Climate change and water budget: According to the recent observations, consider increasing risk of drought
Figure: Comparison of the SPEI drought index value accumulated in 1993–2022 observed for 3 months D Value: panel (panelone) SPEI, group (group observed from 1981-2010 (B) SPEI-based SPEI estimates 2031-2060 conditions and panels (C) SPEI is based on the 2031–2060 conditions of the WG project. SPEI ≤ -1.5 represents serious drought conditions. Based on the observed climate description in the panel from 1981 to 2010 (one), From 1993 to 2022, severe drought conditions occurred 17 times, and the lowest calculated SPEI was -2.1. Use LOCA2 2031–2060 conditions, four severe droughts occurred between 1993 and 2022, and the lowest SPEI was -1.9. For the results of the observation results of the observation of the use of WG 2031–2060 conditions from 1993-2022, no severe drought occurred, and the minimum SPEI was -1.4. The comparison shows that the average description of WG 2031–2060 climate is warm and dry than the Loca2 2031-2060 description.
Climate change is reshaping our world as we just started to understand. One of the most urgent challenges is to predict how these changes will affect water resources, which is essential for agriculture, industry and daily life. The concept of weather attribution studies the possibility of specific weather events under different climate conditions, and it has become a vital tool in this work. By understanding the role of climate change caused by humans in changing the weather mode, scientists can better predict and prepare for the future. This method is particularly related to managing water resources because it helps predict the drought that may have serious economic and environmental impacts.
With the intensification of climate change, weather attribution has become increasingly important. Nick Martin’s latest research is Nick Martin, the Southwest Research Institute of San Antonio, Texas, discussed how the weather is included in the water budget prediction how to enhance our understanding of future drought. This work was published in the “Water Literature” magazine, comparing the expectations of severe drought in the future in the historical observation, LOCA2’s reduced CMIP6 CMIP6 future climate simulation results and weather attribution statistical forecast predicted future climate. Random weather generators (WG) are statistical simulation tools used to predict weather attribution in the future.
Weather attribution is estimated to observe weather events in different climate scenarios, so the possibility of severe drought under -induced climate change in humans. Studies on the weather prediction of statistical predictions of future climate to guide this work show that, in view of human -induced climate change, the possibility of severe three -month drought is five times higher. In conceptual point of view, it may mean that the possibility of arid in 25 years in 2000 is five times higher, and it is now drought in the five years of 2020. The synthetic future climate generated by WG is restricted or “calibrated”, which produces five times severe drought during 2031-2060. This method is to reflect historical data. The recently observed weather and predict the future climate change to simulate the future weather mode, including drought.
“The weather is attributed to providing changes in the possibility of extreme events observed, including drought, requiring assessment, planning and preparing to relieve future risks from climate change caused by humans. The forecast of the synthesis of future climate reflects the possibility of new extreme events, and provides a framework for water resources planning and risk assessment.
The implementation location is the FRIO River Basin in central southern Texas. Due to the direct communication between the surface water and Edwardz water, this is essential for water resources management. Between 2031 and 2060, WG was calibrated to generate random weather. The climate description provides five times the possibility of severe three -month drought compared with historical observation. Compared with historical norms, this enhanced drought possibility is based on the expectations of significantly higher temperature in the future and the decrease in soil moisture. Based on the recently observed weather, CMIP6’s future climate simulation results and weather attribution research support the expectations of rising temperature and decrease in soil moisture.
In this study, the use of standard precipitated steaming index (SPEI) describes the size and possibility of three months of drought. SPEI is based on precipitation and temperature data and provides a climatic drought index that is sensitive to global warming. The observed three -month water deficit (D) is calculated as a potential dispersion depth with less depth of precipitation. It is a drought measurement value. The drought measurement has been transformed, and standardization and standardization have been transformed to generate SPEI. Based on precipitation and temperature data sets for calculating SPEI, the “standardized” section provides the possibility or probability of three months of drought for three months. The following table shows the drought category of the SPEI value range and the cumulative probability of the selected SPEI value.
The significant increase in the possibility of drought conditions observed recently in extreme events is a key factor in guiding the water resource plan. The possibility of droughts observed in January 2000 is different. As shown in the figure above, the expectations of historical conditions are determined. The decreased CMIP6 CMIP6 future climate simulation results and weather are attributed to the future climate of WG. The three-month water deficit (D) observed in January 2000 was -217 mm. When calculated from the observation from 1981 to 2010, the -1.9 SPEI of -217 mm is -1.9, and the cumulative probability is 0.03, corresponding to severe drought. When the WG projection with weather attribution is determined by 2031-2060, the Spei of-217 MM is -0.9, and the cumulative probability is 0.17 to 0.17. This shows that the possibility of the -217 mm of the -217 mm of three months in December and January was 5.7 (0.17 / 0.03 = 5.67). The possibility of WG in 2031-2060 is expected to be higher than from 1981-2010 to 2010. The climate observed during the period. When calculated using the CMIP6 climate simulation results with LOCA2 from 2031-2060, the SPEI of -217 mm was -1.6 to -1.6, and the cumulative probability was 0.05, which means severe drought. Historically, severe drought (-217 mm of December and January D in November, December and January) in WG is expected to occur in the climate of 3.4 (0.17 / 0.05 = 3.4), it is more likely to be 2031-2060 The above LOCA2 decreased CMIP6 climate simulation results are high.
The importance of these findings is their potential applications in water resources management and planning. By providing an enhanced description of the possibility of future extreme events, the method of this study can provide information for water -saving and distribution strategies, thereby reducing the impact of serious drought. This method can be extended to other regions and water systems, providing valuable tools for the challenges and risks brought about by climate change.
In short, this study shows that weather attribution is a key role in enhanced future water budget predictions. These findings emphasize the needs of innovation methods in water resources management, especially when climate change continues to change the frequency and strength of extreme weather events. As Martin comes, the ability to predict and prepare serious drought is critical to sustainable water management and the toughness of communities that rely on these important resources.
Journal reference
Martin, Nick. “The weather is attributed to the future water budget forecast.” Water Literature, 2023, 10, 219. Doi: https: //doi.org/10.3390/hydrology10120219
About the author
Nick Martin It is a water scientist in Vodanube LLC and Respen, which is located in Colins Fort Collins. His background is surface water and groundwater writers and software developers. He focuses on risk assessment, reducing risk, reliability, elasticity, and sustainable analysis related to climate change in nature and engineering systems. Nick specially studies probability analysis and modeling to quantify uncertainty and define environmental and economic risks. His technical interests include an uncertain analysis of decision -making support and data assimilation. This is part of water flowing, transport modeling, machine learning and deep learning research.
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