Alejandro N. Flores

GPS tomography of the ionospheric electron content with a correlation functional

The authors develop a minimization functional in order to regularize the inverse problem associated with three-dimensional (3D) ionospheric stochastic tomography. This functional is designed to yield, upon minimization, a solution which maximizes the frequency content of the solution below a certain cutoff, while keeping /spl chi//sup 2/ constant. The authors show how this functional can be rewritten in terms of the correlation function of the image, thereby facilitating the algorithmic implementation of the method. They then implement this functional in a Kalman filter and obtain a smoothing algorithm that acts in both space and time. Finally, they use this technique to perform global scale Global Positioning System (GPS) tomography of the ionospheric electron content.

Channel‐reach morphology dependence on energy, scale, and hydroclimatic processes with implications for prediction using geospatial data

Received 28 April 2005; revised 6 January 2006; accepted 10 March 2006; published 20 June 2006. [1] Channel types found in mountain drainages occupy characteristic but intergrading ranges of bed slope that reflect a dynamic balance between erosive energy and channel boundary resistance. Using a classification and regression tree (CART) modeling approach, we demonstrate that drainage area scaling of channel slopes provides better discrimination of these forms than slope alone among supply- and capacity-limited sites. Analysis of 270 stream reaches in the western United States exhibiting four common mountain channel types reveals that these types exist within relatively discrete ranges of an index of specific stream power. We also demonstrate associations among regional interannual precipitation variability, discharge distribution skewness, and means of the specific stream power index of step-pool channels. Finally, we discuss a conceptual methodology for predicting ecologically relevant morphologic units from digital elevation models at the network scale based on the finding that channel types do not exhibit equal energy dissipation.

Impact of Hillslope-Scale Organization of Topography, Soil Moisture, Soil Temperature, and Vegetation on Modeling Surface Microwave Radiation Emission

Microwave radiometry will emerge as an important tool for global remote sensing of near-surface soil moisture in the coming decade. In this modeling study, we find that hillslope-scale topography (tens of meters) influences microwave brightness temperatures in a way that produces bias at coarser scales (kilometers). The physics underlying soil moisture remote sensing suggests that the effects of topography on brightness temperature observations are twofold: 1) the spatial distribution of vegetation, moisture, and surface and canopy temperature depends on topography and 2) topography determines the incidence angle and polarization rotation that the observing sensor makes with the local land surface. Here, we incorporate the important correlations between factors that affect emission (e.g., moisture, temperature, and vegetation) and topographic slope and aspect. Inputs to the radiative transfer model are obtained at hillslope scales from a mass-, energy-, and carbon-balance-resolving ecohydrology model. Local incidence and polarization rotation angles are explicitly computed, with knowledge of the local terrain slope and aspect as well as the sky position of the sensor. We investigate both the spatial organization of hillslope-scale brightness temperatures and the sensitivity of spatially aggregated brightness temperatures to satellite sky position. For one computational domain considered, hillslope-scale brightness temperatures vary from approximately 121 to 317 K in the horizontal polarization and from approximately 117 to 320 K in the vertical polarization. Including hillslope-scale heterogeneity in factors effecting emission can change watershed-aggregated brightness temperature by more than 2 K, depending on topographic ruggedness. These findings have implications for soil moisture data assimilation and disaggregation of brightness temperature observations to hillslope scales.

Insights into the Physical Processes Controlling Correlations Between Snow Distribution and Terrain Properties

This study investigates causes behind correlations between snow and terrain properties in a 27 km2 mountain watershed. Whereas terrain correlations reveal where snow resides, the physical processes responsible for correlations can be ambiguous. We conducted biweekly snow surveys at small transect scales to provide insight into late-season correlations at the basin scale. The evolving parameters of transect variograms reveal the interplay between differential accumulation and differential ablation that is responsible for correlations between snow and terrain properties including elevation, aspect, and canopy density. Elevation-induced differential accumulation imposes a persistent source of varariabity at the basin scale, but is not sufficient to explain the elevational distribution of snow water equivalent (SWE) on the ground. Differential ablation, with earlier and more frequent ablation at lower elevations, steepens the SWE-elevation gradient through the season. Correlations with aspect are primarily controlled by differences in solar loading. Aspect related redistribution of precipitation by wind, however, is important early in the season. Forested sites hold more snow than nonforested sites at the basin scale due to differences in ablation processes, while open areas within forested sites hold more snow than covered areas due to interception. However, as the season progresses energetic differences between open and covered areas within forested sites cause differences induced by interception to diminish. Results of this study can help determine which accumulation and ablation processes must be represented explicitly and which can be parameterized in models of snow dynamics.

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)

Reproducibility of soil moisture ensembles when representing soil parameter uncertainty using a Latin Hypercube-based approach with correlation control

[1] Representation of model input uncertainty is critical in ensemble‐based data assimilation. Monte Carlo sampling of model inputs produces uncertainty in the hydrologic state through the model dynamics. Small Monte Carlo ensemble sizes are desirable because of model complexity and dimensionality but potentially lead to sampling errors and correspondingly poor representation of probabilistic structure of the hydrologic state. We compare two techniques to sample soil hydraulic and thermal properties (SHTPs): (1) Latin Hypercube (LH) based sampling with correlation control and (2) random sampling from SHTP marginal distributions. A hydrology model is used to project SHTP uncertainty onto the soil moisture state for given forcings. For statistical comparison, we generate 20 ensembles for 7 ensemble sizes. Variance in ensemble moment estimates decreases with increasing ensemble size. The LH‐based approach yields less variance in the estimate of ensemble moments at all ensemble sizes, an advantage greatest with small ensembles. Implications for hydrologic uncertainty assessment, data assimilation, and parameter estimation are discussed.

