Stephen J. Burges

Bio: Stephen J. Burges is an academic researcher from University of Washington. The author has contributed to research in topics: Streamflow & Surface runoff. The author has an hindex of 32, co-authored 99 publications receiving 6646 citations. Previous affiliations of Stephen J. Burges include Hokkaido University.

A simple hydrologically based model of land surface water and energy fluxes for general circulation models

Abstract: A generalization of the single soil layer variable infiltration capacity (VIC) land surface hydrological model previously implemented in the Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model (GCM) is described. The new model is comprised of a two-layer characterization of the soil column, and uses an aerodynamic representation of the latent and sensible heat fluxes at the land surface. The infiltration algorithm for the upper layer is essentially the same as for the single layer VIC model, while the lower layer drainage formulation is of the form previously implemented in the Max-Planck-Institut GCM. The model partitions the area of interest (e.g., grid cell) into multiple land surface cover types; for each land cover type the fraction of roots in the upper and lower zone is specified. Evapotranspiration consists of three components: canopy evaporation, evaporation from bare soils, and transpiration, which is represented using a canopy and architectural resistance formulation. Once the latent heat flux has been computed, the surface energy balance is iterated to solve for the land surface temperature at each time step. The model was tested using long-term hydrologic and climatological data for Kings Creek, Kansas to estimate and validate the hydrological parameters, and surface flux data from three First International Satellite Land Surface Climatology Project Field Experiment (FIFE) intensive field campaigns in the summer-fall of 1987 to validate the surface energy fluxes.

Digital Elevation Model Networks (DEMON): A model of flow over hillslopes for computation of contributing and dispersal areas

Abstract: Current algorithms for computing contributing areas from a rectangular grid digital elevation model (DEM) use the flow-routing model of O'Callaghan and Mark (1984), which has two major restrictions: (1) flow which originates over a two-dimensional pixel is treated as a point source (nondimensional) and is projected downslope by a line (one dimensional) (Moore and Grayson, 1991), and (2) the flow direction in each pixel is restricted to eight possibilities. We show that large errors in the computed contributing areas result for any terrain topography: divergent, convergent, or planar. We present a new model, called digital elevation model networks (DEMON), which avoids the above problems by representing flow in two dimensions and directed by aspect. DEMON allows computation of both contributing and dispersal areas. DEMON offers the main advantage of contour-based models (e.g., Moore et al., 1988), the representation of varying flow width over nonplanar topography, while having the convenience of using rectangular grid DEMs.

Reviving urban streams: land use, hydrology, biology, and human behavior

Abstract: Successful stream rehabilitation requires a shift from narrow analysis and management to integrated understanding of the links between human actions and changing river health. At study sites in the Puget Sound lowlands of western Washington State, landscape, hydrological, and biological conditions were evaluated for streams flowing through watersheds with varying levels of urban development. At all spatial scales, stream biological condition measured by the benthic index of biological integrity (B-IBI) declined as impervious area increased. Impervious area alone, however, is a flawed surrogate of river health. Hydrologic metrics that reflect chronic altered streamflows, for example, provide a direct mechanistic link between the changes associated with urban development and declines in stream biological condition. These measures provide a more sensitive understanding of stream basin response to urban development than do treatment of each increment of impervious area equally. Land use in residential backyards adjacent to streams also heavily influences stream condition. Successful stream rehabilitation thus requires coordinated diagnosis of the causes of degradation and integrative management to treat the range of ecological stressors within each urban area, and it depends on remedies appropriate at scales from backyards to regional storm water systems.

Effect of bias adjustment and rain gauge data quality control on radar rainfall estimation

Abstract: Thirty major storms that passed over Goodwin Creek, a small research watershed in northern Mississippi, were analyzed to assess the bias between radar rainfall estimates at rain gauge locations and the gauge amounts. These storms, each contributing at least 10 mm of storm total rainfall, accumulated approximately 785 mm of rain, which corresponds to about half the average annual rainfall amount for the area. The focus of this study was to demonstrate the importance of (1) bias adjustment of the radar rainfall estimates and (2) the quality control of the rain gauge data used for bias adjustment. The analyses are based on Memphis Weather Surveillance Radar—1988 Doppler radar data, tipping-bucket rain gauge data, and raindrop spectra information collected within the Goodwin Creek catchment. Because of measurement and rainfall estimation uncertainties, radar observations are often combined with rain gauge data to obtain the most accurate rainfall estimates. Rain gauge data, however, are subject to characteristic error sources: for Goodwin Creek, malfunctioning of the tipping-bucket rain gauges was frequently caused by biological and mechanical fouling, and human interference. Therefore careful quality control of the rain gauge data is crucial, and only good quality rain gauge information should be used for adjusting radar rainfall estimates. By using high-quality gauge data and storm-based bias adjustment, we achieved radar rainfall estimates with root-mean-square errors (RMSE) of approximately 10% for storm total rainfall accumulations of 30 mm or more. Differences resulting from radar data processing scenarios were found to be small compared to the effect caused by bias adjustment and using high-quality rain gauge data.

