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After hurricane disturbances in tropical forests, the size structure and species composition are affected by immediate mortality, and subsequent recruitment and individual growth. Often, immediate post-disturbance stand-level data are presented but understanding of the components that affect changes in growth and longer-term responses to forest structure and composition are lacking. To answer questions about how mortality, recruitment, and growth change among successional Plant Functional Types (PFT) through time after a hurricane disturbance, we use long-term census data (1989–2014) collected in the Luquillo Experimental Forest, Puerto Rico. We developed an algorithm to fill missing diameter data from the long-term data set that was collected three months after Hurricane Hugo; and subsequently at five-year intervals. Both the immediate hurricane-induced mortality and subsequent mortality were lower in stems with larger diameters, but varied among successional PFTs Early, Mid, Late, and Palm. Tree growth rates were observed to decrease with time since the hurricane disturbance. Five years after the hurricane, mortality was minimal but then increased gradually with time. In contrast, recruitment was highest five years after the hurricane and then decreased with time. The palm Prestoea montana became the most abundant species in the forest after the hurricane, as it had the lowest immediate hurricane-induced and subsequent mortality, and the highest recruitment. Twenty-five years after the hurricane, the palm and the Late PFT dominate the forest after shifting species composition from pre-hurricane conditions.

Hurricanes commonly disturb and damage tropical forests. Hurricane frequency and intensity are predicted to change under the changing climate. The short-term impacts of hurricane disturbances to tropical forests have been widely studied, but the long-term impacts are rarely investigated. Modeling is critical to investigate the potential response of forests to future disturbances, particularly if the nature of the disturbances is changing with climate. Unfortunately, existing models of forest dynamics are not presently able to account for hurricane disturbances. Therefore, we implement the Hurricane Disturbance in the Ecosystem Demography model (ED2) (ED2-HuDi). The hurricane disturbance includes hurricane-induced immediate mortality and subsequent recovery modules. The parameterizations are based on observations at the Bisley Experimental Watersheds (BEW) in the Luquillo Experimental Forest in Puerto Rico. We add one new plant functional type (PFT) to the model – Palm, as palms cannot be categorized into one of the current existing PFTs and are known to be an abundant component of tropical forests worldwide. The model is calibrated with observations at BEW using the generalized likelihood uncertainty estimation (GLUE) approach. The optimal simulation obtained from GLUE has a mean relative error of −21 %, −12 %, and −15 % for stem density, basal area, and aboveground biomass, respectively. The optimal simulation also agrees well with the observation in terms of PFT composition (+1 %, −8 %, −2 %, and +9 % differences in the percentages of “Early”, “Mid”, “Late”, and “Palm” PFTs, respectively) and size structure of the forest (+0.8 % differences in the percentage of large stems). Lastly, using the optimal parameter set, we study the impact of forest initial condition on the recovery of the forest from a single hurricane disturbance. The results indicate that, compared to a no-hurricane scenario, a single hurricane disturbance has little impact on forest structure (+1 % change in the percentage of large stems) and composition ( % change in the percentage of each of the four PFTs) but leads to 5 % higher aboveground biomass after 80 years of succession. The assumption of a less severe hurricane disturbance leads to a 4 % increase in aboveground biomass.

The Ecosystem Demography model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical, ecological, and biogeochemical dynamics of vertically and horizontally heterogeneous terrestrial ecosystems. In a companion paper (Longo et al., 2019a), we described how the model solves the energy, water, and carbon cycles, and verified the high degree of conservation of these properties in long-term simulations that include long-term (multi-decadal) vegetation dynamics. Here, we present a detailed assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles in Amazon forests. We use multiple measurements from eddy covariance towers, forest inventory plots, and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple timescales, ranging from subdaily to century long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes, and the storage of water, energy, and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 qualitatively captures several emergent properties of the ecosystem found in observations, specifically observed relationships between aboveground biomass, mortality rates, and wood density; however, the slopes of these relationships were not accurately captured. We also identified several limitations, including the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in Earth system models (ESMs) and to realistically represent their impacts on energy, water, and carbon cycles. We also identify several priorities for further model development.

