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.     

FLOOD FREQUENCY ANALYSIS FOR EVALUATING WATERSHED CONDITIONS WITh RAINFALL-RUNOFF MODELS1

ABSTRACT: Many rainfall-runoff modeling studies compare flood quantiles for different land-use and/or flood mitigation scenarios. However, when flood quantiles are estimated using conventional statistical methods, comparisons may be misleading because the estimates often misrepresent the quantile relationship between scenarios. An alternate statistical procedure is proposed, in which rainfall-runoff modeling is used to evaluate an approximate relationship between flood quantiles for different scenarios. Monte Carlo experiments show that the proposed method produces flood quantile estimates that better reflect the differences between scenarios. The ratio between quantiles for different scenarios is more accurate, so comparisons of the scenarios using flood quantiles are more reliable.     

STOCHASTIC ANALYSIS OF TIME-AGGREGATED HYDROLOGIC DATA1

Stochastic models fitted to hydrologic data of different time scales are interrelated because the higher time scale data (aggregated data) are derived from those of lower time scale. Relationships between the statistical properties and parameters of models of aggregated data and of original data are examined in this paper. It is also shown that the aggregated data can be more accurately predicted by using a valid model of the original data than by using a valid model of the aggregated data. This property is particularly important in forecasting annual values because only a few annual values are usually available and the resulting forecasts are relatively inaccurate if models based only on annual data are used. The relationships and forecasting equations are developed for general aggregation time and can be used for hourly and daily, daily and monthly or monthly and yearly data. The method is illustrated by using monthly and yearly streamflow data. The results indicate that various statistical characteristics and parameters of the model of annual data can be accurately estimated by using the monthly data and forecasts of annual data by using monthly models have smaller one step ahead mean square error than those obtained by using annual data models.     

EFFECTIVE USE OF LANDSAT DATA IN HYDROLOGIC MODELS1

ABSTRACT: In a cooperative demonstration project, NASA and the U.S. Army Corps of Engineers (Corps) compared conventional and Landsat-derived land-use data for use in hydrologic models, and the resulting discharge frequency curves were analyzed. When a grid-based data-management system was used on a cell-by-cell basis (size about 1.1 acres or 0.45 hectare), Landsat classification accuracy was only 64 percent, but, when the grid cells were aggregated into watersheds, the classification accuracy increased to about 95 percent. When both conventional and Landsat land-use data were input to the HEC-1 model for generating discharge frequency curves, the differences in calculated discharge were judged insignificant for subbasins as small as 1.0mi² (2.59 km²). For basins larger than 10mi² (25.9km²), use of the Landsat approach is more cost-effective than use of conventional methods. Digital Landsat data can also be used effectively by local and regional agencies for hydrologic analysis by incorporating the data into grid-based data-management systems. The transfer of this new technology is well under way through inclusion in some Corps training courses and through use by both county government personnel and private consultants.     

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.     

ON THE MISMATCH BETWEEN DATA AND MODELS OF HYDROLOGIC AND WATER RESOURCE SYSTEMS1

ABSTRACT: Many difficulties exist in the matching of models with data. This paper identifies elements of this problem and discusses considerations involved in model evaluation. The well known multivariate linear regression model is used to illustrate the distinctions between accuracy and precision and between estimation and prediction (because the model is commonly misused.) No amount of additional data will improve the accuracy of a poor model. A high R², while indicative of a good matching between the observed data and model estimates, is a poor criterion for judging adequacy of the model to make good predictions of future events. Model evaluation also includes the problem of introducing secondary data and proxy variables into a model. Secondary data frequently enter, for example, the mass, energy and water budget equations because of difficulties in measuring the primary variables. Proxy variables arise because of a desire to collapse a vector of incomparable values, say, of water quality into a single number. Review of the above issues indicates that model evaluation is a multi-criterion problem, often imbedded in a larger framework where models are intended to meet multiple objectives. The mismatch of models and data has increasing legal and social consequences.     

