Adham, S., Hussain, A., Minier-Matar, J., Janson, A., & Sharma, R. (2018). Membrane applications and opportunities for water management in the oil & gas industry. Desalination, 440, 2-17. Web.
The importance of water management and its application in the oil industry is the primary focus of Adham et al. (2018) in this article. The oil and gas industry depends much on water management. According to Adham et al. (2018), advanced methods are needed for water treatment to eliminate the present challenges. Membrane bioreactor processes are examined as a viable technological tool that can potentially impact water treatment. This article is crucial in understanding water management principles and their implications on the oil and gas industry.
Azhoni, A., Jude, S., & Holman, I. (2018). Adapting to climate change by water management organizations: Enablers and barriers. Journal of Hydrology, 559, 736-748. Web.
In this article, Azhoni et al. (2018) address climate change in line with water shortage. Water resource management has been affected by various enablers and barriers. The article analyses literature in line with water management programs. This research facilitates a deeper understanding of past and current management issues. The importance of this article is promoting the development of future multi-scale management programs spanning across many organizations.
Datta, A., Ullah, H., & Ferdous, Z. (2017). Water management in rice. In Chauhan, B., Jabran, K., & Mahajan, G. (eds) Rice Production Worldwide. Springer, Cham. Web.
The article by Datta et al. (2017) discusses the challenges of rice production brought about by water shortage. According to the authors, rice production requires plenty of water ( Datta et al., 2017). Due to water shortages in many countries, there is a need for improved rice production methods. The methods highlighted facilitate water conservation, thus alleviating the water challenges. This article is essential in developing viable solutions for agricultural production and water resource management.
Eggimann, S., Mutzner, L., Wani, O., Schneider, M., Spuhler, D., Moy de Vitry, M., Beutler, P., & Maurer, M. (2017). The potential of knowing more: a review of data-driven urban water management. Environmental Science & Technology, 51(5), 2538-2553. Web.
Eggimann et al. (2017) show the impacts of utilizing large volumes of data in urban water management (UWM). The role of data-driven UWM in facilitating sea change is addressed in the article. According to Eggimann et al. (2017), the future of UWM lies in the collection, analysis, and utilization of data. This article is relevant in the current efforts for water management in many urban applications. This information is crucial for integrating network-based and novel technological solutions in water management.
Kamienski, C., Soininen, J., Taumberger, M., Dantas, R., Toscano, A., Salmon Cinotti, T., Maia, R.F., & Neto, A.T. (2019). Smart water management platform: IoT-based precision irrigation for agriculture. Sensors, 19(2), 276. Web.
Agriculture is one of the sectors that should be considered about water resources management, according to Kamienski et al. (2019). This article addresses the need for precision irrigation through smart water management. The development of intelligent water management techniques depends highly on IoT applications. The authors discuss a SWAMP technique that presents a practical solution to water management issues related to agriculture. This article is essential because the techniques and processes highlighted here provide a solution for water management globally with a particular focus on the agricultural sectors.
Nazari, B., Liaghat, A., Akbari, M., & Keshavarz, M. (2018). Irrigation water management in Iran: Implications for water use efficiency improvement. Agricultural Water Management, 208, 7-18. Web.
Water management depends on its utilization in the various sectors of the economy, as highlighted by Nazari et al. (2018). In this article, the issue of irrigation and its impact on water management are discussed. The authors show that Iran’s water shortage can be attributed to inefficiency in irrigation systems. The role of political, legal, social, and technological changes in water use have been elaborated. This article is crucial in understanding the root causes of water problems and how to mitigate them.
Neupane, J., & Guo, W. (2019). Agronomic basis and strategies for precision water management: A review. Agronomy, 9(2), 87. Web.
Jasmine and Guo (2019) argue that agriculture faces an enormous challenge of feeding many people with limited resources available for production. The article illustrates that site-specific solutions of irrigation present the solution to food production challenges. The primary focus is placed on variable-rate irrigation (VRI) techniques and their implications on agriculture. This article is crucial in understanding the current technological solution for resource management and sustainable production. This information can be applied in many countries to increase food production while utilizing scarce water resources.
