Introduction
River water quality reflects multiple activities and processes within its
catchment, including geomorphic processes, vegetation characteristics,
climate, and anthropogenic land uses (Brierley, 2010). Relationships between
water quality and these catchment characteristics are not straightforward
because all of these factors interact over both space and time. For example,
if intensive livestock grazing occurs on steep slopes, surface runoff and
consequently river turbidity is expected to be greater than if grazing
occurs on flatter areas; in other respects, if fertilizers are heavily applied to sandy
soils with high drainage density, rivers will likely become eutrophied over
a period of decades due to legacy nutrients slowly leaking to the rivers
through groundwater (McDowell et al., 2008). The influence of land use on
water quality has also been shown to vary among different climates (Larned
et al., 2004). With all of the various types of intensive land uses that
have occurred across diverse landscapes over hundreds of years, rivers with
degraded water quality are now widespread.
Historically, water quality in rivers was managed to meet minimally
acceptable standards or maximum pollutant load limits (Baron et al., 2002;
Boesch, 2002; Howard-Williams et al., 2010). However, in the last decade, a
greater emphasis has been placed on maximizing the ecosystem services
provided by healthy rivers, which is driving efforts to further improve
water quality (Brauman et al., 2007; Davies-Colley, 2013). Early efforts in
developed countries to improve water quality focused on point-source
pollution, particularly wastewater discharges from factories and treatment
plants (Campbell et al., 2004). While the broad-scale reduction in
point-source pollution elevated many water quality variables above minimal
standards, most rivers globally still have water quality impairments due to
diffuse pollution from fine sediments, nutrients, and other contaminants
(Vorosmarty et al., 2010). Although considerable effort has been directed at
monitoring and reducing diffuse pollution with some success, the legacy of
pollutants from various land uses remains (Boesch, 2002; Kronvang et al.,
2008; Zobrist and Reichert, 2006). Agricultural land uses are by far the
greatest contributors of diffuse pollution globally (Foley et al., 2005;
Vitousek et al., 1997); however, the “intangible” sources of diffuse
pollution make it difficult to assign cause-and-effect relationships between
land use and water quality (Campbell et al., 2004).
Many studies have used theoretical or numerical models to examine
relationships between land use and water quality because of the lack of
consistent water quality monitoring over long periods (bracketing land use
change). While modeling approaches can be useful for catchments where much
is known about its landscape, modeling may not work well for large,
heterogeneous catchments because land–water relationships are complex with
interdependencies, feedbacks, and legacy effects. Empirical studies can shed
light on some of these complexities, but they are only useful for their
particular catchments and may have limited generality or transferability.
Comparisons of many diverse catchments is probably most useful to advance
understanding of broad-scale land–water relationships (Zobrist and Reichert,
2006).
One of the most comprehensive empirical multi-catchment studies to date on
land use–water quality relationships has been the study by Varanka and Luoto (2012)
of 32 boreal rivers in Finland. They analyzed five water quality
variables over 10 years as a function of a suite of physiographic, climate,
and land use variables. A similar study was conducted on many of the same
rivers in Finland, but with a more sophisticated temporal analysis (Ekholm
et al., 2015). In a study of 11 Swiss watersheds, Zobrist and Reichert (2006)
analyzed export coefficients of six water quality variables from
biweekly, flow proportional, composite samples over a 24-year period within
the context of land use.
All of these studies, and most catchment land use studies, assessed land use
(or land use change) as areal coverage. However, land use intensity
– the inputs (e.g., fertilizer, livestock) and activities (e.g., vegetation
removal) of land use – could be a better predictor of environmental impact
for being a more direct measure of impact than land use alone (Blüthgen
et al., 2012; Ramankutty et al., 2006). Unfortunately, our understanding of
the patterns, processes, and impacts of land use intensity is inadequate
because of (1) its complex, multidimensional interactions with other
landscape variables, and (2) the lack of appropriate datasets across broad
spatiotemporal scales (Kuemmerle et al., 2013; Erb et al., 2016). New
Zealand (NZ) provides a valuable test bed for the patterns, processes, and
impacts of land use intensity because over the past 3 decades pasture
area has decreased but livestock densities and fertilizer inputs have
increased (MacLeod and Moller, 2006; StatsNZ, 2015). Like Finland and
Switzerland, NZ has an extensive long-term river water quality monitoring
network, which has allowed for many studies on river water quality state and
trends (Smith et al., 1996, 1997; Scarsbrook et al., 2003; Scarsbrook, 2006;
Ballantine and Davies-Colley, 2014) and effects of land use areal coverage
(Davies-Colley, 2013; Larned et al., 2004, 2016). However, this dataset has
not been assessed as regards changes in land use intensity that have
occurred over the same period.
Here, we investigate long-term relationships between land use intensity,
geomorphic processes, and river water quality in NZ – which provides a
particularly valuable case study because (1) it has had one of the highest
rates of agricultural land intensification over recent decades (OECD/FAO,
2015) and thus serves as a potential indicator for countries that are also
increasing agricultural intensity; (2) it has a long, consistent, and
comprehensive national water quality dataset; and (3) it is
physiographically diverse. We examined monthly data for a suite of water
quality variables that extend over a 26-year period for 77 diverse
catchments. We then compared these states and trends of river water quality
to landscape data that characterized the catchments' geomorphology, soil
properties, and hydro-climatology as well as temporal changes in land use
areal coverage and land use intensity, specifically livestock density and
land disturbance, defined here as bare soil resulting from vegetation loss.
Altogether, these analyses reveal coincident spatiotemporal patterns in land
use intensity and water quality over a quarter of a century. Most of our
analyses were performed at the catchment scale, which integrates the
spatiotemporal changes that are reflected in our water quality measurements
and is the most appropriate scale to manage diffuse pollution
(Howard-Williams et al., 2010).
Study area
New Zealand is a small island nation (∼ 268 000 km2)
located between the South Pacific Ocean to the east and the Tasman Sea to
the west. Its two main islands, North Island and South Island, are located
between 34 and 47∘ S latitude. Being located on the
active boundary between the Australian and Pacific plates, NZ's geology and
geomorphology are very diverse, including active volcanoes, karst regions, a
range of high-fold mountains (the Southern Alps), large coastal plains, and
rolling hills across both hard and soft rocks. Being stretched
latitudinally, with nowhere more than about 150 km from the sea, between two
major ocean waters combined with its topographic variability, NZ also has a
diverse climate with regional extremes, including sub-tropical in the far
north, temperate in the central North Island, extremely wet on the western
side of the Southern Alps (up to 10 m annually), and semi-arid in the rain
shadow to the east of the Southern Alps.
Land use and location of the 77 National River Water
Quality Network (NRWQN) catchments. Catchment ID colors refer to dominant
land use (> 50 %). Catchments with no dominant land use are
black.
New Zealand is the last major habitable landmass to be settled by humans.
Eastern Polynesians first arrived around 1300 AD (Wilmshurst et al., 2008).
Europeans first arrived in the late-1700s, but large-scale settlement did
not begin until the 1840s. Broad-scale agriculture spread shortly after and
has been intensifying since. While we address land use changes at the
national scale in this study, our water quality analyses focus on 77 diverse
catchments across NZ (Fig. 1).
Methods
Water quality data
Water quality data were obtained from NZ's National Rivers Water Quality
Network (NRWQN), which is operated and maintained by the National Institute
of Water & Atmospheric Research (NIWA). This network represents one of
the world's most comprehensive river water quality datasets: 13 water
quality and 2 biomonitoring variables have been measured monthly (via in
situ measurements and grab samples), with supporting flow estimation, from
1989 to 2014 at 77 sites, whose catchments cumulatively drain approximately half
of NZ's land surface (Davies-Colley et al., 2011). Further, this dataset has
been operationally stable throughout its history, which allows us to
calculate trends over this period. For this study, we focused on 11
water quality variables and their coincident flow (Table 1). We did not
analyze ammoniacal nitrogen (NH4) because early NH4 samples were
biased high by laboratory contamination (Davies-Colley et al., 2011).
Water quality variables measured by the National River Water
Quality Network (NRWQN) obtained from monthly grab samples from 1989 to 2014
for 77 catchments. Details on analytical methods can be found in
Davies-Colley et al. (2011).
Variable
Definition (units)
Q
Water discharge (m3 s-1)
Tw
Water temperature (∘C)
DO
Dissolved oxygen (%)
COND
Water conductivity (µS cm-1)
pHW
Water pH (-log10[H+])
CLAR
Horizontal visual water clarity from black disc sighting range (m)
TURB
Water turbidity (NTU)
CDOM
Colored dissolved organic matter, measured as spectrophotometric absorbance of a membrane filtrate at 440 nm (m-1)
TN
Total nitrogen (mg m-3)
NOx
Oxidized nitrogen in nitrate and nitrite forms (mg m-3)
TP
Total phosphorus (mg m-3)
DRP
Dissolved reactive phosphorus (mg m-3)
All water quality variables, except water temperature (Tw),
were flow normalized (for each site separately) in JMP® Pro (v
11.2.1) with local polynomial regression (LOESS) using a quadratic fit, a
tri-cube weighting function, a smoothing window (alpha) of 0.67, and a
four-pass robustness to minimize the weights of outliers (Cleveland and
Devlin, 1988), where flow-adjusted value = raw value – LOESS
value + median value. With LOESS, there is no assumption about the water quality
variable's relationship with flow. For example, although visual clarity
usually decreases systematically with increasing flow (Smith et al., 1997),
algae blooms at low flows can sometimes reduce clarity. LOESS also allowed
us to examine relative water quality changes over long periods.
