Rafael L. Bras

The fate of Amazonian ecosystems over the coming century arising from changes in climate, atmospheric CO2, and land use

There is considerable interest in understanding the fate of the Amazon over the coming century in the face of climate change, rising atmospheric CO2 levels, ongoing land transformation, and changing fire regimes within the region. In this analysis, we explore the fate of Amazonian ecosystems under the combined impact of these four environmental forcings using three terrestrial biosphere models (ED2, IBIS, and JULES) forced by three bias-corrected IPCC AR4 climate projections (PCM1, CCSM3, and HadCM3) under two land-use change scenarios. We assess the relative roles of climate change, CO2 fertilization, land-use change, and fire in driving the projected changes in Amazonian biomass and forest extent. Our results indicate that the impacts of climate change are primarily determined by the direction and severity of projected changes in regional precipitation: under the driest climate projection, climate change alone is predicted to reduce Amazonian forest cover by an average of 14%. However, the models predict that CO2 fertilization will enhance vegetation productivity and alleviate climate-induced increases in plant water stress, and, as a result, sustain high biomass forests, even under the driest climate scenario. Land-use change and climate-driven changes in fire frequency are predicted to cause additional aboveground biomass loss and reductions in forest extent. The relative impact of land use and fire dynamics compared to climate and CO2 impacts varies considerably, depending on both the climate and land-use scenario, and on the terrestrial biosphere model used, highlighting the importance of improved quantitative understanding of all four factors - climate change, CO2 fertilization effects, fire, and land use - to the fate of the Amazon over the coming century.

Ecohydrologic role of solar radiation on landscape evolution

Solar radiation has a clear signature on the spatial organization of ecohydrologic fluxes, vegetation patterns and dynamics, and landscape morphology in semiarid ecosystems. Existing landscape evolution models (LEMs) do not explicitly consider spatially explicit solar radiation as model forcing. Here, we improve an existing LEM to represent coupled processes of energy, water, and sediment balance for semiarid fluvial catchments. To ground model predictions, a study site is selected in central New Mexico where hillslope aspect has a marked influence on vegetation patterns and landscape morphology. Model predictions are corroborated using limited field observations in central NM and other locations with similar conditions. We design a set of comparative LEM simulations to investigate the role of spatially explicit solar radiation on landscape ecohydro-geomorphic development under different uplift scenarios. Aspect-control and network-control are identified as the two main drivers of soil moisture and vegetation organization on the landscape. Landscape-scale and long-term implications of these short-term ecohdrologic patterns emerged in modeled landscapes. As north facing slopes (NFS) get steeper by continuing uplift they support erosion-resistant denser vegetation cover which leads to further slope steepening until erosion and uplift attains a dynamic equilibrium. Conversely, on south facing slopes (SFS), as slopes grow with uplift, increased solar radiation exposure with slope supports sparser biomass and shallower slopes. At the landscape scale, these differential erosion processes lead to asymmetric development of catchment forms, consistent with regional observations. Understanding of ecohydrogeomorphic evolution will improve to assess the impacts of past and future climates on landscape response and morphology.

Estimation of Net Radiation From the Moderate Resolution Imaging Spectroradiometer Over the Continental United States

Net radiation, at the Earths surface, is a key variable of interest in fields such as hydrology, climate research, and agriculture. Retrieval algorithms for estimation of the surface radiation budget (SRB) from remote sensing data generally suffer from two major shortcomings: difficulty in dealing with cloudy-sky conditions and reliance on study-site specific ancillary ground data. In this paper, we use the methodology of Bisht and Bras (BB10) to estimate SRB and its components, using only remote sensing data under all sky conditions. The BB10 framework is applied over seven sites of the Surface Radiation Budget network in the CONtinental United States (CONUS), along with 21 sites of the Atmospheric Radiation Measurement program in the Southern Great Plains. The data from both the Aqua and Terra satellites are used for entire 2006. The study examines instantaneous upwelling and downwelling shortwave, longwave, net shortwave, and net radiations, as well as daily average net shortwave and net radiations. The root-mean-square errors of estimated daily average net radiation and daily average net shortwave radiation when compared to ground observations are 52.42 and 52.21 W · m-2, respectively. An example of the retrieved instantaneous and daily average net radiation is also presented, which highlights the limitation of using only polar-orbiting satellite data in estimating the diurnal cycle of net radiation. Two adaptations to the algorithm are presented that make the production of SRB estimates over the CONUS feasible. Finally, the methodology is applied to produce daily SRB maps for the CONUS, and monthly SRB maps are presented.

Identifying the optimal spatially and temporally invariant root distribution for a semiarid environment

[1]xa0In semiarid regions, the rooting strategies employed by vegetation can be critical to its survival. Arid regions are characterized by high variability in the arrival of rainfall, and species found in these areas have adapted mechanisms to ensure the capture of this scarce resource. Vegetation roots have strong control over this partitioning, and assuming a static root profile, predetermine the manner in which this partitioning is undertaken.A coupled, dynamic vegetation and hydrologic model, tRIBS + VEGGIE, was used to explore the role of vertical root distribution on hydrologic fluxes. Point-scale simulations were carried out using two spatially and temporally invariant rooting schemes: uniform: a one-parameter model and logistic: a two-parameter model. The simulations were forced with a stochastic climate generator calibrated to weather stations and rain gauges in the semiarid Walnut Gulch Experimental Watershed (WGEW) in Arizona. A series of simulations were undertaken exploring the parameter space of both rooting schemes and the optimal root distribution for the simulation, which was defined as the root distribution with the maximum mean transpiration over a 100-yr period, and this was identified. This optimal root profile was determined for five generic soil textures and two plant-functional types (PFTs) to illustrate the role of soil texture on the partitioning of moisture at the land surface. The simulation results illustrate the strong control soil texture has on the partitioning of rainfall and consequently the depth of the optimal rooting profile. High-conductivity soils resulted in the deepest optimal rooting profile with land surface moisture fluxes dominated by transpiration. As we move toward the lower conductivity end of the soil spectrum, a shallowing of the optimal rooting profile is observed and evaporation gradually becomes the dominate flux from the land surface. This study offers a methodology through which local plant, soil, and climate can be accounted for in the parameterization of rooting profiles in semiarid regions.

