Fe. Variaties in geochemie kunnen daarnaast ook grofweg afgeleid worden van sedimentkleur. Zo zijn flood-eenheden vaak donkerder en/of bruiner en wijzen contrasterende kleuren (olijf-grijs versus blauw-grijs) in Kenai Lake mogelijks op veranderingen in dominant brongebied.
Deze multi-proxy studie van jaarlijks gelamineerde/gelaagde sedimenten uit drie verschillende meren in Zuid-Alaska maakt duidelijk dat zowel sedimentatiesnelheden, afzettingsarchitectuur, korrelgrootte als chemische samenstelling van het sediment klimaatsgebonden factoren zijn. Echter, gezien het verkennende karakter van het onderzoek,
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kunnen enkele aanbevelingen gedaan worden voor additionele analyses ter verbetering van de voorgestelde ouderdomsmodellen en klimaatskalibraties. Warven-begrenzingen in dubieuze intervallen zouden gecontroleerd en bijgestuurd kunnen worden aan de hand van slijpplaatjes, waarop men eveneens element-concentraties en mineralen in kaart kan brengen (µ-XRF, SEM, MLA). Een geochemische karakterisering van glazige partikels in tefra- lagen zou een grotere zekerheid bieden omtrent de bronvulkanen en dus ook de corresponderende, historische erupties. Ten slotte is het aangeraden langere boorkernen in zowel Eklutna Lake als Skilak Lake te nemen om het effect van de LIA op sedimentatie in deze meren grondiger te kunnen bestuderen.
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ACKNOWLEDGEMENTS
This pile of paper would never have been the thesis that it is right now if it wasn’t for a whole bunch of passionate, helpful, devoted, supporting and simply wonderful human beings, to whom the following words are dedicated.
First of all, I would like to thank professor Marc De Batist for giving me the opportunity to work on a truly inspiring subject. I experienced a year of breathing in data and breathing out climate, for which I can find no other words than ‘unique’. Many thanks to Maarten Van Daele for his overall enthusiasm in introducing me to and guiding me through the wide world of lake records. I am grateful for your patience. Thank you very much, Jasper Moernaut, for helping me out with lab work and discussions in Gent, in Zürich, from Zürich and from Chile. A sincere thank you to Nore Praet, Philipp Kempf, Sébastien Bertrand, Anna Reusch and Wesley De Boever for all sorts of advice and contributions, going from map making to revealing the secrets of a core logger.
Thanks a lot to my classmates and friends in Gent and Zürich for the relaxing lunch breaks, coffee breaks, tea breaks, snack breaks and other breaks. They were essential! Last but not least, I want to thank my dear family, as they were nothing less than the supporting backbone throughout my student life. What seems to be a cliché, has never been so true.
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TABLE OF CONTENTS
1 INTRODUCTION ...... 1 1.1 Research objectives ...... 1 1.2 Thesis outline ...... 3
2 STUDY AREA ...... 4 2.1 Large-scale geotectonic setting ...... 5 2.1.1 Alaskan-Aleutian subduction zone ...... 5 2.1.2 Paleoseismicity ...... 7 2.1.3 Volcanic setting ...... 9 2.2 Climate patterns in Southern Alaska ...... 10 2.2.1 General tendencies and oscillations ...... 10 2.2.2 Glacial history ...... 15 2.3 Kenai Peninsula and Anchorage area ...... 17 2.4 Eklutna Lake...... 18 2.4.1 Lake basin and watershed ...... 18 2.4.2 Eklutna Water Project and Eklutna Power Project ...... 19 2.4.3 Climate and sedimentation ...... 20 2.5 Kenai Lake ...... 20 2.6 Skilak Lake ...... 21 2.6.1 Lake basin and watershed ...... 21 2.6.2 Climate and sedimentation ...... 22
3 VARVES AS CLIMATE RECORDERS ...... 23 3.1 Glacial varves ...... 23 3.2 Varve chronology ...... 24 3.3 Climate proxies and calibration ...... 25 3.3.1 Varve thickness ...... 25 3.3.2 Grain-size and chemical composition ...... 26
4 MATERIALS AND METHODS ...... 28 4.1 Reflection-seismic profiling and interpretation ...... 28 4.2 Core acquisition ...... 29 4.3 Sedimentological, geophysical and geochemical analyses ...... 32 4.3.1 Macroscopic core description and general correlation strategies ...... 32 4.3.2 Multi-sensor core logging ...... 33 4.3.3 Core photography and colour analysis ...... 33 4.3.4 Grain-size measurements ...... 35 4.3.5 Smear slides for microscopic description ...... 35 4.3.6 X-ray fluorescence (XRF) ...... 35 4.3.1 X-ray computed tomography (medical CT)...... 36 4.4 Dating methods ...... 36 4.4.1 Radionuclide dating ...... 36 4.4.2 Varve counting and measurements of varve thickness ...... 38 4.5 Climate tuning and calibration ...... 41 4.6 Catchment extraction on DEM’s ...... 43 4.7 Spectral analysis ...... 44 4.8 Workflow ...... 44
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5 RESULTS ...... 46 5.1 Eklutna Lake...... 46 5.1.1 Catchment and drainage networks ...... 46 5.1.2 Bathymetric and seismic-stratigraphic setting ...... 47 5.1.3 Macroscopic and microscopic core description ...... 48 5.1.4 MS- and density-evolutions ...... 53 5.1.5 Colour analysis ...... 54 5.1.6 XRF-profiles and -patterns ...... 54 5.1.7 Age models ...... 56 5.1.8 Varve thickness tuning and climate calibration ...... 58 5.1.9 Spectral analysis ...... 63 5.2 Kenai Lake ...... 64 5.2.1 Catchment and drainage networks ...... 64 5.2.2 Bathymetric and seismic-stratigraphic setting ...... 66 5.2.3 Macroscopic and microscopic core description ...... 67 5.2.4 MS- and density-evolutions ...... 71 5.2.5 Colour analysis ...... 72 5.2.6 XRF-profiles and -patterns ...... 72 5.2.7 Age model...... 73 5.2.8 Varve thickness tuning and climate calibration ...... 74 5.2.9 Spectral analysis ...... 78 5.3 Skilak Lake ...... 79 5.3.1 Catchment and drainage networks ...... 79 5.3.2 Bathymetric and seismic-stratigraphic setting ...... 80 5.3.3 Macroscopic and microscopic core description ...... 81 5.3.4 MS- and density-evolutions ...... 86 5.3.5 Colour analysis ...... 86 5.3.6 XRF-profiles and -patterns ...... 87 5.3.7 Age models ...... 87 5.3.8 Climate calibration ...... 91 5.3.9 Spectral analysis ...... 94
6 DISCUSSION ...... 96 6.1 Event-deposits ...... 96 6.1.1 Earthquake-triggered turbidites ...... 96 6.1.2 Tephra-fall deposits ...... 98 6.1.3 Flood- and mega-flood deposits ...... 100 6.2 Varve counting errors ...... 104 6.3 Intra-lake core correlation and event-deposit distribution ...... 105 6.3.1 General principles ...... 105 6.3.2 Eklutna Lake ...... 106 6.3.3 Kenai Lake ...... 109 6.3.4 Skilak Lake ...... 112 6.4 Inter-lake core correlation ...... 116 6.5 Climate proxies...... 118 6.5.1 Varve thickness ...... 118 6.5.2 Grain-size...... 132 6.5.3 Geochemical and mineralogical composition ...... 133
7 CONCLUSIONS AND RECOMMENDATIONS ...... 135
8 REFERENCES ...... 138
9 APPENDICES ...... 150
IX Chapter 1 - Introduction
1 INTRODUCTION
Happy is he who gets to know the reasons for things. - Virgil -
1.1 Research objectives
In times during which our society becomes more and more captivated by a growing desire to understand and even mitigate climate change, it is essential to look into the past in order to better comprehend the present conditions and predict the future ones. Gaining insight in what kind of mechanisms influenced past climate fluctuations and how each of these separate influences manifested themselves, is key in all environmental sciences. Models were developed to explain observations and predict responses of the climate system to specific perturbations (Crucifix, 2012; Bryson, 1993). Though, due to the increasing degree of anthropogenic disruptions of our climate system, natural forcing factors that have been steering climate for the past millions of years, might have changed their working paths. Hence, it is of great importance to be able to separate natural climate variability from anthropogenic fingerprints. However, this is no easy task, as both factors are closely interwoven and interacting with each other (Hegerl et al., 1996).
Past climate changes that go back beyond instrumental records, have been subject of many studies already. Opportunities to reconstruct these pre-instrumental conditions are offered by natural archives, which contain a wealth of invaluable information. The world’s climate archives include tree ring records, peat deposits, ice cores, coral reefs, ocean sediments, but also lake sediments (Ruddiman, 2008). Thanks to their potential to preserve deposited material through time, lake records have been labelled repeatedly as high-resolution archives with often annually to even seasonally resolved qualities. Their strong resolving nature is a result of seasonal, annual, decadal or longer timescale changes in the surrounding environment that define the characteristics of the sediment that finally settles on the bottom of a lake basin. A whole range of climate proxies are thus captured within lake deposits (e.g. Zolitschka, 2007; O’Sullivan, 1983; Brauer et al., 2009). More specifically in case of proglacial lakes, sediment transport towards the basin is largely controlled by the annual cycle of ice- and snowmelt during spring and summer, followed by a period of renewed ice and snow cover during autumn and winter. These cyclic variations in environmental conditions express themselves in the formation of annual layers, also called glacial ‘varves’ (O’Sullivan, 1983).
Throughout this study, laminated to layered records from three proglacial lakes in Southern Alaska (Eklutna Lake, Kenai Lake and Skilak Lake) are the centre of attention. The specific area of interest covers Kenai Peninsula and the surroundings of Anchorage, which is located on top of a major subduction zone, the Alaskan-Aleutian megathrust. Due to the high tectonic activity of the region, a sequence of strong earthquakes struck the lakes’ settings in
1 Chapter 1 - Introduction the past and is expected to have left its imprint in the sampled lacustrine archives (e.g. Hutchinson & Crowell, 2007ab; Ryan et al., 2011). Most well known is the Prince William Sound earthquake that took place in 1964 A.D. and is the second largest earthquake ever recorded instrumentally, with a moment magnitude (Mw) of 9.2 (e.g. Krauskopf et al., 1973). Apart from seismicity and associated tsunamis, the Alaskan-Aleutian megathrust is also responsible for subduction-related volcanism. A continuous series of heavier and weaker eruptions characterises the history of the Alaskan-Aleutian volcanic chain. Traces of these eruptions or at least part of them are supposed to have affected the sedimentary infill of the three studied lakes as well (e.g. de Fontaine et al., 2007; Payne & Blackford, 2008).
Supported by high-resolution seismic data, the content of nineteen short cores are subject to several sedimentological, geophysical and geochemical analyses in order to release their properties and climate proxies. These proxies range from lamination thickness, over lamination architecture, sediment colour and grain-size, up to magnetic susceptibility, density and chemical composition (Brauer et al., 2009). The presumed annual time frames of individual laminations can be tested by comparing lamination counts to independent absolute ages, based on radionuclide dating and/or identified event-deposits. CT-scans of split cores are made in order to facilitate lamination counting and delineation. The power of annually resolved laminations does not only reside in the chance to distinguish annual deposits, but also in the potential to create high-resolution, solid age-depth frameworks, that can link the depth-dependent multi-proxy records to their corresponding ages (Zolitschka, 2007; Ojala et al., 2012).
Lake catchments and drainage networks have to be studied thoroughly to gain a better understanding of where the cored lake sediment came from initially and how changes in climate conditions can influence the supply to different parts of the lakes. Furthermore, a connection between the measured proxies and prevailing paleoclimate conditions should be established. In order to verify assumed proxy-climate relationships, instrumental data from nearby weather stations are requested and compared to the corresponding intervals of the most evident of all climate proxies, being lamination thickness (sedimentation rate), on a multidecadal as well as an interannual timescale. Based on this climate calibration, proxy- records that reach further back in time than any of the instrumental data can be related to specific climate conditions (e.g. Perkins & Sims, 1983; Leemann & Niessen, 1994). Since three individual lake basins are incorporated in this study, mutual differences in how the lacustrine archives were shaped by changing environmental conditions, are assessed as well.
Several specific phenomena that dominate(d) climate fluctuations in Southern Alaska, are expected to be recognised in the proxies of the sampled lake records, such as the Little Ice Age (LIA), Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO) and El Niño-Southern Oscillation (ENSO) (Diedrich & Loso, 2012; Kaufman et al., 2011). As the latter two act on a pseudo-periodic scale, their imprints are supposed to exhibit the same periodicity. Therefore, spectral analyses are executed on the records of lamination thickness with the aim of revealing their most prominent periodicities (Fagel et al., 2008).
2 Chapter 1 - Introduction
1.2 Thesis outline
A concise summary of the geotectonic setting and climatologic history of the study area is provided in Chapter 2. The three involved lake basins and their direct surroundings are introduced in a higher degree of detail. Chapter 3 presents background information on the working mechanisms behind the formation of glacial varves and how these annual deposits have been used as climate recorders in previous studies. Moreover, the possibility that is being offered by annually resolved laminations to create high-resolution age-depth models and chronologies is discussed in the same chapter.
In Chapter 4, the full sequence of used materials and methods is given, starting from reflection-seismic surveying and core acquisition. Subsequently, a series of sedimentological, geophysical and geochemical analysis techniques that are performed on the split cores in order to retrieve proxy records, are explained. Finally, a discussion on more advanced methods to process raw data into ‘ready-to-interpret’ data, can be found in Chapter 4. As the list of used methods is relatively extensive and the feedbacks between those methods quite strong, a schematic flow chart is provided at the end of the chapter to give a clear overview on why each of the listed methods is executed.
Chapter 5 focuses on a detailed reporting of the obtained results. Results from each of the studied lakes are treated separately in a downscaling order, going from the lake’s catchment down to the lake basin and its sedimentary infill, including all of the measured properties. An interpretation and discussion of the described results follow in Chapter 6. Throughout this chapter, the own findings are compared to previous studies and existing theories concerning climate-driven lake sedimentation. Special attention is given to the difference in which identified climatic signals are expressed within lakes and between lakes.
The main conclusions of this thesis are summarised in Chapter 6. Since the nature of the study is rather exploring, several recommendations for future research are done as well.
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2 STUDY AREA
Look deep into nature, and then you will understand everything better. - Albert Einstein -
The region of interest is located in Southern (or Southcentral) Alaska, more precisely on Kenai Peninsula and in the surroundings of Anchorage (59°00’-61°50’N; 147°50-152°00’W). Focus lies on three lake basins, Eklutna Lake, Skilak Lake and Kenai Lake, and their direct environments (Fig. 2.1), up to a scale on which they actively contribute in influencing the annual lake basin dynamics and sedimentation. Throughout this chapter, several relevant topics with a direct application to subjects and results treated in Chapter 6 are discussed.
Figure 2.1: Map of Kenai Peninsula and surroundings. The inset figure in the upper left corner shows the exact location of the peninsula, relative to the Alaskan mainland and the Aleutian chain. Yellow arrows indicate the three studied lakes and Tustumena Lake in Kenai Lowland (see section 4.2). Volcanoes on the opposite side of the Cook Inlet are represented by orange-yellow triangles. A big blue star, 20 km north of Prince William Sound, gives the position of the 1964 A.D. megathrust earthquake epicentre and a smaller blue star, north of Skilak Lake, the epicentre of a less strong earthquake in 1954 A.D. (see section 2.1.2 and 6.1.1.3) (Landsat image: Google Earth).
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2.1 Large-scale geotectonic setting
2.1.1 Alaskan-Aleutian subduction zone
The Alaskan-Aleutian megathrust is a thrust fault that ranges from the Near Islands in the west to Middleton Island (Gulf of Alaska) in the east, for over approximately 3000 km (Fig. 2.2). It embodies the convergent interface along which the subducting Pacific plate and the overriding Bering Sea region of the North American plate meet, also called the Alaskan- Aleutian subduction zone (AASZ) (Hamilton & Shennan, 2005). The AASZ is defined as a Pacific-type arc (Krauskopf et al., 1973) and is responsible for the formation of the Aleutian Ridge, the Aleutian Islands arc, west of the Alaskan Peninsula, and the deep offshore Aleutian Trench. Relative convergence between the two plates and hence subsidence rates range from 54 mm/yr along the Gulf of Alaska up to values exceeding 76 mm/yr south of the Bering Sea, yielding an average of approximately 65 mm/yr in a northwest direction (Fig. 2.2). Ages of the subducting Pacific plate vary from ~33 Ma in the Gulf of Alaska up to ~90 Ma in the westernmost Aleutians. The older and colder the lithosphere becomes when moving to the west, the deeper the resulting trench. At Unimak Pass (Fig. 2.2), a transition occurs from subduction beneath the continental margin of Alaska to the east to subduction beneath the intra-oceanic Aleutian Ridge and Islands to the west (Ryan et al., 2011).
Convergence configurations evolve from a movement close to orthogonal to the arc in the east (Kodiak margin) to an almost trench-parallel convergence in the west (Aleutian margin) (Lallement & Oldow, 2000; Doser, 2006) (Fig. 2.2). The difference in obliquity of plate convergence results in a transverse fault-controlled segmentation of the AASZ, being most prominent in the central and western Aleutians. Furthermore, the steady configuration change goes accompanied by a westward modification in seismic and volcanic activity, which is considered to be partly responsible for the AASZ-segmentation (Ryan et al., 2011). It is mostly the region where arc-perpendicular convergence takes place (Southern Alaska and eastern Aleutians) that exhibits intense volcanic activity and is characterised by a history of megathrust earthquakes. Along the central portion of the Aleutian arc, active volcanism and a relatively high level of seismicity are observed as well, despite the increasing obliquity of convergence. The western Aleutians, on the other hand, differ remarkably from the latter two regions in terms of tectonics, which is due to the progressively growing transform component of motion between the Pacific and North American plates. Evidence of this evolution is the diminishing degree of active volcanism and the lack of large earthquakes or megathrust events in the recorded history (Ryan et al., 2011). Subduction is complicated by collision of the Yakutat block with the North American plate, as a result of ongoing strike- slip motion along the southwest-northeast oriented Fairweather/Queen Charlotte Fault System (Fig. 2.2 and 2.4). Due to its buoyancy, the Yakutat terrain shows strong resistance against subduction, creating a sharp bend in the plate-interface and a shallowing of the dip of the subducted slab to ~3° beneath Prince William Sound (Doser & Brown, 2001).
Destructive plate margins, such as the Alaskan-Aleutian megathrust, are responsible for the most vicious of earth’s tectonic processes and thus pose a real hazard to human society. Their ability to trigger trans-Pacific tsunamis has to be taken into account as well (Ryan,
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2012). Generation of anomalously large tsunamis along the Aleutian margins, suggests a slow source mechanism. However, local tsunami waves can be induced as well by coseismic, offshore sliding (McAdoo et al., 2004). Most of the seismicity arising from the AASZ is the result of slip-induced thrust faulting along the convergent plate interface, extending from depths of 40-60 km to near the base of the trench, due to repeated strain releases at the locked plate contact. Additionally, reverse and strike-slip focal mechanisms along major fault systems in the overriding North American plate are responsible for shallow, crustal earthquakes (Lallement & Oldow, 2000). Deformation takes place as well within the subducting slab, leading to earthquakes with intermediate focal depths (70-300 km) along the Wadati-Benioff zone (Krauskopf et al., 1973).
Figure 2.2: Maps of Southern Alaska and the Aleutian Islands. Locations of important geographical, geomorphologic and tectonic features are indicated. Arrows in the upper image visualise directions and speed of motion between the Pacific and North American plates. Enlarged regions in A and B are framed with dashed boxes in the overview figure. The area depicted in A shows the oceanic sector of the eastern Alaskan-Aleutian arc from Atka Island up to Unimak Pass in the east and B displays the continental sector of the eastern Alaskan- Aleutian arc from Unimak Pass to Kodiak Island (after Ryan et al., 2011).
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2.1.2 Paleoseismicity
Together with those of Chile and Indonesia, the Alaskan-Aleutian megathrust is one of the three world’s most active seismic regions and, as mentioned before, home of repeated, moderate to giant earthquakes. Several sedimentological and archaeological evidences have proven their past occurrences and point towards a giant earthquake recurrence interval of 700-800 years for each of the separate AASZ-segments (Hutchinson & Crowell, 2007ab). One of these earthquake-induced evidences are buried peat-silt couplets, resulting from marsh submergence after coseismic land subsidence (Hamilton et al., 2005; Hamilton & Shennan, 2006). Other recorders and indicators of seismic activity that have been used to date past Alaskan earthquakes and to reconstruct their recurrence intervals are village abandonments, river delta sediments, uplifted Holocene terraces, land-level changes and earthquake- triggered turbidite deposits or sediment deformations in marine and lacustrine records (Hutchinson & Crowell, 2007a; Rymer & Sims, 1976). Seismites within lake sediments have been used in other study areas as well in order to obtain a better insight in their inducing mechanisms (e.g. Bertrand et al., 2008; Schnellmann et al., 2005; Strasser et al., 2007)
Figure 2.3: Map displaying rupture zones (pink areas) and epicentre locations (red circles) of great (Mw>8) and significant (Mw>7) earthquakes since 1900 A.D. (Ryan et al., 2011).
Since 1900 A.D., the AASZ has hosted a series of significant earthquakes (Mw>7). During the mid th 20 century, most of the Alaskan-Aleutian megathrust ruptured in a sequence of Mw>8 events: 1938 A.D., 1946 A.D., 1957 A.D., 1964 A.D. and 1965 A.D. (Fig. 2.3). Each of these megathrust earthquakes occurred along a well defined rupture zone. Despite the high level of activity, seismicity has been rather discontinuous with two regions (Shumagin gap and
Unalaska gap) that did not yet experience large (Mw>8) earthquakes in the past century and hence constitute an imminent threat for future shaking (Fig. 2.3). Only the 1964 A.D. Prince
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William Sound earthquake affected the study area on Kenai Peninsula in a noteworthy manner, since none of the other epicentres was located far enough to the east (Ryan et al., 2011).
On March 27, 1964 A.D., at about 05:36 p.m. local time the unusual severe Prince William
Sound (PWS) earthquake with Mw 9.2 struck the south coast of Alaska. The epicentre of the main shock was situated in northern Prince William Sound (61.04°±0.05°N; 147.73°±0.07°W) (Fig. 2.1, 2.3 and 2.4). This mega-earthquake was both the largest to occur in North America and the second largest recorded earthquake worldwide over the 20th century, ranked behind the 1960 A.D. Great Chilean Earthquake (Mw 9.5) (Doser & Brown, 2001; Hutchinson & Crowell, 2007b). Its main shock was followed by a series of aftershocks, of which there were several with Mw>6 (Krauskopf et al., 1973). The main event was characterised by a rupture length of roughly 700 km, extending from about 100 km east of the epicentre in Prince William Sound in the northeast to the southern end of Kodiak Island in the southwest. Coseismic subsidence extended over an elongated region, including Kenai Peninsula and most of Cook Inlet (Hamilton & Shennan, 2005) (Fig. 2.3 and 2.4). Tremors were accompanied by substantial vertical and horizontal tectonic deformation, and led to fatalities and infrastructure damage (Zweck et al., 2002). The strong ground motion induced many snowslides, rockfalls, seismic seiches in surface water bodies, and both subaerial and submarine landslides (Shennan et al., 2010; Krauskopf et al., 1973; Mavroeidis et al., 2008; McGarr & Vorhis, 1968; Reger et al., 2007; Updike et al., 1988). Extensive harm was recorded in Kenai, Moose Pass an Kodiak, but significant shaking was felt over a large region of Alaska, parts of western Yukon Territory and even British Columbia (Canada). Property damage was largest in Anchorage, due to both the main shock as well as the subsequent landslides. As a result of large-scale seafloor movements, a tsunami wave was generated and swept from the Gulf of Alaska across the length of the Pacific and lapped against Antarctica
(Krauskopf et al., 1973; Ryan, 2012; McAdoo et al., 2004). Tsunami-related damage has been reported along the Gulf of Alaska, the West Coast of the United States and in Hawaii. Submarine landslides as well created local sea waves or tsunamis (Shennan et al., 2009).
Figure 2.4: Tectonic setting of the eastern sector of the Aleutian megathrust and Yakutat microplate. The extent of coseismic surface deformation during the 1964 A.D. Prince William Sound earthquake is indicated (after Shennan et al., 2009).
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East of the Aleutian arc, along the Gulf of Alaska, crustal earthquakes in the overriding plate occur as a result of transmitted deformation stress, associated with the northwestward convergence of the Pacific plate that collides a block of oceanic and continental material into the North American plate (Ryan et al., 2011). According to Haeussler et al. (2002), paleoseismic data indicate that the timing and recurrence interval of megathrust earthquakes is similar to moderate (Mw 6-7), crustal fault-related (Nokleberg et al., 1994) earthquakes, which suggests a possible link between both types of seismic events. Within the greater Anchorage area of Southern Alaska, the only fault with unambiguous evidence of Holocene offset is the east-northeast striking Castle Mountain Fault (Fig. 2.4). The Castle Mountain Fault is located above the Aleutian megathrust and lies in a forearc setting (Haeussler et al., 2002). Further inland, the Denali Fault ruptured in a sequence of earthquakes in 2002 A.D. Krauskopf et al. (1973) relate two great (Mw>8) Yakutat Bay earthquakes in 1899 A.D. to motions along the Chugach-St. Elias Fault, which runs through the same general tectonic region as Prince William Sound (Fig. 2.4). Primary strike-slip movements along the Fairweather Fault (southeast of the Chugach-St. Elias Fault) is held responsible for the Lituya Bay earthquake of 1958 A.D. The Prince William Sound region is located in a zone of transition between strike-slip faulting along the Fairweather/Queen Charlotte Fault and trench-normal subduction along the Aleutian Trench (Fig. 2.4) (Shennan et al., 2009). Stover and Coffman (1993) provide an extensive overview of all seismic events in the state of Alaska starting from 1786 A.D., with indications of origin, hypocentre, magnitude and intensity (Musson et al., 2010).
2.1.3 Volcanic setting
Besides the Aleutian Ridge, Trench and Islands, the physiography of the AASZ is composed of the Alaska Peninsula volcanic arc as well. At least 29 active volcanoes have erupted along this arc during historical times, dating back to 1760 A.D. (Ryan et al., 2011). As mentioned before, volcanic activity is mainly concentrated along the central and eastern portions of the Aleutian arc. Most of the significant eruptions during the last ~300 years are reported and compiled for each volcano separately by the online Smithsonian Global Volcanism Program (http://www.volcano.si.edu). Dates of prehistoric eruptions are also included in the presented eruptive histories. However, these dates are subject to substantial error margins, as they are based on tephrochronologies, radiocarbon dating, varve counting etc.
Only the active volcanoes nearest to Kenai Peninsula and its direct surroundings are of interest for this study, since these are able to eject vitreous particles and fine ashes as far as the three studied lakes. Fig. 2.1 shows the geographically most prominent and/or active volcanoes across the Cook Inlet. Hence, these volcanoes are the best candidates for depositing layers of tephra in Eklutna Lake, Kenai Lake and Skilak Lake.
The last known eruption of Hayes volcano took place about 1000 years ago, after which no important activity has been registered. Mount Spurr and Redoubt, on the other hand, are the most active volcanoes in the upper Cook Inlet (Begét et al., 1994), with Spurr being the highest (3374 m) volcano of the Aleutian arc. The youngest vent of the latter volcano, Crater
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Peak, has been the source of 40 identified Holocene tephra layers. Two historical eruptions of Spurr in 1953 A.D. and 1992 A.D. deposited ash on the city of Anchorage (de Fontaine et al., 2007; Payne & Blackford, 2008). Historical eruptions from Redoubt originated from a vent at the north end of its summit crater. Especially the 1989 A.D. eruption of Redoubt had a severe economic impact on the Cook Inlet region and caused significant disruptions in airline traffic far beyond the volcano (Begét et al., 1994; Schiff et al., 2010; Wallace et al., 2013). Notwithstanding the prominence of the glacier-covered Iliamna stratovolcano, it is source to only a few explosive Holocene eruptions, of which the Volcanic Explosivity Indices (VEI) always remained lower than three (Waythomas et al., 2000). VEI represents a composite estimate of the magnitude of past explosive eruptions and is proposed as a semi- quantitative compromise between poor data and the need in various disciplines to evaluate the record of past volcanism (Newhall & Self, 1982). The Augustine volcano, located within the southern Cook Inlet, is the most active volcano of the eastern Aleutian arc. Historical eruptions of Augustine have been characterised by explosive activity, during which pumiceous tephras were emplaced. The latest episode of edifice collapse was caused by Augustine’s largest historical eruption in 1883 A.D. (Siebert et al., 1995; Begét et al., 1994). No Holocene eruptions have been reported for Douglas and only one with VEI 2 in 2006 A.D. for Fourpeaked. Of great importance in the volcanic history of the Aleutian arc is the 1912 A.D. Katmai-Novarupta eruption with VEI 6, being the world’s largest eruption during the 20th century. Both Katmai and Novarupta volcanoes are located on the Alaskan mainland, opposite to Kodiak Island (Fig. 2.2), 85 km southwest of Fourpeaked (not indicated on Fig. 2.1), only 12 km separated from one another. In response to the major eruption of Novarupta in 1912 A.D., a magma reservoir underneath Katmai drained towards Novarupta, inducing edifice collapse and formation of a wide caldera at Katmai volcano.
Ash layers from several (pre)historic eruptions of volcanoes in the Cook Inlet area have been identified in glacio-lacustrine sediments of Skilak Lake, Tustumena Lake and several peatland sites on Kenai Peninsula, using sediment analysis and high-resolution magnetic susceptibility profiling (Begét et al., 1994; Perkins & Sims, 1983; de Fontaine et al., 2007; Payne & Blackford, 2008; Stihler et al., 1992). Observed tephra layers typically contain fine, grey ash and varying proportions of glass shards, pumice and glass-coated phenocrysts (de Fontaine et al., 2007). Electron microprobe (EMP) analyses of glass shards and mineralogy studies were executed to create geochemical correlations with reference tephras from certain source volcanoes (Begét et al., 1994; Stihler et al., 1992).
2.2 Climate patterns in Southern Alaska
2.2.1 General tendencies and oscillations
2.2.1.1 Climate of Alaska
Alaska’s climate and its variations depend on three primary factors: latitude, continentality and elevation. All three of these parameters influence regional to even local environmental conditions and interact with each other to create a wide range of different climate zones. As
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Alaska extends from about 51°N to 71°N, its high latitude position gives rise to an extreme seasonal variability in the received solar radiation, even in its more southern regions, such as Kenai Peninsula and the surroundings of Anchorage (59°00’-61°50’N). According to Jones et al. (2009), high latitudes are particularly sensitive to climate change, causing alterations to the hydrological and biogeochemical cycles in the Arctic and subarctic during recent decades (Loso et al., 2006; Huse, 2001; Molnia, 2007; Wiles et al., 2008). Continentality signifies the degree in which landmasses are affected by the presence of ocean waters and the seasonal distribution of sea ice. Regions that are under the predominant influence of the sea, such as Kenai Peninsula, are characterised by a moderate seasonal temperature variability and a high humidity. Finally, altitude above sea level influences the climate of a given area as well. Overall, locations at higher elevations are cooler and receive more precipitation (http://oldclimate.gi.alaska.edu/Climate, 20/03/2014). Moreover, the climate of Alaska is a product of a number of different external and internal forcings. The most influential external forcings are solar activity, atmospheric composition, SST of the Pacific Ocean as well as the strengthening or weakening of ocean currents. Changes in one or several of these forcings typically cause changes within the atmosphere (internal forcings), such as the repositioning of the polar jet stream and the Aleutian Low pressure system or the frequency of La Niña and El Niño events (see below and section 2.2.1.2, Fig. 2.5) (http://pafc.arh.noaa.gov, 20/03/2014).
Weather patterns on Kenai Peninsula and surroundings are strongly influenced by the Aleutian Low (AL), a semi-permanent low-pressure centre situated over the North Pacific and in spring and winter driving the Alaskan gyre, which on its turn dominates the air-circulation in the study area (Jones et al., 2009; Wiles et al., 1999). During winter times, the AL strengthens and moves from its position in the Bering Sea eastward to the Gulf of Alaska, and westward toward the Aleutian Islands in July when it weakens (Fig. 2.5) (Jones et al., 2009). In addition to seasonal migration, the AL has two primary decadal modes: positive when the system is generally more easterly and intense, and negative when the system is generally more westerly weak. The intensity of the AL is related to larger-scale atmospheric circulation phenomena, such as the Pacific Decadal Oscillation (see section 2.2.1.3) and the Arctic Oscillation (see section 2.2.1.4) (Bond & Harrison, 2000; Hartmann & Wendler, 2005; Papineau, 2001; Overland et al., 1999). Prolonged phases of a positive AL have been associated with warmer temperatures and increased winter precipitation along the Gulf of Alaska and northwestern North America (Daigle & Kaufman, 2009; Jones et al., 2009; Wiles et al., 2008).
Figure 2.5: Position and strength of the Aleutian Low and Pacific High pressure centres during winter and summer times (http://www.seasonsinthesea.com, 25/05/2014).
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2.2.1.2 El Niño Southern Oscillation (ENSO)
The El Niño Southern Oscillation (ENSO) is the most important coupled ocean-atmosphere phenomenon to cause global climate variability on interannual timescales. El Niño (EN) refers to the state of the sea surface temperature (SST) over the east equatorial Pacific, which is the oceanographic component of the coupled system. The Southern Oscillation (SO) refers to the out-of-phase mass exchange between the Indian Ocean and the Pacific Ocean. A higher than average pressure over one region is in general linked to a lower than average pressure over the alternate region. The SO is characteristic of the atmospheric component of the coupled system (Brassington, 1997). ENSO shows irregular transitions with a periodicity of 3-8 years between two quasi-states, termed El Niño and La Niña. El Niño has been used to describe the conditions of extended warming of the east equatorial Pacific (‘warm event’), diminished Pacific trade easterlies, an intensified subtropical jet stream over the eastern Pacific and southwestern USA and the eastward propagation of the convergence zone to the central equatorial Pacific. La Niña refers to the reversal of the Walker circulation (Julian & Chervin, 1978), coupled with anomalously cool SST’s in eastern Pacific equatorial waters, a strengthening in easterly trade winds and the propagation of the convergence zone to the Indonesian region (Fig. 2.6) (Ruddiman, 2008; Brassington, 1997). Negative values of the multivariate ENSO-index (MEI) represent the cold ENSO-phase (La Niña), while positive MEI- values reflect the warm ENSO-phase (El Niño).
Figure 2.6 : Above: multivariate ENSO-index (MEI) from 1950 A.D. to 2008 A.D. (http://www.esrl.noaa.gov, 25/05/2014). Right: contrast between jet stream positions, pressure centres and prevailing climate conditions (cold, warm, dry, wet) on the North American continent during El Niño (positive ENSO-index) and La Niña (negative ENSO-index) winters (http://snowbrains. com, 25/05/2014).
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The impact of the climate variation in response to the ENSO takes place on a global scale and can be observed as well in North America through an atmospheric teleconnection (Fig. 2.6) (Moore et al., 2003). During El Niño winters, the eastern two-thirds of the state of Alaska are characterised by above normal temperatures and precipitation, while during La Niña winters temperatures are largely below normal (Fig. 2.6) (Papineau, 2001; Hess et al., 2001). Temperature patterns produced during El Niño, La Niña and neutral winters are modified by the concurrent state of the North Pacific SST anomalies, as indicated by the PDO (see section 2.2.1.3) (Papineau, 2001; Hartmann & Wendler, 2005).
2.2.1.3 Pacific Decadal Oscillation (PDO)
The Pacific Decadal Oscillation (PDO) is an ENSO-like phenomenon of Pacific climate variability, though with a longer, decadal-scale periodicity (Fig. 2.7) (Papineau, 2001; MacDonald & Case, 2005). Investigations during the mid and late 1990’s showed that SST’s across the Gulf of Alaska fluctuated over a time period of 20 to 30 years (Biondi et al., 2001). This fluctuation from above normal water temperatures to below and vice versa was termed PDO (Mantua & Hare, 2002; Wiles et al., 2004). When SST’s in the central North Pacific are above normal, SST’s along the coast of Alaska and British Colombia tend to be warmer than normal, which is referred to as the positive phase of the PDO. The negative phase of the PDO, on the other hand, occurs when SST’s in the central North Pacific are below normal (Fig. 2.8) (Mantua & Hare, 2002). PDO-strength is being measured by monitoring SST’s across the North Pacific, which are then used to construct PDO-indices, ranging from about -3 to +3 (Fig. 2.7) (Bond & Harrison, 2000).
Figure 2.7 : PDO-index from 1000 A.D. to 2000 A.D. based on observed monthly values (left) and reconstructed PDO-index from 1900 A.D. to 2014 A.D. (right) (http://en.wikipedia.org, 25/05/2014).
Several independent studies find evidence for just two full PDO-cycles in the past century. Cool PDO-regimes prevailed from 1890-1946 A.D. and again from 1945-1976 A.D., while warm PDO-regimes dominated from 1925-1946 A.D. and from 1977 A.D. through (at least) the mid-1990’s (Fig. 2.7) (Mantua & Hare, 2002). The 1976 A.D. Pacific climate shift, going from a cold PDO-phase to a warm one, and its manifestations and significance in Alaskan climatology during the last half-century, were examined by Hartmann and Wendler (2005).
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They observed that mean annual and seasonal temperatures for the positive phase were remarkably higher than for the negative phase. Likewise, mean cloudiness, wind speeds and precipitation amounts increased, while mean sea level pressure decreased. This pressure decrease resulted in an intensification of the AL in winter and spring, leading to an increased advection of relatively warm and moist air to Alaska and storminess over the state. The exact mechanisms that steer PDO-variability, however, remain unclear (Mantua & Hare, 2002).
Figure 2.8 : During positive PDO-phases, waters off the West Coast are kept warm (left), whereas during negative PDO-phases the same waters are rather cool, causing continental conditions to be drier than normal (http://www.browningnewsletter.com, 25/05/2014).
2.2.1.4 Arctic Oscillation (AO)
The Arctic Oscillation (AO) is an index (Fig. 2.9), which varies stochastically over time with no particular periodicity and reflects the dominant pattern of non-seasonal variations in sea- level pressure north of 20°N. Centred over the Arctic region at a height of 32 to 48 km above the earth’s surface, a zone of very strong winds (polar vortex) exists (Thompson & Wallace, 1998). These winds blow in a counterclockwise direction when viewed from space. Significant modifications in the strength and position of these winds alter storm tracks and winds in the lower atmosphere (Fig. 2.9). The strongest response to changes of this nature appear to occur in the North Atlantic, explaining the close relation of the AO with the North Atlantic Oscillation (NAO) (Ambaum et al., 2001). However, there are indications of a modest correlation between AO and the position and strength of the AL pressure system (Overland et al., 1999). Fig. 2.9 presents the AO-index, constructed for the interval 1899-2011 A.D. The AO-index is based on monthly wintertime sea-level pressure patterns of the Atlantic and Arctic basins. Values of the index range from about -3 to +3. The polar vortex tends to be stronger than normal when the index is positive, on its turn producing lower than normal sea-level pressures over most of the Arctic region (Fig. 2.9) (Thompson & Wallace, 1998).
14 Chapter 2 - Study area
Figure 2.9: Left: AO-index from 1899 A.D. to 2011 A.D. (http://en.wikipedia.org, 25/05/2014). Left: difference in jet stream position and pressure centres during positive and negative AO-phases (http://www.esrl.noaa.gov, 25/05/2014).
