Forest cover loss in the tropics is well known to cause warming at deforested sites, with maximum temperatures being particularly sensitive. Forest loss causes warming by altering local energy balance and surface roughness, local changes that can propagate across a wide range of spatial scales. Consequently, temperature increases result from not only changes in forest cover at a site, but also by the aggregate effects of non-local forest loss. We explored such non-local warming within Brazil's Amazon and Cerrado biomes, the region with the world's single largest amount of forest loss since 2000. Two datasets, one consisting of in-situ air temperature observations and a second, larger dataset consisting of ATs derived from remotely-sensed observations of land surface temperature, were used to quantify changes in maximum temperature due to forest cover loss at varying length-scales. We considered undisturbed forest locations (1 km 2 in extent), and forest loss trends in annuli ('halos'), located 1-2 km, 2-4 km, 4-10 km and 10-50 km from these undisturbed sites. Our research finds significant and substantial non-local warming, suggesting that historical estimates of warming due to forest cover loss under-estimate warming or mis-attribute warming to local change, where non-local changes also influence the pattern of temperature warming.
The interleaving of impermeable and permeable surfaces along a runoff flow path controls the hillslope hydrograph, the spatial pattern of infiltration, and the distribution of flow velocities in landscapes dominated by overland flow. Predictions of the relationship between the pattern of (im)permeable surfaces and hydrological outcomes tend to fall into two categories: (i) generalized metrics of landscape pattern, often referred to as connectivity metrics, and (ii) direct simulation of specific hillslopes. Unfortunately, the success of using connectivity metrics for prediction is mixed, while direct simulation approaches are computationally expensive and hard to generalize. Here we present a new approach for prediction based on emulation of a coupled Saint Venant equation‐Richards equation model with random forest regression. The emulation model predicts infiltration and peak flow velocities for every location on a hillslope with an arbitrary spatial pattern of impermeable and permeable surfaces but fixed soil, slope, and storm properties. It provides excellent fidelity to the physically based model predictions and is generalizable to novel spatial patterns. The spatial pattern features that explain most of the hydrological variability are not stable across different soils, slopes, and storms, potentially explaining some of the difficulties associated with direct use of spatial metrics for predicting landscape function. Although the current emulator relies on strong assumptions, including smooth topography, binary permeability fields, and only a small collection of soils, slope, and storm scenarios, it offers a promising way forward for applications in dryland and urban settings and in supporting the development of potential connectivity indices.
Describing the effects of surface roughness on flow resistance remains a first-order challenge for modeling shallow overland flow using the Saint Venant equations (SVE). This challenge has resulted in a proliferation of roughness schemes relating the properties of a uniform rough surface to bulk velocity and resistance, making selection of an appropriate roughness scheme daunting-especially on heterogeneous surfaces. For hydrological predictions, a roughness scheme is appropriate if the resulting SVE solutions predict outcomes such as water balance partitioning between runoff and infiltration, the hillslope hydrograph, and the discharge velocity with uncertainties that are less than or comparable to uncertainty in measurements. To assess the sensitivity of hydrological predictions to the choice of a roughness scheme, multiple schemes are first calibrated to each other under equilibrium flow conditions while imposing the kinematic wave approximation at the outlet. This approach yields analytical relations between the parameters of different roughness schemes when applied to the same hillslope and discharge. The approach is used to provide a parameterization of five commonly used roughness schemes for a common surface, and the predictions of the schemes for multiple patchily vegetated hillslopes are compared. The results suggest that, once calibrated, there is minimal prediction sensitivity to the choice of roughness scheme across a wide range of rainfall conditions. Operationally, the results demonstrate that parameterizing a roughness schemes is of higher significance for predicting hydrological patterns than the precise formulation employed.
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