Aggregating over land surface heterogeneity systematically biases evapotranspiration estimates in large-scale evaporation models

<p>Land surface models are highly uncertain in estimating evapotranspiration (ET) fluxes, and differ substantially in their projections of how ET will evolve in the future. Biases in estimated ET fluxes will affect the partitioning between sensible and latent heat, and thus alter simulated temperatures and model predictions of droughts and heatwaves. One potential source of bias is the "aggregation bias" that arises whenever nonlinear processes, such as those that regulate ET fluxes, are modeled using averages of heterogeneous inputs. Here we demonstrate that this aggregation bias leads to substantial overestimates in ET fluxes in a typical large-scale land surface model. The proposed methodology can be used to correct for aggregation biases in ET estimates by quantifying the effects of finer-resolution spatiotemporal variability in ET drivers at each modeling time step, without explicitly representing sub-grid heterogeneities in large-scale land surface models. </p>

Averaging over spatiotemporal heterogeneity substantially biases evapotranspiration rates in a mechanistic large-scale land evaporation model

Evapotranspiration (ET) influences land–climate interactions, regulates the hydrological cycle, and contributes to the Earth's energy balance. Due to its feedback to large-scale hydrological processes and its impact on atmospheric dynamics, ET is one of the drivers of droughts and heatwaves. Existing land surface models differ substantially, both in their estimates of current ET fluxes and in their projections of how ET will evolve in the future. Any bias in estimated ET fluxes will affect the partitioning between sensible and latent heat and thus alter model predictions of temperature and precipitation. One potential source of bias is the so-called “aggregation bias” that arises whenever nonlinear processes, such as those that regulate ET fluxes, are modeled using averages of heterogeneous inputs. Here we demonstrate a general mathematical approach to quantifying and correcting for this aggregation bias, using the GLEAM land evaporation model as a relatively simple example. We demonstrate that this aggregation bias can lead to substantial overestimates in ET fluxes in a typical large-scale land surface model when sub-grid heterogeneities in land surface properties are averaged out. Using Switzerland as a test case, we examine the scale dependence of this aggregation bias and show that it can lead to an average overestimation of daily ET fluxes by as much as 10 % across the whole country (calculated as the median of the daily bias over the growing season). We show how our approach can be used to identify the dominant drivers of aggregation bias and to estimate sub-grid closure relationships that can correct for aggregation biases in ET estimates, without explicitly representing sub-grid heterogeneities in large-scale land surface models.

Property Price Modelling, Market Segmentation and Submarket Classifications: A Review

Accurate pricing of the property market is necessary to ensure effective and efficient decision making. Property price is typically modelled using the hedonic price model (HPM). This approach was found to exhibit aggregation bias due to its assumption that the coefficient estimate is constant and fails to consider variation in location. The aggregation bias is minimized by segmenting the property market into submarkets that are distinctly homogeneous within their submarket and heterogeneous across other submarkets. Although such segmentation was found to improve the prediction accuracy of HPM, there appear to be conflicting findings regarding what constitutes a submarket and how the submarkets are to be driven. This paper therefore reviews relevant literature on the subject matter. It was found that, initially, submarkets were delineated based on a priori classification of the property market into predefined boundaries. The method was challenged to be arbitrary and an empirically statistical data-driven property submarket classification was advocated. Based on the review, there is no consensus on the superiority of either of the methods over the another; a combination of the two methods can serve as a means of validating the effectiveness of property segmentation procedures for more accurate property price prediction.

Affective generalization from intergroup contact: Associations between contact-related and outgroup-related empathy, anxiety, and trust

This paper studied affective generalization from intergroup contact, namely when and how affective empathy, anxiety, and trust-related feelings towards specific outgroup members (contact-related affective variables) generalize to the whole outgroup (outgroup-related affective variables). We analysed affective generalization using multilevel models, with items of each affective variable nested within the individual, to avoid aggregation bias due to averaging across items, hence false positives. As hypothesized, we found strong associations, but not perfect correspondence, between contact-related and outgroup-related affective variables. Moreover, we found that category salience facilitated the affective generalization of affective empathy and trust, whereas the quantity of intimate contact facilitated the generalization of anxiety. These findings suggest that affective intergroup climate hinges upon specific contact interactions, and that it is vital to promote positive affective reactions during contact and the formation of more intimate relationships with outgroup members.