2.2.2 Glacial history
Kenai Peninsula and the surroundings of Anchorage comprise mountainous as well as lowland terrains that were shaped by the last major glaciation (Wisconsin) (Fig. 2.10). However, evidences of earlier glaciations can also be observed. During the climax of the last major glaciation the most part of Southern Alaska was covered with the Cordilleran ice sheet (Fig. 2.10), although a few small, ice-free refugia existed (Reger et al., 2007; Péwé et al., 1963). A four-stade model for the last major glaciation or ‘Naptowne glaciation’ is based on the identification of different generations of terminal moraines (type moraines), afterwards attributed to the subsequent Moosehorn (oldest), Killey (intermediate), Skilak (younger) and Elmendorf (youngest) stades (Fig. 2.10) (Reger et al., 2007; Scott, 1982). The earliest and longest stade of the Naptowne glaciation, the Moosehorn expansion phase, was a very complex event, during which ice from the southern Kenai Mountains advanced into the Cook Inlet. In response to climatic change after the climax (~23 cal ka B.P.) of the Moosehorn stade (~32-18.5 cal ka B.P.), the greatly expanded glacier systems became unstable, thinned and began receding, giving rise to recessional moraines in northcentral Kenai Peninsula (Fig. 2.10). During the, relatively short, second stade of the Naptowne glaciation (~18.5-17.5 cal ka B.P.), much of Kenai Lowland became exposed. Double end moraines in the north mark advances of Killey age. The readvance during the Skilak stade (~17.5-16.0 cal ka B.P.) built, among other, the damming, terminal moraine at the western end of the current Skilak Lake basin (Fig. 2.10). Finally, the last stade of the Naptowne glaciation (Elmendorf, ~16-11 cal ka B.P.) was characterised by a near-simultaneous advance of ice from the Chugach and Kenai Mountains down Turnagain Arm, followed by a retreat and deposition of terminal moraines north of Anchorage. Elsewhere, glacier positions associated with the Elmendorf stade were overall close to the front of the Kenai Mountains, though well beyond known Holocene (post-Naptowne) positions (Fig. 2.10) (Reger et al., 2007).
15 Chapter 2 - Study area
Figure 2.10: Above: paleogeography of Southern Alaska during the climax of the last major glaciation (~23 cal ka B.P.). Left: model of the Naptowne glaciation in the Cook Inlet region, with indications of principal ice-flow directions (black arrows) and limits of ice extent during Moosehorn (red), Killey (purple), Skilak (yellow) and Elmendorf (blue) stades (Reger et al., 2007).
Following retreat from the latest Pleistocene advances (Naptowne glaciation), valley glaciers with land-based termini were in retracted positions during the early to middle Holocene. However, two distinct early Neoglacial expansions were centred around 3.3-2.9 and 2.2-2.0 cal ka B.P. and followed by a major advance in 550-720 A.D. in Southern Alaska (Barclay et al., 2009; Calkin, 1988). The Medieval Optimum or Medieval Warm Period (MWP), encompassing at least a few centuries prior to 1180 A.D. is recognised by an overall retreat of land-terminating glaciers (Wiles et al., 2008). During the subsequent Little Ice Age (LIA) expansion started in 1180-1320 A.D. and culminated with two advance phases, in 1540-1710 A.D. and in 1810-1880 A.D. respectively, dated with the precision of tree-ring cross-dating (Calkin et al., 2001; Barclay et al., 2009; Wiles et al., 1999). Denton and Karlén (1973) note that several minor glacier fluctuations were superimposed on the broad expansion intervals of the LIA, taking place at around 1500-1640 A.D., 1710 A.D., 1780 A.D., 1850 A.D., 1890 A.D. and 1916 A.D. These LIA advances were the largest Holocene ice-expansions in Southern Alaska, driven by changes of mountain glacier mass balance (Barclay et al., 2009; Barlow et al., 2012). Especially the second LIA advance phase (1810-1880 A.D.) dominated in the Gulf of Alaska (Calkin, 1988; Daigle & Kaufman, 2009). Most glaciers have an uninterrupted history of continuous post-LIA retreat (e.g. Huse, 2001; Molnia, 2007).
16 Chapter 2 - Study area
2.3 Kenai Peninsula and Anchorage area
Figure 2.11: Geologic map of northern Kenai Peninsula, Cook Inlet and Anchorage surroundings. Chugach Mountains, Kenai Mountains and Kenai Lowland are indicated. Eklutna Lake and Kenai lake are entirely nested within the mountainous regions of Cretaceous metasedimentary rocks (Valdez Group) and mélanges (McHugh Complex), whereas the Skilak Lake basin is partly surrounded by glacial, alluvial and terrace deposits. Structurally, the map area is cut by a number of major faults and postulated faults (Fig. 2.4) (Mankhemthong et al., 2013).
Kenai Peninsula is a large peninsula, protruding from the south coast of Alaska and bordering to the Cook Inlet (west), Prince William Sound (northeast) and the Gulf of Alaska (east and south) (Fig. 2.1 and 2.11). It extends for over approximately 240 km southwest of the Chugach Mountains and the city of Anchorage. The Mesozoic bedrock of the Kenai Mountains (Fig. 2.11) is aligned with the Chugach Mountains (Wilson & Hults, 2013; Nokleberg et al., 1994), running along the southeast spine of the peninsula. Both mountain chains are home to several icefields (e.g. Sargent Icefield and Harding Icefield) out of which many glaciers originate and flow down into the mountain valleys (Reger et al., 2007) (Fig. 2.1). In contrast, the northwestern coast of Kenai Peninsula (Kenai Lowland) is flatter, marshy and covered with numerous small lakes and few larger lakes (e.g. Skilak Lake). The flat to rolling surface was formed by several Wisconsin-age glaciations and is composed of organic-rich late-glacial and Holocene peat, interbedded with layers of volcanic ash (Reger et al., 2007; Wilson & Hults, 2013; Nokleberg et al., 1994). A rain shadow is created by the Kenai Mountains, causing high amounts of precipitation on the eastern side of the mountains and much drier conditions to the west. The Kenai Lowland is characterised by a semi-continental climate with lower precipitation values, colder winter temperatures and warmer or comparable summer temperatures than surrounding coastal locations (Jones et al., 2009).
17 Chapter 2 - Study area
2.4 Eklutna Lake
2.4.1 Lake basin and watershed
Eklutna Lake is a relatively small lake (14.1 km2), located 45 km northeast of Anchorage in the Chugach State Park, nested in the Chugach Mountains at 265 m above sea level (Fig. 2.12). It has an average width of around 1.4 km, a length of 10.5 km and an average and maximum water depth of 36.6 m and 61.2 m respectively. A more elaborate description of the basin bathymetry can be found in section 5.1.2. The narrow lake basin occupies an elongated, glacially steepened depression (Eklutna Valley), naturally dammed at its northwest end by a recessional terminal moraine from the Pleistocene Eklutna Glacier (Brabets, 1993; Hollinger, 2002; Rymer & Sims, 1976). Eklutna Lake is dimictic, which means that the water column mixes from surface to bottom twice each year, typically during spring and autumn (Brabets, 1993).
Figure 2.12: Eklutna Lake and its surroundings. The lake’s catchment (drainage basin boundary) and its inflow and outflow river systems are shown. Glaciers, of which the extensive Eklutna and West Fork Glaciers, cover the higher elevated parts of the drainages within the Chugach Mountains. Water of the lake basin is being diverted via the Eklutna Water Project pipeline and a tunnel leading to the Eklutna hydroelectric power plant (Brabets, 1993).
The drainage basin of Eklutna Lake (311 km2) is dominated by two sub-watersheds, the East
Fork Eklutna Creek basin and the West Fork Eklutna Creek basin, hosting the lake’s two main inlet streams (East Fork and West Fork). Several smaller streams account for the almost negligible remainder of the total runoff towards Eklutna Lake (Fig. 2.12), because the terrain
18 Chapter 2 - Study area they drain is relatively low in elevation and is located within the rain shadow of the higher elevated portions of the catchment that intercept storms originating in the Gulf of Alaska (Fig. 2.1). Moreover, the glacier-free part of the catchment is comparatively well vegetated, causing the fallen precipitation to be taken away by transpiration, infiltration and evaporation prior to runoff (Larquier, 2010). The East Fork as well as the West Fork basin are entirely located within the Chugach Mountains, which are constituted of structurally complex and variably metamorphosed sedimentary and igneous rocks (Wilson & Hults, 2013; Mankhemthong et al., 2013). Both sub-watersheds are characterised by the occurrence of numerous glaciers of different types and sizes (Brabets, 1993; Johnson, 1947), including a series of small, steep glaciers in the East Fork basin (12.2 % glacier cover). The 12 km long Eklutna Glacier on the other hand, covers about 46.6 % of the West Fork basin, making it the lake’s primary water source (Hollinger, 2002) (Fig. 2.12). Remote sensing techniques document a fifty-year history of glacier terminus retreat and thinning. Furthermore, field observations between 1975 A.D. and 1988 A.D. have shown that Eklutna Glacier withdrew about 1.6 km over a time span of 30 years (Brabets, 1993). In 2002, its terminus was approximately 6.4 km away from Eklutna Lake (Hollinger, 2002). Landsat images (Google Earth,
2014) show a current distance of about 8.2 km between glacier front and lake basin. All of these evidences indicate that Eklutna Glacier is in a phase of pronounced negative mass balance.
2.4.2 Eklutna Water Project and Eklutna Power Project
The Eklutna River is responsible for the lake’s outflow, transporting water from the northwestern end of the basin and emptying into Knik Arm (Hollinger, 2002) (Fig. 2.12). For purposes of power generation, the naturally dammed lake was converted into a reservoir after the construction of the first storage dam in 1929 A.D. (Aquatic Ecosystem Restoration Technical Report, 2011). Part of the water from the lake is being diverted to the Municipality’s Eklutna Water Project (Anchorage Water and Wastewater Utility, AWWU, Municipality of Anchorage (2005)) and the Alaska Power Administration’s Eklutna Power Project. The latter project focused on the construction (1928-1929 A.D.) of a hydroelectric power plant at Eklutna, supporting Anchorage and Matanuska Valley power needs (Brabets, 1993; Hollinger, 2002; Alaska Power Administration, 1992). Several infrastructure components needed to be realised: a low-head storage dam at the outlet of Eklutna Lake to increase the water holding capacity, a diversion dam on the Eklutna River (12 km downstream of the lake), a tunnel to channel water from the diversion dam to the penstock, a penstock (tube connecting the tunnel to the power plant), a powerhouse, transmission lines and substations (Hollinger, 2002; Alaska Power Administration, 1992; Johnson, 1947).
The capacity of the Eklutna power generation system was upgraded several times by dam rehabilitation. In March 1964 A.D., the Prince William Sound earthquake caused severe damage to the existing storage dam (Foster & Karlstrom, 1967), after which the current, earth and rockfill structure was built (Aquatic Ecosystem Restoration Technical Report, 2011). During exceptionally wet years, the storage capacity of the lake can be exceeded, causing water to flow over the storage dam into Eklutna River. The last time an overflow of this nature had been reported, was in 1997 A.D. (Larquier, 2010).
19 Chapter 2 - Study area
2.4.3 Climate and sedimentation
The Eklutna Lake area is characterised by a short melting season (May-September), followed by a longer period of snow and ice cover. Throughout winter, flow discharges from creeks stay minimal due to limited precipitation and freezing temperatures (Hollinger, 2002). According to Johnson (1947), monthly mean temperatures stay below freezing point from November through March and reach their minima during December and January (data from weather station at power plant, near Eklutna). From the beginning of summer on, the Eklutna Lake basin starts to collect water and continuously grows towards its utmost storage capacity (Hollinger, 2002). Maximum monthly temperatures occur in June, July and August. The month of August is marked by the greatest amounts of monthly mean precipitation, together with September, July and June. As a consequence, high stream discharges only occur during the period of end May, through September or early October (Johnson, 1947).
Alluvial deposits, consisting chiefly of gravel and sand, can be found at the southern end of Eklutna lake and at the mouths of tributaries along the lake margins. Colluvial deposits are locally accumulated on steep slopes, primarily through the action of gravity and running water. Most sediment enters the lake during the ice-free summer months, when stream runoffs are largest (Brabets, 1993). Two main processes dominate sedimentation in Eklutna Lake: delta progradation and river plume dispersion. The combined flow of the East Fork and West Fork undergoes a rapid decrease in velocity after debouching. As a consequence, the coarse sediment load (bedload) is deposited and an instable delta structure develops. The finer-grained material, on the other hand, is not deposited in the delta area, but continues to move on through the lake as a turbulent plume, commonly referred to as a density or turbidity current, and eventually settles on the bottom of the basin (Brabets, 1993).
2.5 Kenai Lake
Figure 2.13: Kenai River, connecting Kenai Lake and Skilak Lake (see section 2.6), and transporting rain- and meltwater from the Kenai Mountains across the Kenai Lowland, after which it empties in Cook Inlet (http://www.sewardshorefishing.com, 10/07/2014).
Kenai Lake, at 132 m elevation, is a large, zig-zag shaped lake on Kenai Peninsula (Fig. 2.1) and forms the headwaters of the Kenai River (Fig. 2.13). It is 38 km long and has a surface
20 Chapter 2 - Study area area of 55.9 km2, occupying a narrow, angular trough scoured by thick, glacial ice along linear zones of structural weakness in the Valdez Group basement rocks of the Kenai Mountains (Fig. 2.11) (Reger et al., 2007; Wilson & Hults, 2013). The lake basin has a relatively flat bottom with an average depth of ~91 m and a maximum depth of ~165 m. Kenai River commences at the western end of Kenai Lake and is characterised by a turquoise water colour, which is the result of a high concentration of dispersed clay-sized particles that were brought into the lake by meltwaters from several glacier within the watershed (Reger et al., 2007). Overflowing water is further transported towards Skilak Lake (Fig. 2.13). Previous studies on the catchment and sedimentation in Kenai Lake is rather limited to almost non- existing. Therefore, reference is made to results and discussions in Chapter 5 and Chapter 6.
2.6 Skilak Lake
2.6.1 Lake basin and watershed
Figure 2.14: Skilak Lake and its surroundings. The lake’s inflow and outflow river systems are shown, as well as the meltwater providing Skilak Glacier in the Kenai Mountains. In the inset frame, the stippled area indicates a submerged moraine that forms a bar across the lake (Sonett & Williams, 1985).
Skilak Lake is also a proglacial lake, located within the central region of the Kenai Peninsula,
85 km south of Anchorage, in a moraine-dammed, glacially scoured basin (Reger et al., 2007) on the boundary between Kenai Mountains an Kenai Lowland (Fig. 2.1). A set of two end moraines embodies the western terminus of the lake and represents the Skilak stade glaciation (Wisconsin Naptowne glaciation). The lake is approximately 24 km long and becomes as much as 7 km wide in its western portion, possessing a surface area of ~99 km2 and an average depth of 73 m (Perkins & Sims, 1983; Rymer & Sims, 1976; Reger et al., 2007). A submerged terminal moraine cross-cuts the basin transversely at, connecting the east and west landings, and rises over 120 m off the lake floor to separate the lake into a
21 Chapter 2 - Study area
proximal and a distal sedimentary basin (Fig. 2.14). The proximal basin has a surface area of ~67 km2 and a maximum depth of 183 m in its narrowest part where the scouring glacier was confined between rock walls. The distal basin, west of the submerged moraine, has a surface area of ~32 km2 and a maximum depth of 92 m (Perkins & Sims, 1983; Reger et al., 2007). For a more detailed discussion on the basin bathymetry, see section 5.2.2.
At the eastern end of Skilak Lake, Skilak River and Kenai River enter, building out deltas in the proximal basin (Fig. 2.14). The continuation of Kenai River drains the lake at its westernmost end (Fig. 2.14). Skilak River is a glacier-fed meltwater stream, currently originating at the northern end of a small, ice-marginal lake, adjacent to the terminus of Skilak Glacier (Molnia, 2008), a lobe of the Harding Icefield (Perkins & Sims, 1983; Rymer & Sims, 1976; Sonett & Williams, 1985). As glaciers flow across the mouths of adjoining valleys, they often cause these type of recessional, moraine-dammed lakes to form. Moreover, glacier ice dams are often subject to repeated failure, causing floods to occur regularly (Post & Mayo, 1971). Meltwater from the unnamed, ice-marginal lake flows along the Skilak River valley for 13 km towards Skilak Lake with little addition of non-glacial sediment, after which most of the entrained material is launched into the lake waters. The Kenai River drains Kenai Lake and carries a strongly reduced sediment load to Skilak Lake as a consequence of the trapping efficiency of the upstream Kenai Lake and a series of smaller lakes in its watershed (Fig. 2.13). No major streams enter Kenai River between both basins, suggesting that the majority of transported sediment becomes entrained when the river is discharged from Kenai Lake or by reworking of bedload material. Hence, glacier-fed Skilak River is dominant in transporting sediment towards Skilak Lake (Perkins & Sims, 1983).
2.6.2 Climate and sedimentation
As mentioned in the previous section, Skilak River carries the majority of sediment deposited in Skilak Lake. Therefore, variations in meltwater release from the ice-marginal lake, determine changes in stream discharge and thus, sediment transport towards the Skilak Lake basin. Meltwater release, on its turn, is indirectly controlled by annual ablation of Skilak Glacier, which is sensitive to regional temperatures (see section 3.3) (Perkins & Sims, 1983). A study by Perkins and Sims (1983) reveals that piston cores from the proximal as well as the distal basin comprise annually laminated deposits, interbedded with horizons of volcanic ash (Rymer & Sims, 1976). Varves in the proximal basin are thick and mainly composed of turbidite-sequences with erosive contacts. In contrast, cores from the distal basin show thin, regular varves that lack turbidites and slump deposits. The difference in depositional characteristics between both lake areas can be related to the fact that turbidites that originate at the deltas of Skilak and Kenai Rivers, cannot surmount the submerged moraine ridge. Hence, sediments deposited in the distal basin are transported from the proximal deltas by lake currents, after which they passively settle out of the water column when current velocity decreases (Perkins & Sims, 1983; Desloges & Gilbert, 1994). As the western sub-basin is for the most part isolated from major sediment sources, it acts as a settling pond and is less likely to be subjected to turbidity current deposits than is the proximal, eastern basin (Stihler et al., 1992).
22 Chapter 3 - Varves as climate recorders
3 VARVES AS CLIMATE RECORDERS
Nature is painting for us, day after day, pictures of infinite beauty. - John Ruskin -
3.1 Glacial varves
According to the Swedish geologist Gerard De Geer (1858-1943) the term ‘varve’ in English is derived from the Swedish term ‘varv’ or ‘hvarf’ and has been described in 1912 as the whole of any annual sedimentary layer (Zolitschka, 2007). Hence, a varve consists of sediment deposited during spring, summer, fall and winter. Several terms have been proposed to denote sediment deposited throughout individual years: ‘annual laminations’, ‘seasonal rhythmites’ and ‘varves’ (O’Sullivan, 1983). In this study ‘annual lamination’ and ‘varve’ will be used as synonyms. Nonetheless, one should be aware of the conceptual mistake contained by the term ‘annual lamination’, as individual laminations often represent rather seasonal deposits. Varves can be formed in a wide range of different marine, estuarine and, mostly, lacustrine depositional environments, due to a season-controlled variation in clastic, biological and chemical sedimentary processes (Anderson & Dean, 1988). Even though the best known and first acknowledged varve type is the light/dark-coloured, clastic couplet deposited in glacial lakes, non-glacial varves exist as well. The latter group comprises ferrogenic, calcareous and biogenic annual deposits (O’Sullivan, 1983).
Figure 3.1: Model of clastic varve formation all the way through an annual freeze-thaw cycle. During spring/summer (‘summer layer’) coarse-grained minerogenic particles accumulate via runoff (pale or dark) and during fall/winter (‘winter layer’) fine-grained minerogenic particles settle out of suspension (dark or pale) (Zolitschka, 2007).
Glacio-lacustrine varves consist mainly of allochthonous, clastic matter from the local drainage basin or beyond (O’Sullivan, 1983; Anderson & Dean, 1988). They are formed in lakes which are fed by runoff from glaciers or receding glacial ice margins. During spring
23 Chapter 3 - Varves as climate recorders and summer (melting seasons) a plume of sediment is carried into the lake by meltwater, produced through solar insolation by the melting of snow on glaciers and land surfaces surrounding the lake. Moreover, large volumes of water can be contributed when glacial ice starts to melt in the glacier’s ablation zone. Depending on the density of the incoming sediment plume in relation to the density of the lake water, the inflowing water stream spreads as an over-, inter-, or underflow (Sturm & Matter, 1978). After entering the lake, the flow velocity of the incoming stream becomes transformed into turbulence, which reduces the transport capacity for suspended sediment particles (Zolitschka, 2007). As a consequence, coarser material in the sediment plume settles down rapidly, creating the varve’s silty base (‘summer layer’) (Fig. 3.1). This layer may contain fine sand as well and a certain amount of organic fragments, such as leaves or pieces of wood, may additionally be incorporated via runoff. The finer material of the meltwater plume remains in suspension much longer, often until the lake freezes again during fall and winter (non-melting seasons). When the fine grained fraction eventually settles, the second varve component is formed, a clay layer (‘winter layer’). Hence, every silt (sand)/clay-couplet of which each lamina ideally stands out in both colour and composition, represents one year of proglacial sedimentation (Verosub, 2000) (Fig. 3.1). Interbedded, additional coarse-grained laminae are frequently observed and often related to successive annual runoff events (Zolitschka, 2007).
The formation and preservation of annually laminated sediments requires an accumulation environment in which no disturbances (e.g. bottom currents, gas bubbling through organic-rich sediment, bioturbation by benthic organisms) occur. Hence, a partly or entirely anoxic state of bottom waters is favourable for varve conservation (O’Sullivan, 1983; Anderson & Dean, 1988). Anoxic conditions can be permanent or seasonal and can occur naturally or result from anthropogenic activity (e.g. cultural eutrophication) (Zolitschka, 2007). Furthermore, the morphology of the lake basin has to be suited for preservation of deposited sequences. In order to prevent or minimise perturbations by any kind of slope related mass-movements, the lake basin should be flat-bottomed. Lastly, if the basin is deep with a small surface area, it is geomorphologically protected against water column circulation or mixing driven by wind or density differences, due to which bottom deposits can become disrupted and resuspended (O’Sullivan, 1983; Zolitschka, 2007). However, sediment with a low organic content and a high portion of clastics or chemical precipitates may retain its laminated character under conditions of greater circulation or higher oxygen levels. The definition of the fine-structured layering can be enhanced to form sharply delineated laminations during post-depositional compaction when pore water is excluded (Anderson & Dean, 1988).
3.2 Varve chronology
Gerard De Geer was also first to notice the possibility of using glacial varve sequences in order to establish time lines for chronologic studies and correlation purposes (Anderson & Kirkland, 1966). The annual rhythm of lacustrine sediment accumulation provides an internal clock that can be used for continuous dating without interpolations. Replicate varve counts should be executed in order to establish a solid varve chronology and can be based on microscopic analysis of thin sections as well as image analysis techniques (Ojala et al., 2012).
24 Chapter 3 - Varves as climate recorders
Thickness measurements of every individual varve can then be used to create an age-depth model with as many data points as varves that have been counted (Zolitschka, 2007). Radiometric dating and sometimes tephrochronology (in volcanically active regions) or optically stimulated luminescence dating can be applied to annually laminated records in order to independently verify the varve counting age-depth model (Ojala et al., 2012; Stihler et al., 1992; Jirikowic et al., 1993). Furthermore, records can be compared with historic data of events as well. This method is referred to as a multiple dating approach (Zolitschka, 2007).
A varve chronology can only be established if the structure of the varve cycle is clear. Before varves can be counted and their thicknesses measured, a conceptual, and often idealised, model to explain varve formation should be developed (Ojala et al., 2012; Francus et al., 2013). This model is site specific, depends on limnologic and environmental conditions within the catchment and should characterise specific features in varve composition that can be used to determine boundaries between consecutive varves and identify stratigraphic bodies of no chronological meaning (e.g. turbidites, homogenites) (Zolitschka, 2007).
3.3 Climate proxies and calibration
Varved lake sediments are important sources of information concerning past climate and environmental fluctuations and usually contain long and continuous proxy-records of physical, chemical and biological parameters that can be used to interpret the rate, magnitude and direction of natural changes as well as human impact on past environments (Ojala et al., 2012; Anderson & Dean, 1988). The seasonal variability of temperature and precipitation (rainfall and snowfall), responsible for formation of varved lake sediments, has amplitudes twice as large as long-term climatic fluctuations. In addition, varves provide an internal time frame in calendar years and hence, represent independently datable evidences of high-resolution environmental variations (Brauer et al., 2009; Zolitschka, 2007). Instrumental and written (historic) information about specific climate conditions are restricted to the last few centuries and millennia (Zolitschka, 2007). After calibration, lake archives offer the possibility to extend these records further back in time, depending on the stratigraphic reach of the studied depositional successions (Leemann & Niessen, 1994). However, it is important to understand that each of the studied proxy parameters contains only part of the answer to reveal the full range of environmental conditions (Zolitschka, 2007).
3.3.1 Varve thickness
The total thickness of an individual varve is a function of the climate during the year that it is representing. Since climate varies on a yearly basis, so does varve thickness. Consequently, registration of varve thicknesses over substantial stratigraphic intervals, can provide continuous, high-resolution data sets of climate change (Verosub, 2000). In order to establish a relationship between varve thickness and climate parameters for a specific study area, one needs to calibrate records of thickness measurements to instrumental data from stations or other independent evidences for specific climatologic conditions (e.g. PDO-
25 Chapter 3 - Varves as climate recorders phases, LIA advances). Numerous studies have already been using varve thickness as a proxy for paleoenvironmental conditions, of which a few are mentioned in the following paragraph.
A study on lacustrine deposits in the glacier-dammed, proglacial Iceberg Lake in Southern Alaska by Diedrich and Loso (2012), for instance, suggests a minor varve-thickening in response to the onset of the LIA glacial advance in this region (eastern Chugach Mountains). Ólafsdóttir et al. (2013) concur with the latter observation and attribute the enhanced delivery of sediment to Hvítárvatn (Iceland) to a peak in glacial erosion during the LIA. Additionally, spectral analyses on varve thicknesses point towards a reflection of high- frequency cyclicities (e.g. AMO, NAO) in lacustrine sedimentation rates (Ólafsdóttir et al., 2013). Several other authors (e.g. Kaufman et al., 2011; Tian et al., 2011) discuss the degree of correspondence between varve thicknesses in lakes in the North Pacific region and PDO, with Tian et al. (2011) suggesting thicker annual deposits during cold PDO-phases after a short time lag of three years. Kaufman et al. (2011), on the other hand, reveal a positive relation between varve thickness, warm PDO-phases and a strengthened Aleutian Low on multidecadal timescales, whereas interannual correlations between the latter variables seem to be insignificant in Shadow Bay (southwest Alaska). The existing or non-existing relations between forcing mechanisms and annual lake deposits rely on the impact of associated long- or short-term climate changes on sediment discharge towards the basin of interest. Long-term variations in varve thickness are often related to adjustments in glacier balance as a result of prolonged periods of colder/warmer temperatures (e.g. LIA, PDO, ENSO) (Leonard, 1986; Leonard, 1997; Ohlendorf et al., 1997). On short timescales, increased varve thicknesses have been attributed to heavy precipitation (Lotter & Birks, 1997; Ohlendorf et al., 1997), elevated annual/summer temperatures (Perkins & Sims, 1983; Loso et al., 2006; Cockburn & Lamoureux, 2008) and limited annual snowfall (Perkins & Sims, 1983). Perkins and Sims (1983) explain the apparent inverse correlation between varve thickness and mean annual cumulative snowfall in Skilak Lake with the fact that snowfall increases the albedo of the meltwater providing Skilak Glacier, on its turn inhibiting ablation of the sediment-laden glacial ice. In contrast, Cockburn and Lamoureux (2008) suggest that sediment yields in two High Arctic watersheds amplify in response to increased winter snowfall.
3.3.2 Grain-size and chemical composition
Climatic variations, at least lasting for a few days, can produce variations in the coarseness of the grain-size within the summer phase of glacial varve couplets (Peach & Perrie, 1975). Conceptual models for varve formation establish the link between processes occurring in the lake’s watershed, such as river floods or snowmelt, to specific lamina within the varve structure (Francus et al., 2013). Hence, the internal architecture of annual deposits allows to determine climatic cycles or events with periods of less than one year (Peach & Perrie, 1975). This way, the seasonality of coarse flood deposits can be deduced through analysis of their microstratigraphic position within the annual cycle of sedimentation (Brauer et al., 2009). Episodic river inflow can represent conditions ranging from regular annual glacio-lacustrine meltwater events (‘first flush’) to erratically-timed flash floods from thunderstorms or other high-precipitation occurrences (Anderson & Dean, 1988). Physical properties of such layers,
26 Chapter 3 - Varves as climate recorders with grain-size being an important one, posses an intrinsic value as indicator of hydrological processes (Francus et al., 2013). For instance, Kaufman et al. (2011) defined a strong relation between maximum annual grain-size and maximum spring daily discharge in varved sediments from Shadow Bay (southwest Alaska).
Moreover, flood-triggered detrital matter often shows a mineralogical and geochemical contrast with the surrounding, autochthonous sediment (Brauer et al., 2009; Thorndycraft et al., 1998). After studying the geochemistry of surface sediments from Patagonian fjords using XRF, Bertrand et al. (2012) conclude that under relatively cold climate conditions, chemical weathering is weak and the inorganic geochemical composition of the fjords sediments is primarily controlled by hydrodynamic (intensity of river discharge) mineralogical sorting. Chemical element-concentrations and -ratios can thus be related to grain-size and, indirectly, to environmental conditions. Cuven et al. (2010) as well succeeded in establishing a connection between chemical measurements of several elements and grain- sizes of finely laminated sediment from two Arctic lakes. Results show that Si and Zr are associated with very coarse silt and sand deposits, K and Fe with clay-rich layers and Ti with silty facies. Additionally, they found the ratio of K/Ti to be the best marker for upper varve boundaries, making it a useful tool for varve identification (Cuven et al., 2010).
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4 MATERIALS AND METHODS
I am among those who think that science has great beauty. A scientist in his laboratory is not only a technician. He is also a child placed before natural phenomena which impress him like a fairy tale. We should not allow it to be believed that all scientific progress can be reduced to mechanisms, machines, gearings, even though such machinery has its own beauty. - Marie Curie -
In this chapter, a series of used methods is mentioned and the underlying reasons why these methods were chosen to reach the intended objectives, discussed. Often they show a strong relation with each other and cannot be separated from one another. In order to visualise this interdependence, a workflow chart was constructed and can be found in section 4.8.
4.1 Reflection-seismic profiling and interpretation
High-resolution seismic data were collected during a limnogeological expedition of the RCMG (Renard Centre of Marine Geology, Ghent University) in June 2012. Kenai Lake (02- 04/06), Skilak Lake (06-14/06), and Eklutna Lake (26-28/06) were surveyed with a small research vessel. On each lake a GeoPulse pinger sub-bottom profiler source/receiver on an inflatable catamaran was used to acquire a grid of reflection-seismic profiles. Depending on the geometry of the basin, several longitudinal as well as transverse profiles were navigated. The acoustic signal, with a central frequency of 3.5 kHz, reached a maximum penetration depth of approximately 80 m in the lakes’ sedimentary infill. A GPS-system was used to provide horizontal positioning and help with navigation.
Raw seismic data (ELICS format) were converted into the industry standard SEG-Y file format, developed by the Technical Standards Committee of the Society of Exploration Geophysicists (SEG), for digital processing and analysis using the Kingdom Suite 8.7 (Seismic Micro- Technology Inc.). Seismic data analysis and interpretation of profiles was mainly executed to develop a general overview of the bathymetric and stratigraphic environment of the coring sites, which could then be related to the nature of the sedimentary successions in the retrieved core samples (see section 4.2). Furthermore, studying seismic facies allows to distinguish large-scale event-deposits, such as mass-transport deposits and turbidites (chaotic seismic facies), from background sedimentation (continuously layered seismic facies). Water depths were estimated assuming a seismic propagation velocity of 1450 m/s. Depths below the lake bottom were calculated using an average propagation velocity of ~1600 m/s. Based on a seismic interpretation and time-depth conversion of lake bottom levels, bathymetry maps could be constructed for every lake (Fig. 4.1, 4.2 and 4.3).
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4.2 Core acquisition
During the RCMG summer expedition of 2012 several core samples were collected as well. Sediment cores from Skilak Lake and Kenai Lake were acquired using a bob-corer (a gravity short-corer with hammering apparatus) from the RCMG, operated from a survey vessel equipped with an ‘over-the-side’ winch mechanism to easily lower and raise the plastic coring tubes (6.3 cm diameter). The corer, to which additional lead weights were attached, was lowered to the lake bottom. When reaching the bottom, a hammer weight was repeatedly lifted and subsequently released with the coring rope in order to pound the corer further into the sediment. This technique allowed a recovery of relatively long lake records despite the cohesiveness of the predominantly silty to clayey deposits. Though, one should be aware of the risk of assembling the sedimentary top section more than once due to an insufficient or oblique first entrance of the corer into the sediment, especially in large water depths. The exact coring sites were determined by an early in-field interpretation of the acquired seismic lines and previously existing bathymetry maps of the study area. In order to collect the desired continuous and complete lake records, steep slope environments or other instable settings were avoided. On Eklutna Lake, bob-coring was executed from a smaller zodiac boat, using a short hammer-extension to facilitate the handling. As a consequence, penetration depths of the coring tube were rather limited in this lake.
Figure 4.1: Geomorphologic setting and seismically derived bathymetry of Kenai Lake. Coring locations are labelled from KE12-01 up to KE12-09. Bathymetry iso-lines are drawn every 25 m, grading from light (shallow) to darker blue (deep) zones. Yellow trapezoids represent nearby weather stations from which data time series were used to compare to lake record proxies (see section 4.5) (Landsat image: Google Earth).
Nine cores were collected from the zig-zag shaped Kenai Lake (Fig. 4.1). The southernmost eight coring sites (KE12-02, KE12-03, KE12-04, KE12-05, KE12-06, KE12-07, KE12-08 and
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KE12-09) are located within the lake’s main basin, of which KE12-04, KE12-05, KE12-06 and KE12-09 in its deeper portions, with water depths reaching up to more than 175 m. Acquisition of KE12-02, KE12-03, KE12-07 and KE12-08, on the other hand, took place on gradually shoaling slopes. Finally, core KE12-01 was retrieved from a small, rather shallow (<50 m) sub-basin, connected to the north of the Kenai Lake main basin, providing a direct outflow towards Kenai River and eventually Skilak Lake on the border between Kenai Mountains and Kenai Lowland (Fig. 4.2).
Figure 4.2: Geomorphologic setting and seismically derived bathymetry of Skilak Lake. Coring locations are labelled from SK12-01 up to SK12-04, SK12-07 and SK12-10. Bathymetry iso-lines are drawn every 25 m, grading from light (shallow) to darker blue (deep) zones. Yellow trapezoids represent nearby weather stations from which data time series were used to compare to lake record proxies (see section 4.5). Note the overlap of the easternmost part of the map with the westernmost part of the map of Kenai Lake in Fig. 4.1. The inset frame shows Tustumena Lake in the Kenai Lowland, ~28 km SW of Skilak Lake (Landsat image: Google Earth).
Lacustrine sediments from Skilak Lake were assembled on six different spots (Fig. 4.2), of which sites SK12-01 and SK12-04 are located on the flat lake bottom in the deepest part of the main basin, at water depths of more than 200 m. SK12-02, SK12-03 and SK12-07 were retrieved from the northwestward shoaling basin slopes, whereas the sampling point of SK12-10 is situated right on top of an intermediate slope platform, north of a small land protrusion. No records were taken close to the lake’s main inflow area in the easternmost part of Skilak Lake, where Kenai River (outflow Kenai Lake, Fig. 4.1) and the meltwater stream from Skilak Glacier (Skilak River) debouch.
Five sediment cores (EK12-01, EK12-02, EK12-03, EK12-04 and EK12-05) were gathered, equally spread over the entire length of the smaller, SSE-NW-oriented Eklutna Lake (Fig. 4.3). Three coring tubes (EK12-03, EK12-04 and EK12-05) were lowered within the deepest, central
30 Chapter 4 - Materials and methods part of the southeastern sub-basin and two (EK12-01 and EK12-02) within the northwestern sub-basin. From the latter, EK12-01 was taken out of the flat lake bottom, whereas EK12-02 sediments were sampled near the onset of the basin slope. Coring locations in Eklutna Lake, going from SSE to NW, follow a pattern of stepwise increasing distances from the meltwater inflow (East Fork and West Fork) from Eklutna and West Branch Glacier (Fig. 4.3).
Figure 4.3: Geomorphologic setting and seismically derived bathymetry of Eklutna Lake. Coring locations are labelled from EK12-01 up to EK12-05. Bathymetry iso-lines are drawn every 5 m, grading from light (shallow) to darker blue (deep) zones. Yellow trapezoids represent nearby weather stations from which data time series were used to compare to lake record proxies (see section 4.5) (Landsat image: Google Earth).
Core lengths vary strongly between sites, depending on the local nature of the sampled sediment and technical factors. On average, cores from Kenai Lake, Skilak Lake and Eklutna Lake reach lengths of 74.6 cm, 139.6 cm and 71.8 cm respectively, which lies in the reach of standard short cores. In cases when cores were too long in terms of practical considerations (exceeding 1 m), they were cut in halve, resulting in an A and B part.
Worth mentioning is the ‘failed’ attempt to recover useful records from Tustumena Lake, which is located approximately 28 km SW of Skilak Lake, in the Kenai Lowland (Fig. 4.2). All but one of the acquired cores from this lake were shorter than 23 cm. Specific causes for this phenomenon are still unclear, but large water depths might have induced difficulties. Moreover, a doubling/tripling/quadrupling of the sedimentary top section, inherent to the use of the hammering bob-corer, was most probably the case in all of the collected cores.
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Therefore, samples from Tustumena Lake were not selected to look further into throughout this climate-oriented study.
4.3 Sedimentological, geophysical and geochemical analyses
4.3.1 Macroscopic core description and general correlation strategies
At the RCMG, all cores were split into two halves by opening liners with a circular saw and subsequently dividing the inside sediments using a wire or metal plates. Sediment surfaces of each core half were cleaned and smoothened. Thereafter, the cleaned surfaces of master cores were described macroscopically in a relatively high detail, whereas the other cores were described mainly by correlation with master cores. A ‘master core’ of a lake can be defined as a core that shows the most complete record or has the potential to offer a maximum of information concerning the study of interest and should contain a sedimentary sequence which is more or less representative for a part of the lake or the entire lake. During macroscopic description, attention was paid to sediment colour, grain-size, textures, structures (e.g. layering/laminations, nature of contacts, grain-size grading) etc.
By looking closely at the lake records, a first division between background sequences and intercalated event-deposits could be implemented (Dott Jr., 1996). Background sedimentation generally is described as the deposition of material in between geological events or ‘zero-time’ occurrences (e.g. flooding, slumping, turbidite currents, volcanic eruptions, tsunamis, seiches). In the studied cores, background sediments are represented by continuous series of silty to clayey laminations (or layers when exceeding a thickness of 1 cm). This type of successions have already been the main object of interest in many studies around the earth when examining interannual, interdecadal and/or intercentury climate variability (e.g. Lotter, 1991; Gajewski et al., 1997; Moore et al., 2001; Trachsel et al., 2010).
Most events manifest themselves as fining-upward grain-size sequences. In case of turbidites, they are characterised by the successive Bouma-facies (Bouma, 1962; Middleton & Hampton, 1973) and usually display a massive, graded, sandy base, followed by a central body of fine-grained sandy to silty material with occasional prominent laminations, which is on its turn overlain by a final clay cap as a result of suspension settling. Furthermore, all of the mass-waste deposits in the studied cores show, in a more or lesser extent, a gradual colour change, going from a dark base to a lighter top. Volcanic eruptions along the Alaskan-Aleutian chain can leave a distinct imprint in the sedimentary record as well. Depending on the distance between volcano and coring location, the prevailing wind direction during eruption, the eruption intensity and the preservation potential of the lake basin dynamics, vitreous tephra layers with variable thicknesses can become deposited and preserved (e.g. Begét et al., 1994; de Fontaine et al., 2007; Schiff et al., 2010). Recognition of these event-units or other marker layers/laminations are key in creating a correlation between different cores from the same lake and even between lakes. When looking for corresponding deposits, sediment colour, structure/texture, composition and respective position within the sedimentary successions were taken into account.
