process based models
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2022 ◽  
Vol 314 ◽  
pp. 108802
Author(s):  
Rui Zhang ◽  
Jianhong Lin ◽  
Fucheng Wang ◽  
Nicolas Delpierre ◽  
Koen Kramer ◽  
...  

2022 ◽  
Author(s):  
Pauline Brémond ◽  
Anne-Laurence Agenais ◽  
Frédéric Grelot ◽  
Claire Richert

Abstract. Flood damage assessment is crucial for evaluating flood management policies. In particular, properly assessing damage to the agricultural assets is important because they may have greater exposure and are complex economic systems. The modelling approaches used to assess flood damage are of several types and can be fed by damage data collected post-flood, from experiments or based on expert knowledge. The process-based models fed by expert knowledge are subject of research and also widely used in an operational way. Although identified as potentially transferable, they are in reality often case-specific and difficult to reuse in time (updatbililty) and space (transferability). In this paper, we argue that process-based models are not doomed to be context specific as far as the modelling process is rigorous. We propose a methodological framework aiming at verifying the conditions necessary to develop these models in a spirit of capitalisation by relying on four axes which are: i/ the explicitation of assumptions, ii/ the validation, iii/ the updatability, iv/ the transferability. The methodological framework is then applied to the model we have developed in France to produce national damage functions for the agricultural sector. We show in this paper that the proposed methodological framework allows an explicit description of the modelling assumptions and data used, which is necessary to consider a reuse in time or a transfer to another geographical area. We also highlight that despite the lack of feedback data on post-flood damages, the proposed methodological framework is a solid basis to consider the validation, transfer, comparison and capitalisation of data collected around process-based models relying on expert knowledge. In conclusion, we identify research tracks to be implemented to pursue this improvement in a spirit of capitalisation and international cooperation.


Author(s):  
Mischa Turschwell ◽  
Roman Ashauer ◽  
Max Campbell ◽  
Rod Connolly ◽  
Sean Connolly ◽  
...  

Predicting the impacts of multiple stressors is important for informing ecosystem management, but is impeded by a lack of a general framework for predicting whether stressors interact synergistically, additively, or antagonistically. Here we use process-based models to study how interactions generalise across three levels of bio-logical organisation (physiological, population, and community) for a simulated two-stressor experiment on a seagrass model system. We found that the same underlying processes could result in synergistic, additive or antagonistic interactions, with interaction type depending on initial conditions, experiment duration, stressor dynamics, and consumer presence. Our results help explain why meta-analyses of multiple stressor experimental results have struggled to identify predictors of consistently non-additive interactions in the natural environment. Experiments run over longer temporal scales, with treatments across gradients of stressor magnitude, are needed to identify the processes that underpin how stressors interact and provide useful predictions to management.


2021 ◽  
Vol 25 (12) ◽  
pp. 6185-6202
Author(s):  
Ather Abbas ◽  
Sangsoo Baek ◽  
Norbert Silvera ◽  
Bounsamay Soulileuth ◽  
Yakov Pachepsky ◽  
...  

Abstract. Contamination of surface waters with microbiological pollutants is a major concern to public health. Although long-term and high-frequency Escherichia coli (E. coli) monitoring can help prevent diseases from fecal pathogenic microorganisms, such monitoring is time-consuming and expensive. Process-driven models are an alternative means for estimating concentrations of fecal pathogens. However, process-based modeling still has limitations in improving the model accuracy because of the complexity of relationships among hydrological and environmental variables. With the rise of data availability and computation power, the use of data-driven models is increasing. In this study, we simulated fate and transport of E. coli in a 0.6 km2 tropical headwater catchment located in the Lao People's Democratic Republic (Lao PDR) using a deep-learning model and a process-based model. The deep learning model was built using the long short-term memory (LSTM) methodology, whereas the process-based model was constructed using the Hydrological Simulation Program–FORTRAN (HSPF). First, we calibrated both models for surface as well as for subsurface flow. Then, we simulated the E. coli transport with 6 min time steps with both the HSPF and LSTM models. The LSTM provided accurate results for surface and subsurface flow with 0.51 and 0.64 of the Nash–Sutcliffe efficiency (NSE) values, respectively. In contrast, the NSE values yielded by the HSPF were −0.7 and 0.59 for surface and subsurface flow. The simulated E. coli concentrations from LSTM provided the NSE of 0.35, whereas the HSPF gave an unacceptable performance with an NSE value of −3.01 due to the limitations of HSPF in capturing the dynamics of E. coli with land-use change. The simulated E. coli concentration showed the rise and drop patterns corresponding to annual changes in land use. This study showcases the application of deep-learning-based models as an efficient alternative to process-based models for E. coli fate and transport simulation at the catchment scale.


