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Mason Roberts
Mason Roberts

Adjustment Computations: Spatial Data Analysis


Adjustment Computations is the classic textbook for spatial information analysis and adjustment computations, providing clear, easy-to-understand instruction backed by real-world practicality. From the basic terms and fundamentals of errors to specific adjustment computations and spatial information analysis, this book covers the methodologies and tools that bring accuracy to surveying, GNSS, GIS, and other spatial technologies. Broad in scope yet rich in detail, the discussion avoids overly-complex theory in favor of practical techniques for students and professionals. This new sixth edition has been updated to align with the latest developments in this rapidly expanding field, and includes new video lessons and updated problems, including worked problems in STATS, MATRIX, ADJUST, and MathCAD.




Adjustment Computations: Spatial Data Analysis


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As spatial technologies expand in both use and capability, so does our need for professionals who understand how to check and adjust for errors in spatial data. Conceptual knowledge is one thing, but practical skills are what counts when accuracy is at stake; Adjustment Computations provides the real-world training you need to identify, analyze, and correct for potentially crucial errors.


The Damped Bundle Adjustment Toolbox (DBAT) is a free, open-source, toolbox for bundle adjustment. The purpose of DBAT is to provide an independent, open-source toolkit for statistically rigorous bundle adjustment computations. The capabilities include bundle adjustment, network analysis, point filtering, forward intersection, spatial intersection, plotting functions, and computations of quality indicators such as posterior covariance estimates and parameter correlations. DBAT is written in the high-level Matlab language and includes several processing example files. The input formats have so far been restricted to PhotoModeler export files and Photoscan (Metashape) native files. Fine-tuning of the processing has so far required knowledge of the Matlab language. This paper describes the development of a scripting language based on the XML (eXtensible Markup Language) language that allow the user a fine-grained control over what operations are applied to the input data, while keeping the needed programming skills at a minimum. Furthermore, the scripting language allows a wide range of input formats. Additionally, the XML format allows simple extension of the script file format both in terms of adding new operations, file formats, or adding parameters to existing operations. Overall, the script files will in principle allow DBAT to process any kind of photogrammetric input and should extend the usability of DBAT as a scientific and teaching tool for photogrammetric computations.


Given the high resolution and quality of NEXRAD data, the NWShas an opportunity todepart from lumped parameter modeling and more effectivelyaccount for the spatial andtemporal variability of precipitation. For example, currentprocedures at RFC's usually involvethe generation of mean areal precipitation (MAP) values derivedfrom point raingage data. These average inputs are normally for 6 hour computational timeincrements and for watershedsseveral hundred square miles in area. These models arecalibrated using up to 45 years ofhistorical streamflow and precipitation data, with the resultthat the hydrologic model parametersare inherently related to the spatial and temporal scale ofcalibration. In contrast, NEXRAD willprovide hourly rainfall measurements over a 4 x 4 km grid,representing unprecedentedresolution for the United States.


Rather than moving directly to a gridded or other highresolution distributed parameterhydrologic model that is based on the NEXRAD grid, asemi-distributed modeling approach hasbeen adopted in HRL as a first step towards utilization of theNEXRAD data (Smith, 1995). Inthis format, a basin currently being modeled by an RFC would bedisaggregated into severalconstituent sub-basins. Instead of using point raingagemeasurements to compute MAP valuesfor each entire basin, MAP values for each sub-basin would bederived from the griddedNEXRAD values. Unit hydrographs would be developed from standardmethods orgeomorphological analysis and used to convert runoff volumes todischarge values. AMuskingum-Cunge routing operation will be used to translatehydrographs to the nextdownstream computational point. The goal of the overall researchis to provide RFC personnelwith: 1) hydrologic tools to model sub-basins, 2) guidelines asto what degree to disaggregatea lumped basin to capture essential spatial rainfall variability,and 3) guidelines as to theadjustment of calibrated model parameters to account for fineroperational modeling scale. Future research will address the development of griddeddistributed parameter models.


However, in this approach, some adjustment must be made to thehydrologic modelparameters as they are derived at a basin scale and 6-hour timestep and then used operationallyat sub-basin scale with 1-hour NEXRAD data. Runoff volumesgenerated at the calibration scalewill be different than those generated at the operational scale. Thus, the NWS must understandhow to adjust model parameters when calibrating at one scale andoperationally forecasting ata different spatial and temporal scale.


The greatest surface runoff was computed when the watershedwas modeled at the finestspatial scale. Thus, as a basin is disaggregated into smallersub-basins to capture the spatialvariability of rainfall as measured by NEXRAD, more surface flowwould be generated if thesame hydrologic model parameters were used as in calibration. Thesub-basin representationwould no longer represent a calibrated system; the SAC-SMAparameters governing thegeneration of surface flow would need adjustment. Figure 1 alsoshows that parametersassociated with the generation of interflow and supplemental baseflow would need adjustment.


Statistics is the science of data. Statistical reasoning, methods, design and data analysis are crucial for all disciplines in which data are present. As collection of data grows in all sectors of employment, the Department of Statistics prepares students to meet the challenges of data science, including design of experiments, data collection, curation, analysis, and interpretation. 041b061a72


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