SIMULATION OF THE EFFECTS OF URBANIZATION ON BASIN STREAMFLOW1

ABSTRACT: The hydrologic modeling of streamflow in the Waterford River Basin has been conducted as part of comprehensive investigations of the effects of urbanization on water resources in the basin. Using a detailed input data base, continuous simulation of streamflow in the study area has been done by means of the HSPF model, which has been calibrated for the existing conditions and then applied to several future land use scenarios. The basin climate and geology contribute to high conversion of precipitation into streamflow under the existing conditions. Consequently, future urban development in the study basin should not increase the annual streamflow, but would contribute to increases in peak flows and the incidence of flooding because of the increased speed of runoff. If the impervious area in the basin is doubled, the peak flows may increase by about 20 percent.     

The Forest‐Streamflow Relationship in China: A 40‐Year Retrospect1

Abstract: The relationship between forests and streamflows has long been an important research interest in China. The purpose of this paper is to summarize progress and lessons learned from the forest‐streamflow studies over the past four decades in China. To better measure the research gaps between China and other parts of the world, a brief global review on the findings from paired watershed studies over the past 100 years was also provided. In China, forest management shifted in the later 1990s from timber harvesting to forest restoration. Forest‐streamflow research was accordingly changed from assessing harvesting impacts to evaluating both harvesting and forestation effects. Over the past four decades, Chinese forest hydrology research has grown substantially. Significant progress has been made on measuring individual processes, but little solid, long‐term data were available to assess the relationship between forest changes and streamflows because of an absence of standard paired watersheds. In addition, misuse of statistical analyses was often found in the literature. A unique opportunity exists in China to study the forestation effects on streamflow as several large‐scale forestation programs are being implemented. Such an opportunity should include a robust paired watershed design under an integrated watershed ecosystem framework to avoid repeating the lessons already learned. Recommendations on future forest‐streamflow research directions in China are provided.     

Analyzing the Effects of Groundwater Pumping on an Urban Stream‐Aquifer System

When the cone of influence of a pumping well reaches a nearby river, the resulting hydraulic gradient can induce enhanced seepage of streamflow into the aquifer. The rate of seepage is often modeled using analytical solutions that are simple to apply but may not reproduce field data due to mathematical assumptions not being met in the field. Furthermore, the appropriateness of such models has not been investigated in detail due to difficulty in measuring streamflow loss in the field. In this study, a field experiment was conducted on a reach of the South Platte River near Denver, Colorado to estimate pumping‐induced streamflow loss. A network of stream gauges, monitoring wells, and in situ measurements was used to observe streamflow rates, groundwater levels, and temperature to assess if pumping wells have a significant impact on streamflow, and to compare observed streamflow depletion against analytical solutions. Data collected suggest that pumping wells have a noticeable impact on streamflow. The analytical solutions proved accurate if streamflow was low and constant but performed poorly if streamflow was high and variable. Therefore, for this reach, the use of analytical solutions to predict streamflow may only be appropriate under low‐flow, constant‐flow conditions. Methods and results can be used to guide other streamflow depletion studies and to inform cases of pumping‐induced streamflow depletion, particularly in regard to water rights.     

MODELING STREAMFLOW FROM ARTIFICIALLY DRAINED AGRICULTURAL WATERSHEDS IN ILLINOIS1

ABSTRACT: Evaluation of the applicability and validity of hydrologic simulation models for various cropping systems in different hydrogeologic and soil conditions is needed for a range of spatial scales. We calibrated and tested the ADAPT model for simulating streamflow from 552 to 1,985 km² watersheds in central Illinois, where more than 79 percent of the land is used for maize‐soybean production and tile drainage is common. Model calibration was performed with a seven year period (1987 through1993) of measured streamflow from one of the watersheds, and model testing was done using independent weather and measured streamflow data from the two neighboring watersheds for the same seven year period. Simulations of annual streamflow were accurate with a coefficient of determination and Willmott's index of agreement of 0.98 and 0.99, respectively. For simulation of monthly streamflow, Willmott's index of agreement ranged from 0.93 to 0.95. For simulation of daily streamflow, Willmott's index of agreement ranged from 0.84 to 0.85. The daily simulations challenged the temporal and spatial resolution of our measured precipitation data. Discrepancies between simulated and measured data may result from the model's inability to effectively address frozen soils and snowmelt runoff processes and in accurately representing evapotranspiration.     