We assessed water quality states and trends with ANZECC (2000) guidelines,
which are the 20th percentile of the first decade of the NRWQN record
for reference. These guidelines are “trigger values” that when
exceeded trigger a management response to protect ecosystem health (Hart et
al., 1999). Although these trigger values are not effects-based standards
(which would be difficult to define for the wide variety of NZ ecosystems),
they do provide a useful reference for comparing water quality states and
trends. Upland and lowland catchments, distinguished by the 150 m elevation
threshold, have different guidelines that take into account that lowland
rivers are typically more turbid and nutrient rich.
Landscape variables characterizing the 77 catchments of the
National River Water Quality Network (NRWQN). More details on sources for
these data can be found in Methods section.
Variable
Definition (units)
Source (resolution/scale)
Morphometric variables
Area (A)
Total catchment area above monitoring site (km2)
National Elevation Dataset (30 m)
Drainage density (Dd)
Total length of streams per catchment area (km km-2)
River Environment Classification, v2 (1 : 24 000)
Catchment slope (Sc)
Mean slope across entire catchment (degrees)
National Elevation Dataset (30 m)
Ruggedness (Rr)
Standard deviation of catchment slope (degrees)
National Elevation Dataset (30 m)
Soil variables
Silt-clay percentage (SC%)
Percentage of catchment surface soils dominated by clayey or silty soils (%)
Fundamental Soil Layers (1 : 63 360)
Soil depth (Zs)
Mean maximum potential rooting depth across catchment (m)
Fundamental Soil Layers (1 : 63 360)
Soil pH (pHS)
Mean pH at 0.2–0.6 m depth across catchment (-log10[H+])
Fundamental Soil Layers (1 : 63 360)
Cation exchange capacity (CEC)
Weighted mean CEC at 0–0.6 m depth across catchment (cmoles [+] kg-1)
Fundamental Soil Layers (1 : 63 360)
Organic matter percentage (OM%)
Weighted mean of total carbon at 0–0.2 m depth across catchment (%)
Fundamental Soil Layers (1 : 63 360)
Phosphate retention (Pret)
Weighted mean of phosphate retention at 0–0.2 m depth across catchment (%)
Fundamental Soil Layers (1 : 63 360)
Hydro-climatological variables
Median annual precipitation (MAP)
Median annual precipitation averaged across catchment (mm yr-1)
NIWA National Climate Database (5 km)
Median annual temperature (MAT)
Median annual temperature averaged across catchment (∘C)
NIWA National Climate Database (5 km)
Median annual sunshine (MAS)
Median annual sunshine hours averaged across catchment (hours yr-1)
NIWA National Climate Database (5 km)
Median discharge (Q50)
Median discharge from NRWQN samples during 1989–2014 (m3 s-1)
NRWQN (catchment)
Relative water storage (RWS)
Proportion of annual Q50 stored in reservoirs/lakes (m3 m-3)
Freshwater Environments New Zealand (1 : 50 000)
Land use and land disturbance variables
Land use
Percent of catchment that is occupied by each land use (%); see Table 3 for land uses
Land Cover Database (LCDB, v 4.1), 2001 (1 ha)
High-producing pasture disturbance (DHG)
Percent of high-producing grasslands within catchment that is disturbed (%), based on aggregate of 463 m pixels within catchment
de Beurs et al. (2016) (463 m; 8-day)
Plantation forestry disturbance (DPF)
Percent of plantation forestry within catchment that is disturbed (%), based on aggregate of 463 m pixels within catchment
de Beurs et al. (2016) (463 m; 8-day)
Catchment disturbance (DC)
Percent of catchment that is disturbed (%), based on aggregate of 463 m pixels within catchment
de Beurs et al. (2016) (463 m; 8-day)
Stock unit density (SUD)
Catchment-averaged stock unit density for dairy (da), beef (be), deer (de), and sheep (sh) in 2011 (SU ha-1); subscripts are used to isolate SUD by livestock type
Ausseil et al. (2013) (1 ha)
Change in stock unit density (SUD2012-1990)
Difference between SUD in 2012 and 1990 (SU ha-1)
Statistics NZ (territorial authority)
Physiographic data
Water quality metrics and trends were compared to a suite of landscape
variables (Table 2). Catchment morphometrics (area, slope, ruggedness) were
obtained from a 30 m digital elevation model (DEM) that we rescaled (in
order to align with other gridded spatial datasets) from the 25 m DEM
produced by Landcare Research (LCR). This 25 m DEM was interpolated from
20 m contours of the national TOPOBASE digital topographic dataset supplied
by Land Information NZ (LINZ; scale: 1 : 50 000). Catchment area (A)
is the drainage area (in km2) above the NRWQN station, derived using
Arc Hydro tools in ArcGIS 9.3.1 in combination with the River Environment
Classification (REC, v2.0), the national hydrography dataset derived from a
30 m hydrologically correct DEM (Snelder et al., 2010). Mean catchment slope
(Sc) was derived from the same software package, using a
3 × 3 cell window. We defined ruggedness (Rr) as the standard
deviation of the 30 m slope grid for each catchment (Grohmann
et al., 2011). Drainage density (Dd) was calculated from the
ratio of the total length of REC streams to catchment area (in km km-2).
Soil data were obtained from the 1 : 63 360 Fundamental Soils Layers
(FSL), which is maintained by LCR. Methods and data descriptions for this
soils database are described in Webb and Wilson (1995) and Newsome et
al. (2008). Catchment-scale soil variables (mean value across catchment) that
we included in our analysis for being expected to be related to water quality
were soil depth (Zs), percent of catchment dominated by silty and
clayey surface soils (SC%), soil pH (pHs), cation exchange
capacity (CEC), organic matter percentage (OM%), and phosphate retention
(Pret). Phosphate retention is a measure (in %) of the amount
of phosphate that is removed from solution by the soil via sorption
(Saunders, 1965). Thus, soils with high Pret have low P
availability for plant growth.
Median annual precipitation (MAP), median annual temperature
(MAT), and median annual sunshine (MAS) averaged across
each catchment was obtained from NIWA's National Climate Database, which
contained 5 km gridded daily weather data (Tait and Turner, 2005). Our
values for these three variables represent the median annual precipitation
(total mm yr-1), temperature (mean ∘C), and sunshine (hours yr-1) for
the period 1981–2010. Relative water storage (RWS) was calculated
as the proportion of the annual catchment water yield (i.e., total volume of
water leaving the catchment in a year) stored in lakes and reservoirs.
Reservoir/lake storage was obtained from the Freshwater Ecosystems of NZ
(FENZ) database, described in Snelder (2006). The last hydro-climatological
variable we included in our analyses was the median discharge
(Q50), which was calculated from the NRWQN “flow stamping” at
times of water quality sampling from 1989 to 2014.
Land use classification used in this study, aggregated from the
LUCAS (v11) and LCDB (v4.1) land use/cover datasets.
Class (abbreviation)
Description
LUCAS
LCDB
2012 national coverage (%)
classes
classes
LUCAS/LCDB
Non-plantation forest (NF)
All non-plantation forests ≥ 5 m; does not include Manuka/Kanuka
71
68, 69
29.2/23.9
Plantation forest (PF)
All forests that are planted for the purpose of harvesting
72, 73
64, 71
7.9/7.6
Shrub/grassland (SG)
All shrubs < 5 m and grasses that are not intensively managed
74, 76
41–44, 50–58
33.0/25.4
High-producing grassland (HG)
High-quality pasture grasses that are intensively managed
75
40
21.6/33.0
Perennial cropland (PC)
Orchards and vineyards
77
33
0.4/0.4
Annual cropland (AC)
All annual crops and cultivated bare ground
78
30
1.4/1.4
Open water (OW)
Rivers, lakes/reservoirs, ponds, and estuaries
79
20–22
1.9/2.0
Vegetated wetland (VW)
Herbaceous or woody vegetation periodically flooded; includes mangroves
80
45–47, 70
0.5/0.7
Urban (UR)
Built-up areas, infrastructure, transportation networks, and urban parks/open spaces
81
1–5
0.8/0.9
Barren/Other (BO)
Bare rock, sand, gravel and other areas not dominated by vegetation, including mining and permanent ice/snow
82
6–16
3.3/4.8
Land use and intensity data
There are two national land use datasets for NZ. The Land Use and Carbon
Analysis System (LUCAS) was developed by the NZ Ministry for the Environment
(MfE, 2012) for reporting and accounting of carbon fluxes and greenhouse gas
emissions, as required by the United Nations Framework on Climate Change and
the Kyoto Protocol. Accordingly, LUCAS uses 1990 as its reference year and
maps land use in 12 classes for 2008 and 2012. The Land Cover Database
(LCDB) was developed by LCR, with contributions from MfE, Department of
Conservation, Ministry for Primary Industries, and Regional Councils (LCR,
2015). LCDB contains 35 land use classes for 1996, 2001, 2008, and 2012.
