Atmospheric rivers drive monthly rainfall availability in New Zealand

doi:10.21203/rs.3.rs-3103804/v1

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Atmospheric rivers drive monthly rainfall availability in New Zealand

https://doi.org/10.21203/rs.3.rs-3103804/v1

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Atmospheric rivers (ARs) are elongated channels of enhanced water vapour transport in midlatitudes and contribute significantly to annual rainfall in northern and western New Zealand. Based on data from 529 rain gauge sites and detected landfalling ARs over New Zealand from 1950 to 2020, this study indicates that the presence of ARs generally leads to normal or wetter months, and a lack of ARs generally leads to dry months in the warm season in northern and western New Zealand. For eastern New Zealand, ARs do not generally dominate monthly rainfall availability in line with annual observations. Results in this study raise the need for continued understanding of the presence or lack of ARs on water resources month to month to provide further guidance on dealing with rising uncertainties in water supply, hydropower generation, and flood control in a changing climate.

Earth and environmental sciences/Hydrology

Earth and environmental sciences/Climate sciences/Hydrology

Atmospheric rivers (ARs) are filamentary channels of enhanced poleward water vapour transport in the lower troposphere, primarily occurring in midlatitudes^1–7. They are a key component of the global water cycle, and their presence is often associated with frontal systems^2,4–6 and cyclones^5,6,8–13. Extreme precipitation is related to ARs globally ¹⁴, and the absence of ARs may lead to hydrological droughts in many locations, one of these being New Zealand¹⁵, where AR frequency and intensity have been projected to increase in the southern mid-latitudes with climate change¹⁶.

New Zealand spans from 34°S to 47°S and is surrounded by ocean. Mountain ranges in the country trend northeast-southwest, peaking at 3764m. A prevailing westerly airflow results in the more extreme precipitation typically observed on the western (windward) side of the mountainous areas^17–20. Heavy rainfall led by extratropical cyclones and frontal systems is typical in the northern North Island of the country^21–23. More than 50% of the annual rainfall in these western and northern areas is attributed to ARs, whereas the eastern regions see the opposite, with annual rainfall mainly provided by non-AR rainfall events^24,25.

Several studies have recently investigated hydroclimatological connections in New Zealand between ARs and extreme precipitation^24–28, ARs and historical flood events²⁹ and the climate drivers of ARs that lead to extreme floods³⁰. However, there is still a need to further investigate the role of ARs spatially at higher resolution temporal scales (than annually) for water resource management applications, such as reservoir operations.

One of the central challenges in large reservoir operations is that they are often constructed for multiple purposes, such as flood control, hydropower generation, irrigation, and potable water supplies. In such cases, their management can be complex with different competing objectives with longer-term planning often based on seasonal and terrestrial hydrological information (i.e. rain gauges, river gauges, and available storage) and may not be integrated with weather forecasts. Climate change introduces further uncertainties in the timing and magnitude of inflows and outflows^31,32, where those used for flood control and water supply are particularly vulnerable ³³. Understanding the role of ARs on rainfall availability at higher monthly temporal resolutions will therefore provide useful insights for assessing potential impacts and the feasibility of different reservoir operation approaches, such as forecast informed reservoir operation (FIRO) by incorporating precipitation forecasts^34,35.

The role of ARs on rainfall availability at the monthly timescale remains unknown in New Zealand, i.e., whether the presence of ARs consistently leads to wet months or the absence of ARs leads to dry months. As such, this study attempts to address this issue. In doing so, we first compare accumulated rainfall from ARs and non-AR rainfall events on a monthly scale to assess in which instances ARs are the primary source of rainfall. Secondly, a comparison between rainfall accumulation sourced from ARs and non-AR rainfall events at a monthly scale is carried out in three monthly rainfall scenarios we define as (i) wet months indicated by anomalously high monthly rainfall, (ii) normal months indicated by normal monthly rainfall, and (iii) dry months indicated by anomalously low monthly rainfall. The assessments will provide insight into the ARs’ role in rainfall availability in New Zealand.

