Tropical peatlands are characterized by highly organic, heterogeneous, and compressible peat soils. Without sampling and disturbing the soil, peat hydraulic and discharge parameters can be estimated from analyzing the in situ water level rise and recession. Such an analysis allows for the representation of the hydraulic behavior of a peatland from water level, precipitation, and topography data. Water level is measured in several remote tropical peatlands, whereas in situ precipitation is often not. Gridded satellite precipitation products provide an alternative, but are coarse and highly uncertain. Here, we introduce an algorithm for the hydrological parameterization of water level dynamics using satellite-based precipitation, and apply it to a tropical peatland in Brunei, while accounting for representativeness errors in the precipitation data. First, we adapt the rise and recession analysis developed by Cobb & Harvey (2019) for use with Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission (IMERG) precipitation estimates. The adapted rise analysis reduces the average error in the slope of the master rise curve with IMERG data from 21% to 3%. The average daily recession overestimation with IMERG data is reduced from 0.45 cm day -1 to 0.18 cm day -1. We also quantify the sensitivity of our rise analysis to precipitation errors using an ensemble of erroneous precipitation time series. Second, the adapted master rise and recession curves are used to fit soil hydraulic and discharge function parameters within the peatland-specific module of the NASA Catchment Land Surface Model. Our method enables the retrieval of accurate hydrological parameters for our case study, and should be tested in other peatland regions and with other satellite-based precipitation products.

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 (PEATCLSMTrop,Nat) and drained (PEATCLSMTrop,Drain) tropical peatlands. The operational CLSM version (which includes peat as a soil class) and PEATCLSMTrop,Nat were forced with global meteorological input data and evaluated over the major tropical peatland regions in Central and South America, the Congo Basin, and Southeast Asia. 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 over all three study regions. Over Southeast Asia, an additional simulation with PEATCLSMTrop,Drain was run to address the large fraction of drained tropical peatlands in this region. PEATCLSMTrop,Drain outperformed both CLSM and PEATCLSMTrop,Nat over drained sites. Despite the overall improvements of both tropical PEATCLSM modules, 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.