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.     

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.     

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.     

Stationarity of Streamflow Records and Their Influence on Bankfull Regional Curves1

Haucke, Jessica and Katherine A. Clancy, 2011. Stationarity of Streamflow Records and Their Influence on Bankfull Regional Curves. Journal of the American Water Resources Association (JAWRA) 47(6):1338–1347. DOI: 10.1111/j.1752‐1688.2011.00590.x Abstract: Bankfull regional curves, which are curves that establish relationships among channel morphology, discharge, drainage area, are used extensively for stream restoration. These curves are developed upon the assumption that streamflows maintain stationarity over the entire record. We examined this assumption in the Driftless Area of southwestern Wisconsin where agricultural soil retention practices have changed, and precipitation has increased since the 1970s. We developed a bankfull regional curve for this area using field surveys of bankfull channel performed during 2008‐2009 and annual series of peak streamflows for 10 rivers with streamflow records ranging from the 1930s to 2009. We found bankfull flows to correlate to a 1.1 return period. To evaluate gage data statistics, we used the sign test to compare our channel morphology to historic 1.5 return period discharge (Q_1.5) for five time periods: 1959‐1972, 1973‐1992, 1993‐2008, 1999‐2008, and the 1959‐2008 period of record. Analysis of the historic gage data indicated that there has been a more than 30% decline in Q_1.5 since 1959. Our research suggests that land conservation practices may have a larger impact on gaging station stationarity than annual precipitation changes do. Additionally, historic peak flow data from gages, which have records that span land conservation changes, may need to be truncated to represent current flow regimes.     

Improved Bankfull Channel Geometry Prediction Using Two‐Year Return‐Period Discharge1

He, Laien and Gregory V. Wilkerson, 2011. Improved Bankfull Channel Geometry Prediction Using Two‐Year Return‐Period Discharge. Journal of the American Water Resources Association (JAWRA) 47(6):1298–1316. DOI: 10.1111/j.1752‐1688.2011.00567.x Abstract:  Bankfull discharge (Q_bf) and bankfull channel geometry (i.e., width, W_bf; mean depth, D_bf; and cross‐section area, A_bf) are important design parameters in stream restoration, habitat creation, mined land reclamation, and related projects. The selection of values for these parameters is facilitated by regional curves (regression models in which Q_bf, W_bf, D_bf, and A_bf are predicted as a function of drainage area, A_da). This paper explores the potential for the two‐year return‐period discharge (Q₂) to improve predictions of W_bf, D_bf, and A_bf. Improved predictions are expected because Q₂ estimates integrate the effects of basin drainage area, climate, and geology. For conducting this study, 29 datasets (each representing one hydrologic region) spanning 14 states in the United States were analyzed. We assessed the utility of using Q₂ by comparing statistical measures of regression model performance (e.g., coefficient of determination and Akaike’s information criterion). Compared to using A_da, Q₂ is shown to be a “clearly superior” predictor of W_bf, D_bf, and A_bf, respectively, for 21, 13, and 25% of the datasets. By contrast, A_da yielded a clearly superior model for predicting W_bf, D_bf, and A_bf, respectively, for 0, 0, and 14% of the datasets. Our conclusion is that it alongside with developing conventional regional curves using A_da it is prudent to develop regional curves that use Q₂ as an independent variable because in some cases the resulting model will be superior.     

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.     

STATISTICAL ANALYSIS OF THE IMPACT OF GROUND WATER PUMPAGE ON LOW-FLOW HYDROLOGY1

ABSTRACT: Ground-water pumpage withdrew 57 cubic feet per second from aquifers beneath the Yahara River Basin in 1970. Forty-six cubic feet per second were exported by the diversion of treated wastewater from the drainage basin. The low-flow hydrology of the upper Yahara River has been impacted by this diversion. Prior to 1959, the wastewater was discharged into the river, augmenting the baseflow during low-flow periods. As much as 85% of streamflow was due to effluent discharge. In 1959 the wastewater was transferred from the river basin. The result was a decrease of about one-third in mean annual streamflow, and a decrease of more than 50% in the 7Q₂ and 7Q₁₀. Regression analysis showed the annual 7-day low-flow and 60-day low-flow have a statistically significant correlation with mean annual flow. Using predictions of future mean annual discharge of the river with increasing interbasin transfers, it is shown that by 1990 there is a significant probability that in some years the 60-day low-flow in the river will be zero.     

