Skip to main content

Advertisement

Log in

Prioritization-based management of the watershed using health assessment analysis at sub-watershed scale

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

Examining the problems and prioritization of various parts of the watershed is one of the essential factors for presenting programs and action plans for the adaptive management of the watershed. In other words, presenting executive measures should be based on specific problem-dependent variables, determinant criteria, and effective indicators in the watershed. However, the spatial prioritization of watersheds using a problem-based health assessment approach has yet to be described. Understanding the challenges of the watershed is thus an inescapable requirement for good planning and implementation of natural resource projects, which leads to the prevention of degradation in constantly changing ecosystems and, ultimately, successful natural resource management. The health assessment of the watershed would be the best framework to identify problems and effective variables leading to sustainable watershed management, so that, at the watershed scale, a health assessment is a valuable method for assessing and identifying effective human, ecological, and environmental resource management strategies. It leads to a proper classification of effective elements and the assessment of degrees of controllability, allowing watershed managers to focus their efforts on priority sub-watersheds to efficiently address current challenges. However, such a comprehensive approach has seldom been considered. The current study, therefore, employed the health analysis initiative for the prioritization of sub-watersheds of the Mikhsaz Watershed, Mazandaran Province, Iran. The watershed health was conceptualized and consequently outlined based on various effective and problem-oriented criteria using the pressure–state–response (PSR) framework. Toward that, the PSR framework was customized and corresponding watershed indicators of pressure (P), state (S), and response (R) were conceptualized according to 17 climatic, hydrologic, physical, and anthropogenic factors. The results showed that biologic density and ratio of the number of permitted to unauthorized livestock contributed to pressure indicator at the tune of 36.54%. Hydrologic factors controlled state and response statuses at a contribution rate of 56.07 and 80.11%, respectively. Accordingly, pressure, state, and response indices were found to be 0.68, 0.61, and 0.75 leading to a dominant relatively healthy status of the watershed health (i.e., 46%) with an overall index of 0.68. Besides, pressure, state, response indices were calculated, and associated effective variables were recognized for each sub-watershed led to a prioritization zoning map. The sub-watershed prioritization map can be utilized for designating optimal strategy for the sustainable and of course problem-oriented management of the study watershed.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. However, all necessary data and information have been provided in the present manuscript.

References

  • Adhami, M., & Sadeghi, S. H. R. (2016). Sub-watershed prioritization based on sediment yield using game theory. Journal of Hydrology, 541, 977–987. https://doi.org/10.1016/j.jhydrol.2016.08.008

    Article  Google Scholar 

  • Adinarayana, J. (2003). Spatial decision support system for identifying priority sites for watershed management schemes. In Proc. First Interagency Conference on Research in the Watersheds (pp. 405–408).

  • Aher, P. D., Adinarayana, J., & Gorantiwar, S. D. (2013). Prioritization of watersheds using multi-criteria evaluation through fuzzy analytical hierarchy process. Agricultural Engineering International CIGR Journal, 15, 11–18.

    Google Scholar 

  • Aher, P. D., Singh, K. K., & Sharma, H. C. (2010). Morphometric characterization of Gagar watershed for management planning. Twenty third national convention of agricultural engineers and national seminar (pp. 6–7). Mahatma Phule Agril University.

    Google Scholar 

  • Ahmed, R., Sajjad, H., & Husain, I. (2018). Morphometric parameters-based prioritization of sub-watersheds using fuzzy analytical hierarchy process: A case study of lower Barpani watershed, India. Natural Resources Research, 27, 67–75.

    CAS  Google Scholar 

  • Alilou, H., Rahmati, O., Singh, V. P., Choubin, B., Pradhan, B., Keesstra, S., Ghiasi, S. S., & Sadeghi, S. H. R. (2019). Evaluation of watershed health using Fuzzy-ANP approach considering geo-environmental and topo-hydrological criteria. Journal of Environmental Management, 232, 22–36.

