Fears about AI data centers' water use have exploded. Experts say the reality is far more complicated than people think. Last month, journalist Karen Hao posted a Twitter thread in which she acknowledged that there was a substantial error in her blockbuster book Empire of AI. Hao had written that a proposed Google data center in a town near Santiago, Chile, could require "more than one thousand times the amount of water consumed by the entire population"--a figure which, thanks to a unit misunderstanding, appears to have been off by a magnitude of 1,000. In the thread, Hao thanked Andy Masley, the head of an effective altruism organization in Washington, DC, for bringing the correction to her attention. Masley has spent the past several months questioning some of the numbers and rhetoric common in popular media about water use and AI on his Substack.

At least nine people, including three journalists, have been killed and several others wounded in an Israeli drone attack on Beit Lahiya in northern Gaza, according to Palestinian media. The attack on Saturday reportedly targeted a relief team that was accompanied by journalists and photographers. At least three local journalists are among the dead. The Palestinian Journalists' Protection Center said in a statement that "the journalists were documenting humanitarian relief efforts for those affected by Israel's genocidal war" and called on Gaza ceasefire mediators to pressure Israeli Prime Minister Benjamin Netanyahu to move forward with implementing the agreed truce and prisoner exchange. Israel has rejected opening talks on the second phase of the ceasefire between it and Hamas, which would require it to negotiate over a permanent end to the war, a key Hamas demand.

Understanding and adapting to climate change is paramount for professionals such as urban planners, emergency managers, and infrastructure operators, as it directly influences urban development, disaster response, and the maintenance of essential services. Nonetheless, this task presents a complex challenge that necessitates the integration of advanced technology and scientific insights. Recent advances in LLMs present an innovative solution, particularly in democratizing climate science. They possess the unique capability to interpret and explain technical aspects of climate change through conversations, making this crucial information accessible to people from all backgrounds Rillig et al. [2023], Bulian et al. [2023], Chen et al. [2023]. However, given that LLMs are generalized models, their performance can be improved by providing additional domain-specific information. Recent research has been focusing on augmenting LLMs with external tools and data sources to ensure that the information provided is scientifically accurate: for example, leveraging authoritative data sources such as ClimateWatch Kraus et al. [2023] and findings from the IPCC AR6 reports Vaghefi et al. [2023] helps in refining the LLM's outputs, ensuring that the information is grounded in the latest research.

Due to shortage of water resources and increasing water demands, the joint operation of multireservoir systems for balancing power generation, ecological protection, and the residential water supply has become a critical issue in hydropower management. However, the numerous constraints and nonlinearity of multiple reservoirs make solving this problem time-consuming. To address this challenge, a deep reinforcement learning approach that incorporates a transformer framework is proposed. The multihead attention mechanism of the encoder effectively extracts information from reservoirs and residential areas, and the multireservoir attention network of the decoder generates suitable operational decisions. The proposed method is applied to Lake Mead and Lake Powell in the Colorado River Basin. The experimental results demonstrate that the transformer-based deep reinforcement learning approach can produce appropriate operational outcomes. Compared to a state-of-the-art method, the operation strategies produced by the proposed approach generate 10.11% more electricity, reduce the amended annual proportional flow deviation by 39.69%, and increase water supply revenue by 4.10%. Consequently, the proposed approach offers an effective method for the multiobjective operation of multihydropower reservoir systems.

Acquiring a better understanding of drought impacts becomes increasingly vital under a warming climate. Traditional drought indices describe mainly biophysical variables and not impacts on social, economic, and environmental systems. We utilized natural language processing and bidirectional encoder representation from Transformers (BERT) based transfer learning to fine-tune the model on the data from the news-based Drought Impact Report (DIR) and then apply it to recognize seven types of drought impacts based on the filtered Twitter data from the United States. Our model achieved a satisfying macro-F1 score of 0.89 on the DIR test set. The model was then applied to California tweets and validated with keyword-based labels. The macro-F1 score was 0.58. However, due to the limitation of keywords, we also spot-checked tweets with controversial labels. 83.5% of BERT labels were correct compared to the keyword labels. Overall, the fine-tuned BERT-based recognizer provided proper predictions and valuable information on drought impacts. The interpretation and analysis of the model were consistent with experiential domain expertise.