Both datasets use a minimum mapping area of 1 ha, and use many of the same
data and methods to map land use. There are however, some key differences in
their class designations and classifications that are important to our
analyses: (1) LUCAS includes Manuka/Kanuka as forest, whereas LCDB
designates Manuka/Kanuka as shrub; (2) LUCAS lumps all post-1989 forests
into one class, whereas LCDB differentiates between indigenous and
plantation forests; (3) LUCAS uses a conservative approach to map
high-producing grasslands, whereas LCDB uses phenological information to
provide more accurate estimations of high-producing grassland. Because of
our focus on (water quality-impacting) plantation forests and high-producing
grasslands, we used the LCDB (v4.1) for the midpoint year 2001 for our
spatial and statistical analyses. We used LUCAS only to quantify long-term
changes from 1990 to 2012, before the LCDB was initiated in 1996. Table 3
describes the land use classes we used in this research, which classes are
included from both datasets, and the national comparison between LUCAS and
LCDB for 2012.
There are numerous metrics for land use intensity (Erb et al., 2013). At the
catchment scale, we used livestock density as a metric for all grasslands;
and we used land disturbance, defined here as bare soil resulting from
vegetation loss, as a metric for high-producing grasslands and plantation
forests. We also used national-scale annual fertilizer data (1989–2014) from
StatsNZ (2015) to compare long-term trends of river nutrient concentrations
to nutrient inputs. Livestock numbers for dairy cattle, beef cattle, sheep,
and deer (at 1 ha resolution) for each catchment were derived from maps
provided by Ausseil et al. (2013), which is representative for the year
2011. To assess total livestock impact, we multiplied each livestock type by
its AgriBase stock unit (SU) coefficient: sheep = 0.95 SU, deer = 1.9 SU,
beef cattle = 5.3 SU, and dairy cattle = 6.65 SU (Woods et al.,
2006). The total SU for each catchment was then normalized by total
catchment area, expressed as stock unit density (SUD) in SU ha-1.
Changes in SUD from 1990 to 2012 (SUD2012-1990) were
assessed using district-level data from StatsNZ (2015) on total numbers of
sheep, deer, beef cattle, and dairy cattle. These livestock numbers were
then aggregated for each catchment and multiplied by their respective SU
coefficient. Stock unit densities were then compared between 1990 and 2012
to assess change in livestock intensity in each catchment. For Whakatane and
Kawerau districts, 1993 was used because 1990 data were unavailable.
Land disturbance (i.e., bare soil resulting from vegetation loss) was
quantified for all high-producing grasslands (DHG) and
plantation forests (DPF), as well as the whole catchment
(DC) for the period 2000–2013. The methods for calculating
and validating disturbance are described in de Beurs et al. (2016). Briefly,
MODIS BRDF corrected reflectance data (MCD43A4) at 463 m spatial resolution
and 8-day temporal resolution was used to calculate Tasseled Cap
brightness, greenness, and wetness based on the coefficients following Lobser
and Cohen (2007). These indices consist of linear combinations of all seven
MODIS reflectance bands to represent general image brightness which is
comparable to albedo, image greenness which is comparable to the better
known vegetation indices such as NDVI and EVI, and image wetness which is
linked to the amount of water captured in the vegetation, most comparable to
normalized difference water indices. Missing pixels were ignored. We then
calculated the mean and standard deviation of each tasseled cap index for
each combination of land cover class (LCR, 2015) and climatic region for
each 8-day time period. We then used these measures to standardize the
calculated tasseled cap indices. To determine how disturbed each pixel was
at any point in time, we then calculated the forest and grassland
disturbances. The forest disturbance index is calculated as the standardized
brightness minus the standardized greenness and wetness. The idea is that
disturbed forests appear brighter and less green and less wet than
undisturbed forests. The grassland index is the negative sum of all indices,
indicating that disturbed grasslands appear darker, less green, and less wet
than undisturbed grasslands. MODIS disturbance data were visually validated
against 7500 random pixels from Landsat imagery and corresponding 15 high-resolution Orbview-3 and Ikonos images. The overall accuracy of the
disturbance index based on Landsat data was 98 %.
Statistical methods
We used non-parametric Spearman rank correlation coefficients
(rs) to look at relationships between variables because many
of the relationships were curvilinear. Statistical significance was taken to
be an alpha of 0.05. Bivariate comparisons between all variables (Tables 1–3) were performed to explore for associations and identify correlated
variables before later multivariate analyses. Median values (from the
26-year
monthly time series) for water quality variables at each site were used when
compared to physiographic and land use variables of their corresponding
catchment. Stepwise regression was then used to rank order the relative
contributions of multiple landscape variables associated with each major
water quality variable. Stepwise regression was used because it accounts for
correlations among the independent landscape variables. The order of
variables in the stepwise regression model and the sign of their coefficient
(proportional [+] vs. inverse [-]) provides an objective measure of the
contribution of each landscape variable to river water quality. The level of
entry into the model was set to p = 0.05. All the above statistical
analyses were performed in JMP® Pro (v 11.2.1).
Temporal trends in flow-normalized water quality (1989–2014) and
disturbance (2000–2013) data were assessed with the seasonal Kendall (SK)
test, which was corrected for temporal autocorrelation using the rkt
R package;
missing values were ignored. We also calculated the SK slope
estimators (SKSEs) using the same R package. Because some NRWQN sites had
multiple measurements in some months, a few records (no more than five) were
removed from each site in order to ensure 12 monthly values for each year
for the SKSE test. There were also occasional missing values for some
variables throughout the time series, particularly in the early years. Of
particular note, there were no TN values for 1994 as a result of
contamination by leaking ammonia refrigerant during storage of frozen
subsamples. HV1 did not have data for 18 months from 2012 to 2014.
In order to make trend comparisons among sites and derive an estimate of
percent change per year, we normalized SKSE values by dividing them by the
raw data median to give the relative SKSE (RSKSE) in percent change
per year (Smith et al., 1996). Given that water temperature
(Tw) uses an arbitrary scale in ∘C, we only report
SKSE values for this variable. We also used the trend categories of
Scarsbrook (2006): (1) no significant trend – the null hypothesis
for the SK test was not rejected (p > 0.05);
(2) significant increase/decrease – the null hypothesis for the SK
test was rejected (p < 0.05); and (3) “meaningful”
increase/decrease – the trend was significant and the
magnitude of the trend (RSKSE) was greater than 1 % per year. A 1 %
change per year translates to slightly more than 10 % change per decade
(due to compounding), a rate of change that is easily detectable and
observable.
Statistical description of landscape variables for the 77 NRWQN
catchments. Refer to Tables 2 and 3 for variable descriptions.
Variable
Units
Minimum
Median
Maximum
Mean ± SD
Morphometric variables
Area (A)
km2
26
1126
20 539
2639 ± 3714
Drainage density (Dd)
km km-2
1.30
1.59
2.61
1.60 ± 0.16
Catchment slope (Sc)
degrees
3.4
15.9
30.3
16.3 ± 6.8
Ruggedness (Rr)
degrees
3.4
10.8
15.8
10.6 ± 2.4
Soil variables
Silt-clay percentage (SC%)
%
0
47.3
98.7
44.0 ± 31.6
Soil depth (Zs)
m
0.55
0.96
1.50
1.02 ± 0.22
Soil pH (pH)
-log10[H+]
4.8
5.6
6.5
5.6 ± 0.3
Cation exchange capacity (CEC)
cmoles [+] kg-1
11.6
18.7
33.5
18.8 ± 4.6
Organic matter percentage (OM%)
%
2.8
6.7
23.2
7.2 ± 2.9
Phosphate retention (Pret)
%
19.9
39.0
77.8
41.5 ± 12.2
Hydro-climatological variables
Median annual precipitation (MAP)
mm yr-1
533
1652
7044
1778 ± 873
Median annual temperature (MAT)
∘C
5.0
9.9
15.1
9.9 ± 2.4
Median annual sunshine (MAS)
hours yr-1
1325
1856
2116
1841 ± 146
Median discharge (Q50)
m3 s-1
0.4
26.0
515.0
69.6 ± 112.6
Relative water storage (RWS)
m3 m-3
0
0
29.2
1.1 ± 3.7
Land use variables
Non-plantation forest (NF)
%
0.1
20.5
94.1
26.7 ± 23.3
Plantation forest (PF)
%
0
3.3
69.8
8.2 ± 12.3
Shrub/grassland (SG)
%
0.4
21.7
82.3
26.6 ± 20.2
High-producing grassland (HG)
%
0
21.6
91.2
30.9 ± 26.2
Perennial cropland (PC)
%
0
0
1.3
0.1 ± 0.2
Annual cropland (AC)
%
0
0.1
7.9
0.6 ± 1.4
Open water (OW)
%
0
0.4
25.6
1.9 ± 4.3
Vegetated wetland (VW)
%
0
0.1
2.2
0.3 ± 0.4
Urban (UR)
%
0
0.1
5.8
0.4 ± 0.7
Barren/other (BO)
%
0
1.3
30.0
4.4 ± 6.5
Land disturbance variables
Catchment disturbance (DC)
%
0
3.4
10.5
3.6 ± 2.1
HG disturbance (DHG)
%
0
4.4
34.9
6.0 ± 6.4
PF disturbance (DPF)
%
0
9.9
27.8
10.4 ± 6.7
Stock unit density (SUD)
SU ha-1
0
2.2
16.1
3.2 ± 3.1
Dairy SUD (SUDda)
SU ha-1
0
0.2
15.4
1.2 ± 2.4
Beef SUD (SUDbe)
SU ha-1
0
0.5
3.5
0.7 ± 0.8
Sheep SUD (SUDsh)
SU ha-1
0
0.6
4.5
1.2 ± 1.3
Deer SUD (SUDde)
SU ha-1
0
0
0.2
0 ± 0
Results
Physiographic characteristics
The 77 NRWQN catchments were physiographically diverse in terms of
morphometric, soil, and hydro-climatological variables (Table 4; Table S1 in the Supplement).
