Hydrological Data Integration for Environmental Risk Assessment

Nurhayati; Mursal Ghazali; Syaharuddin; Ratri Retno Utami

doi:10.18535/ijsrm/v13i11.ah02

Abstract

This study presents a systematic literature review aimed at integrating hydrological data into environmental risk assessment. The literature sources were obtained from Scopus, DOAJ, and Google Scholar, focusing on publications from the last 10 years. The discussed literature emphasizes the crucial role of hydrological data integration in enhancing the accuracy and quality of environmental risk assessments. The study details various proposed approaches, including a risk-based eco-hydrological approach, spatial geo-statistical techniques, the development of the meta-scientific-modeling (MSM) framework, automated assessment of local sensor networks, and the creation of web-based software platforms. The research findings illustrate the active efforts of researchers to find more effective and comprehensive ways of utilizing hydrological data in environmental risk evaluation. Each proposed approach has its strengths and limitations, with specific considerations related to data complexity, computational requirements, and analytical skills. In addition to highlighting the necessity of coordinated and integrated techniques to enhance future risk assessments, this study lays the groundwork for a thorough knowledge of the function that hydrological data integration plays in the context of environmental risk assessment.

Keywords

Hydrological DataEnvironmental Risk AssessmentData Integration

References

Aryal, J. (n.d.). Evaluation and Visualization of Hydrological Sensor Network : an Integrated Approach Using MODIS Images.Google Scholar ↗
Brauman, K. A. (2015). Hydrologic ecosystem services: linking ecohydrologic processes to human well-being in water research and watershed management. Wiley Interdisciplinary Reviews: Water, 2(4), 345–358. https://doi.org/10.1002/WAT2.1081DOI ↗Google Scholar ↗
Cui, X., Guo, X., Wang, Y., Wang, X., Zhu, W., Shi, J., Lin, C., & Gao, X. (2019). Application of remote sensing to water environmental processes under a changing climate. In Journal of Hydrology (Vol. 574, pp. 892–902). https://doi.org/10.1016/j.jhydrol.2019.04.078DOI ↗Google Scholar ↗
Fatichi, S., Vivoni, E. R., Ogden, F. L., Ivanov, V. Y., Mirus, B., Gochis, D., Downer, C. W., Camporese, M., Davison, J. H., Ebel, B., Jones, N., Kim, J., Mascaro, G., Niswonger, R., Restrepo, P., Rigon, R., Shen, C., Sulis, M., & Tarboton, D. (2016). An overview of current applications, challenges, and future trends in distributed process-based models in hydrology. In Journal of Hydrology (Vol. 537, pp. 45–60). https://doi.org/10.1016/j.jhydrol.2016.03.026DOI ↗Google Scholar ↗
Fotakis, D., Sidiropoulos, E., & Loukas, A. (2014). Integration of a hydrological model within a geographical information system: Application to a forest watershed. Water (Switzerland), 6(3), 500–516. https://doi.org/10.3390/w6030500DOI ↗Google Scholar ↗
Girsang, N. A. P., Rahmatunnisa, M., & Rizkiyansyah, F. K. (2021). INTEGRITAS PENYELENGGARA PEMILU (Study Tentang Pemutakhiran Data Pemilih Di Kabupaten Simalungun Pada Pemilu Tahun 2019). Jurnal Ilmiah Muqoddimah: Jurnal Ilmu Sosial, Politik Dan Hummanioramaniora, 5(1), 125. https://doi.org/10.31604/jim.v5i1.2021.125-137DOI ↗Google Scholar ↗
Guan, G., Wang, Y., Yang, L., Yue, J., Li, Q., Lin, J., & Liu, Q. (2022). Water-Quality Assessment and Pollution-Risk Early-Warning System Based on Web Crawler Technology and LSTM. International Journal of Environmental Research and Public Health, 19(18). https://doi.org/10.3390/ijerph191811818DOI ↗Google Scholar ↗
Hiola, T. T., & Badjuka, B. Y. M. (2021). Health risk assessment of consuming mackerel scads (Decapterus macarellus) contaminated by mercury. AACL Bioflux, 14(5), 2987–2999.Google Scholar ↗
Hristopulos, D. T., Varouchakis, E. A., Skøien, J. O., & Solomatine, D. (2020). Space–time models for hydrological and environmental applications. In Stochastic Environmental Research and Risk Assessment (Vol. 34, Issue 9, pp. 1285–1287). https://doi.org/10.1007/s00477-020-01830-zDOI ↗Google Scholar ↗
