High Intensity Rainfall and Potential Impacts of Climate Change in the Waiohine Catchment

Transcription

1 High Intensity Rainfall and Potential Impacts of Climate Change in the Waiohine Catchment NIWA Client Report: WLG29-5 February 29 NIWA Project: WRC931/1

2 High Intensity Rainfall and Potential Impacts of Climate Change in the Waiohine Catchment Craig Thompson John Sansom James Sturman Brett Mullan NIWA contact/corresponding author Craig Thompson Prepared for Greater Wellington Regional Council NIWA Client Report: February 29 WLG29-5 NIWA Project: WRC931/1 National Institute of Water & Atmospheric Research Ltd 31 Evans Bay Parade, Greta Point, Wellington Private Bag 1491, Kilbirnie, Wellington, New Zealand Phone , Fax All rights reserved. This publication may not be reproduced or copied in any form without the permission of the client. Such permission is to be given only in accordance with the terms of the client's contract with NIWA. This copyright extends to all forms of copying and any storage of material in any kind of information retrieval system.

3 Cover Photo: Tranquil Waiohine River February 29, Alan Blacklock NIWA 29 All rights reserved. This publication may not be reproduced or copied in any form without the permission of the client. Such permission is to be given only in accordance with the terms of the client's contract with NIWA. This copyright extends to all forms of copying and any storage of material in any kind of information retrieval system.

4 Contents Executive Summary iv 1. Introduction 1 2. Typical weather sequences leading to high river levels Descriptions of a selection of storm sequences resulting in high river levels in Waiohine River 7 3. High intensity rainfall Design rainfalls Temporal rainfall patterns Probable maximum precipitation Method of estimating probable maximum precipitation Comparison of probable maximum precipitation with 1-year storm rainfall Temporal pattern of probable maximum precipitation Climate change Global perspective Climate change scenarios for Wairarapa (Waiohine River Catchment) Implications of changes in rainfall extremes References Appendix 1. Daily River flow records for Waiohine River, Appendix 2. Maps of weather sequences leading to high river flows in Waiohine catchment Appendix 3. Comparison of quantiles from at-site and HIRDSV2 analyses Appendix 4. Analysis of Temporal Patterns Analysis from breakpoint data Analysis from pseudo-breakpoints 72 Reviewed by: Approved for release by: Dr Andrew Tait Dr Andrew Laing

5 Executive Summary As part of providing long-term flood protection to the community of Greytown, Wairarapa, Greater Wellington Regional Council (GWRC) has commissioned the National Institute of Water & Atmospheric Research (NIWA) to assess current high intensity rainfalls in the Waiohine River catchments identify the types of storms that lead to high river levels; provide estimates of design rainfalls and probable maximum precipitation; and provide climate model projections for three warming scenarios (denoted as low, mid-range and high) in order to estimate likely changes in design rainfall events for two future periods ending in 25 and 21. Maximum river flows over a 5 day period were used to identify the type of weather pattern that led to high river levels in the Waiohine River and Mangatarere Stream. From a listing of more than 2 events over a 5 year period, three typical weather patterns were identified. The first is characterised by a progression of a trough (frontal) system across New Zealand, preceded by a strong northwest airflow and followed by a west or southwest airflow. On occasions the front may become slow moving across central New Zealand. This sequence is the most common of all events leading to high river levels. The second weather pattern is characterised by north Tasman Sea depressions and former tropical cyclones that cross over or near the southern North Island. Although this type of weather pattern is infrequent by comparison with the number of frontal systems crossing New Zealand, nevertheless they can provide high rainfalls and river levels. The third weather sequence is characterised by a disturbed westerly wind regime across New Zealand. Although infrequent, they can persist for several days with strong westerly onshore winds enhancing the orographic component of precipitation as the air is forced to ascend the Tararua Ranges. 9 9 The estimation of high intensity design rainfalls relies on the fitting of extreme value distributions to time series of maximum rainfalls. Many extreme rainfall data series have relatively short records when compared to the largest recurrence interval required. Regional frequency analysis is the preferred method of analysis since data from many sites within a region of influence are pooled to determine robust estimates of the distribution s parameters. Regional frequency analysis is the approach used by NIWA s HIRDS (High Intensity Rainfall Design System) software. The procedure fits a threeparameter generalised extreme value (GEV) distribution, and produces storm rainfall depths for a range of durations from 1 minutes to 72 hours and for a range of recurrence intervals from 2 to 15 years. Examples of depth-duration-frequency tables for four sites within and near the Waiohine River catchment are presented showing the variation that can occur. For example, the 2-year 24-hour rainfalls vary from about 1 to 25 mm. A corresponding map of the 1-year 24-hour rainfall indicates a gradient from lowland to the Tararua ranges from 125 to over 5 mm. High temporal resolution data from NIWA and GWRC archives from sites within and near the Waiohine River catchment were used to investigate storm temporal patterns for a range of durations and rainfall accumulations. The analysis involved partitioning storm rainfall totals according to storm High intensity rainfall and potential impacts of climate change in the Waiohine catchment iv

6 duration and accumulation, and temporal patterns produced. The analysis showed that at least for the largest 1 storm events, some sites in the Tararua Range display a near uniform or slightly S-shaped temporal pattern. For the vast majority of fixed duration storms there was large variability in temporal patterns with no typical or characteristic storm rainfall profiles that could be used in conjunction with high intensity design rainfalls. The most extreme form of high intensity rainfall is probable maximum precipitation, which is the greatest depth of precipitation for a given duration that is physically possible over a region. In New Zealand, extreme rainfalls result from a range of weather patterns from former tropical cyclones to broad stationary frontal systems, and all involve a complex interaction between the dynamic storm processes and orography leading to orographic enhancement of precipitation from the forced ascent of moist air masses across mountain ranges. A procedure to estimate probable maximum precipitation for New Zealand considers it in terms of maximising the availability of storm moisture and the contribution from orographic uplift. 9 9 Although frontal systems crossing southern North Island frequently produce high river levels in the Waiohine River and Mangatarere Stream, ex-tropical cyclones are considered the prototype probable maximum precipitation storm for the North Island. Past tropical cyclones paths are likely down either side of the North Island: however those that move down the eastern side of the North Island will produce a larger estimate of probable maximum precipitation in Wairarapa than these moving down the west coast due to the Tararua Ranges significantly reducing the moisture potential to the Wairarapa and its river catchments. For the Waiohine River and Mangatarere Stream, the catchment averaged probable maximum precipitation is estimated at 78 mm and 57 mm respectively for an east coast tracking tropical storm. All estimates of probable maximum precipitation involve a level of uncertainty, and there is no objective way of assessing their accuracy. The usual practice is to compare probable maximum precipitation against the 1-year average recurrence interval. For the Waiohine River catchment this ratio varies from between 1.5 to nearly 4 and is consistent with other New Zealand and international studies. As noted above there is a large variety of temporal patterns, but for very extreme and rare rainfall events the temporal pattern tends to be relatively uniform with single peak intensity. Temporal patterns for probable maximum precipitation events showed that about 5 percent of the total accumulation occurs about half-way through the storm event. Likely future changes in rainfall extremes are assessed through scenarios of temperature increase, as generated by a suite of global climate models forced by scenarios from the Intergovernmental Panel on Climate Change (IPCC) of increasing greenhouse gas concentration. Three temperature scenarios (low, mid-range and high) are considered, for two future periods in the 21 st century i.e. for the 24s, 29s. High intensity rainfall and potential impacts of climate change in the Waiohine catchment v