Most notable with regards to its direct influence on runoff and water
quality was median annual precipitation (MAP), which ranged from
533 to 7044 mm yr-1. When combined with the wide range of catchment areas
(A), median discharge (Q50) varied over 3 orders
of magnitude, from 0.4 to 515 m3 s-1, and annual water yield from 103 to
3475 mm yr-1. In terms of soil, about a quarter of the catchments had very
sandy surface soils (SC% < 10) and a quarter had
fine-textured soils (SC% > 70). Phosphate retention
(Pret), an important variable for fertilizer management and
consequently water quality, was particularly high (> 57 %;
10th percentile) for seven catchments in the central North Island.
Several physiographic variables (Table 2) displayed strong latitudinal
trends from north to south and many were strongly correlated (p < 0.001;
Fig. S1 in the Supplement). In consideration of these relationships and perceived
importance for water quality (sensu Varanka and Luoto, 2012), we
used the following subset of minimally correlated physiographic variables
for subsequent multivariate analyses: catchment slope (Sc),
silt-clay percentage (SC%), phosphate retention
(Pret), and median flow (Q50).
Land use areal coverage and temporal changes
Land use in NZ, like physiography, varied widely, and our 77 catchments
captured this diversity (Fig. 1; Table S2). In 2001, 13 catchments were
dominated by non-plantation forests (NF), while three catchments were
dominated by intensively managed plantation forests (PFs); 13
catchments were dominated by shrub/grassland (SG) that was not
intensively managed. The most dominant land use was grasslands that were
intensively managed (high-producing grasslands; HG), covering the
majority of the area for 31 catchments. Open water (OW) was the
majority land use for only one catchment and relatively high (> 10 %)
for two others. Barren/other (BO), which was largely bare
rock, was relatively high (> 10 %) for 13 mountainous
catchments. Urban (UR) coverage rarely exceeded 1 %, with only
one catchment greater than 2 %. Annual cropland (AC) exceeded
1 % in 11 catchments, but never exceeded 8 %. Vegetated wetland
(VW) and perennial cropland (PC) were minimal in all
catchments, each rarely exceeding 1 %.
In general, NF, SG, and BO areas dominated
mountainous catchments with high Sc and low Zs;
while HG dominated most lowland catchments with low
Sc, high Zs, and high pHs. Like
HG, PF mostly occurred on flat areas (rs= -0.48 with Sc) with thick soils (0.35 with Zs)
that were less acidic (0.31 with pHs). Given the relative
dominance of catchment land use, relationships with physiographic variables,
and potential effects on water quality in NZ rivers (Davies-Colley, 2013;
Howard-Williams et al., 2010), the land use variables used for subsequent
multivariate analyses were NF, SG, HG,
PF, and OW.
Land use areal coverage did not change much from 1990 to 2012 across NZ
(Fig. 2) or in many catchments (Table S2). The greatest change was a
13.4 % increase in PF in GS1, which was almost entirely accounted
for by a 13 % decrease in SG; 13 other catchments
experienced small increases (3.0–6.6 %) in PF, accounted for by
decreases in SG or HG or both. HM3 and HM4 had the
greatest increases in HG at 3.4 and 2.0 %, respectively.
HG for the other 75 catchments remained
virtually unchanged (< 0.4 %) or decreased. WH3 had the greatest
decrease in HG at -4.8 %. Land use areal coverage change in other
catchments was negligible.
Changes in land use areal coverage, livestock, and
fertilizer inputs across New Zealand 1989/1990 vs. 2011/2012. Nitrogen
fertilizers include urea and ammonium sulfate. Phosphorus fertilizers
include superphosphate and diammonium phosphate.
Land use intensity and temporal changes
Changes in total stock unit density between 1990 and 2012
(SUD2012-1990) were also minor with only two catchments
changing more than 1.6 SU ha-1 over this period (Table S3). Temporal changes
in SUD2012-1990 for 56 of the 77 catchments were within the
range of -1.0 to 1.0 SU ha-1. Although land use areal coverage and total
livestock densities changed little over the period 1990–2012, livestock
types changed considerably for many catchments (Table S3) and
across NZ (Fig. 2). The general pattern was dairy cattle replacing sheep.
The number of dairy cattle from 1990 to 2012 increased in 72 catchments,
with a mean increase of 0.6 SU ha-1 for all catchments, whereas the number of
sheep decreased in all 77 catchments (mean = -0.9 SU ha-1). Deer and beef
cattle numbers changed little: 0.0 and -0.2 SU ha-1, respectively.
When 2011 livestock densities were compared with physiographic variables,
the strongest relationships were found with combined SUD of dairy
and beef cattle (hereafter SUDcattle; Fig. S2).
SUDcattle decreased strongly with increasing slope,
Sc (rs= -0.79), but increased with
Zs (0.43), pHs (0.32), and Pret
(0.27). SUDcattle also increased with MAT (0.68) and
MAS (0.42), but decreased with MAP (-0.34). Thus, the highest
cattle densities were found in catchments, such as WA3 (with the highest
SUDcattle at 15.7 SU ha-1), that were relatively flat, warm,
sunny, and dry, with deep soils that had relatively high pH and high
P retention. HG had similar, but less
strong, correlations with these same physiographic variables.
Catchment disturbance (DC) varied widely over both space and
time between 2000 and 2013 (Table S4). The maximum amount of
DC at one time was 35.7 % for WN3 on 7 April 2003, almost
entirely due to bare pastures. DC exceeded 15 % on six other
occasions (264 days in total) in this catchment. In general, the North
Island (Fig. 3) had a greater extent and intensity of disturbance than the
South Island (Fig. 4). The most intense disturbances occurred as a result of
plantation forest harvests, and these disturbances were on average visible
for about 1.5 years up to about 4 years, with exceptions lasting more than 6 years.
Indeed, DC was strongly correlated to PF coverage
(rs= 0.51). The catchment with the highest median
DC (10.5 %) was RO3, which had 69.8 % of its catchment in
PF and 17.7 % in HG; 14 other catchments had
DC above 5 %, and two-thirds of these were dominated by
either PF or HG.
Disturbance frequency of North Island per 463 m pixel,
based on interpretation of MODIS data 2000–2013.
Disturbance frequency of South Island per 463 m pixel,
based on interpretation of MODIS data 2000–2013.
We also analyzed disturbance of plantation forests (DPF) and
high-producing grasslands (DHG) separately for each catchment.
For catchments with at least 21.4 km2 (100 MODIS pixels, for the sake
of statistical robustness) of plantation forest, the mean (±SD)
DPF (from 2000 to 2013) was 10.6 ± 5.6 %. The
catchments with the highest DPF were those with low mean
annual precipitation, MAP (rs= -0.42). There were
no significant relationships between DPF and any of the other
physiographic variables. For catchments with at least 21.4 km2 of
high-producing grasslands, the mean (±SD) DHG was 6.0 ± 6.4 %. The catchments with the highest DHG were
those with low mean annual sunshine (MAS; rs= -0.25), low mean annual temperature (MAT; -0.30), high catchment
slope (Sc; 0.25), and high ruggedness (Rr;
0.31). The six catchments with the highest DHG (> 15 %)
all had low phosphate retention (Pret; < 32 %).
While it is assumed that greater densities of livestock lead to
greater pasture disturbance, we did not find a proportional relationship
between SUD and DHG among
catchments. In fact, the highest median DHG was found for
catchments with low SUD (rs= -0.45). Over
time, however, we observed a fairly strong trend (rs= 0.50)
of lower DHG with decreasing SUD
(-SUD2012-1990). In all there were seven catchments with
significant or meaningful decreases in DHG from 2000 to 2013
(assessed with SKSE), all of which had a negative
SUD2012-1990.
Statistical description of medians of water quality variables for
the 77 NRWQN catchments. Note that the ratio of mean/median can be used as
an index of data skewness.
