Daily storage volume of Melbourne Water's dams, 1940 to 2020
liveDaily observed storage volumes from Melbourne Water for the Cardinia, Greenvale, Maroondah, O'Shannassy, Silvan, Sugarloaf, Tarago, Thomson, Upper Yarra and Yan Yean storages, one row per dam and day. Volumes are measured by telemetry at 8 am and checked against field observations. The record runs from 1 January 1940 to 29 February 2020, in local days.
Also called: Melbourne Water daily storage volumes, Melbourne dam levels daily, Thomson Dam storage history, Water supply daily volume observed for storage dams.
- What does each row hold?
- The volume held in one of Melbourne Water's ten storage dams on one day, in megalitres, and the share of the dam's capacity that volume is. Maroondah, O'Shannassy, Silvan and Yan Yean go back to 1 January 1940. The others start when they came into the record.
- Why is the time 1 pm or 2 pm?
- The publisher's service stores each reading as a moment in UTC, so local midnight appears as 1 pm or 2 pm on the previous day depending on daylight saving. The value is kept as published. The volume itself is recorded at 8 am each day.
- Why does it stop in February 2020?
- The portal's file of the complete record ends on 29 February 2020 and has not been extended. Some days have no volume, and those cells are null.
Made for agents
Connect your AI agent
Any agent that connects to publicdata.au/mcp can find this dataset, read its 5 fields, count and filter across all 218,426 rows and diff its versions. Every answer names the version and carries Melbourne Water's attribution. No key.
Then ask it: What does Daily storage volume of Melbourne Water's dams, 1940 to 2020 hold, and what changed in the newest version?
claude mcp add --transport http publicdata https://publicdata.au/mcpOne command. No key, no account, no sign-up.
Settings, then Connectors, then Add custom connector, and paste https://publicdata.au/mcp.
On the web and in the desktop app.
https://publicdata.au/mcpStreamable HTTP. Cursor, VS Code or any client that connects to a remote MCP server.
Using https://publicdata.au/llms.txt, how many people died on Queensland roads in 2025, by month?For an assistant that reads the web and has no connector.
The dashboard above, live. Click to filter, add panels, share a link or embed a frame.
Query APIFilter and count from a URLaggregate?group=dam&metric=avg.volume_percent_full&record_date=eq.2020-02-28T13%3A00%3A00
% full by dam where recorded at (utc) is 2020-02-28T13:00:00: 92.5 Sugarloaf, 90.7 Greenvale, 87.3 Silvan, 86.7 Maroondah.
Download it as Excel, CSV, JSON and 6 more formats
Pick a format and a version. Excel is picked first because it opens in the tools most offices have. The URL is yours to keep. A dated version never changes.
In your own tools
Use it in Excel, R, Python and more
Excel and Power BI read the CSV from its address and refresh from it. The R and Python packages take any dataset on this site by its slug, so a new dataset needs no new release. The DuckDB file attaches read-only over HTTPS, and a query reads only the blocks it touches.
The dated URL in the code never changes. https://publicdata.au/d/vic-melbourne-water-daily-storage/latest/ redirects to the newest version.
https://publicdata.au/d/vic-melbourne-water-daily-storage/latest/data.csvIn Excel choose Data, then From Web, and paste this address. Excel keeps it, so Refresh All reads the newest version. A sheet holds about a million rows; past that, load the query to the Data Model.
https://publicdata.au/d/vic-melbourne-water-daily-storage/latest/data.csvIn Power BI Desktop choose Get data, then Web, paste this address and choose Anonymous when asked how to sign in. A scheduled refresh reads the same address, so the report follows each new version.
library(publicdataau)
df <- pd_read("vic-melbourne-water-daily-storage")
pd_attribution(df)install.packages("publicdataau"). pd_read() fetches the version's Parquet file; pd_rows() and pd_aggregate() ask the query API instead, and pd_connect() attaches the DuckDB file.
import publicdata_au as pd_au
df = pd_au.read("vic-melbourne-water-daily-storage")
df.attrs["publicdata"]["attribution"]pip install "publicdata-au[pandas]". read() fetches the version's Parquet file; rows() and aggregate() ask the query API, and connect() attaches the DuckDB file.