Isotopic Evidence for Lateral Flow and Diffusive Transport, but Not Sublimation, in a Sloped Seasonal Snowpack, Idaho, USA

Oxygen and hydrogen isotopes in snow were measured in weekly profiles during the growth and decline of a sloped subalpine snowpack, southern Idaho, 2011-2012. Isotopic steps (10 parts per thousand, delta O-18;80 parts per thousand, delta D) were preserved relative to physical markers throughout the season, albeit with some diffusive smoothing. Melting stripped off upper layers without shifting isotopes within the snowpack. Meltwater is in isotopic equilibrium with snow at the top but not with snow at each respective collection height. Transport of meltwater occurred primarily along pipes and lateral flow paths allowing the snowpack to melt initially in reverse stratigraphic order. Isotope diffusivities are similar to 2 orders of magnitude faster than estimated from experiments but can be explained by higher temperature and porosity. A better understanding of how snowmelt isotopes change during meltout improves hydrograph separation methods, whereas constraints on isotope diffusivities under warm conditions improve models of ice core records in low-latitude settings.

Persistent Metal Contamination Limits Lotic Ecosystem Heterotrophic Metabolism after More Than 100 Years of Exposure: A Novel Application of the Resazurin Resorufin Smart Tracer

Persistent stress from anthropogenic metal deposition in lotic ecosystems is a global concern. This long-term selective pressure shapes hyporheic microbial assemblages and influences ecosystem functional integrity. We hypothesized that, even after 100 years of adaptation opportunity, ecosystem function remains inhibited by sediment-associated metal stress and that the Resazurin Resorufin Smart Tracer can be used to quantify this impact. The Resazurin Resorufin Smart Tracer system is applied here in a novel capacity as an indicator of ecosystem function by quantifying ecosystem respiration of microbial communities. Hyporheic microbial communities exposed to differing magnitudes of chronic metal stress were compared to pristine reference sites in controlled column experiments. A Markov chain Monte Carlo technique was developed to solve the inverse smart tracer transport equation to derive community respiration data. Results suggest metals inhibit respiration by 13-30% relative to reference sites and this inhibition is directly related to the level of in situ metal stress. We demonstrate the first application of a hydrologic smart tracer as a functional indicator of ecological integrity within anthropogenically influenced flowing water systems and provide data suggesting resilience is limited in hyporheic ecosystems even after more than a century of microbial adaption to chronic pollutants.

Identifying Irrigated Areas in the Snake River Plain, Idaho: Evaluating Performance across Composting Algorithms, Spectral Indices, and Sensors

There are pressing concerns about the interplay between agricultural productivity, water demand, and water availability in semi-arid to arid regions of the world. Currently, irrigated agriculture is the dominant water user in these regions and is estimated to consume approximately 80% of the world’s diverted freshwater resources. We develop an improved irrigated land-use mapping algorithm that uses the seasonal maximum value of a spectral index to distinguish between irrigated and non-irrigated parcels in Idaho’s Snake River Plain. We compare this approach to two alternative algorithms that differentiate between irrigated and non-irrigated parcels using spectral index values at a single date or the area beneath spectral index trajectories for the duration of the agricultural growing season. Using six different pixel and county-scale error metrics, we evaluate the performance of these three algorithms across all possible combinations of two growing seasons (2002 and 2007), two datasets (MODIS and Landsat 5), and three spectral indices, the Normalized Difference Vegetation Index, Enhanced Vegetation Index and Normalized Difference Moisture Index (NDVI, EVI, and NDMI). We demonstrate that, on average, the seasonal-maximum algorithm yields an improvement in classification accuracy over the accepted single-date approach, and that the average improvement under this approach is a 60% reduction in county scale root mean square error (RMSE), and modest improvements of overall accuracy in the pixel scale validation. The greater accuracy of the seasonal-maximum algorithm is primarily due to its ability to correctly classify non-irrigated lands in riparian and developed areas of the study region.

Modeling Package for Assessing the Potential Effects of Hydrologic Change on Stream Form and Integrity

Existing models can be used to assess the potential hydrologic effects of land-use change, but practical tools for translating these results into predictions regarding channel stability and effects on stream biota are currently unavailable to local planners. To improve watershed management in the context of changing land uses, we are developing a flexible, changeable package of small mechanistic models, statistical models, and expert scientific judgment to provide estimates of long-term changes in stream erosion potential, channel morphology, and instream disturbance regime. We are focusing primarily on the development of a Visual Basic / Excel package of stream / land-use management modules that are designed to operate with either continuous or single-event hydrologic input. Based on input channel geometry and flow series, the various modules provide users with estimates of the following characteristics for pre- and post-land use change conditions: (1) the temporal distribution of shear stress, specific stream power, and potential mobility of various particle sizes; (2) potential changes in channel cross sections as a result of altered flow and sedimentation regimes; (3) frequency, depth, and duration of scour; (4) effective discharge / sediment yield; (5) sediment transport capacity based on selected transport equations; and (6) analytical computation of stable channel dimensions. An attractive feature of this approach for stormwater management is a set of user-friendly tools to examine timeintegrated sediment transport and scour characteristics across a range of flows and time periods associated with varying stormwater mitigation schemes. Ultimately, these modules will give end users a suite of tools to compare the erosive potential of hydrographs, to depict channel changes that might result from different land-use management scenarios, and to improve interpretation of biomonitoring information through consistent quantification of stream disturbance regimes.