Reply to comment by H. Lough, Department of Civil Engineering, University of Canterbury, Christchurch, New Zealand, on the paper “Stream depletion predictions using pumping test data from a heterogeneous stream–aquifer system (a case study from the Great Plains, USA)”

1. General remark 2. The study by Kollet and Zlotnik (2003) 3. Remark on the explanation of the drawdown behavior 4. Remark on the re-analysis of the data from piezometer C2d 5. Summar

Causal deep learning models for studying the Earth system

Earth is a complex non-linear dynamical system. Despite decades of research and considerable scientific and methodological progress, many processes and relations between Earth system variables remain poorly understood. Current approaches for studying relations in the Earth system rely either on numerical simulations or statistical approaches. However, there are several inherent limitations to existing approaches, including high computational costs, uncertainties in numerical models, strong assumptions about linearity or locality, and the fallacy of correlation and causality. Here, we propose a novel methodology combining deep learning (DL) and principles of causality research in an attempt to overcome these limitations. On the one hand, we employ the recent idea of training and analyzing DL models to gain new scientific insights into relations between input and target variables. On the other hand, we use the fact that a statistical model learns the causal effect of an input variable on a target variable if suitable additional input variables are included. As an illustrative example, we apply the methodology to study soil-moisture–precipitation coupling in ERA5 climate reanalysis data across Europe. We demonstrate that, harnessing the great power and flexibility of DL models, the proposed methodology may yield new scientific insights into complex non-linear and non-local coupling mechanisms in the Earth system.</p

Implementation and scaling of the fully coupled Terrestrial Systems Modeling Platform (TerrSysMP) in a massively parallel supercomputing environment – a case study on JUQUEEN (IBM Blue Gene/Q)

Continental-scale hyper-resolution simulations constitute a grand challenge in characterizing non-linear feedbacks of states and fluxes of the coupled water, energy, and biogeochemical cycles of terrestrial systems. Tackling this challenge requires advanced coupling and supercomputing technologies for earth system models that are discussed in this study, utilizing the example of the implementation of the newly developed Terrestrial Systems Modeling Platform (TerrSysMP) on JUQUEEN (IBM Blue Gene/Q) of the Jülich Supercomputing Centre, Germany. The applied coupling strategies rely on the Multiple Program Multiple Data (MPMD) paradigm and require memory and load balancing considerations in the exchange of the coupling fields between different component models and allocation of computational resources, respectively. These considerations can be reached with advanced profiling and tracing tools leading to the efficient use of massively parallel computing environments, which is then mainly determined by the parallel performance of individual component models. However, the problem of model I/O and initialization in the peta-scale range requires major attention, because this constitutes a true big data challenge in the perspective of future exa-scale capabilities, which is unsolved

Quantifying the effects of three-dimensional subsurface heterogeneity on Hortonian runoff processes using a fully-coupled numerical, stochastic approach.

The impact of three-dimensional subsurface heterogeneity on hillslope runoff generated by excess infiltration (so called Hortonian runoff) is examined. A fully-coupled, parallel subsurface overland flow model is used to simulate runoff from an idealized hillslope. Ensembles of correlated, Gaussian random fields of saturated hydraulic conductivity are used to create uncertainty and variability (i.e. structure) due to subsurface heterogeneity. A large number of cases are simulated in a parametric manner with variance of the hydraulic conductivity varied over two orders of magnitude. These cases include rainfall rates above, equal and below the geometric mean of the hydraulic conductivity distribution. These cases are also compared to theoretical considerations of runoff production based on simple assumptions regarding (1) the rainfall rate and the value of hydraulic conductivity in the surface cell using a spatially-indiscriminant approach; and (2) a percolation-theory type approach to incorporate so-called runon. Simulations to test the ergodicity of hydraulic conductivity on hillslope runoff are also performed. Results show three-dimensional features (particularly in the vertical dimension) in the hydraulic conductivity distributions that create shallow perching, which has an important effect on runoff behavior that is fundamentally different in character than previous two dimensional analyses. The simple theories are shown to be very poor predictors of the saturated area that might runoff due to excess infiltration. It is also shown that ergodicity is reached only for a large number of integral scales ({approx}30) and not for cases where the rainfall rate is less than the geometric mean of the saturated hydraulic conductivity

Modeling Interactions of Surface-Subsurface Flow Using a Free-Surface Overland Flow Boundary Condition in a Parallel Flow Simulator

