Application of Satellite Microwave Images in Estimating Snow Water Equivalent1

Abstract: Flood forecast and water resource management requires reliable estimates of snow pack properties [snow depth and snow water equivalent (SWE)]. This study focuses on application of satellite microwave images to estimate the spatial distribution of snow depth and SWE over the Great Lakes area. To estimate SWE, we have proposed the algorithm which uses microwave brightness temperatures (Tb) measured by the Special Sensor Microwave Imager (SSM/I) radiometer along with information on the Normalized Difference Vegetation Index (NDVI). The algorithm was developed and tested over 19 test sites characterized by different seasonal average snow depth and land cover type. Three spectral signatures derived from SSM/I data, namely T19V‐T37V (GTV), T19H‐T37H (GTH), and T22V‐T85V (SSI), were examined for correlation with the snow depth and SWE. To avoid melting snow conditions, we have used observations taken only during the period from December 1‐February 28. It was found that GTH, and GTV exhibit similar correlation with the snow depth/SWE and are most should be used over deep snowpack. In the same time, SSI is more sensitive to snow depth variations over a shallow snow pack. To account for the effect of dense forests on the scattering signal of snow we established the slope of the regression line between GTV and the snow depth as a function of NDVI. The accuracy of the new technique was evaluated through its comparison with ground‐based measurements and with results of SWE analysis prepared by the National Operational Hydrological Remote Sensing Center (NOHRSC) of the National Weather Service. The proposed algorithm was found to be superior to previously developed global microwave SWE retrieval techniques.     

INTERACTIVE DISTRIBUTED CONSERVATION PLANNING1

ABSTRACT: In the environmental and agricultural conservation planning process, more efficient and effective tools are needed for planners to assist private landowners with making wiser land use decisions. Current methods are slow, inefficient, and costly. Scientific techniques have not been fully implemented within the planning process, yet such plans are increasingly needed to meet water quality and Total Maximum Daily Load (TMDL) requirements. The objectives of this study are to (a) utilize the web for accessing an integrated science‐based land use decision support system; (b) link decision tools, models, and databases to the user via the web; (c) link distributed models and databases for enhanced planning efficiency; and (d) integrate the above into an easily usable and readily accessible system. The procedures resulting in the initial design involved planning expertise and focus groups' input. The system was developed in partnership with the Natural Resources Conservation Service of the U.S. Department of Agriculture and several state agencies. A survey of 150 certified conservation planners, the end users, was conducted to identify the data sets and planning tools needed. Data, tools, and models then were selected and integrated into a web accessible system. Specifically, the first generation used a web interactive Geographic Information System (GIS) that overlaid onto digital orthoquads and/or soils polygons field boundaries, transportation, hydrologic features (such as drains, rivers, lakes, etc.), and high pesticide risk runoff or infiltration areas. Conservation planners found they could save time with the system. Clients could access the system quickly to help them prepare for meeting with their planner. Previously acquiring GIS maps in some cases had been a lengthy process that limited use of the information in land use decisions.     

FOREST GROWTH IMPACT ON PEAK SNOW PACK MEASUREMENTS AT LONG TERM SNOW COURSES1

ABSTRACT: Snow course surveys in late winter provide stream‐flow forecasters with their best information for making water supply and flood forecasts for the subsequent spring and summer runoff period in mountainous regions of western North America. Snow survey data analyses are generally based on a 30‐year “normal” period. It is well documented that forest cover changes over time will affect snow accumulation on the ground within forests. This paper seeks to determine if forest cover changes over decades at long term snow courses decrease measured peak snow water equivalent (SWE) enough to affect runoff prediction. Annual peak SWE records were analyzed at four snow courses in two different forest types having at least 25 years of snowpack data to detect any decreases in SWE due to forest growth. No statistically significant decreases in annual peak SWE over time were found at any of these four snow courses. The wide range of annual winter precipitation and correspondingly highly variable peak snowpack accumulation, as well as many other weather and site variables, masked any minor trends in the data.     

