The role of hydrograph indices in parameter estimation of rainfall-runoff models

A reliable prediction of hydrologic models, among other things, requires a set of plausible parameters that correspond with physiographic properties of the basin. This study proposes a parameter estimation approach, which is based on extracting, through hydrograph diagnoses, information in the form of indices that carry intrinsic properties of a basin. This concept is demonstrated by introducing two indices that describe the shape of a streamflow hydrograph in an integrated manner. Nineteen mid-size (223-4790 km2) perennial headwater basins with a long record of streamflow data were selected to evaluate the ability of these indices to capture basin response characteristics. An examination of the utility of the proposed indices in parameter estimation is conducted for a five-parameter hydrologic model using data from the Leaf River, located in Fort Collins, Mississippi. It is shown that constraining the parameter estimation by selecting only those parameters that result in model output which maintains the indices as found in the historical data can improve the reliability of model predictions. These improvements were manifested in (a) improvement of the prediction of low and high flow, (b) improvement of the overall total biases, and (c) maintenance of the hydrograph's shape for both long-term and short-term predictions. Copyright © 2005 John Wiley & Sons, Ltd

Radar Z-R relationship for summer monsoon storms in Arizona

Radar-based estimates of rainfall rates and accumulations are one of the principal tools used by the National Weather Service (NWS) to identify areas of extreme precipitation that could lead to flooding. Radar-based rainfall estimates have been compared to gauge observations for 13 convective storm events over a densely instrumented, experimental watershed to derive an accurate reflectivity-rainfall rate (i.e., Z-R) relationship for these events. The resultant Z-R relationship, which is much different than the NWS operational Z-R, has been examined for a separate, independent event that occurred over a different location. For all events studied, the NWS operational Z-R significantly overestimates rainfall compared to gauge measurements. The gauge data from the experimental network, the NWS operational rain estimates, and the improved estimates resulting from this study have been input into a hydrologic model to "predict" watershed runoff for an intense event. Rainfall data from the gauges and from the derived Z-R relation produce predictions in relatively good agreement with observed streamflows. The NWS Z-R estimates lead to predicted peak discharge rates that are more than twice as large as the observed discharges. These results were consistent over a relatively wide range of subwatershed areas (4-148 km2). The experimentally derived Z-R relationship may provide more accurate radar estimates for convective storms over the southwest United States than does the operational convective Z-R used by the NWS. These initial results suggest that the generic NWS Z-R relation, used nationally for convective storms, might be substantially improved for regional application. © 2005 American Meteorological Society

The "Binarity and Magnetic Interactions in various classes of Stars" (BinaMIcS) project

The "Binarity and Magnetic Interactions in various classes of stars" (BinaMIcS) project is based on two large programs of spectropolarimetric observations with ESPaDOnS at CFHT and Narval at TBL. Three samples of spectroscopic binaries with two spectra (SB2) are observed: known cool magnetic binaries, the few known hot magnetic binaries, and a survey sample of hot binaries to search for additional hot magnetic binaries. The goal of BinaMIcS is to understand the complex interplay between stellar magnetism and binarity. To this aim, we will characterise and model the magnetic fields, magnetospheric structure and coupling of both components of hot and cool close binary systems over a significant range of evolutionary stages, to confront current theories and trigger new ones. First results already provided interesting clues, e.g. about the origin of magnetism in hot stars.Comment: 4 pages, 2 figures, proceedings of the SF2A conferenc

Feasibility of MV CBCT-based treatment planning for urgent radiation therapy: dosimetric accuracy of MV CBCT-based dose calculations.