Jayaprathiga, M., Cibin, R., & Sudheer, K. P. (2022). Reliability of Hydrology and Water Quality Simulations Using Global Scale Datasets. Journal of the American Water Resources Association, 58(3), 453–470. https://doi.org/10.1111/1752-1688.13006DOI ↗Google Scholar ↗
Kaikkonen, L., Parviainen, T., Rahikainen, M., Uusitalo, L., & Lehikoinen, A. (2021). Bayesian Networks in Environmental Risk Assessment: A Review. Integrated Environmental Assessment and Management, 17(1), 62–78. https://doi.org/10.1002/ieam.4332DOI ↗Google Scholar ↗
Larramendy, M. L., & Liwszyc, G. E. (2023). General Aspects – Current and Further Perspectives. In Bird and Reptile Species in Environmental Risk Assessment Strategies (pp. 1–5). https://doi.org/10.1039/bk9781837670765-00001DOI ↗Google Scholar ↗
Magalhães, L., Peixoto, A., Manzato, G., & Bezerra, B. (2023). Risk Matrix of Hydrological Disasters Combining Rainfall Thresholds and Social-Environmental Criteria. Advances in Environmental and Engineering Research, 04(01), 1–16. https://doi.org/10.21926/aeer.2301019DOI ↗Google Scholar ↗
Mandala, G. N., Kumar Verma, S., Rajagopal, N. K., Saranya, S., Verma, D., & Samatha, B. (2022). Risk Assessment Model for Quality Management System. MysuruCon 2022 - 2022 IEEE 2nd Mysore Sub Section International Conference. https://doi.org/10.1109/MysuruCon55714.2022.9972698DOI ↗Google Scholar ↗
Marigómez, I. (2024). Environmental risk assessment, marine. In Encyclopedia of Toxicology (pp. 253–259). https://doi.org/10.1016/b978-0-12-824315-2.01094-0DOI ↗Google Scholar ↗
Mcgregor, G. B., Marshall, J. C., Lobegeiger, J. S., Holloway, D., Menke, N., & Coysh, J. (2018). A Risk-Based Ecohydrological Approach to Assessing Environmental Flow Regimes. Environmental Management, 61(3), 358–374. https://doi.org/10.1007/s00267-017-0850-3DOI ↗Google Scholar ↗
Pujol, L., Garambois, P. A., Monnier, J., Finaud-Guyot, P., Larnier, K., & Mosé, R. (2022). Integrated Hydraulic-Hydrological Assimilation Chain: Towards Multisource Data Fusion from River Network to Headwaters. In Springer Water (pp. 195–211). https://doi.org/10.1007/978-981-19-1600-7_12DOI ↗Google Scholar ↗
Salas, D., Liang, X., Navarro, M., Liang, Y., & Luna, D. (2020). An open-data open-model framework for hydrological models’ integration, evaluation and application. Environmental Modelling and Software, 126. https://doi.org/10.1016/j.envsoft.2020.104622DOI ↗Google Scholar ↗
Sege, J., Ghanem, M., Ahmad, W., Bader, H., & Rubin, Y. (2018). Distributed data collection and web-based integration for more efficient and informative groundwater pollution risk assessment. Environmental Modelling and Software, 100, 278–290. https://doi.org/10.1016/j.envsoft.2017.11.027DOI ↗Google Scholar ↗
Sun, Y., Zhang, L., Liu, J., Lin, J., & Cui, Q. (2022). A Data Assimilation Approach to the Modeling of 3D Hydrodynamic Flow Velocity in River Reaches. Water (Switzerland), 14(22). https://doi.org/10.3390/w14223598DOI ↗Google Scholar ↗
Teodosiu, C., Robu, B., Cojocariu, C., & Barjoveanu, G. (2015). Environmental impact and risk quantification based on selected water quality indicators. Natural Hazards, 75(1), 89–105. https://doi.org/10.1007/s11069-013-0637-7DOI ↗Google Scholar ↗
Thakur, J. K., Singh, S. K., & Ekanthalu, V. S. (2017). Integrating remote sensing, geographic information systems and global positioning system techniques with hydrological modeling. In Applied Water Science (Vol. 7, Issue 4, pp. 1595–1608). https://doi.org/10.1007/s13201-016-0384-5DOI ↗Google Scholar ↗
Virkki, V., Alanärä, E., Porkka, M., Ahopelto, L., Gleeson, T., Mohan, C., Wang-Erlandsson, L., Flörke, M., Gerten, D., Gosling, S. N., Hanasaki, N., Müller Schmied, H., Wanders, N., & Kummu, M. (2022). Globally widespread and increasing violations of environmental flow envelopes. Hydrology and Earth System Sciences, 26(12), 3315–3336. https://doi.org/10.5194/hess-26-3315-2022DOI ↗Google Scholar ↗
Zhu, H., Yao, J., Meng, J., Cui, C., Wang, M., & Yang, R. (2023). A Method to Construct an Environmental Vulnerability Model Based on Multi-Source Data to Evaluate the Hazard of Short-Term Precipitation-Induced Flooding. Remote Sensing, 15(6). https://doi.org/10.3390/rs15061609DOI ↗Google Scholar ↗

