ALBERT

Description: Several safe boundaries of critical Earth system processes have already been crossed due to human perturbations; not accounting for their interactions may further narrow the safe operating space for humanity. Using expert knowledge elicitation, we explored interactions among seven variables representing Earth system processes relevant to food production, identifying many interactions little explored in Earth system literature. We found that green water and land system change affect other Earth system processes strongly, while land, freshwater and ocean components of biosphere integrity are the most impacted by other Earth system processes, most notably blue water and biogeochemical flows. We also mapped a complex network of mechanisms mediating these interactions and created a future research prioritization scheme based on interaction strengths and existing knowledge gaps. Our study improves the understanding of Earth system interactions, with sustainability implications including improved Earth system modelling and more explicit biophysical limits for future food production.

Description: In the last few years, and in particular in 2022, the world has experienced numerous extreme droughts with unprecedented direct, cascading, and systemic impacts. Despite more frequent and severe events, drought risk assessment is still incipient compared to that of other meteorological and climate hazards. This is mainly due to the complexity of droughts and the high level of uncertainties in its analysis, but also the lack of community agreement on a common framework to tackle the problem. Here we outline that to effectively assess and manage drought risks, a systemic perspective is needed.

Description: The erosion of biodiversity is among our biggest challenges, as we face the risk of losing close to one million plant and animal species within the coming decades1. Despite numerous and ambitious international agreements that have been reached over several decades, ecosystem degradation leading to biodiversity decline has continued — and even accelerated — in almost all domains of life across marine, freshwater and terrestrial systems2. Indeed, planetary integrity and ecosystem services are now at risk of irreversible changes, with severe consequences for human wellbeing3. The main drivers of global biodiversity decline include habitat degradation and loss caused by changes in land and water use, direct exploitation of organisms, climate change, invasion by non-native species and chemical pollution4. However, our understanding of these drivers, single and in concert, often seems to be too rudimentary to adequately guide mitigation strategies that would be compatible with human activities. Here we argue for better integration of chemical pollution alongside other drivers in research that assesses biodiversity impacts.

Description: Human actions compromise the many life-supporting functions provided by the freshwater cycle. Yet, scientific understanding of anthropogenic freshwater change and its long-term evolution is limited. Here, using a multi-model ensemble of global hydrological models, we estimate how, over a 145-year industrial period (1861–2005), streamflow and soil moisture have deviated from pre-industrial baseline conditions (defined by 5th–95th percentiles, at 0.5° grid level and monthly timestep over 1661–1860). Comparing the two periods, we find an increased frequency of local deviations on ~45% of land area, mainly in regions under heavy direct or indirect human pressures. To estimate humanity’s aggregate impact on these two important elements of the freshwater cycle, we present the evolution of deviation occurrence at regional to global scales. Annually, local streamflow and soil moisture deviations now occur on 18.2% and 15.8% of global land area, respectively, which is 8.0 and 4.7 percentage points beyond the ~3 percentage point wide pre-industrial variability envelope. Our results signify a substantial shift from pre-industrial streamflow and soil moisture reference conditions to persistently increasing change. This indicates a transgression of the new planetary boundary for freshwater change, which is defined and quantified using our approach, calling for urgent actions to reduce human disturbance of the freshwater cycle.

Description: The satellite missions GRACE and GRACE-FO provide a great chance to derive total water storage anomalies (TWSA) from space with global resolution. However, a gap of nearly one year separates the two missions, some months are missing due to technical issues, and with about 300 km the spatial resolution of GRACE/-FO is still too coarse for finer-scale applications. To overcome these challenges, we here provide the Global Land Water Storage data (GLWS2.0) data set. This is a temporally consistent monthly data set of total water storage anomalies, groundwater, soil moisture, and surface water for the time period 2003 to 2019 on a global 0.5° grid over land (except Greenland and Antarctica), publically available on PANGAEA. The data set is developed by globally assimilating GRACE/-FO TWSA into the WaterGAP global hydrology model via the Ensemble Kalman Filter using uncertainty quantification. To our knowledge, only a few studies globally assimilate GRACE/-FO TWSA and most of them focus on hydrological instead of geodetic applications. Thus, here we focus on TWSA as observed by GRACE/-FO and analyze the new data set on the spatial and spectral domain by comparing its specific signatures with those of observations and model simulations, for example via degree variances. Overall, we find that GLWS2.0 is closer to the GRACE/-FO observations than the model simulations while increasing the spatial and spectral resolution. Finally, we use 1030 globally distributed GNSS stations to validate the GLWS2.0 data set and find a better agreement with the station as compared to GRACE/-FO.