    Google Scholar 

  • Ameri, A. A., Pourghasemi, H. R., & Cerda, A. (2018). Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: A comparison among TOPSIS, VIKOR, SAW., CF multi-criteria decision-making models. Science of the Total Environment, 613–614, 1385–1400. https://doi.org/10.1016/j.scitotenv.2017.09.210

    Article  CAS  Google Scholar 

  • Amiri, M., Pourghasemi, H. R., Arabameri, A., Vazirzadeh, A., Yousefi, H., & Kafaei, S. (2019). Prioritization of flood inundation of Maharloo Watershed in Iran using morphometric parameters analysis and TOPSIS MCDM model. Spatial Modeling in GIS and R for Earth and Environmental Sciences (pp. 371–390). Elsevier.

    Google Scholar 

  • Avand, M., Moradi, H., & Ramazanzadeh, M. (2020). Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2020.125663

    Article  Google Scholar 

  • Bihamta, M. R., & Zare Chahouki, M. A. (2016). Principles of statistics for the natural resources science (4th ed., p. 300). Tehran University Publication.

    Google Scholar 

  • Burkhard, B., & Muller, F. (2008). Driver–pressure–state–impact–response. Encyclopedia of ecology (pp. 967–970). Elsevier.

    Google Scholar 

  • Cude, C. G. (2001). Oregon water quality index a tool for evaluating water quality management effectiveness. Journal of the American Water Resources Association, 37, 125–137.

    CAS  Google Scholar 

  • Deb, D., & Talukdar, B. (2010). Remote sensing and geographic information system for assessment, monitoring, and management of flooded and waterlogged areas, North District of Tripura State, India, In: Watershed management 2010 innovations in watershed management under land use and climate change (pp. 1013–1024).

  • Ding, Y., Wang, W., Cheng, X., & Zhao, S. (2005). Ecosystem health assessment in inner Mongolia region based on remote sensing and GIS, archives. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B1), 1029–1034.

    Google Scholar 

  • Evenson, G. R., Kalcic, M., Wang, Y. C., Robertson, D., Scavia, D., Martin, J., Aloysius, N., Apostel, A., Boles, C., & Brooker, M. (2020). Uncertainty in critical source area predictions from watershed-scale hydrologic models. Journal of Environmental Management, 279, 111506.

    Google Scholar 

  • Fallah, M., Kavian, A., & Omidvar, E. (2016). Watershed prioritization in order to implement soil and water conservation practices. Environmental Earth Sciences, 75(18), 1–17.

    Google Scholar 

  • Gari, S. R., Guerrero, C. E. O., Bryann, A., Icely, J. D., & Newton, A. (2018). A DPSIR-analysis of water uses and related water quality issues in the Colombian Alto and Medio Dagua Community Council. Water Science, 32(2), 318–337.

    Google Scholar 

  • Gari, S. R., Newton, A., & Icely, J. D. (2015). A review of the application and evolution of the DPSIR framework with an emphasis on coastal social-ecological systems. Ocean & Coastal Management, 103, 63–77.

    Google Scholar 

  • Grohmann, C. H. (2004). Morphometric analysis in geographic information systems: Applications of free software GRASS and R. Computers & Geosciences, 30, 1055–1067. https://doi.org/10.1016/j.cageo.2004.08.002

    Article  Google Scholar 

  • Gupta, M., & Srivastava, P. K. (2010). Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarh, Gujarat, India. Water International, 35, 233–245.

    Google Scholar 

  • Hazbavi, Z., Baartman, J. E. M., Nunes, J. P., Keesstra, S. D., & Sadeghi, S. H. R. (2018a). a. Changeability of reliability, resilience, and vulnerability indicators with respect to drought patterns. Ecological Indicators, 87, 196–208.

    Google Scholar 

  • Hazbavi, Z., Keesstra, S. D., Nunes, J. P., Baartman, J. E. M., Gholamalifard, M., & Sadeghi, S. H. R. (2018b). Health comparative comprehensive assessment of watersheds with different climates. Ecological Indicators, 93, 781–790. https://doi.org/10.1016/j.ecolind.2018.05.078

    Article  CAS  Google Scholar 

  • Hazbavi, Z., & Sadeghi, S. H. R. (2017). Watershed health characterization using reliability–resilience–vulnerability conceptual framework based on hydrological responses. Land Degradation & Development, 28, 1528–1537.

