B. Imam

Many automatic parameter estimation procedures resemble curve fitting approaches that use computer power to process large amounts of data and utilize the degrees of freedom available in hydrologic models. Current efforts to estimate physically plausible model parameters focus on incorporating information and knowledge derived from the observed data into the calibration process. We extract information from historical streamflow records using in-stream indicators that are intrinsic and representative of the basin hydrological response. Three indicators - Annual Rising Limb Density, Annual Declining Limb Density, and the Annual Normalized Cumulative Runoff - that reflect different hydrological processes were selected for this study. The validity of the indicators was tested using long-term daily streamflow data from 15 mid-size unregulated headwaters in the US. Subsequently, an automatic procedure using those indicators was developed to reduce model parameter uncertainty. This approach was tested on the SAC-SMA hydrologic model and 40 years of daily streamflow data from the Leaf River basin (~1950 km2). The results showed that the initial SAC-SMA parameter range was reduced significantly. Moreover it was found that the resulting parameter ranges provided an uncertainty envelop on the predictions that enclosed different components of the hydrograph very well.

In many parts of the world, operational real-time flood and hydrologic forecasting are hindered by the lack of reliable real-time precipitation observations. The insufficient ground observations have made satellite-based precipitation estimates the only available source for wide coverage data. As the spatial and temporal resolution of satellite-based rainfall estimates continue to improve, assessing the usefulness of these products, particularly in capturing extreme precipitation events becomes an important issue. This presentation demonstrates and discusses a framework for evaluating real-time high resolution precipitation products in terms of their operational utility. As an example of operational high resolution precipitation products, the 3 hourly near real-time, 0.04°x0.04° Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) (Hong et. al., 2004) product is compared against gauge and NEXRAD observations of several heavy precipitation events including tropical storm Erin, which affected Texas and Oklahoma during the period of August 10-20, 2007. For each storm, a swath of precipitation along the storm track is analyzed using both real-time and quality controlled versions of the products. Traditional as well as threshold- based (e.g. verification) performance measures are used to describe differences between NEXRAD and Satellite observations' ability to capture severe storm characteristics within the target area and to assess possible shifts in rainfall amount spectrum. While not fully conclusive, the results indicate that for operational purposes, high resolution satellite-based precipitation estimates can fill in a much needed observational gap during severe storm events.

Artificial neural networks (ANNs) have been broadly applied to many hydrological applications for which their underlying processes are complicated nonlinear. Although many networks, such as multi-layer feedforward neural networks (MFNs), provide excellent capability in function fittings, very often, they are referred to as black-box models. In this study, a multivariate ANN procedure, entitled SOLO (Self-Organizing Linear Output mapping network) is introduced. This model architecture has been designed for rapid estimation of network structure/parameters and system outputs. Furthermore, the SOLO provides features that facilitate insight to the input-output processes, thereby extending its usefulness as a tool for investigations into the underlying processes through the data classification processes. A case study using SOLO model in a hydrologic rainfall-runoff forecasting is demonstrated. Uncertainty of model estimates is also evaluated.

One difficulty for the assessment and management of water resources in the semi-arid region is the lack of reliable precipitation data. A large portion of semi-arid southwestern United States is mountainous, where gauges and radar only provide limited coverage. Under such circumstance, SAHRA has prioritized the development of satellite remotely sensed methods for estimating the precipitation in the region. In this study, precipitation estimation is generated from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) system for the hydrologic applications. This system integrated relevant information from multiple satellites (GOES-8, GOES-10, TRMM, NOAA-15, -16, -17, DMSP F-13, F-14, F15), ground radar, and gauge data. Precipitation estimates are based on the GOES-8 and GOES-10 cloud texture of infrared imagery. With continuous parameter adjustment using limited rainfall sampling from ground radar measurements and low-orbital satelli...

