Agricultural Water Use in China shows Correlation with Climate Change

China’s main use of water is used for agricultural production. In 2005, 64% of water use in China was dedicated to agriculture. Wu et al. (2010) researched the effects of climate change on agricultural water use in China using the Palmer Drought Severity Index (PDSI) and China’s Gross Irrigation Quota (GIQ) to find the relationship between climate change and agricultural water use in the country. The study found that advancements in irrigation technology will be necessary in order to support the growing demand for agricultural water use in China in the face of climate change. Bailey Hedequist
Wu, P., Jin, J., Zhao, X., 2010. Impact of climate change and irrigation technology advancement on agricultural water use in China. Climatic Change 100 797–805.
Pute Wu and colleagues at the National Engineering Research Centre for Water Saving Irrigation at Yangling, People’s Republic of China examined the negative effects of climate change on agricultural water use in China. The study used the Palmer Drought Severity Index (PDSI) to simulate climate change. Data for China’s total irrigated area and quantity of agricultural water used were obtained from the China Compendium of Statistics and the China Water Resources Bulletin 2006 during the period between 1949 and 2004. The Gross Irrigation Quota (GIQ) measures the quantity of agricultural water use in effective irrigation areas. The GIQ allows researchers to know how much water is used annually for irrigation in terms of water quantity per square hectometer (m3/hm2). Finally, PDSI and GIQ data are combined using a statistical regression method to find the relationship between climate change and irrigation.
The study found that during the period between 1949 and 1990, national irrigation supply showed an upward trend. Based on China’s poor advancement in irrigation technology during that period, Wu et al. concluded the increase of agricultural water availability during that period can be attributed to the downward PDSI trend during the same period. Since 1991, irrigation conservation technology has significantly improved in China, which decreases the GIQ. Using a statistical regression method, the GIQ and PDSI data are compared to determine climate change and its effects on irrigation since 1991. The data indicate that there is a significant correlation between climate and irrigation quantity. Without technological advancement to improve irrigation, climate change could have a drastic negative effect on water availability in China.
With a population of 1.3 billion, future dramatic changes in agriculture related to irrigation insufficiency could be extremely problematic for the people of China. As technology continues to advance, future intensity of water consumption could be reduced. Global warming would increase water intensity use by 100% without technological aid. China could face a major water crisis induced by climate change.
Assessment and Future Improvement of Predicting Food Security in the Midst of Climate Change
Climate change models are used by researchers to predict the effects of changes on precipitation and temperature on crop yield around the globe. However, climate change simulations have not fully developed to include other factors such as topographic impacts, natural disasters, pests, weeds, diseases, and general unpredictability of natural processes when assessing the impact of climate change on the world’s food supply. Soussana et al.(2010) evaluated several models and discussed how simulations can be improved to accurately predict future changes in global food security as atmospheric CO2 and global temperatures continue to increase. Bailey Hedequist
Soussanna, J.F, Graux, A.I., Tubiello, F.N., 2010. Improving the use of modeling for projections of climate change impacts on crops and pastures. Journal of Experimental Botany, 1–12.

Jean-Francois Soussana and colleagues at Grassland Ecosystem Research, France reviewed multiple global climate models (GCM’s) and suggested how they could be improved by including variables which will result from changes in precipitation, temperature, soil water stress, abiotic factors, and alterations in microbial interactions. While giving accurate predictions based on weather, temperature, and precipitation, GCM’s may be advanced to include other negative circumstances resulting from climate change, such as weeds and pests, which could significantly transform the globe’s agricultural market. Potential improvements are necessary in order to develop agricultural technology for countries whose main economic resource is agricultural production or those already suffering from hunger and malnourishment.
Included in the study were process-based, generic, global and regional climate change models, and downscaling methods. The authors discuss the importance of the atmosphere-ocean general circulation model (AOGCM) as being the best determiner of greenhouse gas (GHG) predictions. However, this useful type of model is limited in its accuracy regarding topography, random events, and scale resolution. The AOGCM serves as a perfect example of why models need to be improved as the same models are often re-used in different studies. Furthermore, high temperatures, drought, and elevated CO2 complicate projected climate change. Abiotic interactions with CO2 (water, temperature, nutrients, photosynthesis, and the ozone layer) vary from region to region and therefore create a more complex challenge for improving GCMs. Plant species will also have different effects from elevated CO2 exposure based on genotype, management, and environmental shifts. Pests, weeds, and diseases are also expected to increase under climate change but are nevertheless excluded in GCM’s, although their potential impact is great.
The criteria for a good climate change model are not simple, but GCMs need development in several areas in order to predict future changes in food supply with reliable accuracy. The given factors are certainly difficult to determine with complete confidence, but improved implementation of models will greatly further our understanding of the world’s food security in the next century. 

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