Anthropogenic nutrient inputs cause excessive algal growth for nearly half the world’s population

Domain: earth_science
Published: 2025-02-20
Editorial outcome: minor_revision

Abstract
Reference conditions pertain to conditions without anthropogenic influence and serve to gauge the degree of river pollution and identify the best attainable water quality. Here we show estimates of the global human footprint of nitrogen and phosphorus concentrations and potential for related nuisance or harmful algal growth in rivers. We use statistical models based on 1.2 million stream nutrient measurements (from 2005 to 2013) and find global human enrichment of river total nitrogen and total phosphorus is 35% and 14% respectively. The greatest enrichment is in Europe (86 and 30% respectively) and the least in Oceania (9 and 2% respectively). The levels of enrichment translated into an almost doubling of the catchment areas with rivers predicted to have anthropogenically elevated levels of potentially harmful or nuisance algae, affecting ~40% of the world’s population. Focusing management on the difference between current and reference conditions can help protect good water quality while avoiding unrealistic goals where nitrogen and phosphorus are naturally high.

Introduction
Enriched nitrogen and phosphorus in rivers and streams can pollute waters and stimulate autotrophic and heterotrophic states1. Stimulation of autotrophic state often leads to elevated levels of algal biomass, particularly attached algae (periphyton) that can be toxic, lead to taste and odour problems, deplete dissolved oxygen, interfere with recreation, harm biodiversity, and cause objectionable appearance2–5. Many regulatory bodies and jurisdictions rely on the concept of reference conditions as the best attainable state of ecosystems. These reference conditions typically denote the concentration or yield of nitrogen and phosphorus in rivers that would occur with negligible human influence or disturbance6 and serve as a benchmark to gauge how perturbed the river is and determine the potential for improvement7. Knowledge of reference conditions also avoids the setting of catchment objectives that may be unachievable (e.g., when desired nutrient concentrations are below the reference level, so regular methods of nutrient control through land management could not reach the desired concentrations).

Reference conditions have largely been defined in developed countries (defined as having a gross national income per capita >14,005 United States [US] dollars in 2024 by the World Bank) with abundant data and information on river and catchment characteristics. These characteristics have been used to extend reference conditions across classes of similar biophysical parameters like climate, topography, and geology8, thus avoiding irrelevant classifications based on geopolitical boundaries. For example, in the US, reference conditions are classed according to 14 ecoregions that span multiple States9. In the European Union, the Water Framework Directive uses reference conditions to inform thresholds for ‘good’ ecological status and intercalibrates these across member countries10. Biophysical classes recognise that nitrogen and phosphorus losses will vary among sites according to reference conditions as influenced by factors such as climate, topography, and geology11. However, outside of developed nations the analysis and setting of reference conditions is rare12–14.

Various methods are used to estimate reference conditions. The simplest are empirical, using existing sites under “minimally disturbed conditions” (MDC)15. The MDC approach defines reference conditions as those observations from rivers minimally affected by anthropogenic activities (e.g., <5% agricultural or urban land use and not receiving wastewater). However, such sites can be scarce, especially in regions with a long history of agriculture, and may represent only a few catchments. Moreover, rivers may be very sensitive to relatively low levels of urbanization because of lack of sewage treatment and no phosphorus detergent bans in some countries16. Another approach involves utilizing data from periods before a stream became degraded, known as “historical” reference conditions, but in many cases anthropogenic impacts pre-date the earliest water quality analyses. Lastly, the “least disturbed condition” approach groups sample data for sites and sets a reference condition at some subjective quantile or percentile (e.g., 5th percentile) at the lower end of the value’s distribution but carries the risk of setting the reference condition too high if too many degraded sites are included or too low if many reference sites are in the dataset17. Other approaches combine water quality data from monitoring sites with catchment characteristics in mechanistic or stochastic models to estimate reference conditions. These methods benefit from a larger data pool and potentially produce outputs that are less biased and more representative of local conditions. Mechanistic approaches involve models like the Soil Water Assessment Tool that describe processes within the catchment and can be manipulated to output estimated reference conditions by setting the variables that capture anthropogenic influence to nil18,19. These models can provide accurate estimates of reference conditions but typically demand substantial fine-grained data, which may be lacking in many regions, and need to be fine-tuned for each catchment. Stochastic approaches group sites based on classifications that explain variation between sites and estimate reference conditions by setting anthropogenic variables (e.g., urban or cropland cover within the catchment above each sampling sites) within those classes to nil8,9. Stochastic models are favoured for their adaptability and ability to cover extensive areas but rely on the availability of sites within classes to make predictions across nutrient ecoregions.

In our work we utilise recently available global databases for nitrogen and phosphorus loads20,21 and the likelihood of periphyton accrual (based on median nutrient concentrations22) to predict global references concentrations of both nitrogen and phosphorus forms in streams. We use an approach that is consistent globally and does not rely primarily upon data from developed countries. Then we compare these estimates with current nitrogen and phosphorus concentrations to assess the extent of anthropogenic influence on current nutrient concentrations. Finally, we predict standing stocks of periphyton under reference and current anthropogenic influence (in baseflow during warmer months) to indicate the degree to which eutrophication has harmed global rivers. These predictions can identify the anthropogenic influences on nutrient pollution and to inform policy to target actions to improve water quality on a global scale. They can also inform efforts to protect downstream waters, such as lakes and marine systems, which are also subject to problems of eutrophication23.

Results
Controls on nitrogen and phosphorus losses under current and reference conditions
We used ~1.2 million measurements of nitrate-nitrogen, total nitrogen, dissolved reactive phosphorus and total phosphorus for up to 2618 catchments to generate models to predict median concentrations during baseflow for months likely to see periphyton growth (May–Oct or Nov-Apr for temperate zones in the Northern and Southern hemispheres or annually in the tropics). Additional information is available on the data process (Supplementary Fig. 1), modelling process (Supplementary Notes 1–3) and performance (Supplementary Notes 4 and 5), and outputs (Supplementary Notes 6–8). Our predictions for 14,241 catchments at level 6 of HydroBasins15 performed well, as classed by ref. 24 based on the root mean square error and coefficient of determination. Post-processing, we noted a small amount (<1%) of total nitrogen data with low concentrations that could be considered as outliers. We chose not to remove them owing to their small number and close fit to the 1:1 line (testing data; see Supplementary Note 3).

Amongst the models, anthropogenic (i.e., ‘human’) effects (see also Supplementary Note 1 and Supplementary Fig. 2), accounting for land use, soil fertility, and population density, were 3–5 times more important than the next most important predictor for all nutrient forms except dissolved reactive phosphorus where human effects were 1.5 times more important. Population density and soil fertility (represented by Olsen phosphorus) were the most important components of the human effect predictor (see Supplementary Fig. 1). The remaining predictors included a combination of known drivers of nutrient loss25 such as: potential evapotranspiration, runoff, and slope, but also latitude which maybe acting as an amalgam of these predictors or to signal a seasonal effect (i.e., latitude was used to exclude data from colder months). Notably, slope was less important for nitrate-nitrogen than other nutrient forms given the lower adsorption of nitrate to soils relative to other forms of nitrogen and phosphorus. Our analysis indicated a strong influence of intensively farmed land, enriched soil fertility, and greater population density (especially on flat land) on increased river nutrient concentrations, as have others16,26. In many statistical approaches a dominant factor (like percentage cropland) is used to isolate the human effect9. Our method takes this a step further and accounts for multiple factors that could accrue to cause a human effect (e.g., total phosphorus is generally transported as sediment so slope of farmland can influence loads to rivers). We therefore combined these factors while avoiding any potential effect from multi-collinearity amongst variables (e.g., land uses).

We isolated sites under MDC in our dataset (n = 615) as the closest proxy for reference conditions available. MDC were defined as: Olsen P concentrations of ≤5mg kg−1, population density of ≤0.001 persons km−2 and ≥80% forestland and no cropland or urban land (see also Supplementary Note 5). To standardise variation in biophysical conditions we contrasted the median values for sites under MDC to the predicted median reference values for terrestrial biomes as this would allow us to predict macroscale (up to global) variation in freshwater biogeochemistry27. Some analytes had few sites in each biome under MDC and more likely to be skewed than data-rich biomes. Of those with >5 observations, all were within the 95% confidence intervals for dissolved reactive phosphorus (n=7), total phosphorus (n=7), and nitrate-nitrogen (n=6), while only one out of six biomes fell outside of the 95% confidence intervals for total nitrogen (Fig. 1). These contrasts suggest that reference conditions were well predicted by the model. We recognise that this is not a truly independent comparison (i.e., these sites are also included in the original model) but was necessary owing to the paucity of sites under MDC. We also compared our predicted values to estimates of published reference conditions for least disturbed conditions, MDC, and statistical approaches. While much of the published data exists for catchments that do not overlap with biomes or our catchments (i.e., less studied areas), reference values for total nitrogen and total phosphorus produced by MDC or statistical methods were in the same range as those predicted across 14,241 catchments globally (Fig. 2). Too few published values were available to make the same comparison for dissolved reactive phosphorus or nitrate-nitrogen.