HYDROLOGIC SIMULATION OF THE LITTLE WASHITA RIVER EXPERIMENTAL WATERSHED USING SWAT1

ABSTRACT: Precipitation and streamflow data from three nested subwatersheds within the Little Washita River Experimental Watershed (LWREW) in southwestern Oklahoma were used to evaluate the capabilities of the Soil and Water Assessment Tool (SWAT) to predict streamflow under varying climatic conditions. Eight years of precipitation and streamflow data were used to calibrate parameters in the model, and 15 years of data were used for model validation. SWAT was calibrated on the smallest and largest sub‐watersheds for a wetter than average period of record. The model was then validated on a third subwatershed for a range in climatic conditions that included dry, average, and wet periods. Calibration of the model involved a multistep approach. A preliminary calibration was conducted to estimate model parameters so that measured versus simulated yearly and monthly runoff were in agreement for the respective calibration periods. Model parameters were then fine tuned based on a visual inspection of daily hydrographs and flow frequency curves. Calibration on a daily basis resulted in higher baseflows and lower peak runoff rates than were obtained in the preliminary calibration. Test results show that once the model was calibrated for wet climatic conditions, it did a good job in predicting streamflow responses over wet, average, and dry climatic conditions selected for model validation. Monthly coefficients of efficiencies were 0.65, 0.86, and 0.45 for the dry, average, and wet validation periods, respectively. Results of this investigation indicate that once calibrated, SWAT is capable of providing adequate simulations for hydrologic investigations related to the impact of climate variations on water resources of the LWREW.     

Observed Changes in Climate and Streamflow in the Upper Rio Grande Basin

Observed streamflow and climate data are used to test the hypothesis that climate change is already affecting Rio Grande streamflow volume derived from snowmelt runoff in ways consistent with model‐based projections of 21st‐Century streamflow. Annual and monthly changes in streamflow volume and surface climate variables on the Upper Rio Grande, near its headwaters in southern Colorado, are assessed for water years 1958–2015. Results indicate winter and spring season temperatures in the basin have increased significantly, April 1 snow water equivalent (SWE) has decreased by approximately 25%, and streamflow has declined slightly in the April–July snowmelt runoff season. Small increases in precipitation have reduced the impact of declining snowpack on trends in streamflow. Changes in the snowpack–runoff relationship are noticeable in hydrographs of mean monthly streamflow, but are most apparent in the changing ratios of precipitation (rain + snow, and SWE) to streamflow and in the declining fraction of runoff attributable to snowpack or winter precipitation. The observed changes provide observational confirmation for model projections of decreasing runoff attributable to snowpack, and demonstrate the decreasing utility of snowpack for predicting subsequent streamflow on a seasonal basis in the Upper Rio Grande Basin.     

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.     

RELATION OF NITRATE CONCENTRATIONS TO BASEFLOW IN THE RACCOON RIVER,IOWA1

ABSTRACT: Excessive nitrate‐nitrogen (nitrate) export from the Raccoon River in west central Iowa is an environmental concern to downstream receptors. The 1972 to 2000 record of daily streamflow and the results from 981 nitrate measurements were examined to describe the relation of nitrate to streamflow in the Raccoon River. No long term trends in streamflow and nitrate concentrations were noted in the 28‐year record. Strong seasonal patterns were evident in nitrate concentrations, with higher concentrations occurring in spring and fall. Nitrate concentrations were linearly related to streamflow at daily, monthly, seasonal, and annual time scales. At all time scales evaluated, the relation was improved when baseflow was used as the discharge variable instead of total streamflow. Nitrate concentrations were found to be highly stratified according to flow, but there was little relation of nitrate to streamflow within each flow range. Simple linear regression models developed to predict monthly mean nitrate concentrations explained as much as 76 percent of the variability in the monthly nitrate concentration data for 2001. Extrapolation of current nitrate baseflow relations to historical conditions in the Raccoon River revealed that increasing baseflow over the 20th century could account for a measurable increase in nitrate concentrations.     

