Analyzing Temporal Trends of Urban Evaporation Using Generalized Additive Models

Abstract

This study aimed to gain new insights into urban hydrological balance (in particular, the evaporation from paved surfaces). Hourly evaporation data were obtained simultaneously from two high-resolution weighable lysimeters. These lysimeters are covered in two pavement sealing types commonly used for sidewalks in Berlin, namely cobble-stones and concrete slabs. A paired experiment in field conditions is designed to determine the mechanism by which these two types of soil sealing affect the evaporation rate under the same climatic conditions. A generalized additive model (GAM) is applied to explain how the climatic conditions interact with soil sealing and to evaluate the variation of evaporation rate according to pavement type. Moreover, taking the advantage of the fact that the experimental design is paired, the study fits a new GAM where the response variable is the difference between the evaporation rate from the two lysimeters and its explanatory variables are the climatic conditions. As a result, under the same climatic conditions, cobble-stones are more prone to increasing the evaporation rate than concrete slabs when the precipitation accumulated over 10 h, solar radiation, and wind speed increases. On the other hand, concrete slabs are more inclined to increase the evaporation rate than cobblestones when the relative humidity increases. GAM represents a robust modeling approach for comparing different sealing types in order to understand how they alter the hydrological balance.

Diversity of Glossinidae (Diptera) species in The Gambia in relation to vegetation

“…Models were fitted using the observed number of vectors as the outcomes and Generalized Addictive Models (GAM) were used to model the impact of vegetation cover type on the abundance of Glossinidae species, Stomoxys calcitran and Atylotus agrestis . This model offers a middle ground between simple models, such as linear regression model and more compound machine learning models such as neural networks ( Aljoumani et al, 2022 ). They can be fitted to complex, nonlinear relationships and produce good predictions, while still being able to produce inferential statistics and comprehend and elucidate the underlying model structure.…”