DE EFFICIËNTE WEG NAAR KLIMAATADAPTIEVE STEDEN

Jonas
Blancke

Het klimaat staat niet stil. De wereldwijde opwarming nadert snel de grens van 1,5 °C boven het pre-industriële niveau, de limiet die beleidsmakers in het Klimaatakkoord van Parijs hebben vastgelegd. Nieuwsberichten tonen reeds lang de impact van deze nieuwe realiteit. Dit was ook het geval in 2024, van hevige regenval in Oost-Europa tot extreme hittegolven in Zuid-Europa. Aanpassingen zijn onvermijdelijk, maar de vragen blijven: hoe, waar en wanneer? Hiervoor is in eerste instantie klimaatdata nodig, ook voor onze steden. Maar hier wringt het schoentje: de resolutie van globale modellen schiet tekort om ons ook inzichten te geven over de temperatuurverschillen binnen een stad. 

Traditionele stedelijke klimaatmodellen daarentegen leveren deze details wel, maar ze zijn traag en kosten veel energie. Het simuleren van klimaatdata draagt hierdoor ironisch genoeg een beetje bij aan het klimaatprobleem. Reden te meer voor Jonas Blancke om in zijn masterproef een sneller en energiezuiniger stedelijk klimaatmodel te ontwikkelen dat gebruik maakt van bestaande data en artificiële intelligentie. 

 

STEDEN ZIJN HOTSPOTS

Waarom focussen op steden? Niet alleen huisvesten zij het grootste deel van de wereldbevolking, maar ook groeien stedelijke gebieden voortdurend. Daarnaast speelt het stedelijk hitte-eilandeffect een belangrijke rol. Dit fenomeen doet zich voor doordat materialen zoals asfalt en beton overdag grote hoeveelheden warmte absorberen. 's Nachts komt deze warmte terug langzaam vrij. Hierdoor kunnen de temperaturen in steden tot wel 10 °C hoger oplopen dan in het omliggende platteland. Deze extra hitte komt bovenop de wereldwijde klimaatopwarming. Tijdens hittegolven worden de gevolgen in steden dan ook schrijnend zichtbaar, met een toename van hitte-gerelateerde sterfgevallen, vooral onder kwetsbare groepen zoals ouderen en kinderen. 

 

GEDULD IS NIET ALTIJD EEN SCHONE ZAAK  

De traditionele methode om temperatuur te berekenen voor een klimaatscenario steunt op complexe fysische vergelijkingen die niet eenvoudig door een gewone computer kunnen worden berekend. Daarom worden ze uitgevoerd op grote krachtige computers, maar zelfs dan kost het veel tijd, vooral voor grote gebieden en lange periodes. Zo lopen huidige modellen dan ook soms achter de feiten aan. Het kan bijvoorbeeld een jaar duren om een gedetailleerde berekening te maken voor een zomer. Bovendien is het energieverbruik van deze simulaties enorm. 

Een supercomputer wordt ingezet voor het uitvoeren van traditionele modelsimulaties.

Een supercomputer wordt ingezet voor het uitvoeren van traditionele modelsimulaties.

Dit probleem kan worden opgelost door de complexe fysica met machine learning te vervangen door statistische relaties die veel minder rekenkracht vergen. Het gebruikte algoritme leert de verbanden tussen de temperatuur enerzijds en ruimtelijke (zoals gebouwhoogte) en temporele (zoals windsnelheid) variabelen anderzijds. Dit gebeurt via een reeks beslissingen die splitsingen maken op basis van de waarden van deze variabelen. Het resultaat van deze zogenaamde beslissingsbomen is dan de voorspelde temperatuur voor een specifiek punt in tijd en ruimte. Het grote voordeel van dit model is dat het, zodra het getraind is, simulaties meer dan een miljoen keer sneller kan uitvoeren dan traditionele modellen. Dit kan bovendien op een gewone laptop. 

 

VAN EEN ENKELE STAD TOT VOLLEDIG EUROPA 

Is het gebruik van machine learning nieuw in stedelijke klimaatstudies? Neen, maar het verschil zit hem in de toepasbaarheid van het model. Voorgaande modellen trainden hun model met temperatuurdata van één stad, afkomstig van temperatuursimulaties of observaties. Hierdoor zullen deze echter minder accuraat zijn wanneer je het zou willen gebruiken voor een totaal andere stad. 

Het nieuw voorgestelde model daarentegen steunt op een gigantische openbare dataset, afkomstig van temperatuursimulaties uit een traditioneel klimaatmodel. Concreet werd hiervoor het UrbClim-model gebruikt, dat werd ontwikkeld door het Vlaams Instituut voor Technologisch Onderzoek (VITO). Door het model te trainen op verschillende steden binnen Europa is het generiek toepasbaar over alle grote steden in Europa, zelfs in regio’s waarvoor momenteel nauwelijks temperatuurdata beschikbaar is.

En wat blijkt? Dit machine learning model weet de temperatuurpatronen en variaties uit UrbClim verrassend goed na te bootsen. Voor temperatuurvoorspellingen op hectare-niveau is het gemiddelde verschil tussen beide modellen minder dan 1 °C. Een interessant resultaat is dat data van slechts enkele steden al voldoende is om een model te trainen dat betrouwbare voorspellingen kan maken voor andere steden. Het is dus zelfs niet eens noodzakelijk om te steunen op een hele grote dataset dat afkomstig is van een duur traditioneel model. Het is echter wel belangrijk om aandacht te hebben voor de representativiteit van de geselecteerde steden.  Zo zal een model getraind op steden uit Zuid-Europa minder goed presteren voor steden met een totaal ander klimaat, zoals bijvoorbeeld Reykjavik in Ijsland. 

Vergelijking tussen data van een globaal klimaatproduct (links) en het machine learning model (rechts), dat nauw aansluit bij UrbClim.

Vergelijking tussen data van een globaal klimaatproduct (links) en het machine learning model (rechts), dat nauw aansluit bij UrbClim.

 

STADSPLANNING VOOR MORGEN

Op basis van de verkregen resultaten kunnen we zelfs nog een stap verder dromen. We kunnen het model zo aanpassen dat het algemene toekomstige klimaatscenario’s specifiek voor steden kan simuleren. Deze informatie vormt een waardevol startpunt voor het duurzaam inrichten van steden, afgestemd op de specifieke behoeften van elke wijk. Door daarbij extra oog te hebben voor de meest kwetsbare groepen, kunnen we stedelijke omgevingen creëren die niet alleen klimaatbestendig zijn, maar ook sociaal rechtvaardig en leefbaar voor iedereen.

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