07-20-2026, 01:49 PM
i just bought flood insurance for my home for the first time in years.
Cal delenda est
Flooding, Lake Lag, California Water situation
Cal delenda est
07-20-2026, 02:54 PM
Tulare Lake was once the largest freshwater body west of the Mississippi River, stretching over 100 miles long and 30 miles wide. It disappeared more than 130 years ago when settlers drained it to convert the wetland into farmland. The basin has since remained largely dry, with the land used for agriculture, roads, and infrastructure.
In spring 2023, the lake returned after a series of atmospheric rivers and record Sierra Nevada snowmelt brought unprecedented rainfall and runoff into the Tulare Basin. Because the valley has no natural outlet, water collected in the old lakebed, refilling it rapidly. Satellite imagery showed the lake expanding from under 100,000 acres to over 120,000 acres in just months.
After 130 Years Gone, Ghost Lake Suddenly Returns and Wipes Nearly 100,000 Acres off the Map
In spring 2023, the lake returned after a series of atmospheric rivers and record Sierra Nevada snowmelt brought unprecedented rainfall and runoff into the Tulare Basin. Because the valley has no natural outlet, water collected in the old lakebed, refilling it rapidly. Satellite imagery showed the lake expanding from under 100,000 acres to over 120,000 acres in just months.
After 130 Years Gone, Ghost Lake Suddenly Returns and Wipes Nearly 100,000 Acres off the Map
Audaces fortuna iuvat
Tulare Lake was once the largest freshwater body west of the Mississippi River, stretching over 100 miles long and 30 miles wide. It disappeared more than 130 years ago when settlers drained it to convert the wetland into farmland. The basin has since remained largely dry, with the land used for agriculture, roads, and infrastructure.
In spring 2023, the lake returned after a series of atmospheric rivers and record Sierra Nevada snowmelt brought unprecedented rainfall and runoff into the Tulare Basin. Because the valley has no natural outlet, water collected in the old lakebed, refilling it rapidly. Satellite imagery showed the lake expanding from under 100,000 acres to over 120,000 acres in just months.
After 130 Years Gone, Ghost Lake Suddenly Returns and Wipes Nearly 100,000 Acres off the Map
In spring 2023, the lake returned after a series of atmospheric rivers and record Sierra Nevada snowmelt brought unprecedented rainfall and runoff into the Tulare Basin. Because the valley has no natural outlet, water collected in the old lakebed, refilling it rapidly. Satellite imagery showed the lake expanding from under 100,000 acres to over 120,000 acres in just months.
After 130 Years Gone, Ghost Lake Suddenly Returns and Wipes Nearly 100,000 Acres off the Map
Audaces fortuna iuvat
07-20-2026, 06:55 PM
cardcrimson dateline='[url=tel:1784572429' Wrote: 1784572429[/url]']
Claude had a field day with the hyperbole (my word) and projections from the article, especially as ocean temps in the Pacific have only been reliably monitored for 50 to 75 years. Tough to project a 1 in 4000 year event with such little base data.
If one of the assumptions is that it double or quadruples the chance of a catastrophic CA flood holds, the state is well overdue for one.
Without getting political, I don't think the state is prepared for one. A while back, I had an interesting conversation with one of the lead experts at Contra Costa Flood Control. Basically when it hits, we are in deep trouble, as much of the county was under water then, and it wasn't as bad as what happened to the Central Valley and the LA Basin. . ..
Actually, it is not tough at all. If you measure the temperature every year for 50 years, you will get a sample mean and standard deviation. If you then get a single measurement, you can calculate quite easily how many standard deviations you are from the mean (that’s the z-score). A measurement 3.5 standard deviations above the mean corresponds to the 99.977th percentile, so you would expect only 0.023% of measurements to be as extreme or more extreme. That is roughly 1 in 4,300 which is where you get the estimate.
Now, that doesn’t mean there aren’t some potential problems:
1) The most obvious is the assumption that temperature distributions are Normally distributed. Reasonable assumption, but there might also reasonably be fat tails. A 50 year history probably doesn’t let you test for deviations from Normality.