The goal of the Water cycle Solutions Network is to improve and optimize the sustained ability of water cycle researchers, stakeholders, organizations and networks to interact, identify, harness, and extend research results to augment decision support tools and meet national needs. WaterNet will engage relevant NASA water cycle research resources and community-of-practice organizations, to develop what we term an "actionable database" that can be used to communicate and connect water cycle research results (WCRs) towards the improvement of water-related Decision Support Tools (DSTs). An actionable database includes enough sufficient knowledge about its nodes and their heritage so that connections between these nodes are identifiable and robust. Recognizing the many existing highly valuable water-related science and application networks, we will focus the balance of our efforts on enabling their interoperability in a solutions network context. We will initially focus on ide...

A cloud-masked fractional snow-covered area (SCA) product gridded at 1 km was developed from the advanced very high resolution radiometer for the Colorado River and upper Rio Grande basins for 1995–2002. Cloud cover limited SCA retrievals on any given 1-km2 pixel to on average once per week. There were sufficient cloud-free scenes to map SCA over at least part of

Snow covered area (SCA) maps gridded at 1-km2 were analyzed to assess the persistence of snowcover in the Southwestern U.S. The maps used were derived from AVHRR scenes using a three-part cloud masking procedure and spectral unmixing algorithm, and give fractional snow cover per 1-km2 pixel. Areas with persistent snowcover exceeding 25% SCA were relatively reproducible from year to year,

ABSTRACT In this paper an improved streamflow forecast in arid/semi-arid regions are presented. An ARIMA model is developed for forecasting seasonal streamflows in Salt River in Arizona. Different seasons are defined based on hydrologic characteristics and statistical similarities between different months of the data in the historical period. The snow budget over watershed is also estimated based on a weighting method and is considered as an important indicator for forecast modification. The correlation between snow budget and streamflow at the entrance to the Roosevelt Reservoir has been calculated and has been used to improve the statistical forecasts by partitioning the performance of historical data into real-time forecast of the streamflow. Results of this study have shown the capability of proposed algorithm for seasonal streamflow forecast in Salt River Basin.

The newly developed Common Land Model (CLM2) is one of the most complex land surface models among others and its performance needs to be evaluated with observations. The main goal of this study is to examine CLM2 in estimating soil moisture and runoff at regional scales. NASA Land Data Assimilation System (LDAS) is currently processing three land surface model runs

... eng. uci. edu/persiann/) at CHRS (Center for Hydrometeorology and Remote Sensing), Univer-sity of California at Irvine. See Chapter 2 for further details. ... ru. ac. za/institutes/iwr and looking for the" Hydrological Models and Software" link. See Chapter 3 for further details. ...

Snow provides a substantial portion of the annual water supply in the semi-arid southwestern U.S. Consequently, the timely availability of accurate snow cover information is highly important to water resources managers and analysts. Satellite-based estimates of snow-covered area (SCA), which is a qualitative indicator of potential water supply, are increasingly being used to obtain quantitative estimates of Snow-pack water storage and its seasonal evolution through merging with ground-based measurements of snow water equivalent (SWE). Using the Salt-Verde Basin, which provides water supply to the City of Phoenix as an example, a comparison was conducted between the total volume of water stored in the snow pack when computed using binary (snow/no-snow) and fractional (percent snow) SCA products. Both products are derived from NOAA-AVHRR imagery and merged with SWE maps interpolated from the NRCS's snow telemetry data (SNOTEL) to create high-resolution distributed snow-water volu...

The exchange of water and energy through the land and atmospheric interaction occurs at various space and time scales. Modeling these exchanges, in general, and more specifically, adequate capturing of land surface hydrologic processes such as soil moisture and runoff generation requires reliable modeling and measurement of precipitation at fine time scale. The maturity of Satellite-based rainfall estimates is now sufficient to consider the value of such products in improving land surface models. This study addresses the measurement and bias correction of PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) rainfall to sub-daily scale of 0.25ox0.25o with emphasis on its potential use in land-surface hydrologic applications. The satellite-based PERSIANN system estimates surface rainfall based on infrared temperature and local image texture from geostationary satellites. Model parameters of PERSIANN are frequently adjusted when passive ...