    Google Scholar 

  • Hazbavi, Z., Sadeghi, S. H. R., Gholamalifard, M., & Davoudirad, A. A. (2020). Watershed health assessment using pressure-state-response (PSR) framework. Land Degradation & Development, 31, 3–19.

    Google Scholar 

  • Hlaing, K. T., Haruyama, S., & Aye, M. M. (2008). Using GIS-based distributed soil loss modeling and morphometric analysis to prioritize watershed for soil conservation in Bago river basin of Lower Myanmar. Frontiers of Earth Science in China, 2, 465–478.

    Google Scholar 

  • Huang, D., Zhang, R., Huo, Z., & Mao, F. (2012). An assessment of multidimensional flood vulnerability at the provincial scale in China based on the DEA method. Natural Hazards, 64(2), 1575–1586. https://doi.org/10.1007/s11069-012-0323-1

    Article  Google Scholar 

  • Jain, M. K., & Das, D. (2010). Estimation of sediment yield and areas of soil erosion and deposition for watershed prioritization using GIS and remote sensing. Water Resources Management, 24, 2091–2112.

    Google Scholar 

  • Janssen, J., Krol, M. S., Schielen, R. M. J., Hoekstra, A. Y., & de Kok, J. L. (2010). Assessment of uncertainties in expert knowledge illustrated in fuzzy rule-based models. Ecological Modelling, 221, 1245–1251.

    Google Scholar 

  • Javed, A., Khanday, M. Y., & Rais, S. (2011). Watershed prioritization using morphometric and land use/land cover parameters: A remote sensing and GIS based approach. Journal of the Geological Society of India, 78, 63.

    Google Scholar 

  • Jhariya, D. C., Kumar, T., & Pandey, H. K. (2020). Watershed prioritization based on soil and water hazard model using remote sensing, geographical information system, and multi-criteria decision analysis approach. Geocarto International, 35, 188–208.

    Google Scholar 

  • Kalin, L., & Hantush, M. M. (2009). An auxiliary method to reduce potential adverse impacts of projected land developments: Subwatershed prioritization. Environmental Management, 43, 311.

    Google Scholar 

  • Ketema, A., & Dwarakish, G. S. (2020). Prioritization of sub-watersheds for conservation measures based on soil loss rate in Tikur Wuha watershed Ethiopia. Arabian Journal of Geosciences, 13, 1–16.

    Google Scholar 

  • Kundu, S., Khare, D., & Mondal, A. (2017). Landuse change impact on sub-watersheds prioritization by analytical hierarchy process (AHP). Ecological Informatics, 42, 100–113.

    Google Scholar 

  • Lei, X., Chen, W., Avand, M., Janizadeh, S., Kariminejad, N., Shahabi, H., Costache, R., Shahabi, H., Shirzadi, A., & Mosavi, A. (2020). GIS-based machine learning algorithms for gully erosion susceptibility mapping in a semi-arid region of Iran. Remote Sensing, 12, 2478.

    Google Scholar 

  • Liaqat, A. K. R., Rehman, A. Z., & Yusuf, A. (2011). Morphometric analysis of drainage basin using remote sensing and GIS techniques: A case study of Etmadpur tehsil, Agra district UP. International Journal of Research in Chemistry and Environment, 1, 36–45.

    Google Scholar 

  • Liu, D., & Hao, S. (2017). Ecosystem health assessment at county-scale using the pressure-state-response framework on the Loess Plateau, China. International Journal of Environmental Research and Public Health, 14, 2.

    Google Scholar 

  • Mallya, G., Gupta, A., Hantush, M. M., & Govindaraju, R. S. (2020). Uncertainty quantification in reconstruction of sparse water quality time series: Implications for watershed health and risk-based TMDL assessment. Environmental Modelling & Software., 131, 104735.

    Google Scholar 

  • Mishra, A., Kar, S., & Singh, V. P. (2007). Prioritizing structural management by quantifying the effect of land use and land cover on watershed runoff and sediment yield. Water Resources Management, 21, 1899–1913.

