Minimizing Unintended Consequences of Water Resources Decisions

Sowby, Robert B.; Hotchkiss, Rollin H.

doi:10.1061/(asce)wr.1943-5452.0001623

Cited by 10 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The unintended consequence [3] is a declining lake. Great Salt Lake, like the Colorado River [4], is an environmental gauge, a vital sign of the West's water resources. After years of neglect, the lake is starving not just for water but for attention.…”

Section: Contrasting Conditionsmentioning

confidence: 99%

“…Facing an environmental crisis, we are compelled to rethink our relationship with Great Salt Lake, to treat it like the precious asset it is rather than a casual afterthought. It's a lesson we should have learned with the loss of the Aral Sea in Central Asia [24], Lake Urmia in Iran [25], and other saline lakes around the world [4,26]. But we are learning it now.…”

Section: The Assetmentioning

confidence: 99%

See 1 more Smart Citation

Transforming Great Salt Lake from Afterthought to Asset

Sowby

2023

Earth

Self Cite

View full text Add to dashboard Cite

In a 36-year period that coincides with my lifetime, Great Salt Lake, one of the world’s largest terminal lakes and a critical ecosystem in the Western Hemisphere, went from its largest to its smallest recorded size. In this opinion piece, I argue that the fundamental problem is that we Utahns and other stakeholders have treated Great Salt Lake as an afterthought instead of an asset. I describe the conditions that led to this point, some transformations now taking place, and the new hope that the lake will recover.

show abstract

Section: Contrasting Conditionsmentioning

confidence: 99%

Section: The Assetmentioning

confidence: 99%

Transforming Great Salt Lake from Afterthought to Asset

Sowby

2023

Earth

Self Cite

View full text Add to dashboard Cite

show abstract

“…Water decision-makers implement policies in the present to meet current and future needs. Appropriately assessing these needs, and the ability to meet them, remains critical Water 2024, 16, 396 2 of 18 to the success of a water policy, since changes to the built and natural environment cannot be adjusted instantaneously [12,13]. Existing evaluative approaches, however, lack useinspired and participatory structures [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…Understanding these complexities and ensuring that evaluations account for community needs and realities is central to ensuring that decisions can meet their intended outcomes [18]. Even when decisions are made with the best of intentions, they run the risk of producing unintended consequences within these complex and interconnected human and natural systems [13].…”

Section: Introductionmentioning

confidence: 99%

Understanding Hydrologic, Human, and Climate System Feedback Loops: Results of a Participatory Modeling Workshop

Rajah,

Atkins,

Tang

et al. 2024

Water

View full text Add to dashboard Cite

Groundwater depletion threatens global freshwater resources, necessitating urgent water management and policies to meet current and future needs. However, existing data-intensive approaches to assessments do not fully account for the complex human, climate, and water interactions within transboundary groundwater systems. Here, we present the design of and findings from a pilot participatory modeling workshop aiming to advance understanding of the hydrologic–human–climate feedback loops underpinning groundwater systems. Using participatory modeling tools and methods from the system dynamics tradition, we captured the mental models of researchers from water, social, data, and systems sciences. A total of 54 feedback loops were identified, demonstrating the potential of this methodology to adequately capture the complexity of groundwater systems. Based on the workshop outcomes, as an illustrative example, we discuss the value of participatory system modeling as a conceptualization tool, bridging perspectives across disciplinary silos. We further discuss how outcomes may inform future research on existing knowledge gaps around groundwater issues, and in doing so, advance interdisciplinary, use-inspired research for water decision-making more broadly.

show abstract

“…The lake is also faced with water quality impairment caused by elevated levels of water quality parameters (WQPs) such as total dissolved solids (TDS) by natural and anthropogenic activities associated with population growth and urbanization including, but not limited to, irrigation, industrial, and municipal practices [6,7]. Some of these anthropogenic are unintended consequences that need to be minimized [8]. TDS-related economic damages to the CRB are estimated to be over USD 300 million per year, destroying millions of acres of irrigated lands [2,3,[9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

Adjovu

Stephen

Ahmad

2023

Water

View full text Add to dashboard Cite

Total dissolved solids (TDS) concentration determination in water bodies is sophisticated, time-consuming, and involves expensive field sampling and laboratory processes. TDS concentration has, however, been linked to electrical conductivity (EC) and temperature. Compared to monitoring TDS concentrations, monitoring EC and temperature is simpler, inexpensive, and takes less time. This study, therefore, applied several machine learning (ML) approaches to estimate TDS concentration in Lake Mead using EC and temperature data. Standalone models including the support vector machine (SVM), linear regressors (LR), K-nearest neighbor model (KNN), the artificial neural network (ANN), and ensemble models such as bagging, gradient boosting machine (GBM), extreme gradient boosting (XGBoost), random forest (RF), and extra trees (ET) models were used in this study. The models’ performance were evaluated using several performance metrics aimed at providing a holistic assessment of each model. Metrics used include the coefficient of determination (R2), mean absolute error (MAE), percent mean absolute relative error (PMARE), root mean square error (RMSE), the scatter index (SI), Nash–Sutcliffe model efficiency (NSE) coefficient, and percent bias (PBIAS). Results obtained showed varying model performance at the training, testing, and external validation stage of the models, with obtained R2 of 0.77–1.00, RMSE of 2.28–37.68 mg/L, an MAE of 0.14–22.67 mg/L, a PMARE of 0.02–3.42%, SI of 0.00–0.06, NSE of 0.77–1.00, and a PBIAS of 0.30–0.97 across all models for the three datasets. We utilized performance rankings to assess the model performance and found the LR to be the best-performing model on the external validation datasets among all the models (R2 of 0.82 and RMSE of 33.09 mg/L), possibly due to the established existence of a relationship between TDS and EC, although this may not always be linear. Similarly, we found the XGBoost to be the best-performing ensemble model based on the external validation with R2 of 0.81 and RMSE of 34.19 mg/L. Assessing the overall performance of the models across all the datasets, however, revealed GBM to produce a superior performance based on the ranks, possibly due to its ability to reduce overfitting and improve generalizations. The findings from this study could be employed in assisting water resources managers and stakeholders in effective monitoring and management of water resources to ensure their sustainability.

show abstract

Minimizing Unintended Consequences of Water Resources Decisions

Cited by 10 publications

References 13 publications

Transforming Great Salt Lake from Afterthought to Asset

Transforming Great Salt Lake from Afterthought to Asset

Understanding Hydrologic, Human, and Climate System Feedback Loops: Results of a Participatory Modeling Workshop

A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

Contact Info

Product

Resources

About