Kuo-lin Hsu, Hoshin V. Gupta, Xiaogang Gao, Soroosh Sorooshian, and Bisher Imam Department of Hydrology and Water Resources, University of Arizona, Tucson, Arizona, USA Received 23 July 2001; revised 3 April 2002; accepted 3 April 2002; published 19 December 2002. [1] ...

In many parts of the world, operational real-time flood and hydrologic forecasting are hindered by the lack of reliable real-time precipitation observations. The insufficient ground observations have made satellite-based precipitation estimates the only available source for wide coverage data. As the spatial and temporal resolution of satellite-based rainfall estimates continue to improve, assessing the usefulness of these products, particularly in capturing extreme precipitation events becomes an important issue. This presentation demonstrates and discusses a framework for evaluating real-time high resolution precipitation products in terms of their operational utility. As an example of operational high resolution precipitation products, the 3 hourly near real-time, 0.04°x0.04° Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) (Hong et. al., 2004) product is compared against gauge and NEXRAD observa...

ABSTRACT Diurnal variability is an important yet poorly understood aspect of the warm-season precipitation regime over southwestern North America. In an effort to improve its understanding, diurnal variability is investigated numerically using the fifth-generation Pennsylvania State University (PSU)-NCAR Mesoscale Model (MM5). The goal herein is to determine the possible influence of spatial resolution on the diurnal cycle. The model is initialized every 48 h using the operational NCEP Eta Model 212 grid (40 km) model analysis. Model simulations are carried out at horizontal resolutions of both 9 and 3 km. Overall, the model reproduces the basic features of the diurnal cycle of rainfall over the core monsoon region of northwestern Mexico and the southwestern United States. In particular, the model captures the diurnal amplitude and phase, with heavier rainfall at high elevations along the Sierra Madre Occidental in the early afternoon that shifts to lower elevations along the west slopes in the evening. A comparison to observations (gauge and radar data) shows that the high-resolution (3 km) model generates better rainfall distributions on time scales from monthly to hourly than the coarse-resolution (9 km) model, especially along the west slopes of the Sierra Madre Occidental. The model has difficulty with nighttime rainfall along the slopes, over the Gulf of California, and over Arizona. A comparison of surface wind data from three NCAR Integrated Sounding System (ISS) stations and the Quick Scatterometer (QuikSCAT) to the model reveals a low bias in the strength of the Gulf of California low-level jet, even at high resolution. The model results indicate that outflow from convection over northwestern Mexico can modulate the low-level jet, though the extent to which these relationships occur in nature was not investigated.

A method to improve the GOES Precipitation Index (GPI) technique by combining satellite microwave and infrared (IR) data is proposed and tested. Using microwave-based rainfall estimates, the method, termed the Universally Adjusted GPI (UAGPI), modifies both GPI parameters (ie, ...

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

Examination of the Northridge fractured connections has revealed that the material as well as the manufacturing parameters associated with the fracture behaviour of these connections were largely random. This in turn implies that the fracture resistance of these connections was also random. With these observations in mind, a simplified two-dimensional model is used in this paper to evaluate the reliability

The aim of this paper is to present advanced modelling techniques for dynamic analysis of steel railway bridges. Finite element analyses of a case study skew bridge are carried out and the results are compared with available field measurements. Initially, eigenvalue analyses of different models are carried out in order to obtain the fundamental mode shapes and bridge frequencies and

Recent advances in global precipitation estimation using satellite information make it a viable and, in many regions unique input alternative for hydrologic modeling and water resources management. However, differences in algorithms result in different precipitation estimates at various hydrologically relevant scales. This study evaluates the mean areal precipitation estimates derived from four satellite-based precipitation estimation products (3B42-RT, 3B42-V6, PERSIANN, and