2) The estimates of the mean and standard deviation are that, estimates. At a sample size of 50, the standard deviation estimate is generally (95% of the time) within about 20% of the true standard deviation. That means that the estimated z-score could be too high if the estimated standard deviation was too low (you happened to be in a period of less variability than the underlying average). If it turned out that we underestimated the variability by 20%, then it turns out that the “real” z-score (but bear in mind, unknowable, as we can’t measure the underlying distribution) could be inflated by 1/0.8, and would be about 2.8 if recalculated, which translates to a percentile of 99.75% or an approximate frequency of 1 in 400.
3) The mean is also an estimate (though estimating the mean tends to be more accurate than estimating the standard deviation), but 95% of the sample means are within 0.278 standard deviations of the true mean.
4) We are assuming that we are sampling from a single population of temperature measurements, but none of this is true if the mean temperature or variability in temperature is changing over time.
I think it is very reasonable to take the estimate of a 1 in 4000 El Niño with a grain of salt, but also it is not worth quibbling with whether this is a 1 in 4000 event, a 1 in 400 event, or a 1 in 40,000 event. It is a massive anomaly compared to the historical record; a magnitude this large has not been seen before, so the effects are very unpredictable.
BC
cardcrimson dateline='[url=tel:1784572429' Wrote: 1784572429[/url]']
Claude had a field day with the hyperbole (my word) and projections from the article, especially as ocean temps in the Pacific have only been reliably monitored for 50 to 75 years. Tough to project a 1 in 4000 year event with such little base data.
If one of the assumptions is that it double or quadruples the chance of a catastrophic CA flood holds, the state is well overdue for one.
Without getting political, I don't think the state is prepared for one. A while back, I had an interesting conversation with one of the lead experts at Contra Costa Flood Control. Basically when it hits, we are in deep trouble, as much of the county was under water then, and it wasn't as bad as what happened to the Central Valley and the LA Basin. . ..
Actually, it is not tough at all. If you measure the temperature every year for 50 years, you will get a sample mean and standard deviation. If you then get a single measurement, you can calculate quite easily how many standard deviations you are from the mean (that’s the z-score). A measurement 3.5 standard deviations above the mean corresponds to the 99.977th percentile, so you would expect only 0.023% of measurements to be as extreme or more extreme. That is roughly 1 in 4,300 which is where you get the estimate.
Now, that doesn’t mean there aren’t some potential problems:
1) The most obvious is the assumption that temperature distributions are Normally distributed. Reasonable assumption, but there might also reasonably be fat tails. A 50 year history probably doesn’t let you test for deviations from Normality.
2) The estimates of the mean and standard deviation are that, estimates. At a sample size of 50, the standard deviation estimate is generally (95% of the time) within about 20% of the true standard deviation. That means that the estimated z-score could be too high if the estimated standard deviation was too low (you happened to be in a period of less variability than the underlying average). If it turned out that we underestimated the variability by 20%, then it turns out that the “real” z-score (but bear in mind, unknowable, as we can’t measure the underlying distribution) could be inflated by 1/0.8, and would be about 2.8 if recalculated, which translates to a percentile of 99.75% or an approximate frequency of 1 in 400.
3) The mean is also an estimate (though estimating the mean tends to be more accurate than estimating the standard deviation), but 95% of the sample means are within 0.278 standard deviations of the true mean.
4) We are assuming that we are sampling from a single population of temperature measurements, but none of this is true if the mean temperature or variability in temperature is changing over time.
I think it is very reasonable to take the estimate of a 1 in 4000 El Niño with a grain of salt, but also it is not worth quibbling with whether this is a 1 in 4000 event, a 1 in 400 event, or a 1 in 40,000 event. It is a massive anomaly compared to the historical record; a magnitude this large has not been seen before, so the effects are very unpredictable.
BC
07-21-2026, 05:55 AM
(07-20-2026, 09:39 AM)BostonCard Wrote: The Sacramento Bee had a good explanation:
https://www.sacbee.com/news/california/a...67041.html
Specifically, the temperature anomaly, which is expected to reach about 3.5 standard deviations above the mean, is a 1 in 4000 year event.