Gully erosion was evidence of land degradation in the southern Piedmont, site of the Calhoun Critical Zone Observatory (CCZO), during the cotton farming era. Understanding of the underlying gully erosion processes is essential to develop gully erosion models that could be useful in assessing the effectiveness of remedial and soil erosion control measures such as gully backfilling, revegetation, and terracing. Development and validation of process-based gully erosion models is difficult because observations of the formation and progression of gullies are limited. In this study, analytic formulations of the two dominant gullying processes, namely, plunge pool erosion and slab failure, are utilized to simulate the gullying processes in the 4-km2 Holcombe's Branch watershed. In order to calibrate parameters of the gully erosion model, gully features (e.g., depth and area) extracted from a high-resolution LiDAR map are used. After the calibration, the gully model is able to delineate the spatial extent of gullies whose statistics are in close agreement with the gullies extracted from the LiDAR DEM. Several simulations with the calibrated model are explored to evaluate the effectiveness of various gully remedial measures, such as backfilling and revegetation. The results show that in the short-term, the reshaping of the topographical surface by backfilling and compacting gullies is effective in slowing down the growth of gullies (e.g., backfilling decreased the spatial extent of gullies by 21–46% and decreased the average depth of gullies by up to 9%). Revegetation, however, is a more effective approach to stabilizing gullies that would otherwise expand if no gully remedial measures are implemented. Analyses of our simulations show that the gully stabilization effect of revegetation varies over a wide range, i.e., leading to 23–69% reduction of the spatial extent of gullies and up to 45% reduction in the depth of gullies, depending on the selection of plant species and management practices.

Hurricanes can cause severe damage to tropical forests. To understand the nature of hurricane impacts, we analyze and compare immediate effects from category-4 hurricane María in 2017 and category-3 hurricane Hugo in 1989 at Bisley Experimental Watersheds (BEW) in the Luquillo Experimental Forest, Puerto Rico. We show that hurricane María caused lower mortality than hurricane Hugo, even though hurricane María was a stronger event with higher sustained wind. The lower mortality was due to the combination of lower accumulated cyclone energy at the site and more wind-resistant forest structure and composition at the time of disturbance. We compare our study site with a nearby location that has the same forest type, Luquillo Forest Dynamics Plot (LFDP), and describe the similarities and differences of mortality and impact factors between the two sites during the two events. During hurricane Hugo, LFDP experienced much lower mortality than BEW, even though the accumulated cyclone energy at LFDP was higher. The difference in mortality was due to contrasting forest structure and composition of the two sites. Our results demonstrate that forest structure and composition at the time of the disturbance were more critical to hurricane-induced mortality at the two sites than accumulated cyclone energy.

This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).

Attempts to estimate the influence of erosion on the carbon (C) cycle are limited by difficulties in accounting for the fate of mobilized organic material and for the uncertainty associated with land management practices. This study proposes a method to quantify the uncertainty introduced by the influence of land management on soil organic C (SOC) generation and decomposition at eroding soils. The framework is implemented in tRIBS-ECO (Triangulated Irregular Network-based Real-time Integrated Basin Simulator-Erosion and Carbon Oxidation). tRIBS-ECO is a spatially and depth-explicit model of C dynamics coupled with a process-based hydro-geomorphic model. We assess the impact of soil erosion on the net soil-atmosphere CO2 exchange at the Calhoun Critical Zone Observatory, one of the most severely agriculturally eroded regions in the U.S. Measurements of SOC storage are used from different catena positions. We demonstrate that the spatiotemporal variations of land management practices introduce significant uncertainty in estimates of the erosion-induced CO2 exchange with the atmosphere. Observations and simulations suggest that a substantial portion of eroded organic material is buried in alluvial sediments at the study site. According to results, recent reforestation led to a partial decline in soil and SOC erosion rates. It is suggested that the representation of the fine spatiotemporal variability of the dynamics of eroded C is important in the computation of C budgets in regional and global scales.

