Enhancing speed and scalability of the ParFlow simulation code

Regional hydrology studies are often supported by high resolution simulations of subsurface flow that require expensive and extensive computations. Efficient usage of the latest high performance parallel computing systems becomes a necessity. The simulation software ParFlow has been demonstrated to meet this requirement and shown to have excellent solver scalability for up to 16,384 processes. In the present work we show that the code requires further enhancements in order to fully take advantage of current petascale machines. We identify ParFlow's way of parallelization of the computational mesh as a central bottleneck. We propose to reorganize this subsystem using fast mesh partition algorithms provided by the parallel adaptive mesh refinement library p4est. We realize this in a minimally invasive manner by modifying selected parts of the code to reinterpret the existing mesh data structures. We evaluate the scaling performance of the modified version of ParFlow, demonstrating good weak and strong scaling up to 458k cores of the Juqueen supercomputer, and test an example application at large scale.Comment: The final publication is available at link.springer.co

Response of Convective Boundary Layer and Shallow Cumulus to Soil Moisture Heterogeneity: A Large‐Eddy Simulation Study

In this study, the impact of varying soil moisture heterogeneity (spatial variance and structure) on the development of the convective boundary layer and shallow cumulus clouds was investigated. Applying soil moisture heterogeneity generated via spatially correlated Gaussian random fields based on a power law model and idealized atmospheric vertical profiles as initial conditions, three sets of large‐eddy simulations provide insight in the influence of soil moisture heterogeneity on the ensuing growth of the convective boundary layer and development of shallow cumulus clouds. A sensitivity on the strong, weak, and unstructured soil moisture heterogeneity is investigated. The simulation results show that domain‐averaged land surface sensible heat and latent heat flux change strongly with changing soil moisture variance because of the interactions between surface heterogeneity and induced circulations, while domain means of soil moisture are identical. Vertical profiles of boundary layer characteristics are strongly influenced by the surface energy partitioning and induced circulations, especially the profiles of liquid water and liquid water flux. The amount of liquid water and liquid water flux increases with increasing structure. In addition, the liquid water path is higher in case of strongly‐structured heterogeneity because more available energy is partitioned into latent heat and more intensive updrafts exist. Interestingly, the increase of liquid water path with increasing soil moisture variance only occurs in the strongly structured cases, which suggests that soil moisture variance and structure work conjunctively in the surface energy partitioning and the cloud formation

Towards the representation of groundwater in the Joint UK Land Environment Simulator

Groundwater is an important component of the hydrological cycle with significant interactions with soil hydrological processes. Recent studies have demonstrated that incorporating groundwater hydrology in land surface models (LSMs) considerably improves the prediction of the partitioning of water components (e.g., runoff and evapotranspiration) at the land surface. However, the Joint UK Land Environment Simulator (JULES), an LSM developed in the United Kingdom, does not yet have an explicit representation of groundwater. We propose an implementation of a simplified groundwater flow boundary parameterization (JULES‐GFB), which replaces the original free drainage assumption in the default model (JULES‐FD). We tested the two approaches under a controlled environment for various soil types using two synthetic experiments: (1) single‐column and (2) tilted‐V catchment, using a three‐dimensional (3‐D) hydrological model (ParFlow) as a benchmark for JULES’ performance. In addition, we applied our new JULES‐GFB model to a regional domain in the UK, where groundwater is the key element for runoff generation. In the single‐column infiltration experiment, JULES‐GFB showed improved soil moisture dynamics in comparison with JULES‐FD, for almost all soil types (except coarse soils) under a variety of initial water table depths. In the tilted‐V catchment experiment, JULES‐GFB successfully represented the dynamics and the magnitude of saturated and unsaturated storage against the benchmark. The lateral water flow produced by JULES‐GFB was about 50% of what was produced by the benchmark, while JULES‐FD completely ignores this process. In the regional domain application, the Kling‐Gupta efficiency (KGE) for the total runoff simulation showed an average improvement from 0.25 for JULES‐FD to 0.75 for JULES‐GFB. The mean bias of actual evapotranspiration relative to the Global Land Evaporation Amsterdam Model (GLEAM) product was improved from −0.22 to −0.01 mm day−1. Our new JULES‐GFB implementation provides an opportunity to better understand the interactions between the subsurface and land surface processes that are dominated by groundwater hydrology

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

The imprint of climate and geology on the residence times of groundwater

Surface and subsurface flow dynamics govern residence time or water age until discharge, which is a key metric of storage and water availability for human use and ecosystem function. Although observations in small catchments have shown a fractal distribution of ages, residence times are difficult to directly quantify or measure in large basins. Here we use a simulation of major watersheds across North America to compute distributions of residence times. This simulation results in peak ages from 1.5 to 10.5 years, in agreement with isotopic observations from bomb-derived radioisotopes, and a wide range of residence times—from 0.1 to 10,000 years. This simulation suggests that peak residence times are controlled by the mean hydraulic conductivity, a function of the prevailing geology. The shape of the residence time distribution is dependent on aridity, which in turn determines water table depth and the frequency of shorter flow paths. These model results underscore the need for additional studies to characterize water ages in larger systems

Infiltration from the pedon to global grid scales: an overview and outlook for land surface modelling

Infiltration in soils is a key process that partitions precipitation at the land surface in surface runoff and water that enters the soil profile. We reviewed the basic principles of water infiltration in soils and we analyzed approaches commonly used in Land Surface Models (LSMs) to quantify infiltration as well as its numerical implementation and sensitivity to model parameters. We reviewed methods to upscale infiltration from the point to the field, hill slope, and grid cell scale of LSMs. Despite the progress that has been made, upscaling of local scale infiltration processes to the grid scale used in LSMs is still far from being treated rigorously. We still lack a consistent theoretical framework to predict effective fluxes and parameters that control infiltration in LSMs. Our analysis shows, that there is a large variety in approaches used to estimate soil hydraulic properties. Novel, highly resolved soil information at higher resolutions than the grid scale of LSMs may help in better quantifying subgrid variability of key infiltration parameters. Currently, only a few land surface models consider the impact of soil structure on soil hydraulic properties. Finally, we identified several processes not yet considered in LSMs that are known to strongly influence infiltration. Especially, the impact of soil structure on infiltration requires further research. In order to tackle the above challenges and integrate current knowledge on soil processes affecting infiltration processes on land surface models, we advocate a stronger exchange and scientific interaction between the soil and the land surface modelling communities