Impact of Length of Dataset on Streamflow Calibration Parameters and Performance of APEX Model

Due to resource constraints, long‐term monitoring data for calibration and validation of hydrologic and water quality models are rare. As a result, most models are calibrated and, if possible, validated using limited measured data. However, little research has been done to determine the impact of length of available calibration data on model parameterization and performance. The main objective of this study was to evaluate the impact of length of calibration data (LCD) on parameterization and performance of the Agricultural Policy Environmental eXtender model for predicting daily, monthly, and annual streamflow. Long‐term (1984‐2015) measured daily streamflow data from Rock Creek watershed, an agricultural watershed in northern Ohio, were used for this study. Data were divided into five Short (5‐year), two Medium (15‐year), and one Long (25‐year) streamflow calibration data scenarios. All LCD scenarios were calibrated and validated at three time steps: daily, monthly, and annual. Results showed LCD affected the ability of the model to accurately capture temporal variability in simulated streamflow. However, overall average streamflow, water budgets, and crop yields were simulated reasonably well for all LCD scenarios.     

Modeling Summer Month Hydrological Drought Probabilities in the United States Using Antecedent Flow Conditions

Climate change raises concern that risks of hydrological drought may be increasing. We estimate hydrological drought probabilities for rivers and streams in the United States (U.S.) using maximum likelihood logistic regression (MLLR). Streamflow data from winter months are used to estimate the chance of hydrological drought during summer months. Daily streamflow data collected from 9,144 stream gages from January 1, 1884 through January 9, 2014 provide hydrological drought streamflow probabilities for July, August, and September as functions of streamflows during October, November, December, January, and February, estimating outcomes 5‐11 months ahead of their occurrence. Few drought prediction methods exploit temporal links among streamflows. We find MLLR modeling of drought streamflow probabilities exploits the explanatory power of temporally linked water flows. MLLR models with strong correct classification rates were produced for streams throughout the U.S. One ad hoc test of correct prediction rates of September 2013 hydrological droughts exceeded 90% correct classification. Some of the best‐performing models coincide with areas of high concern including the West, the Midwest, Texas, the Southeast, and the Mid‐Atlantic. Using hydrological drought MLLR probability estimates in a water management context can inform understanding of drought streamflow conditions, provide warning of future drought conditions, and aid water management decision making.     

Temporal Changes in Streamflow and Attribution of Changes to Climate and Landuse in Wisconsin Watersheds

Previous historic trends analyses on 21st Century hydrologic data in the United States generally focus on annual flow statistics and have continued to use USGS hydro‐climatic data network (HCDN) stations, although post‐1988 diversions and runoff regulations are not reflected in the HCDN. Using a more recent dataset, Geospatial Attributes of Gages for Evaluating Streamflow, version II (GAGES II), compiled by Falcone (2012), which includes more watersheds with reference conditions, a comprehensive analysis of changes in seasonal, and annual streamflow in Wisconsin watersheds is demonstrated. Given the pronounced influence of seasonal hydrology in Wisconsin watersheds, the objective of this study is to elucidate the nature of temporal (annual, seasonal, and monthly) changes in runoff. Considerable temporal and regional variability was found in annual and seasonal streamflow changes between the two historic periods 1951‐1980 and 1981‐2010 considered in the study. For example, the northern watersheds show relatively small changes in streamflow discharge ranging from ?6.0 to 4.2%, while the southern watersheds show relatively large increases in streamflow discharge ranging from 13.1 to 18.2%. To apportion streamflow changes to climate and nonclimatic factors, a method based on potential evapotranspiration changes is demonstrated. Results show that nonclimatic factors account for more than 60% of changes in annual runoff in Wisconsin watersheds considered in the study.     

ASSESSMENT OF IMPACTS OF CLIMATE CHANGE ON WATER QUALITY IN THE SOUTHEASTERN UNITED STATES1

ABSTRACT: An assessment of current and future water quality conditions in the southeastern United States has been conducted using the EPA BASINS GIS/database system. The analysis has been conducted for dissolved oxygen, total nitrate nitrogen and pH. Future streamflow conditions have been predicted for the region based on the United Kingdom Hadley Center climate model. Thus far, the analyses have been conducted at a fairly coarse spatial scale due to time and resource limitations. Two hydrologic modeling techniques have been employed in future streamflow prediction: a regional stochastic approach and the application of a physically based soil moisture model. The regional model has been applied to the entire area while the physically based model is being used at selected locations to enhance and support the stochastic model. The results of the study reveal that few basins in the southeast exhibit dissolved oxygen problems, but that several watersheds exhibit high nitrogen levels. These basins are located in regions of intense agricultural activity or in proximity to the gulf coast. In many of these areas, streamflow is projected to decline over the next 30–50 years, thus exacerbating these water quality problems.     

