Effects of Rainfall and Ground‐Water Pumping on Streamflow in Mākaha,O’ahu,Hawai’i1

Abstract: Land‐use/land‐cover changes in Mākaha valley have included the development of agriculture, residential dwellings, golf courses, potable water supply facilities, and the introduction of alien species. The impact of these changes on surface water and ground water resources in the valley is of concern. In this study, streamflow, rainfall, and ground‐water pumping data for the upper part of the Mākaha valley watershed were evaluated to identify corresponding trends and relationships. The results of this study indicate that streamflow declined during the ground‐water pumping period. Mean and median annual streamflow have declined by 42% (135 mm) and 56% (175 mm), respectively, and the mean number of dry stream days per year has increased from 8 to 125. Rainfall across the study area appears to have also declined though it is not clear whether the reduction in rainfall is responsible for all or part of the observed streamflow decline. Mean annual rainfall at one location in the study area declined by 14% (179 mm) and increased by 2% (48 mm) at a second location. Further study is needed to assess the effect of ground‐water pumping and to characterize the hydrologic cycle with respect to rainfall, infiltration, ground‐water recharge and flow in the study area, and stream base flow and storm flow.     

Long‐Term Streamflow Response to Climatic Variability in the Loess Plateau,China1

Abstract: The Loess Plateau region in northwestern China has experienced severe water resource shortages due to the combined impacts of climate and land use changes and water resource exploitation during the past decades. This study was designed to examine the impacts of climatic variability on streamflow characteristics of a 12‐km² watershed near Tianshui City, Gansu Province in northwestern China. Statistic analytical methods including Kendall’s trend test and stepwise regression were used to detect trends in relationship between observed streamflow and climatic variables. Sensitivity analysis based on an evapotranspiration model was used to detect quantitative hydrologic sensitivity to climatic variability. We found that precipitation (P), potential evapotranspiration (PET) and streamflow (Q) were not statistically significantly different (p > 0.05) over the study period between 1982 and 2003. Stepwise regression and sensitivity analysis all indicated that P was more influential than PET in affecting annual streamflow, but the similar relationship existed at the monthly scale. The sensitivity of streamflow response to variations of P and PET increased slightly with the increase in watershed dryness (PET/P) as well as the increase in runoff ratio (Q/P). This study concluded that future changes in climate, precipitation in particular, will significantly impact water resources in the Loess Plateau region an area that is already experiencing a decreasing trend in water yield.     

Mid‐Range Streamflow Forecasts Based on Climate Modeling – Statistical Correction and Evaluation1

Abstract: Mid‐range streamflow predictions are extremely important for managing water resources. The ability to provide mid‐range (three to six months) streamflow forecasts enables considerable improvements in water resources system operations. The skill and economic value of such forecasts are of great interest. In this research, output from a general circulation model (GCM) is used to generate hydrologic input for mid‐range streamflow forecasts. Statistical procedures including: (1) transformation, (2) correction, (3) observation of ensemble average, (4) improvement of forecast, and (5) forecast skill test are conducted to minimize the error associated with different spatial resolution between the large‐scale GCM and the finer‐scale hydrologic model and to improve forecast skills. The accuracy of a streamflow forecast generated using a hydrologic model forced with GCM output for the basin was evaluated by forecast skill scores associated with the set of streamflow forecast values in a categorical forecast. Despite the generally low forecast skill score exhibited by the climate forecasting approach, precipitation forecast skill clearly improves when a conditional forecast is performed during the East Asia summer monsoon, June through August.     

Trends in Western U.S. Snowpack and Related Upper Colorado River Basin Streamflow1