Models incorporating interactions between surface and subsurface flow are commonly based on the conductance concept that presumes a distinct interface at the land surface, separating the surface from the subsurface domain. In these models the subsurface and surface domains are linked via an exchange flux that depends upon the magnitude and direction of the hydraulic gradient across the interface and a proportionality constant (a measure of the hydraulic connectivity). Because experimental evidence of such a distinct interface is often lacking in the field, a more general coupled modeling approach would be preferable. We present a more general approach that incorporates a two-dimensional overland flow simulator into the parallel three-dimensional variably saturated subsurface flow code ParFlow developed at LLNL. This overland flow simulator takes the form of an upper, free-surface boundary condition and is, thus, fully integrated without relying on the conductance concept. Another advantage of this approach is the efficient parallelism of ParFlow, which is exploited by the overland flow simulator. Several verification and simulation examples are presented that focus on the two main processes of runoff production: excess infiltration and saturation. The usefulness of our approach is demonstrated in an application of the model to an urban watershed. The influence of heterogeneity of the shallow subsurface on overland flow and transport is also examined. The results show the uncertainty in flow and transport predictions due to heterogeneity. This is important in determining, for example, total maximum daily loads of surface water systems

Demonstrating fractal scaling of residence time distributions on the catchment scale using a fully-coupled, variably-saturated groundwater and land surface model and a Lagrangian particle tracking approach

The influence of the vadose zone, land surface processes, and macrodispersion on scaling behavior of residence time distributions (RTDs) is studied using a fully coupled watershed model in conjunction with a Lagrangian, particle-tracking approach. Numerical experiments are used to simulate groundwater flow paths from recharge locations along the hillslope to the streambed. These experiments are designed to isolate the influences of topography, vadose zone/land surface processes, and macrodispersion on subsurface RTDs of tagged parcels of water. The results of these simulations agree with previous observations that RTDs exhibit fractal behavior, which can be identified from the power spectra. For cases incorporating residence times that are influenced by vadose zone/land surface processes, increasing macrodispersion increases the slope of the power spectra. In general the opposite effect is demonstrated if the vadose zone/land surface processes are neglected. The concept of the spectral slope being a measure of stationarity is raised and discussed

Haematopoietic stem cell migration to the ischemic damaged kidney is not altered by manipulating the SDF-1/CXCR4-axis

Background. Haematopoietic stem cells (HSC) have been shown to migrate to the ischemic kidney. The factors that regulate the trafficking of HSC to the ischemic damaged kidney are not fully understood. The stromal cell-derived factor-1 (SDF-1)/CXCR4-axis has been identified as the central signalling axis regulating trafficking of HSC to the bone marrow. Therefore, we hypothesized that SDF-1/CXCR4 interactions are implicated in the migration of HSC to the injured kidney

The groundwater-land-surface-atmosphere connection: soil moisture effects on the atmospheric boundary layer in fully-coupled simulations

This study combines a variably-saturated groundwater flow model and a mesoscale atmospheric model to examine the effects of soil moisture heterogeneity on atmospheric boundary layer processes. This parallel, integrated model can represent spatial variations in land-surface forcing driven by three-dimensional (3D) atmospheric and subsurface components. The development of atmospheric flow is studied in a series of idealized test cases with different initial soil moisture distributions generated by an offline spin-up procedure or interpolated from a coarse-resolution dataset. These test cases are performed with both the fully-coupled model (which includes 3D groundwater flow and surface water routing) and the uncoupled atmospheric model. The effects of the different soil moisture initializations and lateral subsurface and surface water flow are seen in the differences in atmospheric evolution over a 36-hour period. The fully-coupled model maintains a realistic topographically-driven soil moisture distribution, while the uncoupled atmospheric model does not. Furthermore, the coupled model shows spatial and temporal correlations between surface and lower atmospheric variables and water table depth. These correlations are particularly strong during times when the land surface temperatures trigger shifts in wind behavior, such as during early morning surface heating

How uncertain are precipitation and peak flow estimates for the July 2021 flooding event?

The disastrous July 2021 flooding event made us question the ability of current hydrometeorological tools in providing timely and reliable flood forecasts for unprecedented events. This is an urgent concern since extreme events are increasing due to global warming, and existing methods are usually limited to more frequently observed events with the usual flood generation processes. For the July 2021 event, we simulated the hourly streamflows of seven catchments located in western Germany by combining seven partly polarimetric, radar-based quantitative precipitation estimates (QPEs) with two hydrological models: a conceptual lumped model (GR4H) and a physically based, 3D distributed model (ParFlowCLM). GR4H parameters were calibrated with an emphasis on high flows using historical discharge observations, whereas ParFlowCLM parameters were estimated based on landscape and soil properties. The key results are as follows. (1) With no correction of the vertical profiles of radar variables, radar-based QPE products underestimated the total precipitation depth relative to rain gauges due to intense collision–coalescence processes near the surface, i.e., below the height levels monitored by the radars. (2) Correcting the vertical profiles of radar variables led to substantial improvements. (3) The probability of exceeding the highest measured peak flow before July 2021 was highly impacted by the QPE product, and this impact depended on the catchment for both models. (4) The estimation of model parameters had a larger impact than the choice of QPE product, but simulated peak flows of ParFlowCLM agreed with those of GR4H for five of the seven catchments. This study highlights the need for the correction of vertical profiles of reflectivity and other polarimetric variables near the surface to improve radar-based QPEs for extreme flooding events. It also underlines the large uncertainty in peak flow estimates due to model parameter estimation.</p