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A multi-criteria remote sensing-based data-driven framework for monitoring lake drying and salinization and mapping its environmental impacts

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Abstract

Lakes are natural water resources that are affected by different factors. The increase in soil salinity is one of the major issues created due to the drying up of a lake. In this study, a two-step methodology was used to assess drought vulnerability and salinity variation of the Urmia Lake basin. In this regard, firstly, a multi-criteria intelligence method based on empirical wavelet transform-long short-term memory, which integrated 15 geo-environmental variables extracted from the in-situ observations and satellite datasets, was used for developing drought vulnerability maps of the basin. In the next step, the salinization progress of the basin and its impacts on the environment were investigated using satellite datasets. Results showed that the Southern and Eastern sections of the lake were more prone to severe droughts. It was found that temperature and precipitation variations did not lead to significant shrinkage of the lake, but human activities along with climate changes caused the basin to dry up. The results showed that the biomass production in the basin is affected by salinity, and there is a negative correlation between the salinity index and the normalized difference vegetation index. Also, a positive correlation was found between land subsidence and the density of drilled wells in the basin. The rate of land subsidence varied between − 1.8 and − 8.4 mm/year. The quality of groundwater was investigated for the existing wells in the basin. Results showed that the excessive use of groundwater resources has affected the quality of water.

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Data availability

The used datasets are obtained from Iranian Meteorological Organization and satellite products.

Abbreviations

AI:

Artificial intelligence

AM-FM:

Amplitude modulated-frequency modulated

ANN:

Artificial neural network

CMAP:

CPC merged analysis of precipitation

DC:

Determination coefficient

EMD:

Empirical mode decomposition

EWT:

Empirical wavelet transform

FN:

False negative

GEE:

Google earth engine

GEP:

Gene expression programming

KNN:

K-Nearest neighbour

LST:

Land surface temperature

LSTM:

Long short-term memory

MODIS:

Moderate resolution imaging spectroradiometer

NDVI:

Normalized difference vegetation index

NPV:

Negative predictive value

PPV:

Pixel probability value

RNN:

Recurrent neural network

SAR:

Synthetic aperture radar

SI:

Salinity index

SLC:

Single look complex

SPEI:

Standardized precipitation evapotranspiration index

SPI:

Standardized precipitation index

SST:

Sensitivity

SVM:

Support vector machine

TOL:

Tolerance

TP:

True positive

VIF:

Variance inflation factor

WQI:

Water quality index

References

  • Abbasi A, Khalili K, Behmanesh J, Shirzad A (2019) Drought monitoring and prediction using SPEI index and gene expression programming model in the west of Urmia Lake. Theor Appl Climatol 138(1):553–567

    Article  Google Scholar 

  • AghaKouchak A, Norouzi H, Madani K, Mirchi A, Azarderakhsh M, Nazemi A, Nasrollahi N, Farahmand A, Mehran A, Hasanzadeh E (2015) Aral Sea syndrome desiccates Lake Urmia: call for action. J Great Lakes Res 41(1):307–311

    Article  Google Scholar 

  • Akramkhanov A, Martius C, Park SJ, Hendrickx JMH (2011) Environmental factors of spatial distribution of soil salinity on flat irrigated terrain. Geoderma 163(1–2):55–62

    Article  Google Scholar 

  • Belayneh A, Adamowski J, Khalil B (2016) Short-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet transforms and machine learning methods. Sustain Water Resour Manag 2(1):87–101

    Article  Google Scholar 

  • Bhatt MJ, Patel AD, Bhatti PM, Pandey AN (2008) Effect of soil salinity on growth, water status and nutrient accumulation in seedlings of Ziziphus mauritiana (RHAMNACEAE). J Fruit Ornam Plant Res 16:383–401

    CAS  Google Scholar 

  • Cao H, Fan F, Zhou K, He Z (2016) Wheel-bearing fault diagnosis of trains using empirical wavelet transform. Measure 82:439–449

    Google Scholar 

  • Cheng Y, Zhang K, Chao L, Shi W, Feng J, Li Y (2023) A comprehensive drought index based on remote sensing data and nested copulas for monitoring meteorological and agroecological droughts: a case study on the Qinghai-Tibet Plateau. Environ Model Softw 161:105629