SIMULATION OF TEMPORAL CHANGES IN RAINFALL-RUNOFF CHARACTERISTICS,COON CREEK BASIN,WISCONSIN1

ABSTRACT: Streamflow for 67 years was simulated for Coon Creek at Coon Valley, Wisconsin, for three conditions in the drainage basin: (1) conditions in the 1930s; (2) conditions in the 1970s, excluding flood-detention reservoirs; and (3) conditions in the 1970s, including flood-detention reservoirs. These simulations showed that the changes in agricultural practices over 40 years (1940–80) reduced the 100-year flood by 53 percent (from 38,900 to 18,300 cubic feet per second). The flood-detention reservoirs reduced the 100-year flood by an additional 17 percent (to 15,100 cubic feet per second). The simulation was accomplished by calibrating a precipitation-runoff model to observed rainfall and runoff during two separate periods (1934–40 and 1978–81). Comparisons of model simulations showed that differences between the model calibrations for the two periods were statistically significant at the 95 percent confidence level.     

THE APPLICATION OF HYDROLOGIC MODELS TO SMALL WATERSHEDS HAVING MILD TOPOGRAPHY1

ABSTRACT: The application of hydrologic models to small watersheds of mild topography is not well documented. This study evaluates the applicability of hydrologic models described by Huggins and the Soil Conservation Service to small watersheds by comparing the simulated and actual hydrograph for both gaged and ungaged situations. The annual maximum rainfall events plus storms exceeding 2.5 inches from 25 years of rainfall and runoff data for two small watersheds were selected for the model evaluations. These storms had a variety of patterns and occurred on many different watershed conditions. Simulated and actual hydrographs were compared using a parameter which contained volume, peak, and shape factors. One-half of the selected storms were used to calibrate the models. For both models, there were no significant differences between the simulated and actual runoff volumes and peak runoff rates. Parameters obtained during the calibration process and relationships developed to estimate antecedent moisture and to modify tabulated runoff curve numbers were used to simulate the runoff hydrograph from the remaining storms. These remaining storms or test storms were simulated only once in order to imitate an ungaged situation. In general, both the Huggins and SCS model performed similarly on the test storms, but the level of model performance was lower than that for the calibration storms. For both models, the two-day antecedent rainfall was more important than the five-day in determining antecedent moisture and modifying tabulated curve numbers. The time of concentration which resulted in good hydrograph simulations was about three times larger than that estimated using published empirical relationships.     

A METHOD FOR OBTAINING SLOPE INFORMATION FOR HYDROLOGIC MODELS1

: A method is described for obtaining surface slope information for analysis with other land resource and water quality data in hydrologic models of nonpoint sources of water pollution. The method described requires a point sampling scheme, topographic maps, and a coordinate digitizer. Sample point elevation, slope direction, and slope magnitude are calculated from locations of the sample point and the nearest upper and lower contour lines. Details of the data collection methodology and associated problems are discussed.     

USING SOIL TEXTURE TO ESTIMATE SATURATED HYDRAULIC CONDUCTIVITY AND THE IMPACT ON RAINFALL-RUNOFF SIMULATIONS1

ABSTRACT: In this paper a new set of soil texture data is used to estimate the spatial distribution of saturated hydraulic conductivity values for a small rangeland catchment. The estimates of conductivity are used to re-excite and re-evaluate a quasi-physically based rainfall-runoff model. The performance of the model is significantly reduced with conductivity estimates gleaned from soil texture data rather than the infiltration data used in our previous efforts.     

A SENSITIVITY ANALYSIS OF THE PALMER HYDROLOGIC DROUGHT INDEX1

ABSTRACT: The sensitivity of the Palmer Hydrologic Drought Index to departures from average temperature and precipitation conditions is examined. A time series of zero index values was calculated and then one monthly temperature or precipitation value was perturbed. The resulting time series shows the effects on the index of one anomalous value. Independent series were calculated for temperature anomalies of plus and minus 1, 3, 5, and 10F and for precipitation anomalies of 25, 50, 75, 125, 150, and 200 percent of normal for each calendar month for Colorado, Indiana, Nevada, New York, Oklahoma, South Carolina, South Dakota, Washington, and Wisconsin. Analysis of the time series showed that the period of time required for the index to reflect actual rather than artificial initial conditions could be more than four years. It was also found that the effects of temperature anomalies are insignificant compared to the effects of precipitation anomalies. In some cases, one anomalous precipitation value could result in established wet or dry spells that last for up to two years. Although not examined in detail, the time series suggest that distributions of index values may be asymmetrical and possibly bimodal.     

