Managing Agricultural Water Considering Water Allocation Priority Based on Remote Sensing Data

Remote Sensing - Tập 13 Số 8 - Trang 1536
Biao Luo1, Fan Zhang2, Xiao Liu3, Qi Pan1, Ping Guo1
1Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China
2State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
3State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China

Tóm tắt

To fairly distribute limited irrigation water resources in arid regions, a water allocation priority evaluation method based on remote sensing data was proposed and integrated with an optimization model. First, the water supply response unit was divided according to canal system conditions. Then, a spatialization method was used for generating spatial agricultural output value (income from planting industry) and grain yield (yield of food crops) with the help of NDVI and the potential yield of farmland. Third, the AHP-TOPSIS method was employed to calculate the water allocation priority based on the above information. Finally, the evaluation results were integrated with a nonlinear multiobjective model to optimally allocate agricultural land and water resources, considering the combined objective of minimum envy and proportional fairness. The method was applied to Hetao irrigation area, an arid agriculture-dominant region in Northwest China. After solving the model, optimization alternatives were obtained, which indicate that: (1) the spatial method of agricultural output value can improve the accuracy by around 16% compared with the traditional method, and the spatial method of grain yield also have good accuracy (MAPE = 14.66%); (2) the rank of water allocation priority can reflect more spatial information, and provide practical decision support for the distribution of water resources; (3) the envy index can better improve the efficiency of an allocation system compared to the Gini coefficient method.

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Tài liệu tham khảo

Gong, 2020, Optimization allocation of irrigation water resources based on crop water requirement under considering effective precipitation and uncertainty, Agric. Water Manag., 239, 106264, 10.1016/j.agwat.2020.106264

Saccon, 2018, Water for agriculture, irrigation management, Appl. Soil Ecol., 123, 793, 10.1016/j.apsoil.2017.10.037

Li, M., Fu, Q., Singh, V.P., Liu, D., Li, T., and Zhou, Y. (2020). Managing agricultural water and land resources with tradeoff between economic, environmental, and social considerations: A multi-objective non-linear optimization model under uncertainty. Agric. Syst., 178.

RazaviToosi, 2016, Evaluating water management strategies in watersheds by new hybrid Fuzzy Analytical Network Process (FANP) methods, J. Hydrol., 534, 364, 10.1016/j.jhydrol.2016.01.006

Karnib, 2004, An approach to elaborate priority preorders of water resources projects based on multi-criteria evaluation and fuzzy sets analysis, Water Resour. Manag., 18, 13, 10.1023/B:WARM.0000015347.87047.5b

Kundu, 2017, Landuse change impact on sub-watersheds prioritization by analytical hierarchy process (AHP), Ecol. Inform., 42, 100, 10.1016/j.ecoinf.2017.10.007

Jaiswal, 2015, Fuzzy AHP Based Multi Crteria Decision Support for Watershed Prioritization, Water Resour. Manag., 29, 4205, 10.1007/s11269-015-1054-3

Karatayev, 2017, Priorities and challenges for a sustainable management of water resources in Kazakhstan, Sustain. Water Qual. Ecol., 9–10, 115, 10.1016/j.swaqe.2017.09.002

RazaviToosi, 2019, A Fuzzy Group Decision Making Framework Based on ISM-FANP-FTOPSIS for Evaluating Watershed Management Strategies, Water Resour. Manag., 33, 5169, 10.1007/s11269-019-02423-4

Gómez-Limón, J.A., Gutiérrez-Martín, C., and Montilla-López, N.M. (2020). Agricultural water allocation under cyclical scarcity: The role of priority water rights. Water, 12.