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.     

ESTIMATING STREAMFLOW AND ASSOCIATED HYDRAULIC GEOMETRY,THE MID‐ATLANTIC REGION,USA1

ABSTRACT: Methods to estimate streamflow and channel hydraulic geometry were developed for unpaged streams in the Mid‐Atlantic Region. Observed mean annual streamflow and associated hydraulic geometry data from 75 gaging stations in the Appalachian Plateau, the Ridge and Valley, and the Piedmont Physiographic Provinces of the Mid‐Atlantic Region were used to develop a set of power functions that relate streamflow to drainage area and hydraulic geometry to streamflow. For all three physiographic provinces, drainage area explained 95 to 98 percent of the variance in mean annual streamflow. Relationships between mean annual streamflow and water surface width and mean flow depth had coefficients of determination that ranged from R²= 0.55 to R²= 0.91, but the coefficient of determination between mean flow velocity and mean annual streamflow was lower (R²= 0.44 to R²= 0.54). The advantages of using the regional regression models to estimate streamflow over a conceptual model or a water balance model are its ease of application and reduced input data needs. The prediction of the regression equations were tested with data collected as part of the U.S. Environmental Protection Agency (USEPA) Environmental Monitoring and Assessment Program (EMAP). In addition, equations to transfer streamflow from gaged to ungaged streams are presented.     

SUWANNEE RIVER LONG RANGE STREAMFLOW FORECASTS BASED ON SEASONAL CLIMATE PREDICTORS1

ABSTRACT: A study of the influence of climate variability on streamflow in the southeastern United States is presented. Using a methodology previously applied to watersheds in Australia and the United States, a long range streamflow forecast (0 to 9 months in advance) is developed. Persistence (i.e., the previous season's streamflow) and climate predictors of the previous season are used to forecast the following season's (winter and spring) streamflow of the Suwannee River located in northern Florida. The winter and spring streamflow is historically the most likely to have severe flood events due to large scale cyclonic (frontal) storms. Results of the analysis indicated that a strong El Nino‐Southern Oscillation (ENSO) signal exists at various lead times to the winter and spring streamflow of the Suwannee River. These results are based on the high correlation values of two commonly used measurements of ENSO strength, the Multivariate ENSO Index (MEI) and Sea Surface Temperature Range 1. Using the relationships developed between climate and streamflow, a continuous exceedance probability forecast was developed for two Suwannee River stations. The forecast system provided an improved forecast for ENSO years. The ability to predict above normal (flood) or below normal (drought) years can provide communities the necessary lead time to protect life, property, sensitive wetlands, and endangered and threatened species.     

USE OF STREAMFLOW INCREASES FROM VEGETATION MANAGEMENT IN THE VERDE RIVER BASIN1

ABSTRACT: Although the effects of vegetation management on streamflow have been studied in many locations, the effects of augmented streamflow on downstream water users have not been carefully analyzed. This study examines the routing of streamflow increases that could be produced in the Verde River Basin of Arizona. Reservoir management and water routing to users in the Salt River Valley around Phoenix were carefully modeled. Simulation of water routing with and without vegetation modification indicates that, under current institutional conditions, less than one-half of the streamflow increase would reach consumptive users as surface water. Most of the remainder would accumulate in storage until a year of unusually heavy runoff, when it would add to reservoir spills. Under alternative scenarios, from 39 to 58 percent of the streamflow increase was delivered to consumptive users.     

The Effects of Changing Land Cover on Streamflow Simulation in Puerto Rico

This study quantitatively explores whether land cover changes have a substantive impact on simulated streamflow within the tropical island setting of Puerto Rico. The Precipitation Runoff Modeling System (PRMS) was used to compare streamflow simulations based on five static parameterizations of land cover with those based on dynamically varying parameters derived from four land cover scenes for the period 1953‐2012. The PRMS simulations based on static land cover illustrated consistent differences in simulated streamflow across the island. It was determined that the scale of the analysis makes a difference: large regions with localized areas that have undergone dramatic land cover change may show negligible difference in total streamflow, but streamflow simulations using dynamic land cover parameters for a highly altered subwatershed clearly demonstrate the effects of changing land cover on simulated streamflow. Incorporating dynamic parameterization in these highly altered watersheds can reduce the predictive uncertainty in simulations of streamflow using PRMS. Hydrologic models that do not consider the projected changes in land cover may be inadequate for water resource management planning for future conditions.     