Miller, W. Paul and Thomas C. Piechota, 2011. Trends in Western U.S. Snowpack and Related Upper Colorado River Basin Streamflow. Journal of the American Water Resources Association (JAWRA) 47(6):1197–1210. DOI: 10.1111/j.1752‐1688.2011.00565.x Abstract: Water resource managers in the Western United States (U.S.) are currently faced with the challenge of adapting to unprecedented drought and uncertain impacts of climate change. Recent research has indicated increasing regional temperature and changes to precipitation and streamflow characteristics throughout the Western U.S. As such, there is increased uncertainty in hydroclimatological forecasts, which impact reservoir operations and water availability throughout the Western U.S., particularly in the Colorado River Basin. Previous research by the authors hypothesized a change in the character of precipitation (i.e., the frequency and amount of rainfall and snowfall events) throughout the Colorado River Basin. In the current study, 398 snowpack telemetry stations were investigated for trends in cumulative precipitation, snow water equivalent, and precipitation events. Observations of snow water equivalent characteristics were compared to observations in streamflow characteristics. Results indicate that the timing of the last day of the snow season corresponds well to the volume of runoff observed over the traditional peak flow season (April through July); conversely, the timing of the first day of the snow season does not correspond well to the volume of runoff observed over the peak flow season. This is significant to water resource managers and river forecasters, as snowpack characteristics may be indicative of a productive or unproductive runoff season.     

A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models1

Abstract: Water resources planning and management efficacy is subject to capturing inherent uncertainties stemming from climatic and hydrological inputs and models. Streamflow forecasts, critical in reservoir operation and water allocation decision making, fundamentally contain uncertainties arising from assumed initial conditions, model structure, and modeled processes. Accounting for these propagating uncertainties remains a formidable challenge. Recent enhancements in climate forecasting skill and hydrological modeling serve as an impetus for further pursuing models and model combinations capable of delivering improved streamflow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamflow forecast ensemble members and accounting for cumulative sources of uncertainty. The framework presented here proposes integration and offline coupling of global climate models (GCMs), multiple regional climate models, and numerous water balance models to improve streamflow forecasting through generation of ensemble forecasts. For demonstration purposes, the framework is imposed on the Jaguaribe basin in northeastern Brazil for a hindcast of 1974‐1996 monthly streamflow. The ECHAM 4.5 and the NCEP/MRF9 GCMs and regional models, including dynamical and statistical models, are integrated with the ABCD and Soil Moisture Accounting Procedure water balance models. Precipitation hindcasts from the GCMs are downscaled via the regional models and fed into the water balance models, producing streamflow hindcasts. Multi‐model ensemble combination techniques include pooling, linear regression weighting, and a kernel density estimator to evaluate streamflow hindcasts; the latter technique exhibits superior skill compared with any single coupled model ensemble hindcast.     

MODIS Biophysical States and NEXRAD Precipitation in a Statistical Evaluation of Antecedent Moisture Condition and Streamflow1

Abstract: The potential of remotely sensed time series of biophysical states of landscape to characterize soil moisture condition antecedent to radar estimates of precipitation is assessed in a statistical prediction model of streamflow in a 1,420 km² watershed in south‐central Texas, Moderate Resolution Imaging Spectroradiometer (MODIS) time series biophysical products offer significant opportunities to characterize and quantify hydrologic state variables such as land surface temperature (LST) and vegetation state and status. Together with Next Generation Weather Radar (NEXRAD) precipitation estimates for the period 2002 through 2005, 16 raw and deseasoned time series of LST (day and night), vegetation indices, infrared reflectances, and water stress indices were linearly regressed against observed watershed streamflow on an eight‐day aggregated time period. Time offsets of 0 (synchronous with streamflow event), 8, and 16 days (leading streamflow event) were assessed for each of the 16 parameters to evaluate antecedent effects. The model results indicated a reasonable correlation (r² = 0.67) when precipitation, daytime LST advanced 16 days, and a deseasoned moisture stress index were regressed against log‐transformed streamflow. The estimation model was applied to a validation period from January 2006 through March 2007, a period of 12 months of regional drought and base‐flow conditions followed by three months of above normal rainfall and a flood event. The model resulted in a Nash‐Sutcliffe estimation efficiency (E) of 0.45 for flow series (in log‐space) for the full 15‐month period, ?0.03 for the 2006 drought condition period, and 0.87 for the 2007 wet condition period. The overall model had a relative volume error of ?32%. The contribution of parameter uncertainties to model discrepancy was evaluated.     