Our outputs for catchments at level 6 of HydroBasins (n = 14,241) were derived from observations from 1186 to 2618 catchments (depending on nutrient form). Although there is always the potential that outputs were biased by the number of catchments in each biome, there was no difference between the proportion of observations across biomes and those present at level 6 of HydroBasins except for Deserts & Xeric Shrublands and Boreal Forests/Taiga biomes (see Supplementary Note 8). Owing to the paucity of observations in these two biomes, we elected not to include these data in continental statistics or maps.

Enrichment
We calculated enrichment as the difference between estimated current and estimated reference concentrations relative to reference conditions. The mean percentage of anthropogenic enrichment across nutrients varied from 2% for total phosphorus in Oceania to 123% for nitrate-nitrogen in Europe (Table 1 and Figs. 3–6). Europe had the highest percentage of enrichment across all analytes, followed by Asia and North America. The global average enrichment for nitrate-nitrogen and total nitrogen was 58 and 35%, and 21 and 14% for dissolved reactive phosphorus and total phosphorus, respectively. The mean percentage of nitrate-nitrogen as total nitrogen was 29 and 39% for reference and current conditions, while the same percentage for dissolved reactive phosphorus as total phosphorus was 21 and 24%, respectively. Ref. 28 showed that an increasing population coupled with an expansion of agricultural land increases the percentage of dissolved inorganic nutrients relative to total nutrients which is consistent with our analysis.

Enrichment in Europe reflects a long history of high application of fertilisers (manure or inorganic), especially in Western Europe29. In Asia, enrichment is most pronounced in China, which has substantially increased inorganic fertilization over the past 40 years30. In North America, enrichment is greatest in the U.S. Midwestern states from intensive cropland. Interestingly, in both Asia and North America there appear to be a strong natural source of phosphorus (Figs. 4 and 6). This source correlates to the presence of naturally high-fertility soil types (e.g., Black Chernozems)31, or frequent drought conditions. Drought can lower dilution rates but also increases soil hydrophobicity and infiltration-excess runoff. Drought followed by rainstorms can therefore lead to nitrogen- and phosphorus-rich sediment reaching rivers from increased surface runoff32–34.

Potential for periphyton accrual
Predicted median nutrient concentrations were used in combination with the global mean total nitrogen and total phosphorus thresholds to categorize catchments across the globe as acceptable (predicted algal biomass <150mg chlorophyll m−2) or unacceptable when greater than this amount based on prior research35. It should be noted that this analysis focuses on nutrient concentrations because they are directly affected by human activity. Although our analysis was restricted to baseflow in warmer months, we cannot fully account for variation in factors like temperature and light which may affect periphyton growth36,37. We then classified each catchment as ‘acceptable periphyton biomass and nitrogen-limitation’ (catchment type 1), ‘undesirable periphyton biomass and nitrogen-limitation’ (catchment type 2), ‘acceptable periphyton biomass and co-limitation’ (catchment type 3), ‘undesirable periphyton biomass and co-limitation’ (catchment type 4), ‘acceptable periphyton biomass and phosphorus-limitation’ (catchment type 5), or ‘undesirable periphyton biomass and phosphorus-limitation’ (catchment type 6). Under reference conditions <1% of the world had undesirable periphyton biomass limited by nitrogen or phosphorus and 2.7% by co-limitation, together these accounted for rivers where ~10% of the world’s population (~777 million) live, relatively evenly spread across continents (Tables 2 and 3 and Fig. 7). However, under current conditions, ~1, 3 and 10% of the global landmass contained catchments likely to have undesirable levels of periphyton biomass caused by nitrogen, phosphorus or a combination of both nutrients being enriched, respectively. Together these catchments contain ~38% of the world’s population (~3.1 billion people), with the greatest numbers living in Europe (80%) and North America (43%) (Table 3). Areas in an acceptable state under reference conditions that were limited by nitrogen, phosphorus or both nutrients amounted to ~52, 10 and 29% of the globe containing 43, 10 and 42% of the population, respectively. However, under current conditions, areas that were limited by nitrogen, phosphorus or both nutrients and had acceptable levels of periphyton biomass accounted for ~21, 16 and 24% of the global land mass, respectively (Table 2), accounting for ~23, 18 and 46% of the world’s population, respectively (Table 3).

The areas with co- or phosphorus-limitation and enrichment likely to induce undesirable periphyton biomass were predicted to occur in temperate regions with a history of phosphorus additions and intensive agriculture38 (Fig. 4). However, there were also substantial areas in Africa and Australia that likely exhibit undesirable levels of biomass probably through a combination of low rainfall and high summer temperatures. Under reference conditions, the area with undesirable levels of periphyton biomass (largely caused by enrichment of both nitrogen and phosphorus) decreased substantially, especially in Europe, North America, and Asia. Areas in Australia and north Africa were still likely to have undesirable levels of biomass owing to high temperatures and low rainfall.

Although many periphyton thresholds rely on total nutrients, as well as the equations we used to estimate periphyton accrual, we dissolved nutrients may be more available to periphyton39. This suggests that our predictions of periphyton accrual based on total nitrogen and phosphorus in rivers could underestimate the level of undesirable periphyton as population and resource use rates increase globally and shift N and P to more available forms. Additionally, while we focus here on attached algae, the nutrient enrichments can also stimulate toxic suspended algal blooms in larger slower flowing rivers40. Our analysis likely underestimates the problems associated with nutrient enrichment in streams and future research could link models of suspended algal blooms in rivers and as influenced by nutrients with our approach41.

Methods
Dataset
Observations for concentrations of dissolved reactive phosphorus (n=305,741), total phosphorus (n=300,840), nitrate-nitrogen (n=297,783) and total nitrogen (n=308,118) checked, filtered, and harmonised to 2005–2013 from readily available, global (e.g., United Nations Environment Programme) or country-specific (e.g., United States Geological Survey or Land, Air, Water, Aotearoa) databases48. We chose 2005–2013 as a period of consistent data quality, to average across variation associated with continental or global climatic trends49, and to minimize changing influences from factors such as changes in land use and land use practices50. The processes and justification used to check, filter, and harmonise the observations are available elsewhere48. Additional work22 also matched and filtered sites in a catchment to those where concentrations were measured no more than 50km upstream of a site where discharge was measured daily51. Discharge data were separated into baseflow and stormflow using a recursive digital filter52 and used to filter out those samples taken where stormflow was ≥25% of mean daily discharge. We used this percentile to avoid including data taken during stormflow where periphyton was likely to be swept away or growth was likely inhibited by suspended sediment and light limitation36.

We then filtered and generated medians (and the number of observations) of data for temperate and polar regions to values measured from May to October in the Northern Hemisphere and for November to April in the Southern Hemisphere. These corresponded to periods of likely peak growth53. Year-round data were used to generate medians and the number of observations for tropical regions. After checking, filtering, harmonising, and matching to baseflow conditions, the number of observations and catchments for dissolved reactive phosphorus were 78,896 and 2202, 108,188 and 2618 for total phosphorus, 87,662 and 2372 for nitrate-nitrogen and 38,092 and 1186 for total nitrogen, respectively.

Predictor variables relating to categorical and continuous factors known to affect nitrogen and phosphorus losses to water were sourced from a wide variety of sources and are listed in the Supplementary Table 1. These included several variables indicative of human activity such as land use and land use intensity8,9,54,55. Continuous variables like percentage cropland were expressed as an average across each catchment. However, where more than one categorical variable was present in a catchment (e.g., biomes), we used the category that covered the greatest area within the catchment.