Variable
Units
Minimum
Median
Maximum
Mean ± SD
Tw
∘C
7.2
12.2
16.9
12.4 ± 2.4
DO
%
75.5
100.8
113.1
100.0 ± 4.7
COND
µS cm-1
39
92
528
113 ± 83
pHW
-log10[H+]
6.9
7.7
8.5
7.7 ± 0.3
CLAR
m
0.1
1.5
9.8
2.1 ± 1.8
TURB
NTU
0.3
2.1
82
4.2 ± 9.4
CDOM
m-1
0.1
0.7
4.6
0.9 ± 0.8
TN
mg m-3
40
259
2162
369 ± 361
NOx
mg m-3
1
107
1852
230 ± 302
TP
mg m-3
3
15
115
24 ± 24
DRP
mg m-3
0.5
5.0
66.2
8.6 ± 11.2
Water quality characteristics and trends
Median monthly values of water quality variables for the 77 catchments
ranged widely (Table 5; Table S5). Some rivers had exceptional water quality
all around, while others had either current issues with multiple variables
or worsening temporal trends (assessed with SKSE from 1989 to 2014; Table 6).
Because of the dependence of water quality on flow, we first assessed
temporal trends in Q. Only two catchments had significant increases
in Q, with one also being meaningful. Three catchments had
significant decreases in Q and five others also had meaningful
decreases in Q.
River water quality trends from 1989 to 2014. The table reports
numbers of sites (out of 77) in different categories of water quality time
trend. All variables were flow-adjusted except flow and water temperature.
Significant trends were taken to be those with a p value < 0.05 in
the seasonal Kendall test. Meaningful trends were taken to be those that
also had a magnitude (RSKSE) greater than 1 % per year.
Direction of trend
River water quality variable (1989–2014)
Q
Tw
DO
COND
pHw
CLAR
TURB
CDOM
TP
DRP
TN
NOx
Meaningful increase
1
0
0
4
0
29
17
1
8
17
27
24
Significant increase
1
21
6
48
12
5
1
1
6
3
6
3
No significant trend
67
54
42
19
48
39
50
56
52
49
39
37
Significant decrease
3
2
29
6
17
2
0
13
4
5
3
1
Meaningful decrease
5
0
0
0
0
2
9
6
7
3
2
12
Water temperatures (Tw) were not particularly high for any of
the catchments; however, 21 rivers had significant increases in
Tw, possibly the signature of climate change. Because of its
strong latitudinal trend (stronger than any land use effect),
Tw was not analyzed further. Dissolved oxygen (DO)
was close to 100 % for most catchments, but was particularly low
(< 90 %) for two catchments: one affected by peri-urban activities
(AK2) and one affected by discharge from a large pulp mill (RO2). Temporal
trends in DO from 1989 to 2014 were relatively minor (RSKSE < 0.5 % yr-1), except for RO2 which had a significant increase
attributable to progressive improvements in treatment of organic waste from
its large pulp mill. Conductivity (COND) was relatively low
(< 115 µS cm-1) for all South Island catchments and varied
considerably for the North Island (54–528 µS cm-1). Most catchments
(52/77) experienced significant or meaningful increases in COND
from 1989 to 2014. Water pH (pHw) was neutral to alkaline for
all rivers, which have been described as calcium–sodium bicarbonate waters
by Close and Davies-Colley (1990), and only displayed minor changes over the
26-year study period.
Median visual water clarity (CLAR) was exceptionally high
(> 5 m) for seven catchments and very low (< 1 m) for
22 catchments. Since 1989, CLAR has improved in almost half of the rivers,
and has worsened in four rivers (Table 6; Table S5). Water turbidity (TURB) was strongly
inversely proportional to CLAR (rs= -0.97) and
generally followed opposite trends of CLAR. Colored dissolved organic matter (CDOM) was low
for most of the rivers, with only five catchments greater than 2.0 m-1;
19 of the catchments have experienced significant or meaningful decreases
in CDOM since 1989, possibly due to the loss of wetlands across NZ. Only one
catchment had a meaningful increase in CDOM.
Total nitrogen (TN) was relatively high (> 455 mg m-3) for almost a third of the catchments, with the vast majority
(17/23) of these being lowland catchments. Most of these catchments also had
relatively high NOx; 33 catchments had significant
or meaningful increases in TN from 1989 to 2014, while only five
had significant or meaningful decreases in TN (Table 6).
NOx not only had a similar number of increasing temporal
trends but also had meaningful decreases for 12 catchments. Total
phosphorus (TP) followed a similar geographical pattern as
TN; 18 of the 23 catchments with relatively high TP
(> 30 mg m-3) were lowland catchments. Most of the catchments
with relatively high TP (18/23) also had relatively high
dissolved reactive phosphorus (DRP) (> 9.5 mg m-3); 17 catchments had
meaningful increases in DRP, compared to only three with
meaningful decreases. There was more of a balance in temporal trends of
TP, with eight meaningful increases and seven meaningful
decreases.
In addition to the expected correlations between CLAR and
TURB, and among the nitrogen and phosphorus constituents,
several other significant relationships existed among the water quality
variables (Fig. S3). Taking into consideration this broad multicollinearity,
we focused our multivariate analyses on several key water quality variables,
particularly those that experienced the most changes from 1989 to 2014
(Table 6): CLAR, TN, NOx, TP, and
DRP.
Water quality relationships with physiography, land use,
livestock density, and disturbance
CLAR generally decreased with A
(-0.37; all following parentheses in this section are rs
unless specified). Except for TURB (0.32), no other water quality
variables had significant relationships with catchment area. Several water
quality variables correlated with catchment slope (Sc),
including TN (-0.72), TP (-0.63), and DRP
(-0.65), meaning N and P concentrations were relatively high in lowland (low
slope) catchments. DRP (0.65) and TP (0.61) were directly
proportional to mean annual temperature (MAT), but this association
probably arises because the highest phosphorus values occurred mainly in
lowland catchments and some of the northernmost catchments, with temperature
being strongly correlated with altitude and latitude. DRP also had
a significant relationship with soil phosphate retention, Pret
(0.35). No other strong physiographic relationships emerged from our
analyses.
Correlations of water quality (median values) vs. the major land
uses, livestock densities, and median catchment disturbance of the 77 NRWQN
catchments. All values represent Spearman correlation coefficients
(rs). Non-significant relationships (p≥0.05) are denoted by NS. Tw was not included
because of its strong latitudinal trend. DO and pHw
were not included because they had no significant relationships with land
use. SUDcattle is the combination of dairy and beef cattle.
HG
SG
NF
PF
OW
SUDda
SUDbe
SUDcattle
SUDsh
SUDde
DC
DHG
DPF
COND
0.57
-0.53
NS
0.53
NS
0.44
0.63
0.60
0.35
NS
NS
-0.25
NS
CLAR
-0.45
NS
0.28
-0.31
NS
-0.41
-0.49
-0.49
-0.40
NS
NS
NS
-0.27
TURB
0.46
NS
-0.27
0.28
NS
0.38
0.50
0.48
0.40
NS
NS
NS
NS
CDOM
0.56
-0.55
NS
0.24
-0.29
0.48
0.53
0.57
0.24
NS
NS
-0.33
NS
TN
0.82
-0.56
-0.37
0.46
-0.25
0.79
0.75
0.85
0.60
0.26
NS
-0.40
NS
NOx
0.70
-0.53
-0.25
0.44
-0.25
0.77
0.65
0.79
0.51
0.28
NS
-0.39
NS
TP
0.66
-0.54
-0.32
0.48
NS
0.58
0.66
0.72
0.42
NS
NS
-0.24
NS
DRP
0.59
-0.65
NS
0.50
-0.43
0.58
0.58
0.66
0.31
NS
NS
-0.32
NS
The strongest relationships between water quality and land use areal
coverage (Table 7) included HG, which
had strong positive relationships with several water quality variables
except CLAR, which decreased as HG increased. The
lesser-managed SG had generally opposite
relationships with water quality, but note that SG did not have
significant relationships with TURB or CLAR.
NF followed the same trends as SG,
but had fewer significant relationships with water quality. PF, on the other hand, followed the same trends as
HG, with poorer water quality being associated with greater
coverage of PF; although correlations were not as strong as
HG. CDOM, DRP, and all N constituents had
significant negative correlations with OW, meaning
that water quality improved with greater OW coverage, plausibly due
to entrapment of fine sediment and nutrients.
Water quality was significantly correlated with all SUD
metrics (Table 7; Fig. S4), except deer (SUDde), which only had
relatively weak relationships with TN and NOx. The
nutrients and CDOM had the strongest correlations with
SUDcattle, which includes both dairy and beef cattle.
COND, CLAR, and TURB had the strongest (slightly)
correlations with SUDbe. Overall, degraded water quality was
strongly associated with high livestock densities, even stronger than areal
coverage of HG.
No significant correlations between water quality and total catchment
disturbance (DC) were found; however, there were significant
associations when disturbance was isolated by high-producing grasslands
(DHG) and plantation forest (DPF; Table 7).
Unexpectedly, CLAR and TURB were not correlated to
DHG, and surprisingly, the rest of the water quality variables
had a significant inverse relationship with DHG.
Conversely, CLAR was the only water quality variable correlated to
plantation forest disturbance, DPF (rs= -0.27).
Some interesting results emerged when temporal trends in water
quality (via SKSE) were assessed for catchments with high disturbance. Of
the 15 catchments with Dc greater than 5 %, six had
meaningful increases in TURB; while only one had a meaningful
decrease in TURB. Most of these 15 catchments also experienced
significant increases in TN (9 catchments; 7/9 also meaningful)
and NOx (10 catchments; 8/10 also meaningful).
