In most regions of the world, and particularly in developing countries, the possibility and reliability of hydrologic predictions is severely limited, because conventional measurement networks (e.g. rain and stream gauges) are either nonexistent or sparsely located. This study, therefore, investigates various systems methods and newly available data acquisition techniques to evaluate their potential for improving hydrologic predictions in poorly gaged and ungaged watersheds.Part One of this study explores the utility of satellite-remote-sensing-based rainfall estimates for watershed-scale hydrologic modeling at watersheds in the Southeastern U.S. The results indicate that satellite-based rainfall estimates may contain significant bias which varies with watershed size and location. This bias, of course, then propagates into the hydrologic model simulations. However, model performance in large basins can be significantly improved if short-term streamflow observations are available for model calibration.Part Two of this study deals with the fact that hydrologic predictions in poorly gauged/ungauged watersheds rely strongly on a priori estimates of the model parameters derived from observable watershed characteristics. Two different investigations of the reliability of a priori parameter estimates for the distributed HL-DHMS model were conducted. First, a multi-criteria penalty function framework was formulated to assess the degree of agreement between the information content (about model parameters) contained in the precipitation-streamflow observational data set and that given by the a priori parameter estimates. The calibration includes a novel approach to handling spatially distributed parameters and streamflow measurement errors. The results indicated the existence of a significant trade-off between the ability to maintain reasonable model performance while maintaining the parameters close to their a priori values. The analysis indicates those parameters responsible for this discrepancy so that corrective measures can be devised. Second, a diagnostic approach to model performance assessment was developed based on a hierarchical conceptualization of the major functions of any watershed system. "Signature measures" are proposed that effectively extract the information about various watershed functions contained in the streamflow observations. Manual and automated approaches to the diagnostic model evaluation were explored and were found to be valuable in constraining the range of parameter sets while maintaining conceptual consistency of the model.

This study evaluated the potential of using the Surface Energy Balance Algorithm for Land (SEBAL) as a means for estimating evapotranspiration (ET) for local and regional scales in Southern Idaho. The original SEBAL model was refined during this study to provide better estimation of ET in agricultural areas and to make more reliable estimates of ET from other surfaces as well, including mountainous terrain. The modified version of SEBAL used in this study, termed as SEBALID (lD stands for Idaho) includes standardization of the two SEBAL "anchor" pixels, the use of a water balance model to track top soil moisture, adaptation of components of SEBAL for better prediction of the surface energy balance in mountains and sloping terrain, and use of the ratio between actual ET and alfalfa reference evapotranspiration (ETr) as a means for obtaining the temporal integration of instantaneous ET to daily and seasonal values. Validation of the SEBALID model at a local scale was performed by comparing lysimeter ET measurements from the USDA-ARS facility at Kimberly, Idaho, with ET predictions by SEBAL using Landsat 5 TM imagery. Comparison of measured and predicted ET values was challenging due to the resolution of the Landsat thermal band (120m x 120 m) and the relatively small size of the lysimeter fields. In the cases where thermal information was adequate, SEBALID predictions were close to the measured values of ET in the lysimeters. Application of SEBALID at a regional scale was performed using Landsat 7 ETM+ and Landsat 5 TM imagery for the Eastern Snake Plain Aquifer (ESP A) region in Idaho during 2000. The results indicated that SEBALID performed well for predicting daily and seasonal ET for agricultural areas. Some unreasonable results were obtained for desert and basalt areas, due to uncertainties of the prediction of surface parameters. In mountains, even though validation of results was not possible, the values of ET obtained reflected the progress produced by the refinements made to the original SEBAL algorithm.