Modelling anthropogenic effects
The anthropogenic effect was estimated based on data for eight variables: population density, soil Olsen phosphorus concentration, and six land classes as given in Supplementary Table 1 and Supplementary Fig. 1. The anthropogenic effect is computed by the weighted sum of the measurements on each of these variables. The weights were modelled as coming from independent normal distributions with a common mean and variance to be computed as part of the model fitting process. The variables were normalised to have approximately similar variances and increase with greater anthropogenic activity. The six land class variables are all percentages, and their relative sizes correspond to their expected relative weightings. Land classes which cover a large proportion of the area are expected to have more weight than land classes which cover only a small proportion of the area. However, to account for skewed distributions, percentage land classes were arcsine-square root-transformed, while also adjusting an increase in land uses associated with in anthropogenic activity such as land under crops, pasture or urban, with decreases in rangeland, forests, and lakes. For example, an increase in cropland, pasture land or urban land was expressed in rangeland as equal to the arcsine transformed ratio of (100-rangeland)/100). Population density and Olsen phosphorus have very skewed distributions so were log transformed. To make them similar in scale to the other variables, these variables were normalised to Olsen phosphorus = log(1.8578 × Olsen phosphorus) and Population density = log(0.0282 × Population Density). Following adjustments and normalisation, the standard deviations of Olsen phosphorus and population density were within the standard deviations of the other variables. In respect to various responses (e.g., total phosphorus, total nitrogen etc.), the eight variables relating to anthropogenic effect are further summarized by their first common latent component named ‘human effect’ using partial least squares56. We used this method to avoid potential effects from multi-collinearity amongst variables (especially land uses). The resulting distribution of human effect was normally distributed across each analyte. Human effect was calculated for each analyte as Eqs. (1–4) with correlation coefficients of 0.61, 0.48, 0.57 and 0.53, respectively:

Dissolved reactive phosphorus human effect = 0:6482 × Olsen phosphorus + 0:3410 × Pasture + 0:1977 × Lentic + 0:4610 × Population Density + 0:3031 × Forest + 0:3113 × Urban − 0:1323 × Rangeland + 0:0759 × Crop (1)

Nitrate-nitrogen human effect = 0:2636 × Olsen phosphorus − 0:1019 × Pasture + 0:1031 × Lentic + 0:2264 × Urban + 0:4273 × Crop + 0:1119 × Rangeland + 0:3126 × Forest + 0:7520 × Population Density (2)

Total phosphorus human effect = 0:2468 × Olsen phosphorus + 0:0537 × Pasture + 0:6057 × Population Density + 0:5141 × Urban + 0:4055 × Forest + 0:1068 × Lentic + 0:0720 × Rangeland + 0:1736 × Crop (3)

Total nitrogen human effect = 0:6960 × Olsen phosphorus + 0:2229 × Pasture + 0:3031 × Rangeland + 0:0401 × Crop + 0:1548 × Forest + 0:3505 × Urban + 0:1513 × Lentic + 0:4503 × Population Density (4)

Predicting water quality
We natural log-transformed concentration data and split it into a training and testing set, containing 75 and 25% of the total sites, respectively before modelling. The split ensures the distribution of the associated concentrations were similar between training, testing, and the original data (see Supplementary Table 2).

We used a random forest model to estimate the median of natural log transformed dissolved reactive phosphorus, total phosphorus, nitrate-nitrogen, and total nitrogen concentrations respectively for each site as a function of anthropogenic effects and variables capturing catchment characteristics (see Supplementary Table 1). We also included the number of observations used to derive a site median as the sample weight. A random forest model is an amalgam of classification and regression trees that partitions and minimises differences in water quality concentrations based on binary splits of predictor variables. The approach does not assume any distribution and is able to fit non-linear relationships and cope with the effects of high order interactions. The splits are made using a random subset of predictor variables to compare a bootstrapped sample of water quality concentrations. Including the random subsets increases the accuracy of prediction when averaged over the forest. The models also maximise their accuracy of prediction for unseen data as the number of trees increases, meaning that the final model is less likely to be overfitted (here we use a ten-fold change for cross-validation).

To find the best random forest model for each analyte concentration, we utilized the performance measurement mean absolute error (MAE) in two steps (see Supplementary Tables 3–7 and Supplementary Figs. 3–6). The first step used the training data to tune hyperparameters outputting the number of predictor variables available at each split with up to 900 trees per forest (as little improvement is found going beyond this number57) and a minimal node size. We started with the full model which included all the terms in the model, then searched for the best hyperparameters based on the parameter grid by minimum MAE and a ten-fold change cross-validation. To tune hyperparameters, we dropped the lowest importance predictor to get a second model and repeated this process until only the fixed parameters remained. As our aim was to determine the effect of humans on water quality concentrations and to extend the reference values (at near zero anthropogenic effect) to biophysically similar regions we fixed Latitude, Human effect, and Biome into the models. For each concentration we obtained 23 models with tuned hyperparameters.

In the second step we applied each of the 23 models to the testing data and calculated the MAE. We choose the model with smallest MAE and where models were similar, the fewest terms. The array and performance of models assessed for each nutrient is given in see Supplementary Tables 3–7.

Final model performance was assessed by comparing observations with predicted values for the training and (independent) testing datasets. Terms in the final model along with their relative importance are given in Supplementary Figs. 3–6. The performance of the final models was summarised as the coefficient of determination (R2) of a regression of observations vs predictions, and the root mean square error which represents the mean deviation between predicted and observed values (Supplementary Table 7). Confidence intervals (95%) for each model with and without human effects were estimated by bootstrap resampling one thousand times, with replacement.

Spatial implementation of modelling outputs
The final validated models were used to predict median concentrations of dissolved reactive phosphorus, nitrate-nitrogen, total phosphorus, and total nitrogen globally in ArcGIS (v10.1, Redlands, CA) under current conditions and under reference conditions. To estimate reference conditions, the terms within equations for Human Effects were set at Olsen phosphorus = 2mg kg−1 (effectively unfertilised soil under native land use58), a population density of 0.001 persons km−2, and 0% cropland, pasture land or urban land (with an equal split between forest or rangeland).

The final validated models were converted from the R-programming language into Python for implementation within ArcGIS to extend predictions out to areas without observations. The performance of the Python implementation of the tuned models was similar but not identical to the development in R because different training and test data are inevitably chosen. The outputs are given in Supplementary Table 8 and Supplementary Figs. 7–12.

In addition to checking model performance, we also checked each model’s spatial predictions by plotting the residuals (see Supplementary Fig. 13) and predictions of reference conditions by comparing predictions to those catchments under MDC (i.e., most likely to be under reference conditions in our database). We defined MDC as: Olsen phosphorus concentrations of ≤5mg kg−1, population density of ≤0.001 persons km−2 and ≥80% forestland or rangeland and no cropland or urban land. Note that this is different from ‘reference conditions’ defined above to allow more sites to qualify as under MDC. The model was considered to have adequately predicted reference conditions if those catchments under reference conditions fell within the 95% confidence interval for its respective biome.

From the Python implementation, raster grids were created with a spatial resolution of 0.025°, which corresponded to the coarsest grid cell associated with the input data listed in Supplementary Table 1. These grids were then used with the input variables in Supplementary Table 1 and the validated models to predict concentrations for sites at HydroBASINS level 6 (n = 14241 catchments59). This level was equivalent to the median level of our catchments.

We used these predictions to generate two outputs. The first was to estimate the percentage anthropogenic effect at each site taken as the ratio of the difference between predicted current and reference concentrations and predicted current concentrations. Although the raw data are given for each site and catchment, we also summarised these data as the area-weighted mean in each biome and included estimates of variation and confidence intervals so that policymakers with a paucity of data could determine the likelihood of anthropogenic effects and the need to remediate them.

The second output was to extend published work4 that looked at the likelihood of nuisance levels of periphyton biomass in streams and rivers under reference conditions based on a Redfield nitrogen: phosphorus ratio of 7:1 (by mass) and the mean thresholds of periphyton biomass for total nitrogen and total phosphorus in the water column. Owing to variation in the response of periphyton to either nitrogen or phosphorus limitation we adjusted ratio to <7 and ≥15 for nitrogen and phosphorus limitation, respectively, and introduced classes for co-limitation by both nutrients whose ratio was ≥7 and <1536,60–62. We note that at high nutrient concentrations the relationship to periphyton biomass is weaker, but nonetheless still significant53. We used the equation of ref. 53 derived from relationships between total nitrogen and phosphorus (from the United States, New Zealand, Australia, Canada, and Europe) and maximum chlorophyl-a biomass (log(max.chl) = 0.349.log(total nitrogen) + 0.256.log(total phosphorus) + 0.722) to confirm that our concentrations of total nitrogen (0.800mg L−1) and phosphorus (0.046mg L−1) met expectations in the literature for the boundary between acceptable and undesirable biomass (~150mg chl-a m−2)35,63,64.