OPERATIONAL APPLICATIONS OF SATELLITE SNOW COVER OBSERVATIONS1

ABSTRACT: Several federal and state water resources agencies and NASA have recently completed an Applications Systems Verification and Transfer (ASVT) project on the operational applications of satellite snow cover observations. When satellite snow cover data were tested in both empirical seasonal runoff estimation and short term modeling approaches, a definite potential for reducing forecast error was evident. Three years of testing in California resulted in reduction of seasonal stream flow forecast error was evident. Three years of testing in California resulted in reduction of seasonal stream flow forecast error from 15 percent to 10 percent on three study basins; and modeling studies on the Boise River basin in Idaho indicated that satellite snow cover could be used to reduce short term forecast error by up to 9.6 percent (5 day forecast). Potential benefits from improved satellite snow cover based predictions across the 11 western states total 10 million dollars for hydropower and 28 million dollars for irrigation annually. The truly operational application of the new technology in the West, however, will only be possible when the turnaround time for all data is reduced to 72 hours, and the water management agencies can be assured of a continuing supply of operational snow cover data from space.     

Effect of Snow Cover Conditions on the Hydrologic Regime: Case Study in a Pluvial‐Nival Watershed,Japan1

Abstract: Hydrologic monitoring in a small forested and mountainous headwater basin in Niigata Prefecture has been undertaken since 2000. An important characteristic of the basin is that the hydrologic regime contains pluvial elements year‐round, including rain‐on‐snow, in addition to spring snowmelt. We evaluated the effect of different snow cover conditions on the hydrologic regime by analyzing observed data in conjunction with model simulations of the snowpack. A degree‐day snow model is presented and applied to the study basin to enable estimation of the basin average snow water equivalent using air temperature at three representative elevations. Analysis of hydrological time series data and master recession curves showed that flow during the snowmelt season was generated by a combination of ground water flow having a recession constant of 0.018/day and diurnal melt water flow having a recession constant of 0.015/hour. Daily flows during the winter/snowmelt season showed greater persistence than daily flows during the warm season. The seasonal water balance indicated that the ratio of runoff to precipitation during the cold season (December to May) was about 90% every year. Seasonal snowpack plays an important role in defining the hydrologic regime, with winter precipitation and snowmelt runoff contributing about 65% of the annual runoff. The timing of the snowmelt season, indicated by the date of occurrence of the first significant snowmelt event, was correlated with the occurrence of low flow events. Model simulations showed that basin average snow water equivalent reached a peak around mid‐February to mid‐March, although further validation of the model is required at high elevation sites.     

Spatiotemporal Variability of Snow Depletion Curves Derived from SNODAS for the Conterminous United States, 2004‐2013

Assessment of water resources at a national scale is critical for understanding their vulnerability to future change in policy and climate. Representation of the spatiotemporal variability in snowmelt processes in continental‐scale hydrologic models is critical for assessment of water resource response to continued climate change. Continental‐extent hydrologic models such as the U.S. Geological Survey National Hydrologic Model (NHM) represent snowmelt processes through the application of snow depletion curves (SDCs). SDCs relate normalized snow water equivalent (SWE) to normalized snow covered area (SCA) over a snowmelt season for a given modeling unit. SDCs were derived using output from the operational Snow Data Assimilation System (SNODAS) snow model as daily 1‐km gridded SWE over the conterminous United States. Daily SNODAS output were aggregated to a predefined watershed‐scale geospatial fabric and used to also calculate SCA from October 1, 2004 to September 30, 2013. The spatiotemporal variability in SNODAS output at the watershed scale was evaluated through the spatial distribution of the median and standard deviation for the time period. Representative SDCs for each watershed‐scale modeling unit over the conterminous United States (n = 54,104) were selected using a consistent methodology and used to create categories of snowmelt based on SDC shape. The relation of SDC categories to the topographic and climatic variables allow for national‐scale categorization of snowmelt processes.     