7 Future changes in high intensity rainfall are computed using a multiplicative factor which varies with the projected temperature increase, and also with the duration and average recurrence interval of the storm event. In all the scenarios, there are increases in extreme rainfalls for the 24s, 29s, when compared to the current climate, and these increases are larger with greater warming. For example, for a mid-range temperature increase, a current 24-hour 1-year storm event is projected to become a bout a 5-year storm event: i.e., halving the average recurrence interval, for specified storm duration with a specified rainfall depth or intensity. Design rainfalls are expected to increase by between 2 and 18 percent for the 24s, and between 2 and 41 percent by the 29s. This information has important implications for planning of major infrastructure and developments that will need to cope with climatic conditions later in the 21 st century. The development of low-lying land already subject to flood risk, roads and bridges are examples of infrastructure where the increase in both intensity and frequency of high intensity rainfall will have significant potential impacts. Thus serious consideration of these likely impacts by council and communities is recommended. The IPCC 4 th Assessment Report has noted that adaptation will be necessary to address impacts resulting from the warming which is already unavoidable due to past emissions, and that a portfolio of adaptation and mitigation is required to reduce the risks of climate change. Because the future course of climate warming is uncertain, and greenhouse gas concentrations in the atmosphere are likely to increase for several decades yet, disaster management and risk prevention strategies should adopt a precautionary framework. It is recommended that the mid-range and high climate change scenarios are used in council guidelines with serious consideration by council and communities of their likely impacts, although if international action curbs the growth of greenhouse gas concentration in the atmosphere during the 21 st century, then the high scenario may not eventuate. 9 9 High intensity rainfall and potential impacts of climate change in the Waiohine catchment vi

8 1. Introduction The Waiohine River is a relatively long and narrow catchment on the eastern side of the Tararua Range 1. From its headwaters in the Tararua Range, the river flows through alluvial greywacke deposits to the southern Wairarapa plains and feeds into the Ruamahanga River just east of Greytown. Tributaries of the Waiohine River, the Mangatarere and Kaipaitangata Streams, with their headwaters in the foothills of the Tararua Range, join the Waiohine River northeast of Greytown. The catchment outline is given in Figure 1, together with the principal road networks, urban areas of the region, and sites of the river flow and rain gauge records used in this report. Details of the river flow and rainfall sites are given also provided in Table 1. As part of providing long-term flood protection to the community of Greytown, Wairarapa, the Greater Wellington Regional Council is developing a flood plain management plan for the Waiohine River catchment. Greater Wellington Regional Council has requested NIWA to provide information on the extreme rainfall climate and potential influences from climate change for the catchment. Specifically the outcomes sought by Greater Wellington are: Typical weather sequences leading to significant flood events that resulted in high river levels in the Waiohine River catchment. Design storm rainfall analysis, including probable maximum precipitation, and temporal patterns. A climate change analysis for the 21 st century to assess the potential impacts of rainfall in the Waiohine River catchment. 1 High intensity rainfall and potential impacts of climate change in the Waiohine catchment 1

9 Figure 1. Location map of rain gauges and river flow recorders in and near Waiohine River catchment. NIWA 29 High intensity rainfall and potential impacts of climate change in the Waiohine catchment 2

10 Table 1: River flow and rainfall recording sites in and near Waiohine catchment Site Authority Latitude ( S) Longitude ( E) Period of Record River Flow Waiohine at Gorge GW present Mangatarere GW present Rainfall Angle Knob GW present Carkeek GW present Valley Hill GW present Phelps GW present Bull Mound GW present McIntosh GW present Waingawa NZED East Taratahi MetService/NIWA Typical weather sequences leading to high river levels Records of maximum river flow (m 3 /s) were provided by Ms Laura Watts (Greater Wellington Regional Council Environmental Scientist) for the Waiohine River and Mangatarere Stream. Flow records for the Waiohine River cover the period from December 1954 to October 28, and for the Mangatarere Stream there is a much shorter flow record from February 1999 to September 28. A graphical view of recorded river flows for the Waiohine River catchment, provided by Dr Alistair McKerchar (NIWA), is given in Appendix 1, and shows the instances over the 55 year record when there were high river levels. The maximum flow records from the Waiohine River have been used as the basis to evaluate significant flooding events in the two rivers. A very simple index was used to identify the largest flows: accumulations of the maximum flows over 5 day periods divided by 1. This is a relative index used for comparison purposes, and was derived to capture those events that resulted in high river levels over several days, and which have a potential for widespread flooding over the southern Wairarapa floodplain. The index may miss short-duration high river level events, and by way of High intensity rainfall and potential impacts of climate change in the Waiohine catchment 3

11 example 2 of the 1 largest annual floods in the Waiohine River are not identified by the index proposed. Further, the accumulation over a period of 5 days corresponds to the typical period of weather sequences (i.e. 5 to 7 days) that involves an eastwards progression of anticyclones and troughs of low pressure, and its subsequent hydrological response of rising and falling river levels. A listing of the largest storm events based on this simple index is given in Table 2, with a brief description of the weather pattern that produced the high river flows in the Waiohine River. A sequence of synoptic weather maps for Australasia for each of the storms selected is given in Appendix 2. The maps are for 12 noon New Zealand Standard Time and have been extracted from NIWA archives of maps from the National Center for Environmental Prediction (NCEP) numerical weather model. From an analysis of weather maps for the identified storms, it is possible to identify two or three predominant types of weather sequences. Frontal Systems: This type of weather sequence is characterized by a trough of low pressure in the Tasman Sea preceded by a strengthening northwest airflow over New Zealand. The frontal systems within the trough usually extend from the north Tasman Sea to the southern oceans and are active frequently with waves or shallow depressions moving southeast along them. The trough and front move eastwards onto New Zealand and on occasions may become slow-moving across central New Zealand. Following the passage of the front over the North Island, a showery west or southwest airflow predominates. This type of weather pattern is by far the most common type of event to result in raised river levels in the southern regions of the North Island. North Tasman Sea depressions and tropical cyclones: This type of weather pattern leading to high rivers in the Wairarapa occurs when a depression forms in the north Tasman off the coast of Australia, and moves southeastwards towards and crosses the North Island. A strong onshore southeasterly flow on the southern side of the depression orographically enhances the rainfall, but this tends to ease as the depression moves away from New Zealand. While this type of weather sequence is infrequent by comparison with the number of frontal systems crossing over the Wairarapa, they do produce some high rainfall totals and subsequent high river flows. Large rainfalls and river flows can also occur when cyclones of tropical origin pass nearby the Wairarapa. Potentially very destructive leading to extensive flooding and other water and wind related damage, cyclones of tropical origin are confined mostly to the period from October April. High intensity rainfall and potential impacts of climate change in the Waiohine catchment 4