We classified each catchment into one of six classes. Rules for class one were if the nitrogen:phosphorus (as total nitrogen and total phosphorus) ratio is <7 and the median total nitrogen concentration is <0.800mg L−1, the site will have a quantity of periphyton biomass that is acceptable and is nitrogen-limited (catchment type 1). Rules for class two were if the nitrogen:phosphorus (as total nitrogen and total phosphorus) ratio is <7 and the median total nitrogen concentration is ≥0.800mg L−1, the site will have a quantity of periphyton biomass that is undesirable and is nitrogen-limited (catchment type 2). Rules for class three were if the nitrogen:phosphorus (as total nitrogen and total phosphorus) ratio is ≥7 and <15 and either the median total nitrogen concentration is <0.800mg L−1 or median total phosphorus concentration is <0.046mg L−1 the site will have a quantity of periphyton biomass that is acceptable and is co-limited (catchment type 3). Rules for class four were if the nitrogen:phosphorus (as total nitrogen and total phosphorus) ratio is ≥7 and <15 and both the median total nitrogen concentration is ≥0.800mg L−1 and median total phosphorus concentration is >0.046mg L−1 the site will have a quantity of periphyton biomass that is acceptable and is co-limited (catchment type 4). Rules for class five were if the nitrogen:phosphorus (as total nitrogen and total phosphorus) ratio is ≥15 and the median total phosphorus concentration is <0.046mg L−1, the site will have a quantity of periphyton biomass that is acceptable and is phosphorus-limited (catchment type 5). Finally, rules for class six were if the nitrogen:phosphorus (as total nitrogen and total phosphorus) ratio is ≥15 and the median total phosphorus concentration is ≥0.046mg L−1, the site will have a quantity of periphyton biomass that is undesirable and is phosphorus-limited (catchment type 6).

We removed catchments in biomes where rivers were less likely to be permanently flowing due to a lack of rainfall (e.g., Desert & Xeric shrublands) or have originated from areas with a 50–90 or >90% chance of being under permafrost65 (e.g., Taiga).