Quote:These sea surface temperatures are almost 3.5 standard deviations above average for this time of year – the equivalent of a once-in-4,000-year event under normal conditions. ...
I appreciate the lesson, BC. You always contribute solid information, Your point #4 (a time-based component to the measure) was the first thing I would have called out.
I read that part of the article differently than how it seems you took it. I read it to say that the CURRENT temperatures are 3.5 standard deviations above the past temperatures at this time. I did not see any justification that the current standard deviations predict the standard deviation of the eventual peak. Common sense would suggest there is a correlation, but that is far from prediction.
So, I'd add another grain of salt: the assumption that the magnitude of an early trend predicts the magnitude of the peak, or even the duration above a threshold. It may. But it may just indicate an earlier peak (ie, a time shift of the whole effect) or a broader but not necessarily higher peak.
Oh, and for those considering flood insurance, maybe you want to get a whole-house generator, and get your tree foliage thinned out and make sure your roof is in good shape. I worry more about the effects of the wind and soaked ground than I do about flooding. But that depends on where one's house is.
(07-20-2026, 09:39 AM)BostonCard Wrote: The Sacramento Bee had a good explanation:
https://www.sacbee.com/news/california/a...67041.html
Specifically, the temperature anomaly, which is expected to reach about 3.5 standard deviations above the mean, is a 1 in 4000 year event.
Quote:These sea surface temperatures are almost 3.5 standard deviations above average for this time of year – the equivalent of a once-in-4,000-year event under normal conditions. ...
I appreciate the lesson, BC. You always contribute solid information, Your point #4 (a time-based component to the measure) was the first thing I would have called out.
I read that part of the article differently than how it seems you took it. I read it to say that the CURRENT temperatures are 3.5 standard deviations above the past temperatures at this time. I did not see any justification that the current standard deviations predict the standard deviation of the eventual peak. Common sense would suggest there is a correlation, but that is far from prediction.
So, I'd add another grain of salt: the assumption that the magnitude of an early trend predicts the magnitude of the peak, or even the duration above a threshold. It may. But it may just indicate an earlier peak (ie, a time shift of the whole effect) or a broader but not necessarily higher peak.
Oh, and for those considering flood insurance, maybe you want to get a whole-house generator, and get your tree foliage thinned out and make sure your roof is in good shape. I worry more about the effects of the wind and soaked ground than I do about flooding. But that depends on where one's house is.
07-21-2026, 10:41 AM
I didn’t want to get too into it because it is even more technical/wonky, but the other major assumption that went into the math is that the observations are independent, which they are clearly not; there is a very strong correlation in the near term, and since this is an oscillating pattern, probably a strong inverse correlation over the course of an oscillation.
I have no idea how much these simplifying assumptions affect the calculation of just how common (or rare) the temperature anomaly is. I take this as a back-of-the envelope “assume a spherical cow” kind of simplifying assumptions that makes the calculation much easier (trivial in today’s era of computers and calculators) to convey that this is quite an outlier in terms of ocean temperatures. I think it is fine for a newspaper article; I would not rely on it if I were an insurance adjustor.
BC
I have no idea how much these simplifying assumptions affect the calculation of just how common (or rare) the temperature anomaly is. I take this as a back-of-the envelope “assume a spherical cow” kind of simplifying assumptions that makes the calculation much easier (trivial in today’s era of computers and calculators) to convey that this is quite an outlier in terms of ocean temperatures. I think it is fine for a newspaper article; I would not rely on it if I were an insurance adjustor.
BC
I didn’t want to get too into it because it is even more technical/wonky, but the other major assumption that went into the math is that the observations are independent, which they are clearly not; there is a very strong correlation in the near term, and since this is an oscillating pattern, probably a strong inverse correlation over the course of an oscillation.
