SPACE-TIME MODELING OF VECTOR HYDROLOGIC SEQUENCES1

Stochastic modeling of vector hydrologic sequences is examined with a general class of space-time autoregressive integrated moving average (STARIMA) models. The models describe spatial and temporal autocorrelatjon, through dependent variables lagged both in space and time. The model structures incorporate a hierarchical ordering scheme to map the vector of observations into a network configuration. The neighboring structure used introduces a physical/geographical hierarchy to enable the model identification procedures to assist in determining appropriate correlative relationships. The three-stage iterative space-time model building procedure is illustrated using average monthly streamfiow data for a four-station network of the Southeastern Hydropower System.     

WATERSHED CLASSIFICATION USING CANONICAL CORRESPONDENCE ANALYSIS AND CLUSTERING TECHNIQUES: A CAUTIONARY NOTE1

ABSTRACT: Watershed classification using multivariate techniques requires the incorporation of continuous datasets representing controlling environmental variables. Often, out of convenience and availability rather than importance to the structure of the system being modeled, the environmental data used originate from a variety of sources and scales. To demonstrate the importance of appropriate environmental data selection, classifications of six‐digit hydrologic units (1:24,000) across selected geographic areas within the Interior Columbia River Basin were produced. Canonical correspondence analysis was used to select and test environmental variables important in predicting Rosgen stream types and valley bottom classes. Then, hierarchical agglomerative clustering was used to group (classify) watersheds based on these variables. Statistically significant results were derived from the use of organized classification data with presumed predictive relationships to watershed properties, and a random distribution of environmental variables from the same datasets provided similar results. The results contained herein demonstrate that these analysis techniques do not necessarily select meaningful variables from a broad spectrum of data and that significant results are easily generated from randomly associated data. It is suggested that classifications produced using these multivariate techniques, especially when using multi‐scale data or data of unknown significance, are subject to invalid inferences and should be used with caution.     

ASSESSMENT OF REMOTE SENSING INPUT TO HYDROLOGIC MODELS1

Remotely sensed variables such as land cover type and snow-cover extent can currently be used directly and effectively in a few specific hydrologic models. Regression models can also be developed using physiographic and snow-cover data to permit estimation of discharge characteristics over extended periods such as a season or year. Most models, however, are not of an appropriate design to readily accept as input the various types of remote sensing parameters that can be obtained now or in the future. Because this new technology has the potential for producing hydrologic data that has significant information content on an areal basis, both inexpensively and repetitively, effort should be devoted now to either modifying existing models or developing new models that can use these data. Minor modifications would at least allow the remote sensing data to be used in an ancillary way to update the model state variables, whereas major structural modifications or new models would permit direct input of the data through remote sensing compatible algorithms. Although current remote sensing inputs to hydrologic models employ only visible and near infrared data, model modification or development should accommodate microwave and thermal infrared data that will be more widely available in the future.     

Data Management Dimensions of Social Water Science: The iUTAH Experience

Integrating social and hydrologic sciences for understanding water systems is challenged by data management complexities. Contemporary mandates for open science and data sharing necessitate better understanding of the implications of social science data types. In the context of an interdisciplinary water research program that endeavors to integrate and share social science and biophysical data, we highlight the array of data types and issues associated with social water science. We present a multi‐dimensional classification of social water science data that provides insight into data management considerations for each data type. Recommendations for cyberinfrastructure, planning, and policy are offered.     