The Ecosystem Demography Model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical and biogeochemical cycles of dynamic ecosystems while considering the role of vertical structure of plant communities and the heterogeneity of such structures across the landscape. In a companion paper, we described in detail how the model solves the energy, water, and carbon cycles, and verified the excellent conservation of such properties in long-term simulation. Here, we present a thorough assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles, with focus on the Amazon forest. We used multiple measurements from eddy covariance towers, forest inventory plots and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple time scales ranging from sub-daily to century-long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes and the storage of water, energy and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 also simulates emerging properties of the ecosystem found in observations, such as the relationship between biomass and mortality rates and wood density, although the relationships predicted by the model were biased. We also identified some of the model limitations, such as the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration, and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in ESMs, to realistically represent the role of forest structure and composition on energy, water, and carbon cycles, as well as the priority areas for further model development.

Developing high-quality long-term data sets at uniform space–time resolution is essential for improved climate studies. This article processes the outputs from two global and regional climate models, the Community Climate System Model (CCSM3) and the Regional Climate Model driven by the Hadley Centre Coupled Model (RegCM3). The results are bias-corrected time series of atmospheric variables corresponding to Intergovernmental Panel on Climate Change (IPCC's) historical (20C3M) and future (A2) climate scenarios over the Amazon Basin. We use a series of simple but effective interpolation approaches to produce hourly climate data sets at 1° by 1° grid cells. A quantile-based mapping approach is used to reduce the biases of temperature and precipitation in CCSM3 and RegCM3. Adjustments are also made on specific humidity and downwelling longwave radiation to avoid inconsistency between those variables and bias-corrected temperature values. We also interpolated an already bias-corrected Parallel Climate Model data set (PCM1) from 3-hourly to the hourly resolution. The final climate data sets can be used as forcing of ecosystem and hydrologic models to study climate changes and impact assessments over the Amazon Basin.

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 Holcombe's 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 14.5 g m−2 yr−1 to a maximum sink of −18.2 g m−2 yr−1. The small-scale complexity of C erosion and burial driven by topography exerts a strong control on the landscape's capacity to serve as a C source or a sink.

Using 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 its diverse land surface radiation regime and precipitation type, within the TRMM domain. Promising results are presented using ShARP over snow-covered land surfaces and in the vicinity of coastlines, in comparison with the land rainfall retrievals of the standard TRMM 2A12, version 7, product. The results show that ShARP can significantly reduce the rainfall overestimation due to the background snow contamination and markedly improve detection and retrieval of rainfall in the vicinity of coastlines. During the calendar year 2013, compared to TRMM 2A25, it is demonstrated that over the study domain the root-mean-square difference can be reduced up to 38% annually, while the improvement can reach up to 70% during the cold months of the year.

In this study we propose a probabilistic approach for coupled distributed hydrological-hillslope stability models that accounts for soil parameters uncertainty at basin scale. The geotechnical and soil retention curve parameters are treated as random variables across the basin and theoretical probability distributions of the Factor of Safety (FS) are estimated. The derived distributions are used to obtain the spatio-temporal dynamics of probability of failure, in terms of parameters uncertainty, conditioned to soil moisture dynamics. The framework has been implemented in the tRIBS-VEGGIE (Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator-VEGetation Generator for Interactive Evolution)-Landslide model and applied to a basin in the Luquillo Experimental Forest (Puerto Rico) where shallow landslides are common. In particular, the methodology was used to evaluate how the spatial and temporal patterns of precipitation, whose variability is significant over the basin, affect the distribution of probability of failure, through event scale analyses. Results indicate that hyetographs where heavy precipitation is near the end of the event lead to the most critical conditions in terms of probability of failure. Copyright © 2015 John Wiley & Sons, Ltd.