Climate change and urbanization causes the increasing challenges of flooding in urban watersheds. Even the rivers identified as non-vulnerable are causing catastrophic damage due to heavy flooding. So, several satellite and radar-based precipitation data are considered to study the watersheds with no gauge station or need recent precipitation data. Weather Radar (NEXRAD)arch, the accuracy of satellite-based precipitation data, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR), and radar-based precipitation data, Next Generation Weather Radar (NEXRAD), is evaluated in rainfall-runoff simulation considering Hydrological Engineering Centre-Hydrologic Modeling System (HEC-HMS) and Personal Computer Storm Water Management Model (PCSWMM), respectively.The primary research proposes a framework for modeling the rainfall-runoff process using PERSIANN-CDR and a floodplain map in an ungauged urban watershed. The one-dimensional Hydrologic Engineering Centre-River Analysis System (HEC-RAS) model generates a flood inundation map for the pertinent flooding occurrences from the acquired peak hydrograph, providing a quantifiable display of the inundation extent percentage. The second research uses the PCSWMMs to show the extent of flooding. It also employs the compromise programming method (CPM) to rank the most critical sub-catchments based on three parameters: slope, surface area, and impervious area. Three low-impact development (LID) strategies over the watershed determine the best flood management option. Therefore, the overall study presents a comprehensive framework for flood management in urban watersheds that integrates satellite precipitation data, hydrologic modeling, and LID strategies. The framework can provide an accurate flood-prone zone and help prioritize critical sub-catchments for flood management options. The study proposes using HEC-HMS and PCSWMM models to simulate and analyze interactions between rainfall, runoff, and the extent of the flood zone. Furthermore, LID can be applied to reduce flooding in urban watersheds. Overall, the framework can be helpful for policymakers and system managers to build the watershed's resilience during catastrophic flooding events caused by climate change and urbanization.

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No description available.

For the process of social and economic development, hydropower energy has an important role such as being renewable, clean, and having less impact on the environment. In decision of the hydropower potential of a study area, the preliminary condition is the availability of the gages in the area. However, in Turkey, the gages in working order are limited and getting decreased in recent years. Therefore, the satellite based precipitation estimates has been gaining importance to predict runoff for ungauged basins. In this study, &Ccedil / oruh basin, which is located in the north-eastern part of Turkey, is selected to perform hydrologic modeling. The input precipitation data for the model are provided from the observations at meteorological stations and the Tropical Rainfall Measuring Mission (TRMM) satellite products (3B42 and 3B43). TRMM satellite is used to monitor and study the rainfall distribution. The precipitation radar on the TRMM is the first radar to make precipitation estimation from the space. Using both precipitation data, HEC-HMS, being well known hydrological model, is applied to the &Ccedil / oruh Basin for 2005 and 2003 water years. To distinguish the differences in the runoff simulations and water budget, comparisons are done with respect to flow monitoring stations. Statistical criteria show that model simulation results obtained from TRMM 3B42 products are promising in estimating the water potential in ungauged basins.

A method for precisely synchronizing an external serial data stream to the pulse-per-second (PPS) output signal from a global navigation satellite-based system (GNSS) receiver was investigated. A signal timing device was designed that used a digital signal processor (DSP) with serial inputs and input captures to generate time stamps for asynchronous serial data based on an 58593.75 Hz internal timer. All temporal measurements were made directly in hardware to eliminate software latency. The resolution of the system was 17.1 µs, which translated to less than one millimeter of horizontal position error at travel speeds typical of most agricultural operations. The dynamic error of a TTS was determined using a rotary test fixture. Tests were performed at angular velocities ranging from 0 to 3.72 rad/s and a radius of 0.635 m. Average latency from the TTS was shown to be consistently near 0.252 s for all angular velocities and less variable when using a reflector based machine target versus a prism target. Sight distance from the target to the TTS was shown to have very little effect on accuracy between 4 and 30 m. The TTS was determined to be a limited as a position reference for dynamic GNSS and vehicle auto-guidance testing based on angular velocity. The dynamic error of a GNSS receiver was determined using the rotary test fixture and modeled as discrete probability density functions for varying angular velocities and filter levels. GNSS position and fixture data were recorded for angular velocities of 0.824, 1.423, 2.018, 2.618, and 3.222 rad/s at a 1 m radius. Filter levels were adjusted to four available settings including; no filter, normal filter, high filter, and max filter. Each data set contained 4 hours of continuous operation and was replicated three times. Results showed that higher angular velocities increased the variability of the distribution of error while not having a significant effect on average error. The distribution of error tended to change from normal distributions at lower angular velocities to uniform distributions at higher angular velocities. Internal filtering was shown to consistently increase dynamic error for all angular velocities.

No description available.