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4.3.2 Multi-sensor core logging
One half of every core was scanned with a Geotek Multi-Sensor Core Logger (MSCL) at the RCMG in order to obtain high-resolution, downcore data sets of geophysical properties, comprising gamma density (gamma attenuation and core radius), magnetic susceptibility (MS), fractional porosity (FP) and several spectrophotometric parameters (CIE L*a*b*, RGB colour values and reflectivity), measured over discrete steps of 0.2 cm along a central pathway. All of the sensors were set to touch the foil-covered sediment surface before measuring. MSCL-data and MS-measurements in particular appeared to be of major importance in providing sufficient certainty for an inter-core correlation throughout intervals were prominent event-deposits or visual marker horizons were absent (Loizeau et al., 2003). Changes in MS often correspond to changes in sedimentary provenance and/or grain-size dependent changes in mineralogy (de Fontaine et al., 2007).
Tephra layers are typically characterised by strong MS-peaks, enabling the detection of tephra horizons that are too thin to observe with the naked eye or microscope (‘cryptotephra’), but of which the presence is indicated by an elevated MS (de Fontaine et al., 2007). As a result of the overall fining-upward texture of mass-waste deposits, they exhibit a typical evolution of upward lowering MS-values (Bertrand et al., 2008). Gamma density data can give more information on lithology and/or porosity variations. Colour records from spectrophotometric analyses often point out changes in sediment composition.
4.3.3 Core photography and colour analysis
Aside from the measurements of different geophysical properties, the Geotek MSCL provides a photography line scan system as well. This function was only used on core KE12-09. Pictures of SK12-10 were taken with the Jai CV L105 3 CCD Colour Line Scan Camera with a resolution of 350 dpi (dots per inch), which is part of the AVAATECH X-Ray Fluorescence Core Scanner equipment at ETH Zürich. For every other core, digital pictures of the sediment surface were taken with a CANON EOS 450D camera in an equally illuminated box. Picture segments of ~15 cm long were stitched together to recreate entire core surfaces. Afterwards, a slight contrast-enhancement was applied to the pictures in order to facilitate distinction between different sedimentary units (Last & Smol, 2001).
In addition to the MSCL spectrophotometric measurements that were conducted directly on sediment surfaces, higher-resolution (pixel-scale) colour analyses of untouched core pictures were executed with Strati-Signal 1.0.5, a multipurpose software for stratigraphic signal analysis (Ndiaye, 2007). Along manually selected, longitudinal scanlines, positions of picture pixels in the CIE L*a*b* colour space were determined (Fig. 4.4 and 4.5). Lightness- or L*- values reflect variations between black (0) and white (100), a*-values between green (negative) and red (positive) and b*-values between blue (negative) and yellow (positive). The high-resolution data output allows the identification of subtle variations in sediment colour and hence, composition on an annual or longer timescale. On its turn, variability in composition can be assigned to climate-related processes.
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Figure 4.4: Strati-Signal user interface after loading a data file, in this case an untouched core picture of EK12- 01. The ‘Scanline’ frame (green box) in the ‘Signal acquisition’ window allows the adjustment of the scanline (green line) width and position (x and y of starting and ending point). The colour space of choice for analysis (here CIE L*a*b*) can be selected in the ‘Colour space’ frame (yellow box) together with the contribution percentage of each band (here 100 % a*). The ‘Data analysis’ window (Fig. 4.5) appears when left-clicking on the ‘Get selected data’ icon (red box).
Figure 4.5: Strati-Signal user interface after conducting a colour analysis of the a*-component (Fig. 4.4). The ‘Data analysis’ window displays variations of a* in a graph view (orange box) and an 8-bits visual representation of the original data (blue box). Raw data can be shown and downloaded in the ‘Data’ menu (pink box).
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4.3.4 Grain-size measurements
Grain-size measurements were executed using a Malvern Mastersizer 2000 at ETH Zürich, which is based on the principle of laser diffraction and able to detect a wide range of particle sizes (0.02 – 2000 µm). Data were collected for several stratigraphic units of interest. For instance, a series of grain-size measurements on dark bases and light-coloured top- sediment of background laminations were conducted along entire master core lengths in order to assess seasonal and longer timescale grain-size variations. Moreover, variability between lakes could be studied as well (Last & Smol, 2001). Prior to measuring, sediment samples were dissolved in water with natrium polyphosphate (calgon) to break up aggregates and release the finest grain-size fraction (clay). Ultrasound at 100 % of its maximum energy was applied to further split up aggregated sediment particles, without risking the break-up of individual fragile particles, such as diatom frustules, since their abundance appeared to be negligible in the analysed samples. Mastersizer output data were used to calculate grain-size distribution statistics with GRADISTAT Version 8.0 Excel software (Blott & Pye, 2001), using the Folk and Ward graphical method (Folk & Ward, 1957).
4.3.5 Smear slides for microscopic description
In order to study the cored sediment at a microscopic scale, several smear slides were made primarily from intervals in which grain-size measurements were conducted as well. Smear slides were prepared according to the role model of Rothwell (1989). A microscopic examination helped to confirm and visualise instrumental grain-size measurements and macroscopic observations. Furthermore, smear slides offer the opportunity to identify minerals, finely distributed organic material and possible microfossils, based on their optical characteristics and morphology. Sediment properties in different smear slides were mainly described relative to each other in means of grain-size and composition. For example, differences between base- and top-sediment of laminations, variations between unit-types and their evolutions through time (core depth) and space (between lakes) were assessed in a semi-quantitative manner.
4.3.6 X-ray fluorescence (XRF)
Split halves of master cores were scanned with the energy dispersive AVAATECH X-Ray Fluorescence Core Scanner at ETH Zürich for a rapid and non-destructive determination of the elemental composition of the sediment (Rothwell & Rack, 2006). The XRF-technique is based on the principle of photoelectric fluorescence of characteristic secondary X-rays from a sample surface, after being stimulated by irradiation with primary X-rays emitted from a tube source (Last & Smol, 2001). Scans were made at 10 kV, 30 kV and 50 kV, using a step size (resolution) of 10 mm, a downcore slit size of 10 mm and a crosscore slit size of 12 mm. The elements that were measured, range from Al to Rh (10 kV), from Zn to Bi (30 kV) and from Ag to Ba (50 kV). Attention has to be paid to the occurrence of artificial peaks of Rh, Ga, Bi, Ag, Sn and Te, since their concentrations are highly influenced by the XRF core
35 Chapter 4 - Materials and methods scanner (rhodium anode X-ray source, collimator, solderings etc.). Cores that comprise continuous, well developed, laminated background sequences, were subject to scans with a higher resolution. This way, master core EK12-01 was scanned at a resolution of 1 mm and master core SK12-10 with a step size of 0.5 mm. Counting times for each measurement vary and were chosen in function of the used resolution. Smaller step sizes and downcore slit sizes require longer counting times to recover high-quality data.
XRF-output values do not represent absolute element-concentrations, but are expressed as total counts. Hence, XRF-data were used to analyse relative changes and differences in chemical composition of the lake deposits through time and space. According to Shanahan et al. (2008), studies based on visual methods alone may not completely characterise the lamination/layer-components and can be enhanced significantly by executing complementary in situ geochemical techniques, such as XRF. The obtained high-resolution chemical profiles could be brought into relation with grain-size distributions. Especially element-ratios were used for this purpose, since they can enhance the amplitude of grain- size-related variability of individual elements. Moreover, when plotting element-ratios, the porosity dependency of individual element-counts becomes ruled out.
4.3.7 X-ray computed tomography (medical CT)
The master cores of every lake were imaged with a medical X-ray CT-scanner (Siemens SOMATOM Definition Flash) at the Ghent University Hospital (UZ Gent). In a medical CT- scanner the X-ray source and detector revolve around a central object in a helical path, which allows to make full body scans (Last & Smol, 2001). Given the ‘poor’ spatial resolution of 2 mm, the used X-ray tube needs to have a large focal spot size, on its turn enabling a very fast scanning. The specific greyscales within each of the voxels (volume elements with voxel size of 0.6 mm) in the acquired three-dimensional CT-images, are a reflection of the measure in which the X-ray beam has been attenuated along its pathway and depends on the density as well as the composition of the sediment (Orsi et al., 1994). However, individual influences from density and composition cannot be disentangled based on scans alone. White voxels correspond to a complete attenuation of the signal, whereas black ones indicate no attenuation at all. CT-images were mainly used to facilitate varve delineation and varve counting in intervals where core pictures were unclear (see section 4.4.2.2).
4.4 Dating methods
4.4.1 Radionuclide dating
Radionuclide dating (Pb/Cs) was executed on sediment from cores EK12-01 (Eklutna Lake) and SK12-03 (Skilak Lake) to obtain absolute ages of deposits from the last 100 years. In order to do so, a certain amount of samples from undisturbed background laminations were collected and freeze-dried with a Labconco Freezone 4.5 freeze-drier. Since this dating method is grounded on the assumption that mixing of sediment did not occur, samples from
36 Chapter 4 - Materials and methods turbidites or other reworked deposits were backed out. Dried samples were sent to the University of Bordeaux, where dating was conducted, utilising gamma ray spectrometry for determining 120Pb-, 137Cs- and 226Ra-concentrations (millibecquerel per gram).
Figure 4.6: Schematic representation of the 238U-decay series and pathways by which supported and unsupported 210Pb reaches aquatic sediments (after Oldfield & Appleby, 1984).
The principle of 210Pb-dating is based on the 238U-decay series, in which a disequilibrium is introduced due to the escape of its seventh daughter product, the noble gas 222Rn (Fig. 4.6). 222Rn is the immediate parent of 210Pb and its removal or addition to a given ecosystem leads ultimately to an excess or depletion of 210Pb within another. In nature, 222Rn diffuses from minerals exposed at the earth’s surface. Having a half-life of 3.8 days, the short-lived 222Rn decays through a series of very short half-life isotopes to 210Pb, generating an excess 210Pb in the atmosphere. Subsequently, the 210Pb gets removed by dry fallout or wet precipitation and hence, a continuous flux of 210Pb onto land and water establishes. There it is rapidly adsorbed on or incorporated in particulate material, which finally settles in lake or marine environments. Besides this excess 210Pb or ‘unsupported’ 210Pb, there is always a fraction of naturally occurring, ‘supported’ 210Pb in the encountered sediments (Fig. 4.6). The latter fraction is in radioactive secular equilibrium with 226Ra and causes a background activity (Oldfield & Appleby, 1984).
It is the measurement and interpretation of unsupported 210Pb that provides age information. Supported 210Pb can thus be considered as a limit to the sensitivity of the method and a cause for additional uncertainties. Knowing that 210Pb has a half-life of 22.6 years, a time-depth dependency (chronology) can be constructed. The time span covered by 210Pb is about 5-6 half-lives, making it an ideal chronometer for most ecosystem studies where changes have occurred within the last century (Reinikainen et al., 1997). In order to estimate depositional ages, a Constant Flux Constant Sedimentation (CF:CS) model and a Constant Initial Concentration (CIC) model were applied for dating sediment in core SK12-03 and core EK12-01 respectively (see section 5.1.7 and 5.3.7). The CF:CS-model assumes a constant flux of excess 210Pb from the atmosphere and a constant dry-mass sedimentation rate. The CIC-model, on the other hand, assumes a constant initial excess 210Pb- concentration in the accumulated sediment. Both approaches yield a linear time-depth relationship that can be extrapolated further back in time to obtain theoretical age models.
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Measurements of anthropogenic 137Cs in the sediment samples were used to verify the 210Pb- based age model (Benoit & Rozan, 2001). The radio-isotope 137Cs is found outside the laboratorium only as a byproduct of nuclear weapons testing and the occasional nuclear accident (Stihler et al., 1992). Three different maxima of introduction of 137Cs into the geosphere are known over the past 50 years. Extensive testing of nuclear weapons between 1954 A.D. and 1963 A.D. caused a significant increase in radioactive fall-out, reaching its maximum value in 1963 A.D. In 1975 A.D., the highest 137Cs release from the Sellafield installation (nuclear reprocessing site near Seascale, England) was registered, similar to the emissions during the 1986 A.D. nuclear accident at Chernobyl. Usually the maximum fall-out in 1963 A.D. is very well preserved in sediments of lakes and other depositional basins.
4.4.2 Varve counting and measurements of varve thickness
4.4.2.1 The varve assumption and confirmation
First of all, it should be emphasised that, theoretically, one cannot use the term ‘varves’ or ‘annual laminations’ before having confirmed the annually resolved character of the background successions. However, in many cases the visual appearance of a dark- coloured/light-coloured lamination-alternation suggests deposition during yearly cycles of sediment input into glacial lake basins. In this study as well, most of the observed background laminations display a typical glacial or clastic varve ‘look’. This presumption has been kept in mind while executing most of the sedimentological, geophysical and geochemical analyses, since it influences strongly the mode of approach. For example, grain- size samples and smear slides were deliberately taken from assumed summer and winter layers with the intention of studying annual variations.
In order to really establish the annual nature of the observed dark/light-coloured sediment couplets, they needed to be counted and compared to marker depths with absolute ages. As an individual varve represents deposition during one year, ages (varve years A.D.) were assigned to all counted laminations, starting from the top layer, deposited during the spring/summer melt of 2012 A.D. (period of core acquisition). The definition of marker depths was based on 137Cs-peaks and/or event-deposits that could be related to historically reported eruptions, earthquakes or floods. Since the proposed varve ages showed a very close similarity with identified marker ages, background laminations could be considered as annual deposits. Detailed counts were executed repeatedly on at least every master core.
4.4.2.2 High-resolution varve thickness records and age models
In practice, varve counting was executed with FIJI (Fiji Is Just ImageJ), an image-processing package focussed on assisting science research. FIJI offers the possibility to easily upload image-files, after which an enormous variety of processing procedures can be performed. First of all, a scale calibration was conducted on the imported images to establish a relation between image pixels and real horizontal (x) and vertical (y) distances. In order to count annual laminations, a ‘point-selection’-tool was used to mark every transition between
38 Chapter 4 - Materials and methods presumed summer and winter layers on contrast-enhanced core pictures. Thick and/or obvious event-deposits were not involved in the counts. Point marks were set along a straight profile line so that layer thicknesses would not be influenced by lateral shifts in the y-position of sedimentary units (Fig. 4.7). The x- and y-coordinates of every placed mark became automatically registered by FIJI in a data set, which could be downloaded in Excel- format afterwards. Calculations on these output coordinates yielded summer, winter and annual layer thicknesses. To every varve thickness, a corresponding varve year was assigned. Attention has to be paid to the difference between calendar years and varve years. A calendar year starts on the 1st of January and ends on the 31st of December, whereas the period covered by a varve year differs from place to place. In Southern Alaska it is safe to assume that a varve year starts on the 1st of May and ends on the 30th of April (Fig. 4.8) (Brabets, 1993). From May on, melting of winter snow and ice commences, causing summer layers to form. After five months of increased discharges towards the lake basins, the beginning of October heralds a new period of snow accumulation and ice formation, during which suspension settling takes place for over seven months.
Figure 4.7: Varve counting in FIJI on contrast-enhanced core pictures. Point marks with labels (yellow numbers with blue background) were placed on a straight profile (yellow line) at every summer/winter and winter/summer transition. A) Interval in core SK12-10 with perfectly developed dark/light-coloured varve couplets. Note the gradual summer (dark)/winter (bright) and sharp winter (bright)/summer (dark) transitions. B) Interval in core EK12-01 with several varves composed of multiple-pulse summer layers. In this case, individual summer pulses were delimited separately, as thicker and/or differently coloured pulses can be related to spring or summer flood- events. More discussion on this topic follows in section 6.1.3. The importance of CT-images in varve counting is shown in, for instance, Fig. 5.6, 5.10 and 5.11 (Chapter 5).
Continuous varve thickness records allowed the construction of high-resolution age-depth models for several cores, at least one for each lake (master cores). These models were then used as frameworks to assess variations in sediment accumulation rates and other possible
39 Chapter 4 - Materials and methods climate proxies through time and to verify the quality of the 210Pb-based age models. Throughout this study, a lot of references are made to dates that were obtained via the construction of varve counting age models. In those cases, ages are always expressed as varve years A.D. Dates from other sources (e.g. radionuclide dating, historical reports), on the other hand, are expressed as calendar years A.D.
Figure 4.8: Schematic representation of the difference between varve years (vy) A.D. (1 May - 30 April) and calendar years A.D. (1 January - 31 December) over an example time interval from 1990 A.D. to 1993 A.D. Summer (‘S’) layers are red and winter (‘W’) layers blue. ‘Si’ and ‘Cl’ stand for silty and clayey deposits respectively.
Several difficulties were encountered during varve counting. General observations show that summer deposits correspond to gradually lightening-upward units, overlain by brighter winter clay caps. Transitions from summer to winter layers are often extremely gradual and therefore disputable, whereas transitions from winter to summer layers are rather sharp (Fig. 4.7). CT-scans proved to be of great assistance in depth intervals throughout which colour variations of core pictures and/or results of colour analyses did not suffice to delineate separate varves. Since CT-images are sensitive to mineralogy and packing density (porosity) (Berger et al., 2011), voxel colouring varies between bright shades of grey in the denser varve bases and darker shades in the less dense varve tops, which is a pattern inverse to the one of lightness in core pictures (Fig. 5.6, Chapter 5). In case of dubious varve boundaries, winter layers served as important key features. Summer layers can consist of multiple pulses, whereas well developed clay caps only accumulate once a year (Fig. 4.7). However, when taking the existing uncertainties into account, the introduction of certain counting errors into the proposed age models seems to be inevitable (Ojala et al., 2012).
Water contents of a series of samples from cores EK12-01 and SK12-10 were determined through freeze-drying when preparing sediment for radionuclide dating. These mass percentages of water were also used to convert varve thicknesses into mass accumulation rates (MAR in g/cm2/yr), assuming a constant sediment density of quartz of 2.648 g/cm 3. This way, sedimentation rates were corrected for the volume of pore water present in between grains. Core EK12-01 showed no significant changes in water content with sediment depth, which might have been the result of desiccation of the originally moist sediment due to a long-term storage prior to sampling. SK12-10, on the other hand, was sampled immediately after splitting and therefore displayed a clear trend of downcore decreasing water contents. Such trend is characteristic for an increasing sediment compaction at greater burial depths. Hence, only the SK12-10 record was expressed as a variation of both varve thickness and MAR through time. Especially when linking sediment accumulation rates to climate conditions, a MAR-conversion is of interest.
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4.5 Climate tuning and calibration
In order to relate calculated varve thicknesses and MAR’s to specific climate-driven factors (‘climate calibration’) (Leemann & Niessen, 1994; Tian et al., 2011), instrumental data from several Alaskan weather stations were requested via the site of The Alaska Climate Research Centre (http://oldclimate.gi.alaska.edu, 13/03/2014). Monthly precipitation (mm), air temperature (°C) and snowfall (m) data from stations close to the studied lakes were selected (Table 4.1). Anchorage and Homer are first-order stations and therefore offer longer and more complete time series, whereas the other stations cover shorter recording periods, in which data are often missing and/or less reliable. To be able to compare these time series to records of varve thickness and MAR, averages of the different weather parameters needed to be calculated for every varve year (May-April). A different approach was used for snowfall data. Values were summed up for every winter separately and linked to the varve year (or summer) following the winter snowfall. It is after all during summer that snow from the previous winter (i.e. previous varve year) will start to melt and influence stream discharges and thus sediment transport towards the lake (Ohlendorf et al., 1997). Relations between climate parameters and sedimentation rates were assessed on interannual as well as multidecadal timescales.
Table 4.1: List with latitudes, longitudes, elevations and recording information of the selected weather stations. Locations are marked on the maps in Fig. 4.1, 4.2 and 4.3. Due to too fragmentary recordings in Cooper Landing Kenai R, data from this station were eventually banned from the used time series.
Station (AK US) Latitude Longitude Elevation (m above sea level) Recording period (A.D.)
Anchorage 61°11’ N 150°00’ W 35 1952-present
Homer 59°39’ N 151°29’ W 27 1932-present
Cooper Landing Kenai R 60°24’ N 149°54’ W 1069 1941-1956
Cooper Lake Project 60°23’ N 149°41’ W 182 1958-2004
Cooper Landing 5 W 60°29’ N 149°58’ W 105 1975-2013
Eklutna WTP 61°27’ N 149°19’ W 158 2001-2013
Eklutna Lake 61°24’ N 149°07’ W 332 1946-1976
Eklutna 61°25’ N 149°31’ W 0 1941-1955
Eklutna Project 61°28’ N 149°10’ W 351 1952-1998
Melting of ice is just like melting of snow in spring and summer an important factor that influences discharges and annual sediment transport towards lake basins. However, the effect of ice melting on varve thicknesses was considered as less of an interannual, but more of a (multi)decadal-scale signal (Ohlendorf et al., 1997; Brabets, 1993). This background pattern of low-frequency variations was filtered out by calculating running averages (window width of 15 varve years) through the thickness/MAR records and by subtracting these averages from the actual values, resulting in detrended curves (Fig. 4.9) (Ólafsdóttir et al., 2013). Detrending for the last seven years was based on a linear fit connecting the youngest end of the plotted running average with the summer layer thickness of varve year 2012 A.D. Fluctuations in the
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obtained detrended or normalised records were then regarded as being the result of primarily interannual changes in precipitation, temperature and snowfall. Hence, this detrending procedure allows the construction of a record that is ready for a year-to-year tuning to the available climate time series. Note that the latter time series needed to be detrended in a similar manner as done for the thickness/MAR records in order to reveal their interannual variations, using a running average with the exact same window width (15 varve years).
Figure 4.9: Example of data set detrending, based on running averages (here with window width of five x- axis units instead of 15) and construction of combined y-axis parameter curves. Upper left graph: raw data of three different variables (A, B and C) and their running averages. Upper right graph: detrended variables A, B and C (A’, B’ and C’). Lower graph: summed, detrended variables with equal weights and differential weights, demonstrating the significance of weight magnitudes in determining patterns within the final, combined parameter curves.
Tuning of the normalised thickness/MAR records to equally normalised climate time series was conducted with the aim of creating best possible fits and thereby reducing the amount of varve delineation errors. Three principles were used to do so. It was assumed that more sediment would be brought into the lake and varves would be able to grow thicker when 1) varve years were characterised by high amounts of precipitation, causing elevated stream discharges and sediment entrainment/transport (Desloges & Gilbert, 1994), 2) snow accumulation in winter was substantial and followed by a varve year with regular air temperatures (no cold spells) in order to melt the stacked up snow (Cockburn & Lamoureux, 2008), 3) varve years exhibited strongly elevated air temperatures (often coinciding with high amounts of precipitation) independent from snowfall during the preceding winter, since high temperatures are able to melt larger amounts of snow, even at higher altitudes (Loso et al., 2006; Perkins & Sims, 1983), 4) a combination of the previous conditions were fulfilled. As a general rule, it was accepted that peaking of precipitation, air temperature and snowfall are all able to positively influence the formation of thicker varves. Therefore, detrended values for annual precipitation, temperature and snowfall were expressed as percentages of their total range within the recording period and subsequently summed up for each varve year, to obtain an equally weighted, ‘all-containing’ climate curve (Fig. 4.9).
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Detrended varve thicknesses from Eklutna Lake were compared to detrended climate curves from Anchorage, Homer, Eklutna WTP, Eklutna Lake, Eklutna and Eklutna Project, whereas detrended thickness/MAR records from Kenai Lake and Skilak Lake were compared to curves from Anchorage, Homer, Cooper Lake Project and Cooper Landing 5 W. Taking core pictures and CT-images into consideration during tuning, varve boundaries were shifted (up to a few varve years) to new positions in order to create better fits with the peaks and troughs in the climate time series. Based on these tuned records, a final variation on the all-containing climate curve was created by assigning different weights instead of equal weights to the percentage values of precipitation, temperature and snowfall. These weights were fine-tuned by maximising the linear Pearson correlation coefficient (Pearson’s r) between the differentially weighted, all-containing climate curves and the tuned thickness/MAR plots. By executing this fine-tuning process, the individual contributions of precipitation, temperature and snowfall in the observed patterns of sediment accumulation could be estimated roughly. Moreover, a series of correlation coefficients between individual, detrended climate parameters and detrended, tuned/untuned thickness/MAR records were computed in order to further assess the influence of different climate factors on varve thickness. The obtained contributions were then used to reconstruct the dominant forcing parameters further back beyond the reach of instrumental records.
In addition to the interpretation of annual averages for precipitation, monthly data were of great importance as well, since it is heavy rainfall during shorter time periods that causes extreme runoffs to occur and streams to evolve into torrential currents. As a consequence, more sediment becomes eroded and transported during these events.
4.6 Catchment extraction on DEM’s
Accumulation of clastic sediment in a lake basin is a direct result of the entrainment and transportation of material in drainage streams of different scales throughout the catchment. The measure in which this transport takes place, determines the amount and type (e.g. composition and grain-size) of sediment that will finally settle on the lake bottom. Furthermore, discharges, energy levels and entrainment capacities of the draining gullies, streams and rivers are strongly correlated with weather and, more generally, climate conditions (Cockburn & Lamoureux, 2008). Thus, it is important to have a clear image of where the cored sediment came from initially in order to determine how its properties can be related to variations in climate. Therefore, catchments and drainage networks of all three lakes were extracted using Global Mapper 13, a GIS data processing application, and Google Earth. Digital Elevation Models (DEM’s) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were downloaded in GeoTIFF file-format from the online USGS Global Data Explorer (http://gdex.cr.usgs.gov/gdex, 03/04/2014) and imported into Global Mapper. Algorithms embedded within the latter software allow an automatic determination of stream paths and delineation of watersheds based on the loaded terrain surface data. Information from Global Mapper was combined with elevation and geographical data from Google Earth in order to construct relatively complete overviews of drainage architecture in the area of the studied lakes.
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4.7 Spectral analysis
Besides simple colour analyses, Strati-Signal software (Ndiaye, 2007) offers many other functions that can be used to reveal stratigraphic signals. The cyclicity module deals with periodicity study, which is an important aspect in paleoclimatology, since many climate- related processes act on a more or less periodic base. A series of different procedures are embedded within this module, such as the spectral methods including Fourier analysis. The most important goal of a spectral analysis is to transform a time series of data into a spectrum of all its possible frequencies or harmonics (conversion of time domain to frequency domain). Because frequency is the inverse of periodicity, all periods in the given signal can be derived as well. Hence, spectral analysis can ease the detection of any existing cyclicity in a stratigraphic signal (Fagel et al., 2008; Ólafsdóttir et al., 2013).
In order to obtain an overview of the scales on which forcing mechanisms influence sedimentation rates in the three lakes of interest, Fourier spectral analyses were conducted on varve thickness/MAR records (including tuned parts from 1932 A.D. to 2012 A.D.). The acquired periodograms were smoothed with a Barlett-type window and were visualised in function of frequency as well as periodicity of the correlated sine and cosine functions. Stronger correlations yield higher scores on the signals’ power scale and are thus more dominant in determining varve thickness/MAR variations through time. Resulting periodograms were compared between lakes and to known cyclicities or oscillations that control natural climate variability in the study area, such as the Pacific Decadal Oscillation and the El Niño-Southern Oscillation (see section 2.2.1).
4.8 Workflow
The relatively extensive sequence of methods used throughout this study, the reasoning why they were used and how they can be interpreted to obtain relevant results are schematically shown in Fig. 4.10. This workflow chart represents the foundation on which the course of proceedings is built.
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Figure 4.10: Overview scheme with the workflow of the presented study. In the yellow box on the left side, the applied methods are given. Core sediment analysis techniques are framed with a dashed line. The central part of the scheme is held for procedures, logical connections and preliminary results, whereas the most important products are displayed in the blue box on the right.
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5 RESULTS
The reason I call myself by my childhood name is to remind myself that a scientist must also be absolutely like a child. If he sees a thing, he must say that he sees it, whether it was what he thought he was going to see or not. See first, think later, then test. But always see first. Otherwise you will only see what you were expecting. - Douglas Adams -
The results of the listed methods and procedures in Chapter 4 are discussed for each lake separately, in a downscaling order, going from the lake’s catchment, over the general basin morphology and sedimentary infill, to the more local sequence of deposits caught within the obtained cores. Results from Eklutna Lake are described most thoroughly, allowing parts about Kenai Lake and Skilak Lake to be shorter by referring to similar results from Eklutna Lake. Throughout this chapter a certain amount of figures are shown that already contain information based on interpretation of results and further discussion. Therefore, in Chapter 6, references to these figures are regularly used.
5.1 Eklutna Lake
5.1.1 Catchment and drainage networks
The catchment of Eklutna Lake is confirmed to be quite small, as it is merely covering the surrounding slopes of the Chugach Mountains. Though, south of the lake, the watershed boundaries reach up to the permanently snow covered mountain peaks and crests, from which several glaciers flow down into the West Fork and East Fork valleys. The West Fork valley stream receives meltwater from the major West Branch and Eklutna Glaciers, whereas the East Fork is being fed by a series of smaller, less developed glaciers. A mountain ridge separates both drainages from one another. These two meltwater carrying rivers, into which several lower Strahler order streams and gullies merge when moving downstream, are the most important contributors to the water input flux towards the lake. A Strahler stream order is an attribute that can be assigned to every link in a river network and represents the size of the contributing basin and the structural complexity of the river network that drains the basin (Strahler, 1957). When leaving the southern portion of Eklutna Lake and moving northwestward along the lake margins, numerous low Strahler order streams flow downslope, collectively providing a minor positive contribution to the lake’s water volume. At the northwestern end of the basin, the Eklutna River is responsible for the outward flux towards Knik Arm and eventually Cook Inlet (Fig. 2.1 and 4.3). Additionally, part of the lake water is being diverted to the Municipality’s Eklutna Water Project and the Alaska Power Administration’s Eklutna Power Project (see section 2.4.3).
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Figure 5.1: Catchment of Eklutna Lake (red line) and its most important drainage paths (light blue arrows), superimposed on a DEM of the lake’s direct region. Thicknesses of the arrows are a measure for visually (Google Earth) and theoretically (Martinez et al., 2010) approximated levels of discharge. White areas represent glaciers. Exclusively within the delineated watershed, glaciers and drainage networks are displayed, except for the outflowing Eklutna River.
5.1.2 Bathymetric and seismic-stratigraphic setting
General observations of the bathymetry of Eklutna Lake (Fig. 4.3) indicate the presence of two separate sub-basins, of which the southeastern sub-basin is the larger and deeper one, including the deepest parts of the lake with depths exceeding 55 m. The northwestern sub- basin is slightly shallower, reaching up to a depth of more than 50 m in its southern portion. Both sub-basins are separated from one another by an intermediate lake bottom elevation. Overall, the basin morphology shows rather steep lateral bedrock margins, characteristic for a glacially scoured and overdeepened valley. Close to the meltwater inflow at the lake’s southern terminus, a broad, shallow zone marks the presence of a transitional area between glacial outwash plane and overdeepened lake basin.
Bathymetric settings of each of the five sampling locations were already discussed in section 4.2. Coring sites are located along a central line running through the length of the kinked lake basin, where the most complete and continuous sedimentary sequences can be found. Hence, seismic-stratigraphic settings of all of the retrieved cores are similar. Two examples of representative profiles that cross-cut or run close to two different coring locations are shown in Fig. 5.2. As indicated on both profiles, the cored sequences (black rectangles) only cover the uttermost top layers of the entire lake infill. Underneath the sampled material, an
47 Chapter 5 - Results extensive succession of draped deposits can be observed over an interval of more than 50 m of depth, which displays a certain variation in reflection strength. Some strong reflectors can be traced back until the point where they meet the steep dipping bedrock margins in on- lapping terminations, whereas other seismic units have a more chaotic seismic facies.
Figure 5.2: Seismic profiles, transversely cross-cutting the lake basin. Locations of the profiles are given in the inset figures. The indicated position of core EK12-04 is a lateral projection, whereas the coring site of EK12-02 is located along the actual profile. Black rectangles show how far the sampled sediment reaches back into the lake’s infill. Vertical distances are given in Two-Way travel Time (TWT).
5.1.3 Macroscopic and microscopic core description
5.1.3.1 Macroscopic description
Overall, the recorded sedimentary sequences can be described as successions of laminated to layered deposits, occasionally interrupted by thicker units of lightening-upward material, which often display a deviating colour and darkness (Fig. 5.3). The 93.5 cm long core EK12- 01 from the northwestern sub-basin has been chosen as the master core from Eklutna Lake, because of its relatively deep reaching and continuous record. Descriptions of sediment colour are always based on the original pictures, without any sort of enhancement.
Starting from its base, EK12-01 (Fig. 5.3) is filled with a ~48 cm thick sequence of clearly laminated to layered (>1 cm thick) deposits, of which each individual lamination or layer displays a lightening-upward and fining-upward trend. In the, on average 0.4 cm thick, type A background laminations (Fig. 5.4), sediment colours vary between grey in the bases up to light grey or light beige in the tops. This colour evolution is associated with a gradual grain- size shift from a fine silt base to a clayey top. However, occasionally thicker, darker and coarser type A units occur as well. The relatively uniform succession of these laminae is regularly interrupted by remarkably thicker units, which often approach and even exceed a thickness of 1 cm. Most of them can thus be defined as ‘layers’ (type B and type C in Fig. 5.4). A few obvious examples of these units are present within the depth intervals of 49-56 cm, 66-71.5 cm, 77-83 cm and 87-91 cm (Fig. 5.3). In most cases they possess a darker, brownish colour in their bases and an upward gradation towards a light grey or light beige shade, identical to the tops of the two-layered background laminations (Fig. 5.4). Sediment in the
48 Chapter 5 - Results bases of the dark layers is overall a bit coarser than the grey bases of type A units and can be described as medium silt. When looking closer at these distinctive layers, they appear to exhibit a specific structure. The darker, eye-catching bases are in fact preceded by grey units of varying thickness, which resemble the bases of regular background laminations (type A in Fig. 5.4). An often weak erosive contact separates the grey from the brownish sediment. As already mentioned, these dark bases subsequently become lighter and finer-grained in an upward direction, to end up in top-sediment similar to the kind that can be found in type A laminations. Type B units can thus be described as often browner, darker and coarser layers, that are ‘sandwiched’ between the base and top of type A background laminations. A variation on the three-layered type B unit is the multi-layered type C unit, which comprises more than one ‘sandwiched’ entity (Fig. 5.4). Between a depth of 40 cm and 45.5 cm, a more homogeneous body of dark grey to light grey sediment can be observed (Fig. 5.3). Its base consists of coarse silt that gradually evolves into a bright clay cap. A few blurry laminations are distinguishable in the upper half of this grey package.
The second part of the core, extending from a depth of 40 cm to the top, shows a new sequence of laminated deposits (Fig. 5.3). Though, the overall sediment colour is slightly darker than in the lower part of the core (93.5-45.5 cm), caused by a lack of prominent, bright clay caps. Furthermore, individual laminations are no longer as uniform in terms of colour and darkness. Grain-size patterns, on the other hand, do not show any noteworthy, macroscopically observable changes. Thicknesses and colour of most of the ‘sandwiched’ parts of type B or type C units, which are very abundant in the upper core half, do not deviate that much anymore from the adjacent type A laminae, even though they still display the described ‘sandwich’-structure. In some intervals, laminations become very vague, for instance in the uppermost 5 cm of the core. Only three actual ‘layers’ can be discerned in this second laminated sequence. One of them is present at a depth of 23 cm (Fig. 5.3). Observations of grain-size within this greyish, fining- and lightening-upward layer are similar to those done in the central parts of three-layered type B units. However, this unit does not belong to the type B class due to the absence of an underlying, grey background base. The two other layers (at depths of 10.5 cm and 5-7 cm), on the other hand, exhibit all of the type B characteristics, including the brownish colour and the weak erosive contact at their bases.
When comparing the sedimentary record from master core EK12-01 with the other cores (EK12-02 up to EK12-05), most of the prominent type B and type C units and other thick marker layers and packages can be recognised fairly easily (Fig. 5.3). Core EK12-02, which was acquired in the northwestern sub-basin as well, shows a more or less identical succession of deposits. Though, the entire record seems to be relatively thinner, especially the upper core half and the central, grey entity that is separating the upper from the lower core half. Moreover, individual laminae in the top part of the core are even blurrier and thus harder to distinguish than in EK12-01.
Figure 5.3 (next page): Correlation between master core EK12-01 and other cores EK12-02, EK12-03, EK12-04 and EK12-05 from Eklutna Lake, arranged by increasing distance from the glacial meltwater inflow from the Eklutna and West Branch Glaciers. Core pictures are slightly contrast-enhanced. Stars and red blocks at the side of EK12-01 are locations of smear slide and grain-size analyses respectively. Zones of different dominant sedimentary units are delineated. A more complete image of EK12-01 can be found in Fig. 5.7. The interpretation and reasoning behind the presented correlation is discussed in section 6.3.2.
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The three cores from the southeastern sub-basin, EK12-03, EK12-04 and EK12-05 display strong mutual similarities, except from the fact that they do not reach up to the same depth and the same stratigraphic level. Laminations and layers are relatively well defined and overall thicker than in the northwestern sub-basin. Throughout the entire core lengths, deposits from the southeastern basin exhibit a series of very dominant type B and type C units. Unlike conditions in the upper core halves of EK12-01 and EK12-02, these units are clearly discernable from the lighter-based, less contrast-rich type A background laminations. The remarkably thinned-out, grey body (at 40-45.5 cm in EK12-01) can be recognised in the lower parts of EK12-04 and EK12-05. One of the most striking changes from northwestern to
50 Chapter 5 - Results southeastern sub-basin is the extreme thickening (up to 5 cm) of the ~1 cm thick, greyish layer, that was found on a depth of 23 cm in core EK12-01. Not only its thickness but also its grain-size increases tremendously, resulting in a fine to even medium sandy base in cores EK12-04 and EK12-05. Furthermore, the lower boundary of this unit shows a strong erosive contact with the underlying, deformed sediment in EK12-05, causing a substantial hiatus to be present (~7 cm missing in comparison to EK12-04). Correlation between the Eklutna Lake
cores is further elaborated and discussed in section 6.3.2.
Figure 5.4: Examples of type A, type B and type C units in EK12-01. Core pictures are complemented by CT- images and schematic representations of sedimentary appearance and grain-size patterns. Black-and-white bars show how deposits are interpreted in terms of separate units.
5.1.3.2 Microscopic description and grain-size measurements
No continuous sampling was executed over the core length of EK12-01 (Fig. 5.3). As a consequence, it is impossible to discuss detailed grain-size trends through time. Sediment samples were selected carefully in order to assess variations within individual layers and between different types of units. When relating these observations to downcore macroscopic sediment descriptions, rough estimates concerning grain-size changes in function of coring depth can be made. Instrumental grain-size measurements were often executed on sets of similar units, allowing a verification of the analysis reproducibility.
Results from grain-size measurements and smear slide analyses on top- and/or base- sediment from representative laminations/layers at depths of 15 cm, 19.5 cm, 63.5 cm and 66 cm are displayed in Fig. 5.5. In general, samples appear to consist of multiple sub- populations and, as a consequence, are poorly sorted. Seven different sub-populations can be recognised and their central peaks defined according to the Udden-Wentworth grain-size scale: fine clay, coarse clay, very fine silt, fine silt, medium silt, very coarse silt and very fine sand. The prominence of each of these classes varies between analysed samples. Modes of all frequency curves seem to be influenced by the consistent presence of a fine clay and, sometimes, a smaller, very coarse silt to fine sand sub-population. The fine clay component can be observed as well on the smear slide pictures, in which it manifests as a sort of
51 Chapter 5 - Results background noise. The recurring presence of the almost negligible, coarse components is most probably the result of few grain aggregates, which were not fully partitioned before measuring. At a depth of 15 cm, measurements were performed on base- and top-sediment from a background lamination (type A unit) within the upper, slightly darker core half. The top-sediment is skewed towards a clayey composition, whereas the base-sediment is centred around the very fine silt sub-population. Similar distributions can be found on a depth of 66 cm (background lamination in lower, lighter core half) and 19.5 cm (dark, central part of type B unit in upper, darker core half). However, the base-sediment of the latter unit is slightly coarser, tending towards fine silt. Analysed sediment from a layer at a depth of 63.5 cm is overall coarser, base-sediment as well as material composing the top. As can be seen on the core picture in Fig. 5.5, these samples were taken from a very well developed brownish layer in a type B unit from the lower core half. Grain-sizes display a grading from fine to medium silt at the base up to very fine silt in the beige top.
