WaterUnderground

data compilation

A do-it-yourself Jupyter notebook to constrain sediment permeability

A do-it-yourself Jupyter notebook to constrain sediment permeability

Post by Elco Luijendijk, Junior lecturer in the Department of Structural Geology and Geodynamics at Georg-August-Universität Göttingen and WaterUnderground founder Tom Gleeson (@water_undergrnd), Associate Professor in the Department of Civil Engineering at the University of Victoria.


Most of the groundwater on our planet is located in sedimentary rocks. This is why it is important to know how easy or hard it is for water to flow through pores in sediments, which is governed by permeability. Unfortunately, permeability is extremely variable. Wouldn’t it be great if we could estimate permeability based on sediment types (for which a decent amount of data exist)?

Enter the 150+ year challenge to estimate the permeability of sediments with universal equations. Most of the equations work well for one sediment type, such as pure sands or clay. For instance, the Kozeny-Carman equation from the 1920s tends to work well for most granular materials such as sand or silt. However, pure sands or clays are rare, and most of what’s out there are mixtures.

Evaluating how well existing and new equations work for mixed sediments is tricky business. Searching high and wide only three datasets with 78 samples were found that contained all the required information (grain size distribution, clay mineralogy). Needless to say, more data are needed to improve the predictive equations. In a paper published a few years ago we found that in most cases, the permeability of the sediments could be estimated in a two-step process:

  • calculate the permeability of clay and granular (sand/silt) components, and
  • calculate the permeability of the mixed sediment by taking the geometric mean of the two components weighed by the clay content of the sediment.

The resulting workflow was published as a series of equations that are not particularly easy to work with. That is why we recently decided to take advantage of the general awesomeness of Jupyter notebooks to publish a do-it-yourself notebook to calculate permeability on GitHub (https://github.com/ElcoLuijendijk/permeability_notebooks). For those of you new to Jupyter notebooks: these are documents that contain a readable mix of text, code, data and figures and can be used to publish studies in such a way that you can reproduce the analysis and make the figures yourself (much like R Markdown).

The Jupyter notebooks to calculate permeability consist of a main notebook and additional notebooks to calculate the specific surface area of sediments. Also included are all the calibration datasets Jthat were compiled for the publication. You can use the data to evaluate how well the permeability equations match these datasets, or you can set up a new spreadsheet with data from your own study area which can then be used by the notebook to calculate permeability. The notebook automatically generates several figures like the one below (Figure 1).

There is also an additional notebook that calculates first-order estimates of permeability from well log data collected by geophysical tools that map the density or water content of sediments. Such well log data can be more widely available than detailed sediment records and may help estimate permeability for the deeper subsurface (>100s of m), where permeability data are generally scarcer than at the surface.

Comparing these datasets and equations with the Jupyter notebooks highlight the gaps in quantifying permeability. These notebooks and datasets are out there for the world, so join the effort to make more accurate predictions of permeability (and groundwater flow) in sediments!

Figure 1: Figure produced by the Jupyter notebook showing measured vs calculated permeability using an example dataset of mixed natural sediments.

 

Doing Hydrogeology in R

Doing Hydrogeology in R

Post by Sam Zipper (@ZipperSam), current Postdoctoral Fellow at the University of Victoria and soon-to-be research scientist with the Kansas Geological Survey at the University of Kansas.


Using programming languages to interact with, analyze, and visualize data is an increasingly important skill for hydrogeologists to have. Coding-based science makes it easier to process and visualize large amounts of data and increase the reproducibility of your work, both for yourself and others. 

There are many programming languages out there; anecdotally, the most commonly used languages in the hydrogeology community are Python, MATLAB, and R. Kevin previously wrote a post highlighting Python’s role in the hydrogeology toolbox, in particular the excellent FloPy package for creating and interacting with MODFLOW models. 

In this post, we’ll focus on R to explore some of the tools that can be used for hydrogeology. R uses ‘packages’, which are collections of functions related to a similar task. There are thousands of R packages; recently, two colleagues and I compiled a ‘Hydrology Task View’ which compiles and describes a large number of water-related packages. We found that water-related R packages can be broadly categorized into data retrieval, data analysis, and modelling applications. Though packages related to surface water and meteorological data constitute the bulk of the package, there are many groundwater-relevant packages for each step of a typical workflow.

Here, I’ll focus on some of the packages I use most frequently. 

Data Retrieval:

Instead of downloading data as a CSV file and reading it into R, many packages exist to directly interface with online water data portals. For instance, dataRetrieval and waterData connect to the US Geological Survey water information service, tidyhydat to the Canadian streamflow monitoring network, and rnrfa for the UK National River Flow Archive.

Data Analysis:

Many common data analysis tasks are contained in various R packages. hydroTSM and zoo are excellent for working with timeseries data, and lfstat calculates various low-flow statistics. The EcoHydRology package contains an automated digital filter for baseflow separation from streamflow data.

