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Lakes and Reservoir Water Quality Indices: Evolution, Applications, and Future Directions

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Abstract

Anthropogenic activities severely degrade water quality and cause environmental pollution that threatens the quality of limited water resources. Water Quality Indices (WQIs) have been widely utilized to simplify datasets, and their evolution from traditional to advanced methods requires a systematic analysis to guide effective application. A comprehensive review of WQIs, systematic analysis of the WQI progression—from conventional indices to advanced statistical, machine learning, and artificial intelligence methods. Our key findings detail the genesis, background, leverage, and applications of these tools to manage dynamic water quality challenges, providing valuable insights for policy-makers through a systematic lens. The proposed platform supports decision-makers of lakes and reservoirs in selecting efficient and cost-effective models. Optimized monitoring strategies, policies, pollution control, and adoption of advanced WQIs deliver significant reductions in operational costs and eliminate redundant testing while maintaining protection standards. This review paves the way for designing more robust and globally applicable water quality assessment tools.

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Fig. 1

(Adopted from Kumar et al., 2024)

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Data Availability

All data and models generated during the study are available from the corresponding author by request.

Abbreviations

WQIs:

Water Quality indices

BCWQI:

British Columbia water quality index

OWQI:

Oregon Water quality index

CCMEWQI:

Canadian council of ministers of the environment water quality index

NSFWQI:

National Sanitation foundation water quality index

T:

Temperature

TP:

Total Phosphorus

DO:

Dissolved oxygen

BOD:

Biochemical oxygen demand

HMs:

Heavy Metals

CPI:

Comprehensive Pollution Index

CI:

Contamination Index

OIP:

Overall Index of pollution

EC:

Electrical conductivity

TDS:

Total Dissolved solids

TH:

Total Hardness

NO3 − :

Nitrate

Na + :

Sodium

SO42 − :

Sulfate

Mg2 + :

Magnesium

Fe2 + :

Iron

Ca2 + :

Calcium

Cl − :

Chloride

TN:

Total nitrogen

NPI:

Nemerow pollution index

COD:

Chemical oxygen demand

TSS:

Total Suspended solids

PO43 − :

Phosphate

NH3:

Ammonia

EF:

EnrichmEnt factor

Igeo:

Geo-Accumulation index

HPI:

Heavy Metal pollution index

HEI:

Heavy Metal evaluation index

TSI:

Carlson Trophic state index

CMTSI:

Carlson Modified trophic index

ICOTRO:

Trophic Contamination index

TRIX:

Trophic Index

TSILamp:

Trophic State index

Chla:

Chlorophyll

SD:

Secchi Depth

LCI:

Hulbert's Lake condition index

mWQI:

Microbiological water quality index

DWQI:

Dinius Water quality index

C-BSQGs:

Consensus-based SQGs

CCPI:

CommunIty conservation perception index

GFDMWQI:

Group Fuzzy decision-making water quality index

AI:

Artificial Intelligence

ML:

Machine Learning

ML:

Support Vector regression

RF:

Random Forest

Xgboost:

Extreme Gradient boosting

GB:

Gradient Boosting

AdaBoost:

Adaptive Boosting

KNN:

K-Nearest neighbor

DT:

Decision Tree

MLP:

Multi-Layer perceptron

PCA:

Principal Components analysis

FA:

Factor Analysis

CA:

Cluster Analysis

CDFs:

Cumulative distribution functions

mPECQs:

Mean Probable effect concentration quotients

ANNs:

Artificial Neural networks

BQI:

Benthic Quality index

CWQI:

Climate-oriented water quality index

SCWQI:

Specific Combinatory water quality index

WQI-DET:

Novel Water quality index

SSPI:

Source-Sink landscape pattern index

IPCC:

IntergovErnmental panel for climate change

MPC:

MaximuM permissible concentration

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Authors and Affiliations

  1. Department of Civil Engineering, Urmia University, Urmia, Iran

    Elham Ebrahimi Sarindizaj

  2. Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman

    Mohammad Reza Nikoo

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  1. Elham Ebrahimi Sarindizaj
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  2. Mohammad Reza Nikoo
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Elham Ebrahimi Sarindizaj and Mohammad Reza Nikoo: Conceptualization, Investigation, Analysis, Visualization, Writing – Review & Editing.

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Correspondence to Elham Ebrahimi Sarindizaj.

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Appendix 1

Appendix 1

Table 2 is presented to compare operational demands and costs of conventional and modern WQIs, analyzing computational needs, data requirements, and cost-effectiveness to reveal accuracy-scalability trade-offs (Table 2).

Table 2 Comparative WQIs analysis: Operational parameters & costs
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Table 3 Water quality indices for lakes and reservoirs
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Ebrahimi Sarindizaj, E., Nikoo, M.R. Lakes and Reservoir Water Quality Indices: Evolution, Applications, and Future Directions. Water Air Soil Pollut 237, 13 (2026). https://doi.org/10.1007/s11270-025-08689-2

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