PREDICTION OF PEAK FLOWS ON SMALL WATERSHEDS IN OREGON FOR USE IN CULVERT DESIGN1

ABSTRACT: Using data from 80 Oregon watersheds that ranged in size from 0.54 km² to 27.45 km², equations were developed to predict peak flows for use in culvert design on forest roads. Oregon was divided into six physiographic regions based on previous studies of flood frequency. In each region, data on annual peak flow from gaging stations with more than 20 years of record were analyzed using four flood frequency distributions: type 1 extremal, two parameter-log normal, three parameter-log normal, and log-Pearson type III. The log-Pearson type III distribution was found to be suitable for use in all regions of the State, based on the chi-square goodness-of-fit-test. Flood magnitudes having recurrence intervals of 10, 25, 50, and 100 years were related to physical and climatic characteristics of drainage basins by multiple regression. Drainage basin size was the most important variable in explaining the variation of flood peaks in all regions. Mean basin elevation and mean annual precipitation were also significantly related to flood peaks in two regions of western Oregon. The standard error of the estimate for the regression relationships ranged from 26 to 84 percent.     

THE USE OF FLOOD POTENTIAL INDICES FOR FLOOD PEAK ESTIMATION ON UNGAGED WATERSHEDS1

: The construction of a flood peak index map was attempted for use by hydrologists in the simple format of rainfall maps. Since flood peaks are highly dependent on watershed area, the effect of area was removed. By regression analysis flood peaks of 2.33 and 100-year return periods were found to be proportional to watershed area to the 0.8 and 0.7 powers, respectively. Therefore, indices C_{2 33}= Q_{2 33}/_A0.7 were completed at each gage and plotted on a Pennsylvania map. It was attempted to further remove some of the scatter by regression of C with several other watershed parameters like slope, percent forest cover, and watershed shape, but no significant correlation could be found. The index maps, drawn without attenuation of the scatter, can be used by hydrologists to compute flood peaks as Q = CAⁿ (with n = 0.8 and 0.7 for the 2.33 and 100-year flood peaks, respectively). Flood peak safety factors can be based on visual observation of the index variation in the vicinity of the location for which the flood peak estimate is needed.     

Neural Network Input Selection for Hydrological Forecasting Affected by Snowmelt1

Abstract: Snowmelt largely affects runoff in watersheds in Nordic countries. Neural networks (NN) are particularly attractive for streamflow forecasting whereas they rely at least on daily streamflow and precipitation observations. The selection of pertinent model inputs is a major concern in NNs implementation. This study investigates performance of auxiliary NN inputs that allow short‐term streamflow forecasting without resorting to a deterministic snowmelt routine. A case study is presented for the Rivière des Anglais watershed (700 km²) located in Southern Québec, Canada. Streamflow (Q), precipitations (rain R and snow S, or total P), temperature (T) and snow lying (A) observations, combined with climatic and snowmelt proxy data, including snowmelt flow (Q_SM) obtained from a deterministic model, were tested. NN implemented with antecedent Q and R produced the largest gains in performance. Introducing increments of A and T to the NNs further improved the performance. Long‐term averages, seasonal data, and Q_SM failed to improve the networks.     

BASE FLOW RECESSION RATES,LOW FLOWS,AND HYDROLOGIC FEATURES OF SMALL WATERSHEDS IN PENNSYLVANIA,USA1

This paper examines the relationships between measurable watershed hydrologic features, base flow recession rates, and the Q_7,10 low flow statistic (the annual minimum seven‐day average streamflow occurring once every 10 years on average). Base flow recession constants were determined by analyzing hydrograph recession data from 24 small (>130 km²), unregulated watersheds across five major physiographic provinces of Pennsylvania, providing a highly variable dataset. Geomorphic, hydrogeologic, and land use parameters were determined for each watershed. The base flow recession constant was found to be most strongly correlated to drainage density, geologic index, and ruggedness number (watershed slope); however, these three parameters are intercorrelated. Multiple regression models were developed for predicting the recession rate, and it was found that only two parameters, drainage density and hydrologic soil group, were required to obtain good estimates of the recession constant. Equations were also developed to relate the recession rates to Q_7,10 per unit area, and to the Q_7,10/Q₅₀ ratio. Using these equations, estimates of base flow recession rates, Q_7,10, and streamflow reduction under drought conditions can be made for small, ungaged basins across a wide range of physiography.     