Modelling:

While R does not have an interface to MODFLOW, there are many other models that can be run within R. The boussinesq package, unsurprisingly, contains functions to solve the 1D Boussinesq equation, and the kwb.hantush package models groundwater mounding beneath an infiltration basin. The first and only package I’ve ever made, streamDepletr, contains analytical models for estimating streamflow depletion due to groundwater pumping. To evaluate your model, check out the hydroGOF package which calculated many common goodness-of-fit metrics.

How do I get and learn R?

R is an open-source software program, available here. RStudio is a user-friendly interface for working with R. RStudio has also compiled a number of tutorials to help you get started!

Other Useful Resources

Louise Slater and many co-authors currently have a paper under discussion about ‘Using R in Hydrology’ which has many excellent resources.

While not hydrogeology-specific, there are many packages for generic data analysis and visualization that will be of use to hydrogeologists. In particular, the Tidyverse has a number of packages for reading, tidying, and visualizing data such as dplyr and ggplot2.

Claus Wilke’s Fundamentals of Data Visualization book (free online) was written entirely within R and shows examples of the many ways that R can be used to make beautiful graphs.

Update on the groundwater situation in Cape Town

Update on the groundwater situation in Cape Town

Post by Jared van Rooyen, PhD student in Earth Science at Stellenbosch University, in South Africa.


When the Cape Town water crisis first emerged it took almost a year before active contingencies were put in place. Four major ideas were proposed: (1) Intense water restrictions for municipal water users, (2) greywater recycling facilities, (3) groundwater augmentation of water supplies, and (4) desalination.

Although not all the proposed ideas came to fruition, there was a significant increase in the installation of well points and boreholes for municipal and private use. The national and provincial governments began the investigation and development of three major aquifers in the Western Cape. Unfortunately (or fortunately), the initial estimates for extraction were never realized as a result of poor water quality in the Cape Flats aquifer, power struggles between government parties and typical delays in service delivery in South Africa. In contrast, private groundwater consultants are benefiting from the high demand for groundwater use by residents installing private wells to alleviate the pressures of stringent water restrictions.

There are now two plausible scenarios for the groundwater use situation in the Western Cape: either we have not yet begun to abstract any significant amounts of groundwater, or we lack the data to show if we have. It is difficult to provide empirical evidence on whether groundwater levels are indeed declining and if it is a result of the drought (or abstraction or both). The trouble is that, unlike surface water storage where we can see the direct evidence of the drought, how much water is in an aquifer cannot be directly observed and must be estimated via an indirect method.

Estimating changes in groundwater availability usually requires detailed baseline data to be available, meaning that the state of a resource is relative to the baseline data available and can be over/underestimated as a result. One example of this was the subject of a controversial string of news articles released in the first months of 2019.

The Department of Water Affairs (DWS) released an interactive map of monitoring boreholes across South Africa which includes a record of normalized water levels (0% being the lowest measured water level in meters above sea level (masl) and 100% the highest measured water level) averaged over a province (Figure 1) . The graph shows a decline in average water levels in the last three years, but the record only goes back to 2009 and it is difficult to say if this a drought signal, a result of abstraction, or simply a natural fluctuation over a longer timescale.

Figure 1: Plot showing the severity of groundwater levels in the Western Cape of South Africa, averaged groundwater levels are plotted as a normalized percentage of the lowest and highest recorded levels in the borehole history. Credit: NIWIS DWA South Africa

Respected researcher and geochemist Dr. Meris Mills investigated historical data from the national groundwater archive and found that much of the data before 2015 were too sparse to be considered representative of the groundwater level. Data density and availability still is a major limiting factor in groundwater studies in South Africa.

Dr. Mills found that 55% of boreholes show statistically significant declining water levels and 63% of boreholes recorded an all time low water level after 2015 to late 2018 (since 1978). She concluded that fractured rock aquifers were the least affected and that 37% of boreholes with falling water levels were, in fact, not related to the recent drought. The cause for these declines in water levels are still unknown.

It is still difficult to quantify how much groundwater contributed to the recovery of Cape Town’s dam levels, if at all, but the resultant interest in long term groundwater supply has sparked debate surrounding local groundwater resources.

It is also clear that the effects of the drought on groundwater resources remain to be fully realized, however our groundwater, in general, is more resilient to change than we may think. Depending on the angle you look at it, initial findings may either indicate that groundwater is potentially a lifeline to cities crippled by a water supply crisis, or a time bomb with a delayed fuse.

Data sharing: an update on new and existing initiatives

Data sharing: an update on new and existing initiatives

Post by Anne Van Loon, Gemma Coxon, and Bentje Brauns.


