A Daily Time Series Analysis of StreamWater Phosphorus Concentrations Along an Urban to Forest Gradient

During a 1-year period, we sampled stream water total phosphorus (TP) concentrations daily and soluble reactive phosphorus (SRP) concentrations weekly in four Seattle area streams spanning a gradient of forested to urban-dominated land cover. The objective of this study was to develop time series models describing stream water phosphorus concentration dependence on seasonal variation in stream base flows, short-term flow fluctuations, antecedent flow conditions, and rainfall. Stream water SRP concentrations varied on average by ±18% or ±5.7 μg/L from one week to another, whereas TP varied ±48% or ±32.5 μg/L from one week to another. On average, SRP constituted about 47% of TP. Stream water SRP concentrations followed a simple sine-wave annual cycle with high concentrations during the low-flow summer period and low concentrations during the high-flow winter period in three of the four study sites. These trends are probably due to seasonal variation in the relative contributions of groundwater and subsurface flows to stream flow. In forested Issaquah Creek, SRP concentrations were relatively constant throughout the year except during the fall, when a major salmon spawning run occurred in the stream and SRP concentrations increased markedly. Stream water SRP concentrations were statistically unrelated to short-term flow fluctuations, antecedent flow conditions, or rainfall in each of the study streams. Stream water TP concentrations are highly variable and strongly influenced by short-term flow fluctuations. Each of the processes assessed had statistically significant correlations with TP concentrations, with seasonal base flow being the strongest, followed by antecedent flow conditions, short-term flow fluctuations, and rainfall. Times series models for each individual stream were able to predict ∼70% of the variability in the SRP annual cycle in three of the four streams (r² = 0.57–0.81), whereas individual TP models explained ∼50% of the annual cycle in all streams (r² = 0.39–0.59). Overall, time series models for SRP and TP dynamics explained 82% and 76% of the variability for these variables, respectively. Our results indicate that SRP, the most biologically available and therefore most important phosphorus fraction, has simpler and easier-to-predict seasonal and weekly dynamics.     

DROUGHT,TREE RINGS,AND RESERVOIR DESIGN1

Droughts constitute one of the most important factors affecting the design and operation of water resources infrastructure. Hydrologists ascertain their duration, severity, and pattern of recurrence from instrumental records of precipitation or stream‐flow. Under suitable conditions, and with proper analysis, tree rings obtained from long living, climate sensitive species of trees can extend instrumental records of streamflow and precipitation over periods spanning several centuries. Those tree‐ring “reconstructions” provide a valuable insight about climate variability and drought occurrence in the Holocene, and yield long term hydrological data useful in the design of water infrastructure. This work presents a derivation of drought risk based on a renewal model of drought recurrence, a brief review of the basic theory of tree‐ring reconstructions, and a stochastic model for optimizing the design of water supply reservoirs. Examples illustrate the methodology developed in this work and the supporting role that tree‐ring reconstructed streamflow can play in characterizing hydrologic variability.     

EVALUATION OF PRACTICES IN WATER SUPPLY FORECASTING1

ABSTRACT: Snowmelt runoff is a primary source of water supply in much of the Western United States. Multipurpose planning requires long-range forecasts and the accuracy of the forecasts has a significant effect on economic benefits. In an effort to increase the accuracy of snowrnelt runoff forecasts, selected practices in water supply forecasting were evaluated. These practices include 1) using multiple regression in developing forecasting models;2) using a model that was calibrated to make forecasts an April 1 for making forecasts at other times;3) using maximum snow water equivalent measurements in forecast equations; and 4) using weighted snow water equivalent measurements for making forecasts. The results of a case study indicate that forecasting accuracy is significantly affected by these practices. Goodness-of-fit statistics may not be indicative of the accuracy of forecasts when the prediction equations are used to make forecasts for dates other than that used in calibration. The use of maximum snow water equivalentmeasurements and weighted averages did not improve forecast accuracy.     

CORRELATION OF SELECTED WELL WATER QUALITY PARAMETERS WITH SOIL AND AQUIFER HYDROLOGIC PROPERTIES1

ABSTRACT: The geographical distribution of well water specific electrical conductivity and nitrate levels in a 932 km² ground water quality study area in the Fresno-Clovis, California, indicated that frequently areas of lower ground water salinity were also areas of relatively greater soil and aquifer permeability. From these observations and certain assumptions we hypothesized that the quality of the well water should be better in areas with permeable soils and geological formations. Correlation and multiple linear regression analysis supported this hypothesis for well water salinity. However, well water nitrate levels were significantly negatively correlated with only the estimated equivalent specific yield of the aquifer system. The multiple R² values of the most significant multiple linear regression models showed that only a fourth to a third of the variability in well water specific electric conductivity and nitrate levels could be ascribed to the effects of the hydrogeological parameters considered with more than 90 percent confidence. This indicates that three-fourths to two-thirds of the variability in ground water salinity and nitrate levels may be related to land use. Thus, there is considerable room for land use management techniques to improve ground water quality and reduce its variability.     

