Landscapes: Painting Processes (Painting With Ev Hales Book 2)

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Thus, a rigorous understanding of landscape response to external forcings is important in the contexts of both adaptation and mitigation to climate change. Section 5 emphasizes the importance of the couplings among vegetation, sediment transport, and topography in forecasting future landscape states. Section 6 argues for the importance of long timescales and natural experiments in the geologic record in validating landscape response models before they can be used for forecasts. Section 7 discusses how landscape response models can be useful for evaluating alternative mitigation strategies.

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We also include five supplements that address specific scales and process zones. These supplements serve several purposes.

Examples of societally relevant landscape properties in need of better forecasting include: the elevation of the land surface, including its changes in space and time i. Therefore, it is useful to identify the most significant hazards to provide guidance on which hazards should be prioritized for the development of improved forecasting tools. The risk associated with these hazards can also be divided into a direct and an indirect risk. A direct risk is associated with loss of life and property.

An indirect risk is one that does not directly threaten a large number of lives or properties but may have catastrophic effects in concert with another process e. An example of an indirect risk is the potential release of CO 2 and methane into the atmosphere associated with thawing permafrost and loss of ground ice, thus accelerating the greenhouse effect.

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In our ranking, we focused primarily on direct risks. Secondary variables include runoff and infiltration i. The relationships among primary and secondary variables are, in some cases, clear. For example, warming of the polar regions and continued extraction of fluids from deltaic regions will drive relative sea level rise in many regions. In other cases, the relationships are less clear. For example, how precipitation changes are likely to alter land cover in the future is complex and incompletely understood.

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As such, Figure 1 underscores the need for collaboration among specific geoscientific research communities, including among geomorphologists and hydrologists, ecologists, and the human geographers who forecast land cover changes. Increasing temperatures in such regions are likely to drive land subsidence due to thawing permafrost, leading to a release of soil carbon that feeds back on the global climate system. Dust emission from such areas may increase in the future due to increased aridity and reduced vegetation cover. Such areas are likely to experience more frequent flooding in response to future increases in precipitation intensity.

However, changes in sediment supply from hillslopes could cause valley floors to incise, thus decreasing flood risk in areas adjacent to valley floors. Many of these areas will experience an increase in the frequency and magnitude of flooding and erosion, particularly in areas where land subsidence is occurring simultaneously with the rise in global mean sea level.

Urban areas are mapped based on the Global Land Cover database [ Hansen et al. As the population densities of these areas increase, it is likely that peak flood discharges will be greater due to the lower infiltration rates of impervious surfaces relative to natural surfaces. Hillslope areas are those not included in any of the other process zones. On hillslopes in arid and semiarid regions, increased potential evapotranspiration and fuel loads will likely trigger larger and more severe wildfires resulting in increased rates of soil erosion. The thresholds of mean annual temperature, rainfall, and elevation that define the boundaries between the process zones in Figure 2 are not unique.

By presenting Figure 2 , we are not suggesting that there is a unique global map of process dominance. Such a map will be useful in focusing research in those hotspots. In some cases, the hotspots of landscape response may occur at the boundaries between process domains.

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For example, it is reasonable to expect that land degradation e. The ESS community has a long and successful tradition of investigation at scales from individual hillslope segments to the scale of whole watersheds. However, the ESM community necessarily works at the global scale. For example, future vegetation states have been predicted, but these data usually predict vegetation type only. Instead, many existing models work with leaf area index LAI [e. Quantifying sediment yield in rivers has been a central topic in geomorphology for at least a century.

Sediment yields influence biogeochemical processes in wetlands [ Reddy et al. Most predictive models for global sediment yield focus on the suspended load component of the total load, which is the dominant component of the total load for most large rivers. Pelletier [ ] developed a model that includes mean monthly precipitation, soil texture, and vegetation cover quantified as LAI explicitly Figure 3.

Sediment yield in this model increases in direct proportion to average rainfall and decreases exponentially with increasing LAI. It is straightforward to include the effects of agriculture in the Pelletier [ ] model, at least in a simplified way, as an example of how the land use changes could be explicitly included in current land surface response models.

This is a simplified approach because croplands are bare only part of the time.

