Relationships between land use and water quality are complex with interdependencies, feedbacks, and legacy effects. Most river water quality studies have assessed catchment land use as areal coverage, but here, we hypothesize and test whether land use intensity – the inputs (fertilizer, livestock) and activities (vegetation removal) of land use – is a better predictor of environmental impact. We use New Zealand (NZ) as a case study because it has had one of the highest rates of agricultural land intensification globally over recent decades. We interpreted water quality state and trends for the 26 years from 1989 to 2014 in the National Rivers Water Quality Network (NRWQN) – consisting of 77 sites on 35 mostly large river systems. To characterize land use intensity, we analyzed spatial and temporal changes in livestock density and land disturbance (i.e., bare soil resulting from vegetation loss by either grazing or forest harvesting) at the catchment scale, as well as fertilizer inputs at the national scale. Using simple multivariate statistical analyses across the 77 catchments, we found that median visual water clarity was best predicted inversely by areal coverage of intensively managed pastures. The primary predictor for all four nutrient variables (TN, NO_x, TP, DRP), however, was cattle density, with plantation forest coverage as the secondary predictor variable. While land disturbance was not itself a strong predictor of water quality, it did help explain outliers of land use–water quality relationships. From 1990 to 2014, visual clarity significantly improved in 35 out of 77 (34∕77) catchments, which we attribute mainly to increased dairy cattle exclusion from rivers (despite dairy expansion) and the considerable decrease in sheep numbers across the NZ landscape, from 58 million sheep in 1990 to 31 million in 2012. Nutrient concentrations increased in many of NZ's rivers with dissolved oxidized nitrogen significantly increasing in 27∕77 catchments, which we largely attribute to increased cattle density and legacy nutrients that have built up on intensively managed grasslands and plantation forests since the 1950s and are slowly leaking to the rivers. Despite recent improvements in water quality for some NZ rivers, these legacy nutrients and continued agricultural intensification are expected to pose broad-scale environmental problems for decades to come.

Per-pixel and sub-pixel are two common classification methods in land cover studies. The characteristics of a landscape, particularly the land cover itself, can affect the accuracies of both methods. The objectives of this study were to: (1) compare the performance of sub-pixel vs. per-pixel classification methods for a broad heterogeneous region; and (2) analyze the impact of land cover heterogeneity (i.e., the number of land cover classes per pixel) on both classification methods. The results demonstrated that the accuracy of both per-pixel and sub-pixel classification methods were generally reduced by increasing land cover heterogeneity. Urban areas, for example, were found to have the lowest accuracy for the per-pixel method, because they had the highest heterogeneity. Conversely, rural areas dominated by cropland and grassland had low heterogeneity and high accuracy. When a sub-pixel method was used, the producer’s accuracy for artificial surfaces was increased by more than 20%. For all other land cover classes, sub-pixel and per-pixel classification methods performed similarly. Thus, the sub-pixel classification was only advantageous for heterogeneous urban landscapes. Both creators and users of land cover datasets should be aware of the inherent landscape heterogeneity and its potential effect on map accuracy.

Globally, environmental and social change in water-scarce regions challenge the sustainability of social-ecological systems. WaterSES, a sponsored working group within the Program for Ecosystem Change and Society, explores and compares the social-ecological dynamics related to water scarcity across placed-based international research sites with contrasting local and regional water needs and governance, including research sites in Spain and Sweden in Europe, South Africa, China, and Alabama, Idaho, Oklahoma, and Texas in the USA. This paper aims to provide a commentary on insights into conducting future solutions-oriented research on water scarcity based on the understanding of the social-ecological dynamics of water scarce regions.

Blue spaces such as rivers provide many ecosystem services (ES), including freshwater for consumption, habitat, water quality regulation, and multiple cultural amenities. While many studies have quantified the biophysical supply of ES provided by rivers, fewer have explored the social demand for ES due to the considerable effort involved in collecting these data. The San Marcos River (SMR) and the Texas State University (TXST) students that use this blue space represent a dynamic social-ecological system (SES) where nature experiences shape student values of the system. In this study, we survey the TXST student population about their use, value, and perception of the SMR, a highly used river of which headwaters originate on and flow through campus. From our extensive survey of these students, we find that educational and life experiences matter. Overall, we find that student exposure to the SMR in space, time, and experience does have measurable effects on their use, value, and perception of ES. This SES study demonstrates the importance of life experiences, place-based knowledge, and experiential learning in influencing one’s well-being and value of natural environments.