Improved Bankfull Discharge Prediction Using 2‐Year Recurrence‐Period Discharge1

Abstract:  Knowledge of bankfull discharge (Q_bf) is essential for planners, engineers, geomorphologists, environmentalists, agricultural interests, developments situated on flood prone lands, surface mining and reclamation activities, and others interested in floods and flooding. In conjunction with estimating Q_bf, regionalized bankfull hydraulic geometry relationships, which relate Q_bf and associated channel dimensions (i.e., width, depth, and cross‐section area) to drainage basin area (A_da), are often used. This study seeks to improve upon the common practice of predicting Q_bf using A_da exclusively. Specifically, we hypothesize that predictions of Q_bf can be improved by including estimates of the 2‐year recurrence‐period discharge (Q₂) in regression models for predicting Q_bf. For testing this hypothesis, we used Q_bf estimates from 30 reports containing data for streams that span 34 hydrologic regions in 16 states. Corresponding values of Q₂ and A_da were compiled from flood‐frequency reports and other sources. By comparing statistical measures (i.e., root mean squared error, coefficient of determination, and Akaike’s information criterion), we determined that predicting Q_bf from Q₂ rather than A_da yields consistently better estimates of Q_bf. Other principal findings are (1) data are needed for at least 12 sites in a region for reliable hydraulic geometry model selection and (2) an approximate range of values for Q_bf/Q₂ is 0.10‐3.0.     

PREDICTION OF MASTER RECESSION CURVES AND BASEFLOW RECESSIONS IN THE LUQUILLO MOUNTAINS OF PUERTO RICO1

ABSTRACT: An analysis of hydrograph recessions and rainfall data was performed to estimate the recession constants for two watersheds in the Luquillo mountains of Puerto Rico. To account for seasonal rainfall patterns, the data were grouped into dry and wet seasons. Sets of three Master Recession Curves (MRC) per season for each watershed were developed: one using the Matching Strip Method (MS) and two using variations of the Correlation Method (CM). These variations were the envelope line (CME) and the least squares regression (CMR). Other regression based analytical expressions that consider the streamflow recession as an autore‐gressive or an integrated moving average process were also applied. The regression based methods performed consistently better than the graphical ones and they proved to be faster, easier, and less subjective. The recession constants from these methods were then used to estimate the time it would take the streamflow to reach the critical Q₉₉ flow duration. Based on this study, once the streamflow reaches Q₉₀, water managers have 6 to 12 days warning before streamflow reaches critical levels.     

GENERALIZED LOW-FLOW FREQUENCY RELATIONSHIPS FOR UNGAGED SITES IN MASSACHUSETTS1

ABSTRACT: Regional hydrologic procedures such as generalized least squares regression and streamflow record augmentation have been advocated for obtaining estimates of both flood-flow and low-flow statistics at ungaged sites. While such procedures are extremely useful in regional flood-flow studies, no evaluation of their merit in regional low-flow estimation has been made using actual streamflow data. This study develops generalized regional regression equations for estimating the d-day, T-year low-flow discharge, Q_{d, t}, at ungaged sites in Massachusetts where d = 3, 7, 14, and 30 days. A two-parameter lognormal distribution is fit to sequences of annual minimum d-day low-flows and the estimated parameters of the lognormal distribution are then related to two drainage basin characteristics: drainage area and relief. The resulting models are general, simple to use, and about as precise as most previous models that only provide estimates of a single statistic such as Q_7,10. Comparisons are provided of the impact of using ordinary least squares regression, generalized least squares regression, and streamflow record augmentation procedures to fit regional low-flow frequency models in Massachusetts.     

FORESTS AND OTHER FACTORS ASSOCIATED WITH STREAMFLOWS IN EAST TEXAS1

Effects of proportion of watersheds in forest and watershed physiographic factors on mean annual streamflow (1965-76), median flow, and 12 flood flow characteristics were regionally analyzed for 19 unregulated streams in East Texas. Annual streamflow increased with decreasing proportion of forest area. Differences in annual streamflow between full forest cover and bare watersheds could be as much as 200 mm. Other things being equal, the minimum watershed area required to generate 0.142 cm (5 cfs), a criterion used by the U.S. Corps of Engineering in regulating dredge and fill activity for water pollution abatement in East Texas streams, is 70 km² (27 mi²). Of the 31 physio-climatic parameters analyzed, watershed area, percent forest area, shape index, spring precipitation, and annual temperature were the most significant in affecting streamflow characteristics in East Texas. Using 2-3 of these five variables, all of the 14 streamflow characteristics can be estimated with accuracy ranging from acceptable to excellent levels.     