Streamflow Hydrology Estimate Using Machine Learning (SHEM)

Continuity and accuracy of near real‐time streamflow gauge (streamgage) data are critical for flood forecasting, assessing imminent risk, and implementing flood mitigation activities. Without these data, decision makers and first responders are limited in their ability to effectively allocate resources, implement evacuations to save lives, and reduce property losses. The Streamflow Hydrology Estimate using Machine Learning (SHEM) is a new predictive model for providing accurate and timely proxy streamflow data for inoperative streamgages. SHEM relies on machine learning (“training”) to process and interpret large volumes (“big data”) of historic complex hydrologic information. Continually updated with real‐time streamflow data, the model constructs a virtual dataset index of correlations and groups (clusters) of relationship correlations between selected streamgages in a watershed and under differing flow conditions. Using these datasets, SHEM interpolates estimated discharge and time data for any indexed streamgage that stops transmitting data. These estimates are continuously tested, scored, and revised using multiple regression analysis processes and methodologies. The SHEM model was tested in Idaho and Washington in four diverse watersheds, and the model's estimates were then compared to the actual recorded data for the same time period. Results from all watersheds revealed a high correlation, validating both the degree of accuracy and reliability of the model.     

APPLICATION OF THE SNOWMELT RUNOFF MODEL USING MULTIPLE-PARAMETER LANDSCAPE ZONES ON THE TOWANDA CREEK BASIN,PENNSYLVANIA1

ABSTRACT: The Snowmelt Runoff Model (SRM) is designed to compute daily stream discharge using satellite snow cover data for a basin divided into elevation zones. For the Towanda Creek basin, a Pennsylvania watershed with relatively little relief, analysis of snow cover images revealed that both elevation and land use affected snow accumulation and melt on the landscape. The distribution of slope and aspect on the watershed was also considered; however, these landscape features were not well correlated with the available snow cover data. SRM streamflow predictions for 1990, 1993 and 1994 snowmelt seasons for the Towanda Creek basin using a combination of elevation and land use zones yielded more precise streamflow estimates than the use of standard elevation zones alone. The use of multiple-parameter zones worked best in non-rain-on-snow conditions such as in 1990 and 1994 seasons where melt was primarily driven by differences in solar radiation. For seasons with major rain-on-snow events such as 1993, only modest improvements were shown since melt was dominated by rainfall energy inputs, condensation and sensible heat convection. Availability of GIS coverages containing satellite snow cover data and other landscape attributes should permit similar reformulation of multiple-parameter watershed zones and improved SRM streamflow predictions on other basins.     

Predicting streamflow response to fire-induced landcover change: implications of parameter uncertainty in the MIKE SHE model

Fire is a primary agent of landcover transformation in California semi-arid shrubland watersheds, however few studies have examined the impacts of fire and post-fire succession on streamflow dynamics in these basins. While it may seem intuitive that larger fires will have a greater impact on streamflow response than smaller fires in these watersheds, the nature of these relationships has not been determined. The effects of fire size on seasonal and annual streamflow responses were investigated for a medium-sized basin in central California using a modified version of the MIKE SHE model which had been previously calibrated and tested for this watershed using the Generalized Likelihood Uncertainty Estimation methodology. Model simulations were made for two contrasting periods, wet and dry, in order to assess whether fire size effects varied with weather regime. Results indicated that seasonal and annual streamflow response increased nearly linearly with fire size in a given year under both regimes. Annual flow response was generally higher in wetter years for both weather regimes, however a clear trend was confounded by the effect of stand age. These results expand our understanding of the effects of fire size on hydrologic response in chaparral watersheds, but it is important to note that the majority of model predictions were largely indistinguishable from the predictive uncertainty associated with the calibrated model - a key finding that highlights the importance of analyzing hydrologic predictions for altered landcover conditions in the context of model uncertainty. Future work is needed to examine how alternative decisions (e.g., different likelihood measures) may influence GLUE-based MIKE SHE streamflow predictions following different size fires, and how the effect of fire size on streamflow varies with other factors such as fire location.