Tropical peatlands are among the most carbon-dense ecosystems on Earth, and their water storage dynamics strongly control these carbon stocks. The hydrological functioning of tropical peatlands differs from that of northern peatlands, which has not yet been accounted for in global land surface models (LSMs). Here, we integrated tropical peat-specific hydrology modules into a global LSM for the first time, by utilizing the peatland-specific model structure adaptation (PEATCLSM) of the NASA Catchment Land Surface Model (CLSM). We developed literature-based parameter sets for natural (PEATCLSM Trop,Nat ) and drained (PEATCLSM Trop,Drain ) tropical peatlands. Simulations with PEATCLSM Trop,Nat were compared against those with the default CLSM version and the northern version of PEATCLSM (PEATCLSM North,Nat ) with tropical vegetation input. All simulations were forced with global meteorological reanalysis input data for the major tropical peatland regions in Central and South America, the Congo Basin, and Southeast Asia. The evaluation against a unique and extensive data set of in situ water level and eddy covariance-derived evapotranspiration showed an overall improvement in bias and correlation compared to the default CLSM version. Over Southeast Asia, an additional simulation with PEATCLSM Trop,Drain was run to address the large fraction of drained tropical peatlands in this region. PEATCLSM Trop,Drain outperformed CLSM, PEATCLSM North,Nat , and PEATCLSM Trop,Nat over drained sites. Despite the overall improvements of PEATCLSM Trop,Nat over CLSM, there are strong differences in performance between the three study regions. We attribute these performance differences to regional differences in accuracy of meteorological forcing data, and differences in peatland hydrologic response that are not yet captured by our model. Plain Language SummaryTropical peatlands are wetlands in which plant material accumulates under waterlogged conditions and develops into a dense organic soil layer. Disturbance of their selfregulating hydrology by external factors such as artificial drainage, land use change, and climate change can quickly convert these immense carbon stocks into strong sources of greenhouse gases. Including the hydrology of tropical peatlands into global Earth system models allows us to understand the impact of such external disturbances. We developed the first hydrology modules for natural and drained tropical peatlands to plug into the NASA Goddard Earth Observing System modeling framework. Our results display strong regional differences, and indicate that the accuracy of our model is limited by rainfall data quality and by our understanding of how peatland hydrology differs across the three regions that contain the major tropical peatland areas (Central and South America, the Congo Basin, and Southeast Asia). Nonetheless, simulations
Abstract. In this study, we tested the impact of a revised set of soil, vegetation and land cover parameters on the performance of three different state-of-the-art land surface models (LSMs) within the NASA Land Information System (LIS). The impact of this revision was tested over the South American Dry Chaco, an ecoregion characterized by deforestation and forest degradation since the 1980s. Most large-scale LSMs may lack the ability to correctly represent the ongoing deforestation processes in this region, because most LSMs use climatological vegetation indices and static land cover information. The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction) instead of climatological vegetation indices, and (iii) yearly land cover information instead of static land cover. A relative comparison in terms of water budget components and “efficiency space” for various baseline and revised experiments showed that large regional and long-term differences in the simulated water budget partitioning relate to different LSM structures, whereas smaller local differences resulted from updated soil, vegetation and land cover parameters. Furthermore, the different LSM structures redistributed water differently in response to these parameter updates. A time-series comparison of the simulations to independent satellite-based estimates of evapotranspiration and brightness temperature (Tb) showed that no LSM setup significantly outperformed another for the entire region and that not all LSM simulations improved with updated parameter values. However, the revised soil parameters generally reduced the bias between simulated surface soil moisture and pixel-scale in situ observations and the bias between simulated Tb and regional Soil Moisture Ocean Salinity (SMOS) observations. Our results suggest that the different hydrological responses of various LSMs to vegetation changes may need further attention to gain benefits from vegetation data assimilation.
