George H. Leavesley

Hydrological responses to dynamically and statistically downscaled climate model output

Daily rainfall and surface temperature series were simulated for the Animas River basin, Colorado using dynamically and statistically downscaled output from the National Center for Environmental Prediction/ National Center for Atmospheric Research (NCEP/NCAR) re-analysis. A distributed hydrological model was then applied to the downscaled data. Relative to raw NCEP output, downscaled climate variables provided more realistic simulations of basin scale hydrology. However, the results highlight the sensitivity of modeled processes to the choice of downscaling technique, and point to the need for caution when interpreting future hydrological scenarios.

ASSESSMENT OF CLIMATE CHANGE AND FRESHWATER ECOSYSTEMS OF THE ROCKY MOUNTAINS, USA AND CANADA

The Rocky Mountains in the USA and Canada encompass the interior cordillera of western North America, from the southern Yukon to northern New Mexico. Annual weather patterns are cold in winter and mild in summer. Precipitation has high seasonal and interannual variation and may differ by an order of magnitude between geographically close locales, depending on slope, aspect and local climatic and orographic conditions. The regions hydrology is characterized by the accumulation of winter snow, spring snowmelt and autumnal baseflows. During the 2-3-month spring runoff period, rivers frequently discharge > 70% of their annual water budget and have instantaneous discharges 10-100 times mean low flow. Complex weather patterns characterized by high spatial and temporal variability make predictions of future conditions tenuous. However, general patterns are identifiable; northern and western portions of the region are dominated by maritime weather patterns from the North Pacific, central areas and eastern slopes are dominated by continental air masses and southern portions receive seasonally variable atmospheric circulation from the Pacific and the Gulf of Mexico. Significant interannual variations occur in these general patterns, possibly related to ENSO (El Nino-Southern Oscillation) forcing. Changes in precipitation and temperature regimes or patterns have significant potential effects on the distribution and abundance of plants and animals. For example, elevation of the timber-line is principally a function of temperature. Palaeolimnological investigations have shown significant shifts in phyto- and zoo-plankton populations as alpine lakes shift between being above or below the timber-line. Likewise, streamside vegetation has a significant effect on stream ecosystem structure and function. Changes in stream temperature regimes result in significant changes in community composition as a consequence of bioenergetic factors. Stenothermic species could be extirpated as appropriate thermal criteria disappear. Warming temperatures may geographically isolate cole water stream fishes in increasingly confined headwaters. The heat budgets of large lakes may be affected resulting in a change of state between dimictic and warm monomictic character. Uncertainties associated with prediction are increased by the planting of fish in historically fishless, high mountain lakes and the introduction of non-native species of fishes and invertebrates into often previously simple food-webs of large valley bottom lakes and streams. Many of the streams and rivers suffer from the anthropogenic effects of abstraction and regulation. Likewise, many of the large lakes receive nutrient loads from a growing human population. We concluded that: (1) regional climate models are required to resolve adequately the complexities of the high gradient landscapes; (2) extensive wilderness preserves and national park lands, so prevalent in the Rocky Mountain Region, provide sensitive areas for differentiation of anthropogenic effects from climate effects; and (3) future research should encompass both short-term intensive studies and long-term monitoring studies developed within comprehensive experimental arrays of streams and lakes specifically designed to address the issue of anthropogenic versus climatic effects.

Use of regional climate model output for hydrologic simulations

Abstract Daily precipitation and maximum and minimum temperature time series from a regional climate model (RegCM2) configured using the continental United States as a domain and run on a 52-km (approximately) spatial resolution were used as input to a distributed hydrologic model for one rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango, Colorado; east fork of the Carson River near Gardnerville, Nevada; and Cle Elum River near Roslyn, Washington). For comparison purposes, spatially averaged daily datasets of precipitation and maximum and minimum temperature were developed from measured data for each basin. These datasets included precipitation and temperature data for all stations (hereafter, All-Sta) located within the area of the RegCM2 output used for each basin, but excluded station data used to calibrate the hydrologic model. Both the RegCM2 output and All-Sta data capture the gross aspects of the seasonal cycles of precipit...

The modular modeling system (MMS) : The physical process modeling component of a database-centered decision support system for water and power management

The Modular Modeling System (MMS) is an integrated system of computer software that is being developed to provide the research and operational framework needed to support development, testing, and evaluation of physical-process algorithms, and to facilitate integration of user-selected sets of algorithms into operational physical-process models. MMS uses a module library that contains compatible modules for simulating a variety of water, energy, and biogeochemical processes. A model is created by selectively linking modules from the library using MMS model-building tools. A geographic information system (GIS) interface also is being developed for MMS to support a variety of GIS tools for use in characterizing and parameterizing topographic, hydrologic, and ecosystem features, visualizing spatially and temporally distributed model parameters and variables, and analyzing and validating model results. MMS is being coupled with the Power Reservoir System Model (PRSYM) to provide a database-centered decision support system for making complex operational decisions on multipurpose reservoir systems and watersheds. The U.S. Geological Survey and the Bureau of Reclamation are working collaboratively on a project titled the Watershed Modeling Systems Initiative to develop and apply the coupled MMS — PRSYM models to the San Juan River basin in Colorado, New Mexico, Arizona, and Utah.