Figure 5.5: EK12-01 with grain-size measurement results from representative laminations and layers. Contrast- enhanced pictures of smear slides from corresponding sampling depths are shown as well. Every grain-size measurement is depicted as a frequency and a cumulative frequency curve. Grey dashed lines, running through the graphs, indicate the presence of different sub-populations within the analysed sediment samples. These sub- populations belong to separate grain-size classes, according to the displayed Udden-Wentworth scale. Modes and descriptions of dominant grain-sizes are output results from GRADISTAT 8.0. Indications of ‘summer’, ‘winter’ and ‘flood deposit’ are discussed in section 6.5.2.
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Since the displayed results in Fig. 5.5 are representative for all other measurements on similar depositional units (Fig. 5.3), it is possible to extrapolate these observations for the entire core. More concretely, this assumption implies that grain-size patterns within background laminations remain unchanged throughout the entire sedimentary succession captured within EK12-01. Furthermore, it is clear that darker, differently coloured layers/laminations are relatively coarser, especially when their bases are well developed, like the samples from a depth of 63.5 cm. Hence, these instrumental measurements confirm and present in a higher detail what was observed visually (see section 5.1.3.1).
Smear slides were not that well suited for mineral identification, since individual grains are typically too small to pick up on optical properties. However, quartz grains with undulatory extinction and opaque minerals (probably metal oxides) can be recognised and seem to recur regularly throughout all slides (Fig. 5.5). It is not possible though to observe clear changes in mineralogy between different smear slides.
5.1.4 MS- and density-evolutions
Patterns of magnetic susceptibility (MS) in the Eklutna Lake cores (Fig. 5.3) exhibit a certain amount of common properties. Sequences of uniform background laminae are characterised by relatively steady, though a bit spiky MS-values, which are slightly higher elevated at the level of coarser layer- and lamination-bases. Moreover, as a general rule, it can be concluded that the coarser the sediment, the higher the MS becomes. As MS is closely related to the magnetic properties of minerals within the sediment (Loizeau et al., 2003; de Fontaine et al., 2007), a link between mineralogy and grain-size is suggested. These grain-size/mineralogy- reflecting MS-trends can be observed in, for instance, the 3-5 cm thick packages of fining- and lightening-upward material in cores from the southeastern sub-basin (in EK12-05 at a depth of 8.5-11.5 cm and 40-45 cm) (Fig. 5.3). Furthermore, several extreme peaks appear to occur without any macroscopic or microscopic evidence to explain their presence. In master core EK12-01, these outlier values can be observed at depths of 9 cm, 10 cm, 40 cm and 77 cm. Vitreous tephra layers are most likely to provoke MS-values to increase dramatically (de Fontaine et al., 2007). However, no macroscopic observations of this nature were done, which points towards the presence of cryptotephra ash-horizons.
The overall curved shape of the γ-density evolution throughout EK12-01 (Fig. 5.7) is most probably the result of a higher degree of compaction within stratigraphically deeper deposits during sedimentation and due to post-acquisition core storage. Changes in density inherent to sedimentological properties (mineralogical composition and texture) contribute as well to the observed downcore variations. Higher-frequency fluctuations respond to the alternation of denser lamination- and layer-bases and less dense tops. These shifts have a mineralogy- and/or texture (grain-size)-related cause and are also responsible for greyscale variations in CT-images (Fig. 5.7), as the latter ones depend on density as well as mineralogy.
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5.1.5 Colour analysis
Spectrophotometric data from the Geotek MSCL appeared to be of low quality, which might have been due to an imperfect contact between sediment surface and sensor during scanning. Therefore these measurements were not further included in the study. The same applies to spectrophotometric scans of cores from Kenai Lake and Skilak Lake. Strati-Signal colour analysis results, on the other hand, reveal particular trends in the CIE L*a*b* colour space (Fig. 5.7). The lightness-index shows strong fluctuations, reflecting the variation between the darkness of base-sediment in laminations and layers and the brightness of their clay caps (Fig. 5.4). Striking is the low-frequency, seemingly periodical, wavy pattern that can be discerned in the plot of the a*-index. Almost three full periods of variations between rather reddish (positive) and rather greenish (negative) sediment can be observed along the length of the core. The b*-index, in contrast, does not exhibit spectacular variations, except for a few positive peaks at heights of browner layers, for instance in type B units.
5.1.6 XRF-profiles and -patterns
Only elements with sufficiently high downcore counts were selected for further observations. For example, Zn, Cu, Sn, P and S never reach count numbers higher than 1000, indicating very low element-concentrations and hence rather unreliable results, containing a large amount of background noise. XRF-profiles can be studied on two different scales: large-scale changes throughout the entire core and small-scale shifts within individual laminations and layers. The latter can only be assessed via lamination-resolving scans with a resolution of 1 mm, executed on master core EK12-01.
An overview of several elements that follow specific trends within individual laminations and layers of type A, B and C is provided in Fig. 5.6 (same example intervals as in Fig. 5.4). XRF- curves are grouped when their patterns are alike. Si and Al do not display particularly consistent or uniform trends. In some cases counts tend to increase when moving to finer- grained material, whereas in others an opposite or no significant pattern is visible. The lack of a clear, recurring intra-lamination/layer trend demonstrates that Si- and Al-concentrations are not that sensitive to small-scale grain-size evolutions. In contrast, the other element- plots in Fig. 5.6 show clear grain-size-dependent fluctuations and can be correlated as well with sediment lightness (%L*). Ti and Ca are more abundant in coarser bases, after which counts decrease relatively gradually towards the tops of type A, B and C units. Of both elements, Ti exhibits the sharpest drops at the onsets of clay caps, making it a valuable indicator for the latter grain-size class. Fe and K, on the other hand, display a positive correlation with lightness values and increasing clay content. Both elements run more or less parallel to each other, except for in the thinner type A laminations, where K lacks the explicitness of the Fe-fluctuations. In order to maximise the distinction between coarse bases and fine-grained tops, ratios of K/Ti and Fe/Ti were plotted. Fe/Ti appears to provide the most accurate record of the occurrence of silty bases (negative peaks) and clayey tops (positive peaks). In general, amplitudes of variations in element-counts are larger in type B and type C units, due to the presence of coarser-based (medium silt), intercalated layers.
54 Chapter 5 - Results
Figure 5.6: Pictures and CT-images of three types of laminations/layers (type A, B and C units) that dominate the record of EK12-01 and Eklutna Lake in general. To the right of the CT-images, black-and-white bars show the boundaries between separate units (annual deposits), whereas the red-and-blue bars make a division between sediment that has been deposited during summer (red) and winter (blue). Next to the simplified drawings of the core surfaces, schematic representations of grain-size trends are displayed with triangles. The right part of the figure shows records of lightness (%L*) and a series of XRF element-counts. Arrows indicate dominant trends in representative units for each type. Arrow sizes reflect amplitude magnitudes between base- and top-sediment. Abbreviations ‘S’, ‘W’ and ‘F’ stand for summer, winter and flood deposit respectively (see Chapter 6).
Full element-profiles of EK12-01 with significant counts are shown in Appendix A. Most curves do not exhibit pronounced downcore trends, except for a sometimes slight difference in average values between lower and upper core half. Almost every plot displays a zone of highs and lows between a depth of 40 cm and 55 cm. These remarkable patterns do not coincide with specific features in the sedimentary record. Therefore, they are most probably the result of scanning artefacts. Si (also Al, Ca and Ti in Appendix A) exhibits an overall descending trend from 40 cm of depth up to the top of the core (Fig. 5.7). This evolution can be related to a post-depositional, compaction-driven porosity decrease within stratigraphically deeper sediments, which is also reflected in the gradually darkening greyshade of the CT-image throughout the upper 40 cm. Si and Al seem to be indicators for coarser grain-sizes, as their profiles reach up to higher values at heights of type B and type C units. K/Ti ratios rule out the effect of porosity-controlled element-counts. Clustered type B and type C units result in zones of increased amplitudes of the K/Ti curve (Fig. 5.7).
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5.1.7 Age models
Based on macroscopic and microscopic observations, it can be concluded that records from Eklutna Lake are composed of laminations and layers that are distinguishable from one another by looking at sediment colour, grain-size and composition (XRF). These units can be counted as well in order to verify their potentially annual time frame. Counts were done on master core EK12-01, because of its long stratigraphic range and clear laminations/layers. The counting process is relatively straight-forward when taking the occurrences of type A, B and C units into account, since these are assumed to be deposited during the time span of one year (Fig. 5.6). Especially the winter clay cap criterion (see section 4.4.2.2) plays an important role in recognising individual varves. CT-images are of great help in zones where laminations are difficult to delineate on pictures alone, especially in the rather ‘messy’ upper half of EK12-01. When looking at the examples of type B and type C layers in Fig. 5.6, individual varves could mistakenly be counted as two, due to their multiple-base character. Though, these multiple bases only possess one common, well developed clay cap, which is expressed as a light top layer on sediment surface pictures and a dark band on CT-images. Thick, fining-upward layers at depths of 5-7 cm, 22-23 cm, 40-45.5 cm and 52-54.5 cm are not incorporated in the varve thickness record, but are regarded as stand-alone event-units (see section 6.1) that do not belong to the continuous sedimentary background.
Lamination/layer thickness variations throughout core EK12-01 can be translated into a high-resolution age-depth model (Fig. 5.7). If the counted laminations and layers are considered to be varves (not yet confirmed), the sedimentary archive captured in core EK12- 01 seems to date back to 1856 A.D. Thicknesses of winter clay caps remain relatively constant, whereas summer layers largely determine the total varve thickness. From 1856 A.D up to 1950 A.D., running averages of total varve thickness fluctuate around 0.5 cm with small, positive bumps centred around 1877 A.D., 1895 A.D., 1920 A.D. and 1940 A.D. Thereafter, the curve describes a pronounced rising trend to reach its maximum in ~1960 A.D., followed by a gradual decrease until 2012 A.D. Vertical running intervals in the age-depth model reflect deposition during zero-time events. The latter are covered in detail in section 6.1.
Measuring points of 137Cs display one clear peak at a corrected depth (depth without event- deposits, Arnaud et al. (2006)) of 20 cm and a real core depth of 22 cm, which corresponds to the maximum fall-out in 1963 A.D. (marker age) (Fig. 5.8). According to the varve counting age model, the depth of this peak coincides with ~1965 varve years A.D. Hence, radionuclide 137Cs-dating confirms the annual nature of the laminations and layers in Eklutna Lake, allowing the term ‘varves’ to be used legitimately. Several standard 210Pb-models were tried out to fit the measuring points of 210Pb. Since the CF:CS-model is known to introduce large errors in the presence of uncorrected mixed layers and several measurements yield relatively low 210Pb-concentrations (indication of mixing), the CIC-model is the most appropriate one to use (Sabine Schmidt, personal communication, 26/04/2014). Application of the CIC-model provides a theoretical background sedimentation rate of 0.432 cm/yr, which appears to be too high in the uppermost 5 cm of the core and too low between 15 cm and 28 cm and from 55 cm on (Fig. 5.8). However, within the sampled depth interval, the 210Pb-model fits the varve counting age model relatively well, also validating the varve assumption.
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Figure 5.7: Age-depth model of EK12-01 (bold red curve), combined with corresponding summer, winter and varve thickness records. In agreement with Fig. 5.3, light blue, light green and beige horizontal bands represent type B and C units, thick type B and C units, and other deviating units, respectively. Dashed lines, to the right of the CT-images, symbolise intervals with dominantly type A units. Black dates are assigned by varve counting. Red dates, on the other hand, result from tuning (see section 5.1.8). Orange horizontal lines indicate the presence of cryptotephra, deposited during historically reported eruptions (white dates). CT-scans are presented in greyscale (black to white) as well as in colour scale (blue to red). Curves of γ-density, L*a*b*, Si and K/Ti are added as well. Note that horizontal unit-bands and vertical running graphs (density, L*a*b* and XRF-data) are a function of core depth, whereas thickness records are a function of age (varve years A.D.). The diagonal age-depth model forms the essential link between both groups of properties.
Based on the presented varve counting age-depth framework, several remarkable sedimentary units in core EK12-01 can be dated (Fig. 5.7). For example, the rather homogeneous, grey body that is separating the lower core half from the upper, seems to be
57 Chapter 5 - Results deposited in 1929 varve years A.D. The thick type B deposits at depths of 54.5 cm and 7 cm, belong to the varve years of 1917 A.D. and 1994 A.D, respectively. Finally, the ~1 cm thick, dark-based layer at a depth of 23 cm can be dated as 1964 A.D.
Figure 5.8: Comparison between age model based on varve counts and age model based on 210Pb- and 137Cs- concentrations in sediment from core EK12-01. The right window shows point measurements of 137Cs and 210Pb in function of corrected depth. A CIC-model is fitted to the 210Pb-points, yielding a constant background sedimentation rate of 0.432 cm/yr. In the left window, the 210Pb-based age model and 137Cs-concentrations are placed on top of the varve counting age model in function of real core depth. Extrapolations of the 210Pb-model beyond the reach of sampling points are indicated with dotted lines. The deviating units of 1964 A.D. and 1929 A.D. and type B and type C units at depth intervals of 5-7 cm, 52-54 cm and 63-70 cm were considered as event- deposits (i.e. mixed layers) when selecting freeze-dried samples for measurements of 210Pb and 137Cs.
5.1.8 Varve thickness tuning and climate calibration
The calibration interval over which varve thicknesses are tuned, extends from 1932 A.D. to 2012 A.D. Climate time series that are used, are presented graphically in Fig. 5.9. These series include data from first-order stations Homer (Fig. 4.1) and Anchorage as well as the more local stations of Eklutna WTP, Eklutna Lake, Eklutna and Eklutna Project (Fig. 4.3). Parameter averages over all stations and over all stations except Homer were calculated and plotted. Excluding Homer from the computed averages allows a reconstruction of temperature, precipitation and snowfall patterns that are more consistent with the direct region of Anchorage and Eklutna Lake. Temperature, precipitation and snowfall data from different stations are more or less concurrent. The time series display rather subtle trends throughout the calibration interval, including a 1945-1976 A.D. temperature low.
Figure 5.9 (next page): Correlation between climate data and varve thickness records from EK12-01. Lower frame: data time series (1932-2012 A.D.) from six different weather stations and their averages (solid, black curve and dashed, black curve) for average annual temperature, annual precipitation and annual snowfall. Middle frame: sums of to percentages converted, detrended time series from all stations except Homer of all three weather parameters with equal weights as well as with differential weights. Upper frame: detrended, untuned and tuned varve thickness records with running averages with window widths of five years. The pink running average from the middle frame is projected on top in order to emphasise a certain concurrence. Orange arrows indicate the link between thicker varves and prevailing climate conditions. The exact meaning of the green arrows (mega-flood, ‘MF’) is explained in section 6.1.3.
58 Chapter 5 - Results
The detrended varve thickness record was created by subtracting a running average (1932- 2005 A.D.) and linear fit (2005-2012 A.D.) from the original record. Based on a contrast- enhanced core picture and CT-image, encompassing the interval 1932-2012 A.D., varves were delineated for tuning (Fig. 5.10). Especially laminations at a depth of 18-39.3 cm cause difficulties when it comes to varve separation. Reason for these ambiguities is a succession of type A, but mostly type B and type C units (Fig. 5.7) without obvious boundaries and often missing, clear clay caps. Taking a substantial amount of counting mistakes into consideration, it is useful to tune the detrended thickness record in order to estimate the
59 Chapter 5 - Results
margin of error within the 1932-2012 A.D. interval. Defined principles in section 4.5 serve as a foundation for varve boundary reassessment and relocation (Fig. 5.11). Orange arrows in Fig. 5.9 indicate example peaks in the tuned, detrended varve thickness record, which correspond to specific weather/climate conditions that favour increased sedimentation rates. Peaks ‘A’, ‘B’, ‘C’ and ‘D’ are related to high annual precipitation, a lot of winter snowfall followed by a warm varve year, a relatively warm varve year and a constructive combination of all parameters, respectively. Green arrows mark the position of the thick type B unit at a depth of 5-7 cm that has not been incorporated in the varve thickness record. Interpretation of this unit and its formation mechanisms follows in section 6.1.3.
Figure 5.10: Core interval of EK12-01 over which tuning is conducted. Varve delineations are executed mainly by looking at variations of sediment colour (core picture and CIE L*a*b*) and CT-greyshades. Each counted varve is delimited on both sides by yellow bars. The presented varve thickness record is based on these varve boundaries. A running average trend line and linear fit were used to detrend the thickness record to get rid of its lowest- frequency variations. The result of detrending is shown in Fig. 5.9.
As can be seen in Fig. 5.11, relocations of varve boundaries cause overall shifts of only a few varve years (maximum two) on different locations. The last varve of the tuned interval (1932 A.D.) has been moved over two years in an upward direction. No changes were executed in the initially delineated varves that are neighbouring the 137Cs-spike (22 cm depth), since this is an absolute age-depth level of high certainty (marker depth). Comparison of the tuned
60 Chapter 5 - Results and untuned thickness records implies an average counting error of ±1 year over a time span of 10 varve years. Between marker depths and from the deepest marker depth on, this counting error can be applied accumulatively (see section 6.2). However, the amount of erroneous counts most probably decreases downcore, because varves posses much clearer boundaries in the lower core half (Fig. 5.7). Age estimates of the lightening-upward layer at a depth of 23 cm, initially dated as 1964 A.D., do not change after tuning, as it is located near the 137Cs-peak marker depth. The type B unit at a depth of 5-7 cm (green arrows in Fig. 5.9), on the other hand, seems to become one year younger after tuning (Fig. 5.11).
Equally weighted and differentially weighted, all-containing climate curves are presented in Fig. 5.9 and are computed out of the all-stations-except-Homer averages after detrending with a window width of 15 varve years. Weights that were chosen for the temperature, precipitation and snowfall components in order to maximise the correlation between the weighted curve and the detrended, tuned varve thickness record are equal to 0, 15.38 and 1, respectively. The five-year running average of this weighted curve seems to show corresponding patterns with the five-year running average of the tuned, detrended varve thickness record, but also with the untuned, detrended varve thicknesses (Fig. 5.9).
Figure 5.11: Varve delineation in EK12-01 (yellow and orange bars) before (under) and after (above) tuning. Orange boxes to the side of the initial varve count indicate intervals in which boundaries were shifted in order to create a thickness record with peaks and troughs that are better fitting the climate data. Most adjustments were executed within the lower 21.3 cm (bold, black line). The tuned, detrended record is plotted in Fig. 5.9.
A series of Pearson correlation coefficients is calculated to explore all different sorts of relations between tuned/untuned thickness records, and combined (equally and differentially weighted) and individual climate parameters on an interannual timescale (after detrending along multidecadal component). These coefficients are determined for the raw records as well as for their running averages with window widths of five years (Table 5.1). Mutual relations between non-detrended data for temperature, precipitation and snowfall are examined too (Table 5.2). Overall, coefficients in Table 5.1 increase after taking running averages from annual data and in most cases, after tuning, proving the interest of the latter procedure. However, exceptions on these general rules can be observed. For instance,
61 Chapter 5 - Results correlations with temperature data decrease after tuning. The highest Pearson’s r in Table 5.1 represents the linear degree of correlation between the running averages of the tuned varve thickness record and the precipitation plot, yielding a value of 0.4136 (yellow cell), which is still relatively low since it does not even reach halfway from 0 (no correlation at all) to 1 (perfect linear correlation). The second highest value of 0.4106 (green cell) is obtained by correlating the running averages of the differentially weighted climate data with the running average of the tuned thickness record. Since correlations with differentially weighted data are higher than correlations with equally weighted data, fine-tuning of individual parameter contributions seems to have a positive effect. Out of the individual climate parameters, precipitation shows the strongest positive correlations, followed by temperature and finally snowfall.
Table 5.1: Pearson correlation coefficients between the annual values and running averages (RA) of tuned and untuned, detrended varve thickness records from EK12-01 on one side and annual data and running averages of detrended climate records on the other side. Climate records comprise temperature (T), precipitation (P), snowfall (S), equally weighted percentage sum (Sum) and differentially weighted percentage sum (W Sum) data, derived from calculated averages of all stations except Homer (Fig. 5.9).
T RA P RA S RA Sum RA W Sum RA
Untuned 0.1120 0.1398 0.0963 0.1737 0.1453
RA 0.3234 0.2122 0.0061 0.2575 0.2082
Tuned 0.0821 0.3897 0.0367 0.2460 0.3901
RA 0.1980 0.4136 0.1051 0.3140 0.4106
Table 5.2: Pearson correlation coefficients between annual data and running averages (RA) of non-detrended climate records, calculated for averages of all stations (left) and all stations except Homer (right).
T RA P RA S RA T RA P RA S RA
T 0.5365 -0.0180 T 0.2669 0.1244
RA 0.6774 -0.1165 RA 0.4011 0.2514
P - - -0.1726 P - - -0.0063
RA - - -0.4866 RA - - 0.2543
S - - - - S - - - -
RA - - - - RA - - - -
Correlation coefficients between individual, non-detrended climate parameters are shown in Table 5.2. When taking all stations into account, strong positive correlations between temperature and precipitation arise (0.6774 for running averages), which on their turn are negatively correlated with snowfall. However, one should be careful with the interpretation of this last observation, for snowfall was coupled to the varve year after the winter in which snow actually fell. Thus, the observation of negative correlation coefficients means that heavy snowfall during winter, combined with the following of a relatively warm varve year does not occur quite often, ruling out the dominance of this climate condition in creating thicker varves. When Homer station is not included, a slightly poorer correlation between
62 Chapter 5 - Results temperature and precipitation (0.4011 for running averages) can be observed and an overall positive, but weak correlation between the latter two and snowfall. Hence, when exclusively local climate data are withheld, snowfall and temperature seem to co-operate in a somewhat greater, but still insignificant, extent in strengthening the mechanism that supports sediment transport towards the lake basin on an interannual timescale.
As already mentioned, correlation coefficients between tuned varve thicknesses and proposed climate curves remain relatively low. Though, when taking into consideration that naturally linked weather parameters (e.g. precipitation and temperature) do not reach correlations higher than 0.6774 for their running averages, the seemingly poor climate-varve thickness relations can be put into perspective and, in fact, be re-evaluated as quite close. The exact meaning of the obtained correlation coefficients and fine-tuned weights and in what way they contribute to the reconstruction of paleoclimate is discussed in section 6.5.1.2.
5.1.9 Spectral analysis
Results of a spectral Fourier analysis on the united, non-detrended, tuned (1932-2012 A.D.) and untuned (1856-1932 A.D.) varve thickness records from core EK12-01 are shown in Fig. 5.12. Correlation strengths of several frequencies and thus periodicities of sine and cosine functions are more pronounced than others. Periods of prominent peaks in a downscaling order of power are: 76.9 yr, 15.6 yr, 19.6 yr, 9.3 yr, 31.3 yr, 4.1 yr, 5.4 yr, 6.8 yr and 12.1 yr.
63 Chapter 5 - Results
Figure 5.12 (previous page): Periodograms, expressing the power of the correlation between sine and cosine functions of different frequencies with the united, non-detrended, tuned (1932-2012 A.D.) and untuned (1856- 1932 A.D.) varve thickness records of EK12-01. The correlating power is plotted in function of frequency and periodicity. Coloured, dotted lines mark the most important peaks, of which the periods (76.9 yr, 31.3 yr etc.) are indicated with arrows. Frequencies below 0.25 year-1 or periods below four year were not considered.
Cyclicities with a period lower than four year are not considered for further discussion, since these are too heavily influenced by the introduced varve counting error and therefore not reliable. Hence, all of the ‘cyclic’ signals with smaller periodicities can be looked at as a sort of background noise, caused by ambiguities in the definition of varve boundaries.
5.2 Kenai Lake
5.2.1 Catchment and drainage networks
In comparison to Eklutna Lake, the catchment of Kenai Lake is quite extensive, comprising several ranges and valleys within the Kenai Mountains, a few glacial meltwater streams and small upstream lakes from which overflowing water is transported towards the Kenai Lake basin. This narrow lake receives most of its water from three different source areas (Fig. 5.13).
A first source area is located to the east and southeast of the basin and delivers water from two different valleys. Melt- and rainwater is transported via the Snow River from the upstream Nellie Juan Lake on towards Primrose at the southern end of Kenai Lake, after it has been merged with the meltwater stream that originates at the snout of a large glacier in the higher Kenai Mountains (south of Trail Glacier) and flows through the broad Paradise Valley. Further to the south and west of the Snow River catchment, water drains towards Bear Creek, Resurrection Bay and the Gulf of Alaska, instead of Kenai Lake.
A second source area can be found northeast of the lake basin and includes multiple branching valleys of which the Trail River valley with Trail Glacier and a series of smaller glaciers are responsible for the main water supply. Water from the Trail River gets caught up first in the Upper Trail Lake, after which it flows further into the smaller Middle Trail and Lower Trail Lakes and eventually in Kenai Lake at Crown Point. Overflowing water from Grant Lake and Ptarmigan Lake becomes assembled as well at the level of this major inflow area.
The last main source region can be defined as the wide valley north of the western portion of Kenai Lake, through which a major part of the Seward Highway runs. From the small Summit Lake on, water is transported to the southwest via a meandering river that is constantly enriched with water from lower Strahler order streams and smaller upstream lakes, such as Crescent Lake. Overflowing water from Crescent Lake is drained as well towards Upper Trail Lake and east Kenai Lake. Besides these three principle source areas, a bunch of smaller drainage paths contributes an almost unimportant volume to the lake’s water budget. All of these inflow fluxes are countered by one outflow, the Kenai River. Overflowing water from Kenai Lake becomes transported via Cooper Landing towards Skilak Lake, located at the transition between Kenai Mountains and Kenai Lowland (Fig. 5.13).
64 Chapter 5 - Results
within the delineated watersheds, drainage networks are are networks thedisplayed, for outflowingexcept KenaiRiver. drainage watersheds, delineated the Only within icefields. arrows and glaciers of the presence the of indicate areas Thicknesses region. important discharge. direct of levels approximated represent most lakes’ their the of and line) DEM a red on superimposed arrows), blue (light paths drainage and (yellow Lake 5.13 Figure
: Skilak and line) (red Lake Kenai of Catchments
coloured White
65 Chapter 5 - Results
5.2.2 Bathymetric and seismic-stratigraphic setting
Kenai Lake is composed of three straight segments and a small sub-segment at its northern end (Fig. 5.13). According to the seismically-derived bathymetry map, the lake’s deepest part (>175 m) can be found in the western portion of the east-west orientated segment, southeast of Porcupine Island (Fig. 5.13), where core KE12-09 was retrieved. Across the entire east-west orientated segment, central water depths stay greater than 125 m and in most parts even greater than 150 m. A small elevation in the lake bottom creates a subtle division between the western and eastern parts of this segment. Throughout the outer two lake sectors a gradually shoaling trend dominates. In the western segment, water depths of less than 25 m are reached before deepening again slightly towards the small sub-segment in the north. A shallow water area that quickly evolves into greater depths to the more internal parts of the basin, surrounds Porcupine Island. Similar to Eklutna Lake, the overall basin morphology displays steep lateral margins, again pointing towards its glacial origin.
Figure 5.14: Seismic profiles, transversely cross-cutting the lake basin. Locations of the navigated lines are shown in the insets. All of the indicated coring sites are placed along the actual profiles. Black rectangles show how far the cored sediment reaches back into the lake’s infill. Vertical distances are given in Two-Way travel Time (TWT).
Specifications concerning the exact position of all nine coring sites were treated in section 4.2. Again, the central parts of the basin were determined to be ideal for the collection of climate records. This theory can be illustrated with four example profiles, shown in Fig. 5.14. A thick, layered sedimentary succession fills the deepest part of the basin and is similar to the one that was observed in Eklutna Lake. Cored deposits only comprise the upper few
66 Chapter 5 - Results seismic reflectors. In contrast to the other lines, the visualised profile of Line Kenai35 in the northern sub-segment displays a bumpy acoustic basement, of which the topography is gradually levelled out by the overlying drapes of sediment.
5.2.3 Macroscopic and microscopic core description
5.2.3.1 Macroscopic description
Similar to Eklutna Lake, sampled sediment from Kenai Lake is mainly composed of laminated to sometimes layered successions with regularly intercalated, slightly thicker and often darker, lightening-upward units (Fig. 5.15). Furthermore, in several of the obtained cores, solid packages of structurally rather homogeneous material with thick, coarse bases can be observed. Core KE12-07, retrieved from the western, gradually shoaling lake segment, contains the cleanest and most continuous sedimentary archive and is hence chosen to be the lake’s master core. Although KE12-01 and KE12-08 seem to contain deeper reaching records (Fig. 5.15), their laminations and layers are often extremely difficult or even impossible to distinguish. Untouched pictures were used for descriptions of sediment colour.
The 77 cm long master core KE12-07 (Fig. 5.15) is filled with a continuous succession of olive-grey laminations. Each of these, on average 0.3 cm thick, laminations displays a fining- and lightening-upward trend, going from silt to clay. This succession shows only one remarkable interruption at a depth of 10.5-13.5 cm, characterised by a 2 cm thick, dark grey, silty to sandy base, in which several wood fragments are embedded and which gradually evolves into a lighter grey, clayey cap without organic material. Up to 3 cm below and 1.5 cm above the coarse interruption, sediment shows a slightly deviating colour. No olive-grey, but a rather bluish grey shade becomes dominant. A gradual evolution towards the same colour can also be seen in the bottom 23 cm of the core. Noteworthy as well, is the observation of a ~2x1 cm, flat rock fragment within the bluish grey sediment below the dark unit at 10.5-13.5 cm depth. This piece of rock cannot be seen on the core surface picture, but stands out on the greyscale CT-scan as a vertically positioned, bright body (Fig. 5.17), since it attenuates much more of the incident radiation. Based on the latter principle, several other smaller pebbles can be seen throughout the core (e.g. at 2.5 cm, 43.5 cm and 57 cm). The wood fragments at a depth of 12-13 cm have a similar attenuating effect on CT-images.
Apart from the prominent interruption, laminations in KE12-07 occasionally have a darker appearance, sometimes accompanied by an increased thickness, up to 1 cm. Examples of these darker entities can be seen at depths of 3 cm, 31-32.5 cm, 41.5 cm and 62 cm. Though a lot more are present. When taking a closer look at those units, many of them seem to possess a similar structure as type B units, described for the sediments of EK12-01 (Eklutna Lake) (Fig. 5.4). Just like in the upper part of EK12-01, the typical ‘sandwich’-architecture of these type B units is rather subtle and not as distinct as the extremely well developed layers that were identified in the lower half of EK12-01. Grain-sizes within the dark type B units in KE12-07 also reach up to coarser silt than can be found in the bases of common background laminations. The overall sediment colour however, has a stronger olive-grey touch in
67 Chapter 5 - Results comparison to the more grey/beige/brown shades in EK12-01 and Eklutna Lake in general. Furthermore, the multiple-layer variations on type B units, which were introduced as type C units, can be distinguished as well. A somewhat different, ~1 cm thick entity can be seen on a depth of 25 cm in KE12-07. Its colour is slightly greyer and its relatively thick base of ~0.8 cm is covered by a thin clay cap of ~0.2 cm. On a depth of 45 cm, the identification of a very fine layer (<1 mm) of vitreous particles that belong to the fine sand grain-size fraction, points towards the presence of a tephra-fall deposit.
Figure 5.15: Correlation between master core KE12-07 and other cores KE12-01, KE12-02, KE12-03, KE12-04, KE12-05, KE12-06, KE12-08 and KE12-09 from Kenai Lake, arranged by increasing distance from important glacier- fed meltwater inflows at the easternmost end of the lake. Core pictures are slightly contrast-enhanced. Stars and red blocks at the side of KE12-07 are locations of smear slide and grain-size analyses respectively. Zones of different dominant sedimentary units are delineated. A more complete image of KE12-07 can be found in Fig. 5.17. The interpretation and reasoning behind the presented correlation is discussed in section 6.3.3.
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Since KE12-07 was acquired in the northwestward shoaling basin segment, it shows more similarities with the deposits caught in core KE12-08 and even KE12-01 (northern, shallow sub-segment) than with the sequences in cores from the deep, central segment and southern segment, which are located closer to the two important meltwater inflows at the eastern end of Kenai Lake (Fig. 4.1, 5.13 and 5.15). Moreover, KE12-07, KE12-08 and KE12-01 contain overall less coarse sediment than the other cores. Remarkable is the strong colour shift to a bluish grey tone that takes place in KE12-08 and KE12-01 after respectively 7 cm and 3 cm of olive-grey top-sediment. Just like in KE12-07, this colour change is located a few centimetres above a dark-based entity, which is extremely well developed in KE12-08. There it consists of a 5 cm thick, erosive base, grading from medium sand to silt, overlain by a 0.7 cm thick, brighter, fine silt to clay top layer. In contrast to master core KE12-07, the bluish grey colour remains maintained throughout the lower part of cores KE12-08 and KE12-01. Apart from the colour difference, KE12-08 displays similar laminations as KE12-07, whereas the sedimentary record of KE12-01 is highly confined. Both KE12-08 and KE12-01 reach further back into the stratigraphy of Kenai Lake than master core KE12-07. Especially the content of KE12-01 covers an extensive interval as a result of its compact nature. Furthermore, several brown, coarse-grained (up to medium sand) layers can be observed in cores KE12-08 and KE12-01, for instance at depths of 79-82 cm, 93 cm and 100 cm in KE12- 08 and at 31.5 cm and 52.5 cm in KE12-01. Another, slightly larger rock fragment was found at 6 cm from the bottom of core KE12-08. At a depth of 19 cm in KE12-01 and 46 cm in KE12-08, layers with vitreous particles similar to the ones in KE12-07, are present.
All records from the southern and central lake segment are variations on the same theme (Fig. 5.15). The observed downcore successions are characterised by a top-sequence of laminated to even layered deposits with an overall olive-grey colour, followed by a tick (up to 62 cm in KE12-05) package of lightening- and fining-upward sediment. The latter body possesses a dark grey, medium sand base that gradually evolves into finer material in an upward direction, finally finishing in a light grey, clayey cap. Within the central part of this structurally more homogeneous unit, a faint dark/light interfingering can be distinguished. Underneath, a new interval of olive-grey laminations and layers continues until the core bottom. In general, background deposits from the southern and central segments display similar properties as in cores KE12-07, KE12-08 and KE12-01. Though, laminae are thicker (sometimes layers) and type B and type C units are more distinct in terms of colour, thickness and coarseness. One of the best developed type B units (in KE12-09 at a depth of 15 cm) is incorporated into more bluish grey sediment, similar to the shade observed in the cores from the northern segment and sub-segment.
5.2.3.2 Microscopic description and grain-size measurements
As already explained in the corresponding section about Eklutna Lake, repeated point measurements on different types of units (Fig. 5.15) can help to reconstruct grain-size patterns within these units and between units throughout the entire core. Grain-size distributions and/or smear slide pictures from representative laminations at depths of 19 cm, 26.5 cm, 41 cm and 70.5 cm are shown in Fig. 5.16. In addition, the organic base of the dark,
69 Chapter 5 - Results interrupting body has been sampled as well for grain-size analysis and smear slide study. General observations are largely convening with the ones from EK12-01. The same sub- populations (fine clay, coarse clay, very fine silt, fine silt, medium silt, very coarse silt and very fine sand) can be traced back throughout all of the plots and are weighted differently in every sample. In contrast to Eklutna Lake, the always recurring sub-populations of coarser grain-sizes (sandy) seem to be absent. The consistent presence of a fine clay sub-population, however, is the case and can be seen on smear slide images as well.
Figure 5.16: KE12-07 with grain-size measurement results from representative laminations and layers. Contrast- enhanced pictures of smear slides from corresponding sampling depths are shown as well. Every grain-size measurement is depicted as a frequency and a cumulative frequency curve. Grey dashed lines, running through the graphs, indicate the presence of different sub-populations within the analysed sediment samples. These sub- populations belong to separate grain-size classes, according to the displayed Udden-Wentworth scale. Modes and desc riptions of dominant grain-sizes are output results from GRADISTAT 8.0. Indications of ‘summer’, ‘winter’, ‘flood deposit’ and ‘mega-flood deposit’ are explained in section 6.5.2.
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The type A lamination that was sampled on a depth of 19 cm does not show that much of a difference in grain-size between base and top. Both samples display more or less similar frequency curves, although the base-sediment is slightly more skewed towards fine silt, whereas the top-sediment has a stronger very fine silt component. Laminations at depths of 41 cm and 70.5 cm represent the coarser, middle parts of type B deposits. As already observed in EK12-01, base-sediment of these laminae is coarser than that of two-layered type A units. Though, the exact coarseness still varies between type B units, as can be seen in Fig. 5.16. The dark, distinct entity at a depth of 41 cm is very strongly skewed towards the coarser side of the plot and has a prominent sub-population of very coarse silt. Frequency curves of the top-sediment, on the other hand, resemble those of common background laminae. Samples from the second type B unit at 70.5 cm display a clearly less pronounced difference between top- and base-deposits, although the base is still coarser than the one from the described type A unit (19 cm). The lamination at a depth of 26.5 cm has a dark base, but does not possess the ‘sandwich’-structure, which is characteristic for type B units. When looking at the instrumental grain-size data, its dark base is reflected in a central, fine silt sub-population and grades into top-sediment made of very fine silt. Therefore, this unit is overall coarser-grained than the lamination at 19 cm and can thus be considered as a darker, coarser type A unit. The organic material containing layer was sampled at a depth of 13 cm and is mainly composed of very fine silt. However, the output data from GRADISTAT describe its grain-size as fine silt. This discrepancy is due to the fact that GRADISTAT interprets the entire plot, whereas ‘very fine silt’ is a definition of only the most prominent sub-population without taking the other sub-populations into account. Hence, the GRADISTAT result is being influenced by the larger spread of the plot and thus the sediment’s poorer sorting. A fragment of organic material is presented in the smear slide picture of the dark grey unit (Fig. 5.16). The surrounding mineral grains, however, are not representative for the sample’s overall grain-size.
Analogous to EK12-01, all observations can be extrapolated for the entire core. Hence, one might conclude that type B and type C deposits are on the whole coarser than type A units, especially when it comes to their dark bases. When comparing the general results to EK12- 01, one rather prominent difference can be noticed, which is the relative underdevelopment of fine-grained clay caps in laminations throughout KE12-07. Once again, it is hard to derive mineralogy-related information from the prepared smear slides (Fig. 5.16) due to limited grain-sizes. Just like in EK12-01, quartz with undulatory extinction and some opaque minerals can be identified, mainly in the coarser samples (e.g. base at 70.5 cm).
5.2.4 MS- and density-evolutions
According to the stated rule for Eklutna Lake, MS of coarser, clastic layers is mostly higher than MS of finer-grained material. However, this theory does not always seem to be valid. In cores KE12-05 and KE12-06, values decrease at the bulky, sandy bases of the thick, fining- upward packages and in cores KE12-08 and KE12-07 sudden drops can be seen at heights of the coarse-grained background interruptions (Fig. 5.15). Also, the brown, sandy layer at a depth of around 80 cm in KE12-08 displays a slightly poorer MS. Part of these lowered values
71 Chapter 5 - Results might be the result of the presence of organic material and/or an imperfect contact between point sensor and sediment surface due to the coarseness of the interval, which often leads to voids along the scan path. Though, the other part must be the result of grain-size-related mineralogy. The observed, vitreous layers in cores KE12-07, KE12-08 and KE12-01 are accompanied by pronounced MS-peaks. Furthermore, several similar peaks appear to occur without any macroscopic or microscopic evidence to explain their presence. In master core KE12-07, peaks of this nature are present at depths of 4.5 cm and 8 cm. In core KE12-09, positive outliers are reached around 13 cm and 20 cm. Similar MS-spikes seem to recur in the other cores as well (Fig. 5.15). Just like in Eklutna Lake, the presence of cryptotephra ash- horizons offer the most plausible explanation for these patterns.
