Skip to main content

Advertisement

SpringerLink
Log in

Critical View of Overland Flow Estimation in Semi-arid Catchments Under Land Subsidence with Long-term Field Measurements

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

An investigation is presented in this paper to predict Overland Flows (OF) from Rain-Falls (RF) and a set of covariates in semi-arid catchments. The study area comprises 8 catchments in the basin of Lake Urmia and its vicinity, where the catchments are at risk of land subsidence through mismanagement triggered by groundwater over-abstraction but received wisdom often implicates the problems with climate change. The paper presents a new perspective, where land subsidence by over-abstraction can impact OF regimes and thereby infiltration regimes. A strategy is formulated at three levels to abstract information from the data/results. At innovative Level 1, the research direction is scoped critically and referenced with reality. At Level 2, modelling results are catered for to be fit-for-purpose. At Level 3, the data/results are critically examined for learning. The data/results are found consistent with the observed reality that (i) a fit-for-purpose OF predictive model can be derived in terms of RF and covariates; (ii) indirect evidence reveals flow regimes of infiltration (interflow and percolation), OF and groundwater are altering, such that percolation is likely to decline and undermine aquifer replenishment regimes when exposed to subsidence risk with subsequent permanent damage. This makes a case for research, effective planning and sustainable drainage systems.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

(Source: the East Azerbaijan Natural Resources Dep.). The box limits indicate the 25th and 75th percentiles, the whiskers represent the 10th and 90th percentiles, and the line within the box marks the median

Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

The datasets used and analysed during the current study are available from the corresponding author upon request. The research does not use any human survey data.

References

  • Bahremand A, De Smedt F (2008) Distributed hydrological modeling and sensitivity analysis in torysa watershed. Slovakia Water Resour Manage 22:393–408. https://doi.org/10.1007/s11269-007-9168-x

    Article  Google Scholar 

  • Beven KJ, Kirkby MJ (1979) A physically-based variable contributing area model of basin hydrology. Hydrol Sci Bullet 24(3):43–69

    Article  Google Scholar 

  • Bouwer H (1977) Land subsidence and cracking due to ground-water depletion. Groundwater 15(5):358–364

    Article  Google Scholar 

  • Chow V, Maidment D, Mays L (1988) Applied Hydrology. 1st Ed. 572 p. (McGraw-Hill Science)

  • Green WH, Ampt GA (1911) Studies on Soil Physics. J Agric Sci 4(1):1–24

    Article  Google Scholar 

  • Hilbe J. (2006) A Handbook of Statistical Analyses Using R. Journal of Statistical Software. Book Reviews. 16(6):1–6. https://doi.org/10.18637/jss.v016.b06

  • Horton RE (1933) The Role of Infiltration in the Hydrologic Cycle. Trans Am Geophys Union 14:446–460

    Article  Google Scholar 

  • Jani R, Khatibi R, Sadeghfam S, Zarrinbal E (2023) Climate zoning under climate change scenarios in the basin of Lake Urmia and in vicinity basins. Theoretical and Applied Climatology. 152: 181–199. https://link.springer.com/article/10.1007/s00704-023-04380-w

  • Kampf SK, Faulconer J, Shaw JR, Lefsky M, Wagenbrenner JW, Cooper DJ (2018) Rainfall thresholds for flow generation in desert ephemeral streams. Water Resour Res 54(12):9935–9950

    Article  Google Scholar 

  • Khatibi R, Ghorbani MA, Naghshara S, Aydin H, Karimi VA (2020) framework for ‘Inclusive Multiple Modelling’ with critical views on modelling practices–Applications to modelling water levels of Caspian Sea and Lakes Urmia and Van. J Hydrol 587:124923

    Article  Google Scholar 

  • Khatibi R, Nadiri AA (2021) Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneity. Geosci Front 12(2) :713–724. https://www.sciencedirect.com/science/article/pii/S1674987120301766?via%3Dihub

  • Klute A (1986) Methods of soil analysis. Part 1. Physical and mineralogical methods (2nd Ed.). American Society of Agronomy. (Madison, WI.)

  • Kodihal S, Akhtar MP (2023) Sustainable groundwater recharge potential zone identification: An AHP-OWA approach integrating future rainfall and land-use projections. Water Resour Manage 27:1–20

    Google Scholar 

  • Koue J (2023) Modeling the effects of river inflow dynamics on the deep layers of lake biwa japan. Environ Process 10(62):1–23. https://doi.org/10.1007/s40710-023-00673-4

    Article  Google Scholar 

  • Liu Z, Wang Y, Xu Z, Duan Q (2017) Conceptual hydrological models. Handbook of hydrometeorological ensemble forecasting. pp 1–23

  • Maier F, Meerveld l van (2021) Long-term changes in runoff generation mechanisms for two proglacial areas in the swiss alps ii: subsurface flow. Water Resour Res 57(12): e2021WR030221

  • Nadiri AA, Naderi K, Khatibi R, Gharekhani M (2019) Modelling groundwater level variations by learning from multiple models using fuzzy logic. Hydrol Sci J 64(2):210–226. https://doi.org/10.1080/02626667.2018.1554940