From Global to Local: Providing Actionable Flood Forecast Information in a Cloud‐Based Computing Environment

Global and continental scale flood forecast provide coarse resolution flood forecast, but from the perspective of emergency management, flood warnings should be detailed and specific to local conditions. The desired refinement can be provided by the use of downscaling global scale models and through the use of distributed hydrologic models to produce a high‐resolution flood forecast. Three major challenges associated with transforming global flood forecasting to a local scale are addressed in this work. The first is using open‐source software tools to provide access to multiple data sources and lowering the barriers for users in management agencies at local level. This can be done through the Tethys Platform that enables web water resources modeling applications. The second is finding a practical solution for the computational requirements associated with running complex models and performing multiple simulations. This is done using Tethys Cluster that manages distributed and cloud computing resources as a companion to the Tethys Platform for web app development. The third challenge is discovering ways to downscale the forecasts from the global extent to the local context. Three modeling strategies have been tested to address this, including downscaling of coarse resolution global runoff models to high‐resolution stream networks and routing with Routing Application for Parallel computatIon of Discharge (RAPID), the use of hierarchical Gridded Surface and Subsurface Hydrologic Analysis (GSSHA) distributed models, and pre‐computed distributed GSSHA models.     

A Multi‐Scale Soil Moisture Monitoring Strategy for California: Design and Validation

A multi‐scale soil moisture monitoring strategy for California was designed to inform water resource management. The proposed workflow classifies soil moisture response units (SMRUs) using publicly available datasets that represent soil, vegetation, climate, and hydrology variables, which control soil water storage. The SMRUs were classified, using principal component analysis and unsupervised K‐means clustering within a geographic information system, and validated, using summary statistics derived from measured soil moisture time series. Validation stations, located in the Sierra Nevada, include transect of sites that cross the rain‐to‐snow transition and a cluster of sites located at similar elevations in a snow‐dominated watershed. The SMRUs capture unique responses to varying climate conditions characterized by statistical measures of central tendency, dispersion, and extremes. A topographic position index and landform classification is the final step in the workflow to guide the optimal placement of soil moisture sensors at the local‐scale. The proposed workflow is highly flexible and can be implemented over a range of spatial scales and input datasets can be customized. Our approach captures a range of soil moisture responses to climate across California and can be used to design and optimize soil moisture monitoring strategies to support runoff forecasts for water supply management or to assess landscape conditions for forest and rangeland management.     

APPLICATION OF THE SNOWMELT RUNOFF MODEL USING MULTIPLE-PARAMETER LANDSCAPE ZONES ON THE TOWANDA CREEK BASIN,PENNSYLVANIA1

ABSTRACT: The Snowmelt Runoff Model (SRM) is designed to compute daily stream discharge using satellite snow cover data for a basin divided into elevation zones. For the Towanda Creek basin, a Pennsylvania watershed with relatively little relief, analysis of snow cover images revealed that both elevation and land use affected snow accumulation and melt on the landscape. The distribution of slope and aspect on the watershed was also considered; however, these landscape features were not well correlated with the available snow cover data. SRM streamflow predictions for 1990, 1993 and 1994 snowmelt seasons for the Towanda Creek basin using a combination of elevation and land use zones yielded more precise streamflow estimates than the use of standard elevation zones alone. The use of multiple-parameter zones worked best in non-rain-on-snow conditions such as in 1990 and 1994 seasons where melt was primarily driven by differences in solar radiation. For seasons with major rain-on-snow events such as 1993, only modest improvements were shown since melt was dominated by rainfall energy inputs, condensation and sensible heat convection. Availability of GIS coverages containing satellite snow cover data and other landscape attributes should permit similar reformulation of multiple-parameter watershed zones and improved SRM streamflow predictions on other basins.     