Channel Roughness in North Carolina Mountain Streams

Channel roughness, often described by Manning's n, is used to represent the amount of resistance that flow encounters, and has direct implications on velocity and discharge. Ideally, n is calculated from a long‐term record of channel discharge and hydraulic geometry. In the absence of these data, a combination of photo references and a validated qualitative method is preferable to simply choosing n arbitrarily or from a table. The purpose of this study was to use United States Geological Survey (USGS) streamflow data to calculate roughness coefficients for streams in the mountains of North Carolina. Five USGS gage stations were selected for this study, representing drainage areas between 71.5 and 337 km². Photo references of the study sites are presented. Measured discharges were combined with hydraulic geometry at a cross‐section to calculate roughness coefficients for flows of interest. At bankfull flow, n ranged between 0.039 and 0.064 for the five study sites. Roughness coefficients were not constant for all flows in a channel, and fluctuated over a large range. At all sites, roughness was highest during low‐flow conditions, then quickly decreased as flow increased, up to the bankfull elevation.     

Decoupling Streamflow Responses to Climate Variability and Land Use/Cover Changes in a Watershed in Northern China

Restored annual streamflow (Q_r) and measured daily streamflow of the Chaohe watershed located in northern China and associated long‐term climate and land use/cover data were used to explore the effects of land use/cover change and climate variability on the streamflow during 1961‐2009. There were no significant changes in annual precipitation (P) and potential evapotranspiration, whereas Q_r decreased significantly by 0.81 mm/yr (p < 0.001) over the study period with a change point in 1999. We used 1961‐1998 as the baseline period (BP) and 1999‐2009 the change period (CP). The mean Q_r during the CP decreased by 39.4 mm compared with that in the BP. From 1979 to 2009, the grassland area declined by 69.6%, and the forest and shrublands increased by 105.4 and 73.1%, respectively. The land use/cover change and climate variability contributed for 58.4 and 41.6% reduction in mean annual Q_r, respectively. Compared with the BP, median and high flows in the CP decreased by 38.8 and up to 75.5%, respectively. The study concludes that large‐scale ecological restoration and watershed management in northern China has greatly decreased water yield and reduced high flows due to the improved land cover by afforestation leading to higher water loss through evapotranspiration. At a large watershed scale, land use/cover change could play as much of an important role as climate variability on water resources.     

Evaluating and Extending CLIGEN Precipitation Generation for the Loess Plateau of China1

Abstract: Climate generator (CLIGEN) is widely used in the United States to generate long‐term climate scenarios for use with agricultural systems models. Its applicability needs to be evaluated for use in a new region or climate. The objectives were to: (1) evaluate the reproducibility of the latest version of CLIGEN v5.22564 in generating daily, monthly, and yearly precipitation depths at 12 stations, as well as storm patterns including storm duration (D), relative peak intensity (ip), and peak intensity (rp) at 10 stations dispersed across the Loess Plateau and (2) test whether an exponential distribution for generating D and a distribution‐free approach for inducing desired rank correlation between precipitation depth and D can improve storm pattern generations. Mean absolute relative errors (MAREs) for simulating daily, monthly, annual, and annual maximum daily precipitation depth across all 12 stations were 3.5, 1.7, 1.7, and 5.0% for the mean and 5.0, 4.5, 13.0, and 13.6% for the standard deviations (SD), respectively. The model reproduced the distributions of monthly and annual precipitation depths well (p > 0.3), but the distribution of daily precipitation depth was less well produced. The first‐order, two‐state Markov chain algorithm was adequate for generating precipitation occurrence for the Loess Plateau of China; however, it underpredicted the longest dry periods. The CLIGEN‐generated storm patterns poorly. It underpredicted mean and SD of D for storms ≥10 mm by ?60.4 and ?72.6%, respectively. Compared with D, ip, and rp were slightly better reproduced. The MAREs of mean and SD were 21.0 and 52.1% for ip, and 31.2 and 55.2% for rp, respectively. When an exponential distribution was used to generate D, MAREs were reduced to 2.6% for the mean and 7.8% for the SD. However, ip estimation became much worse with MAREs being 128.9% for the mean and 241.1% for the SD. Overall, storm pattern generation needs improvement. For better storm pattern generation for the region, precipitation depth, D, and rp may be generated correlatively using Copula methods.     

Regional and Temporal Differences in Nitrate Trends Discerned from Long‐Term Water Quality Monitoring Data

Riverine nitrate (NO₃) is a well‐documented driver of eutrophication and hypoxia in coastal areas. The development of the elevated river NO₃ concentration is linked to anthropogenic inputs from municipal, agricultural, and atmospheric sources. The intensity of these sources has varied regionally, through time, and in response to multiple causes such as economic drivers and policy responses. This study uses long‐term water quality, land use, and other ancillary data to further describe the evolution of river NO₃ concentrations at 22 monitoring stations in the United States (U.S.). The stations were selected for long‐term data availability and to represent a range of climate and land‐use conditions. We examined NO₃ at the monitoring stations, using a flow‐weighting scheme meant to account for interannual flow variability allowing greater focus on river chemical conditions. River NO₃ concentration increased strongly during 1945‐1980 at most of the stations and have remained elevated, but stopped increasing during 1981‐2008. NO₃ increased to a greater extent at monitoring stations in the Midwest U.S. and less so at those in the Eastern and Western U.S. We discuss 20th Century agricultural development in the U.S. and demonstrate that regional differences in NO₃ concentration patterns were strongly related to an agricultural index developed using principal components analysis. This unique century‐scale dataset adds to our understanding of long‐term NO₃ patterns in the U.S.     

