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Groundwater-fed irrigation impacts spatially distributed temporal scaling behavior of the natural system: a spatio-temporal framework for understanding water management impacts

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Groundwater-fed irrigation impacts spatially distributed temporal scaling behavior of the natural system: a spatio-temporal framework for understanding water management impacts
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Abstract
Regional scale water management analysis increasingly relies on integrated modeling tools. Much recent work has focused on groundwater–surface water interactions and feedbacks. However, to our knowledge, no study has explicitly considered impacts of management operations on the temporal dynamics of the natural system. Here, we simulate twenty years of hourly moisture dependent, groundwater-fed irrigation using a three-dimensional, fully integrated, hydrologic model (ParFlow-CLM). Results highlight interconnections between irrigation demand, groundwater oscillation frequency and latent heat flux variability not previously demonstrated. Additionally, the three-dimensional model used allows for novel consideration of spatial patterns in temporal dynamics. Latent heat flux and water table depth both display spatial organization in temporal scaling, an important finding given the spatial homogeneity and weak scaling observed in atmospheric forcings. Pumping and irrigation amplify high frequency (sub-annual) variability while attenuating low frequency (inter-annual) variability. Irrigation also intensifies scaling within irrigated areas, essentially increasing temporal memory in both the surface and the subsurface. These findings demonstrate management impacts that extend beyond traditional water balance considerations to the fundamental behavior of the system itself. This is an important step to better understanding groundwater's role as a buffer for natural variability and the impact that water management has on this capacity.
NiederspannungsnetzReed-RelaisVideotechnikComputeranimation
MessungModellbauerImpaktStörgrößeRaumladungsgebietDreidimensionale IntegrationKalenderjahrReaktionsprinzipErderJahreszeit
Optischer HalbleiterverstärkerLunkerUmlaufzeitModellbauerProzessleittechnikMikroklimaEnergieniveauBesprechung/Interview
MonatKalenderjahrErderWasserdampfFlugsimulatorBesprechung/Interview
WasserdampfTechnische Zeichnung
Me 263SchallAngeregter ZustandNiederfrequenzBesprechung/Interview
WasserdampfParallelschaltungSchiffsrumpfMignon <Schreibmaschine>Schwimmer <Technik>KalenderjahrStundeTagEnergieniveauNiederfrequenzSteckverbinderSchiffsrumpfDiagramm
NiederfrequenzWocheSteckverbinderTagKalenderjahrBesprechung/Interview
WindparkUmlaufzeitTemperaturSchieneWaagePuma <Panzer>Kraft-Wärme-KopplungGleitsichtglasIrrlichtKalenderjahrKopfstützeLangwelleVideotechnikJahreszeitUmlaufzeitWindparkRotverschiebungZelle <Mikroelektronik>NiederfrequenzVeränderlicher SternTechnische ZeichnungDiagramm
Maßstab <Messtechnik>Veränderlicher SternBesprechung/Interview
DrehenSchnittmuster
KalenderjahrWasserdampfSchnittmusterBehälterRotverschiebungKlangeffektMikroklimaBasis <Elektrotechnik>RegelstreckeDürreWarmumformen
Transkript: Englisch(automatisch erzeugt)
My name is Reed Maxwell. I'm a professor of hydrology here at Colorado School of Mines, and I direct the Integrated Groundwater Modeling Center. I'm Laura Condon. I'm a Ph.D. student at the Colorado School of Mines. We do know a lot about groundwater-fed irrigation, and we know a lot about first-order impacts from pumping and irrigation. We know about aquifer depletions and impacts to surface water bodies,
but we know a lot less about the ways that these actions impact the behavior of the underlying system. So what's valuable about simulating the process instead of looking for these aspects of the field is that we can run these very carefully controlled numerical experiments. We can look at forcing the model or running the model as if there were no agricultural results at all.
Then we can take that exact same time period and take that exact same weather, exact same climate signal, and then we can add in very carefully or in a very controlled way the groundwater-fed irrigation. We've coupled dynamic water management operations. So we're simulating at high frequencies,
so one-hour simulations for 20 years, moisture-dependent groundwater-fed irrigation. We designed our experiment to be applicable to other semi-arid basins with groundwater-fed irrigation. We are simulating a real basin with heterogeneous, realistic inputs, but we've implemented water management in basically a generic way so that we could apply our findings to other locations.
We use a technique called a Fourier transform, where we look in Fourier space, and really all that is is a way to understand frequencies of information. So just like there's frequencies of sound, you have frequencies of information in the same way.
So we look at, say, groundwater depths or water table levels every hour or every day over the 20 years of our simulation. Because we can look at this information, we look at that time series, and then instead of just looking at the time series itself of the variable,
we look at the correlation or the connections between different frequencies. So how connected is one day to the next versus one week to the next versus one year to the next. So what we have here is a log-log periodogram for an example farm point just to illustrate behavior in a cell where we have irrigation.
So in black, you can see the periodogram of the simulation without irrigation, and in blue, you see the periodogram with irrigation. And then over top, we fit trend lines to periods less than 0.8 years. And so the slope of those trend lines tells you the temporal persistence and basically the memory in the system.
And so what you can see here is that we have an increased annual peak, and that is because of the seasonal cycle of irrigation. And then you can also see that we have a little more subtle shift in the relative importance of high frequency versus low frequency variability. Our main finding is that pumping and irrigation do influence the temporal scale of variability
within the natural system, and we can see spatial patterns in these changes. One of the big implications for water management is that we can start to use these temporal patterns to better understand the dynamics of the system. And we can use this to better understand the sustainability of the system.
So if we see these shifts and we see these shifts in water demand, we can use these systematic shifts to better plan in general for how we manage water, how we use water on the day-to-day basis, and how we manage water on the year-to-year basis. And this is very important because of these seasonal cycles that we see and these wet and dry patterns that we see in the climate
and how we can better prepare, how we can better manage our water for drought, how we can better use our water in wetter years so that we have more storage for dry years.