INSTALL httpfs; LOAD httpfs;
ATTACH 'https://publicdata.au/d/vic-melbourne-water-daily-storage/v/2026-06-26/data.duckdb' AS vic_melbourne_water_daily_storage (READ_ONLY);
SELECT dam, count(*) FROM vic_melbourne_water_daily_storage.records GROUP BY 1 ORDER BY 2 DESC;The DuckDB file attaches read-only over HTTPS and only the blocks a query touches are read. Parquet works the same way: FROM read_parquet(url).
const res = await fetch("https://publicdata.au/api/v1/datasets/vic-melbourne-water-daily-storage/aggregate?group=dam&metric=avg.volume_percent_full&record_date=eq.2020-02-28T13%3A00%3A00");
const { rows, publicdata } = await res.json();
console.log(rows, publicdata.attribution);The query API answers a page on any site as well as Node, with no key. It returns the rows and the attribution the licence asks for.
What is in it
Column names are made snake_case and the publisher's original header is kept beside each one. Blank cells are null. Nothing is added, removed, ranked or summarised.
| Field | Type | Publisher's header | Note |
|---|---|---|---|
| record_date | datetime | recorddate | The day of the reading as the publisher's service stores it, local midnight in UTC. |
| dam | string | dam | |
| volume_ml | number | volume_ML | The volume held, in megalitres. Null where no reading was recorded. |
| volume_percent_full | number | volume_percentfull | The volume as a percentage of the dam's capacity, as published. |
| object_id | integer | ObjectId | The row's id in the publisher's service. |
Download this table as a data dictionary (Excel), or read it as a Frictionless Table Schema at schema.json. The key is object_id.
A sample of 10 rows
From the latest version, newest first by recorded at (UTC), each dam in turn, then in the publisher's order, with every field. A blank cell is shown as null.
| record_date | dam | volume_ml | volume_percent_full | object_id |
|---|---|---|---|---|
| 2020-02-28T13:00:00 | Cardinia | 222822 | 77.66 | 201 |
| 2020-02-28T13:00:00 | Greenvale | 24351 | 90.72 | 202 |
| 2020-02-28T13:00:00 | O'Shannassy | 279 | 8.93 | 203 |
| 2020-02-28T13:00:00 | Yan Yean | 20687 | 68.35 | 204 |
| 2020-02-28T13:00:00 | Upper Yarra | 85591 | 42.67 | 205 |
| 2020-02-28T13:00:00 | Maroondah | 19219 | 86.65 | 206 |
| 2020-02-28T13:00:00 | Silvan | 35306 | 87.29 | 207 |
| 2020-02-28T13:00:00 | Tarago | 24438 | 65.02 | 208 |
| 2020-02-28T13:00:00 | Thomson | 595885 | 55.79 | 209 |
| 2020-02-28T13:00:00 | Sugarloaf | 89072 | 92.53 | 210 |
The first row as JSON
As it appears in data.json.
GET https://publicdata.au/d/vic-melbourne-water-daily-storage/latest/data.json
{
"record_date": "2020-01-24T13:00:00",
"dam": "Cardinia",
"volume_ml": 233973.0,
"volume_percent_full": 81.54,
"object_id": 1
}
Query it without downloading
The query API returns only the rows you ask for, as JSON, NDJSON or CSV, from the same typed rows as the files. It needs no key. Build a query here and run it, then copy the URL or the code into your own work.
How filters work
Each filter is field=operator.value in the query string, and every filter must match. A number or date field compares as a number or date, and a text field compares as text. Values are case-sensitive except with ilike. Prefix not. to negate a filter, as not.eq.value.
| Operator | What it matches |
|---|---|
| eq.value | Equal to the value. |
| neq.value | Not equal to the value. A blank cell does not match. |
| gt.value | Greater than the value. |
| gte.value | Greater than or equal to the value. |
| lt.value | Less than the value. |
| lte.value | Less than or equal to the value. |
| like.*text* | Matches a pattern where * stands for any run of characters. Case-sensitive. |
| ilike.*text* | The same as like, ignoring case. |
| in.(a,b,c) | Equal to any value in the list. A value cannot contain a comma. |
| is.null | Blank in the source, or suppressed by the publisher. |
| Parameter | What it does |
|---|---|
| select | Fields to return, comma-separated. Every field when absent. |
| order | field.asc or field.desc, comma-separated. The publisher's row order when absent. |
| limit | Rows per page, 1 to 10,000. 100 when absent. |
| offset | Rows to skip. The next URL in each answer sets it for you. |
| group | On aggregate, fields to group by, comma-separated. |
| metric | On aggregate, count, sum.field, avg.field, min.field or max.field, comma-separated. count when absent. |
| format | json, ndjson or csv. JSON carries the provenance header, and the others carry it in response headers. |
Without a version the API answers from the newest loaded version, and that answer changes when the publisher releases again. Put versions/<date>/ before rows or aggregate for an answer that never changes. /api/v1/datasets/<slug>/versions lists the loaded versions. The 1 newest version is loaded; versions lists them.
