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An integrated approach for agricultural water resources management under drought with consideration of multiple uncertainties

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Abstract

Interactions across meteorological drought, agricultural drought and irrigation enlarge complexities of simulating and forecasting crop yield under droughts. Besides, contradictory relationships amid multiple objectives considering respective critical thresholds aggravate difficulties in agricultural water resources optimization. To address the above problems, this paper developed a framework of random forest based multi-objective multi-critical control programming with fuzzy boundary intervals (RFMOMCCPFBI) model for agricultural water resources optimization. The MOMCCP is established based on the MOP approach, and it improves upon the MOP in giving lower and upper thresholds of each objective, promising achievement of subjectivities of managers. Moreover, the model enhances the advantages of MCCP in reflecting multiple variations and uncertainties of thresholds by introducing fuzzy boundary intervals. In addition, the crop yield is simulated by random forest approach, which improves calculation efficiency as well as reflect influences of multiple interactive drought and irrigation on crop yield. The RFMOMCCPFBI model is used to Yingke irrigation district to allocate water resources to multiple crops at different growth stages to verify its applications. The results disclose that frequencies of meteorological drought are around 27%, 19% at five growth stages, higher than agricultural drought in future year 2022 to year 2047. Meteorological drought has many times of abrupt changes without significance. Agricultural water-saving potential increases about 20% considering demands of acceptable thresholds of objectives. Water saving potential enlarges with increase of tolerance levels of objective deviations. The model can provide alternatives for managers based on their preferences on objectives.

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References

  • Ali Z, Hussain I, Nazeer A (2020) Measuring and restructuring the risk in forecasting drought classes: an application of weighted Markov chain based model for standardized precipitation evapotranspiration index (SPEI) at one-month time scale. Tell Ser A Dyn Meteorol Oceanogr 72(1):1–10

    Google Scholar 

  • Anderson MC, Zolin CA, Sentelhas PC et al (2016) The evaporative stress index as an indicator of agricultural drought in Brazil: an assessment based on crop yield impacts. Remote Sens Environ 174:82–99

    Article  Google Scholar 

  • Bachmair S, Tanguy M, Hannaford J, Stahl K (2018) How well do meteorological indicators represent agricultural and forest drought across Europe? Environ Res Lett 13(3):034042

    Article  Google Scholar 

  • Dai M, Huang SZ, Huang Q et al (2020) Assessing agricultural drought risk and its dynamic evolution characteristics. Agric Water Manag 231:106003

    Article  Google Scholar 

  • Deb P, Moradkhani H (2022) Assessing irrigation mitigating drought impacts on crop yields with an integrated modeling framework. J Hydrol 609:127760

    Article  Google Scholar 

  • Elisabeth V, Markus GD, Lisa VA et al (2019) The effects of climate extremes on global agricultural yields. Environ Res Lett 14:054010

    Article  Google Scholar 

  • Faiz MA, Zhang YQ, Zhang XZ et al (2022) A composite drought index developed for detecting large-scale drought characteristics. J Hydrol 605:127308

    Article  Google Scholar 

  • Feng PY, Wang B, Liu DL et al (2019) Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia. Agric Syst 173:303–316

    Article  Google Scholar 

  • Fu Q, Zhu C, Jiang Q, Guo H, Zhao K (2015) Water resource management based on trade-off analysis of multi-dimensional critical regulation and control indicators. Water Sci Technol Water Supply 15(3):552–558

    Article  Google Scholar 

  • Guo E, Liu X, Zhang J, Wang Y, Wang C, Wang R, Li D (2017) Assessing spatiotemporal variation of drought and its impact on maize yield in Northeast China. J Hydrol 553:231–247

    Article  Google Scholar 

  • Guo S, Wang J, Zhang F, Wang Y, Guo P (2018) An integrated water-saving and quality-guarantee uncertain programming approach for the optimal irrigation scheduling of seed maize in arid regions. Water 10(7):908

    Article  Google Scholar 

  • Huang JX, Zhuo W, Li Y (2020) Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield. Int J Digital Earth 13(4):504–526

    Article  Google Scholar 

  • Leng GY, Hall J (2019) Crop yield sensitivity of global major agricultural countries to droughts and the projected changes in the future. Sci Total Environ 654:811–821

    Article  CAS  Google Scholar 

  • Li M, Guo P, Zhang L, Zhao J (2015) Multi-dimensional critical regulation control modes and water optimal allocation for irrigation system in the middle reaches of Heihe River basin, China. Ecol Eng 76:166–177

    Article  Google Scholar 

  • Li M, Fu Q, Singh VP et al (2020a) Managing agricultural water and land resources with tradeoff between economic, environmental, and social considerations: A multi-objective nonlinear optimization model under uncertainty. Agric Syst 178:102685