    Google Scholar 

  • Moradi, H. R., Avand, M. T. M. T., & Janizadeh, S. (2019). Landslide susceptibility survey using modeling methods. Spatial modeling in GIS and R for earth and environmental sciences (pp. 259–275). Elsevier.

    Google Scholar 

  • Mosaffaie, J., Salehpour Jam, A., Tabatabaei, M. R., & Kousari, M. R. (2021). Trend assessment of the watershed health based on DPSIR framework. Land Use Policy. https://doi.org/10.1016/j.landusepol.2020.104911

    Article  Google Scholar 

  • Negash, D. A., Moisa, M. B., Merga, B. B., Sedeta, F., & Gemeda, D. O. (2021). Soil erosion risk assessment for prioritization of sub-watershed: The case of Chogo Watershed, Horo Guduru Wollega Ethiopia. Environmental Earth Sciences, 80(17), 1–11.

    Google Scholar 

  • Newbold, S. C., & Siikamäki, J. (2009). Prioritizing conservation activities using reserve site selection methods and population viability analysis. Ecological Applications, 19, 1774–1790.

    Google Scholar 

  • Niraula, R., Kalin, L., Wang, R., & Srivastava, P. (2011). Determining nutrient and sediment critical source areas with SWAT: Effect of lumped calibration. Transactions of the ASABE, 55, 137–147.

    Google Scholar 

  • OECD, O. (1993). Core set of indicators for environmental performance reviews. Environ. M. 83.

  • Pai, N., Saraswat, D., & Daniels, M. (2011). Identifying priority subwatersheds in the Illinois river drainage area in Arkansas watershed using a distributed modeling approach. Transactions of the ASABE, 54, 2181–2196.

    Google Scholar 

  • Patil, G. P. (2007). Statistical geoinformatics of geographic hotspot detection and multicriteria prioritization for monitoring, etiology, early warning, and sustainable management for digital governance in agriculture, environment, and ecohealth. Journal of Indian Society of Agricultural Statistics, 61, 132–146.

    Google Scholar 

  • Peng, B., Huang, Q., Elahi, E., & Wei, G. (2019). Ecological environment vulnerability and driving force of Yangtze river urban agglomeration. Sustainability, 11, 6623.

    Google Scholar 

  • Pham, B.T., Phong, T., Van Avand, M., Al-Ansari, N., Singh, S.K., Le, H., Van Prakash, I. (2020). Improving voting feature intervals for spatial prediction of landslides. Mathematical Problems in Engineering, 2020.

  • Poongodi, R., & Venkateswaran, S. (2018). Prioritization of the micro-watersheds through morphometric analysis in the Vasishta Sub Basin of the Vellar River, Tamil Nadu using ASTER digital elevation model (DEM) data. Data in Brief, 20, 1353–1359. https://doi.org/10.1016/j.dib.2018.08.197

    Article  CAS  Google Scholar 

  • Redman, C. L. (1999). Human impact on ancient environments (p. 288). University of Arizona Press.

    Google Scholar 

  • Saaty, T. L., & Vargas, L. G. (1980). Hierarchical analysis of behavior in competition: Prediction in chess. Behavioral Science, 25, 180–191.

    Google Scholar 

  • Sadeghi, S. H. R., & Hazbavi, Z. (2017). Spatiotemporal variation of watershed health propensity through reliability-resilience-vulnerability based drought index (case study: Shazand Watershed in Iran). Science of the Total Environment, 587, 168–176.

    Google Scholar 

  • Sadeghi, S. H. R., Kia, S. K., Erfanian, M., & Movahed, S. M. S. (2019). Identifying representative watershed for the Urmia lake basin, Iran. Environmental Monitoring and Assessment, 191, 45.

    Google Scholar 

  • Safamanesh, R., Sulaiman, W. N. A., & Firuz Ramli, M. (2006). Erosion risk assessment using an empirical model of pacific south west inter-agency committee method for Zargeh watershed. Iran Journal of Spatial Hydrology, 6(2), 1–17.

