Is Macroporosity Controlled by Complexed Clay and Soil Organic Carbon?

Multi-scale evidence of rapid, climate-induced soil structural changes occurring at yearly to decadal timescales is mounting. As a result, it has become increasingly important to identify the properties and mechanisms controlling the development and maintenance of soil structure and associated macroporosity. This is especially relevant since macroporosity has disproportionate effects on saturated hydraulic conductivity (Ksat) which strongly influences water storage and flux, thus, affecting the water cycle. In this study, we use decision trees and piecewise linear regression to assess the influence of soil and climate properties on effective porosity (EP; a proxy of macroporosity) in both surface and subsurface horizons under varying land-use and management practices. Data from 1,491 pedons (3,679 horizons) spanning five ecoregions representing bioclimate (e.g., potential vegetation) across the conterminous US demonstrate that, at a continental scale, EP in surface (A) and subsurface (B) horizons is strongly dependent on the complexed fraction of the total mass of soil organic carbon (SOC) and clay; a combined fraction that we refer to as complexed organic carbon and clay (COCC). EP showed a slight positive response to COCC in A horizons but increased steeply with increasing COCC in B horizons. This is because the smaller values of COCC in B horizons reflect a larger pool of clay that has a greater potential to accommodate and complex additions of SOC promoting stronger organo-mineral bonds and the concomitant development and maintenance of soil structure in these horizons. In contrast, larger values of COCC in A horizons reflect conditions where all or most of the clay fraction is effectively complexed with SOC resulting in a larger pool of non-complexed soil organic matter with varying contrasting effects on macroporosity that ultimately mute the response of EP to increases in COCC. In surface horizons, indirect factors such as mean annual precipitation and land use were important predictors of EP, whereas COCC was more influential in controlling EP within the subsoil. The EP-COCC relationship also holds within ecoregions but its effect is mitigated by soil and climate interactions suggesting that the effect of climate on this relationship is indirect and complex. Plowed surface horizons and horizons underlying plowed layers showed greater homogenization (due to disturbance effects reducing heterogeneity in the soil) as well as a reduction in the magnitude and rate of change of EP as a function of COCC compared to undisturbed horizons. Our findings suggest that the complexed fraction of clay and SOC is important for controlling macroporosity and Ksat at ecoregion scales and that the EP-COCC relationship may be an important framework for understanding and predicting future land use- and climate-induced changes in soil hydraulic properties

Application of a Hillslope-Scale Soil Moisture Data Assimilation System to Military Trafficability Assessment

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

Developing and Optimizing Shrub Parameters Representing Sagebrush (\u3ci\u3eArtemisia\u3c/i\u3e spp.) Ecosystems in the Northern Great Basin Using the Ecosystem Demography (EDv2.2) Model

Ecosystem dynamic models are useful for understanding ecosystem characteristics over time and space because of their efficiency over direct field measurements and applicability to broad spatial extents. Their application, however, is challenging due to internal model uncertainties and complexities arising from distinct qualities of the ecosystems being analyzed. The sagebrush-steppe ecosystem in western North America, for example, has substantial spatial and temporal heterogeneity as well as variability due to anthropogenic disturbance, invasive species, climate change, and altered fire regimes, which collectively make modeling dynamic ecosystem processes difficult. Ecosystem Demography (EDv2.2) is a robust ecosystem dynamic model, initially developed for tropical forests, that simulates energy, water, and carbon fluxes at fine scales. Although EDv2.2 has since been tested on different ecosystems via development of different plant functional types (PFT), it still lacks a shrub PFT. In this study, we developed and parameterized a shrub PFT representative of sagebrush (Artemisia spp.) ecosystems in order to initialize and test it within EDv2.2, and to promote future broad-scale analysis of restoration activities, climate change, and fire regimes in the sagebrushsteppe ecosystem. Specifically, we parameterized the sagebrush PFT within EDv2.2 to estimate gross primary production (GPP) using data from two sagebrush study sites in the northern Great Basin. To accomplish this, we employed a three-tier approach. (1) To initially parameterize the sagebrush PFT, we fitted allometric relationships for sagebrush using field-collected data, information from existing sagebrush literature, and parameters from other land models. (2) To determine influential parameters in GPP prediction, we used a sensitivity analysis to identify the five most sensitive parameters. (3) To improve model performance and validate results, we optimized these five parameters using an exhaustive search method to estimate GPP, and compared results with observations from two eddy covariance (EC) sites in the study area. Our modeled results were encouraging, with reasonable fidelity to observed values, although some negative biases (i.e., seasonal underestimates of GPP) were apparent. Our finding on preliminary parameterization of the sagebrush shrub PFT is an important step towards subsequent studies on shrubland ecosystems using EDv2.2 or any other process-based ecosystem model

A family of multimodal dynamic maps

"We introduce a family of multimodal logistic maps with a single parameter. The maps domain is partitioned in subdomains according to the maximal number of modals to be generated and each subdomain contains one logistic map. The number of members of a family is equal to the maximal number of modals. Bifurcation diagrams and basins of attraction of fixed points are constructed for the family of chaotic logistic maps.