Les références de temps et de fréquence sont largement distribuées sur réseaux informatique et de communications, pour une large gamme d'applications scientifiques et industrielles. Poussé par une demande pour de meilleures performances, un certain nombre de nouvelles méthodes de transfert de temps et de fréquence sur des réseaux à fibres optiques ont été développées ces dernières années. Dans cette thèse, notre objectif est de développer une approche de transfert de temps et de fréquence multi-utilisateurs, compatible avec les réseaux de télécommunications et compétitive avec la distribution de temps par GNSS. Nous nous intéressons donc aux méthodes pour les réseaux à commutation par paquets, comme le NTP (Network Time Protocol) et le PTP (Precision Timing Protocol). Nous nous concentrons également sur les liaisons “unidirectionnelles”, où les signaux aller et retour entre les nœuds de réseau se propagent sur des fibres distinctes, non au sein d’une même fibre (liaisons “bidirectionnelles”). En particulier, nous utilisons une méthode appelée White Rabbit PTP (WR). Développée au CERN, basée sur PTP, utilisant l’Ethernet synchrone et d'autres techniques pour atteindre des performances élevées, WR réalise une stabilité du temps sous-nanoseconde pour la synchronisation d'instruments sur des réseaux à l'échelle de 10 km. Nous sommes particulièrement intéressés par l'extension de cette méthode pour la distribution de références au niveau régional ou national, sur des liaisons allant jusqu'à 1000 km.Nous étudions d’abord les performances de l'équipement réseau White Rabbit, en particulier le commutateur White Rabbit. Nous y apportons diverses améliorations : sur le verrouillage du commutateur grand maître à la référence externe, améliorant ainsi sa stabilité à court terme de plus d'un ordre de grandeur ; sur la bande passante de verrouillage du commutateur esclave ; et en augmentant le débit des messages PTP entre les commutateurs maître et esclave.Nous étudions ensuite les liaisons WR moyennes et longues distances. Nous construisons un lien unidirectionnel de 100 km en utilisant des bobines de fibres dans le laboratoire. Nous découvrons que la performance à court terme est limitée par la dispersion chromatique de la fibre, tandis que la performance à long terme est dégradée par le bruit thermique. Pour limiter l'effet de la dispersion chromatique sur les liaisons longue distance, nous proposons l'utilisation d'une approche en cascade. Nous réalisons un lien en cascade de 500 km, à nouveau avec des bobines de fibres. Nous utilisons le multiplexage en longueur d'onde dense pour construire ce lien par des passages multiples à travers des bobines plus courtes. Nous obtenons une stabilité de transfert de fréquence de 2 × 10-12 à une seconde de temps d'intégration et de 5 × 10-15 en un jour, limitée par le bruit thermique à long terme. Nous obtenons une stabilité temporelle de 5 ps à une seconde de temps d'intégration, diminuant jusqu'à un minimum de 1,2 ps à 20 secondes et restant inférieure à une nanoseconde pour des durées plus longues. Ces performances sont similaires à court terme, et deux ordres de grandeur meilleures à long terme, qu’un récepteur GPS de bonne qualité. Nous nous attendons à ce que les fluctuations thermiques et donc l'effet du bruit thermique des fibres soient réduits d'un facteur d'environ cinq pour les installations sur le terrain.Enfin, nous faisons des études préliminaires sur l'étalonnage en temps des liaisons WR. Le principal défi est de mesurer l'asymétrie de longueur optique entre les deux fibres utilisées pour le transfert des signaux aller et retour. Nous démontrons une technique d'échange de fibres, en utilisant une liaison suburbaine White Rabbit sur fibre noire. Nous décrivons et testons ensuite une nouvelle méthode variationnelle pour l'étalonnage, impliquant une méthode de mesure différentielle basée sur l'exploitation de deux liaisons WR à différentes longueurs d'onde sur la même liaison. / Time and frequency references are widely distributed over communications and computer networks, for a variety of scientific and industrial applications. Driven by a demand for improved performance, a number of new methods for time and frequency transfer over optical fiber-based networks have been developed in recent years. In this thesis our objective is to develop a scalable network time and frequency transfer approach, providing multi-user dissemination, compatible with large telecommunication networks and competitive with GNSS-based time distribution. Therefore we are concerned with methods for use in packet-based networks, like the Network Time Protocol (NTP) and Precision Timing Protocol (PTP). We also concentrate on “unidirectional” links, where the forward and backward signals between network nodes propagate over separate fibers, not within the same fiber (“bidirectional” links).In particular we use a method called White Rabbit PTP (WR). This is a novel technology developed at CERN, based on PTP while using Synchronous Ethernet and other techniques to achieve high performance. It demonstrates sub-nanosecond time stability and synchronization of arrays of instruments over 10 km scale networks. We are particularly interested in extending this method for large scale distribution of references at regional or national level, over links of up to 1000 km.We first study extensively the default performances and limitations of White Rabbit network equipment, in particular the White Rabbit switch. We make various improvements to its operation: on the locking of the grandmaster switch to the external reference, thus improving its short-term stability by more than an order of magnitude; optimizing the locking bandwidth of the slave switch; and increasing the PTP messaging rate between master and slave switches.We then study medium and long-distance WR links. We construct a 100 km, unidirectional link using fiber spools in the laboratory. We discover that the short-term performance is limited by chromatic dispersion in the fiber, while the long-term performance is degraded by the influence of temperature variations on the fiber. To limit the effect of chromatic dispersion for long-haul links, we propose the use of a cascaded approach. We realise a national scale, cascaded, 500 km link, again utilizing fiber spools. We use Dense Wavelength Division Multiplexing methods to construct this link by mutliple passages through shorter spools. We achieve a frequency transfer stability of 2 × 10−12 at one second of integration time and 5 × 10−15 at one day, limited by thermal noise in the long term. We achieve a time stability of 5 ps at one second of integration time, decreasing to a minimum of 1.2 ps at 20 seconds and remaining below one nanosecond for longer averaging times. These performances are similar in the short term, and two orders of magnitude better in the long term, than good quality GPS receivers. We expect thermal fluctuations and therefore the effect of fiber thermal noise to be suppressed by a factor of approximately five for installations in the field.Finally we make preliminary investigations of time calibration of WR links. The main challenge here is to measure the optical length asymmetry between the two fibers used for signal transfer in the forward and backward directions. We demonstrate a fiber swapping technique, using a mid range, suburban White Rabbit link over dark fiber. We then describe and test a new variational method for calibration, involving a differential measurement method based on operating two WR links at different wavelengths over the same optical fiber link.In conclusion, we demonstrate high performance, long haul White Rabbit links for time and frequency dissemination to multiple users. With the level of frequency transfer performance achieved, White Rabbit PTP provides a competitive and scalable technique for comparing industrial atomic clocks at regional and national scales.

