Your search found 3 records
1 Li, M.; Li, H.; Fu, Q.; Liu, D.; Yu, L.; Li, T. 2021. Approach for optimizing the water-land-food-energy nexus in agroforestry systems under climate change. Agricultural Systems, 192:103201. [doi: https://doi.org/10.1016/j.agsy.2021.103201]
Water resources ; Land resources ; Food security ; Energy ; Nexus ; Agroforestry systems ; Climate change ; Water allocation ; Water supply ; Water use efficiency ; Irrigation water ; Greenhouse gas emissions ; Carbon footprint ; Sustainable Development Goals ; Models / China / Heilongjiang
(Location: IWMI HQ Call no: e-copy only Record No: H050518)
(6.00 MB)
CONTEXT: Agroforestry systems are widely promoted for their economic and environmental benefits. Food, energy, water and land resources in agroforestry systems are inextricably intertwined and expected to be severely impacted by climate change. Socioeconomic development and increasing populations have posed unique challenges for meeting the demand for food, energy, water and land, and the challenge will become more pressing under projected resource shortages and eco-environmental deterioration. Thus, a method of optimizing and sustainably managing the water-land-food-energy nexus in agroforestry systems under climate change must be developed.
OBJECTIVE: This paper develops an optimization model framework for the sustainable management of limited water-land-food-energy resources in agroforestry systems under climate change. The aims are to (1) quantify the interactions and feedbacks within water, land, food and energy subsystems; (2) provide trade-offs among water and energy utilization efficiency, economic benefits and environmental protection in agroforestry systems; and (3) generate optimal policy options among water and land resources for different crops and woodlands in different regions under different climate change patterns.
METHODS: The model framework is based on multiobjective fractional programming, and compromise programming is used to solve it. Climate change patterns are obtained from atmospheric circulation models and representative concentration pathways. The above aims are investigated through an actual nexus management problem in northeast China. Spatiotemporal meteorological and report-based databases, life cycle assessments, Pearson correlation analyses, data envelopment analyses and analytic hierarchy processes are integrated to realize practical application.
RESULTS AND CONCLUSIONS: The results show that climate variation will change the water and land allocation patterns and these changes will be more pronounced for major grain-producing areas. The optimized water allocation decreased (especially for rice, e.g., the optimal average value of the irrigation quota of rice was 4226 m3/ha, while the corresponding actual irrigation requirement of rice was [4200–7200] m3/ha) to improve the water use efficiency, and surface water allocation accounted for two-thirds. Maize had the largest planting area, although planting soybean generated the most greenhouse gases (greenhouse gas emissions from field activities for rice, maize, and soybean were 43.46%, 84.06% and 91.16%, respectively); However, these gases can be absorbed by forests. The model improved the harmonious degree of the resource-economy-environment system from 0.24 to 0.56 after optimization.
SIGNIFICANCES: Integrated models contribute to the sustainable management of water, food, energy and land resources and can consider the complex dynamics under climate change. It can be used as a general model and extended to other agroforestry systems that show inefficient agricultural production.
OBJECTIVE: This paper develops an optimization model framework for the sustainable management of limited water-land-food-energy resources in agroforestry systems under climate change. The aims are to (1) quantify the interactions and feedbacks within water, land, food and energy subsystems; (2) provide trade-offs among water and energy utilization efficiency, economic benefits and environmental protection in agroforestry systems; and (3) generate optimal policy options among water and land resources for different crops and woodlands in different regions under different climate change patterns.
METHODS: The model framework is based on multiobjective fractional programming, and compromise programming is used to solve it. Climate change patterns are obtained from atmospheric circulation models and representative concentration pathways. The above aims are investigated through an actual nexus management problem in northeast China. Spatiotemporal meteorological and report-based databases, life cycle assessments, Pearson correlation analyses, data envelopment analyses and analytic hierarchy processes are integrated to realize practical application.
RESULTS AND CONCLUSIONS: The results show that climate variation will change the water and land allocation patterns and these changes will be more pronounced for major grain-producing areas. The optimized water allocation decreased (especially for rice, e.g., the optimal average value of the irrigation quota of rice was 4226 m3/ha, while the corresponding actual irrigation requirement of rice was [4200–7200] m3/ha) to improve the water use efficiency, and surface water allocation accounted for two-thirds. Maize had the largest planting area, although planting soybean generated the most greenhouse gases (greenhouse gas emissions from field activities for rice, maize, and soybean were 43.46%, 84.06% and 91.16%, respectively); However, these gases can be absorbed by forests. The model improved the harmonious degree of the resource-economy-environment system from 0.24 to 0.56 after optimization.
SIGNIFICANCES: Integrated models contribute to the sustainable management of water, food, energy and land resources and can consider the complex dynamics under climate change. It can be used as a general model and extended to other agroforestry systems that show inefficient agricultural production.