To account for spatial variability of precipitation as well as basin physiographic properties, the National Weather Service (NWS) has developed a distributed version of the hydrologic component of the NWS's flood forecasting model. The structure of the model, which is termed the Hydrology Laboratory - Research Distributed Hydrologic Model (HL-RDHM), has two main components: 1) the Sacramento Soil Moisture Accounting

Distributed hydrologic modeling is currently viewed as a potential pathway to improve streamflow simulations by addressing the sensitivity of runoff generation to the spatial variability of precipitation and basin properties. The US National Weather Service (NWS) initiated the Distributed Modeling Intercomparison Project (DMIP) to guide NWS's distributed modeling research in this regard. This study is based on our participation of

Snowcover and snowpack information are used extensively by water resources managers to estimate peak streamflows and seasonal runoff volumes. In the southwestern US, the NASA EOS Southwestern Regional Earth Science Applications Center is providing these data to users, such as the Salt River Project, to improve hydrologic predictions. Snowpack water volumes are derived from the combination of snow covered area

ABSTRACT Earth is a unique, living planet due to the abundance and vigorous cycling of water throughout the global environment. Water is essential to life and directly impacts society's welfare, progress, and sustainable growth. It is a national priority to use advancements in scientific observations and knowledge to develop solutions to the water challenges faced by society. NASA has collected substantial water cycle information and knowledge that must be transitioned to develop solutions for all twelve National Priority Application (NPA) areas and must establish collaborations and interoperability with existing networks and nodes of research organizations, operational agencies, science communities, and private industry. Therefore, WaterNet: The NASA Water Cycle Solutions Network will foster a Water and Energy cycle Community of Practice who has knowledge of both decision support needs and cutting-edge research results, and therefore can formulate a broad array of solutions to augment decision support tools and meet national needs.

ABSTRACT A semantic web ontology enables semantic data integration and semantic smart searching. Several organizations have attempted to implement smart registration and integration or searching using ontologies. These are the NOESIS (NSF project: LEAD) and HydroSeek (NSF project: CUAHS HIS) data discovery engines and the NSF project GEON. All three applications use ontologies to discover data from multiple sources and projects. The NASA WaterNet project was established to identify creative, innovative ways to bridge NASA research results to real world applications, linking decision support needs to available data, observations, and modeling capability. WaterNet (NASA project) utilized the smart query tool Noesis as a testbed to test whether different ontologies (and different catalog searches) could be combined to match resources with user needs. NOESIS contains the upper level SWEET ontology that accepts plug in domain ontologies to refine user search queries, reducing the burden of multiple keyword searches. Another smart search interface was that developed for CUAHSI, HydroSeek, that uses a multi-layered concept search ontology, tagging variables names from any number of data sources to specific leaf and higher level concepts on which the search is executed. This approach has proven to be quite successful in mitigating semantic heterogeneity as the user does not need to know the semantic specifics of each data source system but just uses a set of common keywords to discover the data for a specific temporal and geospatial domain. This presentation will show tests with Noesis and Hydroseek lead to the conclusion that the construction of a complex, and highly heterogeneous water cycle ontology requires multiple ontology modules. To illustrate the complexity and heterogeneity of a water cycle ontology, Hydroseek successfully utilizes WaterOneFlow to integrate data across multiple different data collections, such as USGS NWIS. However,different methodologies are employed by the Earth Science, the Hydrological, and Hydraulic Engineering Communities, and each community employs models that require different input data. If a sub-domain ontology is created for each of these,describing water balance calculations, then the resulting structure of the semantic network describing these various terms can be rather complex, heterogeneous, and overlapping, and will require "mapping" between equivalent terms in the ontologies, along with the development of an upper level conceptual or domain ontology to utilize and link to those already in existence.

The inadequacies of water cycle observations for monitoring long-term changes in the global water system, as well as their feedback into the climate system, poses a major constraint on sustainable development of water resources and improvement of water management practices. Hence, The Group on Earth Observations (GEO) has established Task WA-08-01," Integration of in situ and satellite data for water cycle monitoring," an integrative initiative combining different types of satellite and in situ observations related to ...