Nonstationarity: Flood Magnification and Recurrence Reduction Factors in the United States1

Vogel, Richard M., Chad Yaindl, and Meghan Walter, 2011. Nonstationarity: Flood Magnification and Recurrence Reduction Factors in the United States. Journal of the American Water Resources Association (JAWRA) 47(3):464‐474. DOI: 10.1111/j.1752‐1688.2011.00541.x Abstract: It may no longer be reasonable to model streamflow as a stationary process, yet nearly all existing water resource planning methods assume that historical streamflows will remain unchanged in the future. In the few instances when trends in extreme events have been considered, most recent work has focused on the influence of climate change, alone. This study takes a different approach by exploring trends in floods in watersheds which are subject to a very broad range of anthropogenic influences, not limited to climate change. A simple statistical model is developed which can both mimic observed flood trends as well as the frequency of floods in a nonstationary world. This model is used to explore a range of flood planning issues in a nonstationary world. A decadal flood magnification factor is defined as the ratio of the T‐year flood in a decade to the T‐year flood today. Using historical flood data across the United States we obtain flood magnification factors in excess of 2‐5 for many regions of the United States, particularly those regions with higher population densities. Similarly, we compute recurrence reduction factors which indicate that what is now considered the 100‐year flood, may become much more common in many watersheds. Nonstationarity in floods can result from a variety of anthropogenic processes including changes in land use, climate, and water use, with likely interactions among those processes making it very difficult to attribute trends to a particular cause.     

Evidence for Changing Flood Risk in New England Since the Late 20th Century1

Abstract: Long‐term flow records for watersheds with minimal human influence have shown trends in recent decades toward increasing streamflow at regional and national scales, especially for low flow quantiles like the annual minimum and annual median flows. Trends for high flow quantiles are less clear, despite recent research showing increased precipitation in the conterminous United States over the last century that has been brought about primarily by an increased frequency and intensity of events in the upper 10th percentile of the daily precipitation distribution – particularly in the Northeast. This study investigates trends in 28 long‐term annual flood series for New England watersheds with dominantly natural streamflow. The flood series are an average of 75 years in length and are continuous through 2006. Twenty‐five series show upward trends via the nonparametric Mann‐Kendall test, 40% (10) of which are statistically significant (p < 0.1). Moreover, an average standardized departures series for 23 of the study gages indicates that increasing flood magnitudes in New England occurred as a step change around 1970. The timing of this is broadly synchronous with a phase change in the low frequency variability of the North Atlantic Oscillation, a prominent upper atmospheric circulation pattern that is known to effect climate variability along the United States east coast. Identifiable hydroclimatic shifts should be considered when the affected flow records are used for flood frequency analyses. Special treatment of the flood series can improve the analyses and provide better estimates of flood magnitudes and frequencies under the prevailing hydroclimatic condition.     

MODELING ACTUAL EVAPOTRANSPIRATION FROM FORESTED WATERSHEDS ACROSS THE SOUTHEASTERN UNITED STATES1

ABSTRACT: About 50 to 80 percent of precipitation in the southeastern United States returns to the atmosphere by evapotranspiration. As evapotranspiration is a major component in the forest water balances, accurately quantifying it is critical to predicting the effects of forest management and global change on water, sediment, and nutrient yield from forested watersheds. However, direct measurement of forest evapotranspiration on a large basin or a regional scale is not possible. The objectives of this study were to develop an empirical model to estimate long‐term annual actual evapotranspiration (ART) for forested watersheds and to quantify spatial AET patterns across the southeast. A geographic information system (GIS) database including land cover, daily streamflow, and climate was developed using long term experimental and monitoring data from 39 forested watersheds across the region. Using the stepwise selection method implemented in a statistical modeling package, a long term annual AET model was constructed. The final multivariate linear model includes four independent variables—annual precipitation, watershed latitude, watershed elevation, and percentage of forest coverage. The model has an adjusted R² of 0.794 and is sufficient to predict long term annual ART for forested watersheds across the southeastern United States. The model developed by this study may be used to examine the spatial variability of water availability, estimate annual water loss from mesoscale watersheds, and project potential water yield change due to forest cover change.     

ROLE OF CARBONATE ROCKS IN MODIFYING FLOOD FLOW BEHAVIOR1

ABSTRACT. The interrelationships between the runoff characteristics of watersheds (expressed as the mean annual flood), standard basin parameters (area, drainage properties, and relief), and the parameters which describe the solutional modification of the basins (carbonate rock fractions, sinkhole development, and measures of internal drainage) were used to group 62 carbonate watersheds. Simple binary correlations were obtained by direct plotting of the data. This was followed by multivariate analyses: factor and cluster analyses. Following the cluster analysis, which separated the basins into three groups, the variance within each group was examined again by binary correlations and by factor analysis. Prediction equations for those basins underlain by dolomite rock [QBAR = 12.4 TOT^1.01] and for those basins underlain by carbonate rock with very little surface expression [QBAR = 43.5 TOT^0.87] were proposed. Basins underlain by karstic limestone had a large amount of variance within the data set; therefore no prediction equation could be obtained. (QBAR = mean annual flood, cfs; TOT = total length of all blue lines shown on topographic maps, miles.)     

