Andreas Güntner’s research while affiliated with Universität Potsdam and other places

In this study a regional modelling framework for water mass changes is developed. The approach can introduce geodetic observation types of varying temporal and spatial resolution including their correlated error information. For this purpose a Kalman filter process was set up using a regional parameterisation by space-localising radial basis functions and a process model based on stochastic prediction. The feasibility of the approach is confirmed in a closed-loop simulation experiment using gridded water storage estimates derived from simulated monthly solutions of the GRACE satellite gravimetry mission and considering realistic error patterns. The resulting mass change time series exhibit strongly reduced noise and a very high agreement with the reference model. The modelling framework is designed to flexibly allow a future extension towards combining satellite gravimetry with other geodetic observations such as GNSS station displacements or terrestrial gravimetry.

Individual approaches to observe water dynamics across our landscape, from the land surface to groundwater, are many though they individually only provide glimpses into the real world due to their specific space–time scales. Comprehensive integration across all available observations is still largely lacking, limiting both our ability to reduce scientific knowledge gaps, and to guide land and water management using the best available scientific evidence. We argue that a stronger focus on integration of observational products, while utilising machine learning and accounting for current perceptual understanding is urgently needed to overcome this limitation. Since Europe is warming faster than any other continent, central Europe is undergoing a dramatic hydroclimatic transition about which such integrated observations would provide timely and valuable insights. Here, we present potential and gaps of current and planned observational methods. We argue that hyperresolution (sub km) integrated estimates of landscape water dynamics are feasible, which could significantly improve our ability to simulate vadose zone and groundwater dynamics, ultimately closing gaps in our current perception of hydrological processes in a temperate region under strong influence from climate change. We close by arguing that an interdisciplinary effort of various scientific communities is needed to enable this advancement.

The US–German GRACE (Gravity Recovery and Climate Experiment, 2002–2017) and GRACE-FO (GRACE Follow-On, since 2018) satellite missions observe terrestrial water storage (TWS) variations. Over 20 years of data allow for investigating interannual variations beyond linear trends and seasonal signals. However, the origin of observed TWS changes cannot be determined solely with GRACE and GRACE-FO observations. This study focuses on the northern part of the East African Rift around the lakes of Turkana, Victoria, and Tanganyika. It aims to characterise and analyse the interannual TWS variations compared to meteorological and geodetic observations of the water storage compartments (surface water, soil moisture, and groundwater). We apply the STL (Seasonal-Trend decomposition using LOESS) method to decompose the signal into a seasonal signal, an interannual signal, and residuals. By clustering the interannual TWS dynamics for the African continent, we define the exact outline of the study region. We observe a TWS decrease until 2006, followed by a steady rise until 2016, and then the most significant TWS gain in Africa in 2019 and 2020. Besides meteorological variability, surface water storage variations in the lakes explain large parts of the TWS decrease before 2006. The storage dynamics of Lake Victoria alone contribute up to 50 % of these TWS changes. On the other hand, the significant TWS increase around 2020 can be attributed to nearly equal rises in groundwater and surface water storage, which coincide with a substantial precipitation surplus. Soil moisture explains most of the seasonal variability but does not influence the interannual variations. As Lake Victoria dominates the surface water storage variations in the region, we further investigate the lake and the downstream Nile River. The Nalubaale Dam regulates Lake Victoria's outflow. Water level observations from satellite altimetry reveal the impact of dam operations on downstream discharge and on TWS decreases in the drought years before 2006. On the other hand, we do not find evidence for an impact of the Nalubaale Dam regulations on the strong TWS increase after 2019.

Ground-based soil moisture measurements at the field scale are highly beneficial for different hydrological applications, including the validation of space-borne soil moisture products, landscape water budgeting, or multi-criteria calibration of rainfall–runoff models from field to catchment scale. Cosmic-ray neutron sensing (CRNS) allows for the non-invasive monitoring of field-scale soil moisture across several hectares around the instrument but only for the first few tens of centimeters of the soil. Many of these applications require information on soil water dynamics in deeper soil layers. Simple depth-extrapolation approaches often used in remote sensing may be used to estimate soil moisture in deeper layers based on the near-surface soil moisture information. However, most approaches require a site-specific calibration using depth profiles of in situ soil moisture data, which are often not available. The soil moisture analytical relationship (SMAR) is usually also calibrated to sensor data, but due to the physical meaning of each model parameter, it could be applied without calibration if all its parameters were known. However, its water loss parameter in particular is difficult to estimate. In this paper, we introduce and test a simple modification of the SMAR model to estimate the water loss in the second layer based on soil physical parameters and the surface soil moisture time series. We apply the model with and without calibration at a forest site with sandy soils. Comparing the model results with in situ reference measurements down to depths of 450 cm shows that the SMAR models both with and without modification as well as the calibrated exponential filter approach do not capture the observed soil moisture dynamics well. While, on average, the latter performs best over different tested scenarios, the performance of the SMAR models nevertheless meets a previously used benchmark RMSE of ≤ 0.06 cm3 cm-3 in both the calibrated original and uncalibrated modified version. Different transfer functions to derive surface soil moisture from CRNS do not translate into markedly different results of the depth-extrapolated soil moisture time series simulated by SMAR. Despite the fact that the soil moisture dynamics are not well represented at our study site using the depth-extrapolation approaches, our modified SMAR model may provide valuable first estimates of soil moisture in a deeper soil layer derived from surface measurements based on stationary and roving CRNS as well as remote sensing products where in situ data for calibration are not available.