12 Disturbed westerly wind regimes across New Zealand: This type of weather sequence, which can lead to high river flows in the southern North Island, is characterized by a strong westerly-wind regime across New Zealand and the Tasman Sea with anticyclones moving east to the north of the country, and low pressure systems to south. The strong onshore westerly winds induce precipitation as the air masses are forced by orographic uplift to ascend the Tararua and Ruahine Ranges. Rapidly eastward-moving cold fronts ( disturbances ) within the westerlies intensify the precipitation for several hours before easing. Although this type of weather regime is comparatively rare in producing prolonged flooding the Waiohine River, it can nevertheless persist for several days. High intensity rainfall and potential impacts of climate change in the Waiohine catchment 5

13 Table 2. Significant storm events in the Waiohine catchment Storm Date Weather type Flow Index Peak flow (m 3 /s) Date of peak flow Comment 16-2 January 198 S moving front Jan February Feb Further flooding 26 th 29 Sep-3Oct 2 Frontal Oct 9-13 March 199 N Tasman Low Mar March Mar October 1998 Frontal Oct Further flooding 27 th - 3 th February 24 Frontal Feb 1-14 December 1982 Frontal Dec March 1965 Slow moving Trough Mar 1-14 September 1988 Westerly Sep Atypical La Niña 8-12 November 1961 Frontal Nov 26-3 November 1982 Frontal Nov November 1994 Frontal Nov November Nov 26-3 December 1965 Disturbed W to SW Dec 26 Feb-2Mar Feb Flooding 19 th -2 th 2-24 February 1959 Front/trough Feb 7-11 November 1994 Frontal Nov Multiple peaks in month 5-9 May May 9-13 May 1958 Frontal May December 1982 Frontal Dec October 1964 Trough/front Oct 6-1 August 1991 Frontal Aug High intensity rainfall and potential impacts of climate change in the Waiohine catchment 6

14 18-22 May 1988 Frontal May December 198 Trough Dec Further peak on 28 th 5-9 November 1965 Trough/front Nov October 1964 S Westerly Oct 2-24 September 1981 Disturbed Westerly Sep A peak on 2 th 2.1 Descriptions of a selection of storm sequences resulting in high river levels in Waiohine River A description of the top 8 storms identified in Table 2 is given below, together with a small selection of other notable storm events. The storm descriptions have been ordered on the basis of the decreasing magnitude of the flow index. January 198: An extensive trough of low pressure crossed New Zealand on 17 January bringing relatively high river levels to Waiohine River with a peak flow of 813 m 3 /s on the 16 th. This trough was followed by another large trough system that covered the Tasman Sea and moved onto the South Island late on 18 January and become slow moving over the southern North Island on 19 January before weakening as a result of the near stationary anticyclone northeast of New Zealand. By 2 January a westerly airflow covered the country with remnants of the trough remaining in the northwest Tasman Sea. The Waiohine River rose again on the 19 th and a peak flow of 1424 m 3 /s was recorded on the 2 th, while the nearby Atiwhakatu River peaked at m 3 /s, the fourth highest maximum flow in the 3 year record. October 2: A trough of low pressure moved into the Tasman Sea on 1 October and a strong northwest airflow covered New Zealand. The frontal system in the trough crossed Wairarapa around the middle of the day on 2 October and was followed by a strong showery westerly airflow which persisted over the country until the 7 th. The Waiohine River peaked at 1139 m 3 /s on the 2 nd. The river levels rose again several days later with a maximum recorded flow of 89 m 3 /s on the 9 th just prior to a large depression in the south Tasman Sea, becoming relatively slow-moving east of the country on 12 and 13 October. River levels in the Mangatarere for these two events were 81 m 3 /s and 11 m 3 /s respectively. March 199: On 7 March a shallow trough of low pressure in the northern and western Tasman Sea deepened into a single depression as it moved towards the South Island, while a large slow moving anticyclone lay east of the North Island. Between these High intensity rainfall and potential impacts of climate change in the Waiohine catchment 7

15 systems a moist north to northwest airflow covered the North Island which persisted until the 13 th when an anticyclone moved into the South Tasman Sea towards New Zealand. The Waiohine River rose on the 1 th and remained relatively high until it rose further on the 13 th when a maximum flow of 153 m 3 /s was recorded before receding on the 14 th. October 1998: A large anticyclone north of New Zealand on 18 October moved east to lie east of the country on the 21 st, while a northwest to west airflow covered the Tasman Sea and New Zealand to the south. Within this airstream, a frontal system moved onto the country during the 2 th and lay east of the country by the following day. Following the front/trough complex a disturbed showery southwest airflow became established over New Zealand ahead of an anticyclone that moved into the Tasman Sea on the 22 nd. The Waiohine River recorded a maximum flow of 941 m 3 /s on the 2 th. This event was followed by an even larger peak flow on the 28 th when 145 m 3 /s was recorded, when a deep depression developed in the Tasman Sea during the 27 th and 28 th of October. The depression was preceded by a strong northwest airflow as a result of the anticyclone that entered the Tasman Sea on the 22 nd and became slow moving east of the North Island. February 24: February 24 was a wet month with four significant events in the Waiohine River. On 1 February a trough of low pressure moved quickly into the Tasman Sea as a ridge of high pressure moved slowly off the country. The northwest airflow increased across New Zealand as the front in the trough moved onto the South Island reaching southern North Island on 11 February. The front lay east of the country late on 11 February and was followed by a southwest airflow. The maximum recorded river flow in the Waiohine River of 1362 m 3 /s was the fourth highest maximum river flow in the record. The Mangatarere Stream peaked at 14 m 3 /s which is highest recorded in this short record. This system was followed by a further trough that crossed the Wairarapa and formed a depression east of the South Island on 14 February, and became stationary east of the North Island on the 15 th and 16 th before weakening and moving away on the 17 th. River flows in the Waiohine peaked at 655 m 3 /s on the 16 th, and in the Mangatarere the peak reached 12 m 3 /s. The second storm is notable in that it resulted in widespread flooding to Manawatu and Wanganui districts. December 1982: A depression in the western Tasman Sea on the 8 th 9 th December moved eastwards in the South Tasman Sea while remaining relatively stationary in the north. By 11 December an extensive trough of low pressure covered New Zealand and much of the central and northern Tasman Sea, with a moist, humid northwest airflow covering the eastern Tasman Sea and the North Island. The entire system moved High intensity rainfall and potential impacts of climate change in the Waiohine catchment 8