Estimating Sediment and Nutrient Loads in Four Western Lake Superior Streams

Total suspended solids (TSS) and total phosphorus (TP) have been shown to be strongly correlated with turbidity in watersheds. High‐frequency in situ turbidity can provide estimates of these potential pollutants over a wide range of hydrologic conditions. Concentrations and loads were estimated in four western Lake Superior trout streams from 2005 to 2010 using regression models relating continuous turbidity data to grab sample measures of TSS and TP during differing flow regimes. TSS loads estimated using the turbidity surrogate were compared with those made using FLUX software, a standard assessment technique based on discharge and grab sampling for TSS. More traditional rating curve methodology was not suitable because of the high variability in the particulates vs. discharge relationship. Stream‐specific turbidity and TSS data were strongly correlated (r² = 0.5 to 0.8; p < 0.05) and less so for TP (r² = 0.3 to 0.7; p < 0.05). Near‐continuous turbidity monitoring (every 15 min) provided a good method for estimating both TSS and TP concentration, providing information when manual sample collection was unlikely, and allowing for detailed analyses of short‐term responses of flashy Lake Superior tributaries to highly variable weather and hydrologic conditions while the FLUX model typically resulted in load estimates greater than those determined using the turbidity surrogate, with 17/23 stream years having greater FLUX estimates for TSS and 18/23 for TP.     

AMMONIA IN TEXAS STREAMS DURING LOW FLOW FROM MUNICIPAL WASTEWATER1

ABSTRACT: An investigation of treated municipal wastewaters discharged into Texas streams was conducted to determine the probable effect of concentrations of ammonia in receiving waters, based on existing data on ammonia levels which are lethal to various species of fish. Recorded data for most Texas cities were analyzed. Based on existing toxicity criteria for ammonia of 1/10 TL_m= 0.31 mg/1 NH₃-N, employing known discharge flow rates, and 7-day, 5-year or 7-day, 10-year low flows in Texas streams, appreciable numbers of sites were found to pose a threat to various species of fish. Using the bluegill (Lepomis macrochirus) as a median tolerance limit species, data from 65 cities which met the aforecited requirements, were analyzed. Those included a total of 92 wastewater effluents. Sixty-nine percent of those cities and 70% of their effluents exceeded the 0.31 mg/1 NH₃-N limit in the stream below the discharge point. Thirty-seven percent of the cities equaled or exceeded the 96-hour TL_m concentration limit of 3.1 mg/1 ammonia. Based on the 10 mg/1 NO₃-N standard for intake water for potable supplies, 32% of the effluents resulted in a stream concentration which exceeded 10 mg/1, assuming a straight conversion of NH₃-N to NO₃-N.     

Water Balances of Two Piedmont Headwater Catchments: Implications for Regional Hydrologic Landscape Classification

In the Piedmont of North Carolina, a traditionally water‐rich region, reservoirs that serve over 1 million people are under increasing pressure due to naturally occurring droughts and increasing land development. Innovative development approaches aim to maintain hydrologic conditions of the undisturbed landscape, but are based on insufficient target information. This study uses the hydrologic landscape concept to evaluate reference hydrology in small headwater catchments surrounding Falls Lake, a reservoir serving Raleigh and the greater Triangle area. Researchers collected one year of detailed data on water balance components, including precipitation, evapotranspiration, streamflow, and shallow subsurface storage from two headwater catchments representative of two hydrologic landscapes defined by differences in soils and topographic characteristics. The two catchments are similar in size and lie within the same physiographic region, and during the study period they showed similar water balances of 26‐30% Q, ?4 to 5% ΔS, 59‐65% evapotranspiration, and 9‐10% G. However, the steeper, more elevated catchment exhibited perennial streamflow and nongrowing season runoff ratios (Q/P) of 33%, whereas the flat, low‐lying stream was drier during the growing season and exhibited Q/P ratios of 52% during the nongrowing season. A hydrologic landscape defined by topography and soil characteristics helps characterize local‐scale reference hydrology and may contribute to better land management decisions.     

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.     