Ukraine's state electricity operator has announced blackouts in the capital, Kyiv, and seven other regions of the country in the aftermath of Russia's devastating strikes on energy infrastructure. The move comes as Russian forces continue to pound Ukrainian cities and villages with missiles and drones, inflicting damage on power plants and water supplies, in a grinding war that is nearing its nine-month mark. Ukrenergo, the sole operator of Ukraine's high-voltage transmission lines, initially said in an online statement on Saturday that scheduled blackouts will take place in the capital and the greater Kyiv region, as well as several regions around it – Chernihiv, Cherkasy, Zhytomyr, Sumy, Poltava and Kharkiv. Later in the day, however, the company released an update saying that scheduled outages for a specific number of hours are not enough and instead there will be emergency outages, which could last indefinitely. Ukraine has been grappling with power outages and disruption of water supplies since Russia started unleashing barrages of missile and drone attacks on the country's energy infrastructure last month.

Researchers from York University's Dahdaleh Institute for Global Health Research and Lassonde School of Engineering have revamped their Safe Water Optimization Tool (SWOT) with multiple innovations that will help aid workers unlock potentially life-saving information from water-quality data regularly collected in humanitarian settings.

It's a collision course: We'll need to feed another 2 billion people by mid-century, even as climate change threatens our ability to produce food. Georgia, Florida and other Southeastern states must play a central role if we're to feed the world and simultaneously protect the planet. If we fail to rise to this challenge, we risk a multitude of problems driven by hungry people. And a new report released from the UN Intergovernmental Panel on Climate Change only heightens the concern. Our best chance to get off this collision course is through innovation.

Artificial Intelligence has seeped into every sector for good, making lives easier and better. One such crucial sector which is benefitting from the benefits of AI is the water industry. Water being the most essential natural resource for human life is also available in a small amount. There is only 2.5% of the earth's water which is freshwater. And, out of the total, only 0.5% is available freshwater, hence ringing an alarm for the need to conserve and manage this natural resource better. The water crisis in India is as real as it can get.