Generalized Nondimensional Depth‐Discharge Rating Curves Tested on Florida Streamflow1

Abstract: A discharge rating is a relationship between stage and discharge at a specific point in a river stream or lake outlet structure. Rating curves are useful for interpolating and perhaps extrapolating flow measurements and for use directly in storage routing models. However, rating data and stations are limited. A generalized nondimensional mathematical expression that describes the rating relation of depth and discharge has been developed and tested against observations from 46 stations in West‐Central Florida. Three approaches were tested in sequence to select the best fit. The proposed model is a log‐linear equation with zero intercept and a slope that fits more than 50% of the stations were analyzed. The model is normalized by the depth and discharge values at 10% exceedance from data published by the U.S. Geological Survey. For ungauged applications, Q₁₀ and d₁₀ were derived from a relationship shown to be reasonably well correlated to the watershed drainage area. The average relative error for this parameter set shows that for the flow range up to the Q₁₀ discharge, better than 30% agreement with the USGS rating data can be expected for about 50% of the stations. Further analysis is required to determine why so many stations exhibit such similar behavior and to identify the criteria or parameters governing the differences.     

The Impact of Asynchronicity on Event‐Flow Estimation in Basin‐Scale Hydrologic Model Calibration1

Abstract: The calibration of basin‐scale hydrologic models consists of adjusting parameters such that simulated values closely match observed values. However, due to inevitable inaccuracies in models and model inputs, simulated response hydrographs for multiyear calibrations will not be perfectly synchronized with observed response hydrographs at the daily time step. An analytically derived formula suggests that when timing errors are significant, traditional calibration approaches may generally underestimate the total event‐flow volume. An event‐adaptive time series is developed and incorporated into the Nash‐Sutcliffe Efficiency objective function to diagnose the potential impact of event‐flow synchronization errors. Test sites are the 50 km² Subwatershed I of the Little River Experimental Watershed (LREWswI) in southeastern Georgia, and the 610 km² Little Washita River Experimental Watershed (LWREW) in southwestern Oklahoma, with the Soil and Water Assessment Tool used as the hydrologic model. Results suggest that simulated surface runoff generation is 55% less for LREWswI when the daily time series is used compared with when the event‐adaptive technique is used. Event‐flow generation may also be underestimated for LWREW, but to a lesser extent than it may be for LREWswI, due to a larger portion of the event flow being lateral flow.     

Evaluation of Ground‐Water and Surface‐Water Exchanges Using Streamflow Difference Analyses1

Abstract: In the karstic lower Flint River Basin, limestone fracturing, jointing, and subsequent dissolution have resulted in the development of extensive secondary permeability and created a system of major conduits that facilitate the exchange of water between the Upper Floridan aquifer and Flint River. Historical streamflow data from U.S. Geological Survey gaging stations located in Albany and Newton, Georgia, were used to quantify ground‐water and surface‐water exchanges within a 55.3 km section of the Flint River. Using data from 2001, we compared estimates of ground‐water flux using a time adjustment method to a water balance equation and found that these independent approaches yielded similar results. The associated error was relatively large during high streamflow when unsteady conditions prevail, but much lower during droughts. Flow reversals were identified by negative streamflow differences and verified with in situ data from temperature sensors placed inside large spring conduits. Long‐term (13 years) analysis showed negative streamflow differentials (i.e., a losing stream condition) coincided with high river stages and indicated that streamflow intrusion into the aquifer could potentially exceed 150 m³/s. Although frequent negative flow differentials were evident, the Flint River was typically a gaining stream and showed a large net increase in flow between the two gages when examined over the period 1989‐2003. Ground‐water contributions to this stream section averaged 2‐42 m³/s with a mean of 13 m³/s. The highest rate of ground‐water discharge to the Flint River occurred during the spring when regional ground‐water levels peaked following heavy winter and spring rains and corresponding rates of evapotranspiration were low. During periods of extreme drought, ground‐water contributions to the Flint River declined.     