TEMPORAL CHANGES IN THE YADKIN RIVER FLOW VERSUS SUSPENDED SEDIMENT CONCENTRATION RELATIONSHIP1

ABSTRACT: Dynamic linear models (DLM) and seasonal trend decomposition (STL) using local regression, or LOESS, were used to analyze the 50‐year time series of suspended sediment concentrations for the Yadkin River, measured at the U.S. Geological Survey station at Yadkin College, North Carolina. A DLM with constant trend, seasonality, and a log₁₀ streamflow regressor provided the best model to predict monthly mean log₁₀ suspended sediment concentrations, based on the forecast log likelihood. Using DLM, there was evidence (odds approximately 69:1) that the log₁₀ streamflow versus log₁₀ suspended sediment concentration relationship has changed, with an approximate 20 percent increase in the log₁₀ streamflow coefficient over the period 1981 to 1996. However, sediment concentrations in the Yadkin River have decreased during the decade of the 1990s, which has been accompanied by a concomitant increase in streamflow variability. Although STL has been shown to be a versatile trend analysis technique, DLM is shown to be more suitable for discovery and inference of structural changes (trends) in the model coefficient describing the relationship between flow and sediment concentration.     

Evaluation of a Watershed Model for Estimating Daily Flow Using Limited Flow Measurements1

Abstract: The Soil and Water Assessment Tool (SWAT) model was evaluated for estimation of continuous daily flow based on limited flow measurements in the Upper Oyster Creek (UOC) watershed. SWAT was calibrated against limited measured flow data and then validated. The Nash‐Sutcliffe model Efficiency (NSE) and mean relative error values of daily flow estimations were 0.66 and 15% for calibration, and 0.56 and 4% for validation, respectively. Also, further evaluation of the model’s estimation of flow at multiple locations was conducted with parametric paired t‐test and nonparametric sign test at a 95% confidence level. Among the five main stem stations, four stations were statistically shown to have good agreement between predicted and measured flows. SWAT underestimated the flow of the fifth main stem station possibly because of the existence of complex flood control measures near to the station. SWAT estimated the daily flow at one tributary station well, but with relatively large errors for the other two tributaries. The spatial pattern of predicted flows matched the measured ones well. Overall, it was concluded from the graphical comparisons and statistical analyses of the model results that SWAT was capable of reproducing continuous daily flows based on limited flow data as is the case in the UOC watershed.     

SYNTHESIS OF FLOW-DURATION CURVES FOR UNREGULATED STREAMS IN NEW HAMPSHIRE1

ABSTRACT: Flow-duration curves are concise pictures of flow variability at a point on a stream, and provide essential information for all water-resource planning. In New Hampshire, useful estimates of flow-duration curves for ungaged points on unregulated streams can be made using only information readily available from contour maps: 1) area of the basin above the point of interest; and 2) either the measured mean basin elevation or the elevations of the highest and lowest points in the basin. Measured or estimated mean basin elevation is then used in regression equations to estimate mean flow QC and the flow exceeded 95% of the time, Q₉₅. QC is assumed to occur at the 27% exceedance frequency. Q₀₂, Q₀₅, and Q₃₀ are estimated as multiples of QC. Equations are provided for calculating 95% confidence intervals for future estimates using the method. The dependence of mean flow on elevation is due to positive vertical precipitation gradients and negative vertical evapotranspiration gradients. The dependence of Q₉₅ on elevation appears to be due largely to the fact that it rains more often, that snowmelt takes longer, and that evapotranspiration is reduced at higher elevations.     

WATERSHED SCALING EFFECT ON BASE FLOW NITRATE,VALLEY AND RIDGE PHYSIOGRAPHIC PROVINCE1

ABSTRACT: A study of stream base flow and NO₃‐N concentration was conducted simultaneously in 51 subwatersheds within the 116‐square‐kilometer watershed of East Mahantango Creek near Klingerstown, Pennsylvania. The study was designed to test whether measurable results of processes and observations within the smaller watersheds were similar to or transferable to a larger scale. Ancillary data on land use were available for the small and large watersheds. Although the source of land‐use data was different for the small and large watersheds, comparisons showed that the differences in the two land‐use data sources were minimal. A land use‐based water‐quality model developed for the small‐scale 7.3‐square‐kilometer watershed for a previous study accurately predicted NO₃‐N concentrations from sampling in the same watershed. The water‐quality model was modified and, using the imagery‐based land use, was found to accurately predict NO₃‐N concentrations in the subwatersheds of the large‐scale 116‐square‐kilometer watershed as well. Because the model accurately predicts NO₃‐N concentrations at small and large scales, it is likely that in second‐order streams and higher, discharge of water and NO₃‐N is dominated by flow from smaller first‐order streams, and the contribution of ground‐water discharge to higher order streams is minimal at the large scale.