TURBID WATER BEHAVIOR DURING FLOOD IN THE KAWAJI DAM RESERVOIR

Provision of safe drinking water by water utilities is challenged by disturbances to water quality that have become increasingly frequent due to global changes and anthropogenic impacts. Many water utilities are turning to adaptable and flexible strategies to allow for resilient management of drinking water supplies. The success of resilience-based management depends on, and is enabled by, positive relationships with the public. To understand how relationships between managers and communities spill over to in-home drinking water behavior, we examined the role of trust, risk perceptions, salience of drinking water, and water quality evaluations in the choice of in-home drinking water sources for a population in Roanoke Virginia. Using survey data, our study characterized patterns of in-home drinking water behavior and explored related perceptions to determine if residents’ perceptions of their water and the municipal water utility could be intuited from this behavior. We characterized drinking water behavior using a hierarchical cluster analysis and highlighted the importance of studying a range of drinking water patterns. Through analyses of variance, we found that people who drink more tap water have higher trust in their water managers, evaluate water quality more favorably, have lower risk perceptions, and pay less attention to changes in their tap water. Utility managers may gauge information about aspects of their relationships with communities by examining drinking water behavior, which can be used to inform their future interactions with the public, with the goal of increasing resilience and adaptability to external water supply threats.

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Remote Estimation of Trophic State Index for Inland Waters Using Landsat-8 OLI Imagery

Remote Sensing ◽

10.3390/rs13101988 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1988

Author(s):

Minqi Hu ◽

Ronghua Ma ◽

Zhigang Cao ◽

Junfeng Xiong ◽

Kun Xue

Keyword(s):

Yangtze River ◽

Trophic State ◽

Inland Waters ◽

Landsat 8 ◽

Trophic State Index ◽

Type I ◽

Water Type ◽

Type Ii ◽

Turbid Water ◽

State Index

Remote monitoring of trophic state for inland waters is a hotspot of water quality studies worldwide. However, the complex optical properties of inland waters limit the potential of algorithms. This research aims to develop an algorithm to estimate the trophic state in inland waters. First, the turbid water index was applied for the determination of optical water types on each pixel, and water bodies are divided into two categories: algae-dominated water (Type I) and turbid water (Type II). The algal biomass index (ABI) was then established based on water classification to derive the trophic state index (TSI) proposed by Carlson (1977). The results showed a considerable precision in Type I water (R2 = 0.62, N = 282) and Type II water (R2 = 0.57, N = 132). The ABI-derived TSI outperformed several band-ratio algorithms and a machine learning method (RMSE = 4.08, MRE = 5.46%, MAE = 3.14, NSE = 0.64). Such a model was employed to generate the trophic state index of 146 lakes (> 10 km2) in eastern China from 2013 to 2020 using Landsat-8 surface reflectance data. The number of hypertrophic and oligotrophic lakes decreased from 45.89% to 21.92% and 4.11% to 1.37%, respectively, while the number of mesotrophic and eutrophic lakes increased from 12.33% to 23.97% and 37.67% to 52.74%. The annual mean TSI for the lakes in the lower reaches of the Yangtze River basin was higher than that in the middle reaches of the Yangtze River and Huai River basin. The retrieval algorithm illustrated the applicability to other sensors with an overall accuracy of 83.27% for moderate-resolution imaging spectroradiometer (MODIS) and 82.92% for Sentinel-3 OLCI sensor, demonstrating the potential for high-frequency observation and large-scale simulation capability. Our study can provide an effective trophic state assessment and support inland water management.

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