Unlike scheduled radiotherapy treatments, treatment planning time and resources are limited for emergency treatments. Consequently, plans are often simple 2D image-based treatments that lag behind technical capabilities available for nonurgent radiotherapy. We have developed a novel integrated urgent workflow that uses onboard MV CBCT imaging for patient simulation to improve planning accuracy and reduce the total time for urgent treatments. This study evaluates both MV CBCT dose planning accuracy and novel urgent workflow feasibility for a variety of anatomic sites. We sought to limit local mean dose differences to less than 5% compared to conventional CT simulation. To improve dose calculation accuracy, we created separate Hounsfield unit-to-density calibration curves for regular and extended field-of-view (FOV) MV CBCTs. We evaluated dose calculation accuracy on phantoms and four clinical anatomical sites (brain, thorax/spine, pelvis, and extremities). Plans were created for each case and dose was calculated on both the CT and MV CBCT. All steps (simulation, planning, setup verification, QA, and dose delivery) were performed in one 30 min session using phantoms. The monitor units (MU) for each plan were compared and dose distribution agreement was evaluated using mean dose difference over the entire volume and gamma index on the central 2D axial plane. All whole-brain dose distributions gave gamma passing rates higher than 95% for 2%/2 mm criteria, and pelvic sites ranged between 90% and 98% for 3%/3 mm criteria. However, thoracic spine treatments produced gamma passing rates as low as 47% for 3%/3 mm criteria. Our novel MV CBCT-based dose planning and delivery approach was feasible and time-efficient for the majority of cases. Limited MV CBCT FOV precluded workflow use for pelvic sites of larger patients and resulted in image clearance issues when tumor position was far off midline. The agreement of calculated MU on CT and MV CBCT was acceptable for all treatment sites

Alien Registration- Morin, Joseph E. (Lewiston, Androscoggin County)

https://digitalmaine.com/alien_docs/27530/thumbnail.jp

Spatial patterns in thunderstorm rainfall events and their coupling with watershed hydrological response

Weather radar systems provide detailed information on spatial rainfall patterns known to play a significant role in runoff generation processes. In the current study, we present an innovative approach to exploit spatial rainfall information of air mass thunderstorms and link it with a watershed hydrological model. Observed radar data are decomposed into sets of rain cells conceptualized as circular Gaussian elements and the associated rain cell parameters, namely, location, maximal intensity and decay factor, are input into a hydrological model. Rain cells were retrieved from radar data for several thunderstorms over southern Arizona. Spatial characteristics of the resulting rain fields were evaluated using data from a dense rain gauge network. For an extreme case study in a semi-arid watershed, rain cells were derived and fed as input into a hydrological model to compute runoff response. A major factor in this event was found to be a single intense rain cell (out of the five cells decomposed from the storm). The path of this cell near watershed tributaries and toward the outlet enhanced generation of high flow. Furthermore, sensitivity analysis to cell characteristics indicated that peak discharge could be a factor of two higher if the cell was initiated just a few kilometers aside. © 2005 Elsevier Ltd. All rights reserved

Hydrologic response of a semi-arid watershed to spatial and temporal characteristics of convective rain cells

Rain can be measured and represented in many ways such as point data from rain gauges, grid data from meteorological radar, or interpolated data. In this paper we represent rain fields by implementing a rain cell model of convective rain cells. The rain fields are used as an input to a hydrological model to test the watershed response to spatial and temporal characteristics of the rain cells. As a case study we tested an extreme storm event over a semi-arid watershed in southern Israel. The rain cell model was found to simulate the rain storm adequately. The use of these modeled cells allowed us to test the sensitivity of the watershed hydrological response to rain cell characteristics and it was found that the watershed is mainly sensitive to the starting location of the rain cell. Relatively small changes in the rain cell's location, speed and direction may increase watershed peak discharge by three-fold

Approximating optimization problems over convex functions

Many problems of theoretical and practical interest involve finding an optimum over a family of convex functions. For instance, finding the projection on the convex functions in $H^k(\Omega)$, and optimizing functionals arising from some problems in economics. In the continuous setting and assuming smoothness, the convexity constraints may be given locally by asking the Hessian matrix to be positive semidefinite, but in making discrete approximations two difficulties arise: the continuous solutions may be not smooth, and functions with positive semidefinite discrete Hessian need not be convex in a discrete sense. Previous work has concentrated on non-local descriptions of convexity, making the number of constraints to grow super-linearly with the number of nodes even in dimension 2, and these descriptions are very difficult to extend to higher dimensions. In this paper we propose a finite difference approximation using positive semidefinite programs and discrete Hessians, and prove convergence under very general conditions, even when the continuous solution is not smooth, working on any dimension, and requiring a linear number of constraints in the number of nodes. Using semidefinite programming codes, we show concrete examples of approximations to problems in two and three dimensions