    Article  Google Scholar 

  • Nadiri AA, Razzag S, Khatibi R (2021) Sedghi Z (2021) Predictive groundwater levels modelling by Inclusive Multiple Modelling (IMM) at multiple levels. Earth Sci Inf 14:749–763

    Article  Google Scholar 

  • Nadiri AA, Taheri Z, Khatibi R, Barzegari G, Dideban K (2018a) Introducing a new framework for mapping subsidence vulnerability indices (SVIs): ALPRIFT. Journal of Science of the Total Environment. 628–629: 1043-1057. https://www.sciencedirect.com/science/article/pii/S0048969718304194

  • Nadiri AA, Keyvan Naderi K, Khatibi R, Gharekhani M (2018b) Modelling groundwater level variations by learning from multiple models using fuzzy logic Hydro. Scien J 2019 64(2):210–226. https://doi.org/10.1080/02626667.2018.1554940

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—A discussion of principles. J Hydrol 10:282–290

    Article  Google Scholar 

  • Orchard CM, Lorentz SA, Jewitt GPW (2012) Chaplot, V.A.M. Hydrol Process 27(16):2325–2338

  • Parnas FEA, Abdalla EMH, Muthanna TM (2021) Evaluating three commonly used infiltration methods for permeable surfaces in urban areas using the SWMM and STORM. Hydrol Res 52(1):160–175

    Article  Google Scholar 

  • Rostami AA, Karimi V, Khatibi R, Pradhan B (2020) An investigation into seasonal variations of groundwater nitrate by spatial modelling strategies at two levels by kriging and co-kriging models. J Environ Manage 270:110843

    Article  CAS  Google Scholar 

  • Sadeghfam S, Mirahmadi R, Khatibi K, Mirabbasi R, Nadiri AA (2022) Investigating meteorological/groundwater droughts by copula to study anthropogenic impacts. Scientific Reports. 12: 8285. https://www.nature.com/articles/s41598-022-11768-7

  • Safdari Z, Nahavandchi H, Joodaki G (2022) Estimation of groundwater depletion in Iran’s catchments using well data. Water 14(1):131. https://www.mdpi.com/2073-4441/14/1/131

  • Tao Z, Li M, Si B, Pratt D (2021) Rainfall intensity affects runoff responses in a semi-arid catchment. Hydrol Process 35(4):e14100

    Article  Google Scholar 

  • Vaezi AR (2014) Modeling runoff from semi-arid agricultural lands in northwest Iran. Pedosphere 24(5):595–604

    Article  Google Scholar 

  • Wang N, Chu X (2020) Revised Horton model for event and continuous simulations of infiltration. J Hydrol 589:125215

    Article  Google Scholar 

  • Ward JH Jr (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244

    Article  Google Scholar 

  • Yang M, Zhang Y, Pan X (2020) Improving the Horton infiltration equation by considering soil moisture variation. J Hydrol 586:124864

    Article  Google Scholar 

  • Yu F, Harbor JM (2019) The effects of topographic depressions on multiscale overland flow connectivity: A high-resolution spatiotemporal pattern analysis approach based on connectivity statistics. Hydrol Process 33(10):1403–1419

    Article  Google Scholar 

  • Zhang Zh, Moore JC (2014) Mathematical and physical fundamentals of climate change. Chapter 8: Autoregressive moving average models. Elsevier. pp 239–290

Download references

Acknowledgements

The authors are grateful for the contribution of Dr Daniele Penna of University of Florence, and Dr. Catalina Segura of Oregon State University for their ample support when the first author was visiting University of Florence. Their valuable inputs in the developments of the modelling strategy and interpretation of the results are highly appreciated. Our thanks are also due to Dr Sina Sadeghfam of the University of Maragha for providing the rainfall data as used in Fig. 4ia. This work was supported by The University of Zanjan, Iran. The authors also thank the Natural Resources Department of East Azerbaijan, Iran for their collaboration on the field and access to the filed data. Finally, our thanks are due for the Fulbright Scholarship that allowed the collaboration with Dr Segura.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Soil Science, Agriculture Faculty, University of Zanjan, Zanjan, Iran

    O. Bakhshi Rad & A. R. Vaezi

  2. GTEV-Rex Limited, Swindon, UK

    R. Khatibi

Authors
  1. O. Bakhshi Rad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. R. Vaezi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Khatibi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

OBR: Formal Analysis; Writing – Original Draft Preparation; Visualization.

ARV: Supervision; Writing – Review & Editing.

RK: Conceptualization; Formal Analysis; Validation; Critical Review of the Results; Writing and Editing.

Corresponding author

Correspondence to O. Bakhshi Rad.

Ethics declarations

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 882 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bakhshi Rad, O., Vaezi, A.R. & Khatibi, R. Critical View of Overland Flow Estimation in Semi-arid Catchments Under Land Subsidence with Long-term Field Measurements. Water Resour Manage 38, 2315–2337 (2024). https://doi.org/10.1007/s11269-024-03761-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-024-03761-8

Keywords

Search

Navigation