Survey of methods for soil moisture determination

Existing and proposed methods for soil moisture determination are discussed. These include: (1) in situ investigations including gravimetric, nuclear, and electromagnetic techniques; (2) remote sensing approaches that use the reflected solar, thermal infrared, and microwave portions of the electromagnetic spectrum; and (3) soil physics models that track the behavior of water in the soil in response to meteorological inputs (precipitation) and demands (evapotranspiration). The capacities of these approaches to satisfy various user needs for soil moisture information vary from application to application, but a conceptual scheme for merging these approaches into integrated systems to provide soil moisture information is proposed that has the potential for meeting various application requirements

Analysis of information systems for hydropower operations

The operations of hydropower systems were analyzed with emphasis on water resource management, to determine how aerospace derived information system technologies can increase energy output. Better utilization of water resources was sought through improved reservoir inflow forecasting based on use of hydrometeorologic information systems with new or improved sensors, satellite data relay systems, and use of advanced scheduling techniques for water release. Specific mechanisms for increased energy output were determined, principally the use of more timely and accurate short term (0-7 days) inflow information to reduce spillage caused by unanticipated dynamic high inflow events. The hydrometeorologic models used in predicting inflows were examined to determine the sensitivity of inflow prediction accuracy to the many variables employed in the models, and the results used to establish information system requirements. Sensor and data handling system capabilities were reviewed and compared to the requirements, and an improved information system concept outlined

Understanding climate: A strategy for climate modeling and predictability research, 1985-1995

The emphasis of the NASA strategy for climate modeling and predictability research is on the utilization of space technology to understand the processes which control the Earth's climate system and it's sensitivity to natural and man-induced changes and to assess the possibilities for climate prediction on time scales of from about two weeks to several decades. Because the climate is a complex multi-phenomena system, which interacts on a wide range of space and time scales, the diversity of scientific problems addressed requires a hierarchy of models along with the application of modern empirical and statistical techniques which exploit the extensive current and potential future global data sets afforded by space observations. Observing system simulation experiments, exploiting these models and data, will also provide the foundation for the future climate space observing system, e.g., Earth observing system (EOS), 1985; Tropical Rainfall Measuring Mission (TRMM) North, et al. NASA, 1984

Effective and efficient algorithm for multiobjective optimization of hydrologic models

Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristics of the observed data deemed to be important. One strategy to circumvent this problem is to define several optimization criteria (objective functions) that measure different (complementary) aspects of the system behavior and to use multicriteria optimization to identify the set of nondominated, efficient, or Pareto optimal solutions. In this paper, we present an efficient and effective Markov Chain Monte Carlo sampler, entitled the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm, which is capable of solving the multiobjective optimization problem for hydrologic models. MOSCEM is an improvement over the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution (Pareto set). The efficacy of the MOSCEM-UA algorithm is compared with the original MOCOM-UA algorithm for three hydrologic modeling case studies of increasing complexity

A systems analysis of applications of earth orbital space technology to selected cases in water management and agriculture. Volume 1 - Technical summary

Systems analysis of agricultural and water management information systems utilizing satellite borne multispectral band scanner, radar, and television equipmen

The application of remote sensing to the development and formulation of hydrologic planning models

For abstract, see N76-14576

Incorporation of uncertainties in real-time catchment flood forecasting

Floods have become the most prevalent and costly natural hazards in the U.S. When preparing real-time flood forecasts for a catchment flood warning and preparedness system, consideration must be given to four sources of uncertainty -- natural, data, model parameters, and model structure. A general procedure has been developed for applying reliability analysis to evaluate the effects of the various sources of uncertainty on hydrologic models used for forecasting and prediction of catchment floods. Three reliability analysis methods -- Monte Carlo simulation, mean value and advanced first-order second moment analyses (MVFOSM and AFOSM, respectively) - - were applied to the rainfall -runoff modeling reliability problem. Comparison of these methods indicates that the AFOSM method is probably best suited to the rainfall-runoff modeling reliability problem with the MVFOSM showing some promise. The feasibility and utility of the reliability analysis procedure are shown for a case study employing as an example the HEC-1 and RORB rainfall-runoff watershed models to forecast flood events on the Vermilion River watershed at Pontiac, Illinois. The utility of the reliability analysis approach is demonstrated for four important hydrologic problems: 1) determination of forecast (or prediction) reliability, 2) determination of the flood level exceedance probability due to a current storm and development of "rules of thumb" for flood warning decision making considering this probabilistic information, 3) determination of the key sources of uncertainty influencing model forecast reliability, 4) selection of hydrologic models based on comparison of model forecast reliability. Central to this demonstration is the reliability analysis methods' ability to estimate the exceedance probability for any hydrologic target level of interest and, hence, to produce forecast cumulative density functions and probability distribution functions. For typical hydrologic modeling cases, reduction of the underlying modeling uncertainties is the key to obtaining useful, reliable forecasts. Furthermore, determination of the rainfall excess is the primary source of uncertainty, especially in the estimation of the temporal and areal rainfall distributions.U.S. Department of the InteriorU.S. Geological SurveyOpe

Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques

Author name used in this publication: K. W. Chau2010-2011 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Shuttle imaging radar-C science plan

The Shuttle Imaging Radar-C (SIR-C) mission will yield new and advanced scientific studies of the Earth. SIR-C will be the first instrument to simultaneously acquire images at L-band and C-band with HH, VV, HV, or VH polarizations, as well as images of the phase difference between HH and VV polarizations. These data will be digitally encoded and recorded using onboard high-density digital tape recorders and will later be digitally processed into images using the JPL Advanced Digital SAR Processor. SIR-C geologic studies include cold-region geomorphology, fluvial geomorphology, rock weathering and erosional processes, tectonics and geologic boundaries, geobotany, and radar stereogrammetry. Hydrology investigations cover arid, humid, wetland, snow-covered, and high-latitude regions. Additionally, SIR-C will provide the data to identify and map vegetation types, interpret landscape patterns and processes, assess the biophysical properties of plant canopies, and determine the degree of radar penetration of plant canopies. In oceanography, SIR-C will provide the information necessary to: forecast ocean directional wave spectra; better understand internal wave-current interactions; study the relationship of ocean-bottom features to surface expressions and the correlation of wind signatures to radar backscatter; and detect current-system boundaries, oceanic fronts, and mesoscale eddies. And, as the first spaceborne SAR with multi-frequency, multipolarization imaging capabilities, whole new areas of glaciology will be opened for study when SIR-C is flown in a polar orbit

Online flood forecasting in fast responding catchments on the basis of a synthesis of artificial neural networks and process models

A detailed and comprehensive description of the state of the art in the field of flood forecasting opens this work. Advantages and shortcomings of currently available methods are identified and discussed. Amongst others, one important aspect considers the most exigent weak point of today’s forecasting systems: The representation of all the fundamentally different event specific patterns of flood formation with one single set of model parameters. The study exemplarily proposes an alternative for overcoming this restriction by taking into account the different process characteristics of flood events via a dynamic parameterisation strategy. Other fundamental shortcomings in current approaches especially restrict the potential for real time flash flood forecasting, namely the considerable computational requirements together with the rather cumbersome operation of reliable physically based hydrologic models. The new PAI-OFF methodology (Process Modelling and Artificial Intelligence for Online Flood Forecasting) considers these problems and offers a way out of the general dilemma. It combines the reliability and predictive power of physically based, hydrologic models with the operational advantages of artificial intelligence. These operational advantages feature extremely low computation times, absolute robustness and straightforward operation. Such qualities easily allow for predicting flash floods in small catchments taking into account precipitation forecasts, whilst extremely basic computational requirements open the way for online Monte Carlo analysis of the forecast uncertainty. The study encompasses a detailed analysis of hydrological modeling and a problem specific artificial intelligence approach in the form of artificial neural networks, which build the PAI-OFF methodology. Herein, the synthesis of process modelling and artificial neural networks is achieved by a special training procedure. It optimizes the network according to the patterns of possible catchment reaction to rainstorms. This information is provided by means of a physically based catchment model, thus freeing the artificial neural network from its constriction to the range of observed data – the classical reason for unsatisfactory predictive power of netbased approaches. Instead, the PAI-OFF-net learns to portray the dominant process controls of flood formation in the considered catchment, allowing for a reliable predictive performance. The work ends with an exemplary forecasting of the 2002 flood in a 1700 km² East German watershed