2 Ali, S.; Liu, D.; Fu, Q.; Cheema, M. J. M.; Pham, Q. B.; Rahaman, Md. M.; Dang, T. D.; Anh, D. T. 2021. Improving the resolution of GRACE data for spatio-temporal groundwater storage assessment. Remote Sensing, 13(17):3513. (Special Issue: Remote Sensing and Modelling of Water Storage Dynamics from Bedrock to Atmosphere) [doi: https://doi.org/10.3390/rs13173513]
Groundwater assessment ; Water storage ; Irrigation systems ; Aquifers ; Groundwater table ; Soil moisture ; Evapotranspiration ; Runoff ; Models ; Satellites ; Neural networks / Pakistan / Sindh / Punjab / Indus Basin Irrigation System
(Location: IWMI HQ Call no: e-copy only Record No: H050649)
(9.12 MB) (9.12 MB)
Groundwater has a significant contribution to water storage and is considered to be one of the sources for agricultural irrigation; industrial; and domestic water use. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a unique opportunity to evaluate terrestrial water storage (TWS) and groundwater storage (GWS) at a large spatial scale. However; the coarse resolution of GRACE limits its ability to investigate the water storage change at a small scale. It is; therefore; needed to improve the resolution of GRACE data at a spatial scale applicable for regional-level studies. In this study; a machine-learning-based downscaling random forest model (RFM) and artificial neural network (ANN) model were developed to downscale GRACE data (TWS and GWS) from 1° to a higher resolution (0.25°). The spatial maps of downscaled TWS and GWS were generated over the Indus basin irrigation system (IBIS). Variations in TWS of GRACE in combination with geospatial variables; including digital elevation model (DEM), slope; aspect; and hydrological variables; including soil moisture; evapotranspiration; rainfall; surface runoff; canopy water; and temperature; were used. The geospatial and hydrological variables could potentially contribute to; or correlate with; GRACE TWS. The RFM outperformed the ANN model and results show Pearson correlation coefficient (R) (0.97), root mean square error (RMSE) (11.83 mm), mean absolute error (MAE) (7.71 mm), and Nash–Sutcliffe efficiency (NSE) (0.94) while comparing with the training dataset from 2003 to 2016. These results indicate the suitability of RFM to downscale GRACE data at a regional scale. The downscaled GWS data were analyzed; and we observed that the region has lost GWS of about -9.54 ± 1.27 km3 at the rate of -0.68 ± 0.09 km3/year from 2003 to 2016. The validation results showed that R between downscaled GWS and observational wells GWS are 0.67 and 0.77 at seasonal and annual scales with a confidence level of 95%, respectively. It can; therefore; be concluded that the RFM has the potential to downscale GRACE data at a spatial scale suitable to predict GWS at regional scales.
3 Li, M.; Cao, X.; Liu, D.; Fu, Q.; Li, T.; Shang, R. 2021. Sustainable management of agricultural water and land resources under changing climate and socio-economic conditions: a multi-dimensional optimization approach. Agricultural Water Management, 259:107235. (Online first) [doi: https://doi.org/10.1016/j.agwat.2021.107235]
Agricultural water use ; Water management ; Land resources ; Climate change ; Socioeconomic aspects ; Sustainable development ; Water security ; Water supply ; Water demand ; Water allocation ; Surface water ; Irrigation water ; Water footprint ; Decision making ; Economic development ; Models / China / Songhua River Basin / Heilongjiang / Harbin / Hegang / Shuangyashan / Yichun / Jiamusi / Qitaihe / Mudanjiang / Suihua
(Location: IWMI HQ Call no: e-copy only Record No: H050756)
(5.27 MB)
Conflict between limited water supply and the ever-increasing water demand poses the challenge of synergetic management of agricultural water and land resources (AWLR). Sustainable development strategy and changing environment increase the multi-dimensional characteristic and complexity of the management of AWLR. This paper establishes a model framework for the multi-dimensional optimization of AWLR in a changing environment. The model framework is advantageous of: (1) Comprehensively allocating water and land resources on the basis of clarifying their interactions; (2) Balancing incompatible goals from multiple dimensions including resources, society, economy, ecology, and environment; (3) proposing alternative allocation schemes of AWLR that can response to the changing environment of both natural and socio-economic changes. Allocation schemes of AWLR based on the model framework are generated, analyzed and evaluated. The comprehensiveness, equilibrium, and security of multi-dimensional targets help obtain the optimum adaptation allocation plans of AWLR to cope with changing environment. The real-world case study in Songhua River Basin in Northeast China verifies the feasibility and practicality of the model framework. The study found that the model framework can manage AWLR in a sustainable way and meanwhile provide decision makers alternatives plans of AWLR for different natural and social changing environments, which will further contribute to the alleviation of agricultural water scarcity and the promotion of agricultural sustainable development.
Powered by DB/Text WebPublisher, from