Forests ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1415
Author(s):  
Vladimir Shanin ◽  
Hannu Hökkä ◽  
Pavel Grabarnik

Three competition indices were tested against experimental data on the growth of individual trees in mapped forest stands and outputs of spatially explicit, process-based models of competition. The comparison showed the fundamental importance of taking into account the spatial structure of stands and, particularly, the relative spatial locations of individual trees (spatial asymmetry) when calculating the competition between trees. Although none of the competition indices are able to take into account the specific processes affecting the development of individual trees, these indices can be used in forest dynamics modeling as a simplified representation of competition between trees for resources.


2021 ◽  
Vol 12 (4) ◽  
pp. 1015-1035
Author(s):  
Ana Bastos ◽  
René Orth ◽  
Markus Reichstein ◽  
Philippe Ciais ◽  
Nicolas Viovy ◽  
...  

Abstract. In 2018 and 2019, central Europe was affected by two consecutive extreme dry and hot summers (DH18 and DH19). The DH18 event had severe impacts on ecosystems and likely affected vegetation activity in the subsequent year, for example through depletion of carbon reserves or damage from drought. Such legacies from drought and heat stress can further increase vegetation susceptibility to additional hazards. Temporally compound extremes such as DH18 and DH19 can, therefore, result in an amplification of impacts due to preconditioning effects of past disturbance legacies. Here, we evaluate how these two consecutive extreme summers impacted ecosystems in central Europe and how the vegetation responses to the first compound event (DH18) modulated the impacts of the second (DH19). To quantify changes in vegetation vulnerability to each compound event, we first train a set of statistical models for the period 2001–2017, which are then used to predict the impacts of DH18 and DH19 on enhanced vegetation index (EVI) anomalies from MODIS. These estimates correspond to expected EVI anomalies in DH18 and DH19 based on past sensitivity to climate. Large departures from the predicted values can indicate changes in vulnerability to dry and hot conditions and be used to identify modulating effects by vegetation activity and composition or other environmental factors on observed impacts. We find two regions in which the impacts of the two compound dry and hot (DH) events were significantly stronger than those expected based on previous climate–vegetation relationships. One region, largely dominated by grasslands and crops, showed much stronger impacts than expected in both DH events due to an amplification of their sensitivity to heat and drought, possibly linked to changing background CO2 and temperature conditions. A second region, dominated by forests and grasslands, showed browning from DH18 to DH19, even though dry and hot conditions were partly alleviated in 2019. This browning trajectory was mainly explained by the preconditioning role of DH18 on the impacts of DH19 due to interannual legacy effects and possibly by increased susceptibility to biotic disturbances, which are also promoted by warm conditions. Dry and hot summers are expected to become more frequent in the coming decades, posing a major threat to the stability of European forests. We show that state-of-the-art process-based models could not represent the decline in response to DH19 because they missed the interannual legacy effects from DH18 impacts. These gaps may result in an overestimation of the resilience and stability of temperate ecosystems in future model projections.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Wen-Ping Tsai ◽  
Dapeng Feng ◽  
Ming Pan ◽  
Hylke Beck ◽  
Kathryn Lawson ◽  
...  