Hydrologic data assimilation with a hillslope‐scale‐resolving model and L band radar observations: Synthetic experiments with the ensemble Kalman filter

United States. Army Research Office (U.S. Army RDECOM ARL Army Research Office under grant W911NF-04-1-0119)

Shrunken Locally Linear Embedding for Passive Microwave Retrieval of Precipitation

This paper introduces a new Bayesian approach to the inverse problem of passive microwave rainfall retrieval. The proposed methodology [called the shrunken locally linear embedding algorithm for retrieval of precipitation (ShARP)] relies on a regularization technique and makes use of two joint dictionaries of coincident rainfall profiles and their corresponding upwelling spectral radiative fluxes. A sequential detection-estimation strategy is adopted, which basically assumes that similar rainfall intensity values and their spectral radiances live close to some sufficiently smooth manifolds with analogous local geometry. The detection step employs a nearest neighbor classification rule, whereas the estimation scheme is equipped with a constrained shrinkage estimator to ensure the stability of retrieval and some physical consistency. The algorithm is examined using coincident observations of the active precipitation radar and the passive microwave imager onboard the TRMM satellite. We present promising results of instantaneous rainfall retrieval for some tropical storms and mesoscale convective systems over ocean, land, and coastal zones. We provide evidence that the algorithm is capable of properly capturing different storm morphologies including high-intensity rain cells and trailing light rainfall, particularly over land and coastal areas. The algorithm is also validated at an annual scale for calendar year 2013 versus the standard (version 7) radar (2A25) and radiometer (2A12) rainfall products of the TRMM satellite.

Topographic variability and the influence of soil erosion on the carbon cycle

Soil erosion, particularly that caused by agriculture, is closely linked to the global carbon (C) cycle. There is a wide range of contrasting global estimates of how erosion alters soil-atmosphere C exchange. This can be partly attributed to limited understanding of how geomorphology, topography, and management practices affect erosion and oxidation of soil organic C (SOC). This work presents a physically based approach that stresses the heterogeneity at fine spatial scales of SOC erosion, SOC burial, and associated soil-atmosphere C fluxes. The Holcombes Branch watershed, part of the Calhoun Critical Zone Observatory in South Carolina, USA, is the case study used. The site has experienced some of the most serious agricultural soil erosion in North America. We use SOC content measurements from contrasting soil profiles and estimates of SOC oxidation rates at multiple soil depths. The methodology was implemented in the tRIBS-ECO (Triangulated Irregular Network-based Real-time Integrated Basin Simulator-Erosion and Carbon Oxidation), a spatially and depth-explicit model of SOC dynamics built within an existing coupled physically based hydro-geomorphic model. According to observations from multiple soil profiles, about 32% of the original SOC content has been eroded in the study area. The results indicate that C erosion and its replacement exhibit significant topographic variation at relatively small scales (tens of meters). The episodic representation of SOC erosion reproduces the history of SOC erosion better than models that use an assumption of constant erosion in space and time. The net atmospheric C exchange at the study site is estimated to range from a maximum source of 14.5u2009gu2009m−2u2009yr−1 to a maximum sink of −18.2u2009gu2009m−2u2009yr−1. The small-scale complexity of C erosion and burial driven by topography exerts a strong control on the landscapes capacity to serve as a C source or a sink.

A model of energy budgets over water, snow, and ice surfaces

The recently formulated maximum entropy production (MEP) model over land surfaces has been generalized to water-snow-ice surfaces. Analytical solutions of energy budget in terms of the partition of surface radiative fluxes into (turbulent and/or conductive) heat fluxes at the earth-atmosphere interface are derived as functions of surface temperature (e.g., sea surface temperature). The MEP model does not require data of wind speed, air temperature-humidity, and surface roughness. Test of the MEP model using observations from several field experiments is encouraging. Potential applications of the proposed model for understanding long-term trends in surface heat fluxes and for closing global surface energy budget at the Earths atmosphere are suggested.

Evaluation of ShARP Passive Rainfall Retrievals over Snow-Covered Land Surfaces and Coastal Zones

AbstractUsing satellite measurements in microwave bands to retrieve precipitation over land requires proper discrimination of the weak rainfall signals from strong and highly variable background Earth surface emissions. Traditionally, land retrieval methods rely on a weak signal of rainfall scattering on high-frequency channels and make use of empirical thresholding and regression-based techniques. Because of the increased surface signal interference, retrievals over radiometrically complex land surfaces—snow-covered lands, deserts, and coastal areas—are particularly challenging for this class of retrieval techniques. This paper evaluates the results by the recently proposed Shrunken Locally Linear Embedding Algorithm for Retrieval of Precipitation (ShARP) using data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The study focuses on a radiometrically complex region, partly covering the Tibetan highlands, Himalayas, and Ganges–Brahmaputra–Meghna River basins, which is unique in terms of it...

Statistics of multifractal processes using the maximum entropy method

[1]xa0We have demonstrated that the maximum entropy (ME) principle can be used as a general inference algorithm to derive the probability distributions at different scales of a multifractal process characterized by its scaling properties such as multiscaling moments and geometric mean. In a case study, the ME distributions of topography have been tested using wavelet analysis.