The general density-evolution of slightly decreasing values towards top-sequences in KE12- 07 (Fig. 5.17) is most probably due to a higher degree of compaction in the lower parts of the core as a result of post-depositional diagenesis and post-acquisition storage. However, density changes inherent to sedimentological properties (mineralogical composition and texture) are also contributing factors. Higher-frequency fluctuations again respond to the alternation of denser lamination- and layer-bases and less dense tops, which is visible on CT-images as well (Fig. 5.17). A strong density-drop at the height of the organic fragments is the direct consequence of void space in between the coarse particles.
5.2.5 Colour analysis
Since spectrophotometric data from the Geotek MSCL were also rejected for Kenai Lake, Strati-Signal results of a L*a*b* colour analysis are presented in Fig. 5.17. L*- and b*-indices display rather predictable patterns, with b* explicitly highlighting the bluish intervals (low values). The a*-index on the other hand, is characterised by a more or less recurring pattern, which it has in common with EK12-01. However, the wavy signal that was observed in EK12- 01 is replaced by a sort of saw-tooth motif in KE12-07. Separate saw-teeth can be observed at depths of 57-70 cm, 57-44 cm, 44-15 cm and 15-0 cm. The last one, however, is interrupted by the coarse body with woody fragments of organic material.
5.2.6 XRF-profiles and -patterns
No high-resolution (<1 cm) XRF-scans were conducted on cores from Kenai Lake, as none of the cores contains substantial sequences of unambiguously bordered laminations. KE12-07 was scanned at a resolution of 1 cm, making it only possible to assess general downcore chemical evolutions of elements with sufficiently high counts. Element-profiles of KE12-07 are shown in Appendix B. Each one of these curves shows a similar course, characterised by a more or less steady interval from the core bottom up to a depth of 14 cm, after which counts crash at the level of the very porous layer with organic fragments. Within the upper 10 cm of KE12-07, elements show a second low, though less sharp than the first one. An overall, gradually decreasing trend towards the core top, can be assigned to the same porosity- related reasons that cause the density curve to deflect and X-rays to become less attenuated
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(CT-scan). Indeed, K/Ti indicates that these general trends are due to porosity variations since they become levelled out when calculating an element-ratio (Fig. 5.17). Moreover, the property of K/Ti in marking clay caps proves its quality at a depth of 11 cm, where the fine- grained top of the wood-containing layer exhibits a sudden peak. Given the poor scanning resolution of 1 cm, clay caps of every individual lamination are impossible to discern.
5.2.7 Age model
In a similar manner as described for Eklutna Lake, presumed annual laminae/layers can be counted when taking primarily macroscopic and microscopic observations into account. Radionuclide dating has not been executed on cores from Kenai Lake, causing the confirmation of the annual time frame of laminations to rely entirely on marker depths provided by identified historical events (see section 6.1). Even more than in EK12-01, lamination counting in master core KE12-07 depends on the winter clay cap criterion and the core picture complementing images from CT-scans (Fig. 5.17). The lesser development of fine-grained lamination tops was already mentioned in section 5.2.3.2, but can be recognised as well in the lower degree of contrast within laminations on CT-images. Despite this weaker contrast, CT-scans still prove to be of great importance in core intervals where core surface pictures fail to provide a sufficiently clear counting base. For instance, the uppermost 10 cm of core KE12-07 show very faintly laminated deposits on the contrast- enhanced core picture, which is not prominent enough to secure a reliable delineation of assumed varves, winter- and summer-layers (Fig. 5.17). The corresponding CT-interval, on the other hand, compensates the poor quality of the sediment surface picture. During measurements of lamination thickness, the organic unit (10.5-13.5 cm) and the grey, ~1 cm thick unit (24.5-25.5 cm) were not incorporated.
The age-depth model, presented in Fig. 5.17, is based on the assumption of annually resolved laminations (even if not yet confirmed, see section 6.1). Full records of summer, winter and varve thickness are presented as well in function of age (varve years A.D.). Again, winter layer thicknesses follow a rather constant trend, although with a few outliers, whereas summer layers are more decisive in determining fluctuations of total varve thickness. Based on the varve assumption, the entire length of core KE12-07 seems to cover a time period of 179 years, reaching back to 1833 A.D. The running average of total varve thickness sticks to a more or less stable value of 0.3 cm from 1833 A.D. up to 1865 A.D., followed by a prominent high over the next 25 years. Thereafter, a new period of average sedimentation rates (not corrected for water content) of 0.35 cm/yr is maintained during a time interval of 90 years. Finally, the varve thickness record displays an increasing trend until ~1998 A.D., after which a relatively fast decrease can be observed. Vertical sections in the age model reflect deposition during zero-time occurrences (see section 6.1). Based on the constructed, high- resolution age-depth model (Fig. 5.17), varve years can be coupled to each one of the delimited laminations and layers, including entities that were not used to create thickness records. The grey unit at a depth of 25 cm in KE12-07 can be dated as 1964 A.D., which convenes with the age of the dark-based layer at a depth of 23 cm in core EK12-01. Varve dating of the organic interruption yields an age of 1991 varve years A.D. (Fig. 5.17).
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Figure 5.17: Age-depth model of KE12-07 (bold red curve), combined with corresponding summer, winter and varve thickness records. In agreement with Fig. 5.15, light blue, light green and beige horizontal bands represent type B and C units, thick type B and C units, and other deviating units, respectively. Dashed lines symbolise intervals with dominantly type A units. Black dates are assigned by varve counting. Red dates result from tuning (see section 5.2.8). Orange horizontal lines indicate the presence of tephra and cryptotephra, deposited during historical eruptions (white dates). CT-scans are presented in greyscale as well as in colour scale. Curves of γ- density, L*a*b*, Si and K/Ti are added as well. Note that horizontal unit-bands and vertical running graphs (density, L*a*b* and XRF-data) are a function of core depth, whereas thickness records are a function of age (varve years A.D.). The diagonal age-depth model forms the essential link between both groups of properties.
5.2.8 Varve thickness tuning and climate calibration
Tuning of the measured lamination thicknesses of KE12-07 is based on climate data from four different stations: first-order stations Homer and Anchorage and more local stations Cooper Lake Project and Cooper Landing 5 W (Fig. 4.1). The interval over which calibration is executed, runs from 1932 to 2012 varve years A.D. (Fig. 5.18). Since tuning follows the exact same procedure as explained thoroughly for EK12-01 in section 5.1.8, a reference to those paragraphs suffices to understand the working course for KE12-07.
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Figure 5.18: Correlation between climate data and varve thickness records from KE12-07. Lowest frame: data time series (1932-2012 A.D.) from four different weather stations and their averages for average annual temperature, annual precipitation and annual snowfall. Middle frame: sums of to percentages converted, detrended time series from all stations except Anchorage of all three weather parameters with equal weights as well as with differential weights. Upper frame: untuned and tuned, detrended varve thickness records with five-year running averages. The pink running average from the middle frame is projected on top in order to emphasise a certain concurrence. Orange arrows indicate the link between thicker varves and prevailing climate conditions. The significance of the green arrows (mega-flood, ‘MF’) is clarified in section 6.1.3.
Figure 5.19 (next page): Core interval in KE12-07 over which tuning is conducted. Varve delineations are executed mainly by looking at variations in sediment colour (core picture and L*a*b*) and CT-greyshades. Each counted varve is delimited on both sides by yellow bars. The presented varve thickness record is based on these varve boundaries. A running average and linear fit were used to detrend the thickness record to get rid of its lowest-frequency variations. The result of detrending is shown in Fig. 5.18.
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Climate parameter averages were determined over all stations and over all stations except Anchorage (Fig. 5.18). Excluding Anchorage from the computed averages allows a reconstruction of temperature, precipitation and snowfall patterns that are more consistent with the direct region of Kenai Lake and Kenai Peninsula. For this reason, combined time series only include data from stations other than Anchorage.
Normalisation of the varve thickness plot along a running average and linear fit over the interval 1932-2012 A.D. (Fig. 5.19), yields a detrended thickness record, which is displayed in Fig. 5.18. As can be seen on the varve delineation in Fig. 5.19, almost the entire calibration interval consists of ambiguous varve boundaries. Hence, a certain counting error can be reduced by tuning the detrended record (Fig. 5.20). Orange arrows in Fig. 5.18 mark positions of thick varves after tuning and their relation with prevailing weather conditions during the corresponding varve year. Peak ‘A’, for instance, coincides with circumstances of extreme rainfall and air temperatures higher than average over the interval 1932-2012 A.D. Peak ‘B’, on the other hand, seems to be caused by favouring conditions regarding snowfall, precipitation and temperature. Green arrows indicate the location of the wood-containing layer at a depth of 10.5-13.5 cm in KE12-07. This unit was not involved in the calculated varve thicknesses and is therefore not visible in the thickness record. Further discussion on this body follows in section 6.1.3.3. Several varve boundaries were shifted during tuning (Fig. 5.20). An average counting error of ±1.5 year over a time span of 20 varve years can be
76 Chapter 5 - Results derived. This error will be applied between marker depths and from the deepest marker depth down to the bottom of KE12-07 (see section 6.2). The layer that was originally dated as 1964 A.D., does not experience any changes after tuning, whereas the thicker body with wood fragments (green arrows in Fig. 5.18) is being relocated to 1990 A.D.
Figure 5.20: Varve delineation in KE12-07 (yellow and orange bars) before (under) and after (above) tuning. Orange boxes to the side of the initial varve count indicate intervals in which boundaries were shifted in order to create a thickness record with peaks and troughs that are better fitting the climate data. The detrended, tuned record is plotted in Fig. 5.18.
Table 5.3: Pearson correlation coefficients between annual values and running averages (RA) of tuned and untuned, detrended varve thickness records from KE12-07 on one side and annual data and running averages of detrended climate records on the other side. Climate records comprise temperature (T), precipitation (P), snowfall (S), equally weighted percentage sum (Sum) and differentially weighted percentage sum (W Sum) data, derived from calculated averages of all stations except Anchorage (Fig. 5.18).
T RA P RA S RA Sum RA W Sum RA
Untuned 0.1196 0.2158 0.1945 0.2711 0.2542
RA 0.2099 0.4146 0.2498 0.4158 0.4237
Tuned 0.2762 0.4085 0.0906 0.4045 0.4313
RA 0.2354 0.4550 0.2014 0.4333 0.4586
Table 5.4: Pearson correlation coefficients between annual data and running averages (RA) of non-detrended climate records, calculated for averages of all stations (left) and all stations except Anchorage (right).
T RA P RA S RA T RA P RA S RA
T 0.5849 -0.1012 T 0.5151 -0.1837
RA 0.6999 -0.2937 RA 0.4996 -0.2619
P - - -0.0649 P - - 0.1652
RA - - -0.1995 RA - - 0.4060
S - - - - S - - - -
RA - - - - RA - - - -
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Weights for detrended temperature, precipitation and snowfall data of the differentially weighted, combined climate curve from all stations except Anchorage are equal to 1, 4.26 and 1.16 respectively (Fig. 5.18). Similar to the plots for EK12-01 (Fig. 5.9), a certain correspondence between five-year running averages of the differentially weighted climate curve and tuned/untuned varve thickness records can be observed. Small-scale patterns, rather than general trends seem to convene. For example, the period from 1950 A.D. to 1975 A.D. shows in both weighted curve as in tuned thickness record a same elongated dome. Though, in the time interval preceding this dome, the climate curve displays a more pronounced high than the thickness record. Therefore, the relation between climate and thickness plots seems to be not entirely linear.
Overall, tuning has a positive effect on the computed correlations, which proves its benefit (Table. 5.3). The highest Pearson’s r in Table 5.3 represents the linear relation between running averages of the tuned thickness record and the differentially weighted, all- containing climate plot, yielding a value of 0.4586 (yellow cell), which is higher than the corresponding 0.4106 from Eklutna Lake. The second highest value of 0.4550 (green cell) is obtained by correlating running averages of precipitation and tuned thickness. Out of the three climate parameters, precipitation shows the strongest positive correlation, followed by temperature and finally snowfall. Note that this ranking of importance is the same as obtained for Eklutna Lake, in which precipitation played the most prominent role as well.
Correlation coefficients between individual climate parameters are shown in Table 5.4. When taking all stations into account or when neglecting data from the Anchorage station, strong positive correlations between temperature and precipitation arise (highest value of 0.6999 for running averages). As mentioned before in section 5.1.8, correlations between precipitation and temperature on the one hand and snowfall on the other hand, do not have a specific climatologic meaning, since snowfall data are plotted for the varve year following winter snowfall. Because relations between snowfall and temperature are negative on average, one might say that the constructive combination of these two parameters to create significant variations in interannual sediment discharge towards Kenai Lake, is not dominant.
Correlation coefficients in Table 5.3 remain low, even though they are slightly higher than the ones that were calculated for Eklutna Lake. However, mention can be made again of the naturally linked weather parameters (precipitation and temperature) that do not yield correlations close to 1 either. The exact meaning of the obtained correlation coefficients and fine-tuned weights and how these can be interpreted in order to reconstruct paleoclimate conditions, is discussed in section 6.5.1.2.
5.2.9 Spectral analysis
Both periodograms in Fig. 5.21 exhibit peaks at specific frequencies and periods, of which several seem to coincide with the cyclicity peaks in EK12-01 (see section 5.1.9). Prominent peaks in a downscaling order of power are located at periods of 90.9 yr, 30.3 yr, 4.3 yr, 7.8 yr, 4.1 yr, 5.3 yr, 20 yr, 6 yr, 15.2 yr, 6.7 yr, 12 yr and 9.5 yr. Out of these listed periods, 30.3 yr
78 Chapter 5 - Results corresponds more or less with 31.3 yr in EK12-01 (light blue), 20 yr with 19.6 yr in EK12-01 (dark green), 15.2 yr with 15.6 yr in EK12-01 (pink), 12 yr with 12.1 yr in EK12-01 (yellow) and 9.5 yr with 9.3 yr in EK12-01 (light green). Important to notice, is that the relative power of the coinciding periods in EK12-01 and KE12-07 are not the same. For instance, the light blue peak in KE12-07 occupies the second most important position, while the corresponding peak in EK12-01 is placed only fifth in order of dominance. Because of the same reason as mentioned in section 5.1.9, periods lower than four year are considered to be insignificant.
Figure 5.21: Periodograms, expressing the power of correlation between sine and cosine functions of different frequencies with the united, non-detrended, tuned (1932-2012 A.D.) and untuned (1833-1932 A.D.) varve thickness records of KE12-07. Coloured, dotted lines mark the most important peaks, of which the periods (90.9 yr, 30.3 yr etc.) are indicated with arrows. Frequencies below 0.25 year-1 and periods below four year were not withheld.
5.3 Skilak Lake
5.3.1 Catchment and drainage networks
Watershed boundaries and associated drainage networks of Skilak Lake are shown in Fig. 5.13 (see section 5.2.1.). The catchment of Kenai Lake is indicated on the same map, since both lake systems are connected to each other. Overflowing water from Kenai Lake is transported beyond Cooper Landing throughout the broad Kenai River valley towards the
79 Chapter 5 - Results eastern end of Skilak Lake. Thus, the catchment of Kenai Lake represents a part of the Skilak Lake watershed. However, the direct input from Kenai Lake mainly consists of water, since most of the sediment that enters Kenai Lake, especially coarser-grained material, becomes trapped in its accommodation space. The immediate catchment of Skilak Lake is relatively extensive as well and spreads out over several mountain valleys, upstream lakes, glaciers and their originating icefields.
Two different inflows determine the majority of the lake’s water supply. First of all, the Kenai River is being fed by several tributaries that drain higher elevated areas. For instance, water from Cooper Lake and Upper Russian Lake is transported towards Kenai River via relatively small streams (e.g. Russian River). A wide mountain valley to the north and its eastern branches are part of the Kenai River drainage as well. However, the most important feeding stream arises from the termini of Skilak Glacier and another parallel-flowing glacier more to the east. Meltwater from both glaciers arrives first in a small, ice-marginal lake in front of Skilak Glacier terminus and subsequently runs through a broad outwash plain towards the eastern end of Skilak Lake. The southern margin of the Skilak Lake watershed is difficult to delineate since particular ice-flow patterns within the Harding Icefield determine the exact source area of the valley glaciers. Apart from these two major influxes, several lower Strahler order streams contribute in a lesser extent to the lake’s inflowing water volume. Especially the southern basin margins feature a series of small runoff pathways. At the western end of Skilak Lake, the continuation of Kenai River is responsible for the lake’s outflow and travels through the Kenai Lowland, past Sterling and Soldotna towards Kenai, where it debouches into Cook Inlet.
5.3.2 Bathymetric and seismic-stratigraphic setting
Skilak Lake is composed of one main basin, of which the deeper, central parts follow an S- shape that bends around the Caribou and Frying Pan Islands (Fig. 4.2 and 5.13). The deepest area of the lake is centred in its eastern half, reaching up to water depths larger than 200 m. A submerged moraine cross-cuts the main basin south of the SK12-02 coring location and creates a subtle, theoretical boundary between western and eastern sub-basin (Perkins & Sims, 1983). The western sub-basin is rather shallow in comparison to the eastern one and comprises large areas of shoal water (<25 m) located around the intra-lake islands and close to the Kenai River outlet at the western end of the basin. Lateral margins of the basin again display a steep morphology, giving away the lake’s glacially scoured origin. However, this steepness faints towards the Kenai Lowland, where the basin broadens as the laterally constraining mountain chains disappear.
Bathymetric settings of the six retrieved cores were discussed in section 4.2 (Fig. 4.2). Five coring sites are aligned along the winding, S-shaped, central line of the lake, whereas SK12- 10 was acquired in a more upslope location at the southern margin of the basin. As can be seen on the seismic section of Line Skilak 05 (Fig. 5.22), this upslope sampling site is located on a platform-like feature, on which the sedimentary succession seems to consist of a draped, continuous sequence. The three other profiles in Fig. 5.22 sketch representative seismic-stratigraphic settings for the eastern, deeper sub-basin (Line Skilak 52) and the
80 Chapter 5 - Results western, shallower sub-basin (Line Skilak 19 and Line Skilak 11). Unlike Line Skilak 52, Line Skilak 05, Line Skilak 19 and Line Skilak 11 display a relatively accidented acoustic basement, which is entirely or partially levelled out by the overlying deposits. Just like in Eklutna Lake and Kenai Lake, cored samples only represent top sections of the entire lake infill, which consists of a thick succession of layered reflectors and more chaotic seismic units (Fig. 5.22).
Figure 5.22: Seismic profiles, transversely cross-cutting the basin of Skilak Lake. Locations of the profiles are shown in the inset figures. All of the indicated coring sites are located along the actual profiles. Black rectangles display how far the sampled sediment reaches back into the lake’s infill. On Line Skilak 11, the chaotic seismic facies of a mass-transport deposit (MTD) is indicated, which can be identified in core SK12-02. Vertical distances are given in Two-way Travel Time (TWT).
5.3.3 Macroscopic and microscopic core description
5.3.3.1 Macroscopic description
The three longest cores from Skilak Lake (parts A and B of SK12-03, SK12-07 and SK12-10) are composed of continuously laminated to layered successions of variably coloured (beige, grey, brown, olive) sediment, whereas the background deposits of the three shorter cores (SK12-01, SK12-02 and SK12-04) are interrupted by thick, lightening-upward entities of structurally more homogeneous material (Fig. 5.23), similar to the observed bodies in several cores from Kenai Lake. With its length of 176.2 cm and its sequence of clearly delineated laminae, SK12-10 serves as the first master core for Skilak Lake.
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Master core SK12-10 can be described best as a continuous sequence of laminations, of which the bases are overall darker than the tops. Grain-sizes range from coarser silt up to finer silt and clay within each of the laminae and sediment colours vary within a spectrum of greyish, brownish, beige and olive shades. The upper 17 cm of the core become progressively lighter. Due to excessive light reflection on the sediment surface between 83 cm and 89 cm (unknown reason), the visibility of the present laminae is strongly reduced. From a depth of 17 cm to 77 cm, a succession of extremely well developed, relatively uniform laminations can be observed (Fig. 5.23). Whereas most of the units in SK12-10 belong to the two-layered type A class, several type B units can be observed as well, mainly within the lower part of the core (Fig. 5.24). Type B units are not as distinct as in the lower core half of EK12-01 (Eklutna Lake). Their typical ‘sandwich’-structure is often not clearly visible as a result of the rather blurry core picture and the erosive quality of the dark-based, central parts. At depths of 26.5 cm, 42 cm, 123 cm, 126 cm, 147.5 cm and 169.5 cm, fine layers of vitreous particles were observed macroscopically.
In contrast to SK12-10, which was acquired on a higher elevated slope-platform, SK12-01 and SK12-04 were collected in the deepest part of the eastern sub-basin (Fig. 4.2 and 5.22). Both cores possess an almost identical record. Their top and bottom sections are composed of an alternation between type A laminae and darker, coarser (up to fine sand) type B and type C layers (Fig. 6.9). In the middle sections, two subsequent bodies of lightening- and fining-upward material can be discerned. The stratigraphically higher positioned body is the thicker, most erosive one and grades from medium to coarse sand at its base up to a clayey top cap, whereas the stratigraphically lower positioned body (83-89 cm in SK12-01) has a base of silt to fine sand. In core SK12-02, a similar, but more confined top section can be identified. The remainder of this 114 cm long core is filled with an almost homogenous package without clear colour- or grain-size-evolutions.
SK12-03, which was retrieved only 0.9 km to the SWW of SK12-02 (Fig. 4.2) does not contain homogenous sediment packages and is entirely composed of laminae and layers of type A, B and C. Within the depth interval of 27-105 cm in SK12-03, the concentration of dark-based units seems to increase dramatically and with that, the amount of type B and type C units as well. However, the latter are slightly different from the ones that were defined in records from Eklutna Lake. The colour and coarseness of the base-sediment in the central, ‘sandwiched’ parts of these units does not longer differ from the underlying background bases (Fig. 6.9, see section 5.3.3.2). Finally, sampled sediment from the westernmost coring site of SK12-07 is remarkably similar to the observed sequence in SK12-10, but shows some lamination-architecture properties that are halfway between the ones of SK12-10 and SK12- 03. The latter observation is not surprising given the intermediate bathymetric position of SK12-07, in between SK12-10 and SK12-03. At a depth of 97-99 cm in core SK12-07, a small fining-upward body with a base of fine sand is present. In both SK12-03 and SK12-07, several layers of vitreous fragments were found, at depths of 42 cm and 74.5 cm (SK12-03) and 26.5 cm, 42.5 cm, 111 cm, 113.5 cm, 130 cm and 147 cm (SK12-07).
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83 Chapter 5 - Results
Figure 5.23 (previous page): Correlation between master core SK12-10 and other cores SK12-01, SK12-02, SK12- 03, SK12-04 and SK12-07 from Skilak Lake. Core pictures are slightly contrast-enhanced. Stars and red blocks at the side of SK12-03, SK12-07 and SK12-10 are locations of smear slide and grain-size analyses respectively. Zones of different dominant sedimentary units are delineated. More complete images of SK12-10 and SK12-03 can be found in Fig. 5.26 and 5.27. MS-plots of the upper half of SK12-03 are missing, as this part of the core was sampled for radionuclide dating before scanning. The interpretation and reasoning behind the presented correlation is discussed in section 6.3.4.
Figure 5.24: Pictures and CT-images of two types of laminations/layers that dominate the record of SK12-10. To the right of the CT-images, black-and-white bars show the boundaries between each unit (varves), whereas the red-and-blue bars make a division between sediment that has been deposited during summer (red) and winter (blue). Next to the simplified drawings of the core surfaces, schematic representations of grain-size trends are displayed with triangles. Records of lightness (%L*) are provided as well. Abbreviations ‘S’, ‘W’ and ‘F’ stand for summer, winter and flood deposits, respectively (see Chapter 6).
5.3.3.2 Microscopic description and grain-size measurements
Several representative grain-size measurements from SK12-03, SK12-07 and SK12-10 are shown in Fig. 5.25. All frequency plots from SK12-10 seem to be remarkably similar. Small shifts in the prominence of the present sub-populations determine the difference between base- and top-sediment in each of the laminations. These subtle shifts are in most cases visible on the smear slide pictures as well. The strong resemblance between grain-size distributions of all of the analysed units implies that variations in coarseness throughout the entire core stay very limited. Furthermore, the distinct clay sub-population can be recognised in each of the presented plots and pictures, just like in the other two lakes.
At a depth of 7 cm, base-sediment from a well developed type A unit was analysed, resulting in a grain-size frequency plot with a dominant very fine silt fraction. Two more type A units were analysed on depths of 17.5 cm and 63.5 cm. Bases of both laminae yield the exact same distributions as observed at a depth of 7 cm. Their tops, on the other hand, display minimal weight-relocations towards the coarse clay fraction and are therefore slightly finer- grained. Even the type B unit at a depth of 113 cm is composed of the same sub-populations in the same proportions as the analysed type A units. Note that the overall grain-size of core SK12-10 is finer than already observed in Eklutna Lake and Kenai Lake. The latter is reflected in the scale indication of 100 µm instead of 500 µm in the smear slide pictures.
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Figure 5.25: SK12-03, SK12-07 and SK12-10 with grain-size measurement results from representative laminations and layers. Contrast-enhanced pictures of smear slides from corresponding sampling depths are shown as well. Every grain-size measurement is depicted as a frequency and a cumulative frequency curve. Grey dashed lines, running through the graphs, indicate the presence of different sub-populations within the analysed sediment samples. These sub-populations belong to separate grain-size classes, according to the displayed Udden- Wentworth scale. Modes and descriptions of dominant grain-size are output results from GRADISTAT 8.0. Indications of ‘summer’, ‘winter’ and ‘flood deposit’ are discussed in section 6.5.2.
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Only one sampled unit from SK12-07 is shown in Fig. 5.25, since results from the other analyses were similar to the ones in SK12-10. At a depth of 44 cm, an articulate lamina appears to possess base-sediment that is skewed towards a medium silt grain-size and top- sediment with a very fine silt to clayey composition. Thus, the grain-size distribution of the fine-grained cap does not really differ from units in SK12-10, whereas the base is obviously coarser. Out of the SK12-03 record, smear slides were made of one of the ‘not typical’ type B units (‘M’ in Fig. 6.9). Samples were taken from the first base, first top, second base and second top. As can be seen on the smear slide pictures in Fig. 5.25, the first base is the coarsest, followed by the second base, first top and eventually the second top. Especially smear slides from SK12-10 are not suited for identification of minerals, due to the lack of coarse-grained material. In sediment from a depth of 7 cm, several pennate diatoms were observed, of which one is presented in Fig. 5.25. Individual grains in core SK12-03 are coarser, but still difficult to identify. Again, quartz seems to be dominant. Small opaque grains and a few mica fragments were also recognised.
5.3.4 MS- and density-evolutions
Throughout all of the cores, MS-curves show spiky patterns with several stronger peaks (Fig. 5.23). Again, higher values are reached in intervals with coarser-grained material. For instance, the immediate bases of the thick, lightening-upward bodies in SK12-04 and SK12- 01 display an increased MS, which decreases gradually in an upward direction, together with grain-size. In core SK12-02, the relatively homogenous package that is present beneath a depth of 14 cm does not exhibit particular MS-evolutions, which supports the fact that there are no real grain-size evolutions as well. A series of peak values seems to occur within this body, especially concentrated in darker coloured zones with a probably different mineralogical composition. The vitreous layers that were identified in SK12-03, SK12-07 and SK12-10 coincide with strong MS-peaks. Apart from these peaks, several similar outliers are present in the top sections of every core (except SK12-03, due to missing Geotek-scans), which are presumably the result of cryptotephra horizons (Fig. 5.23).
In SK12-10 (Fig. 5.26) the overall density evolution is relatively constant with a slight trough at the top of section B (88 cm) and a strong drop at the top of section A (0 cm). Both decreases are most probably due to the combination of a stronger sediment compaction at greater depositional depths and post-acquisition storage. Higher-frequency fluctuations in both SK12-10 (Fig. 5.26) and SK12-03 (Fig. 5.27) are due to local changes in grain-size, porosity, mineral grain density and fluid density, which on their turn are strongly related to greyshades and colours on CT-images.
5.3.5 Colour analysis
Results from Strati-Signal L*a*b* colour analyses are shown in Fig. 5.26 and Fig. 5.27. L*- and b*-curves do not show specific trends and can be associated easily with visual observations of sediment surface pictures (e.g. Fig. 5.24). In core SK12-03, for example, L*-indices display
86 Chapter 5 - Results a suddenly increased amplitude of variation and overall lower values throughout the interval with well developed, darker units (27-105 cm). The a*-index exhibits, just like in the other two lakes, distinct wavy to saw-tooth-shaped patterns. This motive is clearest in core SK12- 03, in which seven subsequent waves/teeth can be discerned.
5.3.6 XRF-profiles and -patterns
SK12-10 was scanned at a resolution of 0.5 mm and hence, intra-lamination/layer variability in chemical composition can be studied. Trends within type A, B and C units are the same as the ones observed in Eklutna Lake (Fig. 5.6). However, since type B and C units (Fig. 5.24) are mostly not as strongly expressed as the ones that are shown in Fig. 5.6, the amplitude of the element-variations are slightly less pronounced.
Appendix C shows element-profiles of SK12-10 with significant counts. Curves of Al, Si (Fig. 5.26), K and Rh display two separate sections (bottom-88 cm and 88 cm-top), which are both characterised by upward lowering concentrations. These sections correspond to the A and B parts of the master core and are therefore a result of compaction-driven porosity changes, as can be inferred as well from the downcore density-development in Fig. 5.26. Other prominent elements, such as Ti, Cl and Fe, experience less of an influence from these porosity fluctuations. K/Ti ratios are able to rule out large-scale variations for the most part and to reveal lows at the bottom and top of the core. The latter lows could possibly point towards the presence of sediment with an increased fine-grained component.
5.3.7 Age models
Layers and laminations were counted in SK12-10 as well as in SK12-03. The record from master core SK12-10 is particularly interesting because of its length and substantial intervals of undisturbed, clearly delineated laminae. Observed laminae/layers in core SK12-03, on the other hand, show a strong variation in architecture and thickness, which is promising for creating a thickness record with pronounced trends. Once again, a series of observations and principles were taken into account while counting: the occurrence of presumed annual type A, B and C units (mainly based on sediment colour and grain-size measurements), the clay cap criterion, greyscale variations in CT-images (Fig. 5.26 and 5.27) etc. Layers that can be interpreted as stand-alone event-units were not incorporated in the measurements of varve thickness. More discussion on this topic follows in section 6.1.
When assuming the presence of annual laminations, the sedimentary record of SK12-10 seems to date back to 1340 A.D. (Fig. 5.26), while SK12-03 encompasses a time period going from 1646 A.D. up to 2012 A.D. (Fig. 5.27) even though its core length is not even 20 cm shorter than SK12-10. The latter observation confirms the occurrence of overall thicker annual units in SK12-03. When studying the summer, winter and varve thickness records of both SK12-03 and SK12-10, winter layers again display fairly constant thicknesses, whereas summer layers determine the general variations in total varve thickness. Furthermore, SK12-03
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Figure 5.26: Age-depth model of SK12-10 (bold red curve), combined with corresponding summer, winter and varve thickness records. In agreement with Fig. 5.23, light blue horizontal bands represent type B and C units. Dashed lines symbolise intervals with dominantly type A units. Dates were assigned by varve counting. Orange horizontal lines indicate the presence of tephra and cryptotephra, deposited during historically reported eruptions (white dates). CT-scans are presented in greyscale as well as in colour scale. Curves of γ-density, water content, L*a*b *, Si and K/Ti are added as well. Note that horizontal unit-bands and vertical running graphs (density, water content, L*a*b* and XRF-data) are a function of core depth, whereas thickness records are a function of age (varve years A.D.). The diagonal age-depth model forms the essential link between both groups of properties. In order to compare both in the same age-depth frame, the age model of core SK12-03 (Fig. 5.27) is added (bold violet curve). Bright yellow, vertical bars mark periods of strongly increased sedimentation rates.
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Figure 5.27: Age-depth model of SK12-03 (bold red curve), combined with corresponding summer, winter and varve thickness records. In agreement with Fig. 5.23, light blue and beige horizontal bands represent type B and C units, and other deviating units, respectively. Dashed lines symbolise intervals with dominantly type A units. Dates were assigned by varve counting. Orange horizontal lines indicate the presence of tephra and cryptotephra, deposited during historically reported eruptions (white dates). Curves of γ-density (only in section B, for reason see caption Fig. 5.23) and L*a*b* are added as well. Note that horizontal unit-bands and vertical running graphs (density and L*a*b*) are a function of core depth, whereas thickness records are a function of age (varve years A.D.). The diagonal age-depth model forms the essential link between both groups of properties. In order to compa re both in the same age-depth frame, the age model of core SK12-10 (Fig. 5.26) is added as well (bold violet curve). The bright yellow vertical bar, marks a period of increased sedimentation rates.
89 Chapter 5 - Results indeed seems to show very pronounced patterns with, on the one hand, periods during which a relatively constant varve thickness of around 0.3 cm is maintained (1646-1775 A.D. and 1980-2012 A.D.) and, on the other hand, a time interval during which thicknesses are strongly increased, up to a maximum running average value of 0.8 cm (1770-1980 A.D., bright yellow bar in Fig. 5.27). The period with elevated varve thicknesses largely coincides with the core depth interval in which a higher concentration of dark-based units are observed. Moreover, these higher annual sedimentation rates are reflected as well in the age-depth model, which follows a steeper course from a depth of 27 cm up to 125 cm.
Changes in varve thickness are rather subtle in core SK12-10. From 1340 A.D. up to 1775 A.D., the running average of total varve thickness varies between 0.18 cm and 0.38 cm, displaying some more (1550-1625 A.D., bright yellow bar in Fig. 5.26) or less (1400-1500 A.D.) pronounced highs. After 1775 A.D., a gradual increase in varve thickness commences, followed by an even more gradual decrease from 1830 A.D. up to 2012 A.D. The latter period of higher elevated sedimentation rates (up to a running average value of 0.4 cm/yr) is marked by a second bright yellow bar in Fig. 5.26 and corresponds to the core interval with very clearly delineated laminae and to a subtle steepening in the age-depth model of SK12- 10. In both Fig. 5.26 and Fig. 5.27, age models of core SK12-10 and SK12-03 are shown in the same age-depth frame in order to compare their courses. Striking is that the most recent interval of increased sedimentation rates (second, bright yellow bar in Fig. 5.26 and bright yellow bar in Fig. 5.27) of both models concur, even though this phenomenon is more prominent in SK12-03. When moving downcore, age-depth models from SK12-10 and SK12- 03 start to diverge, first slowly, but from 1925 A.D. on more drastically and seem to return to more or less equal sedimentation rates after 1775 A.D. However, they do not coincide again.
Results from radionuclide dating are presented in Fig. 5.28. 137Cs-concentrations display one clear peak at a corrected depth of 13 cm and a real core depth of 14 cm, which corresponds to the maximum fall-out in 1963 A.D. (marker age), coinciding perfectly with 1963 varve years A.D. according to the varve counting age model. Hence, 137Cs-dating confirms the presumed annual nature of the laminations and layers in Skilak Lake, allowing the term ‘varves’ to be used with certainty. Several 210Pb-models were tried out to fit the measuring points of 210Pb. As CIC-models rely entirely on reliable measurements of surface activity and, in this case, an apparent ‘mixed layer’ with almost constant 210Pb-activities seems to be present (due to bioturbation, sampling, storage...), application of a CF:CS-model is preferable (Sabine Schmidt, personal communication, 26/04/2014). This CF:CS-model yields a theoretical background sedimentation rate of 0.253 cm/yr, which is remarkably lower than the 210Pb-based sedimentation rate for EK12-01, equal to 0.432 cm/yr (Fig. 5.28). The linearly fitted sediment accumulation curve for SK12-03 displays a certain discrepancy with the varve counting age model. Theoretical rates from 210Pb-dating appear to be too high in the first five centimetres of the core and too low throughout the remaining part of the core. This observation agrees more or less with the position of individual 210Pb measuring points relative to the linear model. Nevertheless, the offset between 210Pb-model and varve counts remains small throughout the first 13 cm of the core and only increases at higher depths, therefore confirming the varve assumption once more.
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Figure 5.28: Comparison between age models based on varve counts, and 210Pb- and 137Cs-concentrations of sediment in core SK12-03. The right window shows point measurements of 137Cs and 210Pb in function of corrected depth. A CF:CS-model is fitted to the 210Pb points, yielding a constant background sedimentation rate of 0.253 cm/yr. In the left window, the 210Pb-based age model and 137Cs-concentrations are placed on top of the varve counting age model in function of core depth. Extrapolations of the 210Pb-model beyond the reach of measuring points are indicated with dotted lines. Two small deviating units at depths of 17 cm and 13.5 cm were considered as event-deposits (i.e. mixed layers) when selecting freeze-dried samples for measurements of 210Pb and 137Cs.
5.3.8 Climate calibration
For Skilak Lake a different approach was used to derive information about the relation between varve thickness/MAR and climate data on an interannual timescale. As already mentioned in the sections accompanying Fig. 5.9 (Eklutna Lake) and Fig. 5.18 (Kenai Lake), running averages of tuned and untuned, detrended varve thickness records are almost concurrent, despite small, interannual differences. Hence, it is also possible to acquire weights for the differentially weighted, combined climate curves by maximising Pearson’s r between the running averages of thickness/MAR plots and differentially weighted climate curves. This way, a time-consuming tuning process can be avoided. However, this approach makes it impossible to obtain an impression of the weather parameters that cause more or less sediment to accumulate in the lake basin during specific varve years.
First of all, a detrending of the MAR record for SK12-10 and the varve thickness record for SK12-03 was executed. Fig. 5.29 shows how detrending was done in SK12-10. SK12-03 was handled in a similar fashion. Varve thicknesses of SK12-10 have been converted into MAR’s, since water content measurements with substantial downcore variations were available (Fig. 5.26). Moreover, this conversion yields more realistic values for annual sediment accumulation. Especially in case of a climate calibration, this factor plays an important role.
Running averages of detrended varve thickness/MAR records of SK12-03 and SK12-10 seem to show rather concurring highs and lows. In order to study the relation between sediment accumulation and prevailing climate conditions, the same climate data were used as presented in Fig. 5.18 for Kenai Lake. Equally weighted and differentially weighted, all- containing climate curves from all stations except Anchorage were created for both SK12-10 and SK12-03. As already mentioned, weights were determined by maximising the correlation between running averages of weighted, all-containing climate curves and untuned
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MAR/thickness records. Noteworthy is the apparent dominance of the snowfall parameter (maximum weight of 100 %), whereas precipitation and temperature seem to be negligible (zero-weights). Running averages of the weighted climate curves show certain similarities with the running averages of the MAR/thickness records. However, the match is again rather limited to small-scale patterns, instead of general trends (Fig. 5.30).
Figure 5.29: Core interval over which calibration is conducted in SK12-10. Varve delineations are executed mainly by looking at variations in sediment colour (core picture and L*a*b*) and CT-greyshades. Each counted varve is delimited on both sides by yellow bars. The presented varve thickness record is based on these varve boundaries. A running average trend line and linear fit were used to detrend the thickness record to get rid of its lowest- frequency variations. The result of detrending is shown in Fig. 5.30. Note that the blue and beige vertical bars do not always match with the L*a*b*-curves. This discrepancy is due to the fact that the colour analyses with Strati- Signal were executed on the left side of the original core picture. Especially in the lower half of the presented interval, units are rather inclined, causing a lag between the vertical bars and their corresponding colour indices.
