Datadrevne prognoser for vandstand i vandløb - og undersøgelse af værdien af jordfugtighed som input

Westarp, Samuel

4. semester

Vand og Miljø (cand.polyt.), Kandidat

2022

2022-06-09

Naervaerende rapport omhandler forudsigelse af vandstand i rurale vandløb indtil 36 timer frem ved hjaelp af rent datadrevne modeller med oversvømmelsesvarsling som mål. Det undersøges om realtidsmålinger af jordfugtighedsmålinger kan bidrage til forudsigelseskvaliteten. I et lille opland ses en forbedring med jordfugtighedsdata, men i to større oplande er der ikke evident forbedring. Det konkluderes at den anvendte neurale netvaerksarkitektur kan levere pålidelige prognoser og dermed grundlag for effektiv varsling. Et traenet netvaerk kan overføres til andre vandløb med et mini- mum af data.

Climate change is expected to result in increased rainfall intensities in Danmark and therefor the risk of flooding of rivers causing damage to urban areas on their way will also increase. Modelling of the effects on long terms is important for the physical planing of the areas surrounding the rivers. But short term forecasts of the water level of the rivers are essential for early flood warning systems. This project has focused on short term forecasting models up to 36 hour lead-time for river water level based on machine learning investigating wether such models could benefit from soil moisture measured in real time. The soil moisture in the vadose zone affect the path that precipiation takes to the rivers and therefor it is assumed that knowledge of the soil moisture will help models predict the water level respons to precipitation. The two rivers Elling Å and Romdrup Å in Nothern Jutland have been chosen as cases for the project mainly due to data availability as these rivers do not cause issues with flooding. It is found that measured soil moisture significantly explain some of the variantion seen in the water level respons to precipitation events measured at station Brinkhus N in a small creek part of Elling Å. Similar but weaker results are seen for Romdrup Å although a calculatede estimate of the soil moisture, the so called ’drought index’ published by DMI, give similar results. The results transfer to the forecasting models build using a neural network architechture called TemporalFusionTransformer (TFT), as models for Romdrup Å trained with the drought index do not improove when soil moisture data is included. For Brinkhus N we find slightly better performance with soil moisture in the model. The TFT-architecture produce forecasting models performning at NSE > 0.9 with a 24 hour lead-time over 7 to 9 month of test data. The models are based on weather data published in 10km grid format published free by the Danish Meteorologic Institute, who also plan on releasing forecast data later in 2022. Models trained on data from Elling Å and Romdrup Å are succesfully transfered to four different rivers of varying size and geologi and geography of the watersheds. It results in a big performance gain with little training data compared to models trained from scratch.

Prognosemodel ; Vandstand ; Vandløb ; oversvømmelse

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