THE EFFECT OF DATA INDEPENDENCE IN MODEL CALIBRATION AND MODEL TESTING1

ABSTRACT: With the increased use of models in hydrologic design, there is an immediate need for a comprehensive comparison of hydrologic models, especially those intended for use at ungaged locations (i.e., where measured data are either not available or inadequate for model calibration). But some past comparisons of hydrologic models have used the same data base for both calibration and testing of the different models or implied that the results of model calibration are indicative of the accuracy at ungaged locations. This practice was examined using both the regression equation approach to peak discharge estimation and a unit hydrograph model that was intended for use in urban areas. The results suggested that the lack of data independence in the calibration and testing of regression equations may lead to both biased results and misleading statements about prediction accuracy. Additionally, although split-sample testing is recognized as desirable, the split-samples should be selected using a systematic-random sampling scheme, rather than random sampling, because random sampling with small samples may lead to a testing sample that is not representative of the population. A systematic-random sampling technique should lead to more valid conclusions about model reliability. For models like a unit hydrograph model, which are more complex and for which calibration is a more involved process, data independence is not as critical because the data fitting error variation is not as dominant as the error variation due to the calibration process and the inability of the model structure to conform with data variability.     

PARTITIONING SMALL SCALE SPATIAL VARIABILITY OF RUNOFF AND EROSION ON SAGEBRUSH RANGELAND1

ABSTRACT: Most hydrologic models require input parameters which represent the variability found across an entire landscape. The estimation of such parameters is very difficult, particularly on rangeland. Improved model parameter estimation procedures are needed which incorporate the small-scale and temporal variability found on rangeland. This study investigates the use of a surface soil classification scheme to partition the spatial variability in hydrologic and interrill erosion processes in a sagebrush plant community. Four distinct microsites were found to exist within the sagebrush coppice-dune dune-interspace complex. The microsites explained the majority of variation in hydrologic and interrill erosion response found on the site and were discernable based on readily available soil and vegetation information. The variability within each microsite was quite low and was not well correlated with soil and vegetation properties. The surface soil classification scheme defined in this study can be quite useful for defining sampling procedures, for understanding hydrologic and erosion processes, and for parameterizing hydrologic models for use on sagebrush range-land.     

Valuation of National Park System Visitation: The Efficient Use of Count Data Models, Meta-Analysis, and Secondary Visitor Survey Data

The National Park Service (NPS) currently manages a large and diverse system of park units nationwide which received an estimated 279 million recreational visits in 2011. This article uses park visitor data collected by the NPS Visitor Services Project to estimate a consistent set of count data travel cost models of park visitor willingness to pay (WTP). Models were estimated using 58 different park unit survey datasets. WTP estimates for these 58 park surveys were used within a meta-regression analysis model to predict average and total WTP for NPS recreational visitation system-wide. Estimated WTP per NPS visit in 2011 averaged $102 system-wide, and ranged across park units from $67 to $288. Total 2011 visitor WTP for the NPS system is estimated at $28.5 billion with a 95% confidence interval of $19.7–$43.1 billion. The estimation of a meta-regression model using consistently collected data and identical specification of visitor WTP models greatly reduces problems common to meta-regression models, including sample selection bias, primary data heterogeneity, and heteroskedasticity, as well as some aspects of panel effects. The article provides the first estimate of total annual NPS visitor WTP within the literature directly based on NPS visitor survey data.     

SUBWATERSHED SPATIAL ANALYSIS TOOL: DISCRETIZATION OF A DISTRIBUTED HYDROLOGIC MODEL BY STATISTICAL CRITERIA1

ABSTRACT: Complex hydrologic models, designed for simulating larger watersheds, require a huge amount of input data. Most of these models use spatially distributed data as inputs. Spatial data can be aggregated or disaggregated for use as input to a model, which can impact model outputs. Although, it is efficient to minimize data redundancy by aggregating the spatial data, upscaling reduces the detail/resolution of input information and increases model uncertainty. On the other hand, a large number of model inputs with high degrees of disaggregation take more computer time and space to process. Hence, a balance between striving for a maximum level of aggregation and a minimum level of information loss has to be achieved. This study presents a definition of an appropriate level of discretization, derived by establishing a relationship between a model's efficiency and the number of subwater‐sheds modeled. An entropy based statistical approach/tool called Subwatershed Spatial Analysis Tool (SUSAT) was developed to find an objective choice of an appropriate level of discretization. The new approach/tool was applied to three watersheds, each representing different hydrologic conditions, using a hydrologic model. Coefficients of efficiency and entropy estimated at different levels of discretization were used to validate the success of the new approach.     