Terrestrial gravimetry serves for many geophysical and environmental applications by observing gravity variations that are caused by mass variations in the subsurface, such as water storage changes. Recent advances in quantum technology led to the first commercially available absolute quantum gravimeter (AQG) from the company Exail that is designed for deployment in field surveys. In this paper, we present a technical setup including instrument transport, power supply and site design for mobile AQG field surveys on a regional gravimetric network. We discuss the technical and operational feasibility of the setup and assess the measured gravity data in terms of sensitivity, accuracy and repeatability for a 6-day field survey with an AQG of the B-series with serial number 02 (AQG#B02) in the vicinity of the Geodetic Observatory Wettzell (Bavarian Forest, Germany). This is the first mobile AQG survey of its kind to have been carried out. The repeatability of the absolute gravity value at the same site was 54 or 75 nm s ⁻² on average, depending on its definition. When compared with measurements from an A10 absolute field gravimeter on the same network sites, the accuracy of the AQG was 64 nm s ⁻² on average. While mobile AQG field campaigns were shown to be technically feasible, the overall AQG performance did not yet meet the requirements for typical hydrological applications to assess water storage changes. Meanwhile, the AQG-B series was developed further by the manufacturer, improving the stability of the gravimeter.

We investigate the benefits of future quantum accelerometers based on cold atom interferometry (CAI) on current and upcoming satellite gravity mission concepts. These mission concepts include satellite‐to‐satellite tracking (SST) in a single‐pair (GRACE‐like) and double‐pair constellation as well as satellite gravity gradiometry (SGG, single satellite, GOCE‐like). Regarding instruments, four scenarios are considered: current‐generation electrostatic (GRACE‐, GOCE‐like), next‐generation electrostatic, conservative hybrid/CAI and optimistic hybrid/CAI. For SST, it is shown that temporal aliasing poses currently the dominating error source in simulated global gravity field solutions independent of the investigated instrument and constellation. To still quantify the advantages of CAI instruments on the gravity functional itself, additional simulations are performed where the impact of temporal aliasing is synthetically reduced. When neglecting temporal aliasing, future accelerometers in conjunction with future ranging instruments can substantially improve the retrieval performance of the Earth's gravity field (depending on instrument and constellation). These simulation results are further investigated regarding possible benefit for hydrological use cases where these improvements can also be observed (when omitting temporal aliasing). For SGG, it is demonstrated that, with realistic instrument assumptions, one is still mostly insensitive to time‐variable gravity and not competitive with the SST principle. However, due to the improved instrument sensitivity of quantum gradiometers compared to the GOCE mission, static gravity field solutions can be improved significantly.

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.

The US-German GRACE (Gravity Recovery and Climate Experiment, 2002–2017) and GRACE-FO (GRACE-Follow-On, since 2018) satellite missions observe terrestrial water storage (TWS) variations. Over twenty years of data allow for investigating interannual variations beyond linear trends and seasonal signals. However, the origin of observed TWS changes, whether naturally caused or anthropogenic, cannot be determined solely with GRACE and GRACE-FO observations. This study focuses on the East African Rift region region around lakes Turkana, Victoria, and Tanganyika. It aims to characterise and analyse the interannual TWS variations together with surface water and meteorological observations and determine whether natural variability or human interventions caused these changes. To this end, we apply the STL method (Seasonal Trend decomposition based on Loess) to separate the TWS signals into a seasonal signal, an interannual trend signal, and residuals. By clustering these interannual TWS dynamics for the African continent, we define the exact outline of the study's region. In this area, a TWS decrease until 2006 was followed by a steady increase until around 2016, and Africa's most significant TWS increase occurred in 2019 and 2020. We found that besides precipitation and evaporation variability, surface water storage variations in the large lakes of the region explain large parts of the TWS variability. Storage dynamics of Lake Victoria regulated by the Nalubaale Dam alone contribute up to 50 % of the TWS changes. Satellite altimetry reveals the anthropogenically altered discharge downstream of the dam. It thus indicates that human intervention in the form of dam management at Lake Victoria substantially contributes to the TWS variability seen in the East African Rift region.

This study investigates the decades-long evolution of groundwater dynamics and thermal field in the North German Basin beneath Brandenburg (NE Germany) by coupling a distributed hydrologic model with a 3D groundwater model. We found that hydraulic gradients, acting as the main driver of the groundwater flow in the studied basin, are not exclusively influenced by present-day topographic gradients. Instead, structural dip and stratification of rock units and the presence of permeability contrasts and anisotropy are important co-players affecting the flow in deep seated saline aquifers at depths >500 m. In contrast, recharge variability and anthropogenic activities contribute to groundwater dynamics in the shallow (<500 m) freshwater Quaternary aquifers. Recharge fluxes, as derived from the hydrologic model and assigned to the parametrized regional groundwater model, reproduce magnitudes of recorded seasonal groundwater level changes. Nonetheless, observed instances of inter-annual fluctuations and a gradual decline of groundwater levels highlight the need to consider damping of the recharge signal and additional sinks, like pumping, in the model, in order to reconcile long-term groundwater level trends. Seasonal changes in near-surface groundwater temperature and the continuous warming due to conductive heat exchange with the atmosphere are locally enhanced by forced advection, especially in areas of high hydraulic gradients. The main factors controlling the depth of temperature disturbance include the magnitude of surface temperature variations, the subsurface permeability field, and the rate of recharge. Our results demonstrate the maximum depth extent and the response times of the groundwater system subjected to non-linear interactions between local geological variability and climate conditions.