To obtain a better insight in specific relations between several data sets, values for Pearson’s r were calculated. All of the coefficients that are shown in Table 5.5 and in Table 5.6, are rather low and/or negative, which is not surprising as no tuning has been conducted. In order to get an idea about degrees of linear correlation, one should look at running averages, because they rule out part of the interannual variations. Shifts of one or more years that should have been executed during tuning, do not influence the correlation between running
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Figure 5.30: Correlation between climate data and MAR’s from SK12-10 and varve thicknesses from SK12-03. Lowest frame: data time series (1932-2012 A.D.) from four different weather stations and their averages for average annual temperature, annual precipitation and annual snowfall. Middle frame: sums of to percentages converted, detrended time series from all stations except Anchorage of all three weather parameters with equal weights and differential weights for SK12-10 and SK12-03. Upper frame: detrended, untuned MAR (SK12-10) and varve thickness (SK12-03) records with five-year running averages. The pink running average from the middle frame is projected on top in order to emphasise a certain concurrence.
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Table 5.5: Pearson correlation coefficients between the annual values and running averages (RA) of untuned, detrended MAR records from SK12-10 on one side and detrended, annual data and running averages of climate records on the other side. Climate records comprise temperature (T), precipitation (P), snowfall (S), equally weighted percentage sum (Sum) and differentially weighted percentage sum (W Sum) data, derived from calculated averages of all stations except Anchorage (Fig. 5.30).
T RA P RA S RA Sum RA W Sum RA
Untuned 0.0187 -0.0434 0.1087 0.0428 0.1087
RA -0.0756 -0.0216 0.2421 0.0135 0.2421
Table 5.6: Pearson correlation coefficients between the annual values and running averages (RA) of untuned, detrended varve thickness records from SK12-03 on one side and detrended, annual data and running averages of climate records on the other side. Climate records comprise temperature (T), precipitation (P), snowfall (S), equally weighted percentage sum (Sum) and differentially weighted percentage sum (W Sum) data, derived from calculated averages of all stations except Anchorage (Fig. 5.30).
T RA P RA S RA Sum RA W Sum RA
Untuned 0.0204 -0.0123 -0.0663 -0.0184 -0.0663
RA -0.3796 -0.3797 0.2598 -0.3266 0.2598
averages as much as they destructively influence correlations between annual data. The presented Pearson coefficients between sediment accumulation records and individual climate parameters, confirm the dominance of snowfall in the differentially weighted climate curves (yellow cells). Precipitation, on the other hand, is always negatively correlated. Temperature data display poor correlations as well. In fact, the only coefficients with substantial values are the ones that can be seen in the cells of snowfall and differentially weighted sums (yellow cells), reaching a maximum of 0.2598 for SK12-03 in Table 5.6. Note that the prominent role of snowfall in determining varve thickness stays in contrast to the dominance of precipitation in both Eklutna Lake and Kenai Lake. Mutual correlations between climate parameters are the same as for Kenai Lake (Table 5.4), since identical time series were used. The interpretation and discussion of all of the calculated correlations and determined parameter weights follows in section 6.5.1.2.
5.3.9 Spectral analysis
A spectral Fourier analysis was conducted on the full, non-detrended, untuned MAR record of master core SK12-10 (Fig. 5.31). Just like in Eklutna Lake and Kenai Lake, several periods and frequencies of sine and cosine functions display a stronger correlation power with the MAR record. Periods (>4 yr) of the most pronounced peaks in a downscaling order of power are: 250 yr, 133.3 yr, 4.1 yr, 12.7 yr, 5 yr, 18.6 yr, 4.3 yr, 37.7 yr, 4.7 yr, 15.6 yr and 9.4 yr. Since the content of SK12-10 dates further back in time, spectral analyses yield additional, lower-frequency periodicities (250 yr and 133.3 yr) in comparison to Eklutna Lake and Kenai Lake. Furthermore, a few peak-positions show resemblances with the ones in Fig. 5.12 and Fig. 5.22 and are located at periods of 37.7 yr (31.3 yr in EK12-01 and 30.3 yr in KE12-07), 18.6 yr (19.6 yr in EK12-01 and 20 yr in KE12-07), 15.6 yr (15.6 yr in EK12-01 and 15.2 yr in
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KE12-07), 12.7 yr (12.1 yr in EK12-01 and 12 yr in KE12-07) and 9.4 yr (9.3 yr in EK12-01 and 9.5 yr in KE12-07). Worth mentioning is that the relative powers of these peaks are again different from the ones that were earlier-on observed in Eklutna Lake and Kenai Lake.
Figure 5.31: Periodograms, expressing the power of the correlation between sine and cosine functions of different frequencies with the non-detrended, untuned MAR record of SK12-10. Coloured, dotted lines mark the most important peaks, of which the periods (250 yr, 133.3 yr etc.) are indicated with arrows. Frequencies below 0.25 year-1 and periods below 4 year were not considered.
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6 DISCUSSION
The most exciting phrase to hear in science, the one that heralds the most discoveries, is not ‘Eureka!’ but ‘That’s funny...’ - Isaac Asimov -
6.1 Event-deposits
Event-deposits comprise material that have been accumulated during zero-time occurrences and stand in sharp contrast to background successions. As these units are ‘instantaneously’ formed, they do not represent a substantial time interval and therefore give rise to vertical sections in the proposed age-depth models (see Chapter 5, e.g. Fig. 5.7). Geophysical (e.g. MS-evolutions and -peaks), sedimentological (e.g. grain-size, sediment colour, grading) and geochemical (element-counts and -ratios) data all help to identify event-deposits. When historically reported events (e.g. earthquakes, floods) can be related to these bodies, more marker ages are obtained to strengthen the proposed age-depth frameworks, with which interpreted climate proxies can be associated (see section 6.5). More precise estimates of varve counting errors can be made as well (see section 6.2). In the following sections, three types of identified event-deposits (earthquake-triggered turbidites, flood-triggered turbidites and tephra layers) are discussed for each of the three lakes. Focus lies primarily on the master core records. Variations considering the thickness and general architecture of units throughout the lake basins are elaborated in section 6.3, covering intra-lake correlations.
6.1.1 Earthquake-triggered turbidites
At least one turbidite (Bouma, 1962) in deposits from each of the three lake basins seems to be earthquake-triggered. As expected, the most obvious, historical event to cause density flows of this nature, is the 1964 A.D. Prince William Sound megathrust earthquake (see section 2.1.2). On seismic profiles (e.g. Fig. 5.22), several more of these large-scale mass- waste deposits can be identified further down in the stratigraphy, where they appear as bodies with a chaotic to almost transparent seismic facies (Strupler et al., 2012).
6.1.1.1 Eklutna Lake
In EK12-01 only one unit can be indicated as the direct result of a basin-affecting earthquake, which is the ~1 cm thick, dark grey-based layer at a depth of 23 cm (Fig. 5.7). The March 27, 1964 A.D. Prince William Sound megathrust earthquake is kept responsible for generating this small turbidite at the sampling site of EK12-01 (Fig. 6.1). Fig. 5.11 shows how varve year 1964 A.D. (before and after tuning) seems to fit perfectly with the marker age- providing turbidite of 1964 calendar years A.D. However, March falls within the ‘winter’
96 Chapter 6 - Discussion interval that belongs to calendar year 1964 A.D. and varve year 1963 A.D. (Fig. 4.8). A small shift of the varve boundary at a depth of 23 cm to a depth of 22 cm could solve this delineation error fairly easily, by allocating the turbidite to varve year 1963 A.D. instead of 1964 A.D. Mainly because of the way this entity evolves throughout the lake basin (see section 6.3.2) and because of its stratigraphic position near the 1963 A.D. 137Cs-peak (22 cm depth), an earthquake-triggering can be assigned. Though, only based on the unit’s appearance in EK12-01, the provoking mechanism of the depositing turbidity current cannot be derived.
6.1.1.2 Kenai Lake
Formation of the grey unit with a bright clay cap at a depth of 25 cm in core KE12-07 is assumed to be caused by the 1964 A.D. Prince William Sound earthquake as well (Fig. 5.17 and 6.1). Intra-lake core correlation (see section 6.3.3) is again key in deriving the fact that this layer is most probably earthquake-induced. When comparing the 1964 A.D. marker age to the varve counting age model (Fig. 5.17 and 5.20), the same varve age/calendar age lag as for EK12-01 is encountered. Hence, by relocating one varve boundary, the turbidite can be shifted to 1963 varve years A.D. Furthermore, the consistency between the identified event- deposit and proposed age-depth model confirms for the first time that the varve assumption in case of Kenai Lake was fully legitimate. An earlier corroboration of the laminations’ annual time frame could not be implemented, due to the lack of radionuclide dating.
6.1.1.3 Skilak Lake
Since the identified earthquake-triggered turbidites are more or less tinned out at the rather shallow coring site of master core SK12-10 (Fig. 4.2 and 5.22), focus is shifted to the content of SK12-01, which was acquired in the deepest part of Skilak Lake where turbidites are thickest (Fig. 5.23) (Perkins & Sims, 1983). Deposition of both of the interrupting bodies are considered to be earthquake-induced. The upper, most prominent turbidite, starting at a depth of 78 cm in SK12-01, can be assigned to the strong 1964 A.D. Prince William Sound earthquake (Fig. 6.1). A less heavy (Mw 6.7), more local earthquake with a hypocentre within the subducting plate at ~60 km depth beneath northern Kenai Peninsula (60.71°N; 150.52°W), which took place on October 1954 A.D., might be responsible for the stratigraphically lower and smaller turbidite (Fig. 2.1 and 6.1) (Stover & Coffman, 1993; Doser & Brown, 2001). Observation of two subsequent fining-upward grading patterns within this unit, of which the first one has a thicker and coarser base, implies the possibility of a main shock followed by a slightly weaker aftershock, both destabilising sediment. Finally, a subtle, graded layer with a sandy base can be observed only in core SK12-07 at a depth of 100 cm. Correlation (see section 6.3.4) with master core SK12-10 suggests an age of 1636 A.D. Given the seemingly local character of this unit (correlation with cores from the deeper basin is not possible), there is no certainty regarding its exact formation mechanism (earthquake, flood, extended landslide etc.). A single-segment rupture of Kodiak accompanied by a tsunami at 1440-1620 A.D. might have caused turbidity currents to occur in Skilak Lake, even though this event has only been identified in the Kodiak segment so far (Shennan et al., 2014).
97 Chapter 6 - Discussion
Figure 6.1: Representation of master cores from every lake with indications of event- deposits: earthquake-triggered turbidites (‘EQ’), tephra layers from volcanic eruptions (‘VE’), mega-flood-induced turbidites (‘MF’), flood-induced dam-failure turbidite (‘DF’) and regular flood-induced turbidites (light blue bars). Coloured circles and the yellow bar on the side of EK12-01 correspond to legends used in Fig. 6.4, 6.6 and 6.8.
6.1.2 Tephra-fall deposits
As already mentioned in Chapter 5, a series of anomalously high MS-values, sometimes accompanied by horizons of macroscopically visible, vitreous particles, were recognised in the records of all three lakes. These observations can be associated with the presence of (crypto)tephra deposits. MS is related chiefly to the concentration and grain-size of iron- bearing minerals and is used frequently to locate tephra in lacustrine sediments (de Fontaine et al., 2007; Begét et al., 1994). In order to link the tephra layers to historical eruptions of volcanoes in the Alaskan-Aleutian chain, varve counting dates for each of the tephra layers
98 Chapter 6 - Discussion were determined and compared to eruption histories of the nearest, most active volcanoes (see section 2.1.3, Siebert et al. (2010)) (Fig. 2.1). However, the obtained tephra-bound marker ages are not as independent as those provided by other event-deposits, as documented (pre)historic eruptions are assigned based on varve counts themselves, on which the margin of error grows when moving further downcore (e.g. SK12-10).
6.1.2.1 Eklutna Lake
The oldest, varve dated cryptotephra layer in EK12-01 (Fig. 6.1) corresponds, with an error of ~3 years, to the historically observed October 6, 1883 A.D. eruption of the Augustine Island (VEI 4). Augustine’s latest episode of edifice collapse occurred as a result of its largest historical eruption in 1883 A.D., during which vitric ash clouds were blown in a prevalent, northeastern direction (Siebert et al., 1995). Previous studies point out that ash from the 1883 A.D. eruption has been found as far as Skilak Lake and Moose Pass (Fig. 5.13) (Begét et al., 1994; Payne & Blackford, 2008). The next two tephra horizons in EK12-01 can both be associated with eruptions of Redoubt volcano, more specifically the ones that took place on May 25, 1933 A.D. and December 14, 1989 A.D. (VEI 3). Due to the latter one, the Cook Inlet region underwent severe economic damage and air traffic became affected far beyond the volcano. Since tephra layers from this eruption were detected up to 400 km northeast from the vent in lacustrine as well as peatland records, their presence in cores from Eklutna Lake is perfectly justified (Begét et al., 1994; Payne & Blackford, 2008; Schiff et al., 2010).
Only one centimetre of sediment separates cryptotephra of the 1989 A.D. eruption from a similar layer, attributed to the June 27, 1992 A.D. eruption of the highest volcano of the Aleutian arc, Mount Spurr (VEI 4). During this event, ash was deposited on the city of Anchorage and other areas around Cook Inlet, while the central and western portions of Kenai Peninsula were not affected (de Fontaine et al., 2007; Payne & Blackford, 2008). After tuning (Fig. 5.11), an equally good or even better match between historical dates and varve counts is obtained, especially when it comes to the upper two tephra horizons.
6.1.2.2 Kenai Lake
The situation in Kenai Lake is rather complicated (see section 6.3.3), since tephra layers that occur in the northern segment and sub-segment on the one hand, and the central and southern segments on the other hand, do not seem to correlate (Fig. 5.15). Master core KE12-07 comprises three horizons with volcanic deposits (Fig. 6.1), of which the lowest one is macroscopically discernible and most probably the result of the June 6, 1912 A.D. Katmai- Novarupta eruption. The major Novarupta (volcano in Katmai area, opposite to Kodiak Island, Fig. 2.2) eruption rated 6 on the VEI and is known as the world’s largest during the 20th century, producing a voluminous rhyolitic airfall tephra and causing the magma reservoir under Katmai volcano to drain towards Novarupta. Distal ashes from this eruption have been identified in the neighbouring Skilak Lake and peatland near the city of Sterling, which lies within the original fallout zone of the 1912 A.D. tephra (Begét et al., 1994; Payne & Blackford, 2008; Siebert et al., 1995). The upper two tephra horizons in KE12-07 are of
99 Chapter 6 - Discussion sub-microscopic nature and provoke similar MS-peaks (Fig. 5.15). No historical eruption appears to coincide with the lower one of both, which is located around 1995 varve years A.D. The December 9, 2005 Augustine eruption (VEI 3) can be linked to the youngest cryptotephra layer in KE12-07, if a varve counting error of ~3 years is taken into account (even after tuning, Fig. 5.20). A slight offset between core picture and MS-curve might be responsible for part of the difference between varve counting dates and eruption dates as well. Wallace et al. (2010) mention at least one Vulcanian explosive phase of the 2005 A.D. eruption during which ash clouds from Augustine became transported towards Kenai Lake.
Core KE12-09 contains two cryptotephra-fall deposits that are representative for the central and southern segments of Kenai Lake. After correlation with the age model of KE12-07 (Fig. 5.15 and 5.17), ages of 1975 A.D. and 1992 A.D. are obtained for the lower and upper volcanic horizon, respectively. No real match was found for the 1975 A.D. layer, whereas the 1992 A.D. tephra can be associated with the Mount Spurr eruption, also identified in Eklutna Lake.
6.1.2.3 Skilak Lake
The record of SK12-10 encompasses two cryptotephra horizons, followed by six macroscopically visible laminae of glass shards. When looking at the presented age model (Fig. 5.26 and 6.1), a depth of 5 cm corresponds to 1989 varve years A.D. and hence, the 1989 A.D. eruption of Redoubt (see Eklutna Lake). In a previous study, traces from this eruption have already been distinguished in records from Skilak Lake (Begét et al., 1994). No historical eruptions were found to match the second and fourth tephra layers. The second layer seems to be deposited around 1975 A.D., corresponding to the MS-peak observed in the central and southern segments of Kenai Lake. An unreported eruption of one of the Aleutian volcanoes could thus be responsible for the presence of these tephra particles. The third observed MS-peak in SK12-10 can be contributed to the major Katmai-Novarupta eruption in 1912 A.D. (see Kenai Lake), which has been identified several times before in the sedimentary infill of Skilak Lake (e.g. Begét et al., 1994; Siebert et al., 2010; Rymer & Sims, 1976). Eruptive histories provided by the Smithsonian Global Volcanism Program incorporate large margins of error (up to ±100 years) in their eruption dates when going further back beyond ~1700 A.D. These ages are obtained by radiocarbon dating, tephrochronology or varve counting and are not derived from historical reports. Therefore, the assigned ages of the lower four tephra horizons in SK12-10 (Fig. 6.1) are based on varve counts from this study and linked to historical eruptions, of which the dating error overlaps the varve-based ages. This approach yields one Augustine and three subsequent Redoubt eruptions.
6.1.3 Flood- and mega-flood deposits
6.1.3.1 Definitions
First of all, it is important to give clear definitions of all sedimentary phenomena that are being considered as results of floods and mega-floods (large-scale floods). These deposits often have properties that resemble the ones of the earlier-on described earthquake-
100 Chapter 6 - Discussion triggered turbidites, which is not entirely surprising since the energy of high-discharge, sediment-supplying streams can remobilise material from within the lake basin as well, giving rise to turbiditic currents (Sturm & Matter, 1978). Because flood-triggered event- deposits contain climate-related information, thicknesses of these units were incorporated in the varve thickness/MAR records of Chapter 5 (e.g. Anderson & Dean, 1988; Brauer et al., 2009; Desloges & Gilbert, 1994). However, during early stages of varve counting, thick flood- induced turbidites were delineated separately as their origin had not been interpreted yet (Fig. 4.7). Four categories of slightly different, annual flood-units can be distinguished (Fig. 6.2). Overall they are interpreted as direct results from periods of elevated discharges, causing more and often coarser sediment to be transported towards lake basins. However, changes in grain-size of the entrained material can be related as well to changes in mineralogy and elemental composition, as pointed out several times throughout Chapter 5 and discussed further in section 6.5.2 and 6.5.3.
Typical examples of the first type of flood deposits were found in the lower half of EK12-01 (type B and type C in Fig. 5.4 and 5.6). The described ‘sandwich’-architecture can be interpreted as a succession of 1) a normal spring/early summer ice/snow-melting pulse (i.e. greyish bases in EK12-01, though this colour varies according to the lake of interest), followed by 2) one or more mid-summer, stronger melting pulse(s) or peak(s) in precipitation (darker and/or differently coloured, coarser central part) (Larquier, 2010; Cockburn & Lamoureux, 2008) and finished by 3) suspension settling of fine material during winter times (bright top) (Fig. 6.2). Important in this type of flood-unit, is that the mid- summer, high-discharge periods are associated with deposition of relatively coarser material than can be found in regular varve bases (see section 6.5.2). Note that the transition between the uppermost flood deposit and fine-grained winter cap has a gradual character. As a consequence, the exact boundary between both cannot be delineated with certainty. In most cases, the coarser flood-units are also thicker than background laminations, which is due to the high energy of the supplying stream. Elevated energy levels do not only create higher entrainment capacities for material within the lake’s catchment, but are also able to destabilise more sediment on the basin’s slopes.
The second type of flood-units is a slight variation on the first category (Fig. 6.2). In contrast to the entities discussed in the previous paragraph, these units comprise several melting and/or precipitation pulses of a more or less similar nature and appearance. The most obvious examples of this type have been introduced in Chapter 5 as deviant forms of the type B and type C standards of EK12-01 and occur in the central section of core SK12-03 (zoom 2 in Fig. 6.9). Each of these annual layers contains two or more subsequent, fining- and lightening-upward gradations. Since all of these pulses have similar properties (colour, darkness, grain-size (Fig. 5.25), thickness), it is more likely that ice/snowmelt pulses are dominant in inducing their formation, instead of sudden peaks in precipitation.
A third category includes varves that consist of just one strong pulse and therefore belong to the two-layered type A class. What separates these units from regular background varves, is their often deviating colour, coarser grain-size, increased thickness and sometimes slightly erosive base. Their appearance is a direct result of extreme runoff conditions, due to a heavy
101 Chapter 6 - Discussion spring/summer ice/snowmelt phase exclusively or in combination with heavy precipitation (Fig. 6.2). A few examples of these units can be observed in the upper 10 cm of SK12-10 (Fig. 5.26) or at a depth of 26.5 cm in KE12-07 (Fig. 5.16). When central flood-parts of type B deposits are extremely erosive, they can mistakenly be taken for units of this third category, due to the absence (eroded) of the first, mild melting phase (Fig. 6.2).
Figure 6.2: Schematic representation of chronologic formation mechanisms that influence characteristic architectures of annual, type one, type two and type three flood-units.
The fourth type of flood-units comprises ‘mega’-versions (mega-floods, MF) of the previous three categories. They owe their ‘mega’-property to extremely high discharges and/or circumstances within the lake’s catchment or along the basin slopes which create massive amounts of readily erodible and transportable material (Cockburn & Lamoureux, 2008). For instance in EK12-01, two of these mega-floods can be observed (light green bands in Fig. 6.1).
All of the abovementioned unit-types have in common that they cause varve thicknesses to mount up as a result of increased river discharges during relatively short time periods (days to weeks). This relation can be derived from detrending figures in Chapter 5 (e.g. Fig. 5.10).
102 Chapter 6 - Discussion
Mega-floods (light green bars), however, are not incorporated in these varve thickness measurements, since they would lower the visibility of thickness variations throughout the remaining parts of the record. It is worth mentioning that some of the considered flood deposits are not that much different from regular background varves, which asks for a revision of the concept ‘event-deposit’. Since floods are regarded as events, every spring/summer layer of a background unit could, in theory, be defined as an event-deposit as well, because their formation mechanisms (overflows, interflows and underflows) are the same, although smaller-scaled (Sturm & Matter, 1978). Moreover, a continuously varved sequence embodies an alternation between punctuating events (spring/summer melt) and periods of gradual suspension settling (autumn/winter). From this perspective, the conceptual boundary between background sediments and event-deposits becomes rather blurry. In fact, a better integration between depositional cycles, such as varves, and events should be implemented (Dott Jr., 1996). In order to avoid any confusion in further discussions, only the four categories of abovementioned flood-units will be reckoned as consequences of true events.
6.1.3.2 Eklutna Lake
Flood-containing varves in EK12-01 mainly belong to the first described type in section 6.1.3.1 (Fig. 6.1). These units are easily distinguishable in the lower core half (up to 1928 varve years A.D.) and seem to be clustered around specific ages (1860 A.D., 1875 A.D., 1895 A.D. and 1917 A.D.), whereas they become less pronounced in the upper core half (1930- 2012 varve years A.D.). A minority of type three floods can be discerned as well. Dating of the two mega-floods yields 1917 and 1994 (1995 after tuning) varve years A.D. Recorded weather conditions (Fig. 5.9) in 1995 A.D. imply flooding as a result of more than average winter snowfall, followed by a varve year with regular air temperatures (Loso et al., 2006). Section 6.3 focuses on the reasoning behind the identification of the two rather similar, brownish units as mega-flood-induced turbidites, instead of earthquake-triggered turbidites. The grey body with faint laminations that has been dated as 1929 A.D. is not entirely a result of flooding. Historical reports make mention of a flood-induced partial collapse of the built storage dam at the northwestern end of Eklutna Lake in 1929 A.D., which on its turn caused bottom sediment to be set in motion and redeposited as a turbidite (Hollinger, 2002) (Fig. 6.1).
6.1.3.3 Kenai Lake
Due to the lesser visibility of individual varves on the surface pictures of KE12-07 (Fig. 6.1), it is rather difficult to identify the specific types of flood-units. When linking this record to the other cores from the southern and central segments of Kenai Lake (Fig. 5.15) (see section 6.3.2), most of the correlatable units appear to belong to the first and third flood type. However, the erosive character of the first category can be responsible for generating resembling, but false type three deposits (Fig. 6.2). Higher flood-concentrations can be observed within the upper 10 cm of the core (Fig. 6.1). The bluish grey layer with wood fragments is interpreted as a mega-flood, which affected lake sedimentation in 1991 varve years A.D. Fig. 5.18 indicates that high-discharge conditions in 1991 varve years A.D. were
103 Chapter 6 - Discussion most probably due to melting of substantial amounts of snow, fallen during the preceding winter. However, similar and even more extreme circumstances occurred during the interval 1980-2000 A.D., though did not lead to resembling deposits. Most probably, specific phenomena within the lake’s catchment (tree felling, construction works etc.) played a contributing role in generating easily erodible sediment and fragments of organic material.
6.1.3.4 Skilak Lake
SK12-10 (Fig. 5.26) and SK12-03 (Fig. 5.27) contain sequences of units with strongly differing architectures. Whereas SK12-10 comprises mainly type one and type three floods (as in EK12-01 and KE12-07), the record of SK12-03 is dominated by units of the type two category. In the latter core, flood-layers of this second type reach a higher concentration (clustering) within the interval 1775-1925 A.D. (light yellow band in Fig. 5.27 and Fig. 6.1), while before and after this time period several type one and type three units can be identified. In contrast, throughout the interval 1775-1925 A.D. in core SK12-10 concentrations of flood deposits drop, as nothing more than nicely delineated, slightly thicker varves are present (Fig. 6.1). Hence, equal environmental conditions can create strongly differing lacustrine deposits, depending on the exact position within the lake basin (see section 6.3.4).
6.2 Varve counting errors
Based on the identified, marker age providing event-deposits (especially earthquake- triggered turbidites) in section 6.1, varve counting errors can be reanalysed. Varve thickness/MAR tuning already yielded estimates of erroneous counts, which were said to be applied between marker ages and from the oldest marker age down to the bottom of the cores. Tephra-fall deposits are, in the end, not considered as reliable marker layers, since their dating is not entirely independent and not as certain as, for instance, the 1964 A.D. turbidites, of which dating is supported by 1963 A.D. 137Cs-peaks (Fig. 5.8 and 5.28).
Within the tuned interval (1932-2012 A.D.) of EK12-01, a counting error of ±1 year per 10 varve years was obtained. The first encountered marker age below 1932 A.D. is the 1929 A.D. dam-failure turbidite (Fig. 6.1). Application of the defined counting error from 1929 A.D. down to the bottom of the core (1856 A.D.), results in a cumulative error of ±7.3 years. However, the rather unambiguous varve boundaries throughout the lower core half would, most probably, influence this error to be much smaller. If the 1883 A.D. Augustine eruption is assigned correctly, the margin of error at this depth would be equal to ±3 years, instead of ±4.9 years, according to the tuning-based error derivation.
In case of KE12-07, a margin of error of ±1.5 years per 20 varve years was defined. From 1964 A.D. down to the bottom of the core, no other marker layers of high certainty are present. Therefore, from 1964 A.D. on, a cumulative error can be computed, yielding ±6.1 years at 1833 A.D. Though, one might argue once again that if the 1912 A.D. Katmai- Novarupta eruption is the true provider of tephra at a depth of 45 cm, which is relatively
104 Chapter 6 - Discussion certain, no counting error has been made until this depth, instead of the predicted ±3.9 years. This proves that the theoretical margins of error represent maximum possible deviations from real ages and that they do not necessarily have to be fulfilled.
No tuning has been executed on the records of Skilak Lake. The only estimates of counting errors are thus based on event-deposits and radionuclide dating. The 137Cs-peak in SK12-03 confirms the position of a strongly thinned out 1964 A.D. turbidite. Correlation (see section 6.3.3) with SK12-10 provides a corresponding depth of the latter event-unit, which can be regarded as a marker layer in the record of SK12-10 (Fig. 5.23). Within the remaining part of SK12-10, only the relatively obvious tephra layer that is assumed to be deposited during the 1912 A.D. Katmai-Novarupta eruption, can be regarded as a sort of marker horizon, since it has been identified before during previous studies (e.g. Begét et al., 1994). These two markers define a counting error of ±3 years per 52 varve years, which can be extrapolated to the bottom of the core, resulting in ±33 years of error at 1340 A.D.
After a percentage-conversion of the ultimate varve counting error margins within each of the master cores, values of ±10.0 % (EK12-01), ±7.5 % (KE12-07) and ±5.8 % (SK12-10) are obtained. These values are overall higher than chronological errors associated with records from a worldwide varve data base (VDB) compiled by Ojala et al. (2012), in which erroneous counts generally fall between 1 and 3 %. The elevated error ranges in this study are most likely due to the ambiguous varve boundaries in the core top sections on which error estimates are based, causing the latter to be excessively large.
6.3 Intra-lake core correlation and event-deposit distribution
6.3.1 General principles
Several references to this section (section 6.3) have already been made in preceding sections. All sorts of event-deposits and other marker layers are used to connect horizons and units of corresponding stratigraphic age. Identification of these units is based on sedimentological (colour, grain-size, texture) as well as geophysical (MS, Loizeau et al. (2003)) and geochemical (XRF-counts, Katsuta et al. (2007)) observations. However, it should be remembered that cores were retrieved from often strongly differing settings with respect to bathymetry, distance to important inflows/outflows and relative positions to tephra- providing volcanoes. Therefore, a few generally applicable rules concerning variations in the appearance (Anderson & Kirkland, 1966) of any kind of depositional unit, are summed up in the following paragraph.
Earthquakes are capable of simultaneously destabilising slope sediment on more than one location within a lake basin, after which the remobilised masses move downslope and spread out along the lake bottom, giving rise to mass-transport deposits (MTD’s) with an often contorted or sheared bedding when deposited. During slope failure, turbidity currents are initiated as well. Since the latter are a type of density currents, deposition of graded turbidites concentrates within the deepest parts of the basin (e.g. Schnellmann et al., 2005).
105 Chapter 6 - Discussion
Hence, MTD’s are considered to create topography, whereas turbidites have the tendency to level out existing topographic features. As the amount of easily destabilised slope sediment is a determining factor in whether or not thick slope failure deposits will accumulate when an earthquake occurs, deltaic environments (where important streams enter the lake) and steep slopes are extremely susceptible (Schnellmann et al., 2005). In section 6.1.3.1, it has already been mentioned that flood-induced turbidity currents and even normal spring/summer melt pulses answer to the same principles as earthquake-triggered turbidites. Consequently, their deposits will also be thickest in the deeper lake parts and close to high-discharge river mouths. In contrast to other event-units, tephra deposits are not as strongly subject to bathymetry and proximity of inflows. Whether or not tephra layers are present in a lake or in parts of a lake, is related to the magnitude of the eruption (VEI), the distance between volcano and sampling site, the prevailing wind direction during eruption and preservation-related properties of the local lake dynamics (e.g. de Fontaine et al., 2007; Payne & Blackford, 2008; Schiff et al., 2010).
6.3.2 Eklutna Lake
Correlation of most units in Eklutna Lake answers perfectly to the abovementioned general rules for intra-lake variations (Fig. 6.3). Overall, (mega-)flood deposits and regular background varves thin out in a northwestward direction, because sampling sites along this path experience progressively less influence from the main source of sediment-supply (Fig. 6.4). Moreover, corresponding units become also less coarse-based towards the northwestern sub-basin, which is a side effect of the increasing distance from the glacier-fed streams and hence decreasing sediment-transporting energy of the denser underflows/interflows. In addition, the intermediate lake bottom elevation which separates both sub-basins from each other acts as a natural barrier for the coarsest particles (Fig. 4.3). The relatively thick flood-unit, in EK12-01 dated as 1989 varve years A.D. (after tuning), seems to evolve into a mega-flood in the more proximal cores (Fig. 6.3). Application of the terms ‘flood’ and ‘mega-flood’ in this study is thus bound to the local characteristics of turbidity currents and the nature of their resulting deposits on a specific location within the lake, and not to the general discharge values of the sediment providing streams.
Cores EK12-05 and EK12-04 comprise more or less the same time interval as the upper halve record from the only 14-17 cm longer master core EK12-01. When comparing deposits from the southeastern and northwestern sub-basins over the time span they have in common, sediments from EK12-01 and EK12-02 appear to be relatively darker (Fig. 6.3). From ~1970 A.D. on, winter laminae in EK12-01 become gradually slightly thinner (Fig. 5.7). As earlier-on mentioned in Chapter 5, winter clay caps over the last 78 varve years (1934-2012 A.D.) in the northwestern sub-basin are not as pronounced as during the preceding period, causing the succession of varves to seem darker. This phenomenon is not as apparent in, for instance, EK12-05, although the limited core content below ~54 cm depth (1934 A.D.) restricts the range of comparison. In order to explain these observations, one should be familiar with the anthropogenic activities that are affecting sedimentation in Eklutna Lake and more specifically, activities related to the storage dam at the northwestern end of the lake basin. Before artificial
106 Chapter 6 - Discussion
Figure 6.3: Correlation between master core EK12-01 and other cores EK12-02, EK12-03, EK12-04 and EK12-05 from Eklutna Lake, arranged by increasing distance from the glacial meltwater inflow from the Eklutna and West Branch Glaciers (Fig. 5.3). Identified event-units are indicated and dated (black/red and orange ages in A.D.).
dam construction, a free outflow in times of high water (summer) over the naturally damming moraine via the Eklutna River, was possible. Since the built storage dam on Eklutna Lake has been fully operational, a year-round, controlled lake outflow has been initiated. During summer as well as during winter, water is being pumped out of the water storing lake basin. Pumping during summer has practically no influence on high-energy sedimentation, whereas suspension settling of fine-grained material during winter can become disrupted more easily (Simonds, 1995; Hollinger, 2002). The effect of less pronounced or even absent
107 Chapter 6 - Discussion clay caps is strongest in EK12-02, which is located closest to the artificial outlet. Hence, one might reason that during winter a low-energy, suspended material entraining stream towards the dam originates, preventing deposition of substantial clay caps (Fig. 6.4).
Figure 6.4: Eklutna Lake basin with indication of the spatial distribution of event-deposits from the 1964 A.D. Prince William Sound earthquake, mega-floods in 1994 (1995) A.D., 1988 (1989) A.D. and 1917 A.D., regular floods and the 1929 A.D. flood-induced dam-failure. The size of the coloured circles is a measure for thickness of the corresponding units in each of the retrieved cores. Blue arrows represent the main paths of water- and sediment- supply. Influences of storage dam activities decrease gradually towards the south. Positions of the current and past Eklutna Glacier termini are displayed as well and are linked to lower and higher sediment discharges, respectively. Note that the given position of the past glacier terminus is based on observations of moraine ridges on Google Earth. Though, it is unclear if these ridges are remainders from the last retreat after 1976 A.D. or after the final LIA advance phase.
The measure in which the 1964 A.D. earthquake-triggered turbidite thins out towards the northwestern sub-basin is even more extreme than in case of flood deposits (Fig. 6.3 and 6.4). Most probably the instable delta sediments at the inflow of the West Fork and East Fork streams contributed largely to the formation of a coarse-based, deformation-inducing and erosive (EK12-05) turbidite in the most proximal area of coring sites EK12-05 and EK12-04. The rather subtle thinning of (mega-)flood-units might be due to the fact that drainage streams and gullies are present as well along the lateral margins of the basin, also providing an increased sediment-supply in times of climate-driven, elevated discharges, although in a lesser degree than the glacier-fed rivers (Fig. 6.4). Moreover, flood-runoff enters the lake at its water surface, subsequently producing hyperpycnal flows as well as inter- and overflows, which can spread easily over the entire basin. Turbidity currents, on the other hand, origin on unstable, sloping locations within the basin. As a consequence, the latter are less likely to (re)deposit sediment in more shallow parts of the lake (Simonneau et al., 2013).
Since the 1929 A.D. turbidite does not belong to the category of earthquake-triggered turbidites, nor to the purely flood-induced ones, its spatial distribution deviates as well. The almost homogeneous, grey body is thickest in the northwestern basin, more specifically in
108 Chapter 6 - Discussion
EK12-01 and becomes progressively thinner towards the southeast (Fig. 6.3 and 6.4). Triggering of this turbidity current is assigned to a flood-induced, partial collapse of the storage dam, which was not yet operational at the time (Hollinger, 2002). Positions closer to this dam were therefore covered by a larger amount of remobilised sediment. The preferential settling of material in deeper parts of the basin, can be derived from the fact that the turbidite is thickest in EK12-01, even though the coring site of EK12-02 is located closer to the storage dam. In contrast to other turbidite deposits that are positioned close to their principal source (meltwater influx via West Fork and East Fork), this failure-induced unit does not have a sandy, but a silty base. The lack of coarse sediment in its base, is due to the absence of major delta deposits near the storage dam. Finally, cryptotephra layers recur in the MS-pattern of every single core, which makes them fairly accessible tools for correlation.
6.3.3 Kenai Lake
Thinning of background varves and flood deposits away from the two influxes at the eastern end of the lake implies that the third, northern drainage valley (starting from Summit Lake) is the lesser contributor of the three source areas of Kenai Lake (Fig. 5.13, 6.5 and 6.6). However, the shallowing bathymetric development towards coring sites KE12-07 and KE12- 08 (Fig. 4.1) plays a role in this thinning trend as well. Core KE12-02 overall shows the most voluminous sequence, followed by KE12-03, KE12-04 etc., pointing out that from the two inflows at the eastern end of the lake, the Snow River is the more important one in terms of sediment-supply. The trapping capacity of many, upstream, small lakes (e.g. Grant lake, Upper Trail Lake) within the Trail River catchment (Fig. 5.13) provides an explanation for the fact that sediment accumulation in Kenai Lake is not as much influenced by this drainage network, even though it covers a larger area than the Snow River catchment. Very condensed deposits in KE12-01 result from the isolated setting of the northern sub-segment from direct, major inflows (Fig. 6.6). Moreover, supplied sediment can be conveyed immediately by the outflowing Kenai River, especially extremely fine-grained material of which entrainment energy is lowest.
In contrast to all other floods and background varves, the organic material containing mega- flood-unit that has been dated as 1991 A.D. (1990 A.D. after tuning) in master core KE12-07 is thickest in KE12-08 and KE12-07 and gradually evolves in what can be defined as a normal type one flood towards more southeastern positions within the main lake basin (Fig. 6.5 and 6.6). The nature of this basin-wide evolution suggests that the source area of the coarse material and wood fragments is located closest to coring site KE12-08. Hence, the northern drainage valley is the ideal supplying candidate. This hypothesis is supported by the fact that the mega-flood is thickest in KE12-08, even though the sampling site of KE12-07 is located in deeper waters. Thinning and fining towards KE12-01 is a direct consequence of the sub- segment’s isolated position, as the northern valley river currently debouches into the northernmost part of the northern lake segment, causing transported sediment to cascade down to the south, instead of the north. Remarkable as well, is the rather bluish sediment colour, which increasingly dominates records further to the north (Fig. 6.5). Moreover, the 1991/1990 A.D. flood remains embedded within this same bluish grey sediment throughout the
109 Chapter 6 - Discussion
Figure 6.5: Correlation between master core KE12-07 and other cores KE12-01, KE12-02, KE12-03, KE12-04, KE12- 05, KE12-06, KE12-08 and KE12-09 from Kenai Lake, arranged by increasing distance from major meltwater inflows (Fig. 5.15). Identified event-units are indicated and dated (black/red and orange ages in A.D.).
central and southern lake segments. Interpretation of these colour variations tells us that bluish sediment possibly is a geochemical indicator for the northern valley source region, which is mainly filled with glacial deposits of the Naptowne and Brooks Lake Glaciations (Pleistocene), instead of alluvial and terrace deposits that are present in the Trail River and Snow River catchments (Wilson & Hults, 2013) (Fig. 6.6). When applying this presumption to all cores south of KE12-08, increased basin-wide influences of northern valley inflow can be identified in the interval 1985-1992 A.D. and previous to 1880 A.D. (Fig. 6.1 and 6.6). A shift to rather olive-beige sediment during the last 10 years in KE12-08 and KE12-01 indicates
110 Chapter 6 - Discussion that the eastern glacier-fed rivers would have become dominant over the local northern valley sediment-supply. An explanation for the change in relative importance of the three main inflowing streams is not evident, since this strongly depends on how each of the sub- catchments responds to variations in climate conditions (see section 6.5). The extremely thick and coarse mega-flood deposits in KE12-08 and KE12-07 imply, as mentioned in section 6.1.3.3, that the surface state of the source area most probably contributed to the large sediment input. Substantial amounts of easily erodible sediment could have been provided by, for instance, construction works, felling of trees, landslides due to valley slope failures etc. (Anderson & Dean, 1988). A similar, slightly thinner mega-flood deposit is present at a depth of 79-82 cm in KE12-08.