Outputs for each nutrient concentration under current and reference conditions, along with the class for each catchment is given in an interactive map (https://global-water-quality.agr.nz/).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript uses a comprehensive global dataset of river nutrient concentrations and associated land use / population data to predict the likely river reference conditions across the world. It then compares these with the current conditions, to assess the impact of anthropogenic activities on potential algal growth. It is a really original idea, and an interesting and well-written paper, and offers a way to determine human pressure in regions with poor data coverage. I think that the authors need to address some issues before publication.\nMajor comments\n1) The assumption that high nutrients result in high periphyton accrual (line 72) is probably true for lakes, but the case for rivers is much weaker. What evidence do the authors have that nutrient concentrations are directly related to periphyton biomass or chlorophyll concentration? I would assume that flow, light, shading levels, water temperature and river length also have a major role in determining algal biomass.\n2) The authors have used the Redfield Ratio to determine if algae are P or N co-limited. This may work in relatively pristine environments with little nutrient enrichment, but is quite meaningless in regions with high anthropogenic pressures. In highly-enriched river environments, neither P or N will be limiting algal growth, and they are likely to be light or temperature limited. The authors should have an additional category of “no nutrient limitation”.\nMinor comments\nTitle: Change to Anthropogenic nutrient inputs to rivers cause…..\nLine 47: Should MDC also include a human population threshold? Wastewater inputs are more important than agricultural in many regions.\nLine 95: Could P concentrations related to slope also relate to cities being mainly being situated on flat land?\nLine 150: The statement that algal biomass in rivers is directly related to nutrient concentration is a major assumption that needs justifying (or caveats need to be added).\n152: The periphyton accrual threshold is given as 150 mg/L. I can understand how phytoplankton can be quantified in mg chlorophyll /L, but should attached algae be mg/m2?\n185: The paper focusses on potential for periphyton blooms, but phytoplankton blooms are surely the key parameter to measure, in terms of ecology and human health?\nMaterials and methods, Line 7: Please give the reader some indication of where these data sources generally come from, rather than asking them to follow up the citation.", "reviewer_2": "Overview\nThe authors have carried out an analysis of nutrient data to determine a global assessment of the extent of nutrient enrichment of rivers. The analysis uses a selection of environmental variables that provide both an assessment of anthropogenic nutrient input and factors that are likely to influence transport to the riverine system. They determine enrichment using a ratio of predicted values to reference values derived from the same models in catchments subject to minimal human influence.\nThe modelling approach is comprehensive and well executed. Anthropogenic influence was determined using a derived variable following partial least squares regression. The output from this modelling step was fully described in the Supplementary Materia and was consistent with both my experience and the published literature and thus likely to be an appropriate indicator for inclusion in the subsequent modelling. To predict nutrient concentrations Regression tree models were used to select key variables which were then used to make predictions. The modelling approach split data into training and independent data sets, an important step not always used. To extend the output to other geographic areas a GIS based modelling approach was used. This approach produced similar but not identical variable selection and as a consequence different predictions. Literature data were used to determine the likelihood of excess algal growth in conditions of N, P or co-NP limitation using threshold concentrations of TN and TP.\nThe key results presented in the main body of the paper were\n1. Proportion of global enrichment split by region (Table 1)\n2. Continental estimates of the area of catchments & % of world population likely to have unacceptable/acceptable growth of periphyton\nThese results are presented in both tabular and graphical form.\nValidity\nAs a non-professional statistician my assessment is that the approach used was valid and well executed. The methods section and the supplementary material provide adequate information to support the conclusions. Having attempted analysis of large scale data sets I appreciate the difficulties involved in data cleaning. I was not able to assess this aspect of the approach as details were only available in a referenced publication, but I have no reason to believe that this will not have been well executed.\nSignificance\nThere is clearly a very substantial literature describing the impacts of nutrient enrichment on the water environment and thus it is not surprising that the paper concludes that anthropogenic enrichment affects nearly 50% of the world's population. In that sense there is nothing particularly original in this paper’s findings. However, it does present an interesting and in my opinion analysis of a very large global data set.\nThe paper starts with a good, although not particularly novel, overview of the concept of reference conditions. This is important as the authors use deviation from reference conditions to assess impact. It is this approach that I feel makes the paper worthy of publication.\nImprovements\nKey Results\nIn my opinion the most important results in this paper were the predictions of reference conditions derived from the models. However, these were not the main focus of the paper and I suggest that this needs to be addressed in a significant modification to the paper\nI feel that the paper needs to include more consideration of the reference condition predictions in the main body of the paper. Currently all of the detailed information is only within the supplementary material. My suggestion is that it is this aspect of the work that is novel and needs to be brought into the main body.\nFigure S13 provides much useful information and would warrant further discussion. There is currently much uncertainty over the variation of reference conditions, perhaps supported but another of the authors findings in their analysis of published values (i.e. the significance of country and method), also the importance of latitude in the main models. I would like to see more of this in the main paper.\nSummary\nIn overview I found the paper interesting and well executed. However, in its current form I don't feel it provides sufficient new information to warrant publication, although it has conducted a worthwhile analysis of a very substantial data set. If the paper were to focus more on the issue of reference conditions in the context of geographic variability and their importance in impact assessment I do feel the paper could be worthy of publication\nOther comments\nLine 84 Sub-title. I did not find this sub-title particularly helpful, it needs to be modified\nLine 128 enrichment varied from 3% to 86% (is it not 2% for TP Table 1)\nLine 338 I found the legend to table 1 difficult to understand and consequently was not entirely clear what the table contained. Are the values proportions of catchments or the median enrichment level (O-E)/E derived from the predicted reference values.", "reviewer_3": "I found this paper very impressive. The most noteworthy results for me were how low both nitrogen-nitrate and total phosphorus need to be to avoid unacceptable periphyton concentrations. I think particularly the low nitrate is indicative of the prevalence of N fixing periphyton, requiring much lower concentrations in nitrate. The low concentrations of phosphorus are likely unattainable for most urbanised catchments but this paper at least provides evidence for the requirements of lower P concentrations and how much work there is left to be done. To have modelled across the globe further increases the noteworthy-ness of the work and the high impact.\nThe work is wide ranging because of the variety of fields that feed into the human impact. The combined influence of urban pressures and intensification of agriculture with the various subfactors was an interesting approach as often the human effect is segregated into rural versus non-rural pressures. It might have been useful since so much work has been done to make a stab at the impact of those sub categories to give more clarity of what pressures need to be addressed. I think fields that are related to the sub categories, for example Environmental Planners, might feel that they need more insight into the proportion that population density is impacting the nutrient enrichment, for example. As the developed world puts limits on housing development in sensitive areas, this work could provide real evidence to support those often unpopular decisions that have been taken in catchments across the UK for example.\nThe discussion section was relatively short. I would suggest this is the weakest part of the paper and I think some more thinking could be done about the implications of the work that has been completed. The feat of processing 1.2 M data and making a global assessment of nutrient enrichment is rather exciting. The potential for using these findings are wide ranging - some more of that impact would be great to see in the paper.\nThere were a couple of areas that I either just didn't get or think could be improved. In the list of describing the 6 types of catchments, in description 4 you say if \"both\" conditions are met but then you say \"or\" when listing the conditions. I would have thought this should be \"and\".\nI really like how much care was taken with training datasets, testing sets, checking residuals, looking at patterns. Often this much care is missing from papers and leads to weakness in the modelling. Having read all the supplemental info and the modelling approach, I have confidence in your methods and the care you've taken. One thing I was wondering is whether it's possible to get an idea of how confident you are in your values for enrichment. You have stated values like the global-average enrichment value for nitrate was 21% but this really doesn't mean a lot to me without confidence statements. If your confidence range is something like 15-28% then that's quite different to 5-38%. Also in Table S9, which nutrient are you referring to in column 5? I'd also expect an comment on some clear outliers in the comparison between estimated and actual values (Fig S11). I appreciate they're both in the supplemental data but I haven't found mention of any particular outliers in the main body text - only mention of catchments that were removed due to a lack of water.\nOverall though I found the description of methods and the process excellent - and if this paper is published then something I'd be able to teach my students. I think the main area to improve on is the significance of the results and how they impact the topics chosen for discussion. Perhaps widening the discussion a little to describe that impact will achieve this."}, "author_response": "Point-by-point response to the reviewers’ comments\nReviewers' Comments:\nReviewer #1 (Remarks to the Author)\nThis manuscript uses a comprehensive global dataset of river nutrient concentrations and associated\nland use / population data to predict the likely river reference conditions across the world. It then\ncompares these with the current conditions, to assess the impact of anthropogenic activities on\npotential algal growth. It is a really original idea, and an interesting and well-written paper, and\noffers a way to determine human pressure in regions with poor data coverage. I think that the\nauthors need to address some issues before publication.\nMajor comments\n1) The assumption that high nutrients result in high periphyton accrual (line 72) is probably\ntrue for lakes, but the case for rivers is much weaker. What evidence do the authors have\nthat nutrient concentrations are directly related to periphyton biomass or chlorophyll\nconcentration? I would assume that flow, light, shading levels, water temperature and river\nlength also have a major role in determining algal biomass. Good point, although we filtered\nthe data for warmer months under baseflow, we did not account for variation in light or\ntemperature. We have added a caveat to this effect in the text on “Potential for periphyton\naccrual”. See Minor comment 4. While it is true that the relationship between planktonic\nchlorophyll in lakes is much stronger than it is for rivers and streams, there is a significant\nrelationship, nonetheless. This has been established by several meta-analyses. Here are a\nfew references to illustrate the point.