The API allows 60 requests in 10 seconds from one address. Above that it answers 429 for 10 seconds with a Retry-After header, a RateLimit-Policy header and a JSON body that gives the limit. Every API answer carries the same RateLimit-Policy. A client should wait the Retry-After seconds, or read the files, which have no limit. openapi.json describes this dataset's query paths for client generators and agents.
https://publicdata.au/mcp is a remote MCP server over Streamable HTTP with the same tools, defined in the same place, so a tool added to the pages is added here too. It needs no key and no account. The row tools are held to the query API's limit of 60 queries in 10 seconds from one address, and each call is two queries because it also counts the matching rows. Add it to Claude Code with claude mcp add --transport http publicdata https://publicdata.au/mcp, add the URL in Claude as a custom connector, or give it to any client that connects to remote MCP servers. Each queryable dataset is also a resource at https://publicdata.au/d/<slug>/fields.json, which lists its fields with their types, the publisher's descriptions, their ranges and the values they hold, so an agent can read a dataset's shape before it writes a query.
Smaller files
The same rows split by a field, one JSON file each. Each file carries the same provenance header.
By dam (10 files)
by/dam/index.json lists every file with its row count.
What changed
One version for every release the publisher has made since this site started following the dataset. The date is the day the file changed on the portal.
- 2026-06-26 218,426 rows5 fieldsutf-8-sig320ae3a8bb01
versions.json · changes.json · history.tar.zst (1.5 MB, every version's Parquet and manifest)
Questions
How do I download Daily storage volume of Melbourne Water's dams as a CSV file?
Open https://publicdata.au/d/vic-melbourne-water-daily-storage/latest/data.csv. It redirects to the newest dated version, which is https://publicdata.au/d/vic-melbourne-water-daily-storage/v/2026-06-26/data.csv today. The same path serves Excel, JSON, Parquet, SQLite, DuckDB, NDJSON, Arrow. A dated URL never changes, so use it when the file must stay the same.
Can I open Daily storage volume of Melbourne Water's dams in Excel?
Yes. https://publicdata.au/d/vic-melbourne-water-daily-storage/v/2026-06-26/data.xlsx is a workbook with the 218,426 rows on a records sheet, the field list on a second sheet and the provenance on a third. The CSV also opens in Excel. https://publicdata.au/d/vic-melbourne-water-daily-storage/v/2026-06-26/data.csv.gz is the CSV at about a tenth of the size.
What years does Daily storage volume of Melbourne Water's dams cover?
The current version covers 1940 to 2018. Each release from Melbourne Water becomes a new dated version here, and earlier versions stay online.
How often is Daily storage volume of Melbourne Water's dams updated?
Melbourne Water no longer updates it. This site checks the portal every week and adds a dated version when the file changes.
Can I use Daily storage volume of Melbourne Water's dams commercially?
Yes. CC BY 4.0 allows commercial use, redistribution and derived works as long as the attribution is kept. The attribution string is in this page's side column and inside every file.
Is this the official source for Daily storage volume of Melbourne Water's dams?
No. The publisher is Melbourne Water Corporation, and its page is https://discover.data.vic.gov.au/dataset/water-supply-daily-volume-observed-for-storage-dams-operated-by-melbourne-water. This site republishes the publisher's file without changing its content. The original sits beside every version as source.csv with its SHA-256, so the two can be compared.
How do I get only the rows for one dam?
Every version has one JSON file per value of dam, 10 files in the current version, listed with row counts at https://publicdata.au/d/vic-melbourne-water-daily-storage/v/2026-06-26/by/dam/index.json. For example https://publicdata.au/d/vic-melbourne-water-daily-storage/v/2026-06-26/by/dam/maroondah.json holds the 29,280 rows where dam is Maroondah.
What this site did to the data. Cells were typed, headers were renamed and the encoding was made UTF-8. Rows were left alone. The publisher's file sits beside every version as source.csv so the change can be checked.