DEVELOPMENT OF EARTH SATELLITE TECHNOLOGY FOR THE TELEMETRY OF HYDROLOGIC DATA1

ABSTRACT The use of satellite telemetry is playing a major role in the collection of hydrologic data. Advancing technology and availability of government satellites have permitted many agencies to take advantage of new procedures for acquiring data from automated remote data collection stations. Experiments with Earth satellite technology started in the 1960's and 1970's, with the polar-orbiting National Aeronautics and Space Administration Nimbus and Landsat satellites. Subsequent advancements took place through the development phase to operational systems using the Geostationary Operational Environmental Satellite (GOES) of the National Oceanic and Atmospheric Administration. This satellite system supports more than 2,500 active telemetry sites, of which approximately 1,200 are Geological Survey stream-gaging stations for the collection of hydrologic data. A satellite data collection system is made up of three primary components; a small battery-operated radio, and Earth-orbiting satellite, and an Earth receive and data processing station. The data relay satellites' vast aerial view of the Earth's surface gives satellite telemetry a large advantage over ground-based systems for the collection of real-time hydrologic data for flood warning, reservoir management, irrigation water control, hydropower generation, and the operation of hydrologic stations.     

THE APPLICATION OF RIDGE REGRESSION ANALYSIS TO A HYDROLOGIC TARGET-CONTROL MODEL1

ABSTRACT: Ridge regression analysis is used to investigate the stability of regression estimates over twenty-three years of data in a target-control model. Two target stations in the Wind River Range in Wyoming are studied using two sets of control variables. The predictive ability of ridge regression analysis is compared to that of ordinary regression analysis. The results of this study indicate improved stability of the estimates of ridge regression over ordinary regression. The predictive ability of the ridge regression estimates is as good or better than regression estimates.     

AN EVALUATION OF TWO HYDROLOGIC MODELS FOR CLIMATE CHANGE SCENARIOS1

ABSTRACT: Understanding the effects of climate change on water resources requires coupling atmospheric and hydrologic models. With the wide array of hydrologic models, from simple empirical to complex physically based, it is not clear which is preferable to simulate hydrologic variations over long time scales. To address this issue, a black-box artificial neural network (ANN) model was compared to a distributed parameter conceptual Geographic Information System based Hydrologic Modeling System (GIS-HMS). Both models computed daily direct surface runoff in four sub-basins of the West Branch of the Susquehanna River Basin, Pennsylvania and were evaluated with five objective functions. Overall, results were comparable between models. However, the ANN was favored in the larger sub-basins, while GIS-HMS was more accurate in the smaller catchments. Both models were impaired by the poor spatial and temporal resolution of precipitation data and the simplified representation of antecedent soil-moisture conditions. In the context of climate change, where simulations are limited by computing power, results suggest that both models are appropriate. When detailed simulations are essential, GIS-HMS is a preferable model to use. On the other hand, the ANN model is more suitable when multiple scenarios require immediate analysis and the distributed qualities of runoff are not required.     

HYDROLOGIC SIMULATION OF THE BRANDYWINE BASIN1

ABSTRACT: A deterministic hydrologic model, encompassing the hydrologic regime and all water uses, is developed by integrating empirical hydrologic relationships. The Brandywine Basin, located in southeastern Pennsylvania and northern Delaware, is used as an example to demonstrate this modeling effort. The basin is divided into 19 subwatersheds to account for the spatial variation of resource characteristics. The output of the model is the response of the hydrologic system to various inputs such as precipitation, land use characteristics and policy decisions. This modeling effort is applicable to watersheds similar to the Brandywine Basin in size, and once the model is developed and validated, can be applied continuously in the management and planning of water resources such as predicting the hydrologic effects of proposed projects and simulating hydrologic information.     