I have no idea how much these simplifying assumptions affect the calculation of just how common (or rare) the temperature anomaly is. I take this as a back-of-the envelope “assume a spherical cow” kind of simplifying assumptions that makes the calculation much easier (trivial in today’s era of computers and calculators) to convey that this is quite an outlier in terms of ocean temperatures. I think it is fine for a newspaper article; I would not rely on it if I were an insurance adjustor.
BC
I have no idea how much these simplifying assumptions affect the calculation of just how common (or rare) the temperature anomaly is. I take this as a back-of-the envelope “assume a spherical cow” kind of simplifying assumptions that makes the calculation much easier (trivial in today’s era of computers and calculators) to convey that this is quite an outlier in terms of ocean temperatures. I think it is fine for a newspaper article; I would not rely on it if I were an insurance adjustor.
BC
07-21-2026, 06:48 PM
Claude was indeed calling out the 50 year timeline of historical data. To your point, temp over then millennia as been anything but stable, and the predictions less so. I remember in the 70's, Nat Geo and many scientists were warning about the coming ice age. . . .
As for the 1 in 4000 year event that was implied to potentially drench California like never before, my point is that California has been drenched like never before, on average, about every 150-200 years for at least the last 2000 years. Hyperbole . . . .
As for the 1 in 4000 year event that was implied to potentially drench California like never before, my point is that California has been drenched like never before, on average, about every 150-200 years for at least the last 2000 years. Hyperbole . . . .
Free Phogge!!!! And no, crimson doesn't refer to the Tide.
Claude was indeed calling out the 50 year timeline of historical data. To your point, temp over then millennia as been anything but stable, and the predictions less so. I remember in the 70's, Nat Geo and many scientists were warning about the coming ice age. . . .
As for the 1 in 4000 year event that was implied to potentially drench California like never before, my point is that California has been drenched like never before, on average, about every 150-200 years for at least the last 2000 years. Hyperbole . . . .
As for the 1 in 4000 year event that was implied to potentially drench California like never before, my point is that California has been drenched like never before, on average, about every 150-200 years for at least the last 2000 years. Hyperbole . . . .
Free Phogge!!!! And no, crimson doesn't refer to the Tide.
07-21-2026, 06:59 PM
My point is that you don’t need 4000 years of data to know that an event is a 1 in 4000 anomaly, if certain conditions are met (Normality, stability, independence, etc.). The comment that the temperature in the relevant part of the ocean was 3.5 standard deviations above the fifty year average and that this would be expected to happen approximately 1 in 4000 years is fine.
A 3.5 SD temperature anomaly does not imply a 3.5 SD rainfall anomaly, or at least, it shouldn’t, since many factor other than the temperature of the ocean affect rainfall. If you saw an article implying that we should see 1 in 4000 year rainfall, I think you are correct in skewering it. However, the articles that I’ve seen have been careful to say that that the anomaly refers to temperature, and that typically, El Niños produce heavier rainfall in Southern California, and, at most, speculating that the rainfall might be very heavy. But the articles in the Sacramento Bee that I linked earlier went out of its way to say that this doesn’t always happen, and one of the biggest El Niños in recent history, the “Godzilla” 2015-16 El Niño did not produce outsized rainfall. Here’s the direct quote:
Note that he specifically calls out the relatively small sample size of large El Niño events, which implies that correlating very large temperature anomalies to expected rainfall is fraught.
BC
A 3.5 SD temperature anomaly does not imply a 3.5 SD rainfall anomaly, or at least, it shouldn’t, since many factor other than the temperature of the ocean affect rainfall. If you saw an article implying that we should see 1 in 4000 year rainfall, I think you are correct in skewering it. However, the articles that I’ve seen have been careful to say that that the anomaly refers to temperature, and that typically, El Niños produce heavier rainfall in Southern California, and, at most, speculating that the rainfall might be very heavy. But the articles in the Sacramento Bee that I linked earlier went out of its way to say that this doesn’t always happen, and one of the biggest El Niños in recent history, the “Godzilla” 2015-16 El Niño did not produce outsized rainfall. Here’s the direct quote:
Quote:The 2015-16 El Niño, the strongest on record and dubbed as a “Godzilla” El Niño at the time, did not produce expected drought-busting rains for the state. Swain said this and the tiny sample size of large El Niño events also makes it difficult to communicate the situation.