2.1 Data

AR detection is based on the integrated water vapour transport (IVT) magnitude, as calculated by applying the Eulerian framework^36–38 given as:

$$IVT=\sqrt{{\left(\frac{1}{g}{\int }_{1000hPa}^{300hPa}q\overrightarrow{u} dp\right)}^{2}+{\left(\frac{1}{g}{\int }_{1000hPa}^{300hPa}q\overrightarrow{v} dp\right)}^{2}}$$

where q is specific humidity (kg kg^− 1), $\overrightarrow{u}$ and $\overrightarrow{v}$are zonal and meridional wind vectors (m s^− 1), respectively, g is the gravitational acceleration (9.81 m s^− 2), and $dp$ is the pressure difference between two adjacent atmospheric pressure levels (hPa). To compute IVT, 6-hourly specific humidity and wind vectors at 20 vertical pressure levels (1000hPa-300hPa) were retrieved from the most recent reanalysis dataset version available in the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 reanalysis project ³⁹. Data retrieved covered 1950 to 2020 with a 0.25° spatial resolution over a domain of 0°-70°S and 100°E-120°W.

Station-based daily rainfall records were obtained from New Zealand's national climate database web portal (NIWA, cliflo.niwa.co.nz). 529 rain gauges with daily records for at least 350 months from 1950 to 2020 were selected (Fig. 1). These sites were split into five geographic groups based on their topography, the mean fraction of annual rainfall contributed by ARs from Shu et al. (2021), and pre-determined climate zones. The rain gauge data in the western regions and northern North Island are similar, with over 50% of their annual rainfall provided by ARs, whereas the opposite holds true (majority non-AR) for the remaining regions^24,25.

2.2 AR Detection

The AR detection algorithm developed by Guan and Waliser⁴⁰ (hereafter referred to as GW15) was used in this study. The algorithm involves a set of conditions on IVT grid cells for detecting AR objects at a time step within a domain of interest. The AR object refers to the instantaneous area where IVT cells meet the required conditions. The AR detection procedure and conditions are described by GW15, which considers: the grided IVT magnitude greater than the 85th percentile value centred at a 5-month window at each grid cell for that month, the poleward direction of an AR object, the IVT direction coherence within an AR object, the consistency between an AR object mean IVT direction and its orientation, and the geometry of an AR object (typically the length of an AR object is more than two times to its width). Archived landfalling AR dates from GW15 demonstrate over 90% agreement with other regional-specific AR detection techniques^37,41,42.

Additionally, GW15 has been widely used as a benchmark in different regions for AR tracking studies^12,43,44 and regional-specific AR detection algorithm development^45,46. Figure 2 illustrates an example AR event detected using GW15 from 15–17 July 2021 (UTC), which led to heavy rainfall, causing severe floods, river bank breaks, and landslips in many parts of New Zealand⁴⁷. More than 2000 residents were evacuated during that period, and over 500 properties were damaged, resulting in $140.47 million (NZD) of property damage insurance claims⁴⁸.

2.3 AR-induced Monthly Rainfall and Comparison between AR and Non-AR Monthly Rainfall

Based on geographical coordinates of four grid points enclosing a rain gauge site of interest, all four points that indicate that AR activities last for more than one timestep within a day were archived, rainfall record within that day was labelled as AR-induced rainfall, and a series of rainfall records were then divided into AR-induced and non-AR rainfall series. The monthly AR rainfall, monthly non-AR rainfall, and the fraction of monthly rainfall contributed by ARs were calculated accordingly. ARs are then said to dominate a month’s rainfall when more than 50% of monthly rainfall was attributed to them, and vice versa for non-AR-dominated months. Furthermore, monthly rainfall was standardised based on the monthly mean and standard deviation. A standardised index greater than 1 indicates a wet month, less than − 1 indicates a dry month, and an index between − 1 and 1 indicates a normal month.

Both AR and non-AR monthly rainfall series are not normally distributed. The Mann-Whitney U test⁴⁹ (hereafter U test) was used to determine whether there is a difference between the two. The two-tailed U test was carried out for all months and then for the three scenarios of monthly rainfall regarding AR and non-AR monthly rainfall accumulation. The number of rain gauge sites with the AR monthly rainfall series that have a higher rank sum and passed the U test at a significance level of 0.05 was counted for each geographical group. The procedure for the U test without tied ranks is as follows:

$${U}_{1}={n}_{1}{n}_{2}+\frac{{n}_{1}({n}_{2}+1)}{2}-{T}_{1},$$

$${U}_{2}={n}_{1}{n}_{2}+\frac{{n}_{2}({n}_{1}+1)}{2}-{T}_{2},$$

$$U=\text{m}\text{i}\text{n}\text{i}\text{m}\text{u}\text{m}({U}_{1}, {U}_{2}),$$