    Article  Google Scholar 

  • Li X, Wang X, Guo H, Ma W (2020b) Multi-water resources optimal allocation based on multi-objective uncertain chance-constrained programming model. Water Res Manag 34(15):4881–4899

    Article  Google Scholar 

  • Li Y, Wang CL, Li GQ (2021) Optimal scheduling of integrated demand response-enabled integrated energy systems with uncertain renewable generations: a Stackelberg game approach. Energy Convers Manage 235:113996

    Article  Google Scholar 

  • Li PF, Ma BL, Palta JA (2022) Distinct contributions of drought avoidance and drought tolerance to yield improvement in dryland wheat cropping. J Agron Crop Sci 208(3):265–282

    Article  CAS  Google Scholar 

  • Liu YX, Heuvelink GB, Bai ZG (2020) Analysis of spatio-temporal variation of crop yield in China using stepwise multiple linear regression. Field Crop Res 264:108098

    Article  Google Scholar 

  • Lü H, Yang K, Huang X, Yin H (2021) Design optimization of hybrid uncertain structures with fuzzy-boundary interval variables. Int J Mech Mater Des 17(1):201–224

    Article  Google Scholar 

  • Mardhel V, Pinson S, Allier D (2021) Description of an indirect method (IDPR) to determine spatial distribution of infiltration and runoff and its hydrogeological applications to the French territory. J Hydrol 592:125609

    Article  Google Scholar 

  • Naghibi SA, Ahmadi K, Daneshi A (2017) Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resour Manage 31:2761–2775

    Article  Google Scholar 

  • Najafzadeh M, Niazmardi S (2021) A novel multiple-kernel support vector regression algorithm for estimation of water quality parameters. Nat Resour Res 30(5):3761–3775

    Article  CAS  Google Scholar 

  • Nematian J, Movahhed SR (2019) An extended multi-objective mixed integer programming for water resources management through possibility theory. Ecol Inform 54:100992

    Article  Google Scholar 

  • Qu G, Zhou H, Qu W, Li C (2017) Shapley interval-valued dual hesitant fuzzy Choquet integral aggregation operators in multiple attribute decision making. J Intell Fuzzy Syst 34(3):1827–1845

    Article  Google Scholar 

  • Sergio M, Vicente S, Santiago B et al (2012) Performance of drought indices for ecological, agricultural, and hydrological applications. Earth Interact 16:1–26

    Article  Google Scholar 

  • Sherafatpour Z, Roozbahani A, Hasani Y (2019) Agricultural water allocation by integration of hydro-economic modeling with Bayesian networks and random forest approaches. Water Resour Manage 33:2277–2299

    Article  Google Scholar 

  • Sun L, Mitchell SW, Davidson A (2012) Multiple drought indices for agricultural drought risk assessment on the Canadian prairies. Int J Climatol 32(11):1628–1639

    Article  Google Scholar 

  • Vergni L, Todisco F, Mannocchi F (2015) Analysis of agricultural drought characteristics through a two-dimensional copula. Water Resour Manage 29:2819–2835

    Article  Google Scholar 

  • Wang G, Xiao CL, Qi ZW (2020) Water resource carrying capacity based on water demand prediction in Chang-Ji economic circle. Water 13:16

    Article  CAS  Google Scholar 

  • Wilson TG, Kustas WP, Alfieri JG, Anderson MC, Gao F, Prueger JH, Alstad KP (2020) Relationships between soil water content, evapotranspiration, and irrigation measurements in a California drip-irrigated Pinot noir vineyard. Agric Water Manage 237:106186

    Article  Google Scholar 

  • Zarei AR, Mahmoudi MR, Shabani A (2021) Investigating of the climatic parameters effectiveness rate on barley water requirement using the random forest algorithm, Bayesian multiple linear regression and cross-correlation function. Paddy Water Environ 19(1):137–148

    Article  Google Scholar 

  • Zhou KK, Li JZ, Zhang T et al (2021) The use of combined soil moisture data to characterize agricultural drought conditions and the relationship among different drought types in China. Agric Water Manag 243:106479

    Article  Google Scholar 

Download references

Funding

This study was financially supported by Science and Technology Development Fund of the Yellow River Institute of Hydraulic Research (202209), and National Natural Science Foundation of China (No.51909099, NO.42041007).

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Contributions

YW: Conceptualization, methodology, writing-original draft. XZ: data curation, formal analysis. YJ: software. JH: visualization, validation. XG: writing-review & editing. QL: supervision, funding acquisition.

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Correspondence to Qiangkun Li.

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Wang, Y., Zhang, X., Jia, Y. et al. An integrated approach for agricultural water resources management under drought with consideration of multiple uncertainties. Stoch Environ Res Risk Assess 37, 1763–1775 (2023). https://doi.org/10.1007/s00477-022-02364-2

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