The freshwater ecosystems around the world are degrading, such that maintaining environmental flow (EF) in river networks is critical to their preservation. The relationship between streamflow alterations and, respectively, EF violations, and freshwater biodiversity is well established at the scale of stream reaches or small basins (~<100 km²). However, it is unclear if this relationship is robust at larger scales even though there are large-scale initiatives to legalize the EF requirement. Moreover, EFs have been used in assessing a planetary boundary for freshwater. Therefore, this study intends to carry out an exploratory evaluation of the relationship between EF violation and freshwater biodiversity at globally aggregated scales and for freshwater ecoregions. Four EF violation indices (severity, frequency, the probability to shift to violated state, and probability to stay violated) and seven independent freshwater biodiversity indicators (calculated from observed biota data) were used for correlation analysis. No statistically significant negative relationship between EF violation and freshwater biodiversity was found at global or ecoregion scales. While our results thus suggest that streamflow and EF may not be an only determinant of freshwater biodiversity at large scales, they do not preclude the existence of relationships at smaller scales or with more holistic EF methods (e.g., including water temperature, water quality, intermittency, connectivity etc.) or with other biodiversity data or metrics.

In environmental management and sustainability there is an increasing interest in measurement and accounting of beneficial impact - as an incentive to action, as a communication tool, and to move towards a positive, constructive approach focused on opportunities rather than problems. One approach uses the metaphor of a “handprint”, complementing the notion of environmental footprints, which have been widely adopted for impact measurement and accounting. We analyse this idea by establishing core principles of handprint thinking: handprint encourages actions with positive impacts, connects to analyses of footprint reductions, but adds value to them, and addresses the issue of what action should be taken. We also identify five key decisions that need to be made in performing a (potentially quantitative) handprint assessment, related to scoping of the improvement to be made, how it is achieved, and how credit is assigned, taking into account constraints on action. A case study of the potential water footprint reduction of an average Finn demonstrates how handprint thinking can be a natural extension of footprint reduction analyses. We find that there is a diversity of possible handprint assessments that have the potential to encourage doing good. Their common foundation is “handprint thinking”.

Anthropogenic activities are altering flood frequency-magnitude distributions along many of the world’s large rivers, yet isolating the impact of any single factor amongst the multitudes of competing anthropogenic drivers is a persistent, yet important challenge if we are to mitigate their negative consequences. The Usumacinta River in southeastern Mexico provides an ideal opportunity to study an anthropogenic driver in isolation: tropical forest conversion. This article employs a novel approach to disentangle the anthropogenic signal from climate variability, and provides valuable insights into the impact of forest conversion on flood severity. Here we analyse continuous daily time series of precipitation, temperature, and discharge to identify long-term trends, and compare ratios of catchment-wide precipitation totals to daily discharges in order to account for climatic variability. We also identify an anthropogenic signature of tropical forest conversion at the intra-annual scale, successfully reproduce this signal using a distributed hydrological model (VMOD), and demonstrate that the continued conversion of tropical forest to agricultural land use will further exacerbate large-scale flooding. We find statistically significant increasing trends in annual minimum, mean, and maximum discharges that are not evident in either precipitation or temperature records. We also find that mean monthly discharges have increased between 7% and 75% in the past decade, in contrast to mean monthly precipitation, which shows no statistically significant trend. Model results demonstrate that forest cover loss is responsible for raising the 10-year return peak discharge by 25%, while the total conversion of forest to agricultural use would result in an additional 18% rise. These findings highlight the need for a holistic approach to catchment-wide land management in tropical regions that weights the benefits of agricultural expansion against the consequences of increased flood prevalence, and the economic and social costs that they incur.

Although many suggest that future diets should include more plant-based proteins, animal-sourced foods are unlikely to completely disappear from our diet. Grasslands yield a notable part of the world’s animal protein production, but thus far, there is no global insight into the relationship between current livestock stocking densities and the availability of grassland forage resources. This inhibits acting upon concerns over the negative effects of overgrazing in some areas and utilising the potential for increasing production in others. Previous research has examined the potential of sustainable grazing but lacks generic and observation-based methods needed to fully understand the opportunities and threats of grazing. Here we provide a novel framework and method to estimate global livestock carrying capacity and relative stocking density, i.e. the reported livestock distribution relative to the estimated carrying capacity. We first estimate the aboveground biomass that is available for grazers on grasslands and savannas based on the MODIS Net Primary Production (NPP) approach on a global scale. This information is then used to calculate reasonable livestock carrying capacities, using slopes, forest cover and animal forage requirements as restrictions. With this approach, we found that stocking rates exceed the forage provided by grasslands in northwestern Europe, midwestern United States, southern China and the African Sahel. In this study, we provide the highest resolution global datasets to date. Our results have implications for prospective global food system modelling as well as national agricultural and environmental policies. These maps and findings can assist with conservation efforts to reduce land degradation associated with overgrazing and help identify undergrazed areas for targeted sustainable intensification efforts.

Eating less livestock products can reduce agricultural resource use, but it has also been argued that a portion of livestock products does sustainably contribute to food security. Given that diet change impacts arise through modification of food production systems, evaluating such changes requires explicit modelling of system dynamics. This paper uses a novel global integrated optimisation model, “Aalto OptoFood”, to investigate changes in water use when replacing livestock protein with plant-based protein sources in human diets and reallocating associated cropland and pasture. Our findings reveal that the first cuts are the deepest – moderate reductions in livestock protein decrease water consumption more than suggested by comparing global average crop and livestock water productivities. Further reductions show diminishing returns, with no clear optimum. In terms of water use, it is beneficial to eat less livestock products, even if there is no clear case for or against universal adoption of a vegan diet.

High crop yield variation between years, impacted for example by extreme weather shocks and by other shocks on the food production system, can have substantial effect on food production. This, in turn introduces vulnerabilities within global food system. To mitigate the effects of these shocks there is a clear need for understanding how different adaptive capacity measures link to the crop yield variability. While existing literature provides many local scale studies on this linkage, no comprehensive global assessment yet exists. We assessed reported crop yield variation for wheat, maize, soybean and rice for time period 1981-2009 by measuring both yield loss risk (variation in negative yield anomalies considering all years) and changes in yields during only dry shock and hot shock years. We used machine learning algorithm XGBoost to assess globally the explanatory power of selected gridded anthropogenic indicators (i.e., adaptive capacity measures; such as Human Development Index, irrigation infrastructure, fertilizer use) on yield variation on 0.5 degree resolution, within climatically similar regions to rule out the role of average climate conditions. We found that the anthropogenic indicators explained 40-60% of yield loss risk variation whereas the indicators provided noticeably lower (5-20%) explanatory power during shock years. On continental scale, especially in Europe and Africa the indicators explained high proportion of the yield loss risk variation (up to around 80%). Assessing crop production vulnerabilities on global scale provides supporting knowledge to target specific adaptation measures, thus contributing to global food security.