Reductions in river discharge (water availability) like those from climate change or increased water withdrawal, reduce freshwater biodiversity. We combined two scenarios from the Intergovernmental Panel for Climate Change with a global hydrological model to build global scenarios of future losses in river discharge from climate change and increased water withdrawal. Applying these results to known relationships between fish species and discharge, we build scenarios of losses (at equilibrium) of riverine fish richness. In rivers with reduced discharge, up to 75% (quartile range 4-22%) of local fish biodiversity would be headed toward extinction by 2070 because of combined changes in climate and water consumption. Fish loss in the scenarios fell disproportionately on poor countries. Reductions in water consumption could prevent many of the extinctions in these scenarios.

The Gravity Recovery and Climate Experiment (GRACE) mission ended its operation in October 2017, and the GRACE Follow-On mission was launched only in May 2018, leading to approximately 1 year of data gap. Given that GRACE-type observations are exclusively providing direct estimates of total water storage change (TWSC), it would be very important to bridge the gap between these two missions. Furthermore, for many climate-related applications, it is also desirable to reconstruct TWSC prior to the GRACE period. In this study, we aim at comparing different data-driven methods and identifying the more robust alternatives for predicting GRACE-like gridded TWSC during the gap and reconstructing them to 1992 using climate inputs. To this end, we first develop a methodological framework to compare different methods such as the multiple linear regression (MLR), artificial neural network (ANN), and autoregressive exogenous (ARX) approaches. Second, metrics are developed to measure the robustness of the predictions. Finally, gridded TWSC within 26 regions are predicted and reconstructed using the identified methods. Test computations suggest that the correlation of predicted TWSC maps with observed ones is more than 0.3 higher than TWSC simulated by hydrological models, at the grid scale of 1°r esolution. Furthermore, the reconstructed TWSC correctly reproduce the El Nino-Southern Oscillation (ENSO) signals. In general, while MLR does not perform best in the training process, it is more robust and could thus be a viable approach both for filling the GRACE gap and for reconstructing long-period TWSC fields globally when combined with statistical decomposition techniques.

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Temperate forest soils store large amounts of organic matter and are considered as net sinks for atmospheric carbon dioxide. Information about the sink strength and the turnover time of soil organic carbon (SOC) is required to assess the potential response of soils to climate change. Here we report on stocks, turnover times (TT) and accumulation of SOC in bulk soil and density fractions from genetic horizons of a Podzol in the Fichtelgebirge, Germany. Stocks of SOC, total nitrogen and exchangeable cations determined in nine quantitative soil pits strongly varied with stone content and thickness of horizons in both the organic layer and the mineral soil. On the basis of radiocarbon signatures, mean turnover times of 4, 9 and 133 years, respectively, were calculated for Oi, Oe and Oa horizons from three soil pits, using a non-steady-state model. The Oa horizons accumulated 4-8 g C m À2 year À1 whereas the Oi and Oe horizons were close to steady-state during the past decade. Free particulate organic matter (FPOM) was the most abundant fraction in the Oa and EA horizons with TT of 70-480 years. In the B horizons, mineral associated organic matter (MAOM) dominated with over 40% of total SOC and had TT of 390-2170 years. In contrast to other horizons, MAOM in the Bsh and Bs horizon had generally faster TT than occluded particulate organic matter (OPOM), possibly because of sorption of dissolved organic carbon by iron and aluminium oxides/hydroxides. Our results suggest that organic horizons with relatively short turnover times could be particularly vulnerable to changes in climate or other disturbances.

Flow velocity in rivers has a major impact on residence time of water and thus on high and low water as well as on water quality. For global scale hydrological modeling only very limited information is available for simulating flow velocity. Based on the Manning-Strickler equation, a simple algorithm to model temporally and spatially variable flow velocity was developed with the objective of improving flow routing in the global hydrological model of Water-GAP. An extensive data set of flow velocity measurements in US rivers was used to test and to validate the algorithm before integrating it into WaterGAP. In this test, flow velocity was calculated based on measured discharge and compared to measured velocity. Results show that flow velocity can be modeled satisfactorily at selected river cross sections. It turned out that it is quite sensitive to river roughness, and the results can be optimized by tuning this parameter. After the validation of the approach, the tested flow velocity algorithm has been implemented into the WaterGAP model. A final validation of its effects on the model results is currently performed.