Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates.

The recent rise in atmospheric methane (CH4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year-1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

SOIL-WATERGRIDS, mapping dynamic changes in soil moisture and depth of water table from 1970 to 2014.

We introduce here SOIL-WATERGRIDS, a new dataset of dynamic changes in soil moisture and depth of water table over 45 years from 1970 to 2014 globally resolved at 0.25 × 0.25 degree resolution (about 30 × 30 km at the equator) along a 56 m deep soil profile. SOIL-WATERGRIDS estimates were obtained using the BRTSim model instructed with globally gridded soil physical and hydraulic properties, land cover and use characteristics, and hydrometeorological variables to account for precipitation, ecosystem-specific evapotranspiration, snowmelt, surface runoff, and irrigation. We validate our estimates against independent observations and re-analyses of the soil moisture, water table depth, wetland occurrence, and runoff. SOIL-WATERGRIDS brings into a single product the monthly mean water saturation at three depths in the root zone and the depth of the highest and lowest water tables throughout the reference period, their long-term monthly averages, and data quality. SOIL-WATERGRIDS can therefore be used to analyse trends in water availability for agricultural abstraction, assess the water balance under historical weather patterns, and identify water stress in sensitive managed and unmanaged ecosystems.