Using Paleo Reconstructions to Improve Streamflow Forecast Lead Time in the Western United States

In water stressed regions, water managers are exploring new horizons that would help in long‐range streamflow forecasts. Oceanic‐atmospheric oscillations have been shown to influence streamflow variability. In this study, long‐lead time streamflow forecasts are made using a multiclass kernel‐based data‐driven support vector machine (SVM) model. The extended streamflow records based on tree ring reconstructions were used to provide a longer time series data. Reconstructed data were used from 1658 to 1952 and the instrumental record was used from 1953 to 2007. Reconstructions for oceanic‐atmospheric oscillations included the El Niño‐Southern Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, and North Atlantic Oscillation. Streamflow forecasts using all four oscillations were made with one‐year to five‐year lead times for 21 gages in the western United States. This is the first study that uses both instrumental and reconstructed data of oscillations in SVM model to improve streamflow forecast lead time. SVM model was able to provide “satisfactory” to “very good” forecasts with one‐ to five‐year lead time for the selected gages. The use of all the oscillation indices helped in achieving better predictability compared to using individual oscillations. The SVM modeling results are better when compared with multiple linear regression model forecasts. The findings are statistical in nature and are expected to be useful for long‐term water resources planning and management.     

Satellites viewing our world: The NASA landsat and the NOAA SMS/GOES

Thousands of individuals throughout the world are now users of satellite data. Hundreds of satellites have been launched—military, navigation, communications, educational, weather, and earth resources. One of the weather satellites (the SMS/GOES) and the NASA earth resources mapping satellite (Landsat) are the subjects of this article. Data from these systems have been highly cost beneficial, not only in the United States, but in developed and developing nations all over the globe as well. There is an increasing demand both for data and for training in data use.Data samples are shown and applications are discussed. Strong reference is made to the value of the digital computer in natural and man-made features mapping and monitoring. Procedures for acquiring NASA data are explained so that the reader may order data for his home region, or for other regions throughout the world which are of interest for their agriculture, forestry, hydrology, marine resources, geology, or land use. The cost of data is incredibly low; some products cost as little as three dollars.Also discussed are the remotely based data-collection platforms that acquire ground or water data daily and relay results to the NASA Landsat or to the NOAA SMS/GOES.     

Cyberinfrastructure and Web Apps for Managing and Disseminating the National Water Model

Hydrologic modeling can be used to provide warnings before, and to support operations during and after floods. Recent technological advances have increased our ability to create hydrologic models over large areas. In the United States (U.S.), a new National Water Model (NWM) that generates hydrologic variables at a national scale was released in August 2016. This model represents a substantial step forward in our ability to predict hydrologic events in a consistent fashion across the entire U.S. Nevertheless, for these hydrologic results to be effectively communicated, they need to be put in context and be presented in a way that is straightforward and facilitates management‐related decisions. The large amounts of data produced by the NWM present one of the major challenges to fulfill this goal. We created a cyberinfrastructure to store NWM results, “accessibility” web applications to retrieve NWM results, and a REST API to access NWM results programmatically. To demonstrate the utility of this cyberinfrastructure, we created additional web apps that illustrate how to use our REST API and communicate hydrologic forecasts with the aid of dynamic flood maps. This work offers a starting point for the development of a more comprehensive toolset to validate the NWM while also improving the ability to access and visualize NWM forecasts, and develop additional national‐scale‐derived products such as flood maps.     

