Using nocturnal water level fluctuations for estimating seepage from stormwater detention systems

A simple method, modified from White's method, was developed and verified for estimating seepage from two stormwater detention areas (SDAs) for 2 years, using night‐time changes in surface water levels. The SDAs were located in warm sub‐tropical Florida where the assumption of negligible night‐time evaporation for White's method does not hold true. Daily seepage was estimated using the nocturnal water level fluctuations on no flow days during winter when evaporation losses were insignificant. Specific yield, rather than the composite specific yield, provided accurate seepage estimates. The average annual seepage from the two SDAs was 2.03 m/year. At almost 70% of surface flows, seepage is a significant contributor to regional surface and sub‐surface flows. Comparison of seepage estimates from the night‐time method (NM) and the water balance (WB) method showed that the NM‐based estimates were within the range of the estimates from the WB method. At SDA1, the differences between the NM and WB estimates were 1% and 11%, for the 2 years. The discrepancy between the two estimates became higher (27% and 23%) at SDA2. Larger differences at SDA2 were because of higher error in quantifying pumped inflows for the WB method. Successful performance of NM combined with its low resource (single well monitoring) requirements will help quantify seepage from detention areas and other similar features (e.g. ponds, constructed wetlands) in warmer climates. A scale‐up for the Caloosahatchee River basin showed that seepage from SDA's accounted for 15% of annual river flows indicating the importance of seepage in evaluating water and chemical balances. Copyright © 2015 John Wiley & Sons, Ltd.     

Effects of climate-related variability in storage on streamwater solute concentrations and fluxes in a small forested watershed in the Southeastern United States

Streamwater quality can be affected by climate-related variability in hydrologic state, which controls flow paths and affects biogeochemical processes. Thirty-one years of input/output solute fluxes at Panola Mountain Research Watershed, a small, forested, seasonally water-limited watershed near Atlanta, Georgia, were used to quantify the effects of climatic-related variability in storage on streamwater solute concentrations and fluxes. Streamwater fluxes were estimated for ten solutes from weekly and event sample concentrations using regression-based methods. The most pertinent storage attribute (current or antecedent watershed, shallow, and deep storage) for each solute was determined by fitting separate concentration relationships. The concentration-discharge relationships varied more for reactive solutes such as potassium, sulfate, and DOC and less for weathering products (base cations and dissolved silica) and conservative chloride. Many solutes exhibited higher concentrations when storage levels were lower or wetting up, which was likely the result of the concentrating effects of evapotranspiration and/or the buildup and flushing of weathering products associated with longer residence times. The impacts of storage modeling on annual fluxes varied by solute, ranging from about 5% (magnesium) to 52% (nitrate) as relative standard deviations, and sufficiently removed climate-related patterns observed in streamwater concentrations. Sulfate was particularly mobilized following growing season droughts but only if deep storage was sufficiently recharged, possibly indicating that sulfides in the deep storage pool were oxidized to sulfate during droughts and mobilized when re-wetted. The lack of streamwater sulfate response to 61% declines in atmospheric deposition indicates the importance of watershed biogeochemical processes on controls of streamwater export of sulfate. The approach of explicitly incorporating storage in the streamwater concentration modeling elucidated the effects of climate on streamwater water-quality and may provide insight into the effects of climatic change on future fluxes.     

太湖流域浙江片区出入境水量、水质及污染物通量

太湖流域浙江片区受工业和城市废水以及农田地表径流等人为因素的影响,污染问题日益严重,根据2013年的水文巡测以及水质监测资料,分析了太湖流域浙江片区出入境水量和高锰酸盐指数(COD_Mn)、氨氮(NH3-N)、总氮(TN)和总磷(TP)等水质指标的出入境通量及其时空分布.结果表明,2013年太湖流域浙江片区出入境水量以出境为主,出入境断面的首要污染物是TN,其平均浓度在各出入境断面均处于Ⅴ～劣Ⅴ类水平,COD_Mn、NH₃-N和TP的平均浓度总体上达到Ⅱ～Ⅲ类标准.4种污染物的出入境通量均以出境为主,研究区域北部是污染物的主要入境断面,东部是污染物的主要出境断面.净出境水量是影响污染物净出境通量的关键因子,出入境断面水质是净出境通量的主要影响因子.源头控制农业面源污染、将工业废水和城镇生活污水处理达标后排放是减轻太湖流域浙江片区水体污染的关键,是太湖流域水体污染控制和治理的主要措施.     

Bayesian methods for estimating multi-segment discharge rating curves

This study explores Bayesian methods for handling compound stage–discharge relationships, a problem which arises in many natural rivers. It is assumed: (1) the stage–discharge relationship in each rating curve segment is a power-law with a location parameter, or zero-plane displacement; (2) the segment transitions are abrupt and continuous; and (3) multiplicative measurement errors are of equal variance. The rating curve fitting procedure is then formulated as a piecewise regression problem where the number of segments and the associated changepoints are assumed unknown. Procedures are developed for describing both global and site-specific prior distributions for all rating curve parameters, including the changepoints. Estimation and uncertainty analysis is evaluated using Markov chain Monte Carlo simulation (MCMC) techniques. The first model explored accounts for parameter and model uncertainties in the interpolated area, i.e. within the range of available stage–discharge measurements. A second model is constructed in an attempt to include the uncertainty in extrapolation, which is necessary when the rating curve is used to estimate discharges beyond the highest or lowest measurement. This is done by assuming that the rate of changepoints both inside and outside the measured area follows a Poisson process. The theory is applied to actual data from Norwegian gauging stations. The MCMC solutions give results that appear sensible and useful for inferential purposes, though the latter model needs further efforts in order to obtain a more efficient simulation scheme.     

A new fuzzy linear regression approach for dissolved oxygen prediction

Abstract

A new method for fuzzy linear regression is proposed to predict dissolved oxygen using abiotic factors in a riverine environment, in Calgary, Canada. The proposed method is designed to accommodate fuzzy regressors, regressand and coefficients, i.e. representing full system uncertainty. The regression equation is built to minimize the distance between fuzzy numbers, and generalizes to crisp regression when crisp parameters are used. The method is compared to two existing fuzzy linear regression techniques: the Tanaka method and the Diamond method. The proposed new method outperforms the existing methods with higher Nash-Sutcliffe efficiency, and lower RMSE, AIC and total fuzzy distance. The new method demonstrates that nonlinear membership functions are more suitable for representing uncertain environmental data than the typical triangular representations. A result of this research is that low DO prediction is improved and consequently the approach can be used for risk analysis by water resource managers.

Editor D. Koutsoyiannis; Associate editor T. Okruszko