<p>Tropical peatlands have a specific hydrology that regulates their internal processes and functioning. External disturbances such as drainage, land cover and land use changes, and climate change could disrupt the peat-specific hydrology and convert the immense peatland carbon stocks into strong greenhouse gas (GHG) emitting sources. The need for (more) accurate monitoring of GHG emissions has led to the development of complex biogeochemical models, which highly depend on proper representation of peat-specific land surface hydrology. However, the latter is often inadequately accounted for in global Earth system modeling frameworks.</p><p>In this research, we leverage the PEATCLSM modules recently developed for the Catchment land surface model (CLSM) of the NASA Goddard Earth Observing System framework (Bechtold et al., 2019). These modules were evaluated for northern peatlands, hereafter referred to as PEATCLSM<sub>N</sub>. Here, we present an extended version of PEATCLSM for tropical peatlands with literature-based parameter sets for natural (PEATCLSM<sub>T,Natural</sub>) and drained (PEATCLSM<sub>T,Drained</sub>) tropical peatlands. A suite of modeling experiments was conducted to compare the performance of PEATCLSM<sub>T,Natural</sub>, PEATCLSM<sub>T,Drained</sub>, PEATCLSM<sub>N</sub>, and the currently operational CLSM version that includes peat parameters but no peat-specific model structure (CLSM<sub>O</sub>). Simulations over major tropical peatland regions in Southeast Asia, the Congo Basin, and South and Central America were evaluated with a comprehensive and self-compiled dataset of groundwater table depth (WTD) and evapotranspiration (ET). Preliminary results show that the simulated WTD from CLSM<sub>O</sub> exhibits too much temporal variability and large biases, either positive or negative. The temporal correlation coefficient between simulated and observed WTD for both PEATCLSM<sub>T,Natural</sub> (over undeveloped peatlands only) and PEATCLSM<sub>T,Drained</sub> (over drained peatlands only) is similar to that of PEATCLSM<sub>N</sub>. However, both tropical versions reduce the average absolute bias to a few centimeters. Performance differences across the major tropical peatland regions are discussed.</p><p>Reference: Bechtold, M., De Lannoy, G. J. M., Koster, R. D., Reichle, R. H., Mahanama, S. P., Bleuten, W., et al. (2019). PEAT&#8208;CLSM: A specific treatment of peatland hydrology in the NASA Catchment Land Surface Model.<em> Journal of Advances in Modeling Earth Systems, 11(7),</em> 2130-2162. doi: 10.1029/2018MS001574</p>
Tropical peatlands are among the most carbon-dense ecosystems on Earth, and their water storage dynamics strongly control these carbon stocks. The hydrological functioning of tropical peatlands differs from that of northern peatlands, which has not yet been accounted for in global land surface models (LSMs). Here, we integrated tropical peat-specific hydrology modules into a global LSM for the first time, by utilizing the peatland-specific model structure adaptation (PEATCLSM) of the NASA Catchment Land Surface Model (CLSM). We developed literature-based parameter sets for natural (PEATCLSM Trop,Nat ) and drained (PEATCLSM Trop,Drain ) tropical peatlands. Simulations with PEATCLSM Trop,Nat were compared against those with the default CLSM version and the northern version of PEATCLSM (PEATCLSM North,Nat ) with tropical vegetation input. All simulations were forced with global meteorological reanalysis input data for the major tropical peatland regions in Central and South America, the Congo Basin, and Southeast Asia. The evaluation against a unique and extensive data set of in situ water level and eddy covariance-derived evapotranspiration showed an overall improvement in bias and correlation compared to the default CLSM version. Over Southeast Asia, an additional simulation with PEATCLSM Trop,Drain was run to address the large fraction of drained tropical peatlands in this region. PEATCLSM Trop,Drain outperformed CLSM, PEATCLSM North,Nat , and PEATCLSM Trop,Nat over drained sites. Despite the overall improvements of PEATCLSM Trop,Nat over CLSM, there are strong differences in performance between the three study regions. We attribute these performance differences to regional differences in accuracy of meteorological forcing data, and differences in peatland hydrologic response that are not yet captured by our model. Plain Language SummaryTropical peatlands are wetlands in which plant material accumulates under waterlogged conditions and develops into a dense organic soil layer. Disturbance of their selfregulating hydrology by external factors such as artificial drainage, land use change, and climate change can quickly convert these immense carbon stocks into strong sources of greenhouse gases. Including the hydrology of tropical peatlands into global Earth system models allows us to understand the impact of such external disturbances. We developed the first hydrology modules for natural and drained tropical peatlands to plug into the NASA Goddard Earth Observing System modeling framework. Our results display strong regional differences, and indicate that the accuracy of our model is limited by rainfall data quality and by our understanding of how peatland hydrology differs across the three regions that contain the major tropical peatland areas (Central and South America, the Congo Basin, and Southeast Asia). Nonetheless, simulations