A SURVEY OF HYDROLOGY COURSE CONTENT IN NORTH AMERICAN UNIVERSITIES1

ABSTRACT: This report presents the results of a survey of hydrology faculties of colleges and universities in the United States and Canada. Information is presented on topics covered in classes, allocation of class periods to individual topics, textbooks, prerequisites, computer use, and accreditation categories for hydrology courses offered by engineering departments. Hydrology courses generally require courses in fluid mechanics, mathematics, statistics, and computer science as prerequisites. Topics that receive the largest allocation of time in both introductory and advanced courses include rainfall-runoff relations, the hydrologic cycle, routing and open channel flow, and statistics. Advanced courses place greater emphasis on watershed models than do the introductory courses. Hydrology courses at both levels allocate the smallest amounts of time to snow hydrology ground-water hydrology, and “other topics.” Very few courses include field or experimental work. In a discipline where computer modeling is a major tool, this lack of field and data-collection experience may lead students to underestimate the uncertainties associated with data used to calibrate models and the modeling results themselves. Survey responses on hydrology courses taught in departments other than civil engineering were too few to permit detailed analysis. Most of these courses spend approximately two-thirds of available class time on the same topics as presented in engineering hydrology courses. The balance of class time is spent on topics that emphasize the specialized interest of the particular discipline, such as soil physics and soil moisture in agricultural engineering.     

ECONOMICS AND FEDERAL WATER PROJECTS1

While the science of economics is widely used in Federal water resource development projects, the usual procedure of applying an analytical discipline to arrive at a conclusion is reversed. The “answer” is usually provided ahead of time and economics is assigned the task of justifying the preconceived conclusion. This leads to a series of mitigatory effects in which economics as a science is not allowed to freely function. This article attempts to illustrate some of these “unusual” uses of economics in Federal water projects and note the “answers” we would logically expect from the economic discipline. Even though the theory of economics is often subverted, there are positive aspects to the role of economics in evaluating Federal water projects.     

Use of Predictive Weather Uncertainties in an Irrigation Scheduling Tool Part II: An Application of Metrics and Adjoints

We apply predictive weather metrics and land model sensitivities to improve the Colorado State University Water Irrigation Scheduler for Efficient Application (WISE). WISE is an irrigation decision aid that integrates environmental and user information for optimizing water use. Rainfall forecasts and verification performance metrics are used to estimate predictive rainfall probabilities that are used as input data within the irrigation decision aid. These input data errors are also used within a land model sensitivity study to diagnose important prognostic water movement behaviors for irrigation tool development purposes simultaneously performing the analysis in space and time. Thus, important questions such as “how long can a crop water application be delayed while maintaining crop yield production?” are addressed by evaluating crop growth stage interactions as a function of soil depth (i.e., space), rainfall events (i.e., time), and their probabilistic uncertainties. Editor’s note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Augmenting Watershed Model Calibration with Incorporation of Ancillary Data Sources and Qualitative Soft Data Sources

Watershed simulation models such as the Soil & Water Assessment Tool (SWAT) can be calibrated using “hard data” such as temporal streamflow observations; however, users may find upon examination of model outputs, that the calibrated models may not reflect actual watershed behavior. Thus, it is often advantageous to use “soft data” (i.e., qualitative knowledge such as expected denitrification rates that observed time series do not typically exist) to ensure that the calibrated model is representative of the real world. The primary objective of this study is to evaluate the efficacy of coupling SWAT‐Check (a post‐evaluation framework for SWAT outputs) and IPEAT‐SD (Integrated Parameter Estimation and Uncertainty Analysis Tool‐Soft & hard Data evaluation) to constrain the bounds of soft data during SWAT auto‐calibration. IPEAT‐SD integrates 59 soft data variables to ensure SWAT does not violate physical processes known to occur in watersheds. IPEAT‐SD was evaluated for two case studies where soft data such as denitrification rate, nitrate attributed from subsurface flow to total discharge ratio, and total sediment loading were used to conduct model calibration. Results indicated that SWAT model outputs may not satisfy reasonable soft data responses without providing pre‐defined bounds. IPEAT‐SD provides an efficient and rigorous framework for users to conduct future studies while considering both soft data and traditional hard information measures in watershed modeling.     

Proposition 65 warning regulations must change—and soon!

Proposition 65 (Prop 65) is very much a part of the “right‐to‐know” landscape in California and, as we all know, Prop 65 warnings are especially visible in that state. This much is clear. What may be less clear are the sweeping changes in the “clear and reasonable warning” requirements now scheduled to take effect from August 30, 2018. This date may seem like a long way off, but it is right around the corner in terms of coming into compliance with these dramatic changes. This Washington Watch column summarizes the new warning requirements and the reasons why companies need to focus now on these changes.     