Effectiveness of Multi‐Site Weather Generator for Hydrological Modeling1

Khalili, Malika, François Brissette, and Robert Leconte, 2011. Effectiveness of Multi‐site Weather Generator for Hydrological Modeling. Journal of the American Water Resources Association (JAWRA) 1‐12. DOI: 10.1111/j.1752‐1688.2010.00514.x Abstract: A multi‐site weather generator has been developed using the concept of spatial autocorrelation. The multi‐site generation approach reproduces the spatial autocorrelations observed between a set of weather stations as well as the correlations between each pair of stations. Its performance has been assessed in two previous studies using both precipitation and temperature data. The main objective of this paper is to assess the efficiency of this multi‐site weather generator compared to a uni‐site generator with respect to hydrological modeling. A hydrological model, known as Hydrotel, was applied over the Chute du Diable watershed, located in the Canadian province of Quebec. The distributed nature of Hydrotel accounts for the spatial variations throughout the watershed, and thus allows a more in‐depth assessment of the effect of spatially dependent meteorological input on runoff generation. Simulated streamflows using both the multi‐site and uni‐site generated weather data were statistically compared to flows modeled using observed data. Overall, the hydrological modeling using the multi‐site weather generator significantly outperformed that using the uni‐site generator. This latter combined to Hydrotel resulted in a significant underestimation of extreme streamflows in all seasons.     

Streamflow Changes in the South Atlantic,United States During the Mid‐ and Late 20th Century1

Abstract: Repeated severe droughts over the last decade in the South Atlantic have raised concern that streamflow may be systematically decreasing, possibly due to climate variability. We examined the monthly and annual trends of streamflow, precipitation, and temperature in the South Atlantic for the time periods: 1934‐2005, 1934‐1969, and 1970‐2005. Streamflow and climate (temperature and precipitation) trends transitioned ca. 1970. From 1934 to 1969, streamflow and precipitation increased in southern regions and decreased in northern regions; temperature decreased throughout the South Atlantic. From 1970 to 2005, streamflow decreased, precipitation decreased, and temperature increased throughout the South Atlantic. It is unclear whether these will be continuing trends or simply part of a long‐term climatic oscillation. Whether these streamflow trends have been driven by climatic or anthropogenic changes, water resources management faces challenging prospects to adapt to decadal‐scale persistently wet and dry hydrologic conditions.     

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.     

A Comparative Study of Stream‐Gaging Techniques for Low‐Flow Measurements in Two Virginia Tributaries1

Abstract: Nonpoint source pollution (NPS) studies, such as total maximum daily loads development, often require quantification of flow in small first‐order and second‐order streams. Frequently, stream‐gaging techniques are implemented in flows that are below the manufacturer’s recommended minimum velocity. A comparative analysis of the accuracy of current technologies used in NPS pollution stream‐gaging applications and their applicability in low‐flow conditions was conducted. Nine stream‐gaging methods were evaluated for their field and laboratory performance and control structures were used as the statistical control. Analysis of the field investigation data indicated that Marsh McBirney current meter and the One‐orange method were the most accurate in the field while the results of the laboratory experiments found that the Starflow acoustic Doppler and Valeport Braystoke current meter performed best among the 10 methods. Overall, the Marsh McBirney and Valeport Braystoke current meters exhibited the best performance for both field and laboratory situations.     

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.     

Watershed Evapotranspiration Increased due to Changes in Vegetation Composition and Structure Under a Subtropical Climate1

Abstract: Natural forests in southern China have been severely logged due to high human demand for timber, food, and fuels during the past century, but are recovering in the past decade. The objective of this study was to investigate how vegetation cover changes in composition and structure affected the water budgets of a 9.6‐km² Dakeng watershed located in a humid subtropical mountainous region in southern China. We analyzed 27 years (i.e., 1967‐1993) of streamflow and climate data and associated vegetation cover change in the watershed. Land use/land cover census and Normalized Difference of Vegetation Index (NDVI) data derived from remote sensing were used to construct historic land cover change patterns. We found that over the period of record, annual streamflow (Q) and runoff/precipitation ratio did not change significantly, nor did the climatic variables, including air temperature, Hamon’s potential evapotranspiration (ET), pan evaporation, sunshine hours, and radiation. However, annual ET estimated as the differences between P and Q showed a statistically significant increasing trend. Overall, the NDVI of the watershed had a significant increasing trend in the peak spring growing season. This study concluded that watershed ecosystem ET increased as the vegetation cover shifted from low stock forests to shrub and grasslands that had higher ET rates. A conceptual model was developed for the study watershed to describe the vegetation cover‐streamflow relationships during a 50‐year time frame. This paper highlighted the importance of eco‐physiologically based studies in understanding transitory, nonstationary effects of deforestation or forestation on watershed water balances.     