Abstract. Various regions in the world experience land cover and land use changes. One such a region is the Dry Chaco ecoregion in South America, characterized by deforestation and forest degradation since the 1980s. In this study, we simulated the water balance over the Dry Chaco and assessed the impact of land cover changes thereon, using three different state-of-the-art land surface models (LSMs) within the NASA Land Information System (LIS) with updated parameters. The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based dynamic vegetation parameters instead of default climatological vegetation parameters, and (iii) yearly land cover information instead of static land cover. A relative comparison in terms of water budget components and ‘efficiency space’ for various baseline and revised experiments showed that large regional and long-term differences relate to different LSM structures, whereas smaller local differences resulted from updated soil, vegetation and land cover parameters. Furthermore, different LSM structures redistributed water differently in response to these parameter updates. A time series comparison of the simulations to independent satellite-based estimates of evapotranspiration and brightness temperature showed that no LSM setup significantly outperformed another for the entire region, and that not all LSM simulations improved with updated parameter values. However, the revised soil parameters generally reduced the simulated surface soil moisture bias relative to pixel-scale in situ observations, and the simulated Tb bias relative to regional Soil Moisture Ocean Salinity (SMOS) observations.
<p>The 16.8 million ha of peatlands in the Cuvette Centrale wetland complex in the Congo Basin is one of the largest peatland regions on Earth but still highly understudied. Understanding the hydrological functioning of these peatlands and the effects of external disturbances thereon remains a major challenge. Recent research suggested fundamental hydrological differences between the Congo peatlands and the well-studied Southeast Asian peatlands. The Congo peatlands have a doming gradient that is up to ten times smaller, and they are influenced by river hydrology to some extent.</p> <p>In this study, we explore the Congo peatland hydrology through land surface modeling and data assimilation. We build upon our recently developed tropical PEATCLSM module (Apers et al., 2022) that was parameterized based on data from Southeast Asian peatlands due to the lack of field data from other tropical peatland regions. In a first step, we derive Congo-specific peat hydraulic and discharge function parameters from a scalar parametrization of water level dynamics in the Congo peatlands, using observed water level data at two locations. These Congo-specific parameters differ considerably from the original literature-based parameters from Southeast Asian peatlands. In a second step, we apply our original and Congo-specific parameters in an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture and Ocean Salinity (SMOS) mission. The data assimilation results are used in two ways. First, the effect of these parameters on the simulated peatland hydrology and the observation-minus-forecast Tb residuals is evaluated. It is hypothesized that the new parameters reduce the previously reported modeling errors over the Congo peatlands and reduce the residuals in Tb as well. Second, we analyze the data assimilation diagnostics to learn about other model improvement possibilities. In preliminary results, we found long periods of temporally autocorrelated total water storage increments (difference of pre- and post-update) that coincided with anomalies in river stages measured upstream of the peatlands. Since PEATLCSM neglects possible river influence, this concurrence suggests that the typically used grid-based approach of land surface models should be combined with a river routing scheme over the Congo peatlands.</p> <p>Apers, S., De Lannoy, G. J. M., Baird, A. J., Cobb, A. R., Dargie, G. C., del Aguila Pasquel, J., ... & Bechtold, M. (2022). Tropical peatland hydrology simulated with a global land surface model. <em>Journal of advances in modeling earth systems, 14(3),</em> e2021MS002784.</p>
Background. Peatland wildfires involve flaming vegetation and smouldering peat. The smouldering behaviour strongly depends on peat moisture, which can change significantly and quickly due to weather or human activities. Aims. We simulated wildfire in peatlands at the field scale and, for the first time, included daily variations of peat moisture. Methods. We developed KAPAS II, a cellular automaton that includes flaming and smouldering, and coupled it with PEATCLSM (Catchment Land Surface Model) for peatland hydrology. Key results. Compared with the satellite observations over 90 days of a 2018 wildfire in Borneo, KAPAS II predictions provide good agreement for burn scars (79% accuracy) and for the number of smouldering hotspots (85% accuracy). For the same burn scar, the model predicts that 54 ha of peat would smoulder when considering daily moisture variations, but only 12 ha if moisture was constant. Simulations at the same Borneo location, but in different years from 2000 to 2019, show the importance of seasons and climate events like El Niño. Conclusion. Temporal variations in peat moisture, which are strongly influenced by weather and climate, are important to predict the behaviour and severity of peatland wildfires. Implications. This model improves our understanding of wildfire behaviour in peatlands and can contribute to its mitigation.