FTABLE Generation Method Effects on Instream Fecal Bacteria Concentrations Simulated With HSPF1

Abstract: Computer simulation models are used extensively for the development of total maximum daily loads (TMDLs). Specifically, the Hydrological Simulation Program‐FORTRAN (HSPF) is used in Virginia for the development of TMDLs for bacteria impairments. HSPF estimates discharge from a reach using function tables (FTABLES). The FTABLE relates stream stage, surface area, and volume to discharge from a reach. In this study, five FTABLE estimation methods were assessed by comparing their effect on various simulation outputs. Four “field‐based” methods used detailed cross‐sectional data collected via site surveys. A fifth “digital‐based” method used digital elevation data in combination with the Natural Resources Conservation Service Regional Hydraulic Geometry Curves. Sets of FTABLEs created using each method were used in simulations of instream bacteria concentration for a Virginia watershed. Several statistics relating to instream bacteria including long‐term average concentration, die‐off, and the violation rate of Virginia’s bacteria criterion were compared. The pair‐wise Student’s t‐test was used for the comparison. The HSPF simulations that used FTABLES estimated from digitally based data consistently produced significantly higher long‐term average instream fecal bacteria concentrations, significantly lower instream fecal bacteria die‐off, which is related to differences in residence time in the streams, and significantly higher water quality criterion violation rates.     

Ensemble Streamflow Forecast: A GLUE‐Based Neural Network Approach1

Abstract: While training a Neural Network to model a rainfall‐runoff process, generally two aspects are considered: its capability to be able to describe the complex nature of the processes being modeled and the ability to generalize so that novel samples could be mapped correctly. The general conclusion is that, the smallest size network capable of representing the sample distribution is the best choice, as far as generalization is concerned. Oftentimes input variables are selected a priori in what is called an explanatory data analysis stage and are not part of the actual network training and testing procedures. When they are, the final model will have only a “fixed” type of inputs, lag‐space, and/or network structure. If one of these constituents was to change, one would obtain another equally “optimal” Neural Network. Following Beven and others' generalized likelihood uncertainty estimate approach, a methodology is introduced here that accounts for uncertainties in network structures, types of inputs, and their lag‐space relationships by looking at a population of Neural Networks rather than target in getting a single “optimal” network. It is shown that there is a wide array of networks that provide “similar” results, as seen by a likelihood measure, for different types of inputs, lag‐space, and network size combinations. These equally optimal networks expose the range of uncertainty in streamflow predictions and their expected value results in a better performance than any of the single network predictions.     

Automated Algorithms for Heuristic Base‐Flow Separation1

Abstract: The subjective nature of graphical base‐flow separation combined with the many applications of base‐flow time series derived from continuous streamflow data, motivates the development and application of automated algorithms for heuristic base‐flow separation. Base‐flow time series derived from gauged streamflow support diverse applications in engineering hydrology, catchment analysis, hydrogeologic investigations, regional low‐flow analysis, and recharge estimation. Whether based on graphical procedures for recession analysis or analytical expressions derived from fundamental equations of ground‐water flow, the variety of base‐flow separation algorithms belies the array of base‐flow definitions and interpretations that variously refer to dominant process, source, flow path, and characteristic response time. Algorithms that are invariant in their consistent – though heuristic – characterization of base‐flow response are particularly useful for interbasin comparisons of low‐flow characteristics and hydrologic regionalization. More adaptable algorithms provide application‐specific flexibility in allocating flow components like interflow to either quickflow or slowflow. Four widely used algorithms that produce consistent base‐flow time series using only gauged streamflow records are compared and contrasted with a complementary heuristic algorithm that incorporates hydrologic judgment explicitly, through manual parameterization. The utility of these inherently subjective algorithms is illustrated through a simple example of flow phase separation in a two‐component end‐member mixing model of dissolved chlorides in the Cuyahoga River.     

