The importance of vegetation in understanding terrestrial water storage variations

Trautmann, Tina; Koirala, Sujan; Güntner, Andreas; Jung, Martin

doi:10.5194/hess-26-1089-2022

Cited by 20 publications

(16 citation statements)

References 86 publications

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“…The machinelearning-based constraints of Q and ET are not directly observed, and thus, they are expected to have considerable global and regional uncertainties and biases (Ghiggi et al, 2019;Jung et al, 2020). This could lead to inconsistencies in the water balance (Trautmann et al, 2022). However, the multi-objective optimization may dampen the negative effects of biases, as the model can trade off the different constraints.…”

Section: Model Performancementioning

confidence: 99%

Towards hybrid modeling of the global hydrological cycle

Kraft

Jung

Körner

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. State-of-the-art global hydrological models (GHMs) exhibit large uncertainties in hydrological simulations due to the complexity, diversity, and heterogeneity of the land surface and subsurface processes, as well as the scale dependency of these processes and associated parameters. Recent progress in machine learning, fueled by relevant Earth observation data streams, may help overcome these challenges. But machine learning methods are not bound by physical laws, and their interpretability is limited by design. In this study, we exemplify a hybrid approach to global hydrological modeling that exploits the data adaptivity of neural networks for representing uncertain processes within a model structure based on physical principles (e.g., mass conservation) that form the basis of GHMs. This combination of machine learning and physical knowledge can potentially lead to data-driven, yet physically consistent and partially interpretable hybrid models. The hybrid hydrological model (H2M), extended from Kraft et al. (2020), simulates the dynamics of snow, soil moisture, and groundwater storage globally at 1∘ spatial resolution and daily time step. Water fluxes are simulated by an embedded recurrent neural network. We trained the model simultaneously against observational products of terrestrial water storage variations (TWS), grid cell runoff (Q), evapotranspiration (ET), and snow water equivalent (SWE) with a multi-task learning approach. We find that the H2M is capable of reproducing key patterns of global water cycle components, with model performances being at least on par with four state-of-the-art GHMs which provide a necessary benchmark for H2M. The neural-network-learned hydrological responses of evapotranspiration and grid cell runoff to antecedent soil moisture states are qualitatively consistent with our understanding and theory. The simulated contributions of groundwater, soil moisture, and snowpack variability to TWS variations are plausible and within the ranges of traditional GHMs. H2M identifies a somewhat stronger role of soil moisture for TWS variations in transitional and tropical regions compared to GHMs. With the findings and analysis, we conclude that H2M provides a new data-driven perspective on modeling the global hydrological cycle and physical responses with machine-learned parameters that is consistent with and complementary to existing global modeling frameworks. The hybrid modeling approaches have a large potential to better leverage ever-increasing Earth observation data streams to advance our understandings of the Earth system and capabilities to monitor and model it.

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Section: Model Performancementioning

confidence: 99%

Towards hybrid modeling of the global hydrological cycle

Kraft

Jung

Körner

et al. 2022

Hydrol. Earth Syst. Sci.

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“…As the spatialization of vegetation related parameters remains to be a major challenge (Fisher & Koven, 2020) our ecohydrological metrics can also facilitate exploring alternatives to the plant functional type paradigm. For example, one could test if a linear scaling of the spatial I dp field to spatialize parameters controlling vegetation water storage capacity is sufficient, or better than plant functional type specific parameters (see e.g., Trautmann et al., 2021). Similarly, one could test if model parameters controlling drydown rates (see e.g., Raoult et al., 2021) can be better spatialized using our λ map than by using plant functional types.…”

Section: Summary and Perspectivesmentioning

confidence: 99%

Characterizing the Response of Vegetation Cover to Water Limitation in Africa Using Geostationary Satellites

Küçük

Koirala

Miralles

et al. 2022

J Adv Model Earth Syst

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Hydrological interactions between vegetation, soil, and topography are complex, and heterogeneous in semi‐arid landscapes. This along with data scarcity poses challenges for large‐scale modeling of vegetation‐water interactions. Here, we exploit metrics derived from daily Meteosat data over Africa at ca. 5 km spatial resolution for ecohydrological analysis. Their spatial patterns are based on Fractional Vegetation Cover (FVC) time series and emphasize limiting conditions of the seasonal wet to dry transition: the minimum and maximum FVC of temporal record, the FVC decay rate and the FVC integral over the decay period. We investigate the relevance of these metrics for large scale ecohydrological studies by assessing their co‐variation with soil moisture, and with topographic, soil, and vegetation factors. Consistent with our initial hypothesis, FVC minimum and maximum increase with soil moisture, while the FVC integral and decay rate peak at intermediate soil moisture. We find evidence for the relevance of topographic moisture variations in arid regions, which, counter‐intuitively, is detectable in the maximum but not in the minimum FVC. We find no clear evidence for wide‐spread occurrence of the “inverse texture effect” on FVC. The FVC integral over the decay period correlates with independent data sets of plant water storage capacity or rooting depth while correlations increase with aridity. In arid regions, the FVC decay rate decreases with canopy height and tree cover fraction as expected for ecosystems with a more conservative water‐use strategy. Thus, our observation‐based products have large potential for better understanding complex vegetation‐water interactions from regional to continental scales.

