Evaluation of Ensemble Climate Model development methods based on CMIP5 to investigate the potential of water harvesting from air humidity

Document Type : Research Paper


1 Water sciences and engineering, department, faculty of agricultural and natural resources. Imam Khomeini international university, Qazvin, Iran.

2 Department of Water Engineering, Faculty of Agriculture and Natural Resources, Imam Khomeini International University, Qazvin, Iran.


Recognizing the effects of climate change in different sectors, as well as the integration of GCM models and the development of Ensemble Climate Models (ECM) are vital. In this study, the efficacy of the climate models from the CMIP5 in simulating atmospheric variables impacting the potential for water harvesting was assessed. These variables encompass mean air temperature, wind speed, relative humidity, and the feasible quantity of water harvested from air moisture. Also, assessing the efficiency of the optimization algorithm (Genetic Algorithm) in the development of an ensemble climate model was another goal of this research. It is noteworthy that the present investigation employed data from 16 synoptic stations situated in the northern and northwestern regions of Iran during the statistical period of 1991-2005. Results indicated that the performance of individual climate models in simulating variations in wind speed and relative air humidity is deemed poorly. Conversely, GA has yielded a reduction in both error magnitude and biases in climatic outputs in estimating wind speed and relative air humidity. Furthermore, the evaluation of the efficacy of climate models in estimating the water harvesting potential from air humidity indicates the acceptable performance of ECM in simulating changes in the amount of extractable water from air humidity. In general, the results showed that Manjil and Bandar-Anzali stations are the most suitable areas for the implementation of water harvesting projects from air humidity. Conversely, Arak and Hamedan stations exhibit the least potential for water harvesting. Based on the results, the average water that can be extracted from air humidity in the summer season for Manjil and Bandar-Anzali stations is estimated to be 1.56 and 1.78 (l/day.m2). Also, the seasonal changes of water harvesting potential from air humidity showed that the potential of extracting water in summer is more than the other seasons. This accentuates the urgency of water resource management and agricultural planning, prompting the implementation of substantial measures to use this water source. The potential applications of using this source encompass agricultural sectors, green space irrigation, and potentially catering to a portion of drinking water demands, contingent upon quantity and quality parameters.


Main Subjects

Evaluation of Ensemble Climate Model development methods based on CMIP5 to investigate the potential of water harvesting from air humidity




The efficient utilization of GCMs for simulating atmospheric variables holds crucial significance in water resources planning. The combination of multiple climate models can present an avenue to mitigate the inherent uncertainties associated with these models. However, a pivotal concern revolves around the combination and weighting methodologies for each model within this context. Simultaneously, exploring novel water sources such as air humidity extraction emerges as a viable strategy to mitigate the adverse impacts of water scarcity and drought, especially in arid and semi-arid regions. Notably, alterations in atmospheric variables like air humidity, wind speed, and temperature exert a direct influence on the potential for water harvesting from the air, a process susceptible to climate change dynamics. In this context, this study aims to evaluate the effectiveness of two distinct approaches in developing ensemble climate models, against the utilization of individual models. This evaluation extends to the simulation of atmospheric variables encompassing wind speed, air humidity, average temperature, and the water harvesting potential from the atmosphere.


The research area investigated includes 16 synoptic stations (1991 to 2005), situated within the longitudinal range of 47 degrees and 19 minutes to 53 degrees and 17 minutes, and the latitudinal range of 33 degrees and 25 minutes to 38 degrees and 55 minutes. Additionally, this study draws on data derived from three GCM models from the CMIP5 report within the framework of the CORDEX climate project. The development of an ensemble climate model has been executed utilizing an identical weighting approach and a genetic optimization algorithm. The amount of extractable water from air humidity is determined via . The performance assessment of climate outputs incorporates statistical indexes such as the Correlation Coefficient (CC), Mean Bias Error (MBE), and Relative Bias (RBIAS).

Results and Discussion

Using the optimization technique to combine climate models yields a notable reduction of 60.1, 58.7, 60.9, and 59.7 percent in the RMSE value when compared to individual utilization of CNRM, GFDL, and CCSM4 models and the averaging approach in wind speed simulation. Furthermore, the development of ensemble models corresponds to a substantial enhancement in the accuracy of these models in estimating relative air humidity (average RMSE = 8.25 %, average MBE = 2.17 %). Based on the results, the combination of climate models can have a positive effect on increasing the efficiency of climate outcomes, especially in the reproduction of atmospheric variables such as relative air humidity and wind speed (usually climate models individually have a poor performance in simulating variables such as relative humidity and wind speed). This methodology has also been applied to approximate the extractable water amount from air humidity. An examination of seasonal changes in the potential for water harvesting from air humidity reveals the optimized coefficient-based combined climate model to closely mirror observational data. Additionally, the summer and winter seasons exhibit the highest and lowest capacities for water harvesting from air humidity, respectively.


Leveraging the genetic algorithm for coefficient optimization within climate models and the creation of ensemble climate models presents a valuable approach for exploring changes in atmospheric variables and the feasibility of water harvesting from air humidity in response to climate change. Examination of seasonal alterations in water harvesting potential from air humidity indicates the substantial potential of the summer period for implementing air humidity water harvesting plans, which can hold significance in addressing a portion of agricultural requirements.

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