Challenges in obtaining reliable measurements of point rainfall

Abstract: [1] Extensive data recorded from storm systems passing over the well-instrumented 21.4 km2 Goodwin Creek watershed in northern Mississippi are used to highlight uncertainties associated with the measurement of surface rainfall, focusing on data quality control, gauge calibration, out-of-level gauge orifices, and wind effects on rain gauge catch. Assessment of the wind effect on gauge catch is central to the presented analyses, including an in-depth evaluation of a recent technique for estimating the rainfall undercatch by means of wind and raindrop size information. Our findings emphasize that quantification of the wind effect on rain gauge catch is difficult because of uncertainties associated with measuring rainfall, drop size distribution, and the wind at gauge rim height. On the basis of our evaluation the sophisticated wind effect correction technique that makes use of raindrop size and wind information is much less effective than traditional methods based on rainfall rate and wind observations alone.

Warming and Earlier Spring Increase Western U.S. Forest Wildfire Activity

Abstract: Western United States forest wildfire activity is widely thought to have increased in recent decades, yet neither the extent of recent changes nor the degree to which climate may be driving regional changes in wildfire has been systematically documented. Much of the public and scientific discussion of changes in western United States wildfire has focused instead on the effects of 19th- and 20th-century land-use history. We compiled a comprehensive database of large wildfires in western United States forests since 1970 and compared it with hydroclimatic and land-surface data. Here, we show that large wildfire activity increased suddenly and markedly in the mid-1980s, with higher large-wildfire frequency, longer wildfire durations, and longer wildfire seasons. The greatest increases occurred in mid-elevation, Northern Rockies forests, where land-use histories have relatively little effect on fire risks and are strongly associated with increased spring and summer temperatures and an earlier spring snowmelt.

The future of distributed models: model calibration and uncertainty prediction.

Abstract: This paper describes a methodology for calibration and uncertainty estimation of distributed models based on generalized likelihood measures. The GLUE procedure works with multiple sets of parameter values and allows that, within the limitations of a given model structure and errors in boundary conditions and field observations, different sets of values may be equally likely as simulators of a catchment. Procedures for incorporating different types of observations into the calibration; Bayesian updating of likelihood values and evaluating the value of additional observations to the calibration process are described. The procedure is computationally intensive but has been implemented on a local parallel processing computer.

Potential impacts of a warming climate on water availability in snow-dominated regions

Abstract: All currently available climate models predict a near-surface warming trend under the influence of rising levels of greenhouse gases in the atmosphere. In addition to the direct effects on climate--for example, on the frequency of heatwaves--this increase in surface temperatures has important consequences for the hydrological cycle, particularly in regions where water supply is currently dominated by melting snow or ice. In a warmer world, less winter precipitation falls as snow and the melting of winter snow occurs earlier in spring. Even without any changes in precipitation intensity, both of these effects lead to a shift in peak river runoff to winter and early spring, away from summer and autumn when demand is highest. Where storage capacities are not sufficient, much of the winter runoff will immediately be lost to the oceans. With more than one-sixth of the Earth's population relying on glaciers and seasonal snow packs for their water supply, the consequences of these hydrological changes for future water availability--predicted with high confidence and already diagnosed in some regions--are likely to be severe.

Effective and efficient global optimization for conceptual rainfall‐runoff models

Abstract: The successful application of a conceptual rainfall-runoff (CRR) model depends on how well it is calibrated. Despite the popularity of CRR models, reports in the literature indicate that it is typically difficult, if not impossible, to obtain unique optimal values for their parameters using automatic calibration methods. Unless the best set of parameters associated with a given calibration data set can be found, it is difficult to determine how sensitive the parameter estimates (and hence the model forecasts) are to factors such as input and output data error, model error, quantity and quality of data, objective function used, and so on. Results are presented that establish clearly the nature of the multiple optima problem for the research CRR model SIXPAR. These results suggest that the CRR model optimization problem is more difficult than had been previously thought and that currently used local search procedures have a very low probability of successfully finding the optimal parameter sets. Next, the performance of three existing global search procedures are evaluated on the model SIXPAR. Finally, a powerful new global optimization procedure is presented, entitled the shuffled complex evolution (SCE-UA) method, which was able to consistently locate the global optimum of the SIXPAR model, and appears to be capable of efficiently and effectively solving the CRR model optimization problem.

The climate hazards infrared precipitation with stations--a new environmental record for monitoring extremes.

Abstract: The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.