Figure 6.6: Kenai Lake basin with indication of the spatial distribution of event-deposits from the 1964 A.D. Prince William Sound earthquake, mega-flood in 1991 (1990) A.D. and regular floods from the two major inflows at the eastern end of the lake. The size of the coloured circles is a measure for thickness of the corresponding units in each of the retrieved cores. Bright blue and dark blue (northern valley from Summit Lake) arrows represent the main paths of water- and sediment-supply to Kenai Lake.
The Prince William Sound earthquake of 1964 A.D. caused substantial amounts of slope sediment to become remobilised (Reger et al., 2007). More specifically, the eastern and central segments of Kenai Lake were affected by substantial turbidity currents, as these parts of the basin are located closer to the instable delta structures of Snow and Trail River, and density flows tend to travel downslope to deeper bathymetric settings (Fig. 6.6). The sampling site of
111 Chapter 6 - Discussion
KE12-05 represents the perfect compromise between water depth and proximity to coarse- grained deltas, yielding a very coarse-based turbidite, which is thickest of all retrieved cores.
As noted in section 6.1.2.2, records from the northern segments seem to contain different tephra layers than can be found in the remaining part of the lake (Fig. 6.5). Within those two, separate lake parts, MS-peaks of (crypto)tephras can be connected easily. Reason for the tephra mismatches is quite a dilemma. Theoretically, the 2005 A.D. Augustine (Wallace et al., 2010) and the unknown 1995 A.D. eruptions could have produced ash clouds that only covered the northern segments of Kenai Lake, under specific conditions of prevailing wind directions. Then again, it is rather peculiar that the central and southern segments are characterised by their own tephra marker layers as well. It seems to be implausible that ash clouds coming from any of the Aleutian volcanoes would not affect the northern lake segments, but would deposit an MS-influencing amount of tephra in the more distal, central and southern segments. Possibly, the 1992 A.D. Mount Spurr and unknown 1975 A.D. eruptions produced clouds that covered the entire Kenai Lake catchment and were accompanied by deposition of very little and small ash fragments that would not affect MS- values of the lake’s infill. Due to sediment-entraining spring/summer drainage discharges following the eruptions, ash-fall deposits could have been transported, concentrated and deposited in the lake. Especially zones of deep water and locations closest to the two most important sediment-supplying rivers (southern and central segments) are most likely to become covered with MS-affecting runoff cryptotephra (Van Daele et al., 2014). De Fontaine et al. (2007) suggest a similar theory to explain the high concentration of tephra layers in Paradox Lake, a small lake in the Kenai Lowland, 22 km NW of Skilak Lake. The ability of this lake to capture and preserve volcanic ash-layers is partially attributed to its small size, with a proportionally large drainage basin area to catch tephra.
The question mark in Fig. 6.5 in between cores KE12-01 and KE12-08 indicates that correlation is almost impossible, given the poor visibility of separate layers and varves. Archives of both cores reach further back in time than master core KE12-07. However, their low quality in view of a climate reconstruction makes them difficult to use. When constant sedimentation rates throughout these cores are assumed, KE12-08 and KE12-01 would date back to 1763 varve years A.D. and 1400 varve years A.D., respectively.
6.3.4 Skilak Lake
Cores SK12-01 and SK12-04 were collected in the deepest part of the lake basin (Fig. 4.2 and 6.7). Moreover, their acquisition sites are located closest to the two main inflows and their accompanying deltas. As a consequence, not only background sequences and flood deposits, but also earthquake-triggered turbidites (1964 A.D. and 1954 A.D.) are thickest and coarsest in these cores (Fig. 6.8). Records thin towards the more distal and/or shallower water positions of the other cores. The proposed correlation of the 1964 A.D. turbidite-level with the 137 radionuclide dated core SK12-03 can be validated by the 1963 A.D. Cs-peak at a depth of 14 cm (Fig. 5.28), confirming the varve assumption once more. A relatively similar top section is present in both SK12-02 and SK12-03, which is not surprising since their acquisition sites are
112 Chapter 6 - Discussion
113 Chapter 6 - Discussion
Figure 6.7 (previous page): Correlation between master core SK12-10 and other cores SK12-01, SK12-02, SK12- 03, SK12-04 and SK12-07 from Skilak Lake (Fig. 5.23). Identified event-units are indicated and dated (black and orange ages in A.D.). Deposits from the 1964 A.D. megathrust earthquake comprise turbidites and an MTD.
Figure 6.8: Skilak Lake basin with indication of the spatial distribution of event-deposits from the 1964 A.D. Prince William Sound earthquake and regular floods. The size of the coloured circles is a measure for thickness of the corresponding units in each of the retrieved cores. Blue arrows represent the main paths of water- and sediment- supply. Zones affected by underflows, interflows and overflows initiated by turbidity currents are delimited. Positions of the current and past Skilak Glacier terminus are displayed as well and are linked to lower and higher sediment discharges, respectively. Note that the given position of the past glacier terminus is, in this case, not based on specific observations, but just a way of showing how situations could have been during long lasting periods with colder temperatures.
not that distant from one another (Fig. 6.7 and 6.8). The thick, structureless body in SK12-02 that seems to be present at the same stratigraphic level as the 1964 A.D. turbidites in SK12- 01 and SK12-04, can be interpreted as part of a local MTD, of which the source area most probably comprises the basin slopes north of the coring location (Fig. 5.22 and 6.8). Remobilisation of these sediment drapes and other underwater landslides could have caused a certain amount of material to go into suspension and transform into a density current, travelling down to the deepest parts of the lake where it would settle down as a mega- turbidite (SK12-01 and SK12-04), locally accompanied by erosion (SK12-04).
Only the uppermost parts of the continuously laminated/layered deposits of SK12-03, SK12- 07 and SK12-10 can be correlated with the three other, shorter cores, making it impossible
114 Chapter 6 - Discussion to assess how the stratigraphically lower successions evolve within the deeper parts of the basin (Fig. 6.7). According to the correlation rules in section 6.3.1., an analogous sequence of flood-units and varves as the one in SK12-03, should be present underneath the MTD of SK12-02. However, slope failure most probably induced erosion and intense deformation in the basin plain deposits at the location of SK12-02. If the same thickening trend from SK12- 03 to SK12-04 and SK12-01 can be extended further down the lake’s infill, more and thicker floods than the ones at a depth of 100-107 cm in SK12-04, must be lying underneath.
Figure 6.9: Correlation of flood deposits between cores SK12-04, SK12-03, SK12-07 and SK12-10 of Skilak Lake. Yellow dashed lines in zoom 1 and zoom 2 mark the boundaries between annual deposits. Labels ‘1’-‘3’ and ‘A’-‘M’ in zoom 1 and zoom 2 show how individual units change throughout the lake basin. Light blue sidebars mark type one floods and type two floods in zoom 1 and zoom 2, respectively.
Two different zooms in Fig. 6.9 show how annual flood-units evolve between different cores and thus throughout the basin of Skilak Lake. The three thick, type one floods in SK12-04 (‘1’, ‘2’ and ‘3’) gradually become thinner, finer-grained and eventually fade away into well developed, two-layered varves in SK12-10, lacking the multiple-pulse character of the units in SK12-04. This evolution is due to the more distal and/or shallower water conditions further westwards in the basin and on the slope-platform of SK12-10 (Sturm & Matter, 1978; Perkins & Sims, 1983). These locations are solely affected by finer-grained sediment transporting interflows and overflows and avoided by the coarse material carrying turbiditic underflows, which are responsible for the formation of thick flood deposits (Fig. 6.8). Zoom 1
115 Chapter 6 - Discussion in SK12-03 (Fig. 6.9) is situated right at the top end of the 1775-1925 A.D. interval, in which a high concentration of thicker varves with a lot of type two flood-units are observed (Fig. 6.7 and 5.27). Zoom 2 in Fig. 6.9 displays a representative section from the latter interval. The ‘M’-, ‘C’-, ‘I’- and ‘J’-units, for example, possess similar-looking, double bases, whereas the ‘K’-unit is composed of three different pulses. Similar to zoom 1, the multiple pulses become rather faint in SK12-07 and disappear in SK12-10. However, as was already mentioned in Chapter 5, the period with dramatically increased varve thicknesses in SK12-03 coincides with a period of slightly increased thicknesses in SK12-10 as well. Hence, a thickening of varves due to multiple-pulse sedimentation is also the case at the shallower coring site of SK12-10, even though individual pulses cannot be identified. Once more, it should be emphasised that properties of the flood-containing units in zoom 1 and zoom 2 are different, with zoom 1 showing type one units and zoom 2 showing type two units. Type two units are characteristic for SK12-03 and in a lesser degree for SK12-07 (Fig. 6.9). Just like in Eklutna Lake, cryptotephra as well as macroscopically visible tephra layers can be traced throughout all cores from Skilak Lake, creating a solid correlation frame.
6.4 Inter-lake core correlation
In order to create inter-lake correlations, it is necessary to identify depositional units or horizons which are present in at least two of three lakes. Units of this nature result from processes or events that affect large areas, such as megathrust earthquakes and volcanic eruptions. Climate variations can influence lacustrine sedimentation on a regional scale as well. However, every lake system is able to respond to these climate conditions on their own unique way, making this criterion not entirely suited for inter-lake core correlation purposes (O’Sullivan, 1983; Zolitschka, 2007). Moreover, instrumental climate data indicate subtle differences between annual conditions in the surroundings of Eklutna Lake, on the one hand, and Kenai Lake and Skilak Lake, on the other hand (e.g. Fig. 5.9 and 5.18). As described in section 6.1, several event-units could be traced back in more than one lake. The clearest examples of omnipresent marker layers, comprise all turbidites and mass-transport deposits caused by the 1964 A.D. Prince William Sound megathrust earthquake.
Master cores from each lake are correlated in Fig. 6.10. Varve thickness, sediment colour and flood deposits, all influenced by climate, do not lend themselves for an inter-lake correlation, since they show too much difference between lakes. The 1964 A.D. turbidites, two tephra horizons and age-depth models (based on event-deposits, radionuclide dating and varve counting), on the other hand, are key in connecting corresponding stratigraphic levels. Factors that influence the presence of tephra layers in a specific lake or in part of a lake were treated in section 6.3.1. Traces of the 1989 A.D. Redoubt eruption are present in both Skilak Lake and Eklutna Lake, whereas tephra from the major 1912 A.D. Katmai-Novarupta eruption is identified in Skilak Lake and Kenai Lake. The presented correlation emphasises the limited time-reach of EK12-01 and KE12-07 in comparison to core SK12-10, which contains the second longest record (in terms of time), after SK12-07.
116 Chapter 6 - Discussion
117 Chapter 6 - Discussion
Figure 6.10 (previous page): Inter-lake core correlation between Skilak Lake, Kenai Lake and Eklutna Lake. Sediment surface pictures are accompanied by 8-bits representations of the L*-index., varve thickness records in function of core depth and oscillating a*-indices. Vertical (depth) scales are not uniform, which means that some core pictures are stretched out/compressed. Red dates indicate ultimate time-reaches of the different cores, whereas black dates mark intermediate ages, in most cases belonging to event-deposits. Orange lines represent levels of (crypto)tephra (when dashed, layer is missing). Light blue, light green and beige bars correspond to the colour legends used in all previous figures of Chapter 6. Flood deposits, however, are not indicated separately, but grouped in zones with increased concentrations.
6.5 Climate proxies
6.5.1 Varve thickness
6.5.1.1 Motivation and approach
Whereas section 6.1.3.1 focused on architectures of annual flood-containing units and how these units increase varve thickness, section 6.5.1 is dedicated to the attempt to derive which specific climate conditions are responsible for the generation of thicker varves. Throughout Chapter 5, it has been proven how tuning of varve thickness/MAR records can increase the linear correlation between instrumental time series of climatologic parameters and sediment accumulation on an annual timescale. A calibration of each of the measured varve thicknesses/MAR’s or their running averages to prevailing climate conditions is key in revealing the influence of, for instance, peaks in precipitation or winter snowfall, on discharges and hence sediment transport within the lakes’ catchments and how these influences differ between lake systems (Perkins & Sims, 1983; Leemann & Niessen, 1994). As emphasised in Chapter 5, varve thickness variations are mainly determined by changes in summer layer thickness. Winter layer thicknesses, on the other hand, follow a rather steady course with small fluctuations. Hence, the most important sediment-supply-affecting events predominantly take place throughout summer, which can be ratified by descriptions of flood-influenced varve architectures in section 6.1.3.1.
Prevailing meteorological conditions are, on their turn, the result of a complex interplay between a number of different external and internal forcing factors (e.g. PDO, AO, ENSO) (see section 2.2). All of these processes take place on different timescales, making it difficult to isolate the influences of each of the separate driving forces. In order to solve this problem, the dominant peaks in spectral analysis periodograms of varve thickness/MAR can be interpreted in function of known periodicity ranges of important climate oscillations in Southern Alaska (Northern Pacific) (Fagel et al., 2008; Ólafsdóttir et al., 2013). Consequently, the power of peaks assigned to a specific oscillation represents the degree in which the corresponding variations in climate conditions affect sediment-supply to the lake basins.
6.5.1.2 Climate calibration
The purpose of climate calibration is not to explain variations in varve thickness throughout space (see section 6.3), but through time. Therefore, focus lies on just one (master core) or two cores from each lake. Running averages (window width of 15 years) of the original
118 Chapter 6 - Discussion thickness/MAR records represent multidecadal variations in sediment-supply, presumably driven by multidecadal variations in climate parameters. After low-frequency detrending, the high-frequency interannual fluctuations in sediment accumulation rates are revealed. For each of the lakes, climate-related causes are sought for in order to explain the observed low- frequency as well as higher-frequency varve thickness/MAR changes.
Eklutna Lake
MULTIDECADAL VARIATIONS
As described in Chapter 5, section 5.1.7, varve thickness variations in Fig. 6.11 are characterised by several positive bumps, of which the most pronounced one is centred around 1960 A.D. Previous to ~1944 A.D., waves in the running average seem to have an approximate period of 20 varve years and reflect the clustered type one flood-units (Fig. 5.7). This visually estimated periodicity agrees with the peak of 19.6 years in the spectral analysis periodograms (Fig. 5.12), discussed in section 6.5.1.3. The last, most prominent period of varve thickness increase is located right within the upper, darker core half, in which deposition is considered to be influenced by anthropogenic activities related to the storage dam at the northern end of Eklutna Lake (Fig. 6.4 and 6.11). If these activities had an imprint as well on the occurrence of multiple-pulse and hence thicker varves, cannot be claimed with certainty. Throughout the last 32 years varve thicknesses decrease again, even though the dam-controlled lake-outlet has not been shut down, which argues against an anthropogenic-induced varve thickening. Between 1982 and 1999 A.D. a new subtle high over a period of 17 years seems to be present. A possible link between the question- provoking prominent varve thickness increase in the upper core half and instrumental climate data can be analysed, since this period falls within the calibration interval.
As mentioned before, multidecadal variability in sediment accumulation is being considered as principally the result of changes in glacier balance and thus, the melting of ice (Ohlendorf et al., 1997; Leonard, 1997; Leonard, 1986). Since the most important supplying streams of Eklutna Lake are the glacier-fed West Fork and East fork, this assumption seems to be valid. When comparing varve thicknesses (Fig. 6.11) to instrumental data (Fig. 5.9) over the calibration interval, a seemingly counterintuitive relation can be derived. Lower temperatures are generally regarded as favouring conditions for glacier growth and high amounts of ice- covering snow sheets supposed to protect the existing glaciers from melting as a result of increased surface albedo (Perkins & Sims, 1983). Time series for snowfall and precipitation do not exhibit very clear long-term trends. The average annual temperature curve, on the other hand, displays a strong trough during the interval 1945-1976 A.D. (negative phase of PDO-index, Hartmann & Wendler (2005)), which coincides with a period of elevated varve thicknesses (Fig. 6.12). Thicker varves are on their turn associated with enhanced sediment transport towards the lake basin, due to increased discharges as a result of excessive ice/snowmelt. When any possible influence of storage dam-related activities can be ignored, temperature has to be the determining, low-frequency forcing factor (Fig. 6.12).
119 Chapter 6 - Discussion
Figure 6.11: Layer thicknesses from EK12-01 in function of age. A standardised PDO-index is shown to highlight the negative relation between PDO (temperature) and sedimentation rates, including a small time lag between driving force and sedimentary response. The last stage of LIA glacier advance is indicated, just like the interval with potential influences from the constructed storage dam.
A multidecadal interval with overall sustained colder temperatures can easily lead to glacier expansion, during which the glacier’s terminus would reach further down into the valley (Ohlendorf et al., 1997; Leonard, 1997). Even though average summer temperatures during the interval 1945-1976 A.D. also lowered with ~2 °C, it was still warm enough to melt substantial amounts of glacier ice. Remarkable increases in varve thickness start to occur from 1950 A.D. on and are preceded by five years of colder temperatures (Fig. 6.12). During these five years, glacier maxima could have been reached and hence, ice could have spread to lower altitudes. The quantitative relationship between the histories of glacier termini fluctuations and climate change is complicated by a time lag between climate change and glacier response. Johannesson et al. (1989) relate this time lag to the fact that a climate signal appears as a mass-balance perturbation over the full length of a glacier. Information about it becomes transmitted down-glacier to the terminus at finite velocities over a range of distances and, therefore, arrives at the glacier’s snout spread out in time. In analogy with studied sediment discharge dynamics in, for instance, Lake Silvaplana (Ohlendorf et al., 1997), Hector Lake (Leonard, 1997) and Hvítárvatn (Ólafsdóttir et al., 2013) periods of glacier growth in the catchment of Eklutna Lake seem to be characterised by increased varve thicknesses. A higher sediment availability thanks to more glacial erosion (production of rockflour) during times of larger glaciers, leads to a long-term increase in sedimentation rates. Moreover, when glaciers expand further down valley, they can reach the alluvial outwash plain where several small, subglacial meltwater streams are capable of transporting large quantities of loose sediment towards the lake basin. These conditions distinguish themselves from the current situation, in which there is only one main meltwater river, originating at the bedrock covering glacier snout (Fig. 6.4). Following the 1976 A.D. PDO
120 Chapter 6 - Discussion temperature-shift, sedimentation rates decrease rather steadily. This gradual decline might be due to the exposure of unstable, glaciogenic deposits to fluvial reworking during ice recession (Leonard, 1986) (Fig. 6.12). Besides influencing glacier extent, multidecadal variations in average annual temperature must have affected the amount of ice- and snowmelt during summer as well (Hock, 2003). Lower temperatures during the 1945-1967 A.D. interval are considered to produce less meltwater, therefore mitigating the result of the eroding effect of glacier advance on varve thicknesses. Not only throughout the negative PDO-phase of 1945-1976 A.D., but also before and after this interval, sedimentation rates display patterns opposite to the ones of temperature, with a time lag of ~five years (Fig. 6.12).
Figure 6.12: Relation between average annual temperature (Fig. 5.9) and varve thickness. Remarkable is the increase in varve thickness and hence sediment transport towards Eklutna Lake during a period of overall lower temperatures throughout the negative PDO-phase (1945-1976 A.D.).
INTERANNUAL VARIATIONS
Interannual fluctuations in varve thickness, which are revealed by detrending along a 15-year running average, are interpreted as the result of interannual fluctuations in temperature, precipitation and snowfall (Fig. 5.9) (e.g. Leemann & Niessen, 1994; Ohlendorf et al., 1997). Reasoning behind all of the presented curves in Fig. 5.9 was discussed in Chapter 5. The exact meaning of the fine-tuned weights for the differentially weighted, combined climate curve and the Pearson correlation coefficients are elaborated in the next paragraphs.
A strong dominance of precipitation (weight of 15.38) in the differentially weighted curve is remarkable and suggests that interannual fluctuations of varve thickness can be contributed primarily to evolutions in precipitation. Furthermore, the lower snowfall weight of 1 is not unexpected as well, since peaks in snowfall were, during tuning, only associated with thicker varves in case they coincided with relatively high temperatures. The zero-weight of temperature, however, can be considered as a strange outcome. After all, peaks in precipitation correspond most of the time to peaks in temperature, which is confirmed as well by the fairly high Pearson correlation coefficients between precipitation and temperature (Table 5.2). An explanation for these zero-weights should be searched for in the
121 Chapter 6 - Discussion exact definition of the Pearson correlation coefficient. The latter can be described as the measure of the degree of linear dependence between two variables. Since weights were determined by maximising Pearson’s r between weighted climate curve and tuned thickness record, the occurrence of a zero-weight means that the introduction of a merest influence of the temperature curve has a destructive effect on the linear correlation. In case a rather good correlation exists between precipitation data and varve thickness patterns, adding a temperature component can lower the degree of linear correlation, even though interannual peaks and troughs of temperature and precipitation time series coincide. In order to rule out this weakness, which is inherent to the use of a linear correlation coefficient when calculating combined climate curves, Pearson’s r’s between individual climate parameters and thickness records should be studied. Nonetheless, running averages of the differentially weighted climate curve in the top frame of Fig. 5.9 prove that climate data can be used to explain a significant portion of variations in sediment-supply on shorter timescales.
A more realistic image of varve thickness influencing factors can be obtained from data in Table 5.1. The fact that correlations with precipitation data are again highest, confirm previous observations. Temperature correlations, on the other hand, yield higher values as well, followed by snowfall. Hence, one might conclude that precipitation is dominant in creating high-frequency variations of varve thickness in Eklutna Lake. Patterns of interannual fluctuations in temperature often correspond to the ones of precipitation, automatically generating a certain correlation between varve thickness and temperature. Also, when moderate/higher temperatures coincide with elevated winter snowfall (e.g. orange arrows ‘B’ and green arrows ‘MF’ (mega-flood) in Fig. 5.9) thicker varves can be formed as a result of peaking meltwater discharges, giving rise to correlations with snowfall.
As mentioned in section 4.5, assessment of precipitation data on smaller timescales (monthly, Appendix D) can provide an interesting outlook on the presence of flood-induced units (Anderson & Dean, 1988), especially since precipitation appears to be the most important parameter in generating interannual fluctuations in varve thickness. Forcing factors of several prominent flood deposits in Eklutna Lake (Fig. 6.3) can be (re-)evaluated. The brownish layer at a depth of 3-4 cm in EK12-05 in the proximal sub-basin, for example, could be the result of a flash flood in 2004 A.D. or 2006 A.D. Both of the latter two years are characterised by strong precipitation peaks (150-200 mm) in September and August respectively (Appendix D). In a similar manner, mega-floods dated as 1995 A.D. and 1989 A.D. after tuning, can be related to elevated precipitation values and hence extreme, sediment-entraining runoffs. The 1989 A.D. unit possibly corresponds to a precipitation high of more than 200 mm in August, which is supported by climate data in Fig. 5.9 (orange arrows ‘A’). Rainfall conditions in 1995 A.D., on the other hand, are not favourable at all for generating floods (Appendix D). However, in August 1997 A.D., more than 200 mm of rainfall was registered in the weather station of Anchorage AK US and overflow at the Eklutna dam was reported (Larquier, 2010). After a considered reconfiguration of several varve boundaries, the flood deposit of 1995 A.D. can be assigned to 1997 A.D. This theory stands in contrast to tuning-results (green arrows ‘MF’ in Fig. 5.9), in which the mega-flood at a depth of 5-7 cm in EK12-01 is attributed to excessive snowmelt during the varve year of 1995 A.D. Gage data from Eklutna Lake (Gauge EKLA2) and the nearby Eagle River (Gauge
122 Chapter 6 - Discussion
ERBA2) suggest historical discharge crests in September 1995 A.D. (http://www.lawrencevilleweather.com, 23/07/2014), thereby supporting the latter hypothesis. However, both possibilities are as valid and can be used to explain the presence of mega-flood deposits in cores from Eklutna Lake.
CONCLUSIONS FOR EKLUTNA LAKE
Results of climate calibration in Eklutna lake can be summarised as follows. Multidecadal variations in varve thickness are caused by long-term temperature evolutions, which have an important effect on the mass balance of feeding glaciers and snowmelt. However, the potential impact of a certain anthropogenic influence should not be excluded. Higher- frequency variations, on the other hand, are controlled by a combination of all climate parameters, of which precipitation seems to be the most dominant one. Similar sedimentation controls have been observed in the proglacial, Alpine Oeschinensee (Leemann & Niessen, 1994). Everything aside, all of the calculated correlation coefficients are relatively low (<0.42). In Chapter 5, it was mentioned that the seemingly low degree of correlation should be looked at in perspective and compared to correlation coefficients of naturally related parameters, such as temperature and precipitation. However, two other explanations can be given. First of all, there still might be an unknown amount of erroneous varve delineations despite tuning. Moreover, the appropriateness of the linearity of the used Pearson correlation coefficients could be questioned. A linear rise in temperature, for instance, does not necessarily have to result in a linear increase in varve thickness (Loso, 2009).
Kenai Lake
MULTIDECADAL VARIATIONS
Variations of varve thickness over the entire record of KE12-07 (Fig. 6.13) are completely different from the ones observed in EK12-01 (Fig. 6.11). The 15-year running average is characterised by two prominent bumps, of which the smaller one is centred around 1880 A.D. and the bigger one around 2000 A.D. In contrast, EK12-01 displays a strong varve thickness increase from 1945 A.D. until 1980 A.D (Fig. 6.10). The latter one of two bumps in KE12-07 coincides with a high concentration of flood-units (Fig. 5.17) and can be used for low- frequency climate calibration (Fig. 6.14). In contrast to climate data used for calibration of EK12-01, time series from stations in the neighbourhood of Kenai Lake display stronger long-term trends (Fig. 6.14). Temperature data are relatively similar to these from stations in the surroundings of Anchorage and Eklutna Lake. Precipitation data and snowfall data in particular, display slight domes during the last ~36 years of the record (positive PDO-index, Hartmann & Wendler (2005)). These stronger long-term changes in climate conditions are possibly related to the more prominent oceanic influences on Kenai Peninsula in comparison to the surroundings of Anchorage (http://pafc.arh.noaa.gov, 22/05/2014). Hence, the multi- decadal variability of measured varve thicknesses seems to be determined by a combination of ice-/snowmelt and precipitation and not only by temperature-driven changes in glacier balance, as assumed earlier-on and as appeared to be, more or less, the case in Eklutna Lake.
123 Chapter 6 - Discussion
Figure 6.13: Layer thicknesses from KE12-07 in function of age. A standardised PDO-index is shown to highlight the positive relation between PDO (temperature) and sedimentation rates, including a small time lag between driving force and sedimentary response. The last stage of LIA glacier advance is indicated as well.
124 Chapter 6 - Discussion
Figure 6.14 (previous page): Relation between average annual temperature, annual precipitation, annual snowfall (Fig. 5.18) and varve thickness. In contrast to EK12-01, time series for precipitation and snowfall are shown as well, since they seem to exhibit more pronounced long-term trends, especially snowfall.
In contrast to Eklutna Lake, the catchment of Kenai Lake is not dominated by glaciers (Fig. 5.13). As a consequence, discharges and sediment transport towards the lake are not as strictly controlled by glacier balances and the eroding capacity of ice masses. Only one large glacier and a series of smaller ones are present within the extensive watershed. Moreover, glacial meltwater has to travel over longer distances in order to reach the lake and is intercepted by smaller, sediment-trapping lakes before flowing into the Kenai Lake basin (e.g. Trail River). Higher temperatures after 1976 A.D. (positive PDO-phase) result in the formation of thicker varves in KE12-07 (Fig. 6.14), due to enhanced ice-/snowmelt. Furthermore, this period is characterised as well by increased values for precipitation and snowfall, which contribute to the varve thickening. The protecting effect of snow cover against melting of glacier ice, is considered to be overpowered by snowmelt as a result of higher temperatures (Fig. 6.14). Furthermore, the second varve thickness high, centred around 1880 A.D., might result from a period of increased precipitation. Elevated temperatures are most probably not responsible for this pattern, given the prevailing cold conditions of the last LIA glacier advance during these times (see Skilak Lake)
INTERANNUAL VARIATIONS
Calculated weights for the combined climate curve in Fig. 5.18 (1.16*snowfall, 4.26*precipitation and 1*temperature) show a better distribution than those for Eklutna Lake (1*snowfall, 15.38*precipitation and 0*temperature). When excluding data from the Anchorage weather station, precipitation is most dominant, followed by snowfall and finally temperature. This ranking does not seem to be unlikely, as a similar order was obtained from weight fine-tuning in EK12-01. Similarities between running averages of the weighted climate curve and detrended, tuned and untuned varve thickness records are slightly stronger than observed for Eklutna Lake (Fig. 5.9), though still not entirely linear. However, the coincidence of specific crests and troughs proves again that instrumental time series can be used to explain most of the variations in varve thickness.
When looking at the more accurate correlation coefficients for individual climate parameters in Table 5.3, the relative importance of temperature and snowfall seems to be switched in comparison to the weights mentioned in the previous paragraph. The dominance of temperature over snowfall in determining interannual fluctuations in varve thickness implies that the amount of ice- and snowmelt during summer is largely responsible for the generated drainage discharges and sediment transport (Moore et al., 2001; Loso et al., 2006). How much snow fell during the preceding winter appears to be of almost negligible influence, suggesting that snowfall is not limiting to meltwater production. Precipitation, on the other hand, contributes most prominently to varve thickness-enhancing processes (flooding), just like in Eklutna Lake.
125 Chapter 6 - Discussion
Monthly precipitation data from weather stations in Anchorage (Appendix D) and Homer (Appendix E) can be consulted once more in order to (re-)evaluate the nature of remarkable flood deposits. After tuning, the 1990 A.D. mega-flood was contributed to excessive snowmelt (green arrows ‘MF’ in Fig. 5.18) and possibly loose sediment-providing conditions in the Kenai Lake catchment. Precipitation data in Appendix D and Appendix E, however, suggest that the latter conditions were complemented by heavy precipitation during the month of September, which might have caused flash floods to occur.
CONCLUSIONS FOR KENAI LAKE
Climate calibration in Kenai Lake yields slightly different results from Eklutna lake. First of all, multidecadal variability in sediment accumulation is not only bound to trends in temperature, but also to precipitation and snowfall. The lesser degree of glacier-dominance within the lake’s catchment and the stronger long-term variations of climate parameters within the region of Kenai Lake (Kenai Peninsula) are mainly responsible for this rather ‘mixed’ influence. Moreover, responses to long-term temperature changes are inversely related to these of Eklutna Lake. Higher-frequency varve thickness variations can be associated with the combined effect of all climate parameters, of which precipitation is again the most important one. The same reasons as mentioned for Eklutna Lake, can be summed up in order to explain the relatively low correlation coefficients.
Skilak Lake
MULTIDECADAL VARIATIONS
Since the master core record of Skilak Lake (SK12-10) reaches back over a time period of almost 700 years (672 years, i.e. 1340-2012 A.D.) (Fig. 6.15), a unique opportunity to study the effects of long-term climate variations on lake sedimentation, presents itself. The sampled archive of Skilak Lake continues where EK12-01 (1856-2012 A.D.) and KE12-07 (1833-2012 A.D.) leave off (Fig. 6.7). Therefore, characteristics and consequences of the LIA in Southern Alaska are assayable. In combination with the correlated content of SK12-03 (1646-2012 A.D.), the scale of certain varve thickness patterns in SK12-10 can be magnified and/or new patterns can be revealed, as the coring site of SK12-03 experiences a stronger influence from heavy sediment-loaded underflows (Fig. 6.8). Hence, the record of SK12-03 provides a more detailed image of climate conditions, while SK12-10 has a bigger time reach.
Long-term varve thickness patterns in core SK12-10 (Fig. 6.15) feature a series of highs of different timescales. Three subsequent periods of slightly increased varve thicknesses can be seen between 1400 A.D. and 1500 A.D. Two more prominent highs are present within the interval 1550-1625 A.D., followed by a small peak around 1700 A.D. and a very low- frequency, elongated dome from 1750 A.D. until 1900 A.D. The latter dome is extremely well developed in SK12-03, in which it appears to be composed of four separate clusters of concentrated type two flood-units (Fig. 5.27). When comparing these periods of enhanced sediment-supply to known LIA pulses, the two most prominent bulges seem to coincide
126 Chapter 6 - Discussion roughly with the onsets of advance phases. Barclay et al. (2009) describe how LIA glacier expansion in Southern Alaska culminated with two advance phases in 1540-1710 A.D. and 1810-1880 A.D. (light yellow bands in Fig. 6.15). Calkin (1988) confirms these phases and specifies by mentioning that the second of both pulses dominated in the Gulf of Alaska, which is reflected in the magnitude of the varve thickness increase during this interval. An additional sub-phase of minor glacier expansion at around 1710 A.D. is reported by Denton and Karlén (1973) and can be recognised as well in the record of SK12-10. Strong glacier advances during extensive periods of overall colder temperatures, such as the LIA, would lead to an enhanced production of glacially eroded material and transportation of loose sediment throughout the alluvial outwash plain (Fig. 6.8). Evidences of increased sedimentation rates during LIA are presented by several authors (e.g. Diedrich & Loso, 2012; Ólafsdóttir et al., 2013). This theory to explain the seemingly counterintuitive relation between colder temperatures and higher sediment discharges corresponds to the one mentioned in the paragraph concerning multidecadal climate calibration in Eklutna Lake. Analogous to the situation in Eklutna Lake, varve thickness peaks are only reached after a certain time lag, following the onset of the glaciers’ advance. Currently, the terminus of Skilak Glacier runs off into an ice-marginal lake (Fig. 6.8). This small water body can easily trap glacially eroded material, serving as a buffer zone in reducing the sediment-supply. During prolonged cold spells and periods of glacier expansion, the ice-marginal lake might become overrun by ice, halting sediment trapping and therefore enhancing sedimentation in Skilak Lake.
Figure 6.15: Layer thicknesses from SK12-10 and SK12-03 in function of age. A standardised PDO-index is shown to highlight the negative relation between PDO (temperature) and sedimentation rates, including a small time lag between driving force and sedimentary response. The two main stages of LIA glacier advance in Southern Alaska are indicated, of which the latter one is strongest.
127 Chapter 6 - Discussion
Section 6.1.3.1 describes how type two flood-units in SK12-03 are composed of equally coarse and similar looking sub-units. A sequence (two or more) of pulses, of which properties of each individual pulse resemble the ones of the first pulse, are likely to be generated by a series of successive glacier melting phases during the same year. Hence, not only an increased glacial erosion and transport, but also multiple glacier melting pulses (spring, early summer, late summer etc.) seem to characterise periods of LIA glacier advance.
Figure 6.16: Relation between average annual temperature, annual precipitation, annual snowfall (Fig. 5.30) and varve thickness/MAR of SK12-03 and SK12-10 (Fig. 5.29). In contrast to EK12-01, time series for precipitation and snowfall are shown as well, since they seem to exhibit more pronounced long-term trends, especially snowfall.
Throughout the calibration interval, varve thicknesses/MAR’s in both SK12-03 and SK12-10 show gradually decreasing values, seemingly independent from any of the climate time series (Fig. 6.16). However, a minor thickness increase can be identified during the 1945- 1976 A.D. negative PDO-phase in SK12-03. This observation establishes the positive relation between colder temperatures and higher sediment discharges. Sedimentation in both Eklutna Lake and Skilak Lake is indeed strongly controlled by a seasonal input of glacial meltwater and entrained material, since glacier-fed streams are the most important drainage paths in the lakes’ catchments. The overall decreasing trend in sedimentation rates over the interval 1932-2012 A.D. (Fig. 6.16), can be interpreted as the result of a still ongoing, stepwise glacier re-equilibration to warmer temperatures after the last LIA pulse. EK12-01 as well shows a subtly decreasing thickness record with higher values from 1856 A.D. up to
128 Chapter 6 - Discussion
1925 A.D., which also might be the result of glacial erosion during the last LIA advance phase followed by glacier re-equilibration (Fig. 6.11). In order to validate this suggestion, longer cores should be retrieved from Eklutna Lake, allowing the signature of both LIA pulses to be characterised more accurately.
‘INTERANNUAL’ VARIATIONS
As explained in Chapter 5, parameter weights of the combined climate curve for Skilak Lake are based on maximising the correlation between running averages of differentially weighted climate plot and detrended, untuned thickness/MAR records. As a consequence, the timescale on which climate influences can be studied, extends over several years instead of individual years. Calculated weights are relatively extreme and suggest a strong dominance of snowfall (Fig. 5.30). Zero-weights for precipitation and temperature are obtained in both SK12-03 and SK12-10. However, as mentioned before, the meaning of these weights should not be interpreted too literally. Consecutive years with high sedimentation rates seem to be reflected in combined climate curves as overarching, positive domes, proving an existing link between lake sedimentation and prevailing climate conditions.
Correlation coefficients between individual climate parameters and varve thicknesses/MAR’s in SK12-10 and SK12-03 are less accessible for interpretation, as they are even lower than values for Eklutna Lake and Kenai Lake and/or negative (Table 5.5 and 5.6). Nonetheless, snowfall seems to be the overall most important factor, therefore supporting the calculated weights (see previous paragraph). The snowfall controlled, annual sedimentation is quite remarkable and stands in strong contrast to the other two lakes, in which precipitation has a dominant imprint. Moreover, a dominance of snowfall means that the amount of snow accumulated during the preceding varve year (winter), and not temperature, is a limiting parameter in defining spring/summer discharges and sediment transport towards the lake basin. An explanation for this phenomenon might be related to the difference in elevation between the Skilak Lake watershed and the watersheds of the other two lakes, which are both nested within the Chugach Mountains and Kenai Mountains respectively. Skilak Lake, on the other hand, drains a substantial mountainous area, but also several zones of lower altitude, as it is located at the transition between Kenai Mountains and Kenai Lowland (piedmont lake). During spring and summer, snow sheets below a certain altitude will always melt away, whereas the snow that is overlaying higher mountain tops might survive summer temperatures. When, for example, a particular winter is characterised by high amounts of snowfall, more snow will cover the lower elevated portions of the Skilak Lake catchment and thus, more sediment-loaded meltwater will flow into the lake basin during spring and summer. Overall higher summer temperatures across the Kenai Lowland support the hypothesis of snowfall-limited instead of temperature-limited meltwater production (Jones et al., 2009). Cockburn & Lamoureux (2008) describe how discharge magnitudes in Canadian High Arctic, non-glacial watersheds seem to be limited by total snowpack as well. Though their study area is not entirely comparable with the glacier-dominated Skilak Lake catchment. Furthermore, Cockburn & Lamoureux (2008) point out that interannual variations, and not all variations, are controlled by snowfall, whereas long-term trends are determined by glacier dynamics, which is more or less in line with results from this study.
129 Chapter 6 - Discussion
CONCLUSIONS FOR SKILAK LAKE
Long-term variations of sedimentation rates in Skilak Lake answer to the principles that were introduced for Eklutna Lake. Prolonged intervals of colder temperatures lead, after a lagging period, to the formation of thicker varves with a multiple-pulse character. This theory is confirmed by the presence of several known LIA phases of glacier advance in the records of SK12-10 and SK12-03. The similarity of the sedimentary response between Eklutna Lake and Skilak Lake can be attributed to the strong glacier influence within the catchments of both lakes, and stands in contrast to the rather ‘mixed’ influences in the Kenai Lake watershed. Unlike the situation in Eklutna Lake and Kenai Lake, ‘interannual’ variations in varve thickness/MAR are largely controlled by winter snowfall. The lower elevation of the Skilak Lake catchment might be responsible for this dominance. Again, low linear correlations are due to reasons mentioned for Eklutna Lake and Kenai Lake. In addition, Skilak Lake records were not tuned, which contributes to the poor coefficients. However, these findings stand in sharp contrast with results from Perkins and Sims (1983). In the latter study, positive correlations between varve thickness and mean annual temperature (r=0.574-0.831) and strong negative correlations with annual cumulative snowfall (r=-0.794--0.902) are obtained for the interval 1907-1934 A.D. They reason that varve thickness in Skilak Lake is sensitive to annual temperature and snowfall because Skilak Glacier, the dominant source of sediment, is sensitive to these climate parameters. This study supports a negative relation between temperature and varve thickness, whereas Perkins & Sims (1983) associate elevated temperatures with an increase in duration of glacier ablation and thus an increased annual volume of sediment transported to, and deposited in Skilak Lake. Furthermore, they attribute the inverse relationship between snowfall and varve thickness to the ability of snow sheets to increase the albedo of the glacier surface, inhibiting ablation of the sediment-laden glacial ice.