Characterizing long-term hydrologic-response and sediment-transport for the R-5 catchment

Recently there have been several calls to establish long-term data collection networks to monitor near-surface hydrologic response and landscape evolution. The focus of this paper is a long-term dataset from the International Hydrologic Decade (1965-1974). The small upland catchment, known as R-5, located near Chickasha, Olahoma, has been the subject of considerable attention within the event-based hydrologic modeling community for more than 30 yr. Here, for the first time, 8 yr of continuous near-surface hydrologic-response and sediment-transport data are analyzed to show trends in the catchment's long-term behavior. The datasets include precipitation, temperature, solar radiation, soil-water content, infiltration, water discharge, and sediment discharge. Potential and actual evapotranspiration rates were estimated and used to calculate an average annual water balance for the catchment. Findings include, for example, that rainfall intensity rarely exceeds the threshold for Horton-type runoff, soil-water content is both spatially and temporally variable, and the water and sediment discharge rates are positively correlated. The R-5 data provide a unique opportunity to test (and refine) process-based models of continuous hydrologic response and sediment transport at the catchment scale for applications in the emerging fields of hydroecology and hydrogeomorphology.     

Data integration across borders: a case study of the Abbotsford‐Sumas aquifer (British Columbia/Washington State)1

Abstract: Integrating spatial datasets from diverse sources is essential for cross‐border environmental investigations and decision‐making. This is a little investigated topic that has profound implications for the availability and reliability of spatial data. At present, ground‐water hydrostratigraphic models exist for both the Canadian or for the United States (U.S.) portion of the aquifer but few are integrated across the border. In this paper, we describe the challenges of integrating multiple source, large datasets for development of a ground‐water hydrostratigraphic model for the Abbotsford‐Sumas Aquifer. Growing concerns in Canada regarding excessive withdrawal south of the border and in the U.S. regarding nitrate contamination originating north of the border make this particular aquifer one of international interest. While much emphasis in GIScience is on theoretical solutions to data integration, such as current ontology research, this study addresses pragmatic ways of integrating data across borders. Numerous interoperability challenges including the availability of data, metadata, data formats and quality, database structure, semantics, policies, and cooperation are identified as inhibitors of data integration for cross‐border studies. The final section of the paper outlines two possible solutions for standardizing classification schemes for ground‐water models – once data heterogeneity has been addressed.     

COMPARING METHODS OF HYDROLOGIC REGIONALIZATION1

ABSTRACT: Data splitting is used to compare methods of determining “homogeneous” hydrologic regions. The methods compared use cluster analysis based on similarity of hydrologic characteristics or similarity of characteristics of a stream's drainage basin. Data for 221 stations in Arizona are used to show that the methods, which are a modification of DeCoursey's scheme for defining regions, improve the fit of estimation data to the model, but that is is necessary to have an independent measure of predictive accuracy, such as that provided by data splitting, to demonstrate improved predictive accuracy. The methods used the complete linkage algorithm for cluster analysis and computed weighted average estimates of hydrologic characteristics at ungaged sites.     

A New Open‐Access HUC‐8 Based Downscaled CMIP‐5 Climate Model Forecast Dataset for the Conterminous United States