16 slowly east to lie of New Zealand on the 12 th and a strong southwest airflow on the western side of the trough covered the country. During this event, the Waiohine recorded its highest ever flow of 1558 m 3 /s since monitoring began in 1955, an estimated recurrence interval of just under 5 years (fitting a GEV distribution with the method of mixed L-moments and maximum likelihood estimation). March 1965: A trough of low pressure crossed the Tasman Sea on the 14 th 15 th March and became slow moving over northern New Zealand during the 16 th and 17 th. The Waiohine River rose rapidly on the 17 th, peaking at 847 m 3 /s and receded slowly over the following 5 days as the trough and the strong onshore easterly flow over southern North Island gradually weakened. By the 21 st March the shallow trough over the North Island merged with another higher latitude low pressure trough resulting in a large but shallow cyclonic circulation over much of New Zealand. September 1998: A belt of high pressure remained slow moving in the north Tasman Sea from 5 8 September. A trough of low pressure moved eastwards across the Tasman Sea on 5 September, and the front in the trough reached the southwest of New Zealand late on the 5 th. The front, followed by a strong showery westerly, reached the southwest of the North Island during the afternoon of 6 September, and lay east of the country by the 7 th. The Waiohine River rose quickly during the 6 th and a maximum flow of 114 m 3 /s was recorded. Other notable events: November 1994: On 21 November a large trough in the Tasman Sea reached the South Island while an eastwards moving anticyclone lay northeast of the North Island. Between these systems a strong northwest airflow covered New Zealand. The front in the trough crossed the North Island on 22 November and was followed by a strong showery southwest airflow over New Zealand and an anticyclone in the Tasman Sea. The Waiohine and Atiwhakatu Rivers recorded peak flows of 1231 m 3 /s and 151 m 3 /s respectively. This storm was the third event that resulted in the rivers to rise during the month. On 6 7 November a very strong northwest airflow covered New Zealand and much of the Tasman Sea ahead of a front which crossed the Wairarapa on the 8 th and was followed by a disturbed westerly airflow for the following 6 7 days. The nearby Atiwhakatu River peaked on the 8 th at 182 m 3 /s and was the 5 th highest maximum flow recorded for the river, while the Waiohine River had a maximum flow of 137 m 3 /s before falling. High flows were also measured in both rivers during the 14 and 15 November but were not as extreme as the other two events. High intensity rainfall and potential impacts of climate change in the Waiohine catchment 9

17 November 1965: On 6 November a complex depression in the western Tasman Sea moved southeastwards and merged into a single depression southeast of the South Island on the 7 th. The depression was preceded by a strong northwest airflow that covered the North Island and the seas to the east. A large anticyclone remained slow moving far to the east of the country and as the depression moved further away on the 8 th it still maintained a shallow yet narrow trough of low pressure over the North Island, while a ridge of high pressure in the western Tasman Sea moved south of the country. The Waiohine River began to rise on the 6 th of November, peaking at 1187 m 3 /s on the 7 th before dropping. March 1975: Ex-tropical cyclone Alison moved southwards out of the tropics on 6 7 March 1975 and into the north Tasman Sea reaching northern New Zealand on the 1 th. The cyclone travelled down the western side of New Zealand over the next three days before weakening off the coast of Fiordland on the 14 th. The Waiohine River rose during the 11 th peaking at 986 m 3 /sec on the 12 th, while the nearby Atiwhakatu River recorded a peak flow of 24 m3/s. 3. High intensity rainfall 3.1 Design rainfalls The NIWA software package known as HIRDS (High Intensity Rainfall Design System) (Thompson, 22) is a procedure for estimating extreme rainfall frequency across New Zealand. A regional frequency analysis is performed, which uses data from sites within a suitably defined region to estimate the design variable (e.g. river flows or rainfall) at each site. A benefit of a regional analysis approach is that design rainfalls at single-sites can be severely affected by short-data records with a large uncertainty in their estimates, whereas pooling data usually leads to a reduction in uncertainty and more reliable estimates. One method of regional frequency analysis is the index-rainfall procedure. The procedure involves mapping an index (i.e. the median annual maximum) rainfall from all available sites, and the derivation of a regional rainfall growth curve that relates rainfall depth at difference recurrence intervals, ARI, to the index rainfall. The indexrainfall procedure can be written as X ( T ) = x( T ). X i (T ) is the rainfall estimate i µ i for the T-year rainfall event at site i, µ is the index rainfall chosen to be a relatively i common event such as the median that can be reliably computed from the data, and x (T ) is a the dimensionless rainfall frequency growth curve common to every site in High intensity rainfall and potential impacts of climate change in the Waiohine catchment 1

18 the region. The method assumes that all locations within some defined region can be combined in such a way to produce a single rainfall growth curve that can be used anywhere within that region. All sites in the region are expected to have identical frequency distributions, but this implied assumption of homogeneity is seldom satisfied exactly. However, heterogeneity is less important as a source of error in estimating quantiles (Hosking and Wallis, 1997) than is the case if the frequency distribution is mis-specified. Regions are defined on the basis of a site s region of influence, whereby each site is surrounded by other sites having similar statistical attributes to the site of interest, thus avoiding boundary problems associated with fixed regions. In HIRDS, the index rainfall was taken to be the median annual maximum rainfall. The median is a robust estimator, and has an annual exceedance probability of.5 corresponding to an ARI of 2 years. The median is used in preference to some other location parameter, such as the mean, since it is not usually affected by the skewness of the distribution or by the presence of outliers. The mapping of the median annual maximum rainfall over New Zealand involved fitting thin-plate smoothing splines as implemented by ANUSPLIN (Hutchinson, 2). Details of the general fitting procedure of the median rainfall are described in Thompson (22). In design situations, where at-site median rainfalls are available in addition to the fitted spline surfaces, it is possible to get a pooled estimate, where the variances of the mapped and observed medians influence the relative weightings in the pooled estimate. The other component of the index-rainfall procedure is the regional rainfall frequency growth curve. Growth curves are dimensionless, enabling the estimation of design rainfalls of any specified ARI relative to the index rainfall. Inverse cumulative distribution functions are the basis of the growth curves, and in HIRDS a threeparameter generalised extreme value distribution, GEV, is used with the parameters of the distribution estimated from the first four L-moments. In terms of the T-year rainfall event the inverse cumulative distribution function for a GEV is x( T ) = ξ + α / κ(1 ( log(1 1/ T )) κ k x ( T ) = ξ α( log( log(1 1/ T )) k = where ξ is the location (mode), α is the dispersion (scale) parameter, and κ is the shape parameter. For κ =, the GEV reduces to the usual Gumbel distribution. All sites from within the region of influence are used where each site s relative contribution to the regional estimate is weighted on the basis of its record length, i.e. the longer the record, the larger the relative weighting towards the regional estimate. Because GEV High intensity rainfall and potential impacts of climate change in the Waiohine catchment 11

19 distributions are fitted to data for different durations, they do not always provide consistent quantiles (i.e. design rainfalls cross one another even at moderately large ARI). All output depth duration frequency tables are checked for consistency, and adjusted to ensure that design rainfalls increase with both storm duration and recurrence interval. If g(t ) is the dimensionless regional growth curve relative to the index rainfall, the rainfall growth curve is α / ξ g( T ) = x( T ) / x(2) = 1+ ( YT Y2 ) 1+ ( α / ξ ) Y 2 where Y T is the usual Gumbel reduced variate and ξ and α are the parameters of the GEV distribution. As with the index rainfall, the parameter of the growth curve (i.e. α /ξ) and the shape parameter, κ, are mapped across New Zealand with the thin-plate smoothing spline software package. Pooled estimates from the fitted surface and regional derived estimates are used in the design rainfall phase of HIRDS. These are the essential steps behind the development of version 2 of HIRDS. In addition to the above methods, the research and development version HIRDS also has a module to provide quick and consistent estimates of high intensity rainfalls due to climate change. This module will become available in a later version of HIRDS. Table 3 provides depth-duration-frequency tables derived from the application of regional frequency analysis methods, as used in the HIRDS program, for four sites (Phelps, Valley Hill, Carkeek and Angle Knob) in and near the Waiohine River and Mangatarere Stream catchments. High intensity rainfall and potential impacts of climate change in the Waiohine catchment 12