Last year, Anne Van Loon wrote about data sharing initiatives in hydrology (“Data drought or data flood? 28 May 2018). This post gives an update on existing and new initiatives.

CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) 

The CAMELS datasets are expanding: from the United States and Chile to Great Britain and Australia.  The CAMELS-GB dataset will consist of hydro-meteorological timeseries and catchment attributes for 671 catchments across Great Britain and is expected to be released on the Environmental Information Data Centre later this year.

The Groundwater Drought Initiative

The Groundwater Drought Initiative is collecting more and more groundwater level data and groundwater drought impacts. The Initiative is very happy to welcome new partners and supporters from as far East as Ukraine and as far South as Albania, increasing the number of participating countries and countries currently considering to participate to 23 (see map). Additionally, a first getting-to-know-each-other & info meeting was held at EGU19 with participants from Austria, Belgium, Canada, Estonia, Germany, Latvia, Luxembourg, Netherlands, Norway, UK, Ukraine, and Switzerland. If you are from Bulgaria, Greece, Hungary, Italy, Romania, Slovakia or any of the other yellow countries on the map below and you have groundwater data (or contacts in organisations who could help) or you are interested in groundwater drought, please contact Bentje Brauns (benaun@bgs.ac.uk).

The IAHS Panta Rhei Working Group on Large Sample Hydrology

The IAHS Panta Rhei focus on efforts to facilitate the production and exchange of datasets worldwide.  This year at EGU, the group organised a splinter meeting to discuss the generation of large sample catchment datasets in the cloud and a session (HS2.5.2 Large-sample hydrology: characterising and understanding hydrological diversity) that showcased several recent data- and model-based efforts on large-sample hydrology from new global datasets to large multi-model ensembles.  If you are interested in being updated on the activities of the group then please contact Gemma Coxon (gemma.coxon@bristol.ac.uk) to be added to the mailing list.

There seems to be a lot going on in the world of hydrological data sharing! To share your own story or initiative, please leave a reply below.



Anne Van Loon (website | @AnneVanLoon) is a Senior Lecturer in Physical Geography  in the School of Geography, Earth and Environmental Sciences at the University of Birmingham.

Gemma Coxon (website) is a Postdoctoral Research Associate and Lecturer in Hydrology in the School of Geographical Sciences at the University of Bristol.

Bentje Brauns (website) is a Hydrogeologist at the British Geological Survey.

How deep does groundwater go? Mining (dark) data from the depths

How deep does groundwater go? Mining (dark) data from the depths

Post by Kevin Befus, Assistant Professor at the College of Engineering and Applied Science at the University of Wyoming, in the United States.

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3D geologic data can be hard to come by, and can be even more difficult to combine into a continuous dataset. The cross-sections shown here are directly from 3D groundwater models that I compiled [Befus et al., 2017], primarily from USGS groundwater models, for the U.S. East Coast. I kept each of the regional domains (different color swaths on the map) separate, since I ran into the issue of “border discontinuities” between different models where naming conventions and hydrostratigraphic structure didn’t match up. Kh is the horizontal hydraulic conductivity.

We’ve all been asked (or do the asking), “where does your water come from?” This is a fundamental question for establishing a series of additional questions that can ultimately help define strategies for valuing and protecting a particular water resource.

For groundwater, we could phrase this question differently, and I often do when talking to well owners: How deep is your well? If I get an answer to this, then I can dive into additional questions that can help define more about the local groundwater resource: How deep is the well screen? How long is the screen? Do you know what the water level in the well is? Has it changed over some given time? Seasonally?

These are all useful questions, and they serve to begin establishing the hydraulic conditions of a particular aquifer. I ask these whenever I can.

To do this at a larger scale, we can turn to various governmental agencies that regulate groundwater resources and/or water well drilling and often collect and store groundwater data (e.g., www.waterqualitydata.us/, http://nlog.nl/en/data, http://gin.gw-info.net/service/api_ngwds:gin2/en/gin.html, or http://www.bgs.ac.uk/research/groundwater/datainfo/NWRA.html). There is a wealth of information out there internationally on wells when they were drilled and where the driller first hit water. These driller logs can provide a snapshot in time of the water table elevation and can be extremely useful for tracking hydrologic variability [Perrone and Jasechko, 2017], extracting hydraulic parameters [Bayless et al., 2017],  and for testing model results [Fan et al., 2013]. Unfortunately for us earthy nerds, some governments have restricted access to well installation data for either certain types of wells (i.e., municipal) or for all wells, usually for privacy or safety concerns.