[refR-1] Aryal, J. (n.d.). Evaluation and Visualization of Hydrological Sensor Network : an Integrated Approach Using MODIS Images.Google Scholar ↗

[refR-2] Brauman, K. A. (2015). Hydrologic ecosystem services: linking ecohydrologic processes to human well-being in water research and watershed management. Wiley Interdisciplinary Reviews: Water, 2(4), 345–358. https://doi.org/10.1002/WAT2.1081DOI ↗Google Scholar ↗

[refR-3] Cui, X., Guo, X., Wang, Y., Wang, X., Zhu, W., Shi, J., Lin, C., & Gao, X. (2019). Application of remote sensing to water environmental processes under a changing climate. In Journal of Hydrology (Vol. 574, pp. 892–902). https://doi.org/10.1016/j.jhydrol.2019.04.078DOI ↗Google Scholar ↗

[refR-4] Fatichi, S., Vivoni, E. R., Ogden, F. L., Ivanov, V. Y., Mirus, B., Gochis, D., Downer, C. W., Camporese, M., Davison, J. H., Ebel, B., Jones, N., Kim, J., Mascaro, G., Niswonger, R., Restrepo, P., Rigon, R., Shen, C., Sulis, M., & Tarboton, D. (2016). An overview of current applications, challenges, and future trends in distributed process-based models in hydrology. In Journal of Hydrology (Vol. 537, pp. 45–60). https://doi.org/10.1016/j.jhydrol.2016.03.026DOI ↗Google Scholar ↗

[refR-5] Fotakis, D., Sidiropoulos, E., & Loukas, A. (2014). Integration of a hydrological model within a geographical information system: Application to a forest watershed. Water (Switzerland), 6(3), 500–516. https://doi.org/10.3390/w6030500DOI ↗Google Scholar ↗

[refR-6] Girsang, N. A. P., Rahmatunnisa, M., & Rizkiyansyah, F. K. (2021). INTEGRITAS PENYELENGGARA PEMILU (Study Tentang Pemutakhiran Data Pemilih Di Kabupaten Simalungun Pada Pemilu Tahun 2019). Jurnal Ilmiah Muqoddimah: Jurnal Ilmu Sosial, Politik Dan Hummanioramaniora, 5(1), 125. https://doi.org/10.31604/jim.v5i1.2021.125-137DOI ↗Google Scholar ↗

[refR-7] Guan, G., Wang, Y., Yang, L., Yue, J., Li, Q., Lin, J., & Liu, Q. (2022). Water-Quality Assessment and Pollution-Risk Early-Warning System Based on Web Crawler Technology and LSTM. International Journal of Environmental Research and Public Health, 19(18). https://doi.org/10.3390/ijerph191811818DOI ↗Google Scholar ↗

[refR-8] Hiola, T. T., & Badjuka, B. Y. M. (2021). Health risk assessment of consuming mackerel scads (Decapterus macarellus) contaminated by mercury. AACL Bioflux, 14(5), 2987–2999.Google Scholar ↗

[refR-9] Hristopulos, D. T., Varouchakis, E. A., Skøien, J. O., & Solomatine, D. (2020). Space–time models for hydrological and environmental applications. In Stochastic Environmental Research and Risk Assessment (Vol. 34, Issue 9, pp. 1285–1287). https://doi.org/10.1007/s00477-020-01830-zDOI ↗Google Scholar ↗

[refR-10] Jayaprathiga, M., Cibin, R., & Sudheer, K. P. (2022). Reliability of Hydrology and Water Quality Simulations Using Global Scale Datasets. Journal of the American Water Resources Association, 58(3), 453–470. https://doi.org/10.1111/1752-1688.13006DOI ↗Google Scholar ↗

[refR-11] Kaikkonen, L., Parviainen, T., Rahikainen, M., Uusitalo, L., & Lehikoinen, A. (2021). Bayesian Networks in Environmental Risk Assessment: A Review. Integrated Environmental Assessment and Management, 17(1), 62–78. https://doi.org/10.1002/ieam.4332DOI ↗Google Scholar ↗

