Douglas A. Miller

A Conterminous United States Multilayer Soil Characteristics Dataset for Regional Climate and Hydrology Modeling

Soil information is now widely required by many climate and hydrology models and soil-vegetation-atmosphere transfer schemes. This pa- per describes the development of a multilayer soil characteristics dataset for the conterminous United States (CONUS-SOIL) that specifically addresses the need for soil physical and hydraulic property information over large areas. The State Soil Geographic Database (STATSGO) developed by the U.S. De- partment of Agriculture-Natural Resources Conservation Service served as the starting point for CONUS-SOIL. Geographic information system and Perl computer programming language tools were used to create map coverages of soil properties including soil texture and rock fragment classes, depth-to-bed- rock, bulk density, porosity, rock fragment volume, particle-size (sand, silt, and clay) fractions, available water capacity, and hydrologic soil group. In- terpolation procedures for the continuous and categorical variables describing these soil properties were developed and applied to the original STATSGO data. In addition to any interpolation errors, the CONUS-SOIL dataset reflects the limitations of the procedures used to generate detailed county-level soil

Simulating the river-basin response to atmospheric forcing by linking a mesoscale meteorological model and hydrologic model system

Abstract The purpose of this article is to test the ability of a distributed meteorological/hydrologic model to simulate the hydrologic response to three single-storm events passing over the Upper West Branch of the Susquehanna River Basin. The high-resolution precipitation fields for three storms are provided by observations and by the Penn State–NCAR Mesoscale Meteorological Model (MM5) with three nested domains. The MM5 simulation successfully captures the storm patterns over the study area, although some temporal and spatial discrepancies exist between observed and simulated precipitation fields. Observed and simulated precipitation data for those storms are used to drive the Hydrologic Model System (HMS). The output from HMS is compared to the measured hydrographic streamflow at the outlet of the Upper West Branch. The Curve Number and Green-Ampt methods of rainfall-runoff partitioning are used in HMS and evaluated for streamflow simulation. The results of the hydrologic simulation compare well with observed data when using the Curve Number partitioning, but underestimate observed data when using the Green-Ampt. The likely cause is the lack of heterogeneity in hydraulic parameters. The simulated streamflow with the MM5-simulated precipitation is lower than the simulated streamflow with observed precipitation. The experiments suggest that the subgrid-scale spatial variability in precipitation and hydraulic parameters should be included in future model development

A linked meteorological and hydrological model system: the Susquehanna River Basin Experiment (SRBEX).

Abstract The goal of the Susquehanna River Basin Experiment (SRBEX) is to simulate the basins hydrologic response to atmospheric forcing at various time scales. To reach this goal, SRBEX concentrates on developing climate downscaling methodologies. One downscaling approach links a high-resolution meteorological model and a suite of coupled hydrological models. This paper (1) provides an overview of this linked model system and its elements, (2) describes a series of simulations and sensitivity experiments, and (3) discusses ongoing model development. In a typical simulation, a nested version of the Penn State-NCAR mesoscale meteorological model (MM5) simulates the precipitation from a storm system passing over the river basin. The resulting high-resolution precipitation field, with grid increments as fine as 4 km, then drives the Hydrological Modeling System (HMS). HMS is composed of physically based, interactive surface routing, groundwater, soil water, and channel leakage components. An important aspect of the system is the application of a geographic information system (GIS) to control the high-resolution soils, land-use, and digital terrain data at the model interface. One significant output from the model system is a simple hydrograph, which represents the integration of basin hydrology over time and space. Experiments have been performed to determine the sensitivity of the linked model system results to the rainfall-runoff abstraction method and to the MM5 nesting scheme. Ongoing research aims at expanding the time scales of analysis, improving model efficiency and speed of computation, and changing the present meteorological–hydrological interaction from a linked system to a coupled system.