WATER QUALITY FROM TWO SMALL FORESTED WATERSHEDS IN SOUTHERN ILLINOIS1

Two intermittent streams on oak-hickory watersheds in southern Illinois were gaged with a V-notched weir and sampled with an automatic water sampler. For three years data were collected on flow volume and water quality. Flow volumes show large variations between years and watersheds. Samples were analyzed for Na⁺, K⁺, Ca⁺⁺, Mg⁺⁺, P, and NO^-₃. Water quality was consistently high, although there were significant differences between watersheds. A baseline for water quality has been established for comparison after one of the watersheds is clearcut at a later date.     

CLIMATE FACTOR FOR SMALL-BASIN FLOOD FREQUENCY1

ABSTRACT: A climate factor, C_T, (T = 2–, 25-, and 100-year recurrence intervals) that delineates regional trends in small-basin flood frequency was derived using data from 71 long-term rainfall record sites. Values of C_T at these sites were developed by a regression analysis that related rainfall-runoff model estimates of T-year floods to a sample set of 50 model calibrations. C_T was regionalized via kriging to develop maps depicting its geographic variation for a large part of the United States east of the 105th meridian. Kriged estimates of C_T and basin-runoff characteristics were used to compute regionalized T-year floods for 200 small drainage basins. Observed T-year flood estimates also were developed for these sites. Regionalized floods are shown to account for a large percentage of the variability in observed flood estimates with coefficients of determination ranging from 0.89 for 2-year floods to 0.82 for 100-year floods. The relative importance of the factors comprising regionalized flood estimates is evaluated in terms of scale (size of drainage area), basin-runoff characteristics (rainfall. runoff model parameters), and climate (C_T).     

ADAPTATION OF FLOOD PEAKS AND DESIGN HYDROGRAPHS FROM GAGED TO NEARBY UNGAGED WATERSHEDS1

ABSTRACT: Equations were developed to transform peak flows and to adapt design hydrographs and unit hydrographs from gaged watersheds to ungaged watersheds with similar hydrologic characteristics. Dimensional analysis was used to develop adjustment equations for peak flow and time base, and these equations were reinforced with results from regional flood frequency research. The authors believe that the use of these transformation equations should yield more reliable flood peak values and hydrogrphs than the common use of empirical flood estimating curves or equations.     

NITROGEN AND PHOSPHORUS CONCENTRATIONS IN FOREST STREAMS OF THE UNITED STATES1

ABSTRACT: Seventy to eighty percent of the water flowing in rivers in the United States originates as precipitation in forests. This project developed a synoptic picture of the patterns in water chemistry for over 300 streams in small, forested watersheds across the United States. Nitrate (NO₃^?) concentrations averaged 0.31 mg N/L, with some streams averaging ten times this level. Nitrate concentrations tended to be higher in the northeastern United States in watersheds dominated by hardwood forests (especially hardwoods other than oaks) and in recently harvested watersheds. Concentrations of dissolved organic N (mean 0.32 mg N/L) were similar to those of NO₃～, whereas ammonium (NH₄⁺) concentrations were much lower (mean 0.05 mg N/L). Nitrate dominated the N loads of streams draining hardwood forests, whereas dissolved organic N dominated the streams in coniferous forests. Concentrations of inorganic phosphate were typically much lower (mean 12 mg P/L) than dissolved organic phosphate (mean 84 mg P/L). The frequencies of chemical concentrations in streams in small, forested watersheds showed more streams with higher NO₃^? concentrations than the streams used in national monitoring programs of larger, mostly forested watersheds. At a local scale, no trend in nitrate concentration with stream order or basin size was consistent across studies.     