Li, 2020, The improved bankruptcy method and its application in regional water resource allocation, J. Hydro-Environ. Res., 28, 48, 10.1016/j.jher.2018.07.003

Zhang, 2019, A bi-level multiobjective stochastic approach for supporting environment-friendly agricultural planting strategy formulation, Sci. Total Environ., 693, 133593, 10.1016/j.scitotenv.2019.133593

Christakos, 2017, Space-time metric determination in environmental modeling, J. Environ. Inform., 30, 29

Tang, 2020, Grid-scale agricultural land and water management: A remote-sensing-based multiobjective approach, J. Clean. Prod., 265, 121792, 10.1016/j.jclepro.2020.121792

Tang, 2019, A distributed interval nonlinear multiobjective programming approach for optimal irrigation water management in an arid area, Agric. Water Manag., 220, 13, 10.1016/j.agwat.2019.03.052

Tian, 2020, Spatial-temporal dynamics of cropland ecosystem water-use efficiency and the responses to agricultural water management in the Shiyang River Basin, northwestern China, Agric. Water Manag., 237, 106176, 10.1016/j.agwat.2020.106176

Chen, 2016, Improved GDP spatialization approach by combining land-use data and night-time light data: A case study in China’s continental coastal area, Int. J. Remote Sens., 37, 4610, 10.1080/01431161.2016.1217440

Liang, 2020, GDP spatialization in Ningbo City based on NPP/VIIRS night-time light and auxiliary data using random forest regression, Adv. Space Res., 65, 481, 10.1016/j.asr.2019.09.035

Zhao, M., Cheng, W., Zhou, C., Li, M., Wang, N., and Liu, Q. (2017). GDP spatialization and economic differences in South China based on NPP-VIIRS nighttime light imagery. Remote Sens., 9.

Sutton, 1997, Modeling population density with night-time satellite imagery and GIS, Comput. Environ. Urban Syst., 21, 227, 10.1016/S0198-9715(97)01005-3

Xiao, 2019, A spatialization method for grain yield statistical data: A study on winter wheat of Shandong Province, China, Agron. J., 111, 1892, 10.2134/agronj2018.09.0555

Li, 2019, Stochastic multi-objective decision making for sustainable irrigation in a changing environment, J. Clean. Prod., 223, 928, 10.1016/j.jclepro.2019.03.183

Zhang, Y., Zhang, F., Zhu, H., and Guo, P. (2019). An Optimization-Evaluation Agricultural Water Planning Approach Based on Interval Linear Fractional Bi-Level Programming and IAHP-TOPSIS. Water, 11.

Ahmad, I., Zhang, F., Liu, J., Anjum, M.N., Zaman, M., Tayyab, M., Waseem, M., and Farid, H.U. (2018). A linear bi-level multi-objective program for optimal allocation of water resources. PLoS ONE, 13.

Zhang, 2018, An inexact robust two-stage mixed-integer linear programming approach for crop area planning under uncertainty, J. Clean. Prod., 204, 489, 10.1016/j.jclepro.2018.09.005

Hu, 2016, A multi-objective optimization model with conditional value-at-risk constraints for water allocation equality, J. Hydrol., 542, 330, 10.1016/j.jhydrol.2016.09.012

Chanta, 2011, The minimum p-envy location problem: A new model for equitable distribution of emergency resources, IIE Trans. Healthc. Syst. Eng., 1, 101, 10.1080/19488300.2011.609522

Espejo, 2009, A comparison of formulations and solution methods for the minimum-envy location problem, Comput. Oper. Res., 36, 1966, 10.1016/j.cor.2008.06.013

Chanta, 2014, The minimum p-envy location problem with requirement on minimum survival rate, Comput. Ind. Eng., 74, 228, 10.1016/j.cie.2014.06.001

Liu, 2014, Assessing the impact of urban expansion on potential crop yield in China during 1990–2010, Food Secur., 7, 33, 10.1007/s12571-014-0411-z

Lewis, 1998, Estimating maize production in Kenya using NDVI: Some statistical considerations, Int. J. Remote Sens., 19, 2609, 10.1080/014311698214677

Steininger, 2000, Satellite estimation of tropical secondary forest above-ground biomass: Data from Brazil and Bolivia, Int. J. Remote Sens., 21, 1139, 10.1080/014311600210119

Zheng, 2004, Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA, Remote Sens. Environ., 93, 402, 10.1016/j.rse.2004.08.008