\n- Biggs, B. J. F. 2000. Eutrophication of streams and rivers: dissolved nutrient-chlorophyll.\nJournal of the North American Benthological Society 19:17-31.\n- Busse, L. B., J. C. Simpson, and S. D. Cooper. 2006. Relationships among nutrients, algae,\nand land use in urbanized southern California streams. Canadian Journal of Fisheries and\nAquatic Sciences 63:2621-2638.\n- Dodds, W. K., V. H. Smith, and K. Lohman. 2006. Nitrogen and phosphorus relationships\nto benthic algal biomass in temperate streams Canadian Journal of Fisheries and Aquatic\nSciences 63:1190-1191.\n- Munn, M., J. Frey, and A. Tesoriero. 2010. The Influence of Nutrients and Physical\nHabitat in Regulating Algal Biomass in Agricultural Streams. Environmental Management\n45:603-615.\n2) The authors have used the Redfield Ratio to determine if algae are P or N co-limited. This\nmay work in relatively pristine environments with little nutrient enrichment, but is quite\nmeaningless in regions with high anthropogenic pressures. In highly-enriched river\nenvironments, neither P or N will be limiting algal growth, and they are likely to be light or\ntemperature limited. The authors should have an additional category of “no nutrient\nlimitation”. We accept that temperature and light will likely play an important role in\nlimiting periphyton production and have reiterated this as per our response to the previous\npoints. However, it turns out that the relationship between growth and nutrient\nconcentrations it is not meaningless, just a weaker effect at higher nutrient levels. Below is a\n1\nfigure from the meta-analysis of Dodds, et al 2006. Canadian Journal of Fisheries and Aquatic\nSciences 63:1190-1192. You can see that benthic chlorophyll keeps increasing even as total\nphosphorus reaches very high levels. It also makes it evident that there is a weaker\nrelationship between water column nutrients and benthic chlorophyll as P gets very high,\nbut there still is a relationship. Hence, we are not quite sure that we should create a new\ncategory of no nutrient limitation, but note the possibility in the text, as at least there is less\nnutrient limitation. Also note that this analysis makes it clear that N:P matters and at higher\nnutrient levels, the presence of the other nutrient simulates biomass accrual.\nMinor comments\n1. Title: Change to Anthropogenic nutrient inputs to rivers cause….. Change made.\n2. Line 47: Should MDC also include a human population threshold? Wastewater inputs are more\nimportant than agricultural in many regions. Yes, MDC should include low (i.e. <5%) urban land\nuse and also minimal wastewater treatment discharges. We have corrected this. We also\nmention in the Methods that sites were removed sites if located within 50km of a wastewater\ntreatment plant.\n3. Line 95: Could P concentrations related to slope also relate to cities being mainly being situated\non flat land? Yes, that is a possibility. We’ve now included it in the following sentence “ Our\nanalysis indicated a strong influence of intensively farmed land, enriched soil fertility, and\n2\ngreater population density (e.g., especially on flat land) on increased river nutrient\nconcentrations, as have others”.\n4. Line 150: The statement that algal biomass in rivers is directly related to nutrient concentration\nis a major assumption that needs justifying (or caveats need to be added). Correct, we have\nadded the following two sentences and references just after the opening sentence “It should be\nnoted that this analysis focuses on nutrient concentrations because they are directly affected by\nhuman activity. Although our analysis was restricted to baseflow in warmer months, we cannot\nfully account for variation in factors like hydrology, temperature, and light which may affect\nperiphyton growth”. We note that problems with excessive algal biomass generally occur during\nbaseflow and during warmer months. Francoeur, S. N., Biggs, B. J. F., Smith, R. A. & Lowe, R. L.\nNutrient limitation of algal biomass accrual in streams: seasonal patterns and a comparison of\nmethods. J. N. Am. Benthol. Soc. 18, 242-260 (1999). Hill, W. R. & Fanta, S. E. Phosphorus and\nlight colimit periphyton growth at subsaturating irradiances. Freshwat. Biol. 53, 215-225 (2008).\n5. 152: The periphyton accrual threshold is given as 150 mg/L. I can understand how phytoplankton\ncan be quantified in mg chlorophyll /L, but should attached algae be mg/m2? Yes, and thanks,\nthat was a mistake – corrected to m-2.\n6. 185: The paper focusses on potential for periphyton blooms, but phytoplankton blooms are\nsurely the key parameter to measure, in terms of ecology and human health? Walter, your\nthought on this one? We agree this can be important in some cases. Predominantly very slowly\nflowing rivers (with low dilution rates) without high values of suspended inorganic sediments\n(which intercept light) are the places where phytoplankton blooms are problematic. There are\nsome prominent rivers (e.g. Murray-Darling in Australia, the Ohio River in the United States)\nwhere this has caused serios problems. However, we only know of one publication that starts to\nmake this link (Van Nieuwenhuyse, E. E. & Jones, J. R. Phosphorus chlorophyll relationship in\ntemperate streams and its variation with stream catchment area. Can. J. Fish. Aquat. Sci. 53, 99-\n105 (1996)). We had addressed this at line 220 where we state “Additionally, while we focus\nhere on attached algae, the nutrient enrichments can also stimulate toxic suspended algal\nblooms in larger slower flowing rivers 40. Our analysis likely underestimates the problems\nassociated with nutrient enrichment in streams and future research could link models of\nsuspended algal blooms in rivers and as influenced by nutrients with our approach 41.”\n7. Materials and methods, Line 7: Please give the reader some indication of where these data sources generally come from, rather than asking them to follow up the citation. Ok, examples of\nglobal and country-specific databases have been included.\nReviewer #2 (Remarks to the Author)\nOverview\nThe authors have carried out an analysis of nutrient data to determine a global assessment of the\nextent of nutrient enrichment of rivers. The analysis uses a selection of environmental variables that\nprovide both an assessment of anthropogenic nutrient input and factors that are likely to influence\ntransport to the riverine system. They determine enrichment using a ratio of predicted values to\nreference values derived from the same models in catchments subject to minimal human influence.\nThe modelling approach is comprehensive and well executed. Anthropogenic influence was\ndetermined using a derived variable following partial least squares regression. The output from this\nmodelling step was fully described in the Supplementary Materia and was consistent with both my\nexperience and the published literature and thus likely to be an appropriate indicator for inclusion in\nthe subsequent modelling. To predict nutrient concentrations Regression tree models were used to\n3\nselect key variables which were then used to make predictions. The modelling approach split data\ninto training and independent data sets, an important step not always used. To extend the output to\nother geographic areas a GIS based modelling approach was used. This approach produced similar\nbut not identical variable selection and as a consequence different predictions. Literature data were\nused to determine the likelihood of excess algal growth in conditions of N, P or co-NP limitation\nusing threshold concentrations of TN and TP. No change necessary.\nThe key results presented in the main body of the paper were\n1. Proportion of global enrichment split by region (Table 1)\n2. Continental estimates of the area of catchments & % of world population likely to have\nunacceptable/acceptable growth of periphyton\nThese results are presented in both tabular and graphical form. No change necessary.\nValidity\nAs a non-professional statistician my assessment is that the approach used was valid and well\nexecuted. The methods section and the supplementary material provide adequate information to\nsupport the conclusions. Having attempted analysis of large scale data sets I appreciate the\ndifficulties involved in data cleaning. I was not able to assess this aspect of the approach as details\nwere only available in a referenced publication, but I have no reason to believe that this will not\nhave been well executed. No change necessary.\nSignificance\nThere is clearly a very substantial literature describing the impacts of nutrient enrichment on the\nwater environment and thus it is not surprising that the paper concludes that anthropogenic\nenrichment affects nearly 50% of the world's population. In that sense there is nothing particularly\noriginal in this paper’s findings. However, it does present an interesting and in my opinion analysis of\na very large global data set. No change necessary.\nThe paper starts with a good, although not particularly novel, overview of the concept of\nreference conditions. This is important as the authors use deviation from reference conditions to\nassess impact. It is this approach that I feel makes the paper worthy of publication. No change\nnecessary. We agree that this is the first step, but some consider eutrophication in streams\nunimportant. However, there are negative impacts caused by stream eutrophication. To our\nknowledge, this is the first global analysis to attempt assessing one cause of those global impacts.\nImprovements\nKey Results\nIn my opinion the most important results in this paper were the predictions of reference conditions\nderived from the models. However, these were not the main focus of the paper and I suggest that\nthis needs to be addressed in a significant modification to the paper. Thanks, we agree. We have\ntaken this point on board and expanded the section on the prediction of reference conditions.\nI feel that the paper needs to include more consideration of the reference condition predictions\nin the main body of the paper. Currently all of the detailed information is only within the\nsupplementary material. My suggestion is that it is this aspect of the work that is novel and needs to\nbe brought into the main body. Figure S13 provides much useful information and would warrant\nfurther discussion. There is currently much uncertainty over the variation of reference conditions,\nperhaps supported but another of the authors findings in their analysis of published values (i.e. the\nsignificance of country and method), also the importance of latitude in the main models. I would like\n4\nto see more of this in the main paper. We have moved a significant proportion of the Supplementary\nInformation into the main text and included Supplementary Figure S13 as one of the Extended data\nfigures. We have left the discussion of caveats for reference condition performance in the\nSupplementary Information (referring to it) to keep the main text focused. We also included a\ncomment about the importance of latitude in the model, which we suspect to be acting to show a\nseasonal effect (i.e. it was used to exclude data from colder months) or as an amalgam of other\nbiophysical factors. We have made mention of this in the main text and the Supplementary\nInformation.\nSummary\nIn overview I found the paper interesting and well executed. However, in its current form I don't feel\nit provides sufficient new information to warrant publication, although it has conducted a\nworthwhile analysis of a very substantial data set. If the paper were to focus more on the issue of\nreference conditions in the context of geographic variability and their importance in impact\nassessment I do feel the paper could be worthy of publication. Point taken. In addition to ‘beefing\nup’ the reference condition section we’ve also made additional mention of the impact of the work\nfor policy. However, one of the problems is that eutrophication of streams has not been considered\nan important problem by some in the past. Most of the focus had been on lakes. The references in\nthis paper support the idea that eutrophication is a potential problem in rivers and streams, and we\nhope that this puts the problem in global perspective in addition to the potential contribution to lake\nand marine eutrophication.\nOther comments\n Line 84 Sub-title. I did not find this sub-title particularly helpful, it needs to be modified. We\nchanged it to “ Controls on nitrogen and phosphorus losses under current and reference\nconditions”. Hopefully, this is more relevant to the following text.\n Line 128 enrichment varied from 3% to 86% (is it not 2% for TP Table 1). Well spotted! We also\nnoted that the data in Table 1 was listed as a proportion and a percentage in the text. WE have\nchanged this to percentage for consistency\n Line 338 I found the legend to table 1 difficult to understand and consequently was not entirely\nclear what the table contained. Are the values proportions of catchments or the median\nenrichment level (O-E)/E derived from the predicted reference values. Yes, you have interpreted\nthe calculation correctly. We’ve adjusted the table heading to make that clearer.