Interestingly, TP and DRP significantly increased in only
two of these highly disturbed catchments.
Stepwise regressions of water quality variables (median values) on
landscape descriptors (forward selection, p < 0.05). Signs
of coefficients indicate whether the relationship is proportional (+) or
inverse (-). Int is model intercept. Scatter plots that characterize the
primary and secondary explanatory variables are displayed in Fig. 5.
Water quality variable
Step
Landscape variable
Model estimate
Multivariate sequential r2
CLAR
1
HG
-0.03
0.17
2
OW
0.18
0.27
3
Q50
-0.01
0.35
4
PF
-0.03
0.39
Int
3.16
TN
1
SUDcattle
77.05
0.62
2
HG
4.26
0.68
3
PF
5.16
0.69
4
SC%
1.80
0.72
Int
-33.95
NOx
1
SUDcattle
86.15
0.58
Int
62.65
TP
1
SUDcattle
5.47
0.41
2
PF
0.64
0.52
Int
7.75
DRP
1
SUDcattle
2.23
0.31
2
PF
0.38
0.48
Int
1.14
Multivariate water quality relationships
In order to build on the above correlation analyses, the water quality
variables of CLAR, TN, NOx, TP, and
DRP were each assessed in a multivariate stepwise regression, using
the following 10 physiographic and land use independent variables:
Sc, SC%, Pret, Q50,
NF, SG, HG, PF, OW, and
SUDcattle (Table 8). The residual plots for all five water
quality variables met the assumptions of normality and linearity, but
displayed heteroscedasticity with a wide scatter for high values.
CLAR was correlated to -HG, followed by +OW,
-Q50, and -PF, where signs represent whether the
relationship is positive (+) or inverse (-). Thus, water clarity was
predictably lower for larger rivers that drain larger areas of
high-producing grasslands and/or plantation forests, but improved with
increased open-water coverage (Fig. 5).
Multivariate relationships between major water quality
variables (median value for each site) and land use variables. For each
plot, the primary explanatory variable from the stepwise regression (Table 8) is the x axis, with bubble color representing the secondary explanatory
variable. Note that oxidized nitrogen (NOx) did not have a
secondary explanatory variable. Selected catchments discussed in the text
are labeled.
The combined stock unit density for beef and dairy cattle
(SUDcattle) was the primary predictor for all four nutrient
variables, with TN, TP, and DRP also being
proportional to PF coverage (Table 8). Dissolved
oxidized nitrogen (NOx) was not proportional to PF,
or any other independent variable in the stepwise regression. Coverage of
HG and silt-clay surface soils (SC%) were also
proportional factors for TN. Whether intensity or areal coverage, land use
was the primary and secondary predictor for all five water quality variables
(Fig. 5).
Discussion
River water quality states and trends
We characterized water quality states and trends for 77 river sites across
NZ using a wide range of flows and water quality conditions for each site,
including some small floods. We acknowledge that our analyses did not fully
capture large floods due to their short durations, unlikelihood of occurring
during the preset monthly sampling, and the fact that we relied on grab
samples. These episodic floods are particularly important for water quality
of downstream waters such as lakes and estuaries (Stamm et al., 2014). The
uncertainty surrounding our lack of flood samples could have been mitigated
by composite samples or supplemental flood samples; however, our 26 years of
monthly samples for each site (n = 312) did allow us to confidently report
median conditions and temporal trends in water quality (Moosmann et al.,
2005).
There was a wide range of water quality across NZ rivers (Table 5), with
drastic differences between upland catchments and the more intensively
managed lowland catchments. Overall, lowland rivers had considerably lower
CLAR and higher TURB, TN, NOx,
TP, and DRP. Only two (alpine glacial flour-affected)
upland rivers were below the ANZECC CLAR guideline of 0.6 m, whereas
17 lowland rivers were below the ANZECC guideline of 0.8 m. Similarly, 13
lowland catchments exceeded the ANZECC TN guideline of 614 mg m-3, but only eight upland catchments exceeded the much lower guideline
of 295 mg m-3. Almost three-quarters of these catchments (15/21) also
exceeded the NOx guideline of 444 mg m-3 (lowland) and
167 mg m-3 (upland). There were a similar number of sites exceeding
ANZECC guidelines for TP (33/26 mg m-3 for lowland/upland) and
DRP (10/9 mg m-3 for lowland/upland), each with at least 20 and
most of these were corresponding. Our results on the state and trends of the
77 NRWQN catchments generally accord with earlier NRWQN studies (e.g.,
Ballantine and Davies-Colley, 2014) and a recent publication by Larned et al. (2016),
which analyzed water quality states and trends for 461 NZ river
sites for the period 2004–2013.
River water quality classes for upland (a) and lowland (b) catchments in New Zealand: (I) clean river with high visual water clarity
(CLAR) and low dissolved inorganic nutrients (DIN); (II) sediment-impacted
river with low CLAR and low DIN; (III) nutrient-impacted river with high CLAR
and high DIN; and (IV) sediment- and nutrient-impacted river with low CLAR
and high DIN. Classes are organized by ANZECC (2000) trigger values for
water clarity (x axis) and NOx (y axis). Catchments that
exceed ANZECC guidelines for DRP are indicated by gray-filled markers.
Arrows indicate direction of trend over the 26 years inclusive from 1989 if
significant (dashed) or meaningful (solid). No arrow means the trend was not
significant.
Based on ANZECC (2000) trigger values, we have organized the catchments into
four classes (Fig. 6): (I) clean river with high visual water clarity
(CLAR) and low dissolved inorganic nutrients (DIN), (II) sediment-impacted river with low CLAR and low
DIN,
(III) nutrient-impacted river with high CLAR and high DIN, and (IV) sediment- and nutrient-impacted river with low CLAR and high DIN.
Note that the term “sediment impacted” is a connotation for total suspended
solids (TSS), which includes organic matter as well. In
agriculture-dominated catchments, both mineral sediment and particulate
organic matter can greatly increase TSS (Julian et al., 2008). We use
CLAR as a preferred metric for suspended matter because TSS are not
routinely measured in the NRWQN (or other monitoring networks) while
CLAR correlates strongly to TSS (r = -0.92), and better
than TURB (r = 0.87) (Ballantine et al., 2014). Further,
CDOM in NZ rivers is low with minimal impact on CLAR. We
use NOx as our preferred metric for DIN because it is least
affected by suspended sediment and soil properties (compared to
DRP). However, catchments that exceed ANZECC guidelines for
DRP are indicated in Fig. 6 by gray-filled markers.
When this classification is combined with the SKSE trend analyses (Table 6),
we obtain a clear picture of the current and potential state of NZ rivers
(Fig. 6). Before individual rivers are discussed, we first point out key
differences between the upland and lowland catchments, which will later be
placed within the context of physiography and land use intensity. Most
obvious, and consistent with the findings of Larned et al. (2004), was that
lowland rivers were much more degraded, particularly by sediment. More than
a third of the lowland catchments were either class II or IV (17/44),
whereas only two upland catchments were class II. None of the upland
catchments were class IV, and more than two-thirds were clean rivers (class I).
Both types had a similar number of nutrient-impacted rivers (class III).
Particularly concerning is that almost half of the lowland rivers (19/44)
are currently experiencing meaningful increases (> 1 % per
year) in NOx, DRP, or both. The other striking trend
is that many of the lowland rivers are becoming clearer, with 18/44
experiencing meaningful increases in CLAR – which, plausibly,
has been attributed to increasing riparian fencing to exclude cattle from
channels (Davies-Colley, 2013; Ballantine and Davies-Colley, 2014; Larned et
al., 2016).
While clearer rivers are seen as an improvement in water quality; when
combined with increasing nutrients, warmer water, and lower flows, the
perfect recipe for toxic algae blooms is created (Dodds and Welch, 2000;
Hilton et al., 2006). Only recently has the widespread problem of toxic
algae blooms in NZ rivers been evidenced (Wood et al., 2015; McAllister et
al., 2016), and our results indicate that this problem could worsen given
the increasing trends we found in water temperatures, inorganic nutrients,
and most influential in our opinion, water clarity. Nutrient enrichment and
global warming receive the most attention when it comes to degraded water
quality, but rivers have increasingly become light limited (Hilton et al.,
2006; Julian et al., 2013) such that when clarity improves in warm,
nutrient-rich rivers, algae can proliferate. Particularly problematic for NZ
is that its lowland catchments, which are warmer, have much greater
DRP and NOx, and have longer water residence times,
are the ones becoming appreciably clearer (Fig. 6). If droughts become more
frequent and intense in NZ, toxic algae blooms are also likely to become
more frequent, more widespread, and more problematic. However, this algae
response is complex and depends on a number of interacting factors such that
the apparent potential for increasing algal nuisance might not necessarily
be realized in some rivers (Dodds and Welch, 2000; Hilton et al., 2006).
The role of physiography in dictating land use intensity across
New Zealand
While physiography did not emerge as a significant independent variable in
the multivariate analyses (except TN with SC%),
physiography is important because it largely controls the location and
intensity of agricultural land uses. The greatest coverages of
HG and the highest densities of cattle
(SUDcattle), the two primary explanatory variables for all
five major water quality variables (Table 8), were both found predominantly
in flat areas with deep soils located in warm, sunny, and relatively dry
climates. Livestock in NZ depend almost exclusively on pasture grasses and
thus their productivity is maximized when pasture productivity is maximized.