Ma, 2014, Diverse relationships between Suomi-NPP VIIRS night-time light and multi-scale socioeconomic activity, Remote Sens. Lett., 5, 652, 10.1080/2150704X.2014.953263

Shi, 2015, Modeling and mapping total freight traffic in China using NPP-VIIRS nighttime light composite data, GISci. Remote Sens., 52, 274, 10.1080/15481603.2015.1022420

Ma, 2014, Responses of Suomi-NPP VIIRS-derived nighttime lights to socioeconomic activity in Chinas cities, Remote Sens. Lett., 5, 165, 10.1080/2150704X.2014.890758

Osten, 1988, Selection of optimal regression models via cross-validation, J. Chemom., 2, 39, 10.1002/cem.1180020106

Cao, 2014, Spatialization of electricity consumption of China using saturation-corrected DMSP-OLS data, Int. J. Appl. Earth Obs. Geoinf., 28, 193

Ma, 2020, Gdp spatialization and dynamic analysis based on dmsp/ols images: A case of henan province, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Proceedings of the 2020 International Conference on Geomatics in the Big Data Era (ICGBD), Guilin, China, 15–17 November 2019, Volume XLII-3/W10, 783

Gao, 2018, Shallow groundwater plays an important role in enhancing irrigation water productivity in an arid area: The perspective from a regional agricultural hydrology simulation, Agric. Water Manag., 208, 43, 10.1016/j.agwat.2018.06.009

Barrios, 2016, An AHP-topsis integrated model for selecting the most appropriate tomography equipment, Int. J. Inf. Technol. Decis. Mak., 15, 861, 10.1142/S021962201640006X

Qin, 2008, A MCDM-based expert system for climate-change impact assessment and adaptation planning—A case study for the Georgia Basin, Canada, Expert Syst. Appl., 34, 2164, 10.1016/j.eswa.2007.02.024

Westerhoff, 2015, Using uncertainty of Penman and Penman–Monteith methods in combined satellite and ground-based evapotranspiration estimates, Remote Sens. Environ., 169, 102, 10.1016/j.rse.2015.07.021

Kozlowski, T. (1968). Water Deficit and Plant Growth, Academic Press.

Xue, 2018, Untangling the effects of shallow groundwater and deficit irrigation on irrigation water productivity in arid region: New conceptual model, Sci. Total Environ., 619–620, 1170, 10.1016/j.scitotenv.2017.11.145

Uchida, M., and Kurose, J. (2009, January 19–25). An Information-Theoretic Characterization of Weighted alpha-Proportional Fairness. Proceedings of the IEEE INFOCOM 2009—The 28th Conference on Computer Communications, Rio de Janeiro, Brazil.

Li, 2014, A multi-objective optimal allocation model for irrigation water resources under multiple uncertainties, Appl. Math. Model., 38, 4897, 10.1016/j.apm.2014.03.043

Sun, 2017, Sensitivity of crop water productivity to the variation of agricultural and climatic factors: A study of Hetao irrigation district, China, J. Clean. Prod., 142, 2562, 10.1016/j.jclepro.2016.11.020

Sun, 2013, The impacts of interannual climate variability and agricultural inputs on water footprint of crop production in an irrigation district of China, Sci. Total Environ., 444, 498, 10.1016/j.scitotenv.2012.12.016

Liu, 2012, Spatial distribution of China grain output based on land use and population density, Trans. Chin. Soc. Agric. Eng., 28, 1

Zhang, 2021, Optimal irrigation water allocation in Hetao Irrigation District considering decision makers’ preference under uncertainties, Agric. Water Manag., 246, 106670, 10.1016/j.agwat.2020.106670

He, M., Kimball, J.S., Maneta, M.P., Maxwell, B.D., Moreno, A., Beguería, S., and Wu, X. (2018). Regional crop gross primary productivity and yield estimation using fused landsat-MODIS data. Remote Sens., 10.

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Remote Sensing

Tập 13 Số 8

1536

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