Watershed‐scale hydrologic simulation models generally require climate data inputs including precipitation and temperature. These climate inputs can be derived from downscaled global climate simulations which have the potential to drive runoff forecasts at the scale of local watersheds. While a simulation designed to drive a local watershed model would ideally be constructed at an appropriate scale, global climate simulations are, by definition, arbitrarily determined large rectangular spatial grids. This paper addresses the technical challenge of making climate simulation model results readily available in the form of downscaled datasets that can be used for watershed scale models. Specifically, we present the development and deployment of a new Coupled Model Intercomparison Project phase 5 (CMIP5) based database which has been prepared through a scaling and weighted averaging process for use at the level of U.S. Geological Survey (USGS) Hydrologic Unit Code (HUC)‐8 watersheds. The resulting dataset includes 2,106 virtual observation sites (watershed centroids) each with 698 associated time series datasets representing average monthly temperature and precipitation between 1950 and 2099 based on 234 unique climate model simulations. The new dataset is deployed on a HydroServer and distributed using WaterOneFlow web services in the WaterML format. These methods can be adapted for downscaled General Circulation Model (GCM) results for specific drainage areas smaller than HUC‐8. Two example use cases for the dataset also are presented.     

EFFICIENCY OF HYDROLOGIC DATA COLLECTION SYSTEMS ROLE OF TYPE I AND II ERRORS1

ABSTRACT The efficiency of hydrologic data collection systems is relevant to solution of environmental problems, scientific understanding of hydrologic processes, model-building and management of water resources. Because these goals may be overlapping and non-commensurate, design of data networks is not simple. Identified are four elements of error or risk in such networks: (a) choice of variables and mathematical model for the same process, (b) accuracy of model parameter estimates, (c) acceptance of wrong hypothesis or rejection of correct hypothesis and (d) economic losses associated with error. Of these four, the classical hypothesis testing problem is specifically evaluated in terms of costs of type I and II errors for simple and composite hypotheses; mathematical models for these economic analyses also include costs of sample data and costs of waiting while new data is obtained. An illustrative computational example focuses on the hypothesis that natural recharge might be augmented by a system of pumping wells along an ephemeral channel. The relationship of the hypothesis testing problem to Bayesian decision theory is discussed; it is felt that the latter theory offers a more comprehensive framework for design and use of hydrologic data networks.     

Identifying Urban Features from LiDAR for a High‐Resolution Urban Hydrologic Model

Light Detection and Ranging (LiDAR), is relatively inexpensive, provides high spatial resolution sampling at great accuracy, and can be used to generate surface terrain and land cover datasets for urban areas. These datasets are used to develop high‐resolution hydrologic models necessary to resolve complex drainage networks in urban areas. This work develops a five‐step algorithm to generate indicator fields for tree canopies, buildings, and artificial structures using Geographic Resources Analysis Support System (GRASS‐GIS), and a common computing language, Matrix Laboratory. The 54 km² study area in Parker, Colorado consists of twenty‐four 1,500 × 1,500 m LiDAR subsets at 1 m resolution with varying degrees of urbanization. The algorithm correctly identifies 96% of the artificial structures within the study area; however, application success is dependent upon urban extent. Urban land use fractions below 0.2 experienced an increase in falsely identified building locations. ParFlow, a three‐dimensional, grid‐based hydrological model, uses these building and artificial structure indicator fields and digital elevation model for a hydrologic simulation. The simulation successfully develops the complex drainage network and simulates overland flow on the impervious surfaces (i.e., along the gutters and off rooftops) made possible through this spatial analysis process.     

Land-use forecasting and hydrologic model integration for improved land-use decision support

This paper develops a methodology for integrating a land-use forecasting model with an event scale, rainfall-runoff model in support of improving land-use policy formulation at the watershed scale. The models selected for integration are loosely coupled, structured upon a common GIS platform that facilitates data exchange. The hydrologic model HEC-HMS is calibrated for a specific storm event that occurred within central Washington State. The land-use forecasting model, What If? is implemented to forecast future spatial distributions of low-density residential land-uses under low and high population growth estimates. Forecasted land-use distribution patterns for the years 2015, 2025, and 2050 are then used as land-use data input for the calibrated hydrologic model, keeping all other parameters constant. Impacts to the stream discharge hydrograph are predicted as the study area becomes increasingly developed as forecasted by What If?. The initial results of this integration process demonstrate the synergy that can be generated through the linkage of the selected models. The ability to quantifiably forecast the potential hydrologic implications of proposed land-use policies before their implementation offers land-use decision-makers a valuable tool for discerning which proposed land-use alternatives will be effective at minimizing storm water runoff.     