\nReviewer #3 (Remarks to the Author)\nI found this paper very impressive. The most noteworthy results for me were how low both\nnitrogen-nitrate and total phosphorus need to be to avoid unacceptable periphyton concentrations.\nI think particularly the low nitrate is indicative of the prevalence of N fixing periphyton, requiring\nmuch lower concentrations in nitrate. The low concentrations of phosphorus are likely unattainable\nfor most urbanised catchments but this paper at least provides evidence for the requirements of\nlower P concentrations and how much work there is left to be done. To have modelled across the\nglobe further increases the noteworthy-ness of the work and the high impact. No change necessary.\nThe work is wide ranging because of the variety of fields that feed into the human impact. The\ncombined influence of urban pressures and intensification of agriculture with the various subfactors\nwas an interesting approach as often the human effect is segregated into rural versus non-rural\npressures. It might have been useful since so much work has been done to make a stab at the impact\n5\nof those sub categories to give more clarity of what pressures need to be addressed. I think fields\nthat are related to the sub categories, for example Environmental Planners, might feel that they\nneed more insight into the proportion that population density is impacting the nutrient enrichment,\nfor example. As the developed world puts limits on housing development in sensitive areas, this\nwork could provide real evidence to support those often unpopular decisions that have been taken\nin catchments across the UK for example. We understand that this would be useful. However, our\nanalysis cannot do this because we focused on a combined human effect variable. We had thought\nof using each of the factors making up human effects separately but couldn’t reconcile that their\ncollinearity would likely mask the importance of any sub-factor. However, you can get a ‘flavour’ of\nthe importance of each sub-factor by inspecting the partial least squares coefficients and have\ninserted some text to this effect.\nThe discussion section was relatively short. I would suggest this is the weakest part of the paper\nand I think some more thinking could be done about the implications of the work that has been\ncompleted. The feat of processing 1.2 M data and making a global assessment of nutrient\nenrichment is rather exciting. The potential for using these findings are wide ranging - some more of\nthat impact would be great to see in the paper.\nThere were a couple of areas that I either just didn't get or think could be improved. In the list of describing the 6 types of catchments, in description 4 you say if \"both\" conditions are met but then you say \"or\" when listing the conditions. I would have thought this should be \"and\". Actually, after\nre-reading the methods – class 4 should have said co-limited and unacceptable. We’ve now\ncorrected that.\nI really like how much care was taken with training datasets, testing sets, checking residuals,\nlooking at patterns. Often this much care is missing from papers and leads to weakness in the\nmodelling. Having read all the supplemental info and the modelling approach, I have confidence in\nyour methods and the care you've taken. One thing I was wondering is whether it's possible to get\nan idea of how confident you are in your values for enrichment. You have stated values like the\nglobal-average enrichment value for nitrate was 21% but this really doesn't mean a lot to me\nwithout confidence statements. If your confidence range is something like 15-28% then that's quite\ndifferent to 5-38%. Good point. We have now included confidence intervals in Table 1. We also now\nmention in the Methods how these were derived (bootstrapping and resampling with replacement\nof 1000 datapoints).\nAlso in Table S9, which nutrient are you referring to in column 5? We have changed this to\nAnalyte median or concentration.\nI'd also expect an comment on some clear outliers in the comparison between estimated and\nactual values (Fig S11). I appreciate they're both in the supplemental data but I haven't found\nmention of any particular outliers in the main body text - only mention of catchments that were\nremoved due to a lack of water. WE have re-inspected these data. They are outliers, but we’ve\nchosen not to remove them owing to their small number (<1% of the total N pool) and their close\nalignment to the 1:1 line. There is also some literature to support keeping low (but not high)\noutliers\nin datasets – owing to the fact that we know low concentrations are low, but high concentrations\ncould be an ‘out of range error’.\nOverall though I found the description of methods and the process excellent - and if this paper is\npublished then something I'd be able to teach my students. I think the main area to improve on is\nthe significance of the results and how they impact the topics chosen for discussion. Perhaps\nwidening the discussion a little to describe that impact will achieve this. Thanks, no change required.\nHowever, we have expanded the discussion a bit, but did not want to overburden the manuscript\nwith too much speculation. We hope that the level of consideration now is consistent with your\ncomments.\n6\n7"}, {"round": 2, "reviewer_comments": {"reviewer_2": "This is now an excellent paper, in my opinion the modifications made meet all of my and other reviewers comments. Well done", "reviewer_3": "Point-by-point response to the reviewers’ comments\nReviewers' Comments:\nReviewer #1 (Remarks to the Author)\nThis manuscript uses a comprehensive global dataset of river nutrient concentrations and associated\nland use / population data to predict the likely river reference conditions across the world. It then\ncompares these with the current conditions, to assess the impact of anthropogenic activities on\npotential algal growth. It is a really original idea, and an interesting and well-written paper, and\noffers a way to determine human pressure in regions with poor data coverage. I think that the\nauthors need to address some issues before publication.\nMajor comments\n1) The assumption that high nutrients result in high periphyton accrual (line 72) is probably\ntrue for lakes, but the case for rivers is much weaker. What evidence do the authors have\nthat nutrient concentrations are directly related to periphyton biomass or chlorophyll\nconcentration? I would assume that flow, light, shading levels, water temperature and river\nlength also have a major role in determining algal biomass. Good point, although we filtered\nthe data for warmer months under baseflow, we did not account for variation in light or\ntemperature. We have added a caveat to this effect in the text on “Potential for periphyton\naccrual”. See Minor comment 4. While it is true that the relationship between planktonic\nchlorophyll in lakes is much stronger than it is for rivers and streams, there is a significant\nrelationship, nonetheless. This has been established by several meta-analyses. Here are a\nfew references to illustrate the point.\n- Biggs, B. J. F. 2000. Eutrophication of streams and rivers: dissolved nutrient-chlorophyll.\nJournal of the North American Benthological Society 19:17-31.\n- Busse, L. B., J. C. Simpson, and S. D. Cooper. 2006. Relationships among nutrients, algae,\nand land use in urbanized southern California streams. Canadian Journal of Fisheries and\nAquatic Sciences 63:2621-2638.\n- Dodds, W. K., V. H. Smith, and K. Lohman. 2006. Nitrogen and phosphorus relationships\nto benthic algal biomass in temperate streams Canadian Journal of Fisheries and Aquatic\nSciences 63:1190-1191.\n- Munn, M., J. Frey, and A. Tesoriero. 2010. The Influence of Nutrients and Physical\nHabitat in Regulating Algal Biomass in Agricultural Streams. Environmental Management\n45:603-615.\n2) The authors have used the Redfield Ratio to determine if algae are P or N co-limited. This\nmay work in relatively pristine environments with little nutrient enrichment, but is quite\nmeaningless in regions with high anthropogenic pressures. In highly-enriched river\nenvironments, neither P or N will be limiting algal growth, and they are likely to be light or\ntemperature limited. The authors should have an additional category of “no nutrient\nlimitation”. We accept that temperature and light will likely play an important role in\nlimiting periphyton production and have reiterated this as per our response to the previous\npoints. However, it turns out that the relationship between growth and nutrient\nconcentrations it is not meaningless, just a weaker effect at higher nutrient levels. Below is a\n1\nfigure from the meta-analysis of Dodds, et al 2006. Canadian Journal of Fisheries and Aquatic\nSciences 63:1190-1192. You can see that benthic chlorophyll keeps increasing even as total\nphosphorus reaches very high levels. It also makes it evident that there is a weaker\nrelationship between water column nutrients and benthic chlorophyll as P gets very high,\nbut there still is a relationship. Hence, we are not quite sure that we should create a new\ncategory of no nutrient limitation, but note the possibility in the text, as at least there is less\nnutrient limitation. Also note that this analysis makes it clear that N:P matters and at higher\nnutrient levels, the presence of the other nutrient simulates biomass accrual.\nMinor comments\n1. Title: Change to Anthropogenic nutrient inputs to rivers cause….. Change made.\n2. Line 47: Should MDC also include a human population threshold? Wastewater inputs are more\nimportant than agricultural in many regions. Yes, MDC should include low (i.e. <5%) urban land\nuse and also minimal wastewater treatment discharges. We have corrected this. We also\nmention in the Methods that sites were removed sites if located within 50km of a wastewater\ntreatment plant.\n3. Line 95: Could P concentrations related to slope also relate to cities being mainly being situated\non flat land? Yes, that is a possibility. We’ve now included it in the following sentence “ Our\nanalysis indicated a strong influence of intensively farmed land, enriched soil fertility, and\n2\ngreater population density (e.g., especially on flat land) on increased river nutrient\nconcentrations, as have others”.\n4. Line 150: The statement that algal biomass in rivers is directly related to nutrient concentration\nis a major assumption that needs justifying (or caveats need to be added). Correct, we have\nadded the following two sentences and references just after the opening sentence “It should be\nnoted that this analysis focuses on nutrient concentrations because they are directly affected by\nhuman activity. Although our analysis was restricted to baseflow in warmer months, we cannot\nfully account for variation in factors like hydrology, temperature, and light which may affect\nperiphyton growth”. We note that problems with excessive algal biomass generally occur during\nbaseflow and during warmer months. Francoeur, S. N., Biggs, B. J. F., Smith, R. A. & Lowe, R. L.\nNutrient limitation of algal biomass accrual in streams: seasonal patterns and a comparison of\nmethods. J. N. Am. Benthol. Soc. 18, 242-260 (1999). Hill, W. R. & Fanta, S. E. Phosphorus and\nlight colimit periphyton growth at subsaturating irradiances. Freshwat. Biol. 53, 215-225 (2008).\n5. 152: The periphyton accrual threshold is given as 150 mg/L. I can understand how phytoplankton\ncan be quantified in mg chlorophyll /L, but should attached algae be mg/m2? Yes, and thanks,\nthat was a mistake – corrected to m-2.\n6. 185: The paper focusses on potential for periphyton blooms, but phytoplankton blooms are\nsurely the key parameter to measure, in terms of ecology and human health? Walter, your\nthought on this one? We agree this can be important in some cases. Predominantly very slowly\nflowing rivers (with low dilution rates) without high values of suspended inorganic sediments\n(which intercept light) are the places where phytoplankton blooms are problematic. There are\nsome prominent rivers (e.g. Murray-Darling in Australia, the Ohio River in the United States)\nwhere this has caused serios problems. However, we only know of one publication that starts to\nmake this link (Van Nieuwenhuyse, E. E. & Jones, J. R. Phosphorus chlorophyll relationship in\ntemperate streams and its variation with stream catchment area. Can. J. Fish. Aquat. Sci. 53, 99-\n105 (1996)). We had addressed this at line 220 where we state “Additionally, while we focus\nhere on attached algae, the nutrient enrichments can also stimulate toxic suspended algal\nblooms in larger slower flowing rivers 40. Our analysis likely underestimates the problems\nassociated with nutrient enrichment in streams and future research could link models of\nsuspended algal blooms in rivers and as influenced by nutrients with our approach 41.”