Note that he specifically calls out the relatively small sample size of large El Niño events, which implies that correlating very large temperature anomalies to expected rainfall is fraught.
BC
My point is that you don’t need 4000 years of data to know that an event is a 1 in 4000 anomaly, if certain conditions are met (Normality, stability, independence, etc.). The comment that the temperature in the relevant part of the ocean was 3.5 standard deviations above the fifty year average and that this would be expected to happen approximately 1 in 4000 years is fine.
A 3.5 SD temperature anomaly does not imply a 3.5 SD rainfall anomaly, or at least, it shouldn’t, since many factor other than the temperature of the ocean affect rainfall. If you saw an article implying that we should see 1 in 4000 year rainfall, I think you are correct in skewering it. However, the articles that I’ve seen have been careful to say that that the anomaly refers to temperature, and that typically, El Niños produce heavier rainfall in Southern California, and, at most, speculating that the rainfall might be very heavy. But the articles in the Sacramento Bee that I linked earlier went out of its way to say that this doesn’t always happen, and one of the biggest El Niños in recent history, the “Godzilla” 2015-16 El Niño did not produce outsized rainfall. Here’s the direct quote:
Note that he specifically calls out the relatively small sample size of large El Niño events, which implies that correlating very large temperature anomalies to expected rainfall is fraught.
BC
A 3.5 SD temperature anomaly does not imply a 3.5 SD rainfall anomaly, or at least, it shouldn’t, since many factor other than the temperature of the ocean affect rainfall. If you saw an article implying that we should see 1 in 4000 year rainfall, I think you are correct in skewering it. However, the articles that I’ve seen have been careful to say that that the anomaly refers to temperature, and that typically, El Niños produce heavier rainfall in Southern California, and, at most, speculating that the rainfall might be very heavy. But the articles in the Sacramento Bee that I linked earlier went out of its way to say that this doesn’t always happen, and one of the biggest El Niños in recent history, the “Godzilla” 2015-16 El Niño did not produce outsized rainfall. Here’s the direct quote:
Quote:The 2015-16 El Niño, the strongest on record and dubbed as a “Godzilla” El Niño at the time, did not produce expected drought-busting rains for the state. Swain said this and the tiny sample size of large El Niño events also makes it difficult to communicate the situation.
Note that he specifically calls out the relatively small sample size of large El Niño events, which implies that correlating very large temperature anomalies to expected rainfall is fraught.
BC
07-21-2026, 08:36 PM
I spent part of today looking at NEXRAD with a meteorologist to figure out that some 2023 rainfall in Ventura County was literally off the charts. Do we believe this? The super bright (intense) radar split the ground-based rain gage uprights. Suffice to say that BC is correct. One can extrapolate AEP (or the inverse, return period) from hardly any data at all. Depending on the distribution assumed, 3rd order moments (like skew) can drive the frequency curve and things like skew can be driven by outliers, so knowing how to treat those, and look at confidence limits (for the 0.00025 AEP event your 95% CI starts to diverge like crazy with the amount of real-world data available). All I know is that we are still helping litigate flood events from 2018, 2019, 2021, 2022 (NYE), and 2023 around the state so nothing is once in forever, especially if your house is wrecked.
I spent part of today looking at NEXRAD with a meteorologist to figure out that some 2023 rainfall in Ventura County was literally off the charts. Do we believe this? The super bright (intense) radar split the ground-based rain gage uprights. Suffice to say that BC is correct. One can extrapolate AEP (or the inverse, return period) from hardly any data at all. Depending on the distribution assumed, 3rd order moments (like skew) can drive the frequency curve and things like skew can be driven by outliers, so knowing how to treat those, and look at confidence limits (for the 0.00025 AEP event your 95% CI starts to diverge like crazy with the amount of real-world data available). All I know is that we are still helping litigate flood events from 2018, 2019, 2021, 2022 (NYE), and 2023 around the state so nothing is once in forever, especially if your house is wrecked.
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