the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Regional significance of historical trends and step changes in Australian streamflow
- 1Bureau of Meteorology, Melbourne, Australia
- 2Bureau of Meteorology, Perth, Australia
- 3Bureau of Meteorology, Canberra, Australia
- acurrent address: WaterNSW, Sydney
- 1Bureau of Meteorology, Melbourne, Australia
- 2Bureau of Meteorology, Perth, Australia
- 3Bureau of Meteorology, Canberra, Australia
- acurrent address: WaterNSW, Sydney
Abstract. The Hydrologic Reference Stations is a network of 467 high quality streamflow gauging stations across Australia, developed and maintained by Bureau of Meteorology, as part of ongoing responsibility under the Water Act 2007. Main objectives of the service are to observe and detect climate-driven changes in observed streamflow and to provide a quality controlled dataset for research. We investigate linear and step changes in streamflow across Australia in data from all 467 streamflow gauging stations. Data from 30 to 69 years duration ending in February 2019 was examined. We analysed data in terms of water year totals and for the four seasons. The commencement of water year varies across the country – mainly from February–March in the south to September–October in the north. We summarised our findings for each of the 12 Drainage Divisions defined by Australian Geospatial Fabric (Geofabric), and continental Australia as a whole. We used statistical tests to detect and analyse linear and step changes in seasonal and annual streamflow. Linear trends were detected by Mann-Kendall – Variance Correction Approach (MK3), Block Bootstrap Approach (MK3bs) and Long Term Persistence (Mk4) tests. The Nonparametric Pettitt test was used for step change detection and identification. Regional significance of these changes at the drainage division scale was analysed and synthesised using the Walker test. The Murray Darling Basin, with Australia’s largest river system, showed statistically significant decreasing trends for the region in annual total and all four seasons. Drainage Divisions in New South Wales, Victoria and Tasmania showed significant annual and seasonal decreasing trends. Similar results were found in south-west Western Australia, South Australia and north-east Queensland. There was no significant spatial pattern observed in Central and mid-west Australia, one possibility being the sparse density of streamflow stations and or length of data. Only the Timor Sea drainage division in northern Australia showed increasing trends and step changes in annual and seasonal streamflow and were regionally significant. Most of the step changes occurred during 1970–99. In the south-eastern part of Australia, majority of the step changes occurred in the 1990s, before the onset of the millennium drought. Long term linear trends in observed streamflow and its regional significance are consistent with observed changes in climate experienced across Australia. Findings from this study will assist water managers for long term infrastructure planning and management of water resources under climate variability and change across Australia.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(2779 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
Journal article(s) based on this preprint
Gnanathikkam Amirthanathan et al.
Interactive discussion
Status: closed
-
CC1: 'Comment on hess-2022-199', Conrad Wasko, 18 Jul 2022
As someone who has been using this world leading data set, I very much welcome this contribution. I enjoyed reading this manuscript and hope my suggestions are useful to the authors.
Line 82 & 697: “However, it was not clear how these changes relate to change in rainfall”, and “Further research is required to reveal the association of historical rainfall changes with observed streamflow”. I would argue there is literature that addresses potential drivers, that is rainfall and secondly soil moisture (Wasko et al., 2021; Wasko and Nathan, 2019).
Line 88: I agree but would note that another manuscript focussing on Australia found flood peak timing shifting alongside rainfall peak timing for frequent floods (Wasko et al., 2020).
Line 92: The following manuscript may be relevant (Gu et al., 2020)
Line 166: When I followed the link and clicked on “water year” I got the following definition: “1 July to 30 June.” This is different from what was used in this manuscript.
Line 195: Why was only mean/total streamflow considered when previously a range of percentiles was examined (Zhang et al., 2016)?
Line 230: The Pettit test is biased towards finding step changes in the centre of a time series (Mallakpour and Villarini, 2016) – though clearly the results presented here correspond well to drought periods.
Line 434: You mention the MK3test was used for short term persistence, for consistency should you mention that the MK4test was used for long term persistence?
Line 468: Were the magnitude of the trends (on a site-by-site basis) presented or are they just discussed in text?
Line 645: Does this mean non-bolded values in the table are decreasing? This could be stated here.
Line 715: A recent paper (Peterson et al., 2021) and preprint (https://hess.copernicus.org/preprints/hess-2022-147/) suggest increased evapotranspiration per unit of precipitation as a driver.