[refR-12] Larramendy, M. L., & Liwszyc, G. E. (2023). General Aspects – Current and Further Perspectives. In Bird and Reptile Species in Environmental Risk Assessment Strategies (pp. 1–5). https://doi.org/10.1039/bk9781837670765-00001DOI ↗Google Scholar ↗

[refR-13] Magalhães, L., Peixoto, A., Manzato, G., & Bezerra, B. (2023). Risk Matrix of Hydrological Disasters Combining Rainfall Thresholds and Social-Environmental Criteria. Advances in Environmental and Engineering Research, 04(01), 1–16. https://doi.org/10.21926/aeer.2301019DOI ↗Google Scholar ↗

[refR-14] Mandala, G. N., Kumar Verma, S., Rajagopal, N. K., Saranya, S., Verma, D., & Samatha, B. (2022). Risk Assessment Model for Quality Management System. MysuruCon 2022 - 2022 IEEE 2nd Mysore Sub Section International Conference. https://doi.org/10.1109/MysuruCon55714.2022.9972698DOI ↗Google Scholar ↗

[refR-15] Marigómez, I. (2024). Environmental risk assessment, marine. In Encyclopedia of Toxicology (pp. 253–259). https://doi.org/10.1016/b978-0-12-824315-2.01094-0DOI ↗Google Scholar ↗

[refR-16] Mcgregor, G. B., Marshall, J. C., Lobegeiger, J. S., Holloway, D., Menke, N., & Coysh, J. (2018). A Risk-Based Ecohydrological Approach to Assessing Environmental Flow Regimes. Environmental Management, 61(3), 358–374. https://doi.org/10.1007/s00267-017-0850-3DOI ↗Google Scholar ↗

[refR-17] Pujol, L., Garambois, P. A., Monnier, J., Finaud-Guyot, P., Larnier, K., & Mosé, R. (2022). Integrated Hydraulic-Hydrological Assimilation Chain: Towards Multisource Data Fusion from River Network to Headwaters. In Springer Water (pp. 195–211). https://doi.org/10.1007/978-981-19-1600-7_12DOI ↗Google Scholar ↗

[refR-18] Salas, D., Liang, X., Navarro, M., Liang, Y., & Luna, D. (2020). An open-data open-model framework for hydrological models’ integration, evaluation and application. Environmental Modelling and Software, 126. https://doi.org/10.1016/j.envsoft.2020.104622DOI ↗Google Scholar ↗

[refR-19] Sege, J., Ghanem, M., Ahmad, W., Bader, H., & Rubin, Y. (2018). Distributed data collection and web-based integration for more efficient and informative groundwater pollution risk assessment. Environmental Modelling and Software, 100, 278–290. https://doi.org/10.1016/j.envsoft.2017.11.027DOI ↗Google Scholar ↗

[refR-20] Sun, Y., Zhang, L., Liu, J., Lin, J., & Cui, Q. (2022). A Data Assimilation Approach to the Modeling of 3D Hydrodynamic Flow Velocity in River Reaches. Water (Switzerland), 14(22). https://doi.org/10.3390/w14223598DOI ↗Google Scholar ↗

[refR-21] Teodosiu, C., Robu, B., Cojocariu, C., & Barjoveanu, G. (2015). Environmental impact and risk quantification based on selected water quality indicators. Natural Hazards, 75(1), 89–105. https://doi.org/10.1007/s11069-013-0637-7DOI ↗Google Scholar ↗

[refR-22] Thakur, J. K., Singh, S. K., & Ekanthalu, V. S. (2017). Integrating remote sensing, geographic information systems and global positioning system techniques with hydrological modeling. In Applied Water Science (Vol. 7, Issue 4, pp. 1595–1608). https://doi.org/10.1007/s13201-016-0384-5DOI ↗Google Scholar ↗

[refR-23] Virkki, V., Alanärä, E., Porkka, M., Ahopelto, L., Gleeson, T., Mohan, C., Wang-Erlandsson, L., Flörke, M., Gerten, D., Gosling, S. N., Hanasaki, N., Müller Schmied, H., Wanders, N., & Kummu, M. (2022). Globally widespread and increasing violations of environmental flow envelopes. Hydrology and Earth System Sciences, 26(12), 3315–3336. https://doi.org/10.5194/hess-26-3315-2022DOI ↗Google Scholar ↗

[refR-24] Zhu, H., Yao, J., Meng, J., Cui, C., Wang, M., & Yang, R. (2023). A Method to Construct an Environmental Vulnerability Model Based on Multi-Source Data to Evaluate the Hazard of Short-Term Precipitation-Induced Flooding. Remote Sensing, 15(6). https://doi.org/10.3390/rs15061609DOI ↗Google Scholar ↗