Forests as natural reservoirs and filters can store, release, and purify water through their interactions with hydrological processes. For humans, a clean, stable, and predictable water supply is one of the most valuable ecosystem services provided by forests. Yet, globally, forests have undergone many changes driven by human activities (logging, reforestation, afforestation, agriculture, and urbanization) and natural disturbances (wildfires and insect infestations). From 2010 to 2015, tropical forests declined by 5.5 million ha year −1 , whereas temperate forests expanded by 2.2 million ha year−1 ([ 1 ][1]). The effects of both deforestation and forestation (reforestation and afforestation) on water supply have generated serious concerns and debates ([ 2 ][2], [ 3 ][3]), particularly after recent catastrophic fires in Australia and the western United States. However, hydrological consequences of forest changes are never simple, and future research and watershed management require a systematic approach that considers key contributing factors and a broad spectrum of response variables related to hydrological services. Zhang et al. showed the consistent tendency of deforestation to increase annual streamflow ([ 4 ][4]). More than 80% of deforested watersheds had annual streamflow increases ranging from 0.4 to 599.1%, mainly owing to reduced evapotranspiration after 1.7 to 100% forest cover loss ([ 4 ][4]). The large variations in the magnitude of changes depend on the scale, type, and severity of forest disturbance, climate, and watershed properties ([ 4 ][4], [ 5 ][5]). Larger-scale disturbance tends to cause greater increase in annual streamflow. Hydrological response to fire is similar to the response to logging, but the severity of the impact varies with climate, fuel accumulation, fire intensity, overstory tree mortality, and climate. Fires often cause hydrophobic soils, with reduced soil infiltration and acceleration of surface runoff and soil erosion. In a recent national assessment of the contiguous United States, forest fires had the greatest increase in annual streamflow in semiarid regions, followed by warm temperate and humid continental climate regions, with insignificant responses in the subtropical Southeast ([ 6 ][6]). The hydrological impact of insect infestation is likely less pronounced than those of other disturbances. Large-scale beetle outbreaks in the western United States and British Columbia, Canada, over recent decades were predicted to increase streamflow, with reduced evapotranspiration because of the death of infested trees ([ 5 ][5]). However, further evidence showed negligible impacts of beetle infestation on annual streamflow, owing to increased evapotranspiration of surviving trees and understory vegetation ([ 7 ][7]). Forestation can either reduce annual streamflow or increase it ([ 4 ][4], [ 8 ][8]). Zhang et al. ([ 4 ][4]) found that 60% of the forestation watersheds had annual streamflow reduced by 0.7 to 65.1% with 0.7 to 100% forest cover gain, whereas 30% of them (mostly small watersheds) had annual streamflow increased by 7 to 167.7% with 12 to 100% forest cover gain. Variations in annual streamflow response to forestation are even greater than those caused by deforestation, possibly owing to site conditions prior to forestation and tree species selected. Planting with a single fast-growing exotic species can have greater reduction in annual streamflow than with native species ([ 8 ][8]). Streamflow reductions after forestation are more common in semiarid and arid regions than in the humid subtropics and tropics ([ 4 ][4], [ 5 ][5]). Large-scale reforestation programs in the semiarid Loess Plateau in China caused substantial streamflow reductions that consequently approached water resource limits ([ 9 ][9]). Dry-season low flow is critical for water supply, particularly in the face of more severe droughts under climate change. Low-flow response to forest change can be positive, neutral, or negative ([ 5 ][5], [ 10 ][10]). The variable low-flow responses are mainly attributed to low-flow generation processes, forest characteristics (age, species, and regeneration), forestry practices (retention of riparian buffers, logging methods, and silviculture), changes in soil conditions, and choice of low-flow metrics (daily or 7-day minimum flow). Nevertheless, negative low-flow response is commonly expected if soil water storage and infiltration capacities are impaired by forest disturbances (soil compaction and erosion from logging, and soil water repellency following severe fires), and their recovery through reforestation could take much longer, because of the difficulty in restoring damaged soils ([ 10 ][10]). Generally, climate, watershed properties, forest characteristics, and their interactions are the major drivers for large variations in hydrological responses to forest change ([ 2 ][2], [ 4 ][4]). Zhou et al. assessed global land-cover effects on annual streamflow, based on a general theoretical framework ([ 11 ][11]). They found that hydrological sensitivity to land-cover change was determined by watershed properties (watershed size, slope, configuration, and soil), climate (precipitation or potential evaporation), and their interactions, where land cover and watershed properties jointly indicate water retention ability. Land cover or forest change can cause greater hydrological responses in drier watersheds or those with low water retention capacity. Similarly, McDonnell et al. ([ 12 ][12]) recommended studying watershed storages and water movements in the vertical zone that includes forest canopy, soil, fresh bedrock, and the bottom of groundwater ([ 13 ][13]), to further reveal the mechanisms for variable hydrological response to forest change. The feedback between forests and climate may also introduce complexity. Forests can supply atmospheric moisture