Novoa, V., Ahumada-Rudolph, R., Rojas, O., Sáez, K., de la Barrera, F., & ArumĂ, J. (2019). Understanding agricultural water footprint variability to improve water management in Chile. Science of The Total Environment, 670, 188-199. Web.
The article by Novoa et al. (2019) establishes a connection between agriculture, climate variability, and water management. The authors assess the water footprint of the Cachapoal River agricultural basin (34°S 71°W). In their analysis, Novoa et al. (2019) show that regions with high variability of water availability require formulating and enforcing policies on water management. Sustainable water management begins by understanding the water consumption and variability data. This article is relevant because it addresses the root causes of water management challenges and the viable solutions.
Sun, A., & Scanlon, B. (2019). How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions. Environmental Research Letters, 14(7), 073001. Web.
Sun and Scanlon (2019) discuss how machine learning and Big Data could impact environmental and water management practices. Machine learning has become increasingly important in the deployment of SMART solutions in many sectors. Environmental and water management can benefit from Big Data and machine learning by applying remote sensing and information systems. Increased data availability is crucial in the development of practical solutions to present and future water utilization. This article is relevant to water management because it incorporates technology in finding long-lasting solutions to resource management challenges
Zhupankhan, A., Tussupova, K., & Berndtsson, R. (2018). Water in Kazakhstan, a key in Central Asian water management. Hydrological Sciences Journal, 63(5), 752-762. Web.
Zhupankhan et al. (2018) have addressed the issues pertaining to water management. Their article discusses water management policies in central Asia with a particular focus on Kazakhstan. The article discusses how establishing legal frameworks for water management, and inter-boundary cooperation can alleviate the water deficiency in Central Asia. This article is important because it highlights some viable solutions to water management problems around the globe. The concepts addressed in this article apply to any other country or region.
References
Adham, S., Hussain, A., Minier-Matar, J., Janson, A., & Sharma, R. (2018). Membrane applications and opportunities for water management in the oil & gas industry. Desalination, 440, 2-17. Web.
Azhoni, A., Jude, S., & Holman, I. (2018). Adapting to climate change by water management organizations: Enablers and barriers. Journal of Hydrology, 559, 736-748. Web.
Datta, A., Ullah, H., & Ferdous, Z. (2017). Water management in rice. In Chauhan, B., Jabran, K., & Mahajan, G. (eds) Rice Production Worldwide. Springer, Cham. Web.
Eggimann, S., Mutzner, L., Wani, O., Schneider, M., Spuhler, D., Moy de Vitry, M., Beutler, P., & Maurer, M. (2017). The potential of knowing more: a review of data-driven urban water management. Environmental Science & Technology, 51(5), 2538-2553. Web.
Kamienski, C., Soininen, J., Taumberger, M., Dantas, R., Toscano, A., Salmon Cinotti, T., Maia, R.F., & Neto, A.T. (2019). Smart water management platform: IoT-based precision irrigation for agriculture. Sensors, 19(2), 276. Web.
Nazari, B., Liaghat, A., Akbari, M., & Keshavarz, M. (2018). Irrigation water management in Iran: Implications for water use efficiency improvement. Agricultural Water Management, 208, 7-18. Web.
Neupane, J., & Guo, W. (2019). Agronomic basis and strategies for precision water management: A review. Agronomy, 9(2), 87. Web.
Novoa, V., Ahumada-Rudolph, R., Rojas, O., Sáez, K., de la Barrera, F., & ArumĂ, J. (2019). Understanding agricultural water footprint variability to improve water management in Chile. Science of The Total Environment, 670, 188-199. Web.
Sun, A., & Scanlon, B. (2019). How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions. Environmental Research Letters, 14(7), 073001. Web.
Zhupankhan, A., Tussupova, K., & Berndtsson, R. (2018). Water in Kazakhstan, a key in Central Asian water management. Hydrological Sciences Journal, 63(5), 752-762. Web.