The Role of Climate and Human Influences in the Dry‐Up of the Jinci Springs,China1

Abstract: One of the largest karst springs in North China, the Jinci Springs, dried up and has remained dry since 1994. We develop a correlation analysis with time‐lag and a regression analysis with time‐lag to study the relation between spring flow and precipitation. This allows us to obtain a better understanding of karst hydrological processes by differentiating the contribution of variation in precipitation from anthropogenic impacts on the dry‐up of Jinci Springs. We divided the karstic hydrological processes into two phases: pre‐1961 and post‐1961. In the first phase (i.e., 1954‐1960) the groundwater recharge was affected by precipitation alone, and in the second phase (i.e., 1961‐1994) the groundwater recharge was influenced by both precipitation and human activities. Using precipitation and groundwater recharge data in the first phase, we set up a groundwater recharge model with time‐lags. By running the time‐lags model, we acquired the groundwater recharge likely to occur under the sole effect of precipitation in the second phase. Using a water‐balance calculation, we conclude that the groundwater recharge exhibited statistical stationarity, and the Jinci Springs dry‐up was the result of anthropogenic activities. At least three specific types of anthropogenic activities contributed to the drying‐up of Jinci Springs: (1) groundwater pumping accounts for 51%, (2) the dewatering from coal mining accounts for 33%, (3) and dam‐building 14%. The drying‐up of Jinci Springs meant that the groundwater drained from the aquifer’s fractures, and subsequently changed the structure of the karst aquifer. Although groundwater exploitation has been reduced, the flow at Jinci Springs has not reoccurred.     

Evapotranspiration in the Meso‐Scale Forested Watersheds in Minjiang Valley,West China1

Abstract: Information on evapotranspiration (ET) can help us understand water balance, particularly in forested watersheds. Previous studies in China show that ET was relatively low (30‐40% of total precipitation) in the Minjiang Valley located in the upper reach of the Yangtze River Basin. However, this conclusion was derived from research on small‐scale watersheds (<100 km²). The objective of this paper was to present ET information on meso‐scale watersheds in the Minjiang Valley. Four meso‐scale watersheds (1,700‐5,600 km²) located in the Minjiang Valley were used to estimate ET using the water balance approach. We first generated forest vegetation variables (coniferous forest percentage, forest cover percentage, and derived forest vegetation index) using remote sensing data. Landsat 5 TM satellite images, acquired on June 26, 1994, were selected for the vegetation classification. Actual annual ET was calculated based on 11‐year estimated precipitation and measured streamflow data (1992‐2002). We also calculated potential ET (PET) using an improved Thornthwaite model for all four watersheds for the period of 1992‐1998. PET can provide additional information about potential capacity of water flux to atmosphere in the region. Seasonal (dry and rainy) PET and ET for all studied watersheds were also estimated for comparison purposes as the water balance approach, at shorter than annual scales, would likely provide inaccurate estimates of ET. The dominant vegetations in the Minjiang Valley were grasslands, conifer forests, and shrub‐lands. Our results confirmed that both ET and PET for three studied meso‐scale watersheds in the Minjiang Valley is relatively low (39.5‐43.8 and 28.2‐47.7% for ET and PET, respectively), with an exception of ET in the Yuzixi watershed being 71.1%. This result is generally consistent with previous research at small watershed scales. Furthermore, the low ET across various scales in the Minjiang Valley may be related to the unique deeply cut valley environment.     