Editorial:
Line 102: The reference here is missing from the reference list and was published in 2019 (not 2020).
Line 13: Insert “The” –> “The main objectives…”
Line 215: There are some additional spaces in this sentence.
Line 395: Missing ‘l’ in global.
I am not sure Figure 8 adds much and it could possibly be omitted?
References
Gu, X., Zhang, Q., Li, J., Liu, J., Xu, C.Y., Sun, P., 2020. The changing nature and projection of floods across Australia. J. Hydrol. 584, 124703. https://doi.org/10.1016/j.jhydrol.2020.124703
Mallakpour, I., Villarini, G., 2016. A simulation study to examine the sensitivity of the Pettitt test to detect abrupt changes in mean. Hydrol. Sci. J. 61, 245–254. https://doi.org/10.1080/02626667.2015.1008482
Peterson, T.J., Saft, M., Peel, M.C., John, A., 2021. Watersheds may not recover from drought. Science (80-. ). 372, 745–749. https://doi.org/10.1126/science.abd5085
Wasko, C., Nathan, R., 2019. Influence of changes in rainfall and soil moisture on trends in flooding. J. Hydrol. 575, 432–441. https://doi.org/10.1016/j.jhydrol.2019.05.054
Wasko, C., Nathan, R., Peel, M.C., 2020. Changes in Antecedent Soil Moisture Modulate Flood Seasonality in a Changing Climate. Water Resour. Res. 56, e2019WR026300. https://doi.org/10.1029/2019WR026300
Wasko, C., Shao, Y., Vogel, E., Wilson, L., Wang, Q.J., Frost, A., Donnelly, C., 2021. Understanding trends in hydrologic extremes across Australia. J. Hydrol. 593, 125877. https://doi.org/10.1016/j.jhydrol.2020.125877
Zhang, X.S., Amirthanathan, G.E., Bari, M.A., Laugesen, R.M., Shin, D., Kent, D.M., MacDonald, A.M., Turner, M.E., Tuteja, N.K., 2016. How streamflow has changed across Australia since the 1950s: evidence from the network of hydrologic reference stations. Hydrol. Earth Syst. Sci. 20, 3947–3965. https://doi.org/10.5194/hess-20-3947-2016
- AC1: 'Reply on CC1', Mohammed Bari, 28 Sep 2022
-
RC1: 'Comment on hess-2022-199', Anonymous Referee #1, 27 Jul 2022
Summary:
This manuscript uses statistical tests to show the historical trend of the streamflow in Australian, which is intriguing. The results would be useful for the future study to learn how climate change, evapotranspiration, rainfall pattern or other factors affect the streamflow pattern.
Major Comments:
- For several statistical tests, the 0.1 of P value were used. Nowadays, many research use p value of 0.05, could you give us more detail or explanation why 0.1 has been used.
- Line 675, “abrupt shift in streamflows across water year”, it would be better to show how abrupt percentage is based on the region (spatial) and year (year), like in Table.
- How does the long drought or extreme event affect the statistical test, like linear trend? E.g. after the long drought years, will it become increasing trend?
- I recommend have a table or paragraph to show the raw data format like time interval, and explain how to deal with the raw data (take out extreme abnormal data, delete zero value). This is really important input for the following statistical tests.
- Climate change could make extreme events more often, but it could be possible that the average yearly precipitation will not change. In this case, it might have increasing trend for the wet season, decreasing trend for the dry season and no trend for the annual basis. Do you find any station having this similar situation?
Minor Comments:
- Line 39, please add reference to support the statement
- Line156, 23,2846 km separator is wrong. It should be 232,846
- Figure 1, north arrow is missing
- Line 409, how do you convert GL/year to mm/year
- In section 4.2, the authors mention the Figure 3 several times. Did you mean by Figure 5?
- Line 706, where is the Figure 14?
- AC2: 'Reply on RC1', Mohammed Bari, 28 Sep 2022
-
RC2: 'Comment on hess-2022-199', Nir Krakauer, 26 Aug 2022
This work presents trends in annual and seasonal mean streamflow since 1950 across an expanded Australia-wide network of reference gauges. It was found that streamflow has mostly decreased, which can be thought of as a step change in the 1990s, except for some areas in the far north that saw increasing streamflow. The authors highlight the widespread interannual persistence of streamflow anomalies in Australia, and make use of statistical trend tests that account for autocorrelation. This is a valuable contribution and in my view should be published, subject to minor revisions.