Trends in Raindrop Kinetic Energy with Modeled Climate Warming in the Lake Tahoe Basin

Two means by which climate change may increase surface soil erosion in mountainous terrain are: (1) increasing the frequency of extreme rainfall events and (2) decreasing the duration of snow cover on bare soil. We used output from four general circulation models (GCMs) and two greenhouse gas trajectories to produce a suite of hydrologic variables at a daily time‐step for historic and projected 21st Century conditions. We statistically disaggregated the daily rainfall to hourly, using hourly rainfall from a network of nine weather stations in the Tahoe Basin, and filtered out rain falling on a snowpack. We applied published equations to convert hourly intensity to raindrop kinetic energy (KE) for each day and grid cell in the Basin, averaged across grid cells, and created time series of total annual and maximum annual hourly kinetic energy (TKE and MKE) on snow‐free ground. Using the Generalized Extreme Value distribution, we calculated the significance of long‐term trends in KE on snow‐free ground, and estimated energy levels for return periods of 2, 20, and 100 years. We then detrended the snowpack data and compared the resulting trends in KE with the trends resulting from changes in both rainfall energy and snowpack under two GCMs. Principal findings include (1) upward trends in MKE, (2) stronger upward trends in TKE; and (3) an effect of increasing rainfall intensities on KE in some cases, and a strong effect of reduced snowpack in all cases examined.     

Testing and Improving Temperature Thresholds for Snow and Rain Prediction in the Western United States

The phase of precipitation at the land surface is critical to determine the timing and amount of water available for hydrological and ecological systems. However, there are few techniques to directly observe the precipitation phase and many prediction tools apply a single temperature threshold (e.g., 0°C) to determine phase. In this paper, we asked two questions: (1) what is the accuracy of default and station optimized daily temperature thresholds for predicting precipitation phase and (2) what are the regions and conditions in which typical temperature‐based precipitation phase predictions are most suited. We developed a ground truth dataset of rain vs. snow using an expert decision‐making system based on precipitation, snow depth, and snow water equivalent observations. This dataset was used to evaluate the accuracy of three temperature‐threshold‐based techniques of phase classification. Optimizing the temperature threshold improved the prediction of precipitation phase by 34% compared to using 0°C threshold. Developing a temperature threshold based on station elevation improved the error by 12% compared with using the 0°C temperature threshold. We also found the probability of snow as a function of temperature differed among ecoregions, which suggests a varied response to future climate change. These results highlight a current weakness in our ability to predict the effects of regional warming that could have uneven impacts on water and ecological resources.     

A Web‐Based Decision Support System for Assessing Regional Water‐Quality Conditions and Management Actions1

Booth, Nathaniel L., Eric J. Everman, I‐Lin Kuo, Lori Sprague, and Lorraine Murphy, 2011. A Web‐Based Decision Support System for Assessing Regional Water‐Quality Conditions and Management Actions. Journal of the American Water Resources Association (JAWRA) 47(5):1136‐1150. DOI: 10.1111/j.1752‐1688.2011.00573.x Abstract: The U.S. Geological Survey National Water Quality Assessment Program has completed a number of water‐quality prediction models for nitrogen and phosphorus for the conterminous United States as well as for regional areas of the nation. In addition to estimating water‐quality conditions at unmonitored streams, the calibrated SPAtially Referenced Regressions On Watershed attributes (SPARROW) models can be used to produce estimates of yield, flow‐weighted concentration, or load of constituents in water under various land‐use condition, change, or resource management scenarios. A web‐based decision support infrastructure has been developed to provide access to SPARROW simulation results on stream water‐quality conditions and to offer sophisticated scenario testing capabilities for research and water‐quality planning via a graphical user interface with familiar controls. The SPARROW decision support system (DSS) is delivered through a web browser over an Internet connection, making it widely accessible to the public in a format that allows users to easily display water‐quality conditions and to describe, test, and share modeled scenarios of future conditions. SPARROW models currently supported by the DSS are based on the modified digital versions of the 1:500,000‐scale River Reach File (RF1) and 1:100,000‐scale National Hydrography Dataset (medium‐resolution, NHDPlus) stream networks.     