Back to the original question. How deep is groundwater? I keep this question broad. We can usually answer this question for particular areas where we have access to the right data, but for large parts of the globe, and potentially underneath you right now, we cannot answer this question. The “right data” for a hydrogeologist is some form of information on geologic/stratigraphic layer (or lack of layering) that can be tied to the rock properties. For a surficial, unconfined aquifer, this can be relatively easy, but when we start stacking several geologic units on top of each other or start actually using the groundwater, this question of how deep groundwater is becomes tricky. We could qualify this question by asking how deep “usable” groundwater is, which, of course, depends on our definition of usable water for a specific purpose. Or, we can point (or integrate) through the Earth’s crust, core, and right back to its crust and calculate the huge value of how much water is “in the ground” (and minerals)[Bodnar et al., 2013]. And I haven’t even brought up porosity yet! Or specific storage!

A example of a great public 3D interactive web viewer (https://wateratlas.net/) that integrates groundwater data, geological information, and well construction details produced by the Centre for Coal Seam Gas at the University of Queensland (https://ccsg.centre.uq.edu.au/), which is supported by the University of Queensland and industry partners. For more information on this water atlas, please contact Dr. Sue Vink (s.vink@smi.uq.edu.au) or Alexandra Wolhuter (a.wolhuter@uq.edu.au).

Don’t worry. I won’t go there. I want to harass/encourage the hydro[geo]logic community to get serious about sharing their hydrogeologic data. This does mean metadata (do I hear a collective groan?), but metadata and data management plans are increasingly required to secure funding. CUAHSI’s Hydroshare site (www.hydroshare.org) provides a platform uploading hydro models, and the U.S. Geological Survey has developed a slick web system for exploring hydrogeologic models. But, I’d like to take this further, or at least get a service like that going for anyone who wants to share their models. There is a wealth of crustal structure data out there, and groundwater models are unique in often containing some representation of three-dimensional geology/hydrostratigraphy along with Earth properties. There are some great deterministic, published datasets and models of global hydrogeology [De Graaf et al., 2015; Huscroft et al., 2018], but we can do better. Wouldn’t it be great to have a centralized database to extract an ensemble of hydrogeologic structure used in previous regional or local studies? How about be able to draw a model boundary on a web interface and extract 3D structure for your next model? And compare cross-sections between models in the same area? Want to start fitting your puzzle pieces into the international hydrogeologic puzzle? The question now becomes, how do we do it? A “DigitalCrust” has been proposed [Fan et al., 2015], but is not yet in reach.

Join the movement of a “Digital Earth” [Gore, 1998]!

Here are some examples, initiatives, and free 3D [hydro]geology resources to get you started:

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Kevin Befus leads the groundwater hydrology group in the Civil and Architectural Engineering Department at the University of Wyoming. With his research group, he studies how groundwater systems respond to hydrologic conditions over glacial timescales and in mountainous and coastal environments.  You can follow along with Kevin’s research through any of the links below:

Personal WebpageTwitter Research Group Page | UW Faculty Page

 

 

 

 

 

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References

Bayless, E. R., L. D. Arihood, H. W. Reeves, B. J. S. Sperl, S. L. Qi, V. E. Stipe, and A. R. Bunch (2017), Maps and Grids of Hydrogeologic Information Created from Standardized Water-Well Driller’s Records of the Glaciated United States, U.S. Geol. Surv. Sci. Investig. Report2, 20155105, 34, doi:10.3133/sir20155105.

Befus, K. M., K. D. Kroeger, C. G. Smith, and P. W. Swarzenski (2017), The Magnitude and Origin of Groundwater Discharge to Eastern U.S. and Gulf of Mexico Coastal Waters, Geophys. Res. Lett., 44(20), 10,396-10,406, doi:10.1002/2017GL075238.

Bodnar, R. J., T. Azbej, S. P. Becker, C. Cannatelli, A. Fall, and M. J. Severs (2013), Whole Earth geohydrologic cycle, from the clouds to the core: The distribution of water in the dynamic Earth system, Geol. Soc. Am. Spec. Pap., 500, 431–461, doi:10.1130/2013.2500(13).

Fan, Y., H. Li, and G. Miguez-Macho (2013), Global patterns of groundwater table depth, Science, 339(6122), 940–943, doi:10.1126/science.1229881.

Fan, Y. et al. (2015), DigitalCrust – a 4D data system of material properties for transforming research on crustal fluid flow, Geofluids, 15(1–2), 372–379, doi:10.1111/gfl.12114.

Gore, A. (1998), The Digital Earth: Understanding our planet in the 21st Century, Aust. Surv., 43(2), 89–91, doi:10.1080/00050326.1998.10441850.

De Graaf, I. E. M., E. H. Sutanudjaja, L. P. H. Van Beek, and M. F. P. Bierkens (2015), A high-resolution global-scale groundwater model, Hydrol. Earth Syst. Sci., 19(2), 823–837, doi:10.5194/hess-19-823-2015.