<p dir="ltr">Canopy height is a fundamental metric for extracting valuable information about forested areas. Over the past decade, Lidar technology has provided a straightforward approach to measuring canopy height using various platforms such as terrestrial, unmanned aerial vehicle (UAV), airborne, and satellite sensors. However, satellite Lidar data, even with its global coverage, has a sparse sampling pattern that doesn’t provide continuous coverage over the globe. In contrast, satellites like LANDSAT offer seamless and widespread coverage of the Earth's surface through spectral data. Can we exploit the abundant spectral information from satellites like LANDSAT and ECOSTRESS to infer structural information obtained from Lidar satellites like Global Ecosystem Dynamic Investigation (GEDI)? This study aims to develop a deep learning model that can infer canopy height derived from sparsely observed Lidar waveforms using multi-sensor spectral data from spaceborne platforms. Specifically designed for localized site, the model focuses on county-level canopy height estimation, taking advantage of the relationship between canopy height and spectral reflectance that can be established in a local setting – something which might not exist universally. The study hopes to achieve a framework that can be easily replicable as height is a dynamic metric which changes with time and thus requires repeated computation for different time periods.</p><p dir="ltr">The thesis presents a series of experiments designed to comprehensively understand the influence of different spectral datasets on the model’s performance and its effectiveness in different types of test sites. Experiment 1 and 2 utilize Landsat spectral band values to extrapolate canopy height, while Experiment 3 and 4 incorporate ECOSTRESS land surface temperature and emissivity band values in addition to Landsat data. Tippecanoe County, predominantly composed of cropland, serves as the test site for Experiment 1 and 3, while Monroe County, primarily covered by forests, serves as the test site for Experiment 2 and 4. When compared to the Airborne Lidar dataset from the United States Geological Survey (USGS) – 3D Elevation Program (3DEP), the model achieves a Root Mean Square Error (RMSE) of 4.604m for Tippecanoe County using Landsat features while 5.479m for Monroe County. After integrating Landsat and ECOSTRESS features, the RMSE improves to 4.582m for Tippecanoe County but deteriorates to 5.860m for Monroe County. Overall, the study demonstrates comparable results to previous research without requiring feature engineering or extensive pre-processing. Furthermore, it successfully introduces a novel methodology for integrating multiple sources of satellite data to address this problem.</p>