FOREST HYDROLOGY AND EXTREME RAINFALL FREQUENCY1

Extreme rainfall frequency analysis provides one means to predict, within certain limits of probability, the average time interval between the recurrences of storms of a specified duration and magnitude. This in turn furnishes the forest hydrologist a valuable tool for engineering design and runoff and erosion forecast. A modification in the application of the annual maximum and annual exceedance series analysis described by V. T. Chow can, for special purposes, lead to an even more useful estimate of extreme events on a seasonal basis. This can be particularly important on small forested headwater watersheds where the runoff response to extreme rainfall may vary considerably with seasonal changes in canopy cover and soil moisture characteristics. Although the application of data covering a relatively short period of record has produced some inconsistencies among the frequency diagrams, under certain circumstances for short-term recurrence interval forecast and for non-critical application the analysis of extreme rainfall frequency from less than 20 years data seems justified.     

A HYDROCLIMATOLOGICAL ANALYSIS OF THE RED RWER OF THE NORTH SNOWMELT FLOOD CATASTROPHE OF 19971

ABSTRACT: The flood hydroclimatology of the Grand Forks flood of April 1997, the most costly flood on a per capita basis for a major metropolitan area in United States history, is analyzed in terms of the natural processes that control spring snowmelt flooding in the region. The geomorphological characteristics of the basin are reviewed, and an integrated assessment of the hydroclimatological conditions during the winter of 1996 to 1997 is presented to gain a real‐world understanding of the physical basis of this catastrophic flood event. The Grand Forks flood resulted from the principal flood‐producing factors occurring at either historic or extreme levels, or at levels conducive to severe flooding. Above normal fall precipitation increased the fall soil moisture storage and reduced the spring soil moisture storage potential. A concrete frost layer developed that effectively reduced the soil infiltration capacity to zero. Record snowfall totals and snow cover depths occurred across the basin because of the unusual persistence of a blocking high circulation pattern throughout the winter. A severe, late spring blizzard delayed the snowmelt season and replenished the snow cover to record levels for early April. This blizzard was followed by a sudden transition to an extreme late season thaw due to the abrupt breakdown of the blocking circulation pattern. The presence of river ice contributed to backwater effects and affected the timing of tributary inflows to the main stem of the Red River. Only the absence of spring rains prevented an even more catastrophic flood disaster from taking place. This paper contributes to our understanding of the flood hydroclimatology of catastrophic flood events in an unusual flood hazard region that possesses relatively flat terrain, a north‐flowing river, and an annual peak discharge time series dominated by spring snowmelt floods.     

Watershed management and the Web

Watershed analysis and watershed management are developing as tools of integrated ecological and economic study. They also assist decision-making at the regional scale. The new technology and thinking offered by the advent of the Internet and the World Wide Web is highly complementary to some of the goals of watershed analysis. Services delivered by the Web are open, interactive, fast, spatially distributed, hierarchical and flexible. The Web offers the ability to display information creatively, to interact with that information and to change and modify it remotely. In this way the Internet provides a much-needed opportunity to deliver scientific findings and information to stakeholders and to link stakeholders together providing for collective decision-making. The benefits fall into two major categories: methodological and educational. Methodologically the approach furthers the watershed management concept, offering an avenue for practical implementation of watershed management principles. For educational purposes the Web is a source of data and insight serving a variety of needs at all levels. We use the Patuxent River case study to illustrate the web-based approach to watershed management. A watershed scale simulation model is built for the Patuxent area and it serves as a core for watershed management design based on web applications. It integrates the knowledge available for the Patuxent area in a comprehensive and systematic format, and provides a conceptual basis for understanding the performance of the watershed as a system. Moreover, the extensive data collection and conceptualisation required within the framework of the modeling effort stimulates close contact with the environmental management community. This is further enhanced by offering access to the modeling results and the data sets over the Web. Additional web applications and links are provided to increase awareness and involvement of stakeholders in the watershed management process. We argue that it is not the amount and quality of information that is crucial for the success of watershed management, but how well the information is disseminated, shared and used by the stakeholders. In this respect the Web offers a wealth of opportunities for the decision-making process, but still to be answered are the questions at what scale and how widely will the Web be accepted as a management tool, and how can watershed management benefit from web applications.     