    Article  Google Scholar 

  • Chung J, Gulcehre C, Cho K, Bengio Y (2015) Gated feedback recurrent neural networks. In: International conference on machine learning, vol 37, pp 2067–2075

  • Dazzi C, Papa GL (2019) Soil genetic erosion: new conceptual developments in soil security. Int Soil Water Conserv Res 7(4):317–324

    Article  Google Scholar 

  • Eimanifar A, Mohebbi F (2007) Urmia Lake (northwest Iran): a brief review. Saline Syst 3(1):1–8

    Article  Google Scholar 

  • Gilles J (2013) Empirical wavelet transform. IEEE Trans Signal Process 61:3999–4010

    Article  Google Scholar 

  • Giordano R, Liersch S (2012) A fuzzy GIS-based system to integrate local and technical knowledge in soil salinity monitoring. Environ Model Softw 36:49–63

    Article  Google Scholar 

  • Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18(5–6):602–610

    Article  Google Scholar 

  • Guo H, Bao A, Liu T, Ndayisaba F, He D, Kurban A, De Maeyer P (2017) Meteorological drought analysis in the Lower Mekong Basin using satellite-based long-term CHIRPS product. Sustainability 9(6):901

    Article  Google Scholar 

  • Guo M, Zhou X, Li J, Wu W, Chen Y (2015) Assessment of the salinization processes in the largest inland freshwater lake of China. Stoch Environ Res Risk 29:1823–1833

    Article  Google Scholar 

  • Habibi M, Babaeian I, Schöner W (2021) Changing causes of drought in the Urmia Lake basin-increasing influence of evaporation and disappearing snow cover. Water 13(22):3273

    Article  Google Scholar 

  • Hamzehpour N, Eghbal MK, Bogaert P, Toomanian N (2014) Top soil salinity prediction in South-Western part of Urmia Lake with ground water data. Int J Agric Innov Res 4(1):57–63

    Google Scholar 

  • Hengl T, Heuvelink G, Perčec Tadić M, Pebesma EJ (2012) Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images. Theor Appl Climatol 107(1):265–277

    Article  Google Scholar 

  • Hosseini-Moghari SM, Araghinejad S, Tourian MJ, Ebrahimi K, Döll P (2020) Quantifying the impacts of human water use and climate variations on recent drying of Lake Urmia basin: the value of different sets of spaceborne and in situ data for calibrating a global hydrological model. Hydrol Earth Syst Sci 24(4):1939–1956

    Article  Google Scholar 

  • IWRM: Iran Water Resources Management Company (2016). http://wrm.ir/

  • Karimzadeh S (2016) Characterization of land subsidence in Tabriz basin (NW Iran) using InSAR and watershed analyses. Acta Geod Geophys 51(2):181–195

    Article  Google Scholar 

  • Karimzadeh S, Matsuoka M (2020) Ground displacement in East Azerbaijan Province, Iran, revealed by L-band and C-band InSAR analyses. Sensors 20(23):6913

    Article  Google Scholar 

  • Khan N, Sachindra DA, Shahid S, Ahmed K, Shiru MS, Nawaz N (2020) Prediction of droughts over Pakistan using machine learning algorithms. Adv Water Resour 139:103562

    Article  Google Scholar 

  • Khorrami B, Ali S, Sahin OG, Gunduz O (2023) Model-coupled GRACE-based analysis of hydrological dynamics of drying lake Urmia and its Basin. Hydrol Process. https://doi.org/10.1002/hyp.14893

    Article  Google Scholar 

  • Kikon A, Deka PC (2022) Artificial intelligence application in drought assessment, monitoring and forecasting: a review. Stoch Environ Res Risk Assess 36:1197–1214

    Article  Google Scholar 

  • Kisi O, Gorgij AD, Zounemat-Kermani M, Mahdavi-Meymand A, Kim S (2019) Drought forecasting using novel heuristic methods in a semi-arid environment. J Hydrol 578:124053

