There is growing evidence that climate change impacts ecosystems and socio-economic activities in freshwater environments. Consistent global data of projected streamflow and water temperature are... Show moreThere is growing evidence that climate change impacts ecosystems and socio-economic activities in freshwater environments. Consistent global data of projected streamflow and water temperature are key to global impact assessments, but such a dataset is currently lacking. Here we present FutureStreams, the first global dataset of projected future streamflow and water temperature for multiple climate scenarios (up to 2099) gridded at a 5 arcminute spatial resolution (similar to 10 km at the equator), including recent past data (1976-2005) for comparison. We generated the data using global hydrological and water temperature models (PCR-GLOBWB, DynWat) forced with climate data from five general circulation models. We included four representative concentration pathways to cover multiple future greenhouse gas emission trajectories and associated changes in climate. Our dataset includes weekly streamflow and water temperature for each year as well as a set of derived indicators that are particularly relevant from an ecological perspective. FutureStreams provides a crucial starting point for large-scale assessments of the implications of changes in streamflow and water temperature for society and freshwater ecosystems. Show less
Aim Discharge is a key determinant of biodiversity in rivers. Positive relationships between riverine biodiversity and discharge, also called species-discharge relationships (SDRs), have been... Show moreAim Discharge is a key determinant of biodiversity in rivers. Positive relationships between riverine biodiversity and discharge, also called species-discharge relationships (SDRs), have been widely documented. However, potential human influences on these relationships are typically not considered. We aimed to fill this gap by exploring whether and how the slopes and intercepts of global riverine fish SDRs might be affected by human pressure on the environment. Location Global. Time period Current. Major taxa studied Riverine fishes. Methods We first quantified native riverine fish species richness of 4,430 catchments of >500 km(2) in size with available discharge measurements, using a novel dataset of the global distributions of 11,425 riverine fish species. We then established mixed effects models relating fish species richness to discharge and to two aggregated human pressure variables: the human footprint index (HFI) and the fragmentation status index (FSI). We tested for possible interactions between discharge and the human pressure variables, while accounting for other relevant covariates of large-scale gradients in riverine fish diversity. Results Against our expectations, we found positive coefficients for both HFI and FSI, in addition to a positive interaction between FSI and discharge. We found this consistently for different discharge variables (annual mean, maximum weekly and minimum weekly discharge). These findings suggest that riverine fish species richness tends to be higher in catchments characterized by more anthropogenic alterations of the natural environment. Main conclusions The global congruence between riverine fish species richness and human presence might reflect a commonality of drivers as well as biodiversity data gaps in the most pristine and species-rich catchments. Irrespectively, our results indicate that conflicts between human development and conservation are not easily avoided and highlight the challenges involved in safeguarding global freshwater biodiversity. Show less
Climate change poses a significant threat to global biodiversity, but freshwater fishes have been largely ignored in climate change assessments. Here, we assess threats of future flow and water... Show moreClimate change poses a significant threat to global biodiversity, but freshwater fishes have been largely ignored in climate change assessments. Here, we assess threats of future flow and water temperature extremes to similar to 11,500 riverine fish species. In a 3.2 degrees C warmer world (no further emission cuts after current governments' pledges for 2030), 36% of the species have over half of their present-day geographic range exposed to climatic extremes beyond current levels. Threats are largest in tropical and sub-arid regions and increases in maximum water temperature are more threatening than changes in flow extremes. In comparison, 9% of the species are projected to have more than half of their present-day geographic range threatened in a 2 degrees C warmer world, which further reduces to 4% of the species if warming is limited to 1.5 degrees C. Our results highlight the need to intensify (inter)national commitments to limit global warming if freshwater biodiversity is to be safeguarded. Show less
Dams contribute to water security, energy supply, and flood protection but also fragment habitats of freshwater species. Yet, a global species-level assessment of dam-induced fragmentation is... Show moreDams contribute to water security, energy supply, and flood protection but also fragment habitats of freshwater species. Yet, a global species-level assessment of dam-induced fragmentation is lacking. Here, we assessed the degree of fragmentation of the occurrence ranges of similar to 10,000 lotic fish species worldwide due to similar to 40,000 existing large dams and similar to 3,700 additional future large hydropower dams. Per river basin, we quantified a connectivity index (CI) for each fish species by combining its occurrence range with a high-resolution hydrography and the locations of the dams. Ranges of nondiadromous fish species were more fragmented (less connected) (CI = 73 +/- 28%; mean +/- SD) than ranges of di-adromous species (CI = 86 +/- 19%). Current levels of fragmentation were highest in the United States, Europe, South Africa, India, and China. Increases in fragmentation due to future dams were especially high in the tropics, with declines in CI of similar to 20 to 40 percentage points on average across the species in the Amazon, Niger, Congo, Salween, and Mekong basins. Our assessment can guide river management at multiple scales and in various domains, including strategic hydropower planning, identification of species and basins at risk, and prioritization of restoration measures, such as dam removal and construction of fish bypasses. Show less
Steinmann, Z.J.; Schipper, A.M.; Stadler, K.; Wood, R.; Koning, A. de; Tukker, A.; Huijbregts, M.A. 2018
Streamflow data is highly relevant for a variety of socio-economic as well as ecological analyses or applications, but a high-resolution global streamflow dataset is yet lacking. We created FLO1K,... Show moreStreamflow data is highly relevant for a variety of socio-economic as well as ecological analyses or applications, but a high-resolution global streamflow dataset is yet lacking. We created FLO1K, a consistent streamflow dataset at a resolution of 30 arc seconds (-1 km) and global coverage. FLO1K comprises mean, maximum and minimum annual flow for each year in the period 1960-2015, provided as spatially continuous gridded layers. We mapped streamflow by means of artificial neural networks (ANNs) regression. An ensemble of ANNs were fitted on monthly streamflow observations from 6600 monitoring stations worldwide, i.e., minimum and maximum annual flows represent the lowest and highest mean monthly flows for a given year. As covariates we used the upstream-catchment physiography (area, surface slope, elevation) and year-specific climatic variables (precipitation, temperature, potential evapotranspiration, aridity index and seasonality indices). Confronting the maps with independent data indicated good agreement (R-2 values up to 91%). FLO1K delivers essential data for freshwater ecology and water resources analyses at a global scale and yet high spatial resolution.[GRAPHICS]. Show less
Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially... Show moreQuantifying mean annual flow of rivers (MAF) at ungauged sites is essential for assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF based on a dataset unprecedented in size, using observations of discharge and catchment characteristics from 1885 catchments worldwide, measuring between 2 and 10(6) km(2). In addition, we compared the performance of the regression model with the predictive ability of the spatially explicit global hydrological model PCR-GLOBWB by comparing results from both models to independent measurements. We obtained a regression model explaining 89% of the variance in MAF based on catchment area and catchment averaged mean annual precipitation and air temperature, slope and elevation. The regression model performed better than PCR-GLOBWB for the prediction of MAF, as root-mean-square error (RMSE) values were lower (0.29-0.38 compared to 0.49-0.57) and the modified index of agreement (d) was higher (0.80-0.83 compared to 0.72-0.75). Our regression model can be applied globally to estimate MAF at any point of the river network, thus providing a feasible alternative to spatially explicit process based global hydrological models. (C) 2016 Elsevier B.V. All rights reserved. Show less