Assessing a Multi-Platform Data Fusion Technique in Capturing Spatiotemporal Dynamics of Heterogeneous Dryland Ecosystems in Topographically Complex Terrain

Water-limited ecosystems encompass approximately 40% of terrestrial land mass and play a critical role in modulating Earth’s climate and provisioning ecosystem services to humanity. Spaceborne remote sensing is a critical tool for characterizing ecohydrologic patterns and advancing the understanding of the interactions between atmospheric forcings and ecohydrologic responses. Fine to medium scale spatial and temporal resolutions are needed to capture the spatial heterogeneity and the temporally intermittent response of these ecosystems to environmental forcings. Techniques combining complementary remote sensing datasets have been developed, but the heterogeneous nature of these regions present significant challenges. Here we investigate the capacity of one such approach, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm, to map Normalized Difference Vegetation Index (NDVI) at 30 m spatial resolution and at a daily temporal resolution in an experimental watershed in southwest Idaho, USA. The Dry Creek Experimental Watershed captures an ecotone from a sagebrush steppe ecosystem to evergreen needle-leaf forests along an approximately 1000 m elevation gradient. We used STARFM to fuse NDVI retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS) and Landsat during the course of a growing season (April to September). Specifically we input to STARFM a pair of Landsat NDVI retrievals bracketing a sequence of daily MODIS NDVI retrievals to yield daily estimates of NDVI at resolutions of 30 m. In a suite of data denial experiments we compared these STARFM predictions against corresponding Landsat NDVI retrievals and characterized errors in predicted NDVI. We investigated how errors vary as a function of vegetation functional type and topographic aspect. We find that errors in predicting NDVI were highest during green-up and senescence and lowest during the middle of the growing season. Absolute errors were generally greatest in tree-covered portions of the watershed and lowest in locations characterized by grasses/bare ground. On average, relative errors in predicted average NDVI were greatest in grass/bare ground regions, on south-facing aspects, and at the height of the growing season. We present several ramifications revealed in this study for the use of multi-sensor remote sensing data for the study of spatiotemporal ecohydrologic patterns in dryland ecosystems

Hillslope Asymmetry Maps Reveal Widespread, Multi-Scale Organization

Hillslope asymmetry is the condition in which oppositely-facing hillslopes within an area have differing average slope angles, and indicates aspect-related variability in hillslope evolution. As such, the presence, orientation and magnitude of asymmetry may be a useful diagnostic for understanding process dominance. We present a new method for quantifying and mapping the spatial distribution of hillslope asymmetry across large areas. Resulting maps for the American Cordillera of the Western Hemisphere and the western United States reveal that hillslope asymmetry is widespread, with distinct trends at continental to drainage scales. Spatial patterns of asymmetry correlate with latitude along the American Cordillera, mountain-range orientation for many ranges in the western United States, and elevation in the Idaho Batholith of the Northern Rocky Mountains. Spatial organization suggests that non-stochastic, process-driven controls cause these patterns. The hillslope asymmetry metric objectively captures previously-documented extents and frequencies of valley asymmetry for the Gabilan Mesa of the central California Coast Range. Broad-scale maps of hillslope asymmetry are of interest to a wide range of disciplines, as spatial patterns may reflect the influence of tectonics, atmospheric circulation, topoclimate, geomorphology, hydrology, soils and ecology on landscape evolution. These maps identify trends and regions of hillslope asymmetry, allow possible drivers to be spatially constrained, and facilitate the extrapolation of site-specific results to broader regions

Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems

Dynamical Precipitation Downscaling for Hydrologic Applications Using WRF 4D-Var Data Assimilation: Implications for GPM Era

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

Combined Assimilation of Satellite Precipitation and Soil Moisture: A Case Study Using TRMM and SMOS Data

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)

A Simplified Approach for Estimating Soil Carbon and Nitrogen Stocks in Semi-Arid Complex Terrain

We investigated soil carbon (C) and nitrogen (N) distribution and developed a model, using readily available geospatial data, to predict that distribution across a mountainous, semi-arid, watershed in southwestern Idaho (USA). Soil core samples were collected and analyzed from 133 locations at 6 depths (n=798), revealing that aspect dramatically influences the distribution of C and N, with north-facing slopes exhibiting up to 5 times more C and N than adjacent southfacing aspects. These differences are superimposed upon an elevation (precipitation) gradient, with soil C and N contents increasing by nearly a factor of 10 from the bottom (1100 m elevation) to the top (1900 m elevation) of the watershed. Among the variables evaluated, vegetation cover, as represented by a Normalized Difference Vegetation Index (NDVI), is the strongest, positively correlated, predictor of C; potential insolation (incoming solar radiation) is a strong, negatively correlated, secondary predictor. Approximately 62% (as R2) of the variance in the C data is explained using NDVI and potential insolation, compared with an R2 of 0.54 for a model using NDVI alone. Soil N is similarly correlated to NDVI and insolation. We hypothesize that the correlations between soil C and N and slope, aspect and elevation reflect, in part, the inhibiting influence of insolation on semi-arid ecosystem productivity via water limitation. Based on these identified relationships, two modeling techniques (multiple linear regression and cokriging) were applied to predict the spatial distribution of soil C and N across the watershed. Both methods produce similar distributions, successfully capturing observed trends with aspect and elevation. This easily applied approach may be applicable to other semi-arid systems at larger scales