    Google Scholar 

  • Sarma, S., & Saikia, T. (2012). Prioritization of sub-watersheds in Khanapara-Bornihat area of Assam-Meghalaya (India) based on land use and slope analysis using remote sensing and GIS. Journal of the Indian Society of Remote Sensing, 40, 435–446.

    Google Scholar 

  • Shivhare, N., Rahul, A. K., Omar, P. J., Chauhan, M. S., Gaur, S., Dikshit, P. K. S., & Dwivedi, S. B. (2018). Identification of critical soil erosion prone areas and prioritization of micro-watersheds using geoinformatics techniques. Ecological Engineering, 121, 26–34.

    Google Scholar 

  • Solimini, A. G., Ptacnik, R., & Cardoso, A. C. (2009). Towards holistic assessment of the functioning of ecosystems under the water framework directive. TrAC-Trends in Analytical Chemistry, 28, 143–149.

    CAS  Google Scholar 

  • Suresh, M., Sudhakar, S., Tiwari, K. N., & Chowdary, V. M. (2004). Prioritization of watersheds using morphometric parameters and assessment of surface water potential using remote sensing. Journal of the Indian Society of Remote Sensing, 32, 249–259.

    Google Scholar 

  • Talukdar, S., Ghose, B., Salam, R., Mahato, S., Pham, Q. B., Linh, N. T. T., Costache, R., & Avand, M. (2020). Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms. Stochastic Environmental Research and Risk Assessment, 34(12), 2277–2300.

    Google Scholar 

  • Toosi, S. L. R., & Samani, J. M. V. (2017). Prioritizing watersheds using a novel hybrid decision model based on fuzzy DEMATEL, fuzzy ANP, and fuzzy VIKOR. Water Resources Management, 31, 2853–2867.

    Google Scholar 

  • Tukura, N. G., Akalu, M. M., Hussein, M., & Befekadu, A. (2021). Morphometric analysis and sub-watershed prioritization of Welmal watershed, Ganale-Dawa river basin, Ethiopia: Implications for sediment erosion. Journal of Sedimentary Environment., 6(1), 121–130.

    Google Scholar 

  • Vishwakarma, A., Goswami, A., & Pradhan, B. (2020). Prioritization of sites for managed aquifer recharge in a semi-arid environment in western India using GIS-based multicriteria evaluation strategy. Groundwater for Sustainable Development, 12, 100501.

    Google Scholar 

  • Wang, D., Fu, B., Zhao, W., Hu, H., & Wang, Y. (2008). Multifractal characteristics of soil particle size distribution under different land-use types on the Loess Plateau. China, 72, 29–36. https://doi.org/10.1016/j.catena.2007.03.019

    Article  Google Scholar 

  • Wiegand, A. N., Walker, C., Duncan, P. F., Roiko, A., & Tindale, N. (2013). A systematic approach for modelling quantitative lake ecosystem data to facilitate proactive urban lake management. Environmental Systems Research, 2, 3. https://doi.org/10.1186/2193-2697-2-3

    Article  Google Scholar 

  • Worku, G., Teferi, E., Bantider, A., & Dile, Y. T. (2020). Prioritization of watershed management scenarios under climate change in the Jemma sub-basin of the Upper Blue Nile Basin, Ethiopia. Journal of Hydrology Regional Studies, 31, 100714. https://doi.org/10.1016/j.ejrh.2020.100714

    Article  Google Scholar 

  • Wu, H. (2018). Watershed prioritization in the upper Han river basin for soil and water conservation in the South-to-North water transfer project (middle route) of China. Environmental Science and Pollution Research, 25, 2231–2238.

    CAS  Google Scholar 

  • Yadav, S. K., Dubey, A., Szilard, S., & Singh, S. K. (2018). Prioritisation of sub-watersheds based on earth observation data of agricultural dominated northern river basin of India. Geocarto International, 33, 339–356.

