URBANIZATION IMPACT ON WATER QUALITY DURING A FLOOD IN SMALL WATERSHEDS1

ABSTRACT: The impact of various urban land uses on water flow and quality in streams is being studied by monitoring small streams in the Milwaukee urban area. This paper compares the responses of an urban watershed and an agricultural watershed to an autumn rainfall of 2.2 cm. Flow from the urban basin showed a substantially greater response to the rain than that from the rural. Dilution, resulting from the greater quantities of surface runoff in the urban watershed, caused lower concentrations of sodium, chloride, calcium, magnesium, bicarbonate and total dissolved solids in the urban stream. The total quantity of these materials removed per unit drainage area of the urban basin was much greater, however. Road salt was still among the dominant dissolved materials in the urban water chemistry seven months after the last road salting. Sodium was apparently being released from adsorption by clays in the urban basin. Suspended sediment concentrations and total loads were higher in the urban stream.     

STREAM TEMPERATURE ESTIMATION FROM AIR TEMPERATURE1

ABSTRACT: Air temperatures are sometimes used as substitutes for stream temperatures. To examine the errors associated with this procedure, linear relationships between stream temperatures, T, and air temperatures, T_a, recorded for 11 streams in the central U.S. (Mississippi River basin) were analyzed. Weather stations were an average 42 miles (range 0 to 144 miles) from the rivers. The general equations, T_w= 5.0 + 0.75 T_a and T_w= 2.9 + 0.86 T_a with temperatures in °C, were derived for daily and weekly water temperatures, respectively, for the 11 streams studied. The simulations had a standard deviation between measurements and predictions of 2.7°C (daily) and 2.1°C (weekly). Equations derived for each specific stream individually gave lower standard deviations, i.e., 2.1°C and 1.4°C, respectively. Small, shallow streams had smaller deviations than large, deep rivers. The measured water temperatures follow the air temperatures closely with some time lag. time lags ranged from hours to days, increasing with stream depth. Taking into account these time lags improved the daily temperature predictions slightly. Periods of ice cover were excluded from the analysis.     

CONTRIBUTIONS OF RAINFALL TO CONSTITUENT LOADS IN STORM RUNOFF FROM URBAN CATCHMENTS1

ABSTRACT: Rainfall is a significant source of some constituents, particularly nitrogen species, in storm runoff from urban catchments. Median contributions of rainfall to storm runoff loads of 12 constituents from 31 urban catchments, representing eight geographic locations within the United States, ranged from 2 percent for suspended solids to 74 percent for total nitrite plus nitrate nitrogen. The median contribution of total nitrogen in rainfall to runoff loads was 41 percent. Median contributions of total-recoverable lead in rainfall to runoff loads varied by as much as an order of magnitude between catchments in the same geographic location. This indicates that average estimates of rainfall contributions to constituent loading in storm runoff may not be suitable in studies requiring accurate constituent mass-balance computations.     

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.     

AN ADDITIONAL ANALYSIS OF PEAK-VOLUME RELATIONS AND STANDARDIZATION PROCEDURES1

ABSTRACT: Much has been written about the linear relationship in log space between the runoff volume of a hydrograph and the peak discharge. Three versions of this relation (an original and two standardizations) have been presented and recommended by various authors. In this paper, the standardized equations are compared to the original relationship and the behavior of the coefficient of determination (r²) in each case is discussed. It is shown that the r² of the standardized equations is increased or decreased relative to that of the original relation based upon the magnitude of the original slope. Further implications of these relationships are discussed and demonstrated using a data base of 90 watersheds and over 1,200 separate flood hydrographs.     

Rainfall Runoff Relationships for a Cloud Forest Watershed in Central America: Implications for Water Resource Engineering1

Caballero, Luis A., Alon Rimmer, Zachary M. Easton, and Tammo S. Steenhuis, 2012. Rainfall Runoff Relationships for a Cloud Forest Watershed in Central America: Implications for Water Resource Engineering. Journal of the American Water Resources Association (JAWRA) 48(5): 1022‐1031. DOI: 10.1111/j.1752‐1688.2012.00668.x Abstract: Understanding the basic relationships between rainfall and runoff is vital for effective management and utilization of scarce water resources. Especially, this is important in Central America with widespread potable water shortage during the dry months of the monsoon. Potential good water sources are cloud forests, but little information concerning its potential is available to water supply engineers. Our objective is to define rainfall‐runoff‐base flow relationships for a cloud forest catchment. Flumes were installed for measuring river flow in four subwatersheds in La Tigra National Park, Honduras. One of the four watersheds was a 636‐ha subwatershed (WS1) with 60% cloud forest coverage. Precipitation averaged 1,130 mm/yr over the entire basin. About half of the total rainfall became runoff for the cloud forest watershed whereas, for the adjacent undisturbed forested watershed, the total discharge was <20% of the amount of precipitation. Infiltration rates were generally greater than rainfall rates. Therefore, most rainfall infiltrated into the soil, especially in the upper, steep, and well‐drained portions of the watershed. Direct runoff was generated from saturated areas near the river and exposed bedrock. This research provides compelling evidence that base flow is the primary contributor to streamflow during both wet and dry seasons in cloud forest catchments. Protecting these flow processes over time is critical for the sustained provision of potable water.     

Review of Urban Stormwater Quality Models: Deterministic,Stochastic, and Hybrid Approaches1

Abstract: The growing impact of urban stormwater on surface‐water quality has illuminated the need for more accurate modeling of stormwater pollution. Water quality based regulation and the movement towards integrated urban water management place a similar demand for improved stormwater quality model predictions. The physical, chemical, and biological processes that affect stormwater quality need to be better understood and simulated, while acknowledging the costs and benefits that such complex modeling entails. This paper reviews three approaches to stormwater quality modeling: deterministic, stochastic, and hybrid. Six deterministic, three stochastic, and three hybrid models are reviewed in detail. Hybrid approaches show strong potential for reducing stormwater quality model prediction error and uncertainty. Improved stormwater quality models will have wide ranging benefits for combined sewer overflow management, total maximum daily load development, best management practice design, land use change impact assessment, water quality trading, and integrated modeling.