\n7. Materials and methods, Line 7: Please give the reader some indication of where these data\nsources generally come from, rather than asking them to follow up the citation. Ok, examples of\nglobal and country-specific databases have been included.\nReviewer #2 (Remarks to the Author)\nOverview\nThe authors have carried out an analysis of nutrient data to determine a global assessment of the\nextent of nutrient enrichment of rivers. The analysis uses a selection of environmental variables that\nprovide both an assessment of anthropogenic nutrient input and factors that are likely to influence\ntransport to the riverine system. They determine enrichment using a ratio of predicted values to\nreference values derived from the same models in catchments subject to minimal human influence.\nThe modelling approach is comprehensive and well executed. Anthropogenic influence was\ndetermined using a derived variable following partial least squares regression. The output from this\nmodelling step was fully described in the Supplementary Materia and was consistent with both my\nexperience and the published literature and thus likely to be an appropriate indicator for inclusion in\nthe subsequent modelling. To predict nutrient concentrations Regression tree models were used to\n3\nselect key variables which were then used to make predictions. The modelling approach split data\ninto training and independent data sets, an important step not always used. To extend the output to\nother geographic areas a GIS based modelling approach was used. This approach produced similar\nbut not identical variable selection and as a consequence different predictions. Literature data were\nused to determine the likelihood of excess algal growth in conditions of N, P or co-NP limitation\nusing threshold concentrations of TN and TP. No change necessary.\nThe key results presented in the main body of the paper were\n1. Proportion of global enrichment split by region (Table 1)\n2. Continental estimates of the area of catchments & % of world population likely to have\nunacceptable/acceptable growth of periphyton\nThese results are presented in both tabular and graphical form. No change necessary.\nValidity\nAs a non-professional statistician my assessment is that the approach used was valid and well\nexecuted. The methods section and the supplementary material provide adequate information to\nsupport the conclusions. Having attempted analysis of large scale data sets I appreciate the\ndifficulties involved in data cleaning. I was not able to assess this aspect of the approach as details\nwere only available in a referenced publication, but I have no reason to believe that this will not\nhave been well executed. No change necessary.\nSignificance\nThere is clearly a very substantial literature describing the impacts of nutrient enrichment on the\nwater environment and thus it is not surprising that the paper concludes that anthropogenic\nenrichment affects nearly 50% of the world's population. In that sense there is nothing particularly\noriginal in this paper’s findings. However, it does present an interesting and in my opinion analysis of\na very large global data set. No change necessary.\nThe paper starts with a good, although not particularly novel, overview of the concept of\nreference conditions. This is important as the authors use deviation from reference conditions to\nassess impact. It is this approach that I feel makes the paper worthy of publication. No change\nnecessary. We agree that this is the first step, but some consider eutrophication in streams\nunimportant. However, there are negative impacts caused by stream eutrophication. To our\nknowledge, this is the first global analysis to attempt assessing one cause of those global impacts.\nImprovements\nKey Results\nIn my opinion the most important results in this paper were the predictions of reference conditions\nderived from the models. However, these were not the main focus of the paper and I suggest that\nthis needs to be addressed in a significant modification to the paper. Thanks, we agree. We have\ntaken this point on board and expanded the section on the prediction of reference conditions.\nI feel that the paper needs to include more consideration of the reference condition predictions\nin the main body of the paper. Currently all of the detailed information is only within the\nsupplementary material. My suggestion is that it is this aspect of the work that is novel and needs to\nbe brought into the main body. Figure S13 provides much useful information and would warrant\nfurther discussion. There is currently much uncertainty over the variation of reference conditions,\nperhaps supported but another of the authors findings in their analysis of published values (i.e. the\nsignificance of country and method), also the importance of latitude in the main models. I would like\n4\nto see more of this in the main paper. We have moved a significant proportion of the Supplementary\nInformation into the main text and included Supplementary Figure S13 as one of the Extended data\nfigures. We have left the discussion of caveats for reference condition performance in the\nSupplementary Information (referring to it) to keep the main text focused. We also included a\ncomment about the importance of latitude in the model, which we suspect to be acting to show a\nseasonal effect (i.e. it was used to exclude data from colder months) or as an amalgam of other\nbiophysical factors. We have made mention of this in the main text and the Supplementary\nInformation.\nSummary\nIn overview I found the paper interesting and well executed. However, in its current form I don't feel\nit provides sufficient new information to warrant publication, although it has conducted a\nworthwhile analysis of a very substantial data set. If the paper were to focus more on the issue of\nreference conditions in the context of geographic variability and their importance in impact\nassessment I do feel the paper could be worthy of publication. Point taken. In addition to ‘beefing\nup’ the reference condition section we’ve also made additional mention of the impact of the work\nfor policy. However, one of the problems is that eutrophication of streams has not been considered\nan important problem by some in the past. Most of the focus had been on lakes. The references in\nthis paper support the idea that eutrophication is a potential problem in rivers and streams, and we\nhope that this puts the problem in global perspective in addition to the potential contribution to lake\nand marine eutrophication.\nOther comments\n Line 84 Sub-title. I did not find this sub-title particularly helpful, it needs to be modified. We\nchanged it to “ Controls on nitrogen and phosphorus losses under current and reference\nconditions”. Hopefully, this is more relevant to the following text.\n Line 128 enrichment varied from 3% to 86% (is it not 2% for TP Table 1). Well spotted! We also\nnoted that the data in Table 1 was listed as a proportion and a percentage in the text. WE have\nchanged this to percentage for consistency\n Line 338 I found the legend to table 1 difficult to understand and consequently was not entirely\nclear what the table contained. Are the values proportions of catchments or the median\nenrichment level (O-E)/E derived from the predicted reference values. Yes, you have interpreted\nthe calculation correctly. We’ve adjusted the table heading to make that clearer.\nReviewer #3 (Remarks to the Author)\nI found this paper very impressive. The most noteworthy results for me were how low both\nnitrogen-nitrate and total phosphorus need to be to avoid unacceptable periphyton concentrations.\nI think particularly the low nitrate is indicative of the prevalence of N fixing periphyton, requiring\nmuch lower concentrations in nitrate. The low concentrations of phosphorus are likely unattainable\nfor most urbanised catchments but this paper at least provides evidence for the requirements of\nlower P concentrations and how much work there is left to be done. To have modelled across the\nglobe further increases the noteworthy-ness of the work and the high impact. No change necessary.\nThe work is wide ranging because of the variety of fields that feed into the human impact. The\ncombined influence of urban pressures and intensification of agriculture with the various subfactors\nwas an interesting approach as often the human effect is segregated into rural versus non-rural\npressures. It might have been useful since so much work has been done to make a stab at the impact\nof those sub categories to give more clarity of what pressures need to be addressed. I think fields\nthat are related to the sub categories, for example Environmental Planners, might feel that they\nneed more insight into the proportion that population density is impacting the nutrient enrichment,\nfor example. As the developed world puts limits on housing development in sensitive areas, this\nwork could provide real evidence to support those often unpopular decisions that have been taken\nin catchments across the UK for example. We understand that this would be useful. However, our\nanalysis cannot do this because we focused on a combined human effect variable. We had thought\nof using each of the factors making up human effects separately but couldn’t reconcile that their\ncollinearity would likely mask the importance of any sub-factor. However, you can get a ‘flavour’ of\nthe importance of each sub-factor by inspecting the partial least squares coefficients and have\ninserted some text to this effect.\nThe discussion section was relatively short. I would suggest this is the weakest part of the paper\nand I think some more thinking could be done about the implications of the work that has been\ncompleted. The feat of processing 1.2 M data and making a global assessment of nutrient\nenrichment is rather exciting. The potential for using these findings are wide ranging - some more of\nthat impact would be great to see in the paper.\nThere were a couple of areas that I either just didn't get or think could be improved. In the list of\ndescribing the 6 types of catchments, in description 4 you say if \"both\" conditions are met but then\nyou say \"or\" when listing the conditions. I would have thought this should be \"and\". Actually, after\nre-reading the methods – class 4 should have said co-limited and unacceptable. We’ve now\ncorrected that.\nI really like how much care was taken with training datasets, testing sets, checking residuals,\nlooking at patterns. Often this much care is missing from papers and leads to weakness in the\nmodelling. Having read all the supplemental info and the modelling approach, I have confidence in\nyour methods and the care you've taken. One thing I was wondering is whether it's possible to get\nan idea of how confident you are in your values for enrichment. You have stated values like the\nglobal-average enrichment value for nitrate was 21% but this really doesn't mean a lot to me\nwithout confidence statements. If your confidence range is something like 15-28% then that's quite\ndifferent to 5-38%. Good point. We have now included confidence intervals in Table 1. We also now\nmention in the Methods how these were derived (bootstrapping and resampling with replacement\nof 1000 datapoints).\nAlso in Table S9, which nutrient are you referring to in column 5? We have changed this to\nAnalyte median or concentration.\nI'd also expect an comment on some clear outliers in the comparison between estimated and\nactual values (Fig S11). I appreciate they're both in the supplemental data but I haven't found\nmention of any particular outliers in the main body text - only mention of catchments that were\nremoved due to a lack of water. WE have re-inspected these data. They are outliers, but we’ve\nchosen not to remove them owing to their small number (<1% of the total N pool) and their close\nalignment to the 1:1 line. There is also some literature to support keeping low (but not high) outliers\nin datasets – owing to the fact that we know low concentrations are low, but high concentrations\ncould be an ‘out of range error’.\nOverall though I found the description of methods and the process excellent - and if this paper is\npublished then something I'd be able to teach my students. I think the main area to improve on is\nthe significance of the results and how they impact the topics chosen for discussion. Perhaps\nwidening the discussion a little to describe that impact will achieve this. Thanks, no change required.\nHowever, we have expanded the discussion a bit, but did not want to overburden the manuscript\nwith too much speculation. We hope that the level of consideration now is consistent with your\ncomments."}, "author_response": ""}]

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