Testing and validating environmental models

Abstract Generally accepted standards for testing and validating ecosystem models would benefit both modellers and model users. Universally applicable test procedures are difficult to prescribe, given the diversity of modelling approaches and the many uses for models. However, the generally accepted scientific principles of documentation and disclosure provide a useful framework for devising general standards for model evaluation. Adequately documenting model tests requires explicit performance criteria, and explicit benchmarks against which model performance is compared. A models validity, reliability, and accuracy can be most meaningfully judged by explicit comparison against the available alternatives. In contrast, current practice is often characterized by vague, subjective claims that model predictions show ‘acceptable’ agreement with data; such claims provide little basis for choosing among alternative models. Strict model tests (those that invalid models are unlikely to pass) are the only ones capable of convincing rational skeptics that a model is probably valid. However, ‘false positive’ rates as low as 10% can substantially erode the power of validation tests, making them insufficiently strict to convince rational skeptics. Validation tests are often undermined by excessive parameter calibration and overuse of ad hoc model features. Tests are often also divorced from the conditions under which a model will be used, particularly when it is designed to forecast beyond the range of historical experience. In such situations, data from laboratory and field manipulation experiments can provide particularly effective tests, because one can create experimental conditions quite different from historical data, and because experimental data can provide a more precisely defined ‘target’ for the model to hit. We present a simple demonstration showing that the two most common methods for comparing model predictions to environmental time series (plotting model time series against data time series, and plotting predicted versus observed values) have little diagnostic power. We propose that it may be more useful to statistically extract the relationships of primary interest from the time series, and test the model directly against them.

One-Way Coupling of an Atmospheric and a Hydrologic Model in Colorado

This paper examines the accuracy of high-resolution nested mesoscale model simulations of surface climate. The nesting capabilities of the atmospheric fifth-generation Pennsylvania State University (PSU)– National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) were used to create highresolution, 5-yr climate simulations (from 1 October 1994 through 30 September 1999), starting with a coarse nest of 20 km for the western United States. During this 5-yr period, two finer-resolution nests (5 and 1.7 km) were run over the Yampa River basin in northwestern Colorado. Raw and bias-corrected daily precipitation and maximum and minimum temperature time series from the three MM5 nests were used as input to the U.S. Geological Survey’s distributed hydrologic model [the Precipitation Runoff Modeling System (PRMS)] and were compared with PRMS results using measured climate station data. The distributed capabilities of PRMS were provided by partitioning the Yampa River basin into hydrologic response units (HRUs). In addition to the classic polygon method of HRU definition, HRUs for PRMS were defined based on the three MM5 nests. This resulted in 16 datasets being tested using PRMS. The input datasets were derived using measured station data and raw and bias-corrected MM5 20-, 5-, and 1.7-km output distributed to 1) polygon HRUs and 2) 20-, 5-, and 1.7-km-gridded HRUs, respectively. Each dataset was calibrated independently, using a multiobjective, stepwise automated procedure. Final results showed a general increase in the accuracy of simulated runoff with an increase in HRU resolution. In all steps of the calibration procedure, the station-based simulations of runoff showed higher accuracy than the MM5-based simulations, although the accuracy of MM5 simulations was close to station data for the high-resolution nests. Further work is warranted in identifying the causes of the biases in MM5 local climate simulations and developing methods to remove them.

Spatial interpolation schemes of daily precipitation for hydrologic modeling

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs.

Biotic and Abiotic Processes Controlling Water Chemistry During Snowmelt at Rabbit Ears Pass, Rocky Mountains, Colorado, U. S. A.

The chemical composition of snowmelt, groundwater, and streamwater was monitored during the spring of 1991 and 1992 in a 200-ha subalpine catchment on the western flank of the Rocky Mountains near Steamboat Springs, Colorado. Most of the snowmelt occurred during a one-month period annually that began in mid-May 1991 and mid-April 1992. The average water quality characteristics of individual sampling sites (meltwater, streamwater, and groundwater) were similar in 1991 and 1992. The major ions in meltwater were differentially eluted from the snowpack, and meltwater was dominated by Ca2+, SO42−, and NO3−. Groundwater and streamwater were dominated by weathering products, including Ca2+, HCO3− (measured as alkalinity), and SiO2, and their concentrations decreased as snowmelt progressed. One well had extremely high NO3−. concentrations, which were balanced by Ca2+ concentrations. For this well, hydrogen ion was hypothesized to be generated from nitrification in overlying soils, and subsequently exchanged with other cations, particularly Ca2+.Solute concentrations in streamwater also decreased as snowmelt progressed. Variations in groundwater levels and solute concentrations indicate that most of the meltwater traveled through the surficial materials. A mass balance for 1992 indicated that the watershed retained H+, NH4+, NO3−, SO42− and Cl− and was the primary source of base cations and other weathering products. Proportionally more SO42− was deposited with the unusually high summer rainfall in 1992 compared to that released from snowmelt, whereas NO3− was higher in snowmelt and Cl− was the same. The sum of snowmelt and rainfall could account for greater than 90% of the H+ and NH4+ retained by the watershed and greater than 50% of the NO3−.

Model parameter experiment begins new phase

The Model Parameter Estimation Experiment (MOPEX) is an ongoing international project to help develop techniques for the a priori estimation of parameters used in land surface parameterization schemes of atmospheric and hydrological models. MOPEX is affiliated with both the Prediction in Ungauged Basins (PUBS) and the Global Energy and Water Cycle Experiment (GEWEX),and is supported by the GEWEX American Prediction Project (GAPP) as well as by individual participants. Current procedures for a priori parameter estimation are often based on relationships between model parameters and basin characteristics—that is, soils, vegetation, topography climate, geology, etc. These developed relationships have not been fully validated through rigorous testing using retrospective hydrometeorological data and corresponding land surface characteristics. This is partly because the necessary database needed for such testing has not been available. Moreover, there still exists a gap in our understanding of the links between model parameters and land surface characteristics.