through evapotranspiration and potentially increase precipitation (precipitation recycling) locally and in downwind directions. Therefore, forest change affects not only downstream river flow, but also precipitation and water supply downwind ([ 5 ][5]). Lawrence and Vandecar revealed variable rainfall responses to tropical deforestation across landscapes, depending on deforestation thresholds, such as reduced rainfall by large-scale deforestation and increased rainfall by small clearings ([ 14 ][14]). The effects of forest change on precipitation are likely related to topography, prevailing wind, and climate, because they affect moisture residence time, moisture transportation, and precipitation generation. The lack of observational evidence highlights the need for research on the feedback between climate and forest change at regional or continental scales. Time scale is important for understanding these variations. Hydrological effects of forest change can vary with time as forests regrow. Coble et al. reviewed long-term responses of low flows to logging in 25 small catchments in North America ([ 10 ][10]). They identified dynamic low-flow responses over three distinct time periods associated with the development of forest canopy leaf area index and corresponding evapotranspiration: consistent increase in the first 5 to 10 years, variable responses (increase, no change, or decline) during the next 10 to 20 years, and substantial decline in some (16 out of 25) watersheds multiple decades later. However, no decline in low flows was found in nine watersheds during the third period—likely dependent on similar factors previously identified for variations in low-flow response. The dynamic hydrological responses suggest that long-term studies are critical for fully capturing possible trends and variations in the effects of forest change on water supply ([ 5 ][5]). ![Figure][15] The complex influence of forests on water supply Forests in watersheds play a critical role in regulating downstream water supply and associated ecosystem services. GRAPHIC: N. DESAI/ SCIENCE The consistencies and large variations over space and time in streamflow responses to forest change call for a systematic perspective to elucidate both explanatory (factors affecting hydrological functions) and response (hydrological functions) variables in future studies (see the figure). In the systematic context, explanatory variables, including climate, forest, watershed properties, and their interactions and feedback across multiple spatial-temporal scales that jointly control streamflow responses, should all be assessed. To better clarify the response, a more complete spectrum of hydrological variables, including the magnitude, duration, timing, frequency, and variability of flows, which collectively determine river flow conditions, aquatic functions, and thus ecosystem services such as water supply, should be included in an assessment ([ 15 ][16]). Nonetheless, water-supply assessments often use limited hydrological variables (such as annual mean flows), which could underestimate total hydrological functions or even produce misleading conclusions resulting from different or contrasting responses of various flow variables. A systematic assessment of the effects of deforestation and forestation on water supply requires multidisciplinary collaborations. The classic paired watershed experiment (PWE: one watershed as a control and the others as the treatment) ([ 12 ][12]), mainly designed to assess streamflow response to forest change, has limitations to evaluate interactions and feedback among water, forests, climate, and watershed properties. Future PWEs should systematically consider more variables and processes (flow pathways, water storage and retention, and hydrological sensitivity) with various approaches (isotopic tracing, telemetering, and modeling). With long-term in situ monitoring and growing remote-sensing data, the forest-water nexus at larger spatial scales should be explored using advanced analytical tools (machine learning, and coupled climatic-ecohydrological modeling) within a systematic context. Future assessment should also focus on watershed management tools such as payments for ecosystem services, with the inclusion of more representative water variables to support synergies or trade-offs between hydrological and other ecosystem services provided by forests in a changing environment. 1. [↵][17]1. R. J. Keenan et al ., For. Ecol. Manage. 352, 9 (2015). [OpenUrl][18] 2. [↵][19]1. X. Wei et al ., Glob. Change Biol. 24, 786 (2018). [OpenUrl][20] 3. [↵][21]1. K. D. Holl, 2. P. H. S. Brancalion , Science 368, 580 (2020). [OpenUrl][22][Abstract/FREE Full Text][23] 4. [↵][24]1. M. Zhang et al ., J. Hydrol. (Amst.) 546, 44 (2017). [OpenUrl][25] 5. [↵][26]1. I. F. Creed, 2. M. van Noordwijk 1. I. F. Creed et al ., in Forest and Water on a Changing Planet: Vulnerability, Adaptation and Governance Opportunities. A Global Assessment Report, I. F. Creed, M. van Noordwijk, Eds. (International Union of Forest Research Organizations, 2018). 6. [↵][27]1. D. W. Hallema et al ., Nat. Commun. 9, 1307 (2018). [OpenUrl][28] 7. [↵][29]1. K. M. Slinski, 2. T. S. Hogue, 3. A. T. Porter, 4. J. E. McCray , Environ. Res. Lett. 11, 074010 (2016). [OpenUrl][30] 8. [↵][31]1. S. Filoso, 2. M. O. Bezerra, 3. K. C. B. Weiss, 4. M. A. Palmer , PLOS ONE 12, e0183210 (2017). [OpenUrl][32] 9. [↵][33]1. X. Feng et al ., Nat. Clim. Chang. 6, 1019 (2016). [OpenUrl][34] 10. [↵][35]1. A. A. Coble et al ., Sci. Total Environ. 730, 138926 (2020). [OpenUrl][36] 11. [↵][37]1. G. Zhou et al ., Nat. Commun. 6, 5918 (2015). [OpenUrl][38] 12. [↵][39]1. J. McDonnell et al ., Nat. Sustain. 1, 378 (2018). [OpenUrl][40] 13. [↵][41]1. G. Grant, 2. W. Dietrich , Water Resour. Res. 53, 2605 (2017). [OpenUrl][42] 14. [↵][43]1. 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