20 Table 3. Depth-duration-frequency tables for Phelps (Waiohine River), Valley Hill (Mangatarere Stream), Angle Knob (Waingawa River), and Carkeek (Waiohine River) derived from HIRDSV2 Phelps Duration ARI 1m 2m 3m 6m 2h 6h 12h 24h 48h 72h Valley Hill ARI 1m 2m 3m 6m 2h 6h 12h 24h 48h 72h Angle Knob ARI 1m 2m 3m 6m 2h 6h 12h 24h 48h 72h High intensity rainfall and potential impacts of climate change in the Waiohine catchment 13

21 Carkeek ARI 1m 2m 3m 6m 2h 6h 12h 24h 48h 72h The HIRDS procedure uses a GEV frequency distribution incorporating the flexibility of a shape parameter in fitting extreme value distributions to environmental data. Since data from many sites are pooled together, each site influences to some extent the resultant shape parameter, which can be different to the site-specific shape parameter. As Robson and Reed (1999) note, regional frequency analysis is preferable to sitespecific analyses where the record length is less than the largest recurrence interval to be estimated. Further, several authors have noted (Martins and Stedinger, 2; Rosbjerg and Madsen, 1995; NERC, 1975) that when performing site-specific frequency analyses with small amounts of environmental data (less than about 3 years) a 3-parameter GEV analysis often leads to unreliable quantile estimates. This is due to the difficulty in estimating reliably the shape parameter of the GEV distribution with small amounts of data (Lu and Stedinger, 1992; NERC, 1975), and it is recommended that 2-parameter EV1 distributions be used for short data sets. A comparison of site-specific quantiles with those from HIRDS at Phelps, Carkeek and Angle Knob is given in Appendix 3. No analysis was undertaken for Valley Hill due to the very short annual maximum record. At Carkeek, the at-site quantiles for both and EV1 and GEV fitted distributions are consistently smaller than those given by HIRDS. A comparison using Phelps shows design rainfalls from HIRDS and a sitespecific EV1 analysis (i.e. shape parameter set to zero) are quite similar, suggesting the shape parameter from regional frequency analysis is close to zero and therefore EV1-like. When design rainfalls are compared with a site-specific GEV analysis, the site specific analysis is consistently larger across the range of durations for recurrence intervals larger than about 2 years. For Angle Knob, the site-specific quantiles are substantially larger than those given by HIRDS for durations larger than about 6- hours. This is probably due in part to the differences in the 2 year recurrence interval values, and in part due to the large amount of additional rainfall data being used in High intensity rainfall and potential impacts of climate change in the Waiohine catchment 14

22 HIRDS from sites surrounding Angle Knob all contributing towards the regional parameter estimates in a GEV distribution. In summary: The estimation of high intensity design rainfalls relies on the fitting of extreme value distributions to time series of maximum rainfalls. Many extreme rainfall data series have relatively short records when compared to the largest recurrence interval required. Regional frequency analysis is the preferred and recommended method of analysis since data from many sites within a region of influence are pooled to determine robust estimates of the distribution s parameters. Regional frequency analysis is the approach used by NIWA s high intensity rainfall design software. The procedure fits a three-parameter generalised extreme value distribution, and produces storm rainfalls for a range of durations and recurrence intervals. It is also recommended that with short data set the 3-parameter GEV distribution is not used to fit the data as this can result in unrealistic quantiles, and that 2-parameter distributions such as the EV1 are used. 3.2 Temporal rainfall patterns High temporal resolution breakpoint rainfall data from NIWA s climate archives and tipping bucket rainfall data supplied by Ms Laura Watts (Greater Wellington Regional Council) were used to assess whether there were typical temporal patterns during storm events. For both breakpoint and tipping bucket data similar analyses were undertaken, and details of analyses for Waingawa (breakpoint) and for Angle Knob (tipping bucket) are presented here. For other sites in and east of the Tararua Ranges, the results of the analysis of temporal patterns are in Appendix 4. Tipping bucket data were converted into pseudo-breakpoints by amalgamating periods with a constant rain rate into a breakpoint. Zero rainfall was included as a rate so a single dry period separated wet periods which may have more than one breakpoint. The first analysis of temporal patterns was to construct time-accumulation plots of the 1 largest rainfall events for storm durations of 3, 6, 12, 24 and 48 hours. Figures 2 (Waingawa) and 5 (Angle Knob) show the time-accumulation plots. At Angle Knob, in the Tararua Range, there is a hint at a near uniform or slightly S-shaped distribution for several of the durations, but at Waingawa, on the valley floor, there is no coherent profile. For the corresponding figures in Appendix 4, the sites in the range also suggest temporal pattern similar to Angle Knob, while East Taratahi shows considerable variation. High intensity rainfall and potential impacts of climate change in the Waiohine catchment 15

23 The second analysis of temporal patterns was to assess whether there are any typical rainfall profiles that could be obtained from the breakpoint/tipping bucket data. The method used follows, and is summarised in Figures 3 and 4 for Waingawa. About 1 percent of breakpoints represent the periods between rainfall events, and the vertical dashed line in Figure 3 gives approximately the 9 th percentile value. This point occurs at about 1 minutes. Dry periods of at least this length were used to divide the dataset into separate rainfall events. Note that under this approach, a rainfall event may also have short dry periods among the wet periods, with the duration of the dry period lasting less than 1 minutes. A scatter-plot of the total durations of rainfall events against their total accumulation is shown in the right panel of Figure 3 for Waingawa with the horizontal and vertical lines dividing the plot into quartiles. Within each of these16 boxes, all the rain events were used to provide scaled temporal patterns of rainfall. These are displayed in Figure 4 for durations and accumulations in a particular range of values determined by the quartiles. In Figure 4, scaled plots are shown in each of the boxes if there is more than one event. The 8 events in the second from top left hand most box (in Figure 3 right panel) is shown scaled in the corresponding box in Figure 4. Figure 4 also shows a large variety of temporal patterns with no clear and discernible patterns, although the second box from the top left box (i.e. corresponding to storm durations in the second quartile with accumulations in the top quartile) does suggest some clustering so that 5 percent of the storm rainfall accumulates by about 4 5 percent of the way through the storm. A longer record of breakpoint data for Waingawa would ensure that most of these boxes would appear completely black in colour. High intensity rainfall and potential impacts of climate change in the Waiohine catchment 16

24 Figure 2. Time-accumulation plots of the 1 largest rainfall events at Waingawa for storm durations of 3, 6, 12, 24, and 48 hours. NIWA 29 High intensity rainfall and potential impacts of climate change in the Waiohine catchment 17

25 Figure 3. Waingawa. Left panel: Histogram of logarithm to the base 1 of dry periods derived from breakpoint data. The vertical dashed line is the 9 percentile value and represents the selection point for inter-event dry periods. Right panel: Scatter plot of total accumulation (mm) of rainfall against total duration (minutes) of that accumulation. The horizontal and vertical lines in the scatter-plot identify quartile limits of the data. Note the axes are logarithms to the base 1. NIWA 29 High intensity rainfall and potential impacts of climate change in the Waiohine catchment 18