The very large cattle are not well suited for steep slopes, particularly
dairy cattle, which can weigh more than 500 kg. Deep soils are important
because they absorb and hold more water for plant uptake, and are not as
susceptible to waterlogging, especially in wetter climates. Year-round and
intense grazing is best supported by warm and sunny climates where pasture
grasses are highly productive and recover quickly following intense grazing
such as strip/rotational grazing, which is common in NZ dairy farms.
Another soil property we found to be positively correlated to
SUDcattle was phosphate retention (Pret). The
highest dairy cow densities were found on Allophanic volcanic soils with
high Pret, likely because these soils respond favorably to
P fertilizer and thus can be managed more intensively. However, soils with
high Pret require more P fertilizer, and thus generally have
higher export of DRP to rivers. Our finding of a significant
positive correlation between these two variables is consistent with this
interpretation. Further, we found that high-producing pastures with high
Pret had the lowest disturbance (DHG),
indicating that these intensively managed pastures recover quickly following
grazing. In a more comprehensive study of land disturbance across the North
Island of NZ, de Beurs et al. (2016) also found that Allophanic soils had
the least disturbance among all soil orders. Where high livestock densities
occur in less than ideal conditions, land disturbance is likely. Our
catchment-scale analyses limit our interpretation of specific situations,
but based on our results, field observations, and previous remote sensing
analyses, pasture disturbance in NZ will likely be highest during droughts
on steep, south-facing slopes with thin soils being heavily grazed by sheep.
Under these conditions, grasses will be grazed down to bare soil and recover
very slowly.
Plantation forests (PFs) in NZ also correlated with thick soils with
relatively high Pret on flat areas, particularly the pumice
soils of the central North Island. The porous nature of the pumice soils
allows them to efficiently hold and regulate nutrients, water, and air
while being well-drained and resistant to compaction and flooding. Under
these conditions, radiata pine (the dominant PF species in NZ)
grows rapidly (mean harvest cycle of 28 years) and can be harvested year-round.
Since 1990, however, many of the PF additions have occurred on
steeper slopes in response to carbon credit incentives, greater economic
demand for wood products (PCE, 2013), and the need for soil erosion control
on steep pasture susceptible to land sliding (Parkyn et al., 2006).
Land use intensity and water quality in New Zealand rivers
High-producing pastures and livestock densities
HG coverage was the primary explanatory
variable for visual clarity (CLAR; Table 8, Fig. 5). CLAR
in NZ rivers is mostly influenced by mineral and organic particulates
(Davies-Colley et al., 2014). Livestock reduce visual clarity in multiple
ways, especially in NZ where high densities of multiple types of livestock
tread year-round on relatively steep slopes with highly erodible soils
vegetated by shallow-root, introduced grasses that are susceptible to
destabilization (McDowell et al., 2008). The year-round treading is
particularly important because most NZ regions during winter are very wet
with short days, which increases soil disturbance (pugging and compaction)
and slows recovery times. Where livestock have direct access to rivers,
their trampling of riverbanks and instream disturbance is often the main
contributor to reduced CLAR (Trimble and Mendel, 1995; McDowell et
al., 2008).
The lowland flatter areas in NZ have high HG coverage and high
cattle stock densities (SUDcattle). These lowlands also have
high drainage densities – often increased by artificial drainage. The
influence of HG on CLAR is thus exacerbated by this
interaction of high SUDcattle and artificial drainage.
Interestingly, SUDcattle was not an explanatory variable for
CLAR in the stepwise regression, which is likely a result of two
factors. First, HG and SUDcattle are highly
correlated, and stepwise regression does not include secondary variables
that are explaining the same proportion of variance as the primary
independent variable. Second, we found that CLAR has actually
improved in catchments where SUDcattle is high and/or
has increased (Fig. 6), which we noted earlier could be a result of
increased riparian fencing. In 2003, NZ implemented the Dairying and Clean Streams Accord,
which has led to the exclusion of dairy cattle from
87 % (as of 2012) of perennial rivers greater than 1 m in width (Bewsell
et al., 2007; Howard-Williams et al., 2010; Gunn and Rutherford, 2013). By
excluding (dairy) cattle from channels and riparian zones, the contribution
of riverbank and bed erosion to degraded CLAR has likely been
mitigated and reduced over time (Trimble and Mendel, 1995; Hughes and Quinn,
2014). Indeed, CLAR has been significantly and meaningfully
improving in many of NZ's rivers (Table 6), even those with increasing
SUDcattle, albeit from a fairly degraded condition.
Another potential explanation for improved water clarity at numerous sites
is the considerable decrease in sheep density across the NZ landscape. NZ
had 57.65 million sheep in 1990. By 2012, that number had been reduced by
almost half, to 31.19 million (StatsNZ, 2015). Although cattle are larger
and have a greater treading impact per animal, the much greater number of
sheep means that SUD may be broadly comparable as
regards environmental impact. Another difference is that sheep are generally
placed on steeper, less stable slopes in NZ, where headwater stream channels
are located. Where there are breaks in slope (even small ones), sheep create
tracks of bare soil with their hooves and hillside scars with their bodies
(for scratching and shelter), both of which can enhance soil erosion (Evans,
1997). Further, cattle (using their tongues) leave approximately half the
grass height on the pasture after grazing, whereas sheep (using their teeth)
graze approximately 80 % of grass height (down to bare soil in dire
conditions), leaving it exposed to erosion (Woodward, 1998). Considering all
these factors, sheep can have a greater impact on sediment runoff into
rivers, and consequently visual clarity, than suggested by their aversion to
water vs. cattle's attraction to water. Although not isolated in
our analyses, the particulate fractions of TN and TP have
likely been affected by similar processes as CLAR and may follow
the same temporal trends (Ballantine and Davies-Colley, 2014).
While HG was also strongly correlated to river nutrient
concentrations (Table 7), the primary explanatory variable for all four
major nutrient metrics (Table 8, Fig. 5) was land use intensity as measured
by livestock density of beef and dairy cattle (SUDcattle). The
difference between these two explanatory variables may seem trivial; however,
the distinction is important if we want to understand future trends and
effectiveness of water quality management strategies. As we demonstrated,
the area of land used for HG has not
changed much since 1990 (Fig. 2). In fact, it has decreased or stayed
virtually the same in all but two of the 77 catchments. Yet, nutrient
concentrations have been increasing in many of the rivers (Table 6), which
we attribute to (1) increasing numbers of cattle (mostly dairy) on both
HG and SG, and (2) legacy nutrients being slowly delivered
to the rivers in groundwater. From 1990 to 2012, NZ approximately doubled
its number of dairy cattle, exceeding 6.4 million. (StatsNZ, 2015). This
enormous addition to a country that is only 268 000 km2 in area, has
been accompanied by more than 1.426 million tons of P-based fertilizers
and 335 000 tons of N-based fertilizers annually (1990–2012 mean; StatsNZ,
2015). Of the nutrients consumed by lactating dairy cows, approximately
66 % of P and 79 % of N are returned to the landscape in the form of
urine and feces (Monaghan et al., 2007). This results in about 940 000 tons of P-based
and 260 000 tons of N-based diffuse pollution, which is
an underestimation because clover-rye grass dairy pastures also receive large
inputs from fixed atmospheric N (Ledgard, 2001). Some of these nutrients
will be transported to rivers during subsequent storms, but a majority will
remain (building up) in the landscape to be slowly added to rivers over
decadal timescales (Howard-Williams et al., 2010).
Plantation forests
All water quality variables were significantly correlated to plantation
forest coverage (PF; Table 7), with a negative relationship with
CLAR but positive for all other variables. From the stepwise
regression, PF emerged as an explanatory variable for all major
water quality variables except NOx (Table 8), suggesting that
its dominant impact on river water quality was from surface runoff.
Plantation forestry activities can add a considerable amount of sediment and
nutrient pollution to rivers, especially during and immediately following
harvesting (Fahey et al., 2003; Croke and Hairsine, 2006; Davis, 2005). This
harvesting period of maximum soil disturbance usually lasts about 2 years
(Fahey et al., 2003), but the land cover may remain sparsely vegetated and
susceptible to erosion for several years (but usually not more than 5 years; de
Beurs et al., 2016). The greatest PF impact on sediment runoff, and
thus potentially CLAR, is usually from road sidecast/runoff,
shallow landslides, and channel scouring/gullying (Fahey et al., 2003; Motha
et al., 2003; Fransen et al., 2001).
