Alejandro N. Flores

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

Melvin L. Kunkel et al. — Wed, 28 Sep 2011 16:00:34 PDT

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.

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

Alejandro N. Flores et al. — Tue, 27 Apr 2010 21:27:55 PDT

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.

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

Alejandro N. Flores et al. — Tue, 10 Nov 2009 16:29:39 PST

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

Alejandro N. Flores et al. — Mon, 21 Sep 2009 11:12:17 PDT

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 hillslopescale 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.