KALMAN FILTER ESTIMATION AND PREDICTION OF DAILY STREAM FLOWS: I. REVIEW,ALGORITHM, AND SIMULATION EXPERIMENTS1

An important class of models, frequently used in hydrology for the forecasting of hydrologic variables one or more time periods ahead, or for the generation of synthetic data sequences, is the class of autoregressive(AR) models. As the AR models belong to the family of linear stochastic difference equations, they have both a deterministic and a stochastic component. The stochastic component is often assumed to have a Gaussian distribution. It is well known that hydrologic observations (e.g., stream flows) are heavily affected by noise. To account explicitly for the observation noise, the linear stochastic difference equation is expressed in state variable form and an observation model is introduced. The discrete Kalman filter algorithm can then be used to obtain estimates of the state variable vector. Typically, in hydrologic systems, model parameters, system noise statistics and measurement noise statistics are unknown, and have to be estimated. In this study an adaptive algorithm is discussed which estimates these quantities simultaneously with the state variables. The performance of the algorithm is evaluated by using simulated data.     

Development and Comparison of Multiple Regression Models to Predict Bankfull Channel Dimensions for Use in Hydrologic Models

Channel dimensions are important input variables for many hydrologic models. As measurements of channel geometry are not available in most watersheds, they are often predicted using bankfull hydraulic geometry relationships. This study aims at improving existing equations that relate bankfull width, depth, and cross‐sectional area to drainage area (DA) without limiting their use to well‐gauged watersheds. We included seven additional variables in the equations that can be derived from data that are generally required by hydrologic models anyway and conducted several multiple regression analyses to identify the ideal combination of additional variables for nationwide and regional models for each Physiographic Division of the United States (U.S.). Results indicate that including the additional variables in the regression equations generally improves predictions considerably. The selection of relevant variables varies by Physiographic Division, but average annual precipitation (PCP) and temperature (TMP) were generally found to improve the models the most. Therefore, we recommend using regression equations with three independent variables (DA, PCP, and TMP) to predict bankfull channel dimensions for hydrologic models. Furthermore, we recommend using the regional equations for watersheds within regions from which data were used for model development, whereas in all other parts of the U.S. and the rest of the world, the nationwide equations should be given preference.     

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.     

An imprecise fuzzy risk approach for water quality management of a river system

Uncertainty plays an important role in water quality management problems. The major sources of uncertainty in a water quality management problem are the random nature of hydrologic variables and imprecision (fuzziness) associated with goals of the dischargers and pollution control agencies (PCA). Many Waste Load Allocation (WLA) problems are solved by considering these two sources of uncertainty. Apart from randomness and fuzziness, missing data in the time series of a hydrologic variable may result in additional uncertainty due to partial ignorance. These uncertainties render the input parameters as imprecise parameters in water quality decision making. In this paper an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system subject to uncertainty arising from partial ignorance. In a WLA problem, both randomness and imprecision can be addressed simultaneously by fuzzy risk of low water quality. A methodology is developed for the computation of imprecise fuzzy risk of low water quality, when the parameters are characterized by uncertainty due to partial ignorance. A Monte-Carlo simulation is performed to evaluate the imprecise fuzzy risk of low water quality by considering the input variables as imprecise. Fuzzy multiobjective optimization is used to formulate the multiobjective model. The model developed is based on a fuzzy multiobjective optimization problem with max–min as the operator. This usually does not result in a unique solution but gives multiple solutions. Two optimization models are developed to capture all the decision alternatives or multiple solutions. The objective of the two optimization models is to obtain a range of fractional removal levels for the dischargers, such that the resultant fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision making. The methodology is demonstrated with a case study of the Tunga–Bhadra river system in India.