Selection of appropriate empirical reference evapotranspiration (ETo) estimation models is very important for the management of agriculture, water resources, and environment. Statistical metrics generally used for performance assessment of empirical ETo models, on a station level, often give contradictory results, which make the ranking of methods a challenging task. Besides, the ranking of ETo estimation methods for a given study area based on the rank at different stations is also a difficult task. Compromise programming and group decision-making methods have been proposed in this study for the ranking of 31 empirical ETo models for Peninsular Malaysia based on four standard statistical metrics. The result revealed the Penman-Monteith as the most suitable method of estimation of ETo, followed by radiation-based Priestley and Taylor and the mass transfer-based Dalton and Meyer methods. Among the temperature-based methods, Ivanov was found the best. The methodology suggested in this study can be adopted in any other region for an easy but robust evaluation of empirical ETo models.
Extreme weather events are more detrimental to human culture and ecosystems than typical weather patterns. A multimodel ensemble (MME) of the top-performing global climate models (GCMs) to simulate 11 precipitation extremes was selected using a hybrid method to project their changes in Pakistan. It also compared the bene ts of using all GCMs compared to using only selected GCMs when projecting precipitation extremes for two future periods (2020-2059) and (2060-2099) for four shared socioeconomic pathways (SSPs), SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.Results showed that EC-Earth3-Veg, MRI-ESM2-0 and NorESM2-MM performed best among GCMs in simulating historical and projecting precipitation extremes. Compared to the MME of all GCMs, the uncertainty in future projections of all precipitation indices using the selected GCMs was signi cantly smaller. The MME median of the selected GCMs showed increased precipitation extremes over most of Pakistan. The greater increases were in one-day maximum precipitation by 6-12 mm, ve-day maximum precipitation by 12-20 mm, total precipitation by 40-50 mm, 95th percentile precipitation events by greater than 30 mm, 99th percentile precipitation events by more than 9 mm, days when precipitation ≥ 4 mm by 0-4 days, days when precipitation ≥ 10 mm by 2-6 days, days when precipitation ≥ 20 mm by 1-3 days, and precipitation intensity by 1 mm/day, consecutive wet days by one day, consecutive dry days by 0-4 days in the northern high elevated areas for SSP5-8.5 in the late future. These results emphasize the greater in uence of climate change on precipitation extremes in the northern, high-elevation areas, which provide the majority of the country's water. This emphasizes the necessity to adopt suitable climate change mitigation strategies for sustainable development, particularly in the country's northern regions.
Accurate estimation of evapotranspiration (ET) is vital for water resources development, planning and management, particularly in the present global warming context. A large number of empirical ET models have been developed for estimating ET. The main limitations of this method are that it requires several meteorological variables and an extensive data span to comprehend the ET pattern accurately, which is not available in most developing countries. The efficiency of 30 empirical ET models has been evaluated in this study to rank them for Pakistan to facilitate the selection of suitable models according to the availability of data. Princeton Global Meteorological Forcing daily climate data with a 0.25°×0.25° resolution for 1948–2016 was utilized. The ET estimated using Penman-Monteith (PM) was considered as the reference. Multi-criteria group decision-making (MCGDM) was used for the ranking of the models for Pakistan. The results showed the temperature-based Hamon as the best model for most Pakistan, followed by Hargreaves-Samani and Penman models. Hamon also showed the best performance in terms of different statistical metrics used in the study with a mean bias (PBias) of -50.2%, mean error (ME) of -1.62 mm and correlation coefficient (R2) of 0.65. Ivan showed the best performance among the humidity-based models, Irmak-RS and Ritch among the radiation-based models and Penman among the mass transfer-based models. Northern Pakistan was the most heterogeneous region in the relative performance of different ET models.
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