26 Figure 4. Scaled rainfall patterns at Waingawa within each quartile banding displayed in Figure 3 that occurred with particular durations (x-axis) and accumulations (y-axis). The bottom left hand panel corresponds to the lowest quartiles in both rainfall duration and rainfall accumulations. Rainfall accumulated during the event was scaled by dividing the total rainfall for the event. The duration was scaled by the total storm duration. NIWA 29 High intensity rainfall and potential impacts of climate change in the Waiohine catchment 19

27 For the pseudo-breakpoints, the analysis for Angle Knob (see Figures 5 to 7) was very similar to that for the NIWA breakpoint data except the minimum inter-event dry period decreased from 1 to 18 minutes as analyses of storms with three-hour durations were undertaken in this report in addition to other longer durations that in the scatter-plots of accumulation against duration and subsequent temporal plots only storms longer than 12 minutes with more than 1 mm were considered as no temporal structure was available for shorter ones with less rain. The scaled temporal patterns for fixed duration storms in Figure 7 for Angle Knob show most of these boxes are largely black in colour; typical of the large variability that exists in storms. The large variation is also seen in at the other sites in and near the Waiohine River catchment (Appendix 4). There is no apparent difference between the Tararua foothill site at Valley Hill and the Range sites. In summary: A method for analysing temporal patterns of fixed duration storms is presented. This involves partitioning storm rainfall totals according to duration and accumulation, and extracting the temporal patterns. The analysis showed that at least for the largest 1 storm events, some sites in the Tararua Range display a near uniform or slightly S-shaped temporal pattern. However, for the vast majority of fixed duration storms there was large variability in temporal patterns with no typical or characteristic storm rainfall profiles that could be used in conjunction with high intensity design rainfalls. High intensity rainfall and potential impacts of climate change in the Waiohine catchment 2

28 Figure 5. Time-accumulation plots of the 1 largest rainfall events at Angle Knob for storm durations of 3, 6, 12, 24, and 48 hours. NIWA 29 High intensity rainfall and potential impacts of climate change in the Waiohine catchment 21

29 Figure 6. Angle Knob. Left panel: Histogram of logarithm to the base 1 of dry periods derived from breakpoint data. The vertical dashed line represents the selection point for inter-event dry periods. Right panel: Scatter plot of total accumulation (mm) of rainfall against total duration (minutes) of that accumulation. The horizontal and vertical lines in the scatter-plot identify quartile limits of the data. Note the axes are logarithms to the base 1. NIWA 29 High intensity rainfall and potential impacts of climate change in the Waiohine catchment 22

30 Figure 7. Scaled rainfall patterns at Angle Knob within each quartile banding displayed in Figure 6 that occurred with particular durations (x-axis) and accumulations (yaxis). The bottom left hand panel corresponds to the lowest quartiles in both rainfall duration and rainfall accumulations. Rainfall accumulated during the event was scaled by dividing the total rainfall for the event. The duration was scaled by the total storm duration. NIWA 29 High intensity rainfall and potential impacts of climate change in the Waiohine catchment 23

31 4. Probable maximum precipitation Probable maximum precipitation, PMP, is the greatest depth of precipitation for a given duration that is physically possible over a region for a given storm area in the current climate (WMO, 1986). Climate trends tend to progress rather slowly in that their influence on PMP maybe small compared to other uncertainties in estimating these extreme values. Extreme rainfalls in New Zealand result from a range of weather patterns, from tropical cyclones to broad stationary frontal systems. Most of the storms leading to elevated river levels in the Waiohine River and other river systems in Wairarapa are associated with frontal systems and troughs moving across the southern North Island from the Tasman Sea dumping large amounts of precipitation in the headwaters of the Tararua Ranges. Storm rainfall in mountains and ranges of New Zealand results from dynamic atmospheric processes in the storm system and from orographic enhancement of precipitation due to the forced ascent of moist air masses across the mountain range. With complex and diverse patterns of orography across New Zealand, Tomlinson and Thompson (1992) developed procedures to estimate PMP in a consistent manner, and involves a method that considers PMP in terms of the storm dynamics and the contribution arising from orographic uplift. 4.1 Method of estimating probable maximum precipitation Storm transposition is a technique widely used in PMP studies. It relies on the separation of storm rainfalls into two components being the storm dynamics and orographic components. Total storm rainfall involves the following generalised equation: P = FAFP * T / C where FAFP (Free Atmosphere Forced Precipitation) the part of the storm rainfall due to the storm dynamics and is largely independent of orography. It is that part of the PMP procedure that is moisture maximised and widely transposed. T/C (Orographic Factor) An index of the broad scale orographic influences on storm precipitation. It is a ratio of the 24-hour 1 year rainfall to the 24-hour 1 year rainfall in the absence of orography across New Zealand. The above equation forms the basis of estimating PMP, as well as the orographic and dynamics components of historical storm rainfalls. In the estimation procedure for PMP, the orographic factor is modified to account for a reduction in orographic forcing during the most intense part of the storm (Fenn, 1985). High intensity rainfall and potential impacts of climate change in the Waiohine catchment 24

32 The selection and analysis of major storms in New Zealand is an essential requirement when estimating PMP. Major storms were determined from variety of means: from computer surveys of extreme rainfalls and from large floods identified in SCRCC (1957) and from McKerchar and Pearson (1989). From a list of identified storms, a selection was made of those events that were considered significant in the estimation of PMP for New Zealand. These were classified according to the type of weather system producing the extreme rainfall. Such a scheme serves two purposes. The first is that it can be used to determine regions of New Zealand having similar storm types from which storm transposition limits can be assessed. The second purpose is to determine the type of storm in each homogeneous region that will lead to PMP. Although frontal systems crossing the southern North Island frequently produce the high river levels in the Waiohine catchment, Tomlinson and Thompson (1992) consider cyclones of tropical origin (ex-tropical cyclones) as the prototype PMP storm for the North Island. In determining the most likely level of PMP for Wairarapa, and in particular the Waiohine River catchment, tracks of past tropical cyclones (Revell, 1981, Thompson et al., 1992) indicate ex-tropical cyclones are likely down either side of the North Island with no preferred path. However, an ex-tropical cyclone moving down the east coast of the North Island will produce greater PMP rainfall depths in the Wairarapa because the coastal hills do not impede the movement of humid air mass to any great extent, as is the case if the ex-tropical cyclone had moved down the western side of the island. PMP have been prepared for both situations: 1 percent more precipitation is likely from an east coast cyclone. The difference reflects the intervention of the Tararua Ranges in significantly reducing the moisture potential to the Wairarapa and the river catchments. Figures 8 11 give the spatial pattern of PMP for the southern part of the North Island, including the Waiohine River catchments for durations from hours. The figures can also be found, in a format suitable as GIS layers, on the accompanying CD. Table 4 gives the PMP estimates for the two catchments. In the table the estimates have been rounded down to the nearest 1 mm. Two calculations have been produced: one for a tropical storm moving down the west side (W) of the North Island and one for a storm moving down the east side (E). High intensity rainfall and potential impacts of climate change in the Waiohine catchment 25