Rivers receive a pulse of nutrients during the forest harvest, but
fertilizers are also applied at time of re-planting and sometimes routinely
to enhance growth (Davis, 2005). Radiata pine in the pumice soils of the
central North Island, the dominant area of PF in NZ, are
particularly responsive to both N and P fertilizers and thus likely receive
ample supplements. Like pasture fertilizers, some of these nutrients may be
delivered to rivers during intense precipitation, but there is also a legacy
of nutrients left behind. Fertilizers have been applied to plantation
forests in NZ since the 1950s, with an intense period of application in the
1970s (Davis, 2005). While fertilization rates (tons ha-1 yr-1) have decreased
since 1980, the amount of NOx leaving catchments mostly
covered in PF has significantly and “meaningfully” increased since
1989. None of these catchments had more than 17.7 % HG, none had
major increases in HG (< 0.3 %), none had major increases
in SUDcattle (< 0.7 SU ha-1), and none had a significant
increase in DPF. What the catchments did have in common were
all had gravelly/sandy pumice soils (< 4.5 SC%) and all
were intensively managed as reported by Davis (2005) and as indicated by
high DC (> 6.8 %). The extended periods of
non-vegetated land due to weed control also increases the amount of nutrients
delivered to rivers over the long term (Davis, 2005).
Land disturbance and water quality
So far, we have discussed how land use, livestock densities, and fertilizer
inputs affect water quality, with a focus on sediment and nutrient runoff.
When land is disturbed (i.e., bare soil), sediment/nutrient mobilization can
be enhanced. The most intense and longest lasting disturbances occurred
during plantation forest harvests. Following harvest, we found that the land
remained disturbed for 1–6 years, with a mean of 1.5 years. The overall mean
and median DPF among all catchments was 10 %, which means
that plantation forestry leaves large areas of disturbed land at any one
time. When this bare land is exposed to intense precipitation, large
quantities of sediment and nutrients can be mobilized into the rivers. This
process has been documented for numerous catchments across NZ (Basher et
al., 2011; Hicks et al., 2000; Phillips et al., 2005). Because these
disturbances only last a few years, they typically do not show up as
temporal trends (via SKSE); however, it is possible that they produce enough
readily available sediment to impact water quality for longer periods
(Kamarinas et al., 2016).
The coincidence of rainstorms on disturbed pasture could have the same
effect on sediment/nutrient runoff if the pasture is connected to the stream
network via steep slopes or adjacent channels/canals (Dymond et al., 2010;
Kamarinas et al., 2016). Pastures become disturbed from overgrazing, strip
grazing, pugging/soil compaction, tilling/reseeding, cropping/harvesting, or
landsliding on steep slopes. Given the high intensity of grazing management
in NZ, all of these are common. While DHG was lower than
DPF on average, DHG had a higher maximum (Table 4).
Spatiotemporal patterns in disturbance between these two land uses were
also different (de Beurs et al., 2016). DPF covered large
areas and lasted years at a time, whereas DHG had two
patterns: (1) one related to dairy cattle strip grazing, which were
short lived due to quick recovery times of grasses in fertilized soils; and
(2) more widespread and longer continuous disturbances occurring on steeper
slopes grazed by sheep and beef cattle, particularly following drought
periods. Because our disturbance analyses had a spatial resolution of 463 m,
we likely missed some paddock-scale disturbances. Future work could use
Landsat imagery (30 m resolution) to assess disturbance (sensu de
Beurs et al., 2016).
All six catchments with meaningful increases in DHG had
large increases in dairy cattle density 1990–2012 (Tables S3, S4). Not
surprisingly, all six catchments suffered impacts to water quality. Five of
the six had meaningful increases in DRP and three had meaningful
increases in NOx and TN. One had a meaningful
increase in TURB and three had significant reductions in
DO. One of these catchments, in particular, may provide a glimpse
into NZ's future if agricultural intensification continues. The Waingongoro
River catchment (WA3) is covered almost entirely by HG (91.2 %),
with practically all of this land being used for intensive strip grazing.
The SUDda was 15.0 SU ha-1 in 1990 and increased to 15.4 SU ha-1
by 2012. The DHG from 2000 to 2013 had a strong increasing trend
of 9.8 % yr-1 RSKSE, associated with the intensification of dairy operations
(Wilcock et al., 2009). The result of all this intensification was that WA3
had meaningful increases in TP and DRP. The reason
TN and NOx did not display significant trends here is
because of the extreme monthly variability in river nitrogen concentrations,
possibly due to livestock rotations, fertilizer applications, and
precipitation events. Noteworthy is that these significant trends of
increasing SUDda, DHG, and nutrients are
occurring not only in lowland catchments on the North Island (WA3, HV2) but
also in upland catchments of the North Island (RO6), as well as both lowland
(TK1) and upland (CH3, TK2) catchments on the South Island.
While disturbance was not itself a strong predictor of water quality, it did
help explain outliers of land use–water quality relationships. For example,
streams with high DRP (> 20 mg m-3; 10th percentile) had
one of two dominant land uses, either PF (RO2, RO3) or HG
(HM5, WA3, WA9, HM4, HM2). The one exception was RO4, which had relatively
low coverage of PF (11.2 %) and HG (2.9 %). In fact,
RO4 is dominated by NF (79.1 %). Upon closer examination, we found that
the small areas of PF and HG in RO4 were disturbed
frequently. Further, most of the disturbed forestry occurred on steep slopes
and most of the disturbed pastures (practically all sheep and beef) occurred
on hilly terrain adjacent to stream channels. Our high temporal-resolution
analyses of disturbance showed that even though this catchment is mostly
indigenous forest, intense disturbances on small proportions of developed
land can have a considerable impact on water quality. RO4 is also
experiencing significant increases in TURB and TP, as well
as a significant decrease in Q. Another outlier example was RO3,
which was the only non-HG-dominated catchment with high
NOx (634 mg m-3). RO3 was dominated by PF
(69.8 %), but it had the highest median disturbance (10.5 %) of all
catchments. This catchment also exceeds ANZECC guidelines for DRP
and has experienced meaningful increases in TURB, TN, and
NOx.
We believe that land disturbance and consequently river water quality will
continue to worsen in some NZ catchments based on the following. More
plantation forests were planted 1993–1997 (3810 km2) than any other
5-year period in NZ history (NZFFA, 2014). With a 28-year mean age of harvest, NZ
will experience its greatest coverage and intensity of forest disturbance
around 2025. When combined with drought and intense storms, the potential
for nutrient and sediment mobilization is high, especially given that
approximately 45 % of these plantings occurred on high-producing
grasslands (NZFFA, 2014) where many of the legacy nutrients will be exported
to rivers during forest harvest (Davis, 2014). If carbon prices continue to
stay low, there will be a high likelihood that many of the harvested forests
will be converted to pasture, adding even more nutrients to NZ rivers (PCE,
2013). Given that the central government created a national policy goal of
nearly doubling the export to gross domestic product ratio
by the year 2025 (MBIE, 2015), NZ is
likely to see continued increases in livestock density, fertilizer inputs,
and supplemental feed to support these extra livestock, all of which will
add even more pressure and risks of eutrophication on NZ's rivers.
Conclusions
This study had the overall goal of describing how changes in land use
intensity impact river water quality across broad scales and over long
periods. To address this goal we used a combination of “brute force”
statistical analyses (in terms of hundreds of analyses using a suite of
physiographic, land use, and water quality data for 77 catchments over 26 years)
and careful examination (using multi-resolution data to find patterns
and relationships among these variables). This goal was ambitious and we
likely missed some relationships and details of water quality changes.
However, we found empirical evidence for several key relationships among
land use intensity, geomorphic processes, and water quality, which we now
place into a broader perspective.
The greatest negative impact on river water quality in NZ in recent decades
has been high-producing pastures that require large amounts of fertilizer to
support high densities of livestock. While this finding has been previously
published (Davies-Colley, 2013; Howard-Williams et al., 2010; and references
within), our results and supporting information show that the relationship
between high-producing pastures and water quality is complicated, being
dependent on livestock type/density, disturbance regime, and physiography,
particularly soil type. Dairy cattle receive much of the blame for degraded
water quality because of their high nutrient requirements (Howard-Williams
et al., 2010), but beef cattle can also strongly degrade water quality due
to comparable required inputs and grazing on steeper land with a higher
potential for runoff (McDowell et al., 2008). Further, pasture
designations/boundaries are becoming increasingly blurred by modern cattle
management, with greater movements of dairy and beef cattle among pastures,
greater use of high-producing pastures for beef, over-wintering of dairy
cattle on beef pastures, and cross-breeding (Morris, 2013). While riparian
fencing has plausibly improved the clarity of NZ rivers, the removal of
millions of sheep from steep slopes has also likely played a role that
should be investigated further.
New Zealand is the global leading exporter of whole milk powder, butter, and
sheep products, and NZ's prominence in these industries is likely to
continue over the next decade (OECD/FAO, 2015). In this most recent
environmental review by the Organisation for Economic Co-operation and Development,
NZ, had the highest percent increase (1990–2005) in agricultural
production out of 29 OECD countries, the highest percent increase in
N fertilizer use, and the second highest increase in P fertilizer use.
This agricultural intensification over our study period is reflected in
overall nutrient enrichment of NZ rivers. If cattle continue to be added at
the rates we documented, additional fertilizers and supplemental feed will
be needed. Even if best management practices are adopted to reduce nutrient
export to rivers, there is already a half-century legacy of nutrients
distributed across the NZ landscape that will continue to leak to the rivers
(Larned et al., 2016). Indeed, the full impact of agricultural
intensification on river water quality will not be fully appreciated for
another several decades (Howard-Williams et al., 2010; Vant and Smith,
2004). Having an extensive national network like the NRWQN to document and
study these water quality changes will be important.