The hydrologic cycle is an exquisitely coordinated and balanced interaction between the atmosphere, the ocean, and the land that controls, among other things, the planet's temperature by moving large quantities of matter and energy. The system is incredibly complex with a myriad of positive and negative feedbacks acting at a variety of scales. Much of what we experience in our natural and altered environments results from these complex interactions. Surprisingly (or maybe not) this complexity many times results in beautifully organized expressions of the hydrologic state that are commonly amenable to fairly simple explanations. This paper illustrates hydrologic complexity and organization in the context of the author's and collaborator's work during the past decades, a lot published in Water Resources Research. Topics include the impact of soil moisture on the atmosphere and vice versa, the impact of deforestation on the Amazon cloud climate and precipitation, the estimation of surface energy and mass fluxes, the self-organization of landscapes and river basins over very long time periods, and the roles of vegetation on landscape evolution.

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.

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.

The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacing—relevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.

ECOHYDROLOGY Ecohydrol. 8, 1572–1583 (2015) Published online 11 February 2015 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/eco.1605 Ecohydrological controls on grass and shrub above-ground net primary productivity in a seasonally dry climate Anthony J. Parolari, 1,2 * Michael L. Goulden 3 and Rafael L. Bras 4,5 Nicholas School of the Environment, Duke University, Durham, NC, USA Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA Department of Earth System Science, University of California, Irvine, CA, USA School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA ABSTRACT Seasonally dry, water-limited regions are often co-dominated by distinct herbaceous and woody plant communities with contrasting ecohydrological properties. We investigated the shape of the above-ground net primary productivity (ANPP) response to annual precipitation (P a ) for adjacent grassland and shrubland ecosystems in Southern California, with the goal of understanding the role of these ecohydrological properties on ecosystem function. Our synthesis of observations and modelling demonstrates grassland and shrubland exhibit distinct ANPP-P a responses that correspond with characteristics of the long-term P a distribution and mean water balance fluxes. For annual grassland, no ANPP occurs below a ‘precipitation compensation point,’ where bare soil evaporation dominates the water balance, and ANPP saturates above the P a where deep percolation and runoff contribute to the modelled water balance. For shrubs, ANPP increases at a lower and relatively constant rate across the P a gradient, while deep percolation and runoff account for a smaller fraction of the modelled water balance. We identify precipitation seasonality, root depth, and water stress sensitivity as the main ecosystem properties controlling these responses. Observed ANPP-P a responses correspond to notably different patterns of rain-use efficiency (RUE). Grass RUE exceeds shrub RUE over a wide range of typical P a values, whereas grasses and shrubs achieve a similar RUE in particularly dry or wet years. Inter-annual precipitation variability, and the concomitant effect on ANPP, plays a critical role in maintaining the balance of grass and shrub cover and ecosystem-scale productivity across this landscape. Copyright © 2015 John Wiley & Sons, Ltd. KEY WORDS soil moisture dynamics; seasonal climates; primary production; rain-use efficiency Received 10 July 2014; Revised 10 January 2015; Accepted 15 January 2015 INTRODUCTION Semi-arid ecosystems are often characterized by a patchwork of plant communities, such as the herbaceous and woody vegetation mosaic found in regions with seasonally dry climate. Because of their socio-economic importance and vulnerability to change under future climate and land use conditions (Schlesinger et al., 1990; Rietkerk et al., 2004; Reynolds et al., 2007; Schneider et al., 2007), a mechanistic understanding of the structure and function of such water-limited ecosystems is essential to their proper management. Extensive research has highlighted both bottom-up (i.e. competition for soil moisture and nutrients) and top-down (i.e. fire and herbivory) controls on the productivity of semi-arid woody and herbaceous vegetation (Scholes and Archer, 1997). *Correspondence to: Anthony J. Parolari, Nicholas School of the Environment, Duke University, Durham, NC 27708, USA. E-mail: anthony.parolari@duke.edu Copyright © 2015 John Wiley & Sons, Ltd. The link between primary productivity and precipitation is an important indicator of dryland function. At the scale of an individual site, the response of above-ground net primary productivity (ANPP) to annual precipitation (P a ) varies substantially with soil type, plant functional type (PFT) and climate. ANPP generally increases with P a , with evidence for both linear (Briggs and Knapp, 1995; Huxman et al., 2004; Hsu et al., 2012) and nonlinear (saturating) (Hsu et al., 2012) ANPP-P a responses. However, ANPP-P a correlations are often weak, and in some cases, ANPP is insensitive to P a (Sala et al., 2012). Previous analyses of the ANPP-P a relation have been relatively descriptive, and the ecohydrological basis for these patterns has yet to be explored in detail. Because of nonlinearity in the surface hydrological response to precipitation, interaction between the soil water balance and vegetation productivity may be one factor that controls the shape of the ANPP-P a response. Annual evapotrans- piration, the water flux that facilitates productivity, tends to saturate with increasing annual precipitation (Budyko, 1974).

