Development and Application of Predictive Models of Surface Water Extent to Identify Aquatic Refuges in Eastern Australian Temporary Stream Networks

The goals of the study were: (i) To describe the distribution of summer near-surface water temperatures in lakes of the coterminous United States and southern Canada (ii) to determine the geographic, meteorological and limnological factors related to summer water temperatures and (iii) to develop and test predictive models for summer near-surface water temperatures. We used data from the United States National Lakes Assessments of 2007 and 2012 as well as data collected from several different studies of Canadian lakes. Using multiple regressions, we quantified the general observations that summer water temperatures decreased when going from south to north, from east to west, and from lower elevations to higher elevations. Our empirical model using 8-day average air temperatures, latitude, longitude, elevations and month was able to predict water temperatures in individual lakes on individual summer days with a standard deviation of 1.7 °C for United States lakes and 2.3 °C for lakes in the southern regions of Canada.

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Modeling Surface Water Quality Using the Adaptive Neuro-Fuzzy Inference System Aided by Input Optimization

Sustainability ◽

10.3390/su13084576 ◽

2021 ◽

Vol 13 (8) ◽

pp. 4576

Author(s):

Muhammad Izhar Shah ◽

Taher Abunama ◽

Muhammad Faisal Javed ◽

Faizal Bux ◽

Ali Aldrees ◽

...

Keyword(s):

Water Quality ◽

Surface Water ◽

Predictive Models ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Surface Water Quality ◽

Inference System ◽

Input Optimization ◽

Neuro Fuzzy ◽

Soft Computing Techniques

Modeling surface water quality using soft computing techniques is essential for the effective management of scarce water resources and environmental protection. The development of accurate predictive models with significant input parameters and inconsistent datasets is still a challenge. Therefore, further research is needed to improve the performance of the predictive models. This study presents a methodology for dataset pre-processing and input optimization for reducing the modeling complexity. The objective of this study was achieved by employing a two-sided detection approach for outlier removal and an exhaustive search method for selecting essential modeling inputs. Thereafter, the adaptive neuro-fuzzy inference system (ANFIS) was applied for modeling electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River. A larger dataset of a 30-year historical period, measured monthly, was utilized in the modeling process. The prediction capacity of the developed models was estimated by statistical assessment indicators. Moreover, the 10-fold cross-validation method was carried out to address the modeling overfitting issue. The results of the input optimization indicate that Ca2+, Na+, and Cl− are the most relevant inputs to be used for EC. Meanwhile, Mg2+, HCO3−, and SO42− were selected to model TDS levels. The optimum ANFIS models for the EC and TDS data showed R values of 0.91 and 0.92, and the root mean squared error (RMSE) results of 30.6 µS/cm and 16.7 ppm, respectively. The optimum ANFIS structure comprises a hybrid training algorithm with 27 fuzzy rules of triangular fuzzy membership functions for EC and a Gaussian curve for TDS modeling, respectively. Evidently, the outcome of the present study reveals that the ANFIS modeling, aided with data pre-processing and input optimization, is a suitable technique for simulating the quality of surface water. It could be an effective approach in minimizing modeling complexity and elaborating proper management and mitigation measures.

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From site-focused intervention towards landscape-scale surface water management using Synthetic Stream Networks and Rapid Scenario Screening

Blue-Green Systems ◽

10.2166/bgs.2021.019 ◽

2021 ◽

Author(s):

J. L. Webber ◽

S. Wigley ◽

N. Paling ◽

Z. Kapelan ◽

G. Fu

Keyword(s):

Water Management ◽

Surface Water ◽

Landscape Scale ◽

Practical Implementation ◽

Catchment Management ◽

Stream Networks ◽

Surface Water Management ◽

Novel Approach ◽

Physically Based ◽

A Site

Abstract This research addresses the need to transform success in technical understanding and practical implementation of surface water management (SWM) interventions at a site-scale towards integrated landscape-scale management. We achieve this through targeting the informative preliminary stages of strategic design, where broad, early and effective exploration of opportunities can enhance and direct a regional SWM perspective. We present a new method, ‘Synthetic Stream Networks’ (SSN), capable of meeting these requirements by taking advantage of easily accessible data, likely to be available during regional screening. We find that results from the SSN are validated by existing, ‘downstream’ focused data (90% of the river network is within 30 m of an associated SSN flow path), with the added advantage of extending understanding of surface water exceedance flow paths and watersheds into the upper catchment, thus establishing a foundational and physically based sub-catchment management unit exploring surface water connectivity at a catchment and landscape scale. We also demonstrate collaborative advantages of twinning the new SSN method with ‘Rapid Scenario Screening’ (RSS) to develop a novel approach for identifying, exploring and evaluating SWM interventions. Overall, we find that this approach addresses challenges of integrating understanding from sub-catchment, catchment and landscape perspectives within surface water management.

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Application of a Simple Cold Stub to the Direct Observation of Frozen Hydrated Sand

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100071508 ◽

1973 ◽

Vol 31 ◽

pp. 212-213

Author(s):

John M. Wehrung ◽

Richard J. Harniman

Keyword(s):

United States ◽

Surface Water ◽

Direct Observation ◽

Controlled Study ◽

Clay Particles ◽

Organic Debris ◽

Rock Formations ◽

Water Tables ◽

Permeable Rock ◽

Natural Means

Water tables in aquifer regions of the southwest United States are dropping off at a rate which is greater than can be replaced by natural means. It is estimated that by 1985 wells will run dry in this region unless adequate artificial recharging can be accomplished. Recharging with surface water is limited by the plugging of permeable rock formations underground by clay particles and organic debris.A controlled study was initiated in which sand grains were used as the rock formation and water with known clay concentrations as the recharge media. The plugging mechanism was investigated by direct observation in the SEM of frozen hydrated sand samples from selected depths.

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