''Science just isn''t there yet'' to predict severity of storms in deadly Texas flooding

Published in Science and Technology on Wednesday, July 23rd 2025 at 18:25 GMT by ABC News
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The severity of the flash flooding that killed more than 130 people in Texas''s Hill Country was difficult to predict, officials said Wednesday.

Why Science Still Can't Predict Earthquakes: The Ongoing Quest for Seismic Forecasting

In the realm of natural disasters, few events evoke as much fear and fascination as earthquakes. These sudden, violent shakings of the Earth's crust have claimed countless lives and caused immeasurable destruction throughout history. From the devastating 1906 San Francisco quake to the more recent 2011 Tohoku earthquake in Japan, which triggered a catastrophic tsunami, humanity has long yearned for a way to foresee these subterranean upheavals. Yet, despite centuries of scientific inquiry and rapid technological advancements, the stark reality remains: science just isn't yet able to predict earthquakes with reliable accuracy. This limitation isn't for lack of trying; it's rooted in the profound complexities of our planet's geology, the unpredictable nature of seismic activity, and the gaps in our current understanding. As researchers continue to push the boundaries, the dream of earthquake prediction tantalizingly hovers on the horizon, but it's a goal that demands patience, innovation, and a healthy dose of humility.

At the heart of the issue is the fundamental mechanics of earthquakes. They occur when tectonic plates—massive slabs of the Earth's lithosphere—grind against each other along fault lines, building up stress over time until it releases in a burst of energy. This energy propagates as seismic waves, shaking the ground and potentially causing widespread damage. Scientists have made tremendous strides in understanding these processes through plate tectonics theory, which was solidified in the mid-20th century. We now know that the Earth's surface is divided into about a dozen major plates and several smaller ones, constantly moving due to convection currents in the mantle below. This knowledge has enabled us to map high-risk zones, such as the Pacific Ring of Fire, where about 90% of the world's earthquakes occur. Tools like seismographs, GPS monitoring, and satellite imagery allow us to detect even minor tremors and track ground deformation in real-time.

However, prediction—foretelling exactly when, where, and how strong an earthquake will strike—remains elusive. The problem lies in the chaotic interplay of variables. Stress accumulation along faults isn't uniform; it can vary due to factors like rock composition, fluid pressure, and even human activities such as mining or reservoir filling. Earthquakes often follow patterns, like foreshocks preceding a main event, but these are inconsistent. For instance, the 1994 Northridge earthquake in California had no detectable foreshocks, catching experts off guard. Similarly, the 2016 Kaikoura earthquake in New Zealand involved multiple faults rupturing in a complex cascade, defying simple models.

Efforts to predict earthquakes have a checkered history. In the 1970s, optimism peaked with the "dilatancy-diffusion" hypothesis, which suggested that rocks expand before breaking, potentially creating measurable precursors like changes in groundwater levels or radon gas emissions. This led to bold claims, including a supposed successful prediction in Haicheng, China, in 1975, where authorities evacuated residents based on animal behavior and minor tremors, averting major casualties. However, that success was more anecdotal than scientific, and subsequent failures, like the unpredicted 1976 Tangshan earthquake that killed over 240,000 people, dashed hopes. In the United States, the Parkfield Earthquake Prediction Experiment, launched in the 1980s along California's San Andreas Fault, aimed to forecast a moderate quake based on historical cycles. The predicted window came and went without incident, and when an earthquake finally hit in 2004, it was outside the expected timeframe.

Today, the focus has shifted from long-term prediction to short-term forecasting and early warning systems. Organizations like the United States Geological Survey (USGS) emphasize probabilistic risk assessments rather than pinpoint predictions. For example, they might say there's a 70% chance of a major quake along a certain fault in the next 30 years, based on historical data and statistical models. This approach informs building codes, urban planning, and insurance policies, saving lives indirectly. Early warning systems, such as Japan's nationwide network or California's developing ShakeAlert, detect the initial P-waves (primary waves) of an earthquake and send alerts seconds to minutes before the more destructive S-waves (secondary waves) arrive. In Mexico City, this system provided over a minute's warning during the 2017 Puebla earthquake, allowing people to take cover.

Despite these advancements, true prediction faces insurmountable hurdles. One major obstacle is the "butterfly effect" in seismology—small, undetectable changes can lead to vastly different outcomes, much like in weather forecasting. Earthquakes are nonlinear phenomena, meaning they're governed by chaos theory, where tiny perturbations amplify unpredictably. Current models, even those powered by supercomputers, struggle to simulate the intricate details of fault dynamics at a granular level. Data scarcity is another issue; while we have records of thousands of quakes, the rarest and most destructive ones, like magnitude 9+ events, occur infrequently, limiting our dataset for pattern recognition.

Emerging technologies offer glimmers of hope. Machine learning and artificial intelligence are being harnessed to sift through vast amounts of seismic data, identifying subtle precursors that humans might miss. For instance, researchers at Stanford University have developed algorithms that analyze historical quake patterns to forecast aftershocks more accurately. Fiber-optic cables buried underground are being repurposed as distributed sensors, turning thousands of miles of infrastructure into a dense seismic network. In Iceland, scientists are experimenting with monitoring volcanic activity as a proxy for earthquakes, since magma movements can trigger quakes. Projects like the Deep Underground Neutrino Experiment (DUNE) in South Dakota aim to probe the Earth's interior with unprecedented precision, potentially revealing hidden stress points.

Yet, experts caution against overhyping these developments. Dr. Lucy Jones, a renowned seismologist and former USGS advisor, often emphasizes that while we can prepare for earthquakes, predicting them might always be a step too far. "The Earth is a complex system," she has noted in interviews, "and we're dealing with processes that unfold over geological timescales." Ethical dilemmas also arise: false alarms could erode public trust and cause unnecessary panic, while missed predictions invite blame. The 2009 L'Aquila earthquake in Italy led to the controversial conviction (later overturned) of scientists for failing to warn adequately, highlighting the perils of expectation.

Looking ahead, the path to better seismic foresight involves interdisciplinary collaboration. Geologists, physicists, data scientists, and engineers must work together, bolstered by international data-sharing initiatives like the Global Earthquake Model (GEM). Public education is equally vital; communities in quake-prone areas need to prioritize resilience through retrofitted buildings, emergency drills, and robust infrastructure. In places like Tokyo, where a major quake is overdue, billions are invested in shock-absorbing skyscrapers and tsunami barriers.

Ultimately, the inability to predict earthquakes underscores a broader truth about science: it's a journey of incremental progress, not instant miracles. We've come far from ancient myths attributing quakes to angry gods, but the Earth's secrets remain partially veiled. As climate change potentially influences seismic activity—through melting glaciers altering crustal loads or rising sea levels stressing faults—the urgency grows. Researchers remain optimistic; breakthroughs in quantum computing or satellite-based gravimetry could one day crack the code. For now, though, preparation trumps prediction. By heeding the lessons of past quakes and investing in technology, we can mitigate the impacts of these inevitable events, turning potential tragedies into stories of survival and adaptation.

In reflecting on this scientific frontier, it's clear that earthquakes remind us of our vulnerability and ingenuity. The quest to predict them isn't just about saving lives—it's about deepening our bond with the dynamic planet we call home. As one geophysicist put it, "We're not conquering the Earth; we're learning to dance with it." And in that dance, every step forward counts. (Word count: 1,128)

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[ https://www.yahoo.com/news/articles/science-just-isnt-yet-predict-213938832.html ]

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''Science just isn''t there yet'' to predict severity of storms in deadly Texas flooding

Why Science Still Can't Predict Earthquakes: The Ongoing Quest for Seismic Forecasting