University of Nevada, Las Vegas

Critical Zone Hydrology

Research

The System

Earth’s critical zone is the heterogeneous near surface environment ranging from the top of the canopy to the bottom of the aquifer. The critical zone is governed by a multitude of complex interacting processes sustaining terrestrial life on Earth and it is closely intertwined with human society through issues such as land use and climate change.

Water connects the various components in the critical zone. Water scarcity and water stress already affect regions on every continent and are expected to become even more relevant due to climate change and increasing populations. A quantitative understanding of water flow and transport is fundamental, for example, for sustainable agriculture and the recovery of degraded drylands.

The Challenge

The accurate prediction of water flow and transport in the critical zone on relevant scales ranging from fields and hillslopes to landscapes remains an open challenge. Detailed process understanding is available, however, mainly at much smaller scales.

Applying this small-scale understanding to larger scales is challenging due to a multitude of process interactions, highly nonlinear processes, and an intrinsic multi-scale heterogeneity of terrestrial systems in combination with limited measurement capabilities in the subsurface. In hydrologic models this leads to simplified descriptions, lacking process representations, and unresolved heterogeneous material properties. Therefore, the description of water flow and transport in the critical zone with a forward model typically faces significant uncertainties.

Our Approach

I use data science methods such as data assimilation and machine learning to combine process understanding, process based models, and data across different scales to address this challenge. I actively collaborate at a range of scales including (i) the SEPHAS Lysimeters operated by the the Desert Research Institute (DRI) at the patch scale, (ii), the Landscape Evolution Observatory at Biosphere 2 operated by the University of Arizona at the hillslope scale, and (iii) the Southern Nevada Water Authority (SNWA) at the landscape scale.

SEPHAS Lysimeters
Landscape Evolution Observatory
Southern Nevada Water Authority

Publications

  • Kim, M., Bauser, H. H., Beven, K., and Troch, P. A. (2023). Time-variability of flow recession dynamics: Application of machine learning and learning from the machine. Water Resources Research, 59, e2022WR032690. https://doi.org/10.1029/2022WR032690
  • Bauser, H. H., Kim, M., Ng, W.-R., Bugaj, A., and Troch, P. A. (2022). Richards equation at the hillslope scale: Can we resolve the heterogeneity of soil hydraulic material properties? Water Resources Research, 58, e2022WR032294. https://doi.org/10.1029/2022WR032294
  • Bauser, H. H., Berg, D., and Roth, K. (2021) .Technical note: Sequential ensemble data assimilation in convergent and divergent systems. Hydrology and Earth System Sciences25(6), 3319–3329. https://doi.org/10.5194/hess-25-3319-2021
  • Bauser, H. H., Riedel, L., Berg, D., Troch, P. A., and Roth, K. (2020). Challenges with effectiverepresentations of heterogeneity in soil hydrology based on local water contentmeasurements. Vadose Zone Journal19(1), e20040. https://doi.org/10.1002/vzj2.20040
  • Kreyenberg, P. J., Bauser, H. H., and Roth, K. (2019). Velocity field estimation on density-driven solute transport with a convolutional neural network. Water Resources Research55(8). https://doi.org/10.1029/2019WR024833
  • Berg, D., Bauser, H. H., and Roth, K. (2019). Covariance resampling for particle filter – state and parameter estimation for soil hydrology. Hydrology and Earth System Sciences23(2), 1163–1178. https://doi.org/10.5194/hess-23-1163-2019
  • Bauser, H. H., Berg, D., Klein, O., and Roth, K. (2018). Inflation method for ensemble Kalman filter in soil hydrology. Hydrology and Earth System Sciences22(9), 4921–4934. https://doi.org/10.5194/hess-22-4921-2018
  • Bauser, H. H., Jaumann, S., Berg, D., and Roth, K. (2016). EnKF with closed-eye period – towards a consistent aggregation of information in soil hydrology. Hydrology and Earth System Sciences20(12), 4999–5014. https://doi.org/10.5194/hess-20-4999-2016
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