This work investigates how the integrated land use of northern South America has affected the present day regional patterns of hydrology. A model of the terrestrial ecosystems (ecosystem demography model 2: ED2) is combined with an atmospheric model (Brazilian Regional Atmospheric Modeling System: BRAMS). Two realizations of the structure and composition of terrestrial vegetation are used as the sole differences in boundary conditions that drive two simulations. One realization captures the present day vegetation condition that includes deforestation and land conversion, the other is an estimate of the potential structure and composition of the region's vegetation without human influence. Model output is assessed for differences in resulting hydrometeorology. The simulations suggest that the history of land conversion in northern South America is not associated with a significant precipitation bias in the northern part of the continent, but has shown evidence of a negative bias in mean regional evapotranspiration and a positive bias in mean regional runoff. Also, negative anomalies in evaporation rates showed pattern similarity with areas where deforestation has occurred. In the central eastern Amazon there was an area where deforestation and abandonment had lead to an overall reduction of above-ground biomass, but this was accompanied by a shift in forest composition towards early successional functional types and grid-average-patterned increases in annual transpiration. Anomalies in annual precipitation showed mixed evidence of consistent patterning. Two focus areas were identified where more consistent precipitation anomalies formed, one in the Brazilian state of Pará where a dipole pattern formed, and one in the Bolivian Gran Chaco, where a negative anomaly was identified. These locations were scrutinized to understand the basis of their anomalous hydrometeorologic response. In both cases, deforestation led to increased total surface albedo, driving decreases in net radiation, boundary layer moist static energy and ultimately decreased convective precipitation. In the case of the Gran Chaco, decreased precipitation was also a result of decreased advective moisture transport, indicating that differences in local hydrometeorology may manifest via teleconnections with the greater region.

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 Earth's atmosphere are suggested.

Soil moisture is an important environmental variable that impacts military operations and weapons systems. Accurate and timely forecasts of soil moisture at appropriate spatial scales, therefore, are important for mission planning. We present an application of a soil moisture data assimilation system to military trafficability assessment. The data assimilation system combines hillslope-scale (e.g., 10s to 100s of m) estimates of soil moisture from a hydrologic model with synthetic L-band microwave radar observations broadly consistent with the planned NASA Soil Moisture Active–Passive (SMAP) mission. Soil moisture outputs from the data assimilation system are input to a simple index-based model for vehicle trafficability. Since the data assimilation system uses the ensemble Kalman Filter, the risks of impaired trafficability due to uncertainties in the observations and model inputs can be quantified. Assimilating the remote sensing observations leads to significantly different predictions of trafficability conditions and associated risk of impaired trafficability, compared to an approach that propagates forward uncertainties in model inputs without assimilation. Specifically, assimilating the observations is associated with an increase in the risk of “slow go” conditions in approximately two-thirds of the watershed, and an increase in the risk of “no go” conditions in approximately 40% of the watershed. Despite the simplicity of the trafficability assessment tool, results suggest that ensemble-based data assimilation can potentially improve trafficability assessment by constraining predictions to observations and facilitating quantitative assessment of the risk of impaired trafficability.

