Projecting future streamflow under changing climate and urban land cover across the UK

Author(s):  
Shasha Han ◽  
Louise Slater

<p>Changes in precipitation and land cover are important drivers of change in catchment streamflow, yet quantifying their influence remains a major challenge. This work aims to understand how streamflow may evolve under different scenarios of future precipitation and urbanization across the UK. A collection of catchments from the National River Flow Archive (NRFA) that have experienced significant changes in flows and urbanization were selected. Both historical observations and future projections of precipitation and urban land cover were extracted within each study catchment, for different emissions and socio-economic scenarios including Representative Concentration Pathways (RCPs) and Shared Socio-Economic Pathways (SSPs). Distributional regression models – Generalised Additive Models for Location Scale and Shape (GAMLSS) – were developed using historical precipitation, land cover, and streamflow, and employed to project future streamflow using bias-corrected projections of precipitation and land cover. The results improve our understanding of streamflow response to climate and land cover changes and provide further insights for water resources management and land use development.</p>

2008 ◽  
Vol 74 (10) ◽  
pp. 1213-1222 ◽  
Author(s):  
Jeffrey T. Walton

2016 ◽  
Vol 3 (2) ◽  
pp. 127
Author(s):  
Jati Pratomo ◽  
Triyoga Widiastomo

The usage of Unmanned Aerial Vehicle (UAV) has grown rapidly in various fields, such as urban planning, search and rescue, and surveillance. Capturing images from UAV has many advantages compared with satellite imagery. For instance, higher spatial resolution and less impact from atmospheric variations can be obtained. However, there are difficulties in classifying urban features, due to the complexity of the urban land covers. The usage of Maximum Likelihood Classification (MLC) has limitations since it is based on the assumption of the normal distribution of pixel values, where, in fact, urban features are not normally distributed. There are advantages in using the Markov Random Field (MRF) for urban land cover classification as it assumes that neighboring pixels have a higher probability to be classified in the same class rather than a different class. This research aimed to determine the impact of the smoothness (λ) and the updating temperature (Tupd) on the accuracy result (κ) in MRF. We used a UAV VHIR sized 587 square meters, with six-centimetre resolution, taken in Bogor Regency, Indonesia. The result showed that the kappa value (κ) increases proportionally with the smoothness (λ) until it reaches the maximum (κ), then the value drops. The usage of higher (Tupd) has resulted in better (κ) although it also led to a higher Standard Deviations (SD). Using the most optimal parameter, MRF resulted in slightly higher (κ) compared with MLC.


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