Detecting the Dynamic Linkage between Landscape Characteristics and Water Quality in a Subtropical Coastal Watershed, Southeast China

Geospatial analysis and statistical analysis are coupled in this study to determine the dynamic linkage between landscape characteristics and water quality for the years 1996, 2002, and 2007 in a subtropical coastal watershed of Southeast China. The landscape characteristics include Percent of Built (%BL), Percent of Agriculture, Percent of Natural, Patch Density and Shannon’s Diversity Index (SHDI), with water quality expressed in terms of COD_Mn and NH₄ ⁺–N. The %BL was consistently positively correlated with NH₄ ⁺–N and COD_Mn at time three points. SHDI is significantly positively correlated with COD_Mn in 2002. The relationship between NH₄ ⁺–N, COD_Mn and landscape variables in the wet precipitation year 2007 is stronger, with R² = 0.892, than that in the dry precipitation years 1996 and 2002, which had R² values of 0.712 and 0.455, respectively. Two empirical regression models constructed in this study proved more suitable for predicting COD_Mn than for predicting NH₄ ⁺–N concentration in the unmonitored watersheds that do not have wastewater treatment plants. The calibrated regression equations have a better predictive ability over space within the wet precipitation year of 2007 than over time during the dry precipitation years from 1996 to 2002. Results show clearly that climatic variability influences the linkage of water quality-landscape characteristics and the fit of empirical regression models.     

Empirical Estimation of Stream Discharge Using Channel Geometry in Low‐Gradient,Sand‐Bed Streams of the Southeastern Plains

Manning's equation is used widely to predict stream discharge (Q) from hydraulic variables when logistics constrain empirical measurements of in‐bank flow events. Uncertainty in Manning's roughness (n_M) is the major source of error in natural channels, and sand‐bed streams pose difficulties because flow resistance is affected by flow‐dependent bed configuration. Our study was designed to develop and validate models for estimating Q from channel geometry easily derived from cross‐sectional surveys and available GIS data. A database was compiled consisting of 484 Q measurements from 75 sand‐bed streams in Alabama, Georgia, South Carolina, North Carolina (Southeastern Plains), and Florida (Southern Coastal Plain), with six New Zealand streams included to develop statistical models to predict Q from hydraulic variables. Model error characteristics were estimated with leave‐one‐site‐out jackknifing. Independent data of 317 Q measurements from 55 Southeastern Plains streams indicated the model (Q = A_cR_H^0.6906S^0.1216; where A_c is the channel area, R_H is the hydraulic radius, and S is the bed slope) best predicted Q, based on Akaike's information criterion and root mean square error. Models also were developed from smaller Q range subsets to explore if subsets increased predictive ability, but error fit statistics suggested that these were not reasonable alternatives to the above equation. Thus, we recommend the above equation for predicting in‐bank Q of unbraided, sandy streams of the Southeastern Plains.     

PREDICTION OF TWO-YEAR PEAK STREAM-DISCHARGES USING NEURAL NETWORKS1

ABSTRACT: The cascade correlation neural network was used to predict the two-year peak discharge (Q₂) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q₂ by regression. Our research shows Q₂ was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds.     

Identification of Sediment Sources in Forested Watersheds With Surface Coal Mining Disturbance Using Carbon and Nitrogen Isotopes1

Abstract: Sediments and soils were analyzed using stable carbon and nitrogen isotope ratio mass spectrometry and carbon and nitrogen elemental analyses to evaluate the their ability to indicate land‐use and land management disturbance and pinpoint loading from sediment transport sources in forested watersheds disturbed by surface coal mining. Samples of transported sediment particulate organic matter were collected from four watersheds in the Southern Appalachian forest region of southeastern Kentucky. The four watersheds had different surface coal mining history that were classified as undisturbed, active mining, and reclaimed conditions. Soil samples were analyzed including reclaimed grassland soils, undisturbed forest soils, geogenic organic matter associated with coal fragments in mining spoil, and soil organic matter from un‐mined grassland soils. Statistically significant differences were found for all biogeochemical signatures when comparing transported sediments from undisturbed watersheds and surface coal mining disturbed watersheds, and the results were attributed to differences in erosion sources and the presence of geogenic organic matter. Sediment transport sources in the surface coal mining watersheds were analyzed using Monte Carlo mass balance un‐mixing and it was found that: δ¹⁵N showed the ability to differentiate streambank erosion and surface soil erosion; and δ¹³C showed the ability to differentiate soil organic matter and geogenic organic matter. Results from the analyses suggest that streambank erosion downstream of surface coal mining sites is an especially significant source of sediment in coal mining disturbed watersheds. Further, the results suggest that the sediment transport processes governing streambank erosion loads are taking longer to reach geomorphologic equilibrium in the watershed as compared with the surface erosion processes. The dual‐isotope technique provides a useful method for further investigation of the impact of surface coal mining in the uplands of the watershed upon the geomorphologic state of the channel and the source of organic matter in aquatic systems impacted by surface coal mining.