Conclusions for climate calibration
Overall, it can be concluded that relations between annual sedimentation rates in Eklutna Lake, Skilak Lake and Kenai Lake, and climate conditions are rather complex and unique for each separate lake system. This complexity, combined with many factors of uncertainty (possible anthropogenic influences, questionable linear relations, unknown varve counting errors etc.) makes it fairly hard to create reliable transfer functions that can be used to extent climate records on an annual timescale, beyond the reach of instrumental data.
6.5.1.3 Spectral analysis
As emphasised in the corresponding sections of Chapter 5, positions of several periodicity peaks in each of the obtained periodograms show an intermediate to high degree of coincidence between the three different lakes. Their relative powers however, differ. The peaks with the lowest frequencies (indicated with red lines in Fig. 5.12, 5.21 and 5.31) do not necessarily have a profound significance when it comes to the presence of cyclicities. Both red peaks in EK12-01 and KE12-07 reflect two ‘periods’ extending over the entire lengths of the cores’ thickness records (Fig. 6.11 and 6.13). These two low-frequency waves are a result
130 Chapter 6 - Discussion of the limited time reach of the records rather than of an actual cyclic forcing mechanism. A similar observation can be done for the periodogram of SK12-10, in which the lowest frequency peak represents a period of 340 years. As the entire record of SK12-10 only counts 672 varve years, the described period fits almost two times in the length of the core. The periodicity peak at 133.3 years in the same core, however, could be an indication of large timescale, cyclic climate forcing.
When interpreting the results of spectral analyses, it is important to realise what the exact meaning and reliability of the observed peaks are. In the previous section, relations between sediment accumulation rates (varve thickness and MAR) and climate conditions are studied. These climate conditions and the fashion in which they vary over time are, on their turn, controlled by several, interacting forcing factors, of which some operate in a semi-periodic way and on different timescales (high-frequency and lower-frequency). Hence, these forcing factors indirectly determine varve thicknesses/MAR’s as well. However, it has become clear that each individual lake system answers differently to prevailing climate conditions and that different climate parameters are decisive in determining discharges and sediment transport toward the lake basins. This causes deviating trends in sediment accumulation within master cores of different lakes (Fig. 6.7). On the other hand, the relatively high degree of resemblance between the positions of periodogram peaks suggests that, even though responses of the three lakes are diverse, the scale on which they respond is quite similar. Differences in periodicity powers between lakes can be related to the degree in which lake systems are affected by specific climate parameters generated by the interactions between several external and internal forcings.
According to the pseudo-periodicities of forcings, discussed in section 2.2.1, periodogram peaks in Fig. 5.12, 5.21 and 5.31 are grouped. Overall, changing PDO-indices seem to affect variations in sedimentation rates strongest in Eklutna Lake and Kenai Lake. PDO-controlled varve thickness fluctuations have been observed already by Kaufman et al. (2011) and Tian et al. (2011). In Skilak Lake and Kenai Lake, cyclicities with a 4-5 year period are relatively prominent and can be assigned to the effects of ENSO. As the AO has a rather stochastic character, the presence of peaks over a broad periodicity range might result from this index.
When superimposed, individual oscillations can reinforce or mitigate each other’s effects on prevailing climate conditions in a way that is not yet entirely understood, which complicates interpretation of responses in lacustrine sedimentation as well. For example, the modulating effects of the cold phases of PDO and AO may, when in phase with decreased solar activity, serve to amplify cooling, forcing glacier advance (Wiles et al., 2004). According to Papineau (2001), temperature patterns produced by ENSO are modified by the concurrent state of the PDO sea-surface temperature anomalies. Moreover, the considered oscillations do not display a purely sinusoidal variation, implying that their reflections in varve thickness records are not perfectly sinusoidal as well. Therefore, spectral analysis results have to be treated with caution and need further study and a better understanding to be interpreted correctly with consideration of all its complexities.
131 Chapter 6 - Discussion
6.5.2 Grain-size
Grain-size as a climate proxy is directly related to variations in varve architecture, which has already been discussed thoroughly in previous sections. Varve architecture, on its turn, depends on climate conditions, characteristics of the lake catchment and the relative position of the sampling location within the lake basin (Peach & Perrie, 1975; Francus et al., 2013). The occurrence of spring/summer floods and their formation mechanisms are decisive in determining the structure of annual deposits. In Chapter 5, a division between type A, type B and type C units has been made to describe regular two-layered background varves and two- and multiple-pulse varves, respectively. Section 6.1.3.1 assigns the observed structures to seasonal variations in stream discharges by creating several categories of flood-containing units (type one to type four).
Overall, it can be noted that laminae/layers that are considered to contain flood-induced deposits, are composed of coarser material (Fig. 5.5, 5.16 and 5.25). This observation is not surprising, since flooding is associated with higher discharges and thus higher entrainment energies, causing not only more sediment, but also coarser sediment to be transported towards, and deposited within the lake basins. However, a few exceptions on this general rule can be noticed. Firstly, some of these exceptions might be (partly) the result of a non- precise sampling, during which contamination with the overlying/underlying, coarser/finer deposits occurred. Second of all, when interpreting the measure in which grain-sizes differ between units throughout a specific core, one should be aware of the bathymetric influence on the range of grain-sizes that can accumulate on the studied coring site. In core SK12-10, for example, which was taken from an intermediate slope-platform, there is not as much difference between grain-size distributions in flood deposits (e.g. 113 cm) and in regular background varves (e.g. 63.5 cm). Moreover, shifts between frequency plots of varve bases and tops are even extremely subtle (Fig. 5.25). After all, coarse material-transporting, turbiditic underflows never reach these shallower areas in times of spring/summer melt or flooding (Sturm & Matter, 1978) (Fig. 6.8). Hence, sedimentation in deeper lake portions that are located close to important inflowing streams are most sensitive to climate-related grain- size changes. For instance, grain-size distributions of core EK12-01, exhibit more pronounced differences between bases of flood-units (e.g. 19.5 cm) and bases of normal varves (e.g. 66 cm) (Fig. 5.5). Flood deposits in KE12-07 (e.g. 41 cm) are also remarkably coarser than the other units present (e.g. 19 cm) (Fig. 5.16). Kaufman et al. (2011) suggest positive relations as well between maximum annual grain-size and maximum annual daily discharges, while correlations between varve thickness and maximum annual grain-size are poor in the sediments from Shadow Bay (Southwest Alaska).
Unfortunately, no continuous instrumental grain-size measurements were executed. If for example, every individual varve of EK12-01 would have been sampled and analysed, a better image of variations in discharges through time could be obtained. In that case, grain-size would be considered a proxy-record for stream entrainment energy, controlled by specific climate parameters which were discussed in section 6.5.1. These continuous records could be used to confirm the occurrences of increased flood-concentrations, proposed in correlation figures as well as age-depth models of Chapter 5 (e.g. clustered floods in the lower section
132 Chapter 6 - Discussion of EK12-01 and in SK12-03). Moreover, coarser stratigraphic intervals which were not yet recognised as the result of increased discharges, could be recognised.
6.5.3 Geochemical and mineralogical composition
Information regarding the geochemical composition of sediment can be derived from smear slides, XRF-data and colour analyses (Brauer et al., 2009; Shanahan et al., 2008). Though, smear slides appeared to be suited only for visual confirmation of instrumental grain-size measurements. A few minerals were recognised, but changes in their prominence between slides could not be assessed as a consequence of the limited grain-sizes. However, the undulatory extinction of quartz grains confirms the metamorphic properties of large parts of all three lakes’ catchments (Wilson & Hults, 2013). The presence of few pennate diatoms in the uppermost 10 cm of SK12-10 might be due to recent eutrophication of the Skilak Lake as a result of anthropogenic nutrient input (Ohlendorf et al., 1997; Bigler et al., 2007).
XRF-data were studied on two different timescales: interannual and multidecadal (over entire time span of cored material). Interannual patterns in geochemical composition are illustrated in Fig. 5.6 for several intervals within core EK12-01. The observed trends are confirmed to occur in Skilak Lake as well. Although cores from Kenai Lake were not scanned in a high enough resolution to derive information concerning interannual patterns, similar trends are likely to be present. Fig. 5.6 shows how elements Si, Al, Ti and Ca tend to peak more in the coarser bases of varves and/or flood-units. Si and Al, however, often deviate from this pattern, which means that these elements are not that much restricted to a particular grain- size class as Ti and Ca are to silt. Ti and Ca indeed display very well pronounced count- increases at levels of silty summer deposits. In the type B and type C units, also defined as type one flood-units (Fig. 6.2), peaks are even stronger at the bases of the central, differently coloured floods. The latter observation is a logical consequence from the fact that these flood-units have a grain-size distribution which is skewed more towards the silt class (Fig. 5.5). Fe and K tend to reach higher counts within the winter clay caps and therefore, display trends opposite to the ones of Ti and Ca. An optimised and very detailed clay cap indicator is presented by the element-ratios K/Ti and Fe/Ti, proposed by Cuven et al. (2010). Hence, it appears that consistent peaking of certain elements is strongly bound to the abundance of specific grain-sizes, which implies that mineralogy as well is grain-size dependent. No informed suggestions can be done about the exact changes in mineralogy with varying grain-size, since smear slides could not be used for this purpose. All of these observations, however, do not give away direct information about climate conditions. They rather function as grain-size proxies, which, on their turn, can be linked to specific hydrodynamic processes in the lakes’ watersheds (Bertrand et al., 2012). Thick flood deposits for example, are reflected in XRF-data by increased amplitudes between base- and top-sediment (Fig. 5.6).
All of the principles that are established on an interannual scale can be extended to multidecadal scales as well. Despite the lack of continuous downcore grain-size measurements, evolutions in grain-size can be estimated roughly thanks to geochemical proxies. Si-counts are strongly influenced by density variations as a consequence of
133 Chapter 6 - Discussion
changing degrees of sediment compaction. Clay cap-indicating K/Ti-ratios are freed from this density-dependent influence. Though, no spectacular trends or patterns can be observed in K/Ti-plots, except for increased variation-amplitudes throughout the lower core half of EK12-01, especially at depths of clustered flood-units with a periodicity of 20 years. Rb/Sr- ratios, suggested by Jin et al. (2001) as a proxy for weak chemical weathering under arid and cold climate conditions of the LIA, are not applicable as they do not display noteworthy long-term variations, just like redox potential-reflecting Fe/Mn-ratios (Cohen, 2003).
Finally, colour analysis results and more generally, sediment colour can be interpreted in terms of composition or at least, in terms of changes in composition. One of the most obvious examples of visible colour changes can be seen in the bases of several thick flood deposits. In most cases, these bases are remarkably darker, which is a grain-size-related effect, but in some specific cases, they are browner as well. In Eklutna Lake, several of these brownish flood-units can be discerned within the five cores (Fig. 6.3). The 1995 A.D. dated mega-flood in EK12-01 has a stronger brown colour-component than its corresponding deposits in EK12-03, EK12-04 and definitely in EK12-05, implying that the brownish colour is not a synsedimentary phenomenon. Oxidation shortly after deposition or after core splitting (before photographing) is most probably responsible for these discolorations (Christensen & Björck, 2001; Anderson & Dean, 1988). Another prominent example of changes in sediment colour, can be identified in cores from Kenai Lake (Fig. 6.5). In this case, the increasing dominance of bluish sediment towards the northern lake segments was assigned to a difference in the sediment-contributing source area.
Many of the visible colour changes are reflected in the Strati-Signal colour analysis results as well, since L* indicates variation between black and white, a* between green and red and b* between blue and yellow. As mentioned before, flood-units are characterised by coarser sediment, which is related to darker colours (e.g. Fig. 5.6). Hence, the lightness-index could be used as a flood-indicator. Fig. 6.10, however, shows that this rule is often, but not always applicable. The 8-bits representations of the L*-index make it possible to immediately delineate depth intervals in which sediment is darker (Gan & Scholz, 2013). Within Skilak Lake, the last phase of LIA glacier advance is represented by a very prominent dark zone in both SK12-03 and SK12-10. In contrast to SK12-03, SK12-10 does not contain a higher concentration of flood-units throughout this interval, because of bathymetry-related reasons (Sturm & Matter, 1978). The b*-index, on its turn, is successful in marking the zones of more bluish sediment in Kenai Lake (Fig. 5.17). Though, this does not add any additional information. In contrast, the a*-index seems to show subtle sediment colour variations that cannot be observed visually. Two different interpretations can be used to explain these wavy patterns. The first and most likely explanation assigns the apparent colour variations to a stitching of unevenly-lit photo-segments in order to obtain full core photographs. A second explanation relates the semi-periodic colour variations to a cyclic, climate-related mechanism. However, it is not entirely clear which cyclicity this could be and how it would influence sediment colour (green/red). The average periodicity of variations in the a*-index yields 35 years and therefore, comes closest to the cyclic behaviour of the PDO. Though, the absence of prominent a*-index variations in SK12-10, which was photographed with the Jai CV L105 3 CCD Colour Line Scan Camera, favours the first explanation.
134 Chapter 7 - Conclusions and recommendations
7 CONCLUSIONS AND RECOMMENDATIONS
Science never solves a problem without creating ten more. - George Bernard Shaw -
By executing a series of sedimentological, geophysical and geochemical analyses on the content of several cores from three different, proglacial lakes on Kenai Peninsula and in the surroundings of Anchorage, a diversity of sediment-properties are obtained. Interpretation of these properties serves to construct age-depth models and to derive climatologic information, which on its turn can be associated with specific dates or time intervals.
The framework of each of the intra-lake core correlations and age-depth models is based on identified, historical event-deposits (earthquake-induced turbidites, flood-units, tephra layers), which distinguish themselves from background sequences by their general appearance and properties. Most of the recognised event-deposits have a rather local character and are unique for every separate lake or part of each lake. For instance, in Eklutna Lake, a 1929 A.D. flood-induced, storage dam-failure turbidite can be observed, whereas
Skilak Lake archived ‘zero-time’-deposits of a 1954 A.D. earthquake (Mw 6.7) with a hypocentre located under northern Kenai Peninsula. In contrast to these local stratigraphic units, (mega-)turbidites, triggered by the major 1964 A.D. Prince William Sound earthquake
(Mw 9.2), are present in each of the three lakes and therefore offer the opportunity to create an inter-lake correlation. Tephra layers, deposited during the eruptions of Redoubt in 1989 A.D., Mount Spurr in 1992 A.D. and Novarupta in 1912 A.D. can be traced back in the records of Skilak Lake and Eklutna Lake, Kenai Lake and Eklutna Lake, and Skilak Lake and Kenai Lake, respectively. Hence, the latter marker layers can be used as well to connect synchronous horizons between lakes. Yet, the occurrence of all of the remaining tephras seems to be restricted to maximum one lake. Radionuclide dating of sediment from Eklutna Lake and Skilak Lake supports the assigned event-dates.
Aside from event-deposits, each core is composed of laminated to layered successions. Age- depth frameworks confirm the annual nature of individual laminations and layers, allowing the term ‘varves’ to be used. By counting these clastic varves and measuring their thicknesses, blank intervals in between event-deposits can be filled in, giving rise to high- resolution, solid and continuous age-depth models for at least each lake’s master core. CT- scans appear to be invaluable when it comes to varve delineation throughout intervals where core pictures are rather blurry. The sedimentary record of EK12-01 (Eklutna Lake) reaches back in time to 1856 A.D. ±7 yrs, KE12-07 (Kenai Lake) to 1833 A.D. ±6 yrs, SK12-03 (Skilak Lake) to 1646 A.D. ±15 yrs and SK12-10 to 1340 A.D. ±33 yrs. Hence, the tectonically and volcanically active setting of Southern Alaska, combined with the depositional characteristics (glacial varves) in proglacial lakes, appear to be ideal for creating a decent relation between stratigraphic depth and date of sedimentation. Moreover, age-depth models are
135 Chapter 7 - Conclusions and recommendations indispensible in transforming depth-dependent proxy-datasets in age-dependent proxy-data sets, which is essential for climate calibration.
One of the most obvious climate proxies is annual sediment accumulation or varve thickness. Overall, thicker varves can be associated with the occurrence of different types of flood- units, which are also considered to be event-deposits. Flooding, on its turn, is a consequence of suddenly increased discharges and sediment entrainment capacities of drainage streams within the lakes’ catchments and seems to be concentrated during spring/summer times. Four different categories of annual deposits are considered to contain flood-units: 1) two- or multiple-pulse varves of which the pulse(s) following the first spring/summer melt are thicker, coarser and often differently-coloured, 2) idem as type 1, though all pulses are similar, 3) one-pulse varves that are thicker, coarser and differently-coloured in comparison to regular background varves, 4) large-scale versions of type 1, 2 and 3 (mega-floods). Records of annual sediment accumulation from every lake display variations on various timescales, going from interannual to multidecadal. Furthermore, these variations differ strongly between lakes, suggesting that every studied lake system responds differently to more or less similar climate conditions.
Before calibrating annual sedimentation rates to instrumental climate data (1932-2012 A.D.), the magnitude of an inevitable varve counting error could be minimised by varve thickness/MAR tuning. Calibration was executed on multidecadal scales as well as interannual scales in order to derive how climate parameters steer sediment accumulation in the three lake basins and hence, flooding conditions in their respective catchments. Results from climate calibration should be interpreted in function of two criteria: the degree of glacier-dominance within the watershed and the average elevation of the watershed. Multidecadal variations in Eklutna Lake as well as Skilak Lake are mainly determined by temperature, which can be associated with long-term fluctuations in glacier balance. Indeed, water- and sediment-supplying streams of both lakes are dominated by glacier-fed rivers. However, the positive relation between flooding (i.e. thicker varves) and colder temperatures seems to be rather counterintuitive, though can be explained by increased glacial erosion and transportation of loose sediment toward the lake basins. The ~700 year long time span of core SK12-10 allows to study the effects of LIA glacier advances. Several known LIA advance phases seem to coincide with periods of elevated varve thickness, therefore confirming the stated relation between temperature and sedimentation rates in a glacier- dominated catchment. In contrast to records from Eklutna Lake and Skilak Lake, multidecadal variations of sedimentation rates in Kenai Lake are controlled by temperature as well as snowfall and precipitation. This rather ‘mixed’ signal is a direct consequence of the more complex catchment, in which glacier-input is less dominant and less direct. Interannual climate calibration, on the other hand, points out that high-frequency fluctuations in sediment transport towards the lakes is determined by an interaction between all climate parameters. Nonetheless, precipitation is decisive in both Eklutna Lake and Kenai Lake, whereas snowfall appears to be of great weight in Skilak Lake. The latter observation might be due to the limiting nature of snowfall in producing meltwater within the overall lower elevated catchment of the piedmont type Skilak Lake. Eklutna Lake and Kenai Lake are nested within the higher mountains, where temperature limits the amount of ice- and snowmelt.
136 Chapter 7 - Conclusions and recommendations
Thus, multidecadal and interannual variations in sediment-supply can be related to changes in interacting climate parameters. These varying climate conditions are, on their turn, determined by a complex communication between several atmospheric and oceanic patterns and oscillations, which act on different timescales, often in a pseudo-periodic way. Results from spectral analyses suggest influences from PDO, ENSO and possibly AO. However, these oscillations do not exhibit a perfect sinusoidal behaviour and interact with each other in a complicated manner, which is not entirely understood yet. Moreover, their influences on climate conditions are not always straight-forward, not to mention their influences on sedimentation rates in the three different lakes. A correct interpretation of the resulting periodograms needs further study.
Grain-size as well as geochemical proxies are largely related to varve-architecture (i.e. flood- units) and thus varve thickness. Floods are a direct result of high discharges, which can transport more, but also coarser sediment. Chemical composition is strongly related to grain-size, with Ti being a good indicator for silt (summer layer) and K and Fe for clay (winter layer). Strong fluctuations of K/Ti and Fe/Ti mark positions of prominent flood deposits. A larger contrast between coarse base and fine-grained top exists in the latter.
As this study has a rather exploratory character, several recommendations can be done for future research in order to improve the quality of the age-depth models and climate calibrations. One of the largest sources of uncertainty is the ambiguity of varve boundaries in several core intervals. Thin sections should be prepared and studied to delineate separate varves by identifying continuous grain-size evolutions on interannual scales. Moreover, elemental/mineral scanning and mapping of these thin sections with µ-XRF, SEM and MLA (Mineral Liberation Analyser, Gu et al. (2014)) could finally help to obtain a better insight in mineralogical variations that are related to the grain-size changes. As mentioned before, continuous, downcore grain-size measurements are recommended as well in order to assess subtle, long-term changes. The quality of the proposed age-depth models can be further assessed by examining geochemical properties of tephra layers, associated with specific eruptions of the Alaskan-Aleutian volcanoes (e.g. Begét et al., 1994; Stihler et al., 1992). Finally, acquisition of longer cores would offer the opportunity to study LIA influences in deeper parts of Skilak Lake and in Eklutna Lake.
137 Chapter 8 - References
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149 Chapter 9 - Appendices
9 APPENDICES
Appendix A: XRF-profiles of all elements with significant counts from core EK12-01, having a downcore resolution of 1 mm (Al-Ba) and 1 cm (Rb-Ag). Element-counts are expressed in function of core depth.
150 Chapter 9 - Appendices
Appendix B: XRF-profiles of all elements with significant counts from core KE12-07, having a downcore resolution of 1 cm. Element-counts are expressed in function of core depth.
151 Chapter 9 - Appendices
Appendix C: XRF-profiles of all elements with significant counts from core SK12-10, having a downcore resolution of 0.5 mm. Element-counts are expressed in function of core depth.
152 Chapter 9 - Appendices
Appendix D: Monthly data for precipitation from first-order station Anchorage AK US over the interval 1952-2011 A.D. Average values are highest during months July, August, September and October. Colour-coded cells represent precipitation values of 0-50 mm (blank), 50-100 mm (green), 100-150 mm (blue), 150-200 mm (orange) and >200 mm (red). Note years 1989 A.D., 1990 A.D., 1997 A.D., 2004 A.D. and 2006 A.D., characterised by strong precipitation peaks in August or September.
Year (A.D.) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1952 / / / 11.176 8.636 0.508 69.088 98.044 67.564 95.504 31.75 /
1953 / 17.272 9.144 2.032 22.606 3.048 23.876 126.746 68.326 32.258 2.794 28.194
1954 14.224 4.572 24.638 0.762 3.81 23.114 52.832 54.102 42.164 51.308 23.622 25.4
1955 28.448 77.978 12.954 33.528 0.508 29.972 43.688 82.804 32.258 46.228 14.986 67.818
1956 13.208 63.246 7.112 12.7 11.176 13.208 77.978 40.64 50.8 55.626 59.182 4.826
1957 34.544 17.018 5.08 0.254 0.508 14.224 41.656 51.308 81.534 23.622 38.354 9.144
1958 26.67 1.778 4.826 6.35 26.67 55.626 112.776 42.418 33.274 49.022 35.814 13.716
1959 6.858 24.13 21.59 33.528 12.446 6.604 112.522 78.994 36.068 28.448 17.018 34.036
1960 18.288 11.43 5.588 6.858 11.176 6.604 68.834 68.58 121.666 8.89 14.224 26.416
1961 38.354 11.684 8.636 35.052 11.938 28.448 56.388 49.276 137.922 71.374 16.256 24.13
1962 22.352 18.796 14.732 6.35 38.608 86.36 18.288 48.768 36.83 39.624 12.446 26.924
1963 53.086 34.29 37.592 45.212 11.176 46.228 69.85 71.12 24.892 25.654 3.048 37.846
1964 8.89 29.21 27.178 22.606 24.638 43.942 27.432 54.864 21.082 58.674 68.834 16.256
1965 14.478 17.018 21.082 7.62 12.954 24.384 44.196 40.132 116.84 36.576 47.244 36.576
1966 16.002 20.32 11.176 17.78 19.05 6.858 18.034 62.738 62.23 21.844 28.194 26.924
1967 31.75 25.654 24.892 12.446 27.178 36.576 62.738 75.184 72.644 12.954 43.688 60.96
1968 21.082 42.418 7.366 21.59 40.64 15.748 34.036 17.526 26.67 40.894 27.432 11.43
1969 7.112 18.542 2.54 0 21.844 4.572 54.356 8.382 19.812 22.86 21.336 23.876
1970 21.844 14.478 7.366 6.858 10.922 21.59 51.562 56.642 28.194 41.148 30.734 41.148
1971 6.096 37.846 17.78 16.002 13.208 9.398 72.644 65.532 45.466 54.864 17.018 22.098
1972 14.224 16.002 17.272 18.542 20.574 15.494 10.668 35.56 112.268 73.406 19.304 18.288
1973 18.288 2.794 16.51 8.382 3.556 27.178 15.24 86.36 19.304 44.196 19.812 9.652
1974 0.508 29.21 15.24 15.494 8.636 17.526 30.988 41.148 38.862 66.802 25.654 50.8
1975 10.922 19.558 13.716 43.434 10.16 11.938 33.782 30.226 114.808 17.526 2.54 22.606
1976 24.892 8.382 44.958 18.796 4.064 8.382 15.24 24.638 88.9 32.766 72.136 26.162
1977 34.29 13.208 21.336 48.514 11.684 12.446 34.798 34.29 103.632 48.768 13.462 17.526
1978 9.906 30.226 11.43 0.508 0.762 78.486 45.212 13.716 54.864 41.91 21.59 66.04
1979 5.842 17.526 70.104 23.876 3.81 45.466 97.536 39.624 69.342 64.516 70.358 29.21
1980 32.512 29.972 7.62 4.826 42.672 69.342 57.658 77.724 64.262 77.47 12.446 10.414
1981 23.622 24.638 10.414 4.826 20.574 21.082 111.506 125.984 54.61 88.646 46.99 9.144
1982 0.508 17.526 10.668 6.858 13.716 39.624 61.214 59.182 118.364 74.93 43.688 2.794
1983 5.334 5.842 0 34.544 14.986 16.764 13.97 73.406 58.166 67.818 5.842 12.192
1984 33.02 27.432 2.032 23.622 24.384 27.94 28.194 81.534 65.786 35.052 3.81 27.432
1985 17.78 17.018 21.844 12.7 36.83 25.654 25.146 89.916 80.518 27.178 2.032 37.338
1986 5.08 13.97 43.18 10.668 12.7 8.382 51.308 91.948 72.39 104.394 31.242 36.068
1987 68.834 5.08 4.318 6.096 17.018 27.686 48.006 10.922 48.514 66.04 48.26 28.448
1988 9.652 8.128 16.51 9.398 14.224 20.066 16.256 95.758 32.004 75.184 28.194 38.354
1989 6.604 4.318 5.588 24.892 49.022 28.956 73.406 248.158 99.568 92.202 25.654 41.402
1990 36.068 37.084 11.684 6.858 18.034 38.608 20.574 48.26 168.656 18.542 33.274 45.212
153 Chapter 9 - Appendices
1991 15.748 10.668 16.51 5.842 3.048 4.572 71.628 89.916 86.614 49.022 39.878 46.228
1992 29.718 26.416 7.874 2.032 14.732 30.734 20.066 63.246 71.882 52.832 29.718 17.526
1993 23.876 29.718 7.366 2.286 29.718 4.318 14.478 102.108 108.458 48.26 50.8 7.62
1994 14.986 7.112 38.354 11.43 12.954 34.036 14.478 25.908 42.164 30.734 62.738 38.354
1995 13.208 25.4 22.352 2.032 28.194 23.114 76.454 55.626 74.422 24.13 2.286 2.286
1996 2.794 60.96 10.668 2.032 5.08 12.7 51.816 64.262 49.022 66.802 35.052 6.096
1997 3.048 13.208 0.254 6.35 28.448 15.24 34.544 212.598 64.262 49.022 22.098 45.72
1998 11.43 6.096 1.778 9.906 16.002 68.58 25.654 82.55 18.288 13.716 4.572 37.338
1999 9.398 7.112 15.494 7.366 33.02 27.94 54.61 117.348 80.518 66.802 8.89 36.322
2000 26.416 13.716 12.192 9.906 17.526 36.322 65.532 42.672 82.296 14.986 28.702 14.732
2001 27.94 21.59 22.352 8.636 12.192 6.096 114.046 24.638 28.956 39.878 6.604 5.08
2002 18.288 8.89 40.894 7.366 6.858 25.654 37.084 89.154 85.344 108.712 6.858 42.164
2003 9.906 22.86 8.636 4.318 17.018 24.13 31.242 59.436 49.784 77.724 65.278 53.34
2004 12.446 18.542 21.844 19.558 25.908 24.13 22.352 29.718 186.69 29.972 60.96 43.942
2005 15.494 32.766 26.416 4.064 7.366 20.574 26.162 87.376 116.078 19.812 25.146 22.86
2006 9.398 18.034 18.542 12.446 13.97 35.814 37.338 167.64 90.424 51.308 1.016 60.452
2007 34.036 3.556 4.572 4.318 16.764 27.94 45.974 53.086 109.22 42.926 32.766 15.748
2008 29.464 21.59 10.414 58.928 10.16 16.002 82.55 23.368 81.788 44.958 28.194 25.146
2009 24.638 11.43 27.94 3.302 19.304 14.478 35.56 73.406 29.718 55.88 30.988 19.812
2010 15.494 23.114 15.748 30.734 5.08 30.734 83.058 84.836 23.622 10.922 72.898 22.098
2011 11.938 5.588 / / / / / / / / / /
Average 19.05 20.32 16.256 14.224 16.51 25.4 48.26 68.58 69.088 48.006 28.702 28.194
Appendix E: Monthly data for precipitation from first-order station Homer AK US over the interval 1932-2010 A.D. Average values are highest during months August, September, October, November, December, January and February. Colour-coded cells represent precipitation values of 0-50 mm (blank), 50-100 mm (green), 100-150 mm (blue), 150-200 mm (orange) and >200 mm (red). Note years 1939 A.D., 1940 A.D., 1950 A.D., 1952 A.D., 1969 A.D., 1979 A.D., 1983 A.D. and 2001 A.D., characterised by strong precipitation peaks in October, November, December or January.
Year (A.D.) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1932 / / / / / / / / 57.658 64.262 52.832 110.49
1933 39.624 6.604 49.276 43.942 35.306 26.162 9.398 93.218 39.878 23.114 67.818 0
1934 55.118 25.654 34.798 68.326 39.878 10.668 27.94 100.33 / / / /
1935 / / / / / / / / / / 24.892 59.944
1936 102.362 9.398 30.734 5.588 21.336 22.098 52.578 101.854 33.274 161.29 140.97 52.07
1937 46.736 23.622 49.53 17.78 48.514 11.938 40.64 / / / / /
1938 / / / / / / / / / 134.62 0 108.458
1939 95.25 30.988 46.99 77.216 27.178 42.926 28.448 141.224 90.678 86.36 43.942 203.454
1940 96.012 26.162 56.388 30.734 16.764 8.382 53.848 81.534 116.332 213.868 59.69 95.504
1941 70.104 133.35 97.028 71.882 37.338 85.598 43.18 36.83 41.148 36.576 27.178 112.014
1942 33.528 62.23 52.07 62.23 17.78 25.4 40.386 69.596 67.818 47.752 5.588 23.368
1943 27.432 42.418 11.938 22.86 32.004 18.034 40.132 59.182 93.218 64.262 184.15 127.254
1944 96.52 28.448 37.084 4.826 47.752 37.592 75.946 90.424 56.134 83.82 101.6 45.72
154 Chapter 9 - Appendices
1945 123.19 100.838 96.012 8.382 10.668 9.144 36.576 134.62 98.298 160.528 74.168 33.02
1946 85.598 57.404 34.036 30.48 49.784 9.906 36.576 51.816 66.04 172.72 27.94 33.274
1947 21.844 30.226 38.1 17.526 19.304 7.62 53.594 54.864 91.948 96.774 72.136 70.612
1948 61.214 16.002 11.684 0 17.78 15.748 96.266 60.452 57.15 87.884 24.13 12.954
1949 89.408 10.16 58.42 22.86 19.812 56.134 36.83 43.18 87.884 67.056 79.502 54.864
1950 1542.034 4.064 27.432 69.85 12.7 35.56 25.908 34.036 66.802 59.944 2.032 36.576
1951 37.846 22.86 9.144 36.068 4.064 52.832 18.288 54.356 76.454 35.56 28.956 7.112
1952 17.526 19.812 8.636 16.256 6.858 12.192 42.926 50.546 54.864 116.586 218.186 143.51
1953 24.892 90.678 5.334 37.846 51.816 18.796 4.064 122.174 61.722 97.028 69.596 55.88
1954 28.448 19.304 49.022 0.254 10.922 6.604 48.26 104.902 37.338 117.602 61.976 34.036
1955 53.086 26.416 11.176 27.94 9.144 70.104 23.368 102.108 49.276 94.234 3.048 65.024
1956 26.67 21.59 17.78 37.846 29.718 13.208 36.83 56.896 45.974 35.814 75.946 4.064
1957 23.876 21.082 10.668 19.304 9.398 2.286 57.404 77.216 109.22 92.202 152.4 59.69
1958 94.996 12.192 42.926 22.352 28.448 28.448 62.992 73.406 60.198 52.832 119.888 26.67
1959 20.574 45.72 15.748 88.646 16.256 3.048 84.328 41.656 42.672 57.15 81.788 108.458
1960 59.436 33.274 25.908 33.02 34.798 9.398 66.548 62.992 81.026 71.374 34.29 132.588
1961 91.948 65.278 17.78 37.338 24.384 22.098 76.962 48.006 134.62 53.848 92.71 49.53
1962 45.72 9.144 35.56 37.592 36.576 62.738 25.908 13.208 55.372 81.788 54.61 37.592
1963 52.324 38.1 37.084 25.146 10.668 42.672 47.244 55.88 62.738 79.756 11.43 112.776
1964 25.908 103.378 21.336 30.988 35.306 20.828 24.13 60.198 45.212 83.058 73.152 12.192
1965 24.892 22.86 63.5 29.972 40.64 44.45 58.674 51.816 114.554 42.164 39.37 66.548
1966 32.258 27.432 39.624 15.24 37.338 18.542 70.866 133.35 111.76 64.008 33.528 33.02
1967 23.876 39.116 7.62 18.542 7.366 43.18 21.844 92.71 136.906 60.706 71.882 85.09
1968 17.526 44.958 44.45 15.748 49.784 9.906 4.572 29.464 45.72 54.864 29.718 17.526
1969 9.906 59.436 32.512 9.144 30.988 10.922 30.734 24.892 21.082 217.17 96.774 199.39
1970 10.668 67.31 67.564 52.07 9.144 12.192 31.75 60.198 32.766 93.726 44.958 30.226
1971 17.526 82.55 34.798 25.908 48.26 29.718 49.53 49.53 40.386 107.442 37.592 42.926
1972 19.304 26.162 20.066 30.48 26.162 14.478 19.05 86.614 120.904 80.772 48.006 5.588
1973 21.336 32.004 17.018 15.24 58.42 51.562 8.128 53.34 71.628 40.386 44.958 51.054
1974 18.796 109.22 13.97 33.528 2.032 10.922 39.624 49.276 103.632 84.582 115.824 90.932
1975 63.5 85.852 41.656 55.118 49.276 23.622 25.146 12.954 88.392 48.26 25.908 103.378
1976 67.818 9.398 102.616 50.292 14.224 16.51 8.89 57.912 125.73 96.012 175.26 98.044
1977 111.76 75.692 54.102 40.64 55.626 24.13 7.874 61.468 42.926 89.154 9.906 3.048
1978 47.752 142.748 24.638 16.002 17.272 62.738 39.878 11.938 56.388 107.696 70.866 127
1979 56.134 3.048 44.45 24.13 17.78 27.94 46.482 29.972 87.63 128.016 216.408 133.604
1980 111.506 111.76 54.102 83.058 57.912 34.036 75.946 71.628 65.786 116.078 77.216 30.226
1981 169.672 56.388 152.908 2.54 43.688 14.478 94.742 94.742 55.88 59.944 114.808 107.95
1982 23.876 9.652 36.576 24.384 7.112 23.622 26.67 27.94 131.826 40.132 40.894 104.902
1983 37.084 39.878 17.018 26.67 13.716 12.446 25.4 55.88 49.784 61.722 221.488 5.588
1984 121.158 56.896 30.988 40.386 10.668 22.098 25.908 105.918 98.044 30.734 22.352 48.768
1985 107.442 25.908 93.726 37.592 37.846 21.59 74.422 62.484 77.724 23.368 22.86 59.944
1986 95.758 55.88 1.778 5.588 27.686 7.366 55.88 74.422 105.664 141.224 64.516 197.612
1987 159.004 74.93 62.23 19.558 26.924 27.178 27.432 14.986 130.048 174.498 59.182 50.546
1988 119.634 100.33 110.236 103.886 23.114 22.606 19.558 81.788 32.766 61.722 46.99 139.954
155 Chapter 9 - Appendices
1989 48.26 6.858 0.762 28.702 28.448 16.764 53.34 71.12 103.886 103.632 30.48 67.31
1990 77.978 39.116 8.382 10.16 27.94 30.48 16.256 60.706 156.972 42.164 4.064 37.592
1991 35.56 35.56 49.53 20.574 39.116 33.02 38.1 55.88 86.36 23.368 43.942 149.098
1992 109.982 29.21 95.504 12.446 8.382 11.43 34.544 66.802 26.924 21.082 114.3 65.024
1993 54.102 40.64 50.8 25.654 9.398 15.24 30.226 75.946 100.33 42.926 78.994 110.998
1994 24.384 33.274 62.23 19.05 27.432 19.812 29.972 20.32 84.582 50.292 77.724 97.536
1995 52.578 51.562 25.146 21.59 25.4 28.956 60.198 80.772 104.394 47.752 8.128 10.414
1996 4.572 55.118 5.334 24.638 11.684 31.496 6.858 44.704 81.28 31.242 9.652 22.352
1997 56.642 46.736 10.922 15.748 14.478 29.464 26.67 59.436 122.174 25.654 149.098 /
1998 / / / 19.812 58.674 36.83 42.418 97.79 51.816 66.04 73.406 33.528
1999 67.818 39.116 69.088 22.098 24.892 17.78 27.178 73.152 104.394 96.266 58.674 72.644
2000 46.99 14.478 46.99 9.144 3.556 16.51 65.786 28.448 56.642 65.278 126.492 126.238
2001 219.71 61.214 77.47 36.83 7.112 1.27 79.502 76.454 62.738 46.736 28.702 109.728
2002 68.072 81.28 2.286 6.096 13.462 8.382 40.132 51.816 92.71 187.706 182.626 75.946
2003 43.942 39.624 14.732 24.638 20.32 36.83 17.272 87.884 26.67 73.406 39.116 99.314
2004 24.638 35.052 17.018 26.162 23.114 13.716 19.05 4.572 120.65 74.422 68.834 88.9
2005 9.398 21.59 40.894 27.178 9.398 27.686 20.32 29.464 132.334 49.53 35.814 62.484
2006 19.304 92.71 27.178 22.86 11.684 40.894 17.018 100.076 50.546 42.926 13.208 100.838
2007 54.356 9.144 7.366 42.672 8.128 10.668 32.258 26.162 84.836 64.516 185.166 80.264
2008 30.988 49.53 23.876 24.892 27.94 14.478 61.214 43.18 93.218 57.15 43.434 29.21
2009 37.338 17.526 17.018 7.874 21.082 10.16 23.876 52.07 62.484 43.18 86.36 71.882
2010 5.842 47.752 66.294 57.15 7.874 20.574 88.138 53.848 35.306 108.458 71.374 38.354
Average 76.962 44.45 39.116 29.972 25.146 24.384 39.878 62.992 76.962 79.248 69.088 71.12
156