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.

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>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‐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>

<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>

<p>This study aims at better understanding the impact of deforestation on the local hydrology over the Argentinian Chaco, using land surface modeling and remote sensing data. The Chaco is an ecoregion characterized by unprecedented deforestation since the 1980s, mainly for cattle ranging and soybean production. More specifically, default climatological vegetation parameters (LAI, GVF) and static land cover in state-of-the-art land surface models (LSM), grouped within the NASA Land Information System (LIS), are updated using satellite-based dynamic vegetation parameters and yearly land use maps to feed the models with deforestation.</p><p>The presentation will show a spatio-temporal analysis of long-term water budget simulations using a range of LSMs (Noah, CLM, CLSM) in which dynamically updated vegetation and land cover parameters are included. Our simulations indicate that different LSMs result in a different partitioning of the total water budget, but all indicate an increase in soil moisture and percolation over the deforested areas.  Model output is evaluated using in situ soil moisture data, and various soil moisture retrieval products from SMOS (operational Level 2 and SMOS-IC) and SMAP (operational Level 2) and evapotranspiration data from GLEAM.</p>

<p>The 16.5 million ha Cuvette Centrale peatland complex in the Congo Basin was described for the first time in 2017. However, a proper understanding of the entire hydrological functioning of this peatland complex is a challenge and large-scale land surface models (LSMs) are unlikely to accurately represent the circulation of water in this area. One of the major issues of large-scale LSMs is the quantification of the spatially- and temporally-variable lateral water input from rivers into peatlands.</p><p>In this research, we applied our recently developed tropical peatland-specific module PEATCLSM<sub>Trop,Nat</sub> in a land surface modeling and assimilation scheme that uses L-band brightness temperature (T<sub>b</sub>) data from the Soil Moisture and Ocean Salinity (SMOS) satellite mission. Despite the dense vegetation cover in tropical peatlands, preliminary results showed that the data assimilation improved the water level estimates at 4 evaluation sites over model-only simulations, with mean correlation coefficients of 0.46 for the model-only and 0.63 for the data assimilation estimates, and mean anomaly correlation coefficients of 0.02 for the model-only and 0.26 for the data assimilation estimates. To gain insight into the large-scale hydrology of the Cuvette Centrale peatland complex, we analyzed data assimilation diagnostics and found temporally autocorrelated positive and negative total water storage (tws) increments (=tws correction introduced via data assimilation) over periods of up to four months over the Cuvette Centrale peatlands. This is indicative of a temporarily suboptimal assimilation system, due to a shortcoming in the LSM. Since PEATCLSM<sub>Trop,Nat</sub> does not simulate lateral water input and the positive autocorrelated periods of tws increments coincide with anomalies in river stages measured upstream, it suggests that lateral water input (=flooding) from upstream mineral areas into the peatlands of the Cuvette Centrale is an important but unmodelled process in its hydrology. This means that land use change and a climate change-induced precipitation reduction in upstream mineral areas will influence the local hydrology of the Cuvette Centrale peatland complex, making it even more vulnerable to external disturbances.</p>