    Google Scholar 

  • Yariyan, P., Avand, M., Abbaspour, R. A., Torabi, A., Costache, R., Ghorbanzadeh, O., & Janizadeh, S. (2020). Flood susceptibility mapping using an improved analytic network process with statistical models. Geomatics Natural Hazards and Risk, 11, 2282–2314. https://doi.org/10.1080/19475705.2020.1836036

    Article  Google Scholar 

  • Yousefi, S., Pourghasemi, H. R., Avand, M., Janizadeh, S., Tavangar, S., & Santosh, M. (2020). Assessment of land degradation using machine-learning techniques: A case of declining rangelands. Land Degradation & Development. https://doi.org/10.1002/ldr.3794

    Article  Google Scholar 

  • Yu, G., Yu, Q., Hu, L., Zhang, S., Fu, T., Zhou, X., He, X., Liu, Y., Wang, S., & Jia, H. (2013). Ecosystem health assessment based on analysis of a land use database. Applied Geography, 44, 154–164.

    Google Scholar 

  • Zhang, J., Sun, J., Ma, B., & Du, W. (2017). Assessing the ecological vulnerability of the upper reaches of the Minjiang River. PLoS ONE, 12, e0181825.

    Google Scholar 

  • Zhang, L., Huettmann, F., Liu, S., Sun, P., Yu, Z., Zhang, X., & Mi, C. (2019). Classification and regression with random forests as a standard method for presence-only data SDMs: A future conservation example using China tree species. Ecol. Inform, 52, 46–56. https://doi.org/10.1016/j.ecoinf.2019.05.003

    Article  CAS  Google Scholar 

  • Zhang, M., Xiang, W., Chen, M., & Mao, Z. (2018). Measuring social vulnerability to flood disasters in China. Sustain, 10, 1–14. https://doi.org/10.3390/su10082676

    Article  Google Scholar 

  • Zhao, C., Shao, N., Yang, S., Ren, H., Ge, Y., Zhang, Z., Zhao, Y., & Yin, X. (2019). Integrated assessment of ecosystem health using multiple indicator species. Ecological Engineering, 130, 157–168.

    Google Scholar 

Download references

Acknowledgements

The current study is based on data collected for the first author's Ph.D. dissertation at Tarbiat Modares University in Iran with the assistance of the General Department of Natural Resources and Watershed Management of Mazandaran Province and the people of Mikhsaz Watershed, Iran, whose invaluable assistance to them is recognized. In the case of the corresponding author, the Tarbiat Modares University Agrohydrology Research Group (Grant No. IG-39713) provided partial funding.

Funding

Partial financial support was received from Tarbiat Modares University Agrohydrology Research Group under Grant No. IG-39713).

Author information

Authors and Affiliations

Authors

Contributions

Both authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by ZEG and SHS. The first draft of the manuscript was written by ZEG, and both authors commented on previous versions of the manuscript. Both authors read and approved the final manuscript.

Corresponding author

Correspondence to Seyed Hamidreza Sadeghi.

Ethics declarations

Conflict of interest

The authors declare that they have no established conflicting financial interests or personal partnerships that may have influenced the research presented in this article.

Ethical approval

Both authors whose names appear on the submission states that the manuscript has been only submitted to Water Resources Management (WRM). The submitted work is an original work and has not been published elsewhere in any form or language (partially or in full). The manuscript is a single study not broken into several parts to increase the quantity of submissions and submitted to various journals or to WRM. Results has been presented clearly, honestly, and without fabrication, falsification or inappropriate data manipulation (including image-based manipulation). Authors adhere to discipline-specific rules for acquiring, selecting and processing data. No data, text, or theories by others have been presented as if they were the author’s own (‘plagiarism’). Proper acknowledgements to other works have been given to other works.

Consent to participate

Both authors whose names appear on the submission made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work; drafted the work or revised it critically for important intellectual content; approved the version to be published; and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Consent to publish

The authors give the Publisher the permission of the Author to publish the Work. They signed Transfer of Copyright empowers the Publisher to protect the Work against unauthorized use and to maintain the integrity of the Work from a bibliographical and archival standpoint.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ebrahimi Gatgash, Z., Sadeghi, S.H. Prioritization-based management of the watershed using health assessment analysis at sub-watershed scale. Environ Dev Sustain 25, 9673–9702 (2023). https://doi.org/10.1007/s10668-022-02455-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10668-022-02455-8

Keywords

Navigation