    Article  Google Scholar 

  • Li Y, Chen W (2019) Landslide susceptibility evaluation using hybrid integration of evidential belief function and machine learning techniques. Water 12(1):113

    Article  Google Scholar 

  • Machado RMA, Serralheiro RP (2017) Soil salinity: effect on vegetable crop growth. Management practices to prevent and mitigate soil salinization. Horticulturae 3(2):30

    Article  Google Scholar 

  • Minea I, Iosub M, Boicu D (2022) Multi-scale approach for different type of drought in temperate climatic conditions. Nat Hazards 110(2):1153–1177

    Article  Google Scholar 

  • Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391(1–2):202–216

    Article  Google Scholar 

  • Mukherjee S, Mishra A, Trenberth KE (2018) Climate change and drought: a perspective on drought indices. Curr Clim Change Rep 4(2):145–163

    Article  Google Scholar 

  • Peng T, Zhou J, Zhang C, Fu W (2017) Streamflow forecasting using empirical wavelet transform and artificial neural networks. Water 9(6):406

    Article  Google Scholar 

  • Periasamy S, Ravi KP (2020) A novel approach to quantify soil salinity by simulating the dielectric loss of SAR in three-dimensional density space. Remote Sens Environ 251:112059

    Article  Google Scholar 

  • Phogat V, Mallants D, Cox JW, Šimůnek J, Oliver DP, Awad J (2020) Management of soil salinity associated with irrigation of protected crops. Agric Water Manag 227:105845

    Article  Google Scholar 

  • Rabinovich A, Kelly C, Wilson G, Nasseri M, Ngondya I, Patrick A, Blake WH, Mtei K, Munishi L, Ndakidemi P (2019) We will change whether we want it or not: soil erosion in Maasai land as a social dilemma and a challenge to community resilience. J Environ Psychol 66:101365

    Article  Google Scholar 

  • Seydehmet J, Lv GH, Nurmemet I, Aishan T, Abliz A, Sawut M, Abliz A, Eziz M (2018) Model prediction of secondary soil salinization in the Keriya Oasis, Northwest China. Sustainability 10(3):656

    Article  Google Scholar 

  • Shi H, Hellwich O, Luo G, Chen C, He H, Ochege FU, Van de Voorde T, Kurban A, De Maeyer P (2021) A global meta-analysis of soil salinity prediction integrating satellite remote sensing, soil sampling, and machine learning. IEEE Trans Geosci Remote Sens 60:1–15

    Google Scholar 

  • Xiao C, Chen N, Hu C, Wang K, Gong J, Chen Z (2019) Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach. Remote Sens Environ 233:111358

    Article  Google Scholar 

  • Zare M, Drastig K, Zude-Sasse M (2019) Tree water status in apple orchards measured by means of land surface temperature and vegetation index (LST-NDVI) trapezoidal space derived from Landsat 8 satellite images. Sustainability 12(1):70

    Article  Google Scholar 

  • Zhao X, Chen W (2020) Optimization of computational intelligence models for landslide susceptibility evaluation. Remote Sens 12(14):2180

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by the research Grant of the University of Tabriz (research number: 4877).

Funding

Funding was provided by University of Tabriz.

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Authors and Affiliations

  1. Department of Water Resource Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

    Roghayeh Ghasempour & Mohammad Taghi Aalami

  2. Division of Hydraulics, Department of Civil Engineering, Istanbul Technical University, Istanbul, Turkey

    V. S. Ozgur Kirca

Authors
  1. Roghayeh Ghasempour
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  2. Mohammad Taghi Aalami
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  3. V. S. Ozgur Kirca
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Contributions

RG: Project administration, Investigation, Data Curation, Conceptualization, Methodology, Writing. MTA: Supervision, Conceptualization, Methodology, Review and Editing. VSOK: Conceptualization, Formal analysis, Review and& Editing.

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Correspondence to Roghayeh Ghasempour.

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Ghasempour, R., Aalami, M.T. & Kirca, V.S.O. A multi-criteria remote sensing-based data-driven framework for monitoring lake drying and salinization and mapping its environmental impacts. Stoch Environ Res Risk Assess 37, 4197–4214 (2023). https://doi.org/10.1007/s00477-023-02502-4

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