Huscroft, J., T. Gleeson, J. Hartmann, and J. Börker (2018), Compiling and Mapping Global Permeability of the Unconsolidated and Consolidated Earth: GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0), Geophys. Res. Lett., 45(4), 1897–1904, doi:10.1002/2017GL075860.

Perrone, D., and S. Jasechko (2017), Dry groundwater wells in the western United States, Environ. Res. Lett., 12(10), 104002, doi:10.1088/1748-9326/aa8ac0.

 

Data drought or data flood?

Data drought or data flood?

Post by Anne Van Loon, Lecturer in Physical Geography (Water sciences) at the University of Birmingham, in the United Kingdom.

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The basis for (almost) all scientific work, at least in the earth and environmental sciences, is DATA. We all need data to search for the answers to our questions. There are a number of options to get hold of data; we can measure stuff ourselves in the field or in the lab, generate model data, process data measured by satellites, or use data that other people collected. The last option has the advantage that you can cover much larger temporal and spatial scales than if you do all the measurements yourself, but it is not necessarily much easier or quicker. In this blog I do a quick and dirty tour of large-scale data collection initiatives in hydrology and introduce a new initiative focusing on groundwater drought.

“Hydrometeorological data…” (source: https://cloudtweaks.com/)

The classical way for hydrologists to use other people’s data (also called “secondary data”) is to use national-scale government-funded hydrometeorological databases such as the National River Flow Archive (NRFA, https://nrfa.ceh.ac.uk/) and National Groundwater Level Archive (NGLA, http://www.bgs.ac.uk/research/groundwater/datainfo/levels/ngla.html) in the UK and the US Geological Survey Water Data in the USA (https://water.usgs.gov/data/). This seems a good and reliable source for data, but there are worries, for example that the number of gauges worldwide is decreasing due to various reasons (Mishra & Coulibaly, 2009; https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007RG000243; Hannah et al., 2011; https://onlinelibrary.wiley.com/doi/full/10.1002/hyp.7794) and that paper or microfilm archives are at risk (https://public.wmo.int/en/our-mandate/what-we-do/observations/data-rescue-and-archives). These national data are collated in global databases like the Global Runoff Data Centre (GRCD, http://www.bafg.de/GRDC/EN/Home/homepage_node.html) and the Global Groundwater Network (GGN, https://ggmn.un-igrac.org/), hosted by the International Groundwater Resources Assessment Centre (IGRAC). The problem there is that it is very dependent on the national hydrometeorological institutes to provide data, the records are not always up to date and quality checked, and important meta-data are not always available.

That is the reason that many researchers spend a lot of time combining and expanding these datasets. A few recent examples (NB: not at all an exhaustive list):

These are very helpful, but also quite time consuming for a single person (usually an early-career scientist) or a small group of people to compile and the dataset easily becomes outdated.

On the other side of the spectrum is crowd-sourced or citizen science data. This is already quite common in meteorology, both for weather observations (Weather Observations Website, WOW, http://wow.metoffice.gov.uk/), historic weather data (for example Weather Rescue, https://www.zooniverse.org/projects/edh/weather-rescue/) and climate model data (weather@home, https://www.climateprediction.net/, by Massey et al., 2014 https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/qj.2455 ), but citizen science is starting to get used in hydrology as well. Some examples are (again not exhaustive):

Example of crowd-sourcing hydrological data via an App (source: http://www.crowdhydrology.com/)

Most of these are using citizens as passive data collectors with the scientists doing the analysis and interpretation. The nice thing is that it creates lots of data, but the downside is a lot of local knowledge is underused. There are, however, also initiatives that try to make use of this local knowledge, either from citizens themselves, from the experts in government agencies, or from local scientists who know much more about the local hydrological situation. Some of these are funded projects, such as:

Some of these are not funded, like the UNESCO NE-FRIEND Low flow and Drought group that produced an analysis of the 2015 streamflow drought in Europe after a community effort to collect streamflow data and drought characteristics from partners in countries around Europe (Laaha et al., 2017, https://www.hydrol-earth-syst-sci.net/21/3001/2017/hess-21-3001-2017.html). Or are only partly funded, for example by a COST action that only provides travel funding, as in the case of the FloodFreq initiative in which researchers collected a dataset of long streamflow records for Europe to study floods (Mediero et al. 2015, https://www.sciencedirect.com/science/article/pii/S0022169415004291) or the European Flood Database that could have been developed with support of an ERC Advanced Grant (Hall et al., 2015, https://www.proc-iahs.net/370/89/2015/piahs-370-89-2015.html).