There are a few unclear sentences, such as at line 383: "The main objective is to assess whether the number of locations with significant trends occur at a regional scale or not" and 540: "It is least sensitive to outliers, and skewed distribution makes it most suitable for the analysis of streamflow data". These should be rephrased. In general some proofreading is needed.
The terminology "linear trend" is confusing at times (e.g. Section 3.1), as the MK test is for monotonic but not necessarily linear changes (including step changes).
In Section 3.3, mention that the test for regional significance is actually conservative, as it is based on a null hypothesis of independent trends across stations, when the trends within a region are actually positively correlated.
Check units -- e.g., at line 409, should it be 1.8 mm/year per year?
In Section 5.3, mention the possible role of CO2 increase in reducing vegetation evapotranspiration rate, which could increasing streamflow in certain climatic and geomorphic settings, offsetting the increased evaporation rate due to warming -- cf. for example my 2008 HESS paper "Mapping and attribution of change in streamflow in the coterminous United States".
In Section 5.4, consider mentioning that, given the decadal persistence in streamflow regimes, it will be useful to add the available information on streamflows before 1950 to a future analysis to better seperate trends from oscillations.
- AC3: 'Reply on RC2', Mohammed Bari, 28 Sep 2022
Peer review completion
Interactive discussion
Status: closed
-
CC1: 'Comment on hess-2022-199', Conrad Wasko, 18 Jul 2022
As someone who has been using this world leading data set, I very much welcome this contribution. I enjoyed reading this manuscript and hope my suggestions are useful to the authors.
Line 82 & 697: “However, it was not clear how these changes relate to change in rainfall”, and “Further research is required to reveal the association of historical rainfall changes with observed streamflow”. I would argue there is literature that addresses potential drivers, that is rainfall and secondly soil moisture (Wasko et al., 2021; Wasko and Nathan, 2019).
Line 88: I agree but would note that another manuscript focussing on Australia found flood peak timing shifting alongside rainfall peak timing for frequent floods (Wasko et al., 2020).
Line 92: The following manuscript may be relevant (Gu et al., 2020)
Line 166: When I followed the link and clicked on “water year” I got the following definition: “1 July to 30 June.” This is different from what was used in this manuscript.
Line 195: Why was only mean/total streamflow considered when previously a range of percentiles was examined (Zhang et al., 2016)?
Line 230: The Pettit test is biased towards finding step changes in the centre of a time series (Mallakpour and Villarini, 2016) – though clearly the results presented here correspond well to drought periods.
Line 434: You mention the MK3test was used for short term persistence, for consistency should you mention that the MK4test was used for long term persistence?
Line 468: Were the magnitude of the trends (on a site-by-site basis) presented or are they just discussed in text?
Line 645: Does this mean non-bolded values in the table are decreasing? This could be stated here.
Line 715: A recent paper (Peterson et al., 2021) and preprint (https://hess.copernicus.org/preprints/hess-2022-147/) suggest increased evapotranspiration per unit of precipitation as a driver.
Editorial:
Line 102: The reference here is missing from the reference list and was published in 2019 (not 2020).
Line 13: Insert “The” –> “The main objectives…”
Line 215: There are some additional spaces in this sentence.
Line 395: Missing ‘l’ in global.
I am not sure Figure 8 adds much and it could possibly be omitted?
