Assessment of integrated watershed health based on the natural environment, hydrology, water quality, and aquatic ecology

Ahn, So Ra; Kim, Seong Joon

doi:10.5194/hess-21-5583-2017

Cited by 46 publications

(19 citation statements)

References 29 publications

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“…Climatic conditions of the selected four sub-basins are almost similar but hydrological conditions may vary depending upon factors like elevation, slope, and urbanization. These attributes of four selected sub-basins are different that may affect the hydrological cycle or hydrological conditions of the area [41]. Therefore, the selection of these sub-basins may provide a diverse spatial pattern of drought propagation.…”

Section: Research Area and Datamentioning

confidence: 99%

Application of the Hidden Markov Bayesian Classifier and Propagation Concept for Probabilistic Assessment of Meteorological and Hydrological Droughts in South Korea

et al. 2020

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Drought is one of the most destructive natural hazards and results in negative effects on the environment, agriculture, economics, and society. A meteorological drought originates from atmospheric components, while a hydrological drought is influenced by properties of the hydrological cycle and generally induced by a continuous meteorological drought. Several studies have attempted to explain the cross dependencies between meteorological and hydrological droughts. However, these previous studies did not consider the propagation of drought classes. Therefore, in this study, to consider the drought propagation concept and to probabilistically assess the meteorological and hydrological drought classes, characterized by the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI), respectively, we employed the Markov Bayesian Classifier (MBC) model that combines the procedure of iteration of feature extraction, classification, and application for assessment of drought classes for both SPI and SRI. The classification results were compared using the observed SPI and SRI, as well as with previous findings, which demonstrated that the MBC was able to reasonably determine drought classes. The accuracy of the MBC model in predicting all the classes of meteorological drought varies from 36 to 76% and in predicting all the classes of hydrological drought varies from 33 to 70%. The advantage of the MBC-based classification is that it considers drought propagation, which is very useful for planning, monitoring, and mitigation of hydrological drought in areas having problems related to hydrological data availability.

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Section: Research Area and Datamentioning

confidence: 99%

Application of the Hidden Markov Bayesian Classifier and Propagation Concept for Probabilistic Assessment of Meteorological and Hydrological Droughts in South Korea

et al. 2020

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“…Figure 2a shows the calibrated and validated result of SWAT for daily dam inflow at seven locations and Figure 2b represents the results for eight days of data of SS, T-N, and T-P at one water quality station located nearby PDD where two main rivers are joined. More detailed information on the results for this study are presented in further papers (see [40][41][42][43]). 0.6) [38,39].…”

Section: Swat Modelmentioning

confidence: 99%

“…The calibration and validation results of SWAT for (a) dam inflow and (b) water quality[40][41][42][43].Sustainability 2019, 11, 3397 6 of 15…”

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confidence: 99%

Evaluation of Watershed Scale Aquatic Ecosystem Health by SWAT Modeling and Random Forest Technique

Woo

Jung²,

Lee

et al. 2019

Sustainability

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In this study, we evaluated the aquatic ecosystem health (AEH) with five grades (A; very good to E; very poor) of FAI (Fish Assessment Index), TDI (Trophic Diatom Index), and BMI (Benthic Macroinvertebrate Index) using the results of SWAT (Soil and Water Assessment Tool) stream water temperature (WT) and quality (T-N, T-P, NH 4 , NO 3 , and PO 4 ). By applying Random Forest, one of the machine learning algorithms for classification analysis, each AEH index was trained and graded from the SWAT results. For Han river watershed (34,418 km 2 ) in South Korea, the 8 years (2008~2015) observed AEH data of Spring and Fall periods at 86 locations from NAEMP (National Aquatic Ecological Monitoring Program) were used. The AEH was separately trained for Spring (FAI s , TDI s , and BMI s ) and Fall (FAI a , TDI a , and BMI a ), and the AEH results of Random Forest with SWAT (WT, T-N, T-P, NH 4 , NO 3 , and PO 4 ) as input variables showed the accuracy of 0.42, 0.48, 0.62, 0.45, 0.4, and 0.58, respectively. The reason for low accuracy was from the weak strength of the individual trees and high correlation between the trees composing the Random Forest due to the data imbalance. The AEH distribution results showed that the number of Grade A of total FAI, TDI, and BMI were 84, 0, and 158 respectively and they were mostly located at the upstream watersheds. The number of Grade E of total FAI, TDI, and BMI were 4, 50, and 13 and they were shown at downstream watersheds.

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“…The previous diversity of scenarios where co-occurrence network topology has been used in ecological analysis then implies that it could also be used to develop a framework for the assessment of the biotic integrity of streams across an entire catchment area (Ahn & Kim, 2017;Moyle & Randall, 1998;Smith & Lamp, 2008).…”

Section: Introductionmentioning

confidence: 99%

Using co‐occurrence network topology in assessing ecological stress in benthic macroinvertebrate communities

Simons

Mazor

Theroux

2019

Ecology and Evolution

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Ecological monitoring of streams has often focused on assessing the biotic integrity of individual benthic macroinvertebrate (BMI) communities through local measures of diversity, such as taxonomic or functional richness. However, as individual BMI communities are frequently linked by a variety of ecological processes at a regional scale, there is a need to assess biotic integrity of groups of communities at the scale of watersheds. Using 4,619 sampled communities of streambed BMIs, we investigate this question using co‐occurrence networks generated from groups of communities selected within California watersheds under different levels of stress due to upstream land use. Building on a number of arguments in theoretical ecology and network theory, we propose a framework for the assessment of the biotic integrity of watershed‐scale groupings of BMI communities using measures of their co‐occurrence network topology. We found significant correlations between stress, as described by a mean measure of upstream land use within a watershed, and topological measures of co‐occurrence networks such as network size (r = −.81, p < 10–4), connectance (r = .31, p < 10–4), mean co‐occurrence strength (r = .25, p < 10–4), degree heterogeneity (r = −.10, p < 10–4), and modularity (r = .11, p < 10–4). Using these five topological measures, we constructed a linear model of biotic integrity, here a composite of taxonomic and functional diversity known as the California Stream Condition Index, of groups of BMI communities within a watershed. This model can account for 66% of among‐watershed variation in the mean biotic integrity of communities. These observations imply a role for co‐occurrence networks in assessing the current status of biotic integrity for BMI communities, as well as their potential use in assessing other ecological communities.

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Assessment of integrated watershed health based on the natural environment, hydrology, water quality, and aquatic ecology

Cited by 46 publications

References 29 publications

Application of the Hidden Markov Bayesian Classifier and Propagation Concept for Probabilistic Assessment of Meteorological and Hydrological Droughts in South Korea

Application of the Hidden Markov Bayesian Classifier and Propagation Concept for Probabilistic Assessment of Meteorological and Hydrological Droughts in South Korea

Evaluation of Watershed Scale Aquatic Ecosystem Health by SWAT Modeling and Random Forest Technique

Using co‐occurrence network topology in assessing ecological stress in benthic macroinvertebrate communities

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