Composite measures of watershed health from a water quality perspective

Mallya, Ganeshchandra; Hantush, Mohamed M.; Govindaraju, Rao S.

doi:10.1016/j.jenvman.2018.02.049

Cited by 22 publications

(15 citation statements)

References 32 publications

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“…During risk analysis for a water quality constituent, vulnerability may denote the total magnitude of violations or damage caused in dollar amounts. In order for the vulnerability and thus the watershed health measures to range from 0 to 1 for consistency, Mallya et al [ 48 ] introduced a new measure called opposite of vulnerability :

…”

Section: Methodsmentioning

confidence: 99%

“…Using the risk indices described above, Mallya et al [ 48 ] proposed a conservative measure for watershed health:

…”

Section: Methodsmentioning

confidence: 99%

“…Risk-based frameworks have been used in several studies to evaluate the health of water resources systems such as reservoirs [ 45 ] and watersheds [ 8 , 46 , 47 ]. More recently, Mallya et al [ 48 ] proposed a new vulnerability risk measure and composite watershed health index that conveniently ranges between zero and one and uses reliability, resilience, and vulnerability (R-R-V) risk metrics in its computation. A value of zero indicates poor watershed health with respect to a chosen water quality parameter, whereas a value of one indicates high watershed health.…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Approach to Predict Watershed Health Indices for Sediments and Nutrients at Ungauged Basins

Mallya

Hantush

Govindaraju

2023

Water

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Effective water quality management and reliable environmental modeling depend on the availability, size, and quality of water quality (WQ) data. Observed stream water quality data are usually sparse in both time and space. Reconstruction of water quality time series using surrogate variables such as streamflow have been used to evaluate risk metrics such as reliability, resilience, vulnerability, and watershed health (WH) but only at gauged locations. Estimating these indices for ungauged watersheds has not been attempted because of the high-dimensional nature of the potential predictor space. In this study, machine learning (ML) models, namely random forest regression, AdaBoost, gradient boosting machines, and Bayesian ridge regression (along with an ensemble model), were evaluated to predict watershed health and other risk metrics at ungauged hydrologic unit code 10 (HUC-10) basins using watershed attributes, long-term climate data, soil data, land use and land cover data, fertilizer sales data, and geographic information as predictor variables. These ML models were tested over the Upper Mississippi River Basin, the Ohio River Basin, and the Maumee River Basin for water quality constituents such as suspended sediment concentration, nitrogen, and phosphorus. Random forest, AdaBoost, and gradient boosting regressors typically showed a coefficient of determination R2>0.8 for suspended sediment concentration and nitrogen during the testing stage, while the ensemble model exhibited R2>0.95. Watershed health values with respect to suspended sediments and nitrogen predicted by all ML models including the ensemble model were lower for areas with larger agricultural land use, moderate for areas with predominant urban land use, and higher for forested areas; the trained ML models adequately predicted WH in ungauged basins. However, low WH values (with respect to phosphorus) were predicted at some basins in the Upper Mississippi River Basin that had dominant forest land use. Results suggest that the proposed ML models provide robust estimates at ungauged locations when sufficient training data are available for a WQ constituent. ML models may be used as quick screening tools by decision makers and water quality monitoring agencies for identifying critical source areas or hotspots with respect to different water quality constituents, even for ungauged watersheds.

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…”

Section: Methodsmentioning

confidence: 99%

“…Using the risk indices described above, Mallya et al [ 48 ] proposed a conservative measure for watershed health:

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Machine Learning Approach to Predict Watershed Health Indices for Sediments and Nutrients at Ungauged Basins

Mallya

Hantush

Govindaraju

2023

Water

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“…Water, as an active component, connects various subsystems within a river basin ecosystem (Zanotti et al 2019). Therefore, water quality dynamics can rapidly and systematically indicate the state of ecosystem resilience (Mallya et al 2018). In addition, there is growing concern over e-flows setting arising in aquatic ecosystems (Kuriqi et al 2021).…”

Section: Introductionmentioning

confidence: 99%

A framework to identify critical dynamics of water quality for diagnosing river basin ecosystem resilience and management

Wang²,

Lun³

et al. 2023

Environ. Res. Lett.

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This study proposed a novel framework to identify critical water quality dynamics as early warning signals for diagnosing changes in the resilience of river basin ecosystems. We established empirical linkages between the theoretical background of three resilience capacities (robustness, adaptability and transformability) and water quality dynamics. Then, the processes of resilience degradation and their risk transfer or accumulation have been identified based on the shifts among different states. The methods of time-domain analysis and frequency-domain analysis were integrated into this framework, aiming to identify gradual and transient responses of water quality and its periodic fluctuation characteristics at multiple temporal scales. The time-domain analysis methods obtained the trend, cumulative periodic fluctuation of water quality by extracting the key characteristic parameters from the time-series data. The wavelet transform methods were introduced into the frequency-domain analysis to reveal the water quality fluctuation patterns at specific temporal scales. We tested the proposed framework in a typical agriculture-intensive watershed in eastern China. The results showed that this framework can be effectively used to identify three resilience states of river basin ecosystems. The degraded resilience regions were mainly distributed in the downstream area, which was influenced by their specific land use/cover and different agricultural soil health conditions. Particularly, urban sewage discharge was the main cause of periodic fluctuation in water quality time series at multiple high-frequency scales. The theoretical background of resilience capacities was elaborated in non-equilibrium dynamics before resilience degradation. Thus, this novel framework could reveal the pollution processes and driving mechanisms in different river reaches, and it also can provide adaptive management suggestions according to resilience dynamics traits.

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“…Some latest studies have proposed resilience as a complementary measure to risk for a more robust decisionmaking in WQ management (Sweetapple et al, 2018). These studies focus on the promotion of key drivers, attributes and role players' adaptive capacities to cope with changing conditions rather than the use of control-based risk management (van Den Hoek et al, 2011;Hoque et al, 2012;Mallya et al, 2018).…”

Section: Introductionmentioning

confidence: 99%