THE USE OF BINARY OPTIMIZATION AND HYDROLOGIC MODELS TO FORM RIPARIAN BUFFERS1

ABSTRACT: Riparian buffers are considered important management options for protecting water quality. Land costs and buffer performance, which are functions of local environmental characteristics, are likely to be key attributes in the selection process, especially when budgets are limited. In this article we demonstrate how a framework involving hydrologic models and binary optimization can be used to find the optimal buffer subject to a budget constraint. Two hydrologic models, SWAT and REMM, were used to predict the loads from different source areas with and without riparian buffers. These loads provided inputs for a binary optimization model to select the most cost efficient parcels to form a riparian buffer. This methodology was applied in a watershed in Delaware County, New York. The models were parameterized using readily available digital databases and were later compared against observed flow and water quality data available for the site. As a result of the application of this method, the marginal utility of incremental increases in buffer widths along the stream channel and the set of parcels to form the best affordable riparian buffer were obtained.     

INTEGRATION OF FOREST ECOSYSTEM AND CLIMATIC MODELS WITH A HYDROLOGIC MODEL1

ABSTRACT: The effects of changes in the landscape and climate over geological time are plain to see in the present hydrological regime. More recent anthropogenic changes may also have effects on our way of life. A prerequisite to predicting such effects is that we understand the interactions between climate, landscape and the hydrological regime. A semi-distributed hydrological model (SLURP) has been developed which can be used to investigate, in a simple way, the links between landscape, climate and hydrology for watersheds of various sizes. As well as using data from the observed climate network, the model has been used with data from atmospheric models to investigate possible changes in hydrology. A critical input to such a model is knowledge of the links between landscape and climate. While direct anthropogenic effects such as changes in forested area may presently be included, the indirect effects of climate on landscape and vice versa are not yet modeled well enough to be explicitly included. The development of models describing climate-landscape relationships such as regeneration, development and breakup, water and carbon fluxes at species, ecosystem and biome level is a necessary step in understanding and predicting the effects of changes in climate on landscape and on water resources. Forest is the predominant land cover in Canada covering 453 Mha and productivity/succession models for major forest types should be included in an integrated climate-landscape-water simulation.     

UTILIZATION OF MODELS IN WATER RESOURCES1

ABSTRACT: A 1984 survey of water resources personnel was conducted to determine the current and future uses of mathematical models in planning, design and operations of water resources systems. Eighty-six percent of those responding indicated they have used mathematical models in the last year. Lack of appropriate data, inadequate time and funding to do the modeling and lack of models that represent the “real world” situation were the most frequently mentioned constraints to model use. Microcomputers were seen as having a positive influence on mathematical model use in water resources.     

THE RELATIONSHIP BETWEEN THE TIME BASES OF SIMULATION MODELS AND THEIR STRUCTURE1

ABSTRACT: The time base of a simulation model can be defined as a combination of two time intervals. One is the interval used for input and internal computations. The second is the interval used for the output and calibration of the model. The time base of a model is related on the one hand to the type of applications for which the simulated data are used, and on the other hand to the structure and complexity of the model. The latter may be represented by the number of parameters employed to specify the operation of the model. Using data typical to relatively small watersheds in a semiarid climate, the interaction between the complexity of a series of models and the time bases used by them was studied. This included the effects of the two factors, time base and complexity, on the values of the optimal parameters, prediction of mean annual flow, and general performance of the models. The main conclusion is that if the acceptable time base is longer, the model can be less complex needing fewer parameters. There is also an advantage in using a time base comprising a shorter input time interval and a longer output time interval.     

MODEL PREDICTIONS OF WATERSHED HYDROLOGIC COMPONENTS: COMPARISON AND VERIFICATIION1

ABSTRACT: Model predictions of the relatively simple soil compartment model SESOIL are compared with those of the more data-intensive terrestrial ecosystem hydrology model AGTEHM. Comparisons were performed using data from a deciduous forest stand watershed, a grassland watershed, and two agricultural field plots. Good agreement was obtained between model predictions for annual values of infiltration, evapotranspiration, surface runoff, and groundwater runoff. SESOIL model predictions also compare well with empirical measurements at the forest stand and the grassland watersheds.