33 Figure 8. Estimate of the 6-hour probable maximum precipitation (mm) for southern North Island, including the Waiohine River and Mangatarere Stream catchments (outlined in red). NIWA 29 High intensity rainfall and potential impacts of climate change in the Waiohine catchment 26

34 Figure 9. Estimate of the 12-hour probable maximum precipitation (mm) for southern North Island, including the Waiohine River and Mangatarere Stream catchments (outlined in red). NIWA 29 High intensity rainfall and potential impacts of climate change in the Waiohine catchment 27

35 Figure 1. Estimate of the 24-hour probable maximum precipitation (mm) for southern North Island, including the Waiohine River and Mangatarere Stream catchments (outlined in red). NIWA 29 High intensity rainfall and potential impacts of climate change in the Waiohine catchment 28

36 Figure 11. Estimate of the 72-hour probable maximum precipitation (mm) for southern North Island, including the Waiohine River and Mangatarere Stream catchments (outlined in red). NIWA 29 High intensity rainfall and potential impacts of climate change in the Waiohine catchment 29

37 Table 4. Catchment-averaged estimates of probable maximum precipitation for Waiohine River catchments (after Tomlinson and Thompson, 1992) Waiohine Mangatarere Catchment Catchment area (km 2 ) Index 24-h PMP (mm) Barrier adjustment (W).84 Barrier adjustment (E).94 Depth-area reduction (% index PMP) hour catchment PMP (W) hour catchment PMP (E) Full details of the method to calculate probable maximum precipitation are given in Tomlinson and Thompson (1992). Briefly this involves adjusting the index PMP for (a) the elevation impeding the flow of moisture into the catchment (i.e. barrier adjustment in Table 4) and (b) for the size of the catchment since the index PMP is considered to be an estimate for a 25 km 2 area (i.e. a depth-area reduction in Table 4). The resultant PMP for the catchment is the multiplication of these two factors with the index PMP. Probable maximum precipitation estimates for other durations are related to the 24- hour PMP through factors given in Table 5. Table 5. Duration-variation of probable maximum precipitation as a percentage of the 24-hour estimate (after Tomlinson and Thompson, 1992) Duration (hours) High intensity rainfall and potential impacts of climate change in the Waiohine catchment 3

38 4.2 Comparison of probable maximum precipitation with 1-year storm rainfall All PMP estimates involve a level of uncertainty. However, they represent the best available estimate that can be made given the current level of meteorological knowledge, methods and data. There is also no objective way of assessing the accuracy of PMP estimates (WMO, 1986). The level of the PMP can be assessed by comparing the PMP against the 1-year average recurrence interval estimate. By definition the PMP must exceed the 1-year values, and for New Zealand the ratio of PMP to 1-year storm rainfall varies from about 1.5 to nearly 4 (Tomlinson and Thompson, 1992). This range of ratios is entirely consistent with PMP estimates from overseas studies (e.g. Hansen et al. 1988, Kennedy et al, 1988) where ratios vary between 1.5 and 6. The spatial patterns of the 1-year recurrence interval storm rainfall are given in Figures for durations from 12 hours to 72 hours. These maps are derived from NIWA s high intensity rainfall design software (HIRDS) (Thompson, 22). The maps can also be found, in a format suitable as GIS layers, on the accompanying CD. Catchment PMP estimates are 2 4 times the 1-year estimates and are consistent with the ratios also found by Tomlinson and Thompson (1992) for other Wairarapa and New Zealand sites. High intensity rainfall and potential impacts of climate change in the Waiohine catchment 31

39 Figure 12. Estimate of the 6-hour 1-year average recurrence rainfall (mm) for southern North Island, including the Waiohine River and Mangatarere Stream catchments (outlined in red) from HIRDSv2. NIWA 29 High intensity rainfall and potential impacts of climate change in the Waiohine catchment 32

40 Figure 13. Estimate of the 12-hour 1-year average recurrence interval rainfall (mm) for southern North Island, including the Waiohine River and Mangatarere Stream catchments (outlined in red) from HirdsV2. NIWA 29 High intensity rainfall and potential impacts of climate change in the Waiohine catchment 33

41 Figure 14. Estimate of the 24-hour 1-year average recurrence interval rainfall (mm) for southern North Island, including the Waiohine River and Mangatarere Stream catchments (outlined in red) from HirdsV2. NIWA 29 High intensity rainfall and potential impacts of climate change in the Waiohine catchment 34

42 Figure 15. Estimate of the 72-hour 1-year average recurrence interval rainfall (mm) for southern North Island, including the Waiohine River and Mangatarere Stream catchments (outlined in red) from HirdsV2. NIWA Temporal pattern of probable maximum precipitation Temporal patterns of extreme and rare rainfall events, such as those associated with tropical cyclones tend to be more uniform than those storms that occur more frequently (IEA, 1987). As noted in Section 3 there is a great variety in the storm rainfall temporal patterns with some storm events having several peaks of higher High intensity rainfall and potential impacts of climate change in the Waiohine catchment 35

43 intensity rainfall, while other profiles display single peak intensity. Often, the severe and low frequency storms show the single peak intensity (Miller et al., 1984). With these concepts in mind, Tomlinson and Thompson prepared (1992) temporal patterns of storm rainfall for probable maximum precipitation. Pilgrim and Cordery (1975) propose that temporal patterns be distributed in time according to a pattern of average variability: that is a temporal distribution that differs from a uniform intensity by an average amount. Maximum intensities occurring in the PMP storm should be equal to the mean of the maximum intensities that occurred in all recorded intense storms. Other lesser intensities within the storm are determined in the same way. Pilgrim and Cordery (1975) also consider that the rainfall pattern may not represent the rainfall in the complete storm, but covers an intense period of rainfall that occurs at some stage within the storm. They also assume there is equality between the probable maximum precipitation and the probable maximum flood if the temporal patterns incorporate average variability rainfalls. While the method has been used for evaluating storm profiles for probable maximum precipitation, it can equally apply to providing temporal distributions associated with design rainfalls. Table 6. Temporal accumulation (%) of probable maximum precipitation for storm durations of 12, 24, 48, and 72-hours (from Tomlinson and Thompson, 1992) Duration Percentage accumulation (hours) Tomlinson and Thompson (1992) prepared temporal patterns for probable maximum precipitation from the most extreme storms over the North Island, using the Pilgrim and Cordery method of average variability. Table 6 provides the percentage accumulation of PMP storm rainfall for various durations. For the storm durations provided in the table, there is a high level of consistency in each of the storm subperiods. About 5 percent of the total rainfall accumulation occurs about half-way through the storm event. High intensity rainfall and potential impacts of climate change in the Waiohine catchment 36

