EFFECTS OF CLIMATE VARIABILITY ON RIVERS: CONSEQUENCES FOR LONG TERM WATER QUALITY ANALYSIS¹

Abstract

Associations between the El Nino Southern Oscillation (ENSO) climate pattern and temporal variability in flow and 12 water quality variables were assessed at 77 river sites throughout New Zealand over a 13‐year period (1989 through 2001). Trends in water quality were determined for the same period. All 13 variables showed statistically significant linear regression relationships with values of the Southern Oscillation Index (SOI). The strongest relationships were for water temperature (mean R2= 0.20), dissolved reactive phosphorus (0.18), and oxidized nitrogen (0.17). The association with SOI varied by climate region. The observed patterns were generally consistent with known ENSO effects on New Zealand rainfall and air temperature. Trends in water quality variables for the periods 1989 through 1993, 1994 through 1998, and 1989 through 1998 were reasonably consistent with trends in SOI, even when the influence of river flow was removed from the data. This suggests that SOI effects on water quality are not necessarily a direct consequence of changes in flow associated with rainfall variation. In addition, both Baseline (32 upstream) and Impact (45 downstream) sites showed similar trends, indicating that changes in management were not directly responsible. We conclude that interpretation of long term water quality datasets in rivers requires that climate variability be fully acknowledged and dealt with explicitly in trend analyses.

Trends in water quality of five dairy farming streams in response to adoption of best practice and benefits of long-term monitoring at the catchment scale

“…Positive values (La Niña) are generally characterised by increased moist, rainy conditions to the north-east of the North Island, and reduced rainfall to the south and south-west of the South Island (Kidson and Renwick 2002). Trends in New Zealand water quality were correlated with SOI values over a 13-year period, with statistically significant linear regression relationships being found for 13 variables (Scarsbrook et al 2003). The strongest relationships were for water temperature (mean R 2 ¼ 0.20), FRP (0.18), and NO X -N (0.17).…”

“…The strongest relationships were for water temperature (mean R 2 ¼ 0.20), FRP (0.18), and NO X -N (0.17). The analysis indicated that effects on water quality were not necessarily a direct consequence of changes in flow associated with rainfall variation, and that changes in management were not directly responsible (Scarsbrook et al 2003). The authors suggest that water quality trends in New Zealand should take climatic influences into account when ascribing causes for trends, which implies that monitoring records should be long enough to discriminate land-management consequences from those induced by 3-7 year climate cycles.…”

Observed climate change in Australian marine and freshwater environments

“…As with any monitoring program, the value of sustained high-quality measurements only increases with time, and the New Zealand example allows insights into biological, physical and chemical linkages and changes, which would not be obtainable otherwise (e.g. Scarsbrook et al 2000Scarsbrook et al , 2003.…”

Macroinvertebrate community response to inter‐annual and regional river flow regime dynamics

“…Results from the available long-term hydroecological studies indicate that instream communities are influenced by variations in both the climatological (Bradley and Ormerod, 2001;Daufresne et al, 2003;Scarsbrook et al, 2003) and hydrological regimes (e.g., Lamouroux et al, 2006;Monk et al, 2006, Scarsbrook, 2002Wagner and Schmidt, 2004;Wood et al, 2001). However, attempts to quantify macroinvertebrate community response to river flow variability, and in particular high and low flows, are currently limited in terms of their temporal (typically <5 years study period) and/or geographical coverage (e.g., Caruso 2002;Clausen and Biggs, 1997;Sheldon and Thoms, 2006;Suren and Jowett, 2006).…”