Remote sensing of soil moisture and vegetation water content from space often requires inversion of a zeroth-order approximation of the forward radiative transfer equation in L-band, known as the τ-ω model. This paper shows that the least-squares inversion of the model is not strictly convex and the widely used unconstrained damped least-squares (DLS) can lead to biased retrievals, due to preferential descending paths. In particular, the numerical experiments show that for sparse (dense) vegetation with a low (high) opacity, the DLS tends to overestimate (underestimate) the soil moisture and vegetation water content when the soil is dry (wet). A new Constrained Multi-Channel Algorithm (CMCA) is proposed that confines the retrievals with a priori information about the soil type and vegetation density and accounts for slow temporal changes of the vegetation water content through a smoothing-norm regularization. It is demonstrated that depending on the resolution of the constraints, the algorithm can lead to high-resolution soil moisture retrievals beyond the radiometric spatial resolution. Controlled numerical experiments are conducted and the results are validated against ground-based gauge observations using the passive microwave observations by the Soil Moisture Active Passive (SMAP) Satellite.

[1] In semiarid complex terrain, the combination of elevation and aspect promotes variations in the water and energy balance, resulting in slopes with distinct ecologic and hydrologic properties. Quantifying the differential energy and water dynamics of opposing slopes can provide essential information on the potential effects of climate variability on landscapes. In this study, we use observations from a network of hydrologic sensors deployed on the slopes of a semiarid catchment in central New Mexico, USA, to quantify the ecohydrologic dynamics of two coexisting and contrasting ecosystems: a juniper (Juniperus monosperma) savanna on a north facing slope (NFS) and a creosote (Larrea tridentata) shrubland on a south facing slope (SFS). Our analyses show that: (1) energy loads exert a first-order control on the dynamics of evapotranspiration and soil moisture residence times in the catchment, with vegetation imposing a second-order control at the onset of the growing season; (2) soils exhibit a characteristic progression of moisture and temperature along the slope-aspect continuum that is preserved throughout the year, going from a wetter and cooler NFS to a drier and warmer SFS; (3) there are remarkable differences in the runoff dynamics among the catchment slopes, with a smaller precipitation threshold triggering larger SFS runoff amounts than at its NFS counterpart; and (4) seasonal water balances of the NFS and SFS follow opposite trajectories in the year and point to distinct soil water pools for evapotranspiration demands. The results of this study have important implications for understanding landscape changes in areas of complex topography under current and future climate variability.

[1] A large fraction of soil erosion in temperate climate systems proceeds from gully headcut growth processes. Nevertheless, headcut retreat is not well understood. Few erosion models include gully headcut growth processes, and none of the existing headcut retreat models have been tested against long-term retreat rate estimates. In this work the headcut retreat resulting from plunge pool erosion in the Channel Hillslope Integrated Landscape Development (CHILD) model is calibrated and compared to long-term evolution measurements of six gullies at the Bardenas Reales, northeast Spain. The headcut retreat module of CHILD was calibrated by adjusting the shape factor parameter to fit the observed retreat and volumetric soil loss of one gully during a 36 year period, using reported and collected field data to parameterize the rest of the model. To test the calibrated model, estimates by CHILD were compared to observations of headcut retreat from five other neighboring gullies. The differences in volumetric soil loss rates between the simulations and observations were less than 0.05 m3 yr−1, on average, with standard deviations smaller than 0.35 m3 yr−1. These results are the first evaluation of the headcut retreat module implemented in CHILD with a field data set. These results also show the usefulness of the model as a tool for simulating long-term volumetric gully evolution due to plunge pool erosion.

