Land Cover Change and Hydroclimatic Deviation

Land Cover Change and Hydroclimatic Deviation: A Detailed Examination within the Budyko Framework

O'Hanrahan, Mike (TU Delft Civil Engineering & Geosciences)

Hrachowitz, M. (mentor)
Vizcaino, M. (graduation committee)
Sprokkereef, Eric (graduation committee)
Stam, Jasper (graduation committee)
Bouaziz, Laurène (graduation committee)

Delft University of Technology

Delft University of Technology

Civil Engineering | Environmental Engineering

CATAPUC

2023-09-21

Predicting streamflow in a changing climate poses significant challenges for traditional hydrological models. Static parameter sets result from model calibrations over historical data that increasingly encounter the non-stationary impacts on the hydrological system. Endeavouring toward non-stationary model parameters by incorporating time-adaptive ecosystem-scale root zone storage parameters shows promise for modelling systems under change. The ongoing CATAPUC project aims to further develop, refine, and implement this adaptive modelling approach. This paper continues to build upon this body of work by investigating the evidence for land cover change impacts on root zone storage capacity, focusing specifically on uncertainties in the evaporative balance. Combining long-term hydrometeorological data from the primary study area, the Meuse basin, with the large-sample CAMELS datasets (GB and US) analyses were performed across 283 catchments. Applying the vector operations from Velde et al. (2014) and Jaramillo et al. (2018) to decadal changes within the Budyko framework, we separate climate-related evaporative changes. We isolated the residual component of evaporative change, which is the unknown component affecting parameter estimations. Our findings indicate that this residual component is twice as prominent in evaporative change as the climate component. The aim is to test if land cover changes have contributed significantly to the residual component of evaporative change. A multi-scale approach to land cover change analysis is adopted to bridge the data gap from 1984 to 2019. Implementing ensemble machine-learning methods on Landsat imagery with Google Earth Engine, we develop annual timeseries of (30m) high-resolution multi-class data for this period with accuracies up to 86% for 117 catchments (Meuse and GB). Resulting land cover change estimates suggest that Meuse Basin urbanisation rates may have been significantly underestimated for this period. The Meuse Basin over the most recent 20-year period is found to deviate anomalously in actual evaporation compared with the large sample. A distinct spatial pattern reveals a concentration of deviations in the east of the basin. Calculated by Tempel (2023), the anomaly in the basin corresponds with a relative median error in root zone storage capacity change of −14%. Low flow analysis is performed to remove the possibility that deviations are affected by anomalous contribution to streamflow from additional subsurface flow. We observed that the increase in low flow variability over the same period exhibits a spatial pattern similar to the Meuse Basin anomaly. We found no meaningful causal relationships linking multi-scale interdecadal changes in forest, agriculture, and urban land classes to the observed deviations in the large sample datasets. The implication of this study is that land cover change is likely not a significant driver of evaporative changes, specifically, errors, throughout the record available.

Hydrology
Hydrological Modelling
Root zone storage capacity
Water Balance
Budyko Framework
Remote Sensing
Landsat
Machine Learning
Land Cover
Meuse
CAMELS

http://resolver.tudelft.nl/uuid:0ec1f39b-9b0b-4187-94cf-8440868c916d

Student theses

master thesis

© 2023 Mike O'Hanrahan