AbstractThe behaviors and skills of models in many geosciences (e.g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration. A well-calibrated model can reasonably propagate information from observations to unobserved variables via model physics, but traditional calibration is highly inefficient and results in non-unique solutions. Here we propose a novel differentiable parameter learning (dPL) framework that efficiently learns a global mapping between inputs (and optionally responses) and parameters. Crucially, dPL exhibits beneficial scaling curves not previously demonstrated to geoscientists: as training data increases, dPL achieves better performance, more physical coherence, and better generalizability (across space and uncalibrated variables), all with orders-of-magnitude lower computational cost. We demonstrate examples that learned from soil moisture and streamflow, where dPL drastically outperformed existing evolutionary and regionalization methods, or required only ~12.5% of the training data to achieve similar performance. The generic scheme promotes the integration of deep learning and process-based models, without mandating reimplementation.


2021 ◽  
Vol 193 ◽  
pp. 103209
Author(s):  
Matthew J. Knowling ◽  
Bree Bennett ◽  
Bertram Ostendorf ◽  
Seth Westra ◽  
Rob R. Walker ◽  
...  
Keyword(s):  

Author(s):  
Moritz Feigl ◽  
Benjamin Roesky ◽  
Mathew Herrnegger ◽  
Karsten Schulz ◽  
Masaki Hayashi

Typical applications of process- or physically-based models aim to gain a better process understanding or provide the basis for a decision-making process. To adequately represent the physical system, models should include all essential processes. However, model errors can still occur. Other than large systematic observation errors, simplified, misrepresented, inadequately parametrized or missing processes are potential sources of errors. This study presents a set of methods and a proposed workflow for analyzing errors of process-based models as a basis for relating them to process representations. The evaluated approach consists of three steps: (i) training a machine learning (ml) error-model using the input data of the process-based model and other available variables, (ii) estimation of local explanations (i.e., contributions of each variable to a individual prediction) for each predicted model error using SHapley Additive exPlanations (SHAP) in combination with principal component analysis, (iii) clustering of SHAP values of all predicted errors to derive groups with similar error generation characteristics. By analyzing these groups of different error-variable association, hypotheses on error generation and corresponding processes can be formulated. That can ultimately lead to improvements in process understanding and prediction. The approach is applied to a process-based stream water temperature model HFLUX in a case study for modelling an alpine stream in the Canadian Rocky Mountains. By using available meteorological and hydrological variables as inputs, the applied ml model is able to predict model residuals. Clustering of SHAP values results in three distinct error groups that are mainly related to shading and vegetation emitted longwave radiation. Model errors are rarely random and often contain valuable information. Assessing model error associations is ultimately a way of enhancing trust in implemented processes and of providing information on potential areas of improvement to the model.


2021 ◽  
Author(s):  
Haiyan Hou ◽  
Bing-Bing Zhou ◽  
Fengsong Pei ◽  
Guohua Hu ◽  
Zhongbo Su ◽  
...  

Abstract Anthropogenic land use/cover (LULC) change alters terrestrial gross primary productivity (GPP), which is a major atmospheric carbon sink. Identifying the impacts of future LULC changes on terrestrial GPP has been challenging due to the complexity of incorporating future LULC into ecosystem models. Here, we present eight-scenario-based projections of global spatially explicit LULC at 1km resolution over the period 2015-2100. We further conducted Fourteen experiments to quantify the contribution of LULC change to GPP dynamics relative to that of climate change under different scenarios. We find that global GPP change would be underestimated by 10.92%‒16.16% during 2000-2050 and 1.41%‒14.57% during 2050-2100 when modeled without LULC dynamics—as in most existing GPP modeling efforts. Particularly, LULC-change-dominated areas would account globally for 1.65‒2.20 the size of the Amazon rainforest. Our findings underline the necessity of incorporating future LULC dynamics into process-based models and highlight the non-trivial role of LULC in transitioning toward sustainability.


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