$${\mu }_{U}=\frac{{n}_{1}{n}_{2}}{2},$$

$${\sigma }_{U}=\sqrt{\frac{{n}_{1}{n}_{2}({n}_{1}+{n}_{2}+1)}{12}, }$$

$$z= \frac{U-{\mu }_{U}}{{\sigma }_{U}},$$

where ${n}_{1}$ and ${n}_{2}$ are the size of the AR and non-AR monthly rainfall series, respectively. ${T}_{1}$ and ${T}_{2}$ are the rank sum for AR and non-AR monthly rainfall series, respectively. $U$ is the minimum value of ${U}_{1}$and ${U}_{2}$.

The procedure for the U test with tied ranks follows the same steps except for Eq. (6) for the standard error of U, which is instead given by:

$${\sigma }_{ties}= \sqrt{\frac{{n}_{1}{n}_{2}}{12}\left(\left(n+1\right)-\frac{\sum _{k=1}^{K}{t}_{k}^{3}-{t}_{k}}{n\left(n-1\right)}\right),}$$

where $K$ is the total number of tied ranks, ${t}_{k}$ is the number of ties for sharing the $k$th rank, and $n$ is the sum of ${n}_{1}$and${n}_{2}.$

The method to determine the p-value for the U test depends on the sizes of the series. The exact values can be used and read from a table for one of the samples with a size less than 8 and without tied ranks. For one of the samples with a size greater than 8, the p-value can be determined by z-score (Eq. 7) based on the standard normal distribution table⁵⁰.

3.1 Contribution of ARs to Monthly Rainfall

Figure 3 shows the fraction of monthly rainfall associated with ARs over the five geographical groups of rain gauge sites. The contribution of ARs to monthly rainfall ranges from 0 to 100%, implying the role shift between ARs and non-AR rainfall events from month to month. A median value of over 50% indicates that ARs are the major contributor to monthly rainfall in general for corresponding groups and months. Figure 4 shows that the fraction of rain gauge sites in the AR monthly rainfall series has a higher rank sum, and the difference between AR and non-AR monthly rainfall series is statistically significant. A value over 50% indicates that more than half of rain gauge sites observe ARs are a more significant component for monthly rainfall for corresponding groups and months.

Based on results in Figs. 3 and 4, most rain gauge sites in northern (Fig. 3a and Fig. 4a) and western (Fig. 3b and Fig. 4b) North Island observe ARs significantly contributing to more than 50% of monthly rainfall in months of the warm season (October to February). This is similar to the western South Island (Fig. 3d and Fig. 4d), with ARs significantly contributing to more than 50% of monthly rainfall in the slightly longer warm season (October to March). ARs do not significantly contribute to monthly rainfall in the east of the North and South Islands (Fig. 3c, 3e and Fig. 4c, 4e).

3.2 Role of ARs on Monthly Rainfall Availability in Wet, Normal, and Dry Months

Figure 5 illustrates the fraction of monthly rainfall associated with ARs in wet, dry, and normal months. The percentage of rain gauge sites where AR monthly rainfall series have a higher rank sum and the difference between AR and non-AR monthly rainfall series is statistically significant are illustrated in Fig. 6.

Most rain gauge sites generally observe more than 50% of abnormally high monthly rainfall (wet months) contributed by ARs in March and October in northern North Island (Fig. 5a and Fig. 6a). The majority of rain gauge sites show monthly rainfall in the AR series has a higher rank sum, and the difference between AR and non-AR monthly rainfall series is statistically significant in the two months. For the western North Island, most rain gauge sites see more than 50% of rainfall accumulation in wet months contributed by ARs between October to February (Fig. 5b and Fig. 6b). ARs are the major contributor to the wet month in the western South Island from September to April (Fig. 5d and Fig. 6d).

ARs generally contribute to more than 50% of normal monthly rainfall (normal months) for most rain gauge sites in northern North Island from October to January and April. Most rain gauge sites see more than 50% of normal monthly rainfall contributed by ARs from October to February and April in western North Island, and it is similar in western South Island but includes March (Fig. 5a-b, d and Fig. 6a-b, d).