The databases developed in funded projects are great because there is (researcher) time to develop something new. But it is also hard to maintain the database when the project funding stops and a permanent host then needs to be found. Unfunded projects can benefit from the enthusiasm and commitment of their collaborators, but have to rely on people spending time to provide data and be involved in the analysis and interpretation. These work best if they are rooted in active scientific communities or networks. I already mentioned the NE-FRIEND Low flow and Drought group (http://ne-friend.bafg.de/servlet/is/7402/), which developed into a nice group of scientific FRIENDs, but also organisations like the International Association of Hydrological Sciences (IAHS, https://iahs.info/) and the International Association of Hydrogeologists (IAH, https://iah.org/) play an important role (see Bonnell et al. 2006 – HELPing FRIENDs in PUBs; https://onlinelibrary.wiley.com/doi/full/10.1002/hyp.6196 ). IAHS for example drives the Panta Rhei decade on Change in Hydrology and Society (https://iahs.info/Commissions–W-Groups/Working-Groups/Panta-Rhei.do), which has a number of very active working groups that are driving data sharing initiatives. Another very successful example is HEPEX (https://hepex.irstea.fr/), which is a true bottom-up network with “friendly people who are full of energy” (https://hepex.irstea.fr/hepex-highlights-egu-2018/). These international networks can provide the framework for data sharing initiatives.

The value of international scientific networks for data sharing (source: https://hepex.irstea.fr/)

It also helps if there is one (funded) person driving the data collection and if there is a clear aim or research question that everyone involved is interested in. Also, a clear procedure and format for the data helps. With that in mind, portals have been developed specifically for data sharing in hydrology, for example:

– SWITCH-ON that focusses on open data and virtual laboratories where people can do collective experiments (http://www.water-switch-on.eu/project_pages/index.html).

– Hydroshare, which is a collaborative website where people can upload hydrological data and models (https://www.hydroshare.org/)

The most inclusive are the initiatives (either funded or unfunded) that manage to incorporate local knowledge, so those that do not only collect data, but also work with the data providers for the interpretation of the data. This synthesis aspect is the main strength of these initiatives and a lot can be learned by bringing data and knowledges together, even if no new data is created.

In a NEW initiative we are hoping to combine some of the advantages of the above-mentioned data collection efforts. The Groundwater Drought Initiative (GDI, http://www.bgs.ac.uk/research/groundwater/waterResources/groundwaterDroughtInitiative/home.html) is a three-year initiative starting in April 2018 that aims to develop and support a network of European researchers and stakeholders with an interest in regional- to continental-scale groundwater droughts. Through the GDI network we will collect groundwater level data and groundwater drought impact information for Europe. This is needed because most of the data collection initiatives mentioned above are focussed on floods, not on drought, and most collate data on streamflow, not on groundwater. Since around 65% of the Europe’s drinking water supply is obtained from groundwater and drought is (and will increasingly be) a threat to water security in Europe, it is essential to get a good understanding of groundwater drought and its impacts. Since groundwater drought is typically large-scale and transboundary, data on a pan-European scale is needed to increase this understanding.

The GDI initiative is embedded in the NE-FRIEND Low flow and Drought group and has obtained a bit of funding from the UK Research Council for workshops and some researcher time, but we hope to arouse the interest and the enthusiasm of even more scientists and government employees of various nationalities and regions to be involved in the initiative and to contribute with data, meta-data, local knowledge and interpretation of data. In return the GDI will provide tools to visualise and analyse groundwater droughts, a regional- to continental-scale context of the groundwater drought information, insights into the impacts of major groundwater droughts, access to a network of international groundwater drought researchers and managers, and the opportunity to participate in joint scientific publications. The long-term sustainability of the initiative will hopefully be developed through the network that we will establish and through the link with formal organisations like the European Drought Centre (EDC, http://europeandroughtcentre.com/) and IGRAC (https://www.un-igrac.org/ ), where the groundwater drought data will be stored after the end of the funded project.

If you are interested, please get in touch:

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Anne Van Loon is a catchment hydrologist and hydrogeologist working on drought. She studies the relationship between climate, landscape/ geology, and hydrological extremes and its variation around the world. She is especially interested in the influence of storage in groundwater, human activities, and cold conditions (snow and glaciers) on the development of drought.

Bio taken from Anne’s University of Birmingham page.

Western water wells are going dry

Western water wells are going dry

Post by Scott Jasechko, Assistant Professor of Water Resources at the University of Calgary, in Canada, and by Debra PerronePostdoctoral Research Scholar at Stanford University, in the United States of America.

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Wells are excavated structures, dug, drilled or driven into the ground to access groundwater for drinking, cleaning, irrigating, and cooling. We recently mapped groundwater wells across the 17 western states [1], where half of US groundwater pumping takes place. The western states contain aquifers key to United States food production, including the Central Valley of California and the central High Plains.

Millions of water wells exist in the western US, alone. About three-quarters of these wells have been constructed to supply water for household uses. Nearly one-quarter are used to irrigate crops or support livestock. A smaller fraction (<5 %) supports industry [1].