References
Gu, X., Zhang, Q., Li, J., Liu, J., Xu, C.Y., Sun, P., 2020. The changing nature and projection of floods across Australia. J. Hydrol. 584, 124703. https://doi.org/10.1016/j.jhydrol.2020.124703
Mallakpour, I., Villarini, G., 2016. A simulation study to examine the sensitivity of the Pettitt test to detect abrupt changes in mean. Hydrol. Sci. J. 61, 245–254. https://doi.org/10.1080/02626667.2015.1008482
Peterson, T.J., Saft, M., Peel, M.C., John, A., 2021. Watersheds may not recover from drought. Science (80-. ). 372, 745–749. https://doi.org/10.1126/science.abd5085
Wasko, C., Nathan, R., 2019. Influence of changes in rainfall and soil moisture on trends in flooding. J. Hydrol. 575, 432–441. https://doi.org/10.1016/j.jhydrol.2019.05.054
Wasko, C., Nathan, R., Peel, M.C., 2020. Changes in Antecedent Soil Moisture Modulate Flood Seasonality in a Changing Climate. Water Resour. Res. 56, e2019WR026300. https://doi.org/10.1029/2019WR026300
Wasko, C., Shao, Y., Vogel, E., Wilson, L., Wang, Q.J., Frost, A., Donnelly, C., 2021. Understanding trends in hydrologic extremes across Australia. J. Hydrol. 593, 125877. https://doi.org/10.1016/j.jhydrol.2020.125877
Zhang, X.S., Amirthanathan, G.E., Bari, M.A., Laugesen, R.M., Shin, D., Kent, D.M., MacDonald, A.M., Turner, M.E., Tuteja, N.K., 2016. How streamflow has changed across Australia since the 1950s: evidence from the network of hydrologic reference stations. Hydrol. Earth Syst. Sci. 20, 3947–3965. https://doi.org/10.5194/hess-20-3947-2016
- AC1: 'Reply on CC1', Mohammed Bari, 28 Sep 2022
-
RC1: 'Comment on hess-2022-199', Anonymous Referee #1, 27 Jul 2022
Summary:
This manuscript uses statistical tests to show the historical trend of the streamflow in Australian, which is intriguing. The results would be useful for the future study to learn how climate change, evapotranspiration, rainfall pattern or other factors affect the streamflow pattern.
Major Comments:
- For several statistical tests, the 0.1 of P value were used. Nowadays, many research use p value of 0.05, could you give us more detail or explanation why 0.1 has been used.
- Line 675, “abrupt shift in streamflows across water year”, it would be better to show how abrupt percentage is based on the region (spatial) and year (year), like in Table.
- How does the long drought or extreme event affect the statistical test, like linear trend? E.g. after the long drought years, will it become increasing trend?
- I recommend have a table or paragraph to show the raw data format like time interval, and explain how to deal with the raw data (take out extreme abnormal data, delete zero value). This is really important input for the following statistical tests.
- Climate change could make extreme events more often, but it could be possible that the average yearly precipitation will not change. In this case, it might have increasing trend for the wet season, decreasing trend for the dry season and no trend for the annual basis. Do you find any station having this similar situation?
Minor Comments:
- Line 39, please add reference to support the statement
- Line156, 23,2846 km separator is wrong. It should be 232,846
- Figure 1, north arrow is missing
- Line 409, how do you convert GL/year to mm/year
- In section 4.2, the authors mention the Figure 3 several times. Did you mean by Figure 5?
- Line 706, where is the Figure 14?
- AC2: 'Reply on RC1', Mohammed Bari, 28 Sep 2022
-
RC2: 'Comment on hess-2022-199', Nir Krakauer, 26 Aug 2022
This work presents trends in annual and seasonal mean streamflow since 1950 across an expanded Australia-wide network of reference gauges. It was found that streamflow has mostly decreased, which can be thought of as a step change in the 1990s, except for some areas in the far north that saw increasing streamflow. The authors highlight the widespread interannual persistence of streamflow anomalies in Australia, and make use of statistical trend tests that account for autocorrelation. This is a valuable contribution and in my view should be published, subject to minor revisions.
There are a few unclear sentences, such as at line 383: "The main objective is to assess whether the number of locations with significant trends occur at a regional scale or not" and 540: "It is least sensitive to outliers, and skewed distribution makes it most suitable for the analysis of streamflow data". These should be rephrased. In general some proofreading is needed.
The terminology "linear trend" is confusing at times (e.g. Section 3.1), as the MK test is for monotonic but not necessarily linear changes (including step changes).
In Section 3.3, mention that the test for regional significance is actually conservative, as it is based on a null hypothesis of independent trends across stations, when the trends within a region are actually positively correlated.
Check units -- e.g., at line 409, should it be 1.8 mm/year per year?
In Section 5.3, mention the possible role of CO2 increase in reducing vegetation evapotranspiration rate, which could increasing streamflow in certain climatic and geomorphic settings, offsetting the increased evaporation rate due to warming -- cf. for example my 2008 HESS paper "Mapping and attribution of change in streamflow in the coterminous United States".
In Section 5.4, consider mentioning that, given the decadal persistence in streamflow regimes, it will be useful to add the available information on streamflows before 1950 to a future analysis to better seperate trends from oscillations.
- AC3: 'Reply on RC2', Mohammed Bari, 28 Sep 2022
Peer review completion
Journal article(s) based on this preprint
Gnanathikkam Amirthanathan et al.
Gnanathikkam Amirthanathan et al.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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