When most rain gauge sites experience abnormally low monthly rainfall (dry months), ARs are not the major contributor to monthly rainfall. Most rain gauge sites do not see the AR monthly rainfall series with a higher rank sum in dry months, although ARs can contribute to more than 50% of monthly rainfall in some cases (Figs. 5 and 6). As expected, ARs are not the major contributor to monthly rainfall for most rain gauge sites over eastern New Zealand, regardless of monthly rainfall scenarios.

The role of ARs on monthly rainfall across New Zealand was investigated in this study. Landfalling ARs were detected based on the GW15 AR detection algorithm. 529 rain gauge sites across the country with at least 350 months of daily rainfall records were selected, and AR-induced rainfall events in each time series were identified. Monthly accumulated rainfall associated with ARs and non-AR rainfall events was calculated. These were aggregated into geographic groups, with the fraction of monthly rainfall associated with ARs calculated accordingly. The U test was used to evaluate whether there is a statistically significant difference between the accumulated monthly rainfall associated with ARs and non-AR rainfall events each month and whether ARs generally deliver higher monthly rainfall amounts. Furthermore, the tests were carried out between AR and non-AR monthly rainfall series in wet, normal, and dry month scenarios to reflect AR’s dominance on monthly rainfall availability and consequent monthly rainfall conditions.

For most sites in northern and western New Zealand, particularly in months of the warm season, we observe ARs contributing to more than 50% of monthly rainfall. Furthermore, the presence of ARs generally leads to normal or wet months, providing higher accumulated monthly rainfall than non-AR rainfall events. On the other hand, in dry months, ARs do not generally contribute more to the accumulated rainfall than non-AR rainfall events. For most sites in eastern New Zealand, ARs do not generally deliver higher monthly rainfall than non-AR rainfall events; therefore, monthly rainfall availability in this region is primarily dominated by non-AR rainfall events.

We only investigated AR’s impact on rainfall in this study. However, ARs also play an important role in delivering moisture for large snowfall events^26,28 and should be investigated further.

The results of this study raise the need for continued analysis around the role shift between AR and non-AR rainfall events on monthly rainfall and potential changes to these patterns with climate change. AR frequency and intensity have been projected to increase in the southern mid-latitudes. However, questions surrounding climate change impacts on ARs in New Zealand remain unanswered, particularly for regions where ARs have strong hydrometeorological impacts. For example, those regions that overlap catchments where water is intensively used for water supply, hydropower generation, and irrigation are particularly of interest to determine whether reliable supplies can be maintained throughout dry periods or if alternative water resource management is required. Building on this knowledge base is imperative to help cope with future uncertainties in New Zealand’s climate.

Acknowledgements

The authors thank NIWA for providing access to the New Zealand meteorological data from the National Climate Database and ECMWF for providing access to the ERA5 dataset. The authors wish to acknowledge the Centre for eResearch at the University of Auckland for their help in facilitating this research http://www.eresearch.auckland.ac.nz. The AR data and the AR detection code were provided by Bin Guan via https://ucla.box.com/ARcatalog. The development of the AR detection algorithm and databases was supported by NASA. This research is supported by the National Key Research and Development Program of China (grant no. 2016YFE0201900) and the State Key Laboratory of Hydroscience and Engineering (grant no. 2017KY04).

Author contributions

J.S. contributed to the data analysis, results and figures interpretation, and manuscript writing. C.Z. contributed to the manuscript revision. C.Z., T.L., A.S., and B.M. contributed to the concept design and guided data analysis and results interpretation.

Competing interests

The authors declare no competing financial interests.

Data Availability Statement

Data used in this study are freely available online (registration required):

ERA5 6-hourly data on pressure levels from 1950 to 1978 (preliminary version): https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-preliminary-back-extension?tab=overview

ERA5 6-hourly data on pressure levels from 1979 to present: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview

Daily rainfall: https://cliflo.niwa.co.nz/pls/niwp/wgenf.genform1

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No competing interests reported.

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Atmospheric rivers drive monthly rainfall availability in New Zealand

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Abstract

Figures

1 Introduction

2 Data and Methods

2.1 Data

2.2 AR Detection

2.3 AR-induced Monthly Rainfall and Comparison between AR and Non-AR Monthly Rainfall

3 Results

3.1 Contribution of ARs to Monthly Rainfall

3.2 Role of ARs on Monthly Rainfall Availability in Wet, Normal, and Dry Months

4 Concluding Remarks

Declarations

Acknowledgements

Author contributions

Competing interests

References

Additional Declarations

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