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“…SINDBAD, constrained with multiple observational data streams, has been successfully applied over northern mid-to highlatitudes (e.g., Trautmann et al, 2018) and over the globe (Trautmann et al, 2022) for simulating seasonality and interannual variability of TWS and its components. SINDBAD is a simple conceptual 4-pool water balance model with spatial variations of hydrological parameters depending on remote-sensing and statistical estimates of vegetation fraction and soil water capacity (Trautmann et al, 2022). SINDBAD TWS consists of snow, soil moisture divided into shallow and deep components, and delayed moisture storages.…”

Section: Global Hydrological Model Simulationsmentioning

confidence: 99%

“…In SINDBAD, vegetation indirectly accesses secondary water storage with capillary rise, which contributes to a larger evapotranspiration over some regions, including the regions in Africa (Fig. 9 in Trautmann et al, 2022).…”

Section: Spatial Contributions To the Global Tws Iav And Its Errormentioning

confidence: 99%

“…6d), the TWS changes have been reported due to human impacts such as reducing groundwater abstraction and surging reservoirs as well as increased precipitation (Meghwal et al, 2019;Munagapati et al, 2021). Note that most of these regions were excluded in the analysis of model simulations (Kraft et al, 2022;Trautmann et al, 2022), and trends were removed from all data and simulations to focus on the interannual variability of TWS under natural conditions. Regarding the GRACE errors, the GRACE mascon solution has the leakage error created during spatial interpolation from the original spatial resolution (3 • ) to higher resolution (0.5 • ) (Wiese et al, 2016).…”

Section: Potential Sources Of Errorsmentioning

confidence: 99%

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Diagnosing modeling errors of global terrestrial water storage interannual variability

Lee

Jung²,

Trautmann³

et al. 2022

Preprint

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Abstract. Terrestrial water storage (TWS) is an integrative hydrological state that is key for our understanding of the global water cycle. The TWS observation from GRACE missions has, therefore, been instrumental in calibration and validation of hydrological models and understanding the variations of the hydrological storages. The models, however, still show significant uncertainties in reproducing observed TWS variations, especially for the interannual variability (IAV) at the global scale. Here, we diagnose the regions dominating the variance of globally integrated TWS IAV, and sources of the errors in two data-driven hydrological models that were calibrated against global TWS, snow water equivalent, evapotranspiration, and runoff data: 1) a parsimonious process-based hydrological model, the Strategies to INtegrate Data and BiogeochemicAl moDels (SINDBAD) framework, and 2) a machine learning-physically based hybrid hydrological model (H2M) that combines a dynamic neural network with a water balance concept. While both models agree with GRACE that global TWS IAV is largely driven by the semi-arid regions of southern Africa, Indian subcontinent and northern Australia, and the humid regions of northern South America and Mekong River Basin, the models still show errors such as overestimation of the observed magnitude of TWS IAV at the global scale. Our analysis identifies modeling error hotspots of the global TWS IAV mostly in the tropical regions including Amazon, sub–Saharan regions, and Southeast Asia, indicating that the regions that dominate global TWS IAV are not necessarily the same as those that dominate the error in global TWS IAV. Excluding those error hotspot regions in the global integration yields large improvements of simulated global TWS IAV, which implies that model improvements can focus on improving processes in these hotspot regions. Further analysis indicates that error hotspot regions are associated with lateral flow dynamics, including both sub-pixel moisture convergence and across pixel lateral river flow, or with interactions between surface processes and groundwater. The association of model deficiencies with land processes that delay the TWS variation could, in part, explain why the models cannot represent the observed lagged response of TWS IAV to precipitation IAV in hotspot regions that manifest to errors in global TWS IAV. Our approach presents a general avenue to better diagnose model simulation errors for global data streams to guide efficient and focused model development for regions and processes that matter the most.

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The importance of vegetation in understanding terrestrial water storage variations

Cited by 20 publications

References 86 publications

Towards hybrid modeling of the global hydrological cycle

Towards hybrid modeling of the global hydrological cycle

Characterizing the Response of Vegetation Cover to Water Limitation in Africa Using Geostationary Satellites

Diagnosing modeling errors of global terrestrial water storage interannual variability

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