Study question To examine the hypothesis that experiences with patient-centred endometriosis care are associated with the endometriosis-specific quality of life dimensions ‘emotional wellbeing’ and ‘social support’. Summary answer Positive associations were found between experienced patient-centeredness of care and the quality of life domains ‘emotional well-being’ and ‘social support’. What is known already Women with endometriosis have lower quality of life. Furthermore, research showed that the patient-centeredness of endometriosis care could still be improved. The quality of the provided endometriosis care might impact women’s quality of life, as demonstrated in the field of fertility care. A previous explorative study identified associations between patient-centred endometriosis care and two in five dimensions of endometriosis-specific quality of life (i.e. ‘emotional well-being’ and ‘social support’) but concluded that a more focussed and adequately powered study was needed. Study design, size, duration A secondary regression analysis of two cross-sectional cohort studies was performed. Both studies investigated patient-centeredness of endometriosis care and endometriosis-specific quality of life using respectively the ENDOCARE questionnaire (ECQ) and the Endometriosis Health Profile 30 (EHP-30). In total the data from 300 women was eligible for analysis, exceeding the, according to our power calculation, required sample size of 200 women. Participants/materials, setting, methods The participating women all had surgically proven endometriosis and were recruited by one secondary and two tertiary endometriosis clinics in the Netherlands. The regression analysis focused on the previously found association between the ten dimensions of the ECQ and the EHP-30 domains ‘emotional well-being’ and ‘social support’ rather than all five EHP-30 domains. After the Bonferroni correction to limit type I errors, the adjusted p-value was 0.003 (0.05/20). Main results and the role of chance The participating women had a mean age of 35.7 years and had predominantly been diagnosed with moderate to severe (68.6%). Regarding the EHP-30 domain ‘emotional well-being’, an association was found with the following five patient-centeredness dimensions: ‘respect for patients’ values, preferences and expressed needs’ (p = 0.046, Beta=0.159), ‘coordination and integration of care’ (p = 0.013, Beta=0.193), ‘information, communication and integration of care’ (p = 0.010, Beta=0.258), ‘emotional support and alleviation of fear and anxiety’ (p = 0.010, Beta=0.178), and ‘continuity and transition’ (p = 0.015, Beta=0.179). None of the associations between the EHP-30 domain ‘emotional well-being’ and a dimension of patient-centred endometriosis care, were significant when compared to the Bonferroni corrected p-value (all p ≥ 0.010). The EHP-30 domain ‘social support’ proved to be significantly associated to the following three dimensions of patient-centered endometriosis care (in order of strength): ‘information, communication and integration of care’ (p < 0.001, Beta=0.436), ‘coordination and integration of care’ (p = 0.001, Beta=0.307), and ‘emotional support and alleviation of fear and anxiety’ (p = 0.002, Beta=0.259). Limitations, reasons for caution This cross-sectional studies identified associations rather than proving causality between experiencing less patient-centeredness of care and having lower quality of life. Nevertheless, it is very tangible that some causality exists, either directly or indirectly (e.g. through empowerment) and that by improving patient-centeredness, quality of life might be improved as well. Wider implications of the findings Improving the patient-centeredness of endometriosis care was already considered an important goal, but even more so given its association with women’s quality of life, which is increasingly considered the ultimate measure of health care quality. Trial registration number not applicable
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.