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  1. 1
    A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations

    A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations by D. Aboelyazeed, C. Xu, F. M. Hoffman, F. M. Hoffman, J. Liu, A. W. Jones, C. Rackauckas, K. Lawson, C. Shen

    Published 2023-07-01
    “…Our results showed that the carboxylation rate at 25 <span class="inline-formula"><sup>∘</sup></span>C (<span class="inline-formula"><i>V</i><sub>c,max25</sub></span>) was more impactful than a factor representing water limitation, although tuning both was helpful in addressing biases with the default values. …”
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    Article
  2. 2
    Application of an improved distributed hydrological model based on the soil–gravel structure in the Niyang River basin, Qinghai–Tibet Plateau

    Application of an improved distributed hydrological model based on the soil–gravel structure in the Niyang River basin, Qinghai–Tibet Plateau by P. Wang, P. Wang, P. Wang, Z. Zhou, J. Liu, C. Xu, C. Xu, K. Wang, Y. Liu, J. Li, Y. Li, Y. Jia, H. Wang

    Published 2023-07-01
    “…The temperature root-mean-square error was reduced from 1.16 to 0.86 <span class="inline-formula"><sup>∘</sup>C</span>. In the fully thawed period, the dualistic soil–gravel structure improved the regulation effect of groundwater on flow, thus stabilizing the flow process. …”
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  3. 3
    Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations

    Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations by J. Leng, J. M. Chen, W. Li, X. Luo, M. Xu, J. Liu, R. Wang, C. Rogers, B. Li, Y. Yan

    Published 2024-03-01
    “…However, these models often estimate GPP and ET at coarse daily or monthly steps, hindering analysis of ecosystem dynamics at the diurnal (hourly) scales, and prescribe some essential parameters (i.e., the Ball–Berry slope (<span class="inline-formula"><i>m</i></span>) and the maximum carboxylation rate at 25 °C (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M2" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi>V</mi><mi mathvariant="normal">cmax</mi><mn mathvariant="normal">25</mn></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="27pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="6843e2ad912be589c7d614809a188ea9"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-16-1283-2024-ie00001.svg" width="27pt" height="16pt" src="essd-16-1283-2024-ie00001.png"/></svg:svg></span></span>)) as constant, inducing uncertainties in the estimates of GPP and ET. …”
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