USING ARTIFICIAL NEURAL NETWORKS TO ESTIMATE MISSING RAINFALL DATA1

ABSTRACT: Missing rainfall data from a time series or a spatial field of observations can present a serious obstacle to data analysis, modeling studies and operational forecasting in hydrology. Numerous schemes for replacing missing data have been proposed, ranging from simple weighted averages of data points that are nearby in time and space to complex statistically-based interpolation methods and function fitting schemes. This paper presents a technique for replacing missing spatial data using a backpropagation neural network applied to concurrent data from nearby gauges. Tests performed on a sample of gauges in the Middle Atlantic region of the United States show that this technique produces results that compare favorably to simple techniques such as arithmetic and distance-weighted averages of the values from nearby gauges, and also to linear optimization methods such as regression.     

Trust in Citizen Science Research: A Case Study of the Groundwater Education Through Water Evaluation & Testing Program1

Thornton, Teresa and Jessica Leahy, 2012. Trust in Citizen Science Research: A Case Study of the Groundwater Education Through Water Evaluation & Testing Program. Journal of the American Water Resources Association (JAWRA) 48(5): 1032‐1040. DOI: 10.1111/j.1752‐1688.2012.00670.x Abstract: Data collected by citizen scientists, including K‐12 students, have been validated by the scientific community through quality assurance/quality control tests and publication of results in peer‐reviewed journal articles. However, if citizen science data are to be used by local communities, research is needed to determine which factors contribute to local community member trust in citizen science data, and how to increase the benefits and use of citizen science programs. This article describes the Groundwater Education Through Water Evaluation & Testing (GET WET!) program that employs middle and high school students, state and local government employees, environmental nongovernmental organization leaders, business representatives, college faculty and students, and other volunteers as citizen scientists to create a database of groundwater quality for use as a baseline for local water resources management. Data were gathered through semi‐structured interviews pre‐ and post‐involvement from 40 participants in this citizen science program conducted in five states in the northeastern United States. Results indicate that factors of trust are largely based on interpersonal trust and familiarity. We conclude with recommendations and future research that may improve local community member willingness to trust citizen science data generated by students.     

The Arkansas Valley “Super Ditch”— An Analysis of Potential Economic Impacts1

McMahon, Tyler G. and Mark Griffin Smith, 2012. The Arkansas Valley “Super Ditch”— An Analysis of Potential Economic Impacts. Journal of the American Water Resources Association (JAWRA) 00(0):000‐000. 1‐12. DOI: 10.1111/jawr.12005 Abstract: In Colorado’s Arkansas River basin, urban growth and harsh farming conditions have resulted in water transfers from agricultural to urban uses. Several studies have shown that these transfers have significant secondary economic impacts associated with the removal of irrigated land from production. In response, new methods of sharing water are being developed to allow water transfers that benefit both farm and urban economies, compared with previous permanent transfers that negatively impacted surrounding farm communities. One such project currently under development is the Arkansas Valley “Super Ditch,” which is a rotational crop fallowing plan based on long‐term water leasing designed to provide an annual supply of 25,000 acre‐feet of water (31.6 Mm³). This article analyzes the net benefits of implementing the “Super Ditch” for both the farmers and the surrounding community.     

Supporting Diverse Data Providers in the Open Water Data Initiative: Communicating Water Data Quality and Fitness of Use

Shared, trusted, timely data are essential elements for the cooperation needed to optimize economic, ecologic, and public safety concerns related to water. The Open Water Data Initiative (OWDI) will provide a fully scalable platform that can support a wide variety of data from many diverse providers. Many of these will be larger, well‐established, and trusted agencies with a history of providing well‐documented, standardized, and archive‐ready products. However, some potential partners may be smaller, distributed, and relatively unknown or untested as data providers. The data these partners will provide are valuable and can be used to fill in many data gaps, but can also be variable in quality or supplied in nonstandardized formats. They may also reflect the smaller partners' variable budgets and missions, be intermittent, or of unknown provenance. A challenge for the OWDI will be to convey the quality and the contextual “fitness” of data from providers other than the most trusted brands. This article reviews past and current methods for documenting data quality. Three case studies are provided that describe processes and pathways for effective data‐sharing and publication initiatives. They also illustrate how partners may work together to find a metadata reporting threshold that encourages participation while maintaining high data integrity. And lastly, potential governance is proposed that may assist smaller partners with short‐ and long‐term participation in the OWDI.     