44 5. Climate change 5.1 Global perspective The Intergovernmental Panel on Climate Change (IPCC) issued updated climate change assessments during 27 (IPCC, 27a, b). NIWA has used climate model data from the IPCC Fourth Assessment to update the climate change scenarios for New Zealand. These are described in a guidance manual prepared for the Ministry for the Environment (MfE, 28), and supersede the earlier scenarios (MfE, 24). This report for Greater Wellington Regional Council draws on the new scenario information. The IPCC presented projections for 6 emissions scenarios (called marker scenarios) to cover a wide range of possible future economic, political and social developments during the 21 st century. Figure 16 shows the IPCC projected range of global temperature increases likely out to 21 that occur as a consequence of the 6 emission scenarios, based on simulations from about 2 global climate models. The modelaverage temperature changes over time are shown for three of the marker scenarios in the boxed part of Figure 16, and temperature increases at 21 for all 6 scenarios at the right-hand side. There is a substantial spread in projected warming. One factor causing the spread in temperature increase is the range of plausible emissions scenarios (represented by separation between the coloured lines or, at 21, between the coloured marks near the middle of the grey bars). The second factor is the variation in climate response by the models for the same emissions (represented for warming at 21 by the length of the grey bars). The global-average temperature increase at 21, relative to the average over , varies from +1.1 C (least sensitive model combined with the lowest emission scenario B1) to +6.4 C (most sensitive model with the highest emission scenario A1FI). The multi-model average, or IPCC best estimate, of the global temperature increase for the mid-range A1B scenario is +2.8 C at 21, with a range for A1B between 1.7 and 4.4 C. Projected temperature increases for New Zealand are slightly smaller than the global increases. High intensity rainfall and potential impacts of climate change in the Waiohine catchment 37

45 Figure 16. IPCC projections of global temperature increase. Solid coloured lines are multimodel global averages of surface warming (relative to ) for emission scenarios B1, A1B and A2, shown as continuations of the 2 th century simulations (black line). The coloured shading denotes the ±1 standard deviation range of individual model annual averages. The grey bars at right indicate the best estimate (solid horizontal line within each grey bar) and the likely range across 6 scenarios that span the full range of all IPCC emission scenarios. (Adapted from Figure SPM-5, IPCC 27). 5.2 Climate change scenarios for Wairarapa (Waiohine River Catchment) NIWA has produced downscaled scenarios for New Zealand from 12 global climate models. MfE (28) provides maps and tables of New Zealand regional projections, along with background on the downscaling methodology. The climate models are driven by the increasing greenhouse gas emissions of the A1B scenario between years 2 and 21, and changes in climate are compared to the model simulation of the late 2 th century climate to 1999 (where observed greenhouse gas increases are used in place of the scenario emissions). NIWA has not so far analysed the global climate model data for the other SRES scenarios. However, these other scenarios are taken account of in MfE (28), and also in this report, through the widely used approach of rescaling the A1B changes to match the global warming rate reported in IPCC (27a) and illustrated in Figure 16. For Wairarapa, scenarios of temperature increase have been developed for two future periods and for three combinations of climate models and greenhouse gas emissions. The future periods are centred at 24 (corresponding to an average over ), High intensity rainfall and potential impacts of climate change in the Waiohine catchment 38

46 and 29 ( average). Thus, the temperature increases can be thought of as those experienced up to the years 25 and 21. In all cases, the changes are relative to (199 for short), which is considered to be indicative of the current climate. This convention follows the approach in the MfE guidance manual (MfE, 28) and in IPCC (27a). The 3 model/emission scenarios are as follows: i. the single model with the smallest A1B temperature increase, rescaled down to the lowest IPCC B1 emission scenario (the low scenario); ii. the 12-model average for the A1B emission scenario (the mid scenario); iii. the single model with the highest A1B temperature increase, rescaled up to the highest IPCC A1FI emission scenario (the high scenario). Figure 17 shows the 29 temperature projections for all 12 models, as a function of the emission scenario, with the low, middle and high scenarios marked on the figure. Note that one of the 12 models is substantially warmer than any of the others (3.5 C warming at 29, compared to the second warmest of 2.6 C, for A1B emissions). At his stage, however, the very warm outlier model cannot be eliminated from consideration. High intensity rainfall and potential impacts of climate change in the Waiohine catchment 39

47 Figure 17. IPCC projections of temperature change, downscaled to the Auckland region. Temperature changes are shown for 29 ( average, relative to the average). Vertical coloured bars show the range across 12 climate models for the three emission scenarios known as B1, A1B and A1F1. Stars mark the individual model values. Short horizontal lines mark the positions of the low, middle and high temperature scenarios used in this report. Note that the upper star is well separated from the second most sensitive climate model. NIWA 29 Table 7. Projected annual temperature increase (in C) for Wairarapa, for time periods centred on 24 and 29, and for three scenarios (coldest model under lowest B1 emissions, 12-model average under A1B emissions, hottest model under highest A1FI emissions. Scenario/Period Low: B1 coldest model.26.6 Mid: A1B 12-model average High: A1FI hottest model High intensity rainfall and potential impacts of climate change in the Waiohine catchment 4

48 Table 7 shows the numerical values for the Wairarapa temperature scenarios. The 29 values correspond to those marked on Figure 17. These temperature increases are used, as explained below, to estimate consequent changes in extreme rainfalls. Table 7 provides just a single value for the Wairarapa as a whole. This is reasonable assumption since the downscaling shows that the spatial variation in projected temperatures is very small. Figure 18 illustrates the 12-model average changes to 29 for temperature. Note the very fine contour interval used for temperature. The 29 annual temperature increase under A1B (12-model average) lies between 2.4 and 2.8 C for almost the entire lower North Island. For completeness, Figure 18 also shows the precipitation changes. Under the A1B mid-range emissions, the 12-model average projects small increases in annual precipitation totals of about 2.5% in the high altitude western areas of the Waiohine catchment, and decreases of about 5% in the east around Greytown. These changes apply to mean annual precipitation, and should not be confused with the extreme precipitation which is projected to increase everywhere, even where there is a decline in the mean. Figure 18a: Projected 12-model annual average changes to 29 for temperature ( C) under the A1B emission scenario. NIWA 29 High intensity rainfall and potential impacts of climate change in the Waiohine catchment 41

49 Figure 18b: Projected 12-model annual average changes to 29 for precipitation (%) under the A1B emission scenario. NIWA 29 The IPCC is reluctant to state whether any of their emissions scenarios are more likely to eventuate than others. However, observed emissions are increasing faster than the mid-range A1B emission scenario. Thus, the low B1 scenario, where the projected temperature increase to 21 is less than the linear extrapolation of the observed New Zealand warming over the 2 th century, appears unlikely. Obviously, if international action curbs the growth of greenhouse gas concentration in the atmosphere during the 21 st century, then the high scenario may not eventuate. The Ministry for the Environment in its 24 publication Preparing for climate change: A guide for local government in New Zealand and the recently updated 28 publication Climate change effects and impacts assessment: A guidance manual for local government in New Zealand, 2 nd Edition provides a method showing how high intensity rainfalls can be adjusted for preliminary scenario studies. It advocates that at least two sets of calculations be undertaken for low and high temperature change scenarios. For screening assessment purposes, Table 8 provides the recommended percentage adjustments per degree Celsius of warming to apply to high intensity rainfalls for various durations and average recurrence intervals. The percentage changes in this table for durations of hours are based on results from a regional climate model (MfE, 28). Entries for 1 minute duration are based on the theoretical increase in the amount of water held in the atmosphere for a 1 C increase in temperature. Entries between 1 minutes and 24 hours are derived from a logarithmic interpolation (in time) between the 1 minute and 24 hour periods. High intensity rainfall and potential impacts of climate change in the Waiohine catchment 42