Time‐Dependent Health Risk from Contaminated Groundwater Including Use of Reliability,Resilience, and Vulnerability as Measures

Traditionally, assessment of human health risk caused by contamination of a water supply focuses on the maximum risk to an individual. Here, we introduce a time‐dependent risk assessment method and adapt and explore the reliability, resilience, and vulnerability (RRV) criteria from the surface‐water literature as possible tools for assessing this risk. Time‐dependent risk assessment, including RRV, is applied to two synthetic examples where water quality at a well varies over time. We calculate time‐dependent health risks for discrete periods of exposure to the contaminated water for a variable population. The RRV criteria provide information about time‐dependent risk: probability of an acceptable risk, probability of system recovery, maximum risk, and average exceedance of a prescribed risk threshold. The results demonstrate that episodic contamination events produce fundamentally different time‐dependent risks than long‐term events: these differences, such as generally lower risks for the episodic contamination, can be captured via plots of the risk and the RRV criteria. Furthermore, the evaluation of time‐dependent health risk and the RRV criteria demonstrates significant sensitivity to the shape of the contaminant breakthrough curve, length of exposure, and variability within the population. Overall, analysis of time‐dependent health risks provides substantial insight into the structure of risk, with RRV providing a reasonable framework for the evaluation of these risks.     

EVALUATION OF STORMSEWER CONTROL ALGORITHMS USING A TRANSIENT MIXED FLOW MODEL1

ABSTRACT: a hydraulic transient model that is capable of simultaneously modeling open channel and pressurized flows is used to study active control of a deep tunnel stormwater collection system. The simultaneous occurrence of open channel flow and pressurized flow is termed mixed flow. This paper demonstrates the application of a mixed flow hydraulic model to the development of an active control scheme. It is shown that dynamic conditions can exist in a storm sewer system even under moderate inflow conditions and that these conditions, particularly at the time of full system pressurization, can influence the operation of the dynamic control, so that accurate hydraulic modeling is essential to proper control formulation.     

iTree‐Hydro: Snow Hydrology Update For The Urban Forest Hydrology Model1

Yang, Yang, Theodore A. Endreny, and David J. Nowak, 2011. iTree‐Hydro: Snow Hydrology Update for the Urban Forest Hydrology Model. Journal of the American Water Resources Association (JAWRA) 47(6):1211–1218. DOI: 10.1111/j.1752‐1688.2011.00564.x Abstract: This article presents snow hydrology updates made to iTree‐Hydro, previously called the Urban Forest Effects—Hydrology model. iTree‐Hydro Version 1 was a warm climate model developed by the USDA Forest Service to provide a process‐based planning tool with robust water quantity and quality predictions given data limitations common to most urban areas. Cold climate hydrology routines presented in this update to iTree‐Hydro include: (1) snow interception to simulate the capture of snow by the vegetation canopy, (2) snow unloading to simulate the release of snow triggered by wind, (3) snowmelt to simulate the solid to liquid phase change using a heat budget, and (4) snow sublimation to simulate the solid to gas phase via evaporation. Cold climate hydrology routines were tested with research‐grade snow accumulation and weather data for the winter of 1996‐1997 at Umpqua National Forest, Oregon. The Nash‐Sutcliffe efficiency for open area snow accumulation was 0.77 and the Nash‐Sutcliffe efficiency for under canopy was 0.91. The USDA Forest Service offers iTree‐Hydro for urban forest hydrology simulation through http://www.iTreetools.org .     

A Comprehensive Python Toolkit for Accessing High‐Throughput Computing to Support Large Hydrologic Modeling Tasks

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large‐scale, high‐resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high‐throughput computing (HTC) and cloud computing provide an ideal resource for large‐scale modeling because they are cost‐effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI‐WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national‐scale modeling.