AN APPLICATION RUNOFF MODEL STRATEGY FOR UNGAGED WATERSHEDS1

ABSTRACT: Mathematical models for predicting watershed surface flow responses are available, most of which are elaborate nonlinear numerical surface and channel flow models linked with infiltration models. Such models may be used to make predictions for ungaged areas, assuming an acceptable fitting of the model to the topography and roughness of the real system. For some application purposes, these models are impractical because of their complexity and expensive computer solutions. A procedure is developed that uses a complex model of an ungaged area to derive a simpler parametric nonlinear system model for repetitious simulation with input sequences. The predicted flow outputs are obtained with the simpler model at significant savings of money and time. The procedures for constructing a complex kinematic model of a 40 acre (161,880 m²) reference watershed and deriving the simpler system model are outlined. The results of predictions from both models are compared with a selected set of measured events, all having essentially the same initial conditions. Peak discharges ranged from 3 to 118 ft³/sec (0.085 to 3.34 m³/sec), which includes the largest event of record. The inherent limitations of lumped systems models are demonstrated, including the bias caused by their inability to model infiltration losses after rainfall ceases. Computer costs and times for the models were compared. The derived simple model has a cost advantage when repeated use of a model is required. Such an applications hydrologic model has an engineering tradeoff of reduced accuracy, and lumping bias, but is more economical for certain design purposes.     

COMPUTER PROGRAMMING FOR MAXIMUM MODELING INFORMATION TRANSFER1

Abstract: The diffuculty in understanding the listings of many available hydrologic models programmed in FORTRAN is a serious limitation to their verification and use. By expending more effort in creating clearly written computer programs, more information can be transferred to those interested in modeling hydrologic process. The use of simulation languages to produce more understandable program listings and increase the flow of informationg is recommended. An example is presented in which surface water runoff from storms on small rural watershedds is modeled.     

NONLINEAR PROGRAMMING IN RIVER BASIN MODELING1

ABSTRACT: This paper explores the use of nonlinear programming in river basin water quality modelling. Applications recently reported in the literature, along with the author's experience with nonlinear programming, are reviewed. Results obtained using nonlinear programming are compared with the results obtained by other researchers using linear and dynamic programming to solve river basin water quality optimization problems. These water quality models have objective functions with continuous first partial derivatives, several inequality and variable bound constraints, and are of the form: minizie Σ^j=n_j=1Y_j(X_j) subject to Σ^j=n_j=1a_ijX_jb_i, i=1,2, …, m c_jX_jd_j, j= 1,2, …, n The variable X_i is the maximum allowable ratio of the BOD (biochemical oxygen demand) of the effluent outflow to the BOD of the wastewater inflow for treatment plant j, in the range c_j to d_j. The a_ijd and b_i are constants in the DO (dissolved oxygen) and BOD constraints. The resuks show, given certain assumptions about the data, that nonlinear programming is a better solution method for these problems than is either linear programming or dynamic programming.     

CLIMATE CHANGE,IRRIGATION, AND CROP RESPONSE1

ABSTRACT: A set of simulation models consisting of a weather generator, and irrigation supply, soil moisture and crop growth components was used to evaluate the impacts of climate change on irrigated corn in locations near Albany, New York, Indianapolis, Indiana, and Oklahoma City, Oklahoma. The models evaluated the combined effects of modified water demand, supply and crop management (planting date, cultivar selection, irrigation). Simulations were duplicated for 100-year weather sequences based on current (1961–1988) weather statistics, and statistics modified by outputs from the GFDL GCM runs showing the effects from doubling of atmospheric CO₂. Climate impacts differed greatly with location and management. Effects were most adverse in New York and least damaging in Indiana. At all sites, the beneficial effects of longer growing season and increased water supply were generally overcome by the detrimental impacts of increased evapotranspiration and reduced solar radiation during plant maturing stages. Adverse impacts of climate change can be substantially reduced by irrigation and appropriate selection of planting dates and cultivars.