Description: The ocean mass budget plays a crucial role in predicting future changes in ocean mass and sea level. In recent efforts to reconcile observations from GRACE and GRACE-Follow On satellites with steric-corrected altimetry and models of contributions from land and ice a discrepancy in the mass budget has been reported (Wang et al, 2022; Barnoud et al, 2022), in particular in the period following the launch of GRACE-Follow On. In this study, we aim to compare 20 years of GRACE-observed mass changes with steric-corrected altimetry and GRD-induced sea level changes resulting from landmass changes. To accomplish this, we produce monthly 3D global mass change products with a spatial resolution of 0.5 degrees, covering the period from 2003 to 2022. We improve the processing steps for steric-corrected satellite altimetry by accounting for ocean bottom deformation, removing the global mean contribution of halosteric sea level change, and replacing the radiometer-based wet tropospheric correction with a model-based correction. Our results indicate that both the steric-corrected altimetry and ocean mass reconstruction from GRD-induced sea level change is in agreement with the GRACE observations on both long-term and seasonal time scales and regional scales. We also find that a recent slowdown in GRACE-observed mass change during the GRACE-FO period can be attributed to terrestrial water storage variability driven by a long phase of La Nina and a deceleration in the mass loss of Greenland and Antarctic ice sheets.

Description: Global hydrological models enhance our understanding of the Earth system and support the sustainable management of water, food and energy in a globalized world. They integrate process knowledge with a multitude of model input data (e.g., precipitation, soil properties, and the location and extent of surface waterbodies) to describe the state of the Earth. However, they do not fully utilize observations of model output variables (e.g., streamflow and water storage) to reduce and quantify model output uncertainty through processes like parameter estimation. For a pilot region, the Mississippi River basin, we assessed the suitability of three ensemble-based multi-variable approaches to amend this: Pareto-optimal calibration (POC); the generalized likelihood uncertainty estimation (GLUE); and the ensemble Kalman filter, here modified for joint calibration and data assimilation (EnCDA). The paper shows how observations of streamflow (Q) and terrestrial water storage anomaly (TWSA) can be utilized to reduce and quantify the uncertainty of model output by identifying optimal and behavioral parameter sets for individual drainage basins. The common first steps in all approaches are (1) the definition of drainage basins for which calibration parameters are uniformly adjusted (CDA units), combined with the selection of observational data; (2) the identification of potential calibration parameters and their a priori probability distributions; and (3) sensitivity analyses to select the most influential model parameters per CDA unit that will be adjusted by calibration. Data assimilation with the ensemble Kalman filter was modified, to our knowledge, for the first time for a global hydrological model to assimilate both TWSA and Q with simultaneous parameter adjustment. In the estimation of model output uncertainty, we considered the uncertainties of the Q and TWSA observations. Applying the global hydrological model WaterGAP, we found that the POC approach is best suited for identifying a single “optimal” parameter set for each CDA unit. This parameter set leads to an improved fit to the monthly time series of both Q and TWSA as compared to the standard WaterGAP variant, which is only calibrated against mean annual Q, and can be used to compute the best estimate of WaterGAP output. The GLUE approach is almost as successful as POC in increasing WaterGAP performance and also allows, with a comparable computational effort, the estimation of model output uncertainties that are due to the equifinality of parameter sets given the observation uncertainties. Our experiment reveals that the EnCDA approach performs similarly to POC and GLUE in most CDA units during the assimilation phase but is not yet competitive for calibrating global hydrological models; its potential advantages remain unrealized, likely due to its high computational burden, which severely limits the ensemble size, and the intrinsic nonlinearity in simulating Q. Partitioning the whole Mississippi River basin into five CDA units (sub-basins) instead of only one improved model performance in terms of the Nash–Sutcliffe efficiency during the calibration and validation periods. Diverse parameter sets achieved comparable fits to observations, narrowing the range for at least three parameters. Low coverage of observation uncertainty bands by GLUE-derived model output bands is attributed to model structure uncertainties, especially regarding artificial reservoir operations, the location and extent of small wetlands, and the lack of representation of rivers that may lose water to the subsurface. These uncertainties are also likely to be responsible for significant trade-offs between optimal fits to Q and TWSA. Calibration performed exclusively against TWSA in regions without Q observations may worsen the Q simulation as compared to the uncalibrated model variant. We recommend that modelers improve the realism of the output of global hydrological models by calibrating them against observations of multiple output variables, including at least Q and TWSA. Further work on improving the numerical efficiency of the EnCDA approach is necessary.