Western US water well depths vary widely (Fig. 1). The great majority (90%) of western US well depths range between 12m and 186m. The median western US well depth is 55m. Wells with depths exceeding 200m tap deep aquifers bearing fresh groundwater, such as the basal formations in the Denver Basin aquifer system, and the deeper alluvium in the California Central Valley. Shallow wells are common along perennial rivers, such as the Yellowstone, Platte, and Willamette Rivers.

Fig. 1. Western USA wells depths. Each point represents the location of a domestic, industrial or agricultural well. Blue colors indicate well depths of less than the median (55m), and red-black colors indicate well depths exceeding the median.

The wide variability of well depths across the west (Fig. 1) emphasizes the value of incorporating well depth data when assessing the likelihood that a groundwater well may go dry.

We know wells are going dry in the western US: journalists have identified numerous communities whose well-water supplies have been impacted by declining water tables [2-4]. While several studies have assessed adverse impacts of groundwater storage declines—such as streamflow depletion [5], coastal aquifer salinization [6], eustatic sea level rise [7], land subsidence [8]—few studies address the question: where have wells have gone dry?

Here we put forth a first estimate of the number of western US wells that have dried up (Fig. 2). We compared well depths to nearby well water level measurements made in recent years (2013-2015). We define wells that have likely gone dry as those with depths shallower than nearby measured well water levels (i.e., our estimate of the depth to groundwater).

Fig. 2. Schematic of a well that has gone dry (left) and a well with a bottom beneath the water table (blue) that may still produce groundwater (right). Even wells with submerged bottoms may be impacted by declines in groundwater storage because (i) pumps are situated above the well bottom, (ii) pumping induces a localized drawdown of the water table in unconfined portions of aquifer systems, (iii) well yields may decline if the hydrostatic pressure above the well base declines.

We estimate that between 0.5% and 6 % of western US wells have gone dry [1]. Dry wells are common in some areas where groundwater storage has declined, such as the California Central Valley [9] and parts of the central and southern High Plains aquifer [10,11]. We also identify lesser-studied regions where dry wells are abundant, such as regions surrounding the towns of Moriarty and Portales in central and eastern New Mexico.

Dry wells threaten the convenience of western US drinking water supplies and irrigated agriculture. Our findings emphasize that dry wells constitute yet another adverse impact of groundwater storage losses, in addition to streamflow depletion [5], seawater intrusion [6], sea level rise [7], and land subsidence [8].

Some wells are more resilient to drying (i.e., deeper) and others more vulnerable (i.e., shallower). We show that typical agricultural wells are deeper than typical domestic water wells in California’s Central Valley and Kansas’ west-central High Plains [1]. Our finding implies that reductions to groundwater storage will disproportionately dry domestic water wells compared to agricultural water wells, because domestic wells tend to be shallower in these areas. However, in other areas, such as the Denver Basin, typical domestic wells are deeper than typical agricultural wells. This comparison of different groundwater users’ well depths may help to identify water wells most vulnerable to groundwater depletion, should it occur.

So, what option does one have when a well goes dry?

Groundwater users whose wells have gone dry may consider a number of potential, short-term remedies, some of which may include (i) drilling a new well or deepening an existing well, (ii) connecting to alternative water sources (e.g., water conveyed by centralized infrastructure; water flowing in nearby streams), or (iii) receiving water delivered by truck.

Drilling new wells, deepening existing wells or connecting to alternate water supplies is often costly or unavailable, raising issues of inequality [12]. Receiving water deliveries via truck [13] is but a stopgap, one that may exist in parts of the western United States but not elsewhere, especially if high-use activities (e.g., irrigated agriculture) are intended [14]. In places where water table declines are caused primarily by unsustainable groundwater use, a long-term solution to drying wells may be managing groundwater to stabilize storage or create storage surpluses.

Realizing such sustainable groundwater futures where wells are drying up is a critical challenge. Doing so will be key to meeting household water needs and conserving irrigated agriculture practices for future generations [15]. We conclude that groundwater wells are going dry, highlighting that declining groundwater resources are impacting the usefulness of existing groundwater infrastructure (i.e., wells). The drying of groundwater wells could be considered more frequently when measuring the impacts of groundwater storage declines.

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Scott Jasechko is an assistant professor of water resources at the University of Calgary. In November 2017, Scott joins the faculty of the Bren School of Environmental Science & Management at the University of California, Santa Barbara.

Find out more about Scott’s research at : http://www.isohydro.ca

 

 

 

Debra Perrone is a postdoctoral research scholar at Stanford University with a duel appointment in the Department of Civil and Environmental Engineering and the Woods Institute for the Environment. In November 2017, Debra will join the Environmental Studies Program at the University of California, Santa Barbara as an assistant professor.