[1] 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. In particular, the rooting strategies employed by vegetation can be critical to their survival. However, land surface models currently prescribe rooting profiles as a function of only the plant functional type of interest with no consideration for the soil texture or rainfall regime of the region being modeled. Additionally, these models do not incorporate the ability of vegetation to dynamically alter their rooting strategies in response to transient changes in environmental forcings or competition from other plant species and therefore tend to underestimate the resilience of these ecosystems. To address the simplicity of the current representation of roots in land surface models, a new dynamic rooting scheme was incorporated into the framework of the distributed ecohydrological model tRIBS+VEGGIE. The new scheme optimizes the allocation of carbon to the root zone to reduce the perceived stress of the vegetation, so that root profiles evolve based upon local climate and soil conditions. The ability of the new scheme to capture the complex dynamics of natural systems was evaluated by comparisons to hourly timescale energy flux, soil moisture, and vegetation growth observations from the Walnut Gulch Experimental Watershed, Arizona. Robust agreement was found between the model and observations, providing confidence that the improved model is able to capture the multidirectional interactions between climate, soil, and vegetation at this site.

The entire Piedmont of the Southeastern United States, where the Calhoun Critical Zone Observatory (CCZO) is located, experienced one of the most severe erosive events of the last two centuries. Forested areas were cleared to cultivate cotton, tobacco, and other crops during the nineteenth and early twentieth century and these land use changes, together with intense rainfalls, initiated deep gullying. An accurate mapping of these landforms is important since, despite some gully stabilization and reforestation efforts, gullies are still major contributors of sediment to streams. Mapping gullies in the CCZO area is hindered by the presence of dense canopy, which precludes the identification through aerial photogrammetry and other traditional remote sensing methods. Moreover, the wide spatial extent of the gullies makes the identification and characterization of entire gullies through field surveys a very large and expensive proposition. This work aims to develop a methodology to automatically detect and map gullies based on a set of algorithms and morphological characteristics retrieved by very high resolution (VHR) imagery. A one-meter resolution LiDAR Digital Elevation Model (DEM) is used to derive different morphometric indices, which are combined by using spatial analysis methods and fuzzy logic rules, building up a tool able to automatically identify gullies. This spatial model has been calibrated using, as reference, the perimeters of two relatively large gullies that have been measured during a recent field survey. The entire procedure aims to provide estimates of gully erosion patterns, which characterize the entire CCZO area, and to develop an objective method to measure characteristic features of gullies (i.e., depth and volume).

Spatial patterns of ecosystem productivity arise from the terrain-modulated wetting and drying of the landscape. Using a daily relative greenness (rG) index, we explore the relations between spatial variability of plant productivity and landscape morphology, and how these relations change over time. The rG index is defined as a measure of local vegetation greenness relative to the site's mean greenness, calculated from remotely sensed normalized difference vegetation index. We analyse two semiarid grasslands with pronounced topography, one located in southeastern Arizona, with a mean annual precipitation of 350 mm, and the other in central New Mexico, with a mean annual precipitation of 250 mm. Our results indicate that (1) rG is spatially more uniform after wet conditions (higher biomass) than after dry conditions (lower biomass); (2) differences in the relative frequency distribution of rG among different landscape morphologies (ridges, unchanneled valleys and channels) indicate higher productivity in channels, similar coefficient of variation in all process domains, and higher skewness in the ridges; (3) relatively high correlations between the binned average rG with respect to upstream area, curvature, and annual insolation in more than 80% of the terrain indicate a clear dependence between ecosystem productivity and topography; (4) rG is more sensitive to changes in topographic indices at the wetter Arizona study site. Such improved understanding of vegetation-topography dependence is critical for ecosystem management, testing ecohydrologic models, and offers ideas for the downscaling of coarse-scale satellite-derived vegetation indices. Copyright © 2012 John Wiley & Sons, Ltd.