The Use of LiDAR Terrain Data in Characterizing Surface Roughness and Microtopography

The availability of light detection and ranging data (LiDAR) has resulted in a new era of landscape analysis. For example, improvements in LiDAR data resolution may make it possible to accurately model microtopography over a large geographic area; however, data resolution and processing costs versus resulting accuracy may be too costly. We examined two LiDAR datasets of differing resolutions, a low point density (0.714 points/m2 spacing) 1 m DEM available statewide in Pennsylvania and a high point density (10.28 points/m2 spacing) 1 m DEM research-grade DEM, and compared the calculated roughness between both resulting DEMs using standard deviation of slope, standard deviation of curvature, a pit fill index, and the difference between a smoothed splined surface and the original DEM. These results were then compared to field-surveyed plots and transects of microterrain. Using both datasets, patterns of roughness were identified, which were associated with different landforms derived from hydrogeomorphic features such as stream channels, gullies, and depressions. Lowland areas tended to have the highest roughness values for all methods, with other areas showing distinctive patterns of roughness values across metrics. However, our results suggest that the high-resolution research-grade LiDAR did not improve roughness modeling in comparison to the coarser statewide LiDAR. We conclude that resolution and initial point density may not be as important as the algorithm and methodology used to generate a LiDAR-derived DEM for roughness modeling purposes.

Forecasting runoff from Pennsylvania landscapes

Identifying sites prone to surface runoff has been a cornerstone of conservation and nutrient management programs, relying upon site assessment tools that support strategic, as opposed to operational, decision making. We sought to develop simple, empirical models to represent two highly different mechanisms of surface runoff generation—saturation excess runoff and infiltration excess runoff—using variables available from short-term weather forecasts. Logistic regression models were developed from runoff monitoring studies in Pennsylvania, fitting saturation excess runoff potential to rainfall depth, rainfall intensity, and soil moisture, and infiltration excess runoff potential to rainfall depth and intensity. Testing of the models in daily hindcasting mode over periods of time and at sites separate from where they were developed confirmed a high degree of skill, with Brier Skill Scores ranging from 0.61 to 0.65 and Gilbert Skill Scores ranging from 0.39 to 0.59. These skill scores are as good as models used in weather forecasting. Results point to the capability to forecast site-specific surface runoff potential for diverse soil conditions, with advances in weather forecasting likely to further improve the predictive ability of runoff models of this type.

Aircraft and satellite remote sensing of desert soils and landscapes

Abstract The remote sensing of desert soils and landscapes using Thematic Mapper (TM), Heat Capacity Mapping Mission (HCMM), Simulated SPOT, and Thermal Infrared Multispectral Scanner (TIMS) data is discussed. These studies were all conducted in arid or semiarid study sites. Landsat Thematic Mapper (TM) data for southwestern Nevada discriminated among alluvial fan deposits with different degrees of desert pavement and varnish as well as different vegetation cover. Thermal-infrared data acquired from the Heat Capacity Mapping Mission (HCMM) satellite were used to map the spatial distribution of diurnal surface temperatures and to estimate mean annual soil temperatures in semiarid east central Utah using diurnal data for five dates throughout a year. Simulated SPOT data for northwestern New Mexico identified geomorphic features, such as differences in eolian sand cover and fluvial incision, which are correlated with surface age and geomorphic stability of landscape components. The Thermal Infrared Multispectral Scanner (TIMS), which is an aircraft scanner that provides six-channel spectral capability in the thermal region of the electromagnetic spectrum, was used to depict surface geologic features of the Saline Valley in southeastern California. These research projects are presented as a summary of some of the sensors and analytical techniques that are useful in the study of desert soils and landscapes.

Topographic variables improve climatic models of forage species abundance in the northeastern United States

Question Species distribution modelling has most commonly been applied to presence-only data and to woody species. Can similar methods be used to create detailed predicted abundance maps for forage species? These predictions would be of great value for agricultural management and land-use planning. Location Northeastern USA. Methods We used field data from 31 grazed farms to model abundances for six forage species with three statistical methods: GLM, GAM and Random Forest models. A hierarchical ecological framework encompassing climatic, edaphic and topographic variables related to the plant species requirements for water, light and temperature was used to guide variable selection. Results Although many species distribution modelling studies have used only climatic variables, the inclusion of topography greatly improved explanatory power. Edaphic variables contributed little more beyond the information already provided by climate and topography. Random Forest models had higher overall predictive capability, and were used to produce the final potential abundance maps for the six forage species. Conclusions Climate-only predictions may be suitable for state or regional planning, but topographic variables must be included in species distribution models used to support decision-making at the farm and field scales.

No-till management intensity zones for Pennsylvania.

A classification of management intensity zones for no-till maize production was developed for Pennsylvania using GIS. Zones were based on analysis of Growing Degree Days, drainage characteristics, slope, water holding capacity of the root zone, and rock fragment content. Six zones were distinguished, reflecting the relative management requirements for notill. The map was produced as a tool for farmers and extension agents to recognize the challenges associated with no-till in their area.