Find out more about Debra at: http://debraperrone.weebly.com

 

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References

[1] Perrone D and Jasechko S 2017 Dry groundwater wells in the western United States. Environmental Research Letters 12, 104002 doi: 10.1088/1748-9326/aa8ac0. http://iopscience.iop.org/article/10.1088/1748-9326/aa8ac0

[2] James I, Elfers S, Reilly S et al 2015 The global crisis of vanishing groundwaters. in: USA Today https://www.usatoday.com/pages/interactives/groundwater/

[3] Walton B 2015 In California’s Central Valley, Dry wells multiply in the summer heat. in: Circle of Blue http://www.circleofblue.org/2015/world/in-californias-central-valley-dry-wells-multiply-in-the-summer-heat/

[4] Fleck J 2013 When the well runs dry. in: Albuquerque Journal https://www.abqjournal.com/216274/when-the-well-runs-dry.html

[5] Barlow P M and Leake S A 2012 Streamflow depletion by wells—understanding and managing the effects of groundwater pumping on streamflow. US Geological Survey Circular 1376 (Reston, VA: United States Geological Survey)

[6] Barlow P M, Reichard E G 2010 Saltwater intrusion in coastal regions of North America. Hydrogeol. J. 18 247-260.

[7] Konikow L F 2011 Contribution of global groundwater depletion since 1900 to sea-level rise. Geophys. Res. Lett. 38 L17401

[8] Galloway D, Jones D R and Ingebritsen S E 1999 Land subsidence in the United States. US Geological Survey Circular 1182 (Reston, VA: United States Geological Survey)

[9] Famiglietti J S, Lo M, Ho S L, Bethune J, Anderson K J, Syed T H, Swenson S C, Linage C R D and Rodell M 2011 Satellites measure recent rates of groundwater depletion in California’s Central Valley. Geophys. Res. Lett. 38 L03403

[10] McGuire V L 2014 Water-level Changes and Change in Water in Storage in the High Plains Aquifer, Predevelopment to 2013 and 2011–13  (Reston, VA: United States Geological Survey)

[11] Scanlon B R, Faunt C C, Longuevergne L, Reedy R C, Alley W M, Mcguire V L and McMahon P B 2012 Groundwater depletion and sustainability of irrigation in the US High Plains and Central Valley. Proc. Natl Acad. Sci. 109 9320–5

[12] Famiglietti J S 2014 The global groundwater crisis. Nature Climate Change 4 945-948.

[13] The Times Editorial Board 2016 When it comes to water, do not keep on trucking. in: LA Times http://www.latimes.com/opinion/editorials/la-ed-water-hauling-20160729-snap-story.html

[14] James I 2015 Dry springs and dead orchards. in: Desert Sun http://www.desertsun.com/story/news/environment/2015/12/10/morocco-groundwater-depletion-africa/76788024/

[15] Bedford L 2017 Irrigation, innovation saving water in Kansas. in: agriculture.com http://www.agriculture.com/machinery/irrigation-equipment/irrigation-innovation-saving-water-in-kansas

A new data portal for permeability!

A new data portal for permeability!

Permeability data is tucked many dusty corners of the web and in even dustier reports, books and thesis. The purpose of the Crustal Permeability Data Portal is to ‘unearth’ (pun intended!) permeability data by providing links to online, peer-reviewed permeability data that is open to anyone around the world.

This data portal colldata portalates links to other data sources rather than hosting data and is a community-based effort that grew out of a compilation of papers on Crustal Permeability (Geofluids special edition and forthcoming Wiley book).

A related community-based effort is the Digital Crust which a 4D data system of spatially-located data. The Crustal Permeability Data Portal is different from the Digital Crust since it will not host data and data does not have to be spatially located.

Why should I contribute data?

  • data availability is crucial to the core scientific principle of reproducibility
  • sharing is easy and feels good
  • some journals (e.g. Nature) and most scientific funding agencies (NSF, NSERC, NERC etc.) encourage or require data management and sharing

What are the data requirements?

  • Peer-reviewed, that is published in a peer-reviewed journal, book or report
  • Permeability or other related fluid flow and transport parameters such as porosity, storage etc.
  • Hosted on a publicly available on an online data repository such as figshare or institutional webpages such as the USGS

It’s simple: All you need to do is upload your data and fill out this form.

Communicating research results through comics: is the permeability of crystalline rock in the shallow crust related to depth, lithology, or tectonic setting?

Communicating research results through comics: is the permeability of crystalline rock in the shallow crust related to depth, lithology, or tectonic setting?

Mark Ranjram, a Masters student in my research group, wrote a paper on crystalline permeability that is coming out in a special edition of Geofluids on ‘Crustal Permeability’ early in 2015 (other cool papers in early view here). Here is Mark’s awesome response when I asked him if he wanted to write a plain language summary:

PlainLanguagePermeabilityComic_1Column