Distributed Hydrologic Modeling in the Suburban Landscape: Assessing Parameter Transferability from Gauged Reference Catchments1

Mittman, Tamara, Lawrence E. Band, Taehee Hwang, and Monica Lipscomb Smith, 2012. Distributed Hydrologic Modeling in the Suburban Landscape: Assessing Parameter Transferability from Gauged Reference Catchments. Journal of the American Water Resources Association (JAWRA) 48(3): 546-557. DOI: 10.1111/j.1752-1688.2011.00636.x Abstract: Distributed, process-based models of catchment hydrologic response are potentially useful tools for the assessment of Low Impact Development (LID) techniques in urbanized catchments. Their application is often limited, however, by the lack of continuous streamflow records to calibrate poorly constrained parameters. This article examines the transferability of soil and groundwater parameters from a forested reference catchment to a nearby suburban catchment. We use the Regional Hydro-Ecologic Simulation System (RHESSys) to develop hydrologic models of one gauged forested and one ungauged suburban catchment within the Baltimore Ecosystem Study (BES) study area. We use a parameter uncertainty framework to calibrate soil and groundwater parameters for the forested catchment, and discrete measurements of streamflow from the suburban catchment to assess parameter transferability. Results indicate that the transfer of soil and groundwater parameters from forested reference to nearby suburban catchments is viable, with performance measures for the suburban catchment often exceeding those for the forested catchment. We propose that the simplification of hydrologic processes in urbanized catchments may account for the increase in model performance in the suburban catchment.     

Stage‐Discharge Relationships for U‐, A‐, and W‐Weirs in Un‐submerged Flow Conditions1

Thornton, Christopher I., Anthony M. Meneghetti, Kent Collins, Steven R. Abt, and S. Michael Scurlock, 2011. Stage‐Discharge Relationships for U‐, A‐, and W‐Weirs in Un‐submerged Flow Conditions. Journal of the American Water Resources Association (JAWRA) 47(1):169‐178. DOI: 10.1111/j.1752‐1688.2010.00501.x Abstract: Instream rock weirs are routinely placed into stream systems to provide grade control, reduce streambank erosion, provide energy dissipation, and allow fish passage. However, design and performance criteria for site specific applications are often anecdotal or qualitative in nature, and based upon the experience of the design team. A study was conducted to develop generic state‐discharge relationships for U‐, A‐, and W‐weirs. A laboratory testing program was performed in which scaled, near‐prototype U‐, A‐, and W‐rock weir structures were constructed in 11 configurations. Each configuration encompassed a unique weir shape, bed material, and/or bed slope. Thirty‐one tests were conducted in which each structure was subjected to a sequence of predetermined discharges that minimally included the equivalent of 1/3 bankfull, 2/3 bankfull, and bankfull conditions. All tests were performed in subcritical, un‐submerged flow conditions. Stage‐discharge relationships were developed using multivariant, power regression techniques for each of the U‐, A‐, and W‐rock weirs as a function of the effective weir length, flow depth, mean weir height, rock size, and discharge coefficient. Unique coefficient expressions were developed for each weir shape, and a single discharge coefficient was proposed applicable to the weirs for determining the channel stage‐discharge rating.     

Surface‐Water Nutrient Conditions and Sources in the United States Pacific Northwest1

Wise, Daniel R. and Henry M. Johnson, 2011. Surface‐Water Nutrient Conditions and Sources in the United States Pacific Northwest. Journal of the American Water Resources Association (JAWRA) 47(5):1110‐1135. DOI: 10.1111/j.1752‐1688.2011.00580.x Abstract: The SPAtially Referenced Regressions On Watershed attributes (SPARROW) model was used to perform an assessment of surface‐water nutrient conditions and to identify important nutrient sources in watersheds of the Pacific Northwest region of the United States (U.S.) for the year 2002. Our models included variables representing nutrient sources as well as landscape characteristics that affect nutrient delivery to streams. Annual nutrient yields were higher in watersheds on the wetter, west side of the Cascade Range compared to watersheds on the drier, east side. High nutrient enrichment (relative to the U.S. Environmental Protection Agency’s recommended nutrient criteria) was estimated in watersheds throughout the region. Forest land was generally the largest source of total nitrogen stream load and geologic material was generally the largest source of total phosphorus stream load generated within the 12,039 modeled watersheds. These results reflected the prevalence of these two natural sources and the low input from other nutrient sources across the region. However, the combined input from agriculture, point sources, and developed land, rather than natural nutrient sources, was responsible for most of the nutrient load discharged from many of the largest watersheds. Our results provided an understanding of the regional patterns in surface‐water nutrient conditions and should be useful to environmental managers in future water‐quality planning efforts.