Scientist and Policy-Maker Response Types and Times in Suburban Watersheds

Differences between scientist and policy-maker response types and times, or the “how” and “when” of action, constrain effective water resource management in suburbanizing watersheds. Policy-makers are often rushed to find a single policy that can be applied across an entire, homogeneous, geopolitical region, whereas scientists undertake multiyear research projects to appreciate the complex interactions occurring within heterogeneous catchments. As a result, watershed management is often practiced with science and policy out of synch. Meanwhile, development pressures in suburban watersheds create changes in the social and physical fabric and pose a moving target for science and policy. Recent and anticipated advances in the scientific understanding of urbanized catchment hydrology and pollutant transport suggest that management should become increasingly sensitive to spatial heterogeneities in watershed features, such as soil types, terrain slopes, and seasonal watertable profiles. Toward this end, policy-makers should encourage funding scientific research that characterizes the impacts of these watershed heterogeneities within a geopolitical zoning and development framework.     

The USGS Water Availability and Use Science Program: Needs,Establishment, and Goals of a Water Census

Many reports have recognized the need for a national water census for the United States and have called upon the U.S. Geological Survey to undertake this challenge. For example, the National Science and Technology Council stated: “The United States has a strong need for an ongoing census of water that describes the status of our Nation's water resource at any point in time and identifies trends over time.” Responding to the need for this information, the U.S. Congress established the SECURE Water Act. The directives are to provide a more accurate assessment of the status of the water resources of the United States; determine the quantity of water available for beneficial uses; identify long‐term trends in water availability; assist in determination of the quality of the water resources; and develop the basis for an improved ability to forecast the availability of water for future economic, energy production, and environmental uses. This article provides summary and new information on the process and progress on work to estimate water budget components nationwide, involvement of stakeholder interests, efforts to examine water‐use characteristics throughout the Nation, studies of water availability in geographically focused areas and the initiation of methods to provide open access to existing and new water resources information contributing to Open Water Data Initiative (OWDI) efforts and objectives.     

Informal privatisation of community taps: issues of access and equity

On the basis of a detailed case study this paper questions the equity of centralised piped drinking water supply systems installed by the government of Nepal in rural areas. The study shows how processes of socio-technical interaction and change alter the physical water supply infrastructure of the installed public water supply system, simultaneously altering patterns of access to taps and water. The analysis suggests that this happens through a process of “informal privatisation”, with community taps becoming appropriated by individuals over time, cutting off some families from their access to community tap water while reinforcing the water security of others. This process is deeply shaped by prevailing relations of power and cultural difference along axes of gender, caste and wealth.     

A Water Allocation Decision‐Support Model and Tool for Predictions in Ungauged Basins in Northeast British Columbia,Canada

Pressures on water resources due to changing climate, increasing demands, and enhanced recognition of environmental flow needs result in the need for hydrology information to support informed water allocation decisions. However, the absence of hydrometric measurements and limited access to hydrology information in many areas impairs water allocation decision‐making. This paper describes a water balance‐based modeling approach and an innovative web‐based decision‐support hydrology tool developed to address this need. Using high‐resolution climate, vegetation, and watershed data, a simple gridded water balance model, adjusted to account for locational variability, was developed and calibrated against gauged watersheds, to model mean annual runoff. Mean monthly runoff was modeled empirically, using multivariate regression. The modeled annual runoff results are within 20% of the observed mean annual discharge for 78% of the calibration watersheds, with a mean absolute error of 16%. Modeled monthly runoff corresponds well to observed monthly runoff, with a median Nash–Sutcliffe statistic of 0.92 and a median Spearman rank correlation statistic of 0.98. Monthly and annual flow estimates produced from the model are incorporated into a map‐ and watershed‐based decision‐support system referred to as the Northeast Water Tool, to provide critical information to decision makers and others on natural water supply, existing allocations, and the needs of the environment.