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Neglecting plant cover during the growing season likely leads to an overprediction of sediment yield, but this effect is somewhat counteracted by the fact that cropland soils are disturbed by tillage, thus reducing their shear strength relative to bare undisturbed surfaces. When agriculture is included in the model, the region of largest sediment yields in the U. The challenge of quantifying the geomorphic responses to land use changes reflects the uncertainty in both how sediment yield relates to land use and how land use should be quantified for input into models. Existing global gridded datasets for land use predict the locations and density of transitions between primary land and agricultural land, but it remains unclear how best to quantify the relationships between erodibility and land use type.

The effects of land use changes have been incorporated into empirical models for hillslope sediment yields such as the Universal Soil Loss Equation USLE [ Wischmeier and Smith , ]. The effects of dams in storing sediments released from uplands have not been well quantified on a regional or global basis, but the effect is substantial. Syvitski and Milliman [ ] included anthropogenic effects on sediment yield with drainage basin—specific coefficients that depended on population density and GNP per capita. Nonlinear systems respond at a variable rate to changes in external forcings.

An example of gradual amplification of a forcing change is dust entrainment, which is a major player in Earth's energy balance because dust is a radiatively important aerosol and exerts a significance influence on human health. Bed load sediment transport rates relate to turbulent bed shear stresses in a way that is both nonlinear and threshold dominated [ Gomez and Church , ]. In the southwestern U.

In recent decades, small increases in springtime temperatures have exponentially increased wildfire size and severity [ Westerling et al. In many semiarid areas of the western U. Recent studies of tidal marsh dynamics highlight the importance of multiple stable states, and the associated potential for instability.

For example, vegetation stabilizes tidal marshes by dissipating wave energy and producing and trapping sediment. If the geomorphic response is threshold dominated Figure 5 a , the distribution of responses is partitioned into two very distinct sets of likely responses: both minor and major geomorphic responses may become plausible, thus increasing the degree of uncertainty of the forecast Figure 5 d. Stable landscapes reflect complex feedbacks among surface processes, climate, and vegetation. Slope stability is enhanced through lateral reinforcement by roots [ Schwarz et al.

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Vegetation cover fundamentally influences geomorphic processes and landforms, e. For example, the stability of barrier islands depends on the ability of foredunes to develop in both height and extent [ Houser et al. Resiliency of an island, therefore, is dependent on the regrowth of vegetation through the reemergence of buried plants, seed banks, and colonization from adjacent areas. The case of dune grasses also exemplifies the widely applicable point that we need to understand not just how vegetation affects physical processes, but also how vegetation responds to physical processes [e. Indirect influences of climatic changes and direct human modification of the landscape can alter vegetation cover, which can lead to a new landscape.

The stability and morphology of coastal marshes as sea level rises depend on the ability of salt marsh vegetation to promote sediment deposition and limit erosion [ Morris et al. More extreme modifications such as deforestation, alteration in vegetation type e. In areas of steep topography, this has resulted in large and often damaging debris flows [ Cannon et al. Changes in vegetation can result from changes in sediment supply and water discharge at a distant location. For example, there has been a large loss of cottonwood trees downstream of dams because of a disconnection from the floodplain, caused by both channel incision due to a lack of sediment supply from upstream and lack of inundating flows due to flow regulation [e.

In addition to these changes, invasive species such as salt cedar have taken over in areas downstream of dams in part because of these changes in hydrology [ Friedman et al. Changes in vegetation often trigger dramatic landscape responses by altering the forces both driving and resisting erosion. Changes in resisting forces effectively alter the thresholds between the stable states of landscapes, whereas changes in driving forces alter the ability of the system to overcome those thresholds.

Reducing vegetation cover on hillslopes generally increases erosion by changing both the driving and resisting forces; runoff increases because interception, surface roughness, and water consumption are all reduced, while root cohesion that inhibits erosion is also reduced [e. Changes in vegetation type may directly alter root cohesion, surface roughness, and geomorphic process dominance e. A reduction in transpiration could increase the vapor pressure deficit of the air, thereby exacerbating vegetative water stress and possibly further reducing vegetation cover through wildfire. The importance of vegetation is quite evident in arid and semiarid environments where it limits erosion by wind and water and feeds back to the local climate through evapotranspiration. Specifically, the loss of vegetation through prolonged drought further limits moisture availability through a loss in soil and an increase in the vapor pressure deficit, which increases the potential for degradation through both water and wind [ Middleton and Thomas , ; Breshears et al.

The continued loss of biodiversity may decrease system resiliency to periodic droughts, which may increase the potential for these systems to jump to an irreversible state of desertification.