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Irrigation is an important component of the terrestrial water cycle, but it is often poorly accounted for in models. Recent studies have attempted to integrate satellite data and land surface models via data assimilation (DA) to (a) detect and quantify irrigation, and (b) better estimate the related land surface variables such as soil moisture, vegetation, and evapotranspiration. In this study, different synthetic DA experiments are tested to advance satellite DA for the estimation of irrigation. We assimilate synthetic Sentinel‐1 backscatter observations into the Noah‐MP model coupled with an irrigation scheme. When updating soil moisture, we found that the DA sets better initial conditions to trigger irrigation in the model. However, DA updates to wetter conditions can inhibit irrigation simulation. Building on this limitation, we propose an improved DA algorithm using a buddy check approach. The method still updates the land surface, but now the irrigation trigger is not primarily based on the evolution of soil moisture, but on an adaptive innovation (observation minus forecast) outlier detection. The new method was found to be optimal for more temperate climates where irrigation events are less frequent and characterized by higher application rates. It was found that the DA outperforms the model‐only 14‐day irrigation estimates by about 20% in terms of root‐mean‐squared differences, when frequent (daily or every other day) observations are available. With fewer observations or high levels of noise, the system strongly underestimates the irrigation amounts. The method is flexible and can be expanded to other DA systems, also real‐world cases. Plain Language Summary: Irrigation has an important impact on the terrestrial water cycle. However, it remains poorly simulated by models and it is hard to quantify through satellite observations alone. The combination of models and satellite observations to detect and quantify irrigation has been explored in the last few years. Recently, Sentinel‐1 radar (microwave) observations have been assimilated into the Noah‐MP land surface model in order to quantify irrigation, and better estimate the related land surface variables, such as soil moisture and vegetation. This system has shown benefits but also limitations, which are highlighted and addressed in our study using synthetic experiments. We propose an improved data assimilation algorithm and test it for different sites and levels of model and observation error. For temperate regions, the new method estimates irrigation more accurately (20%) than the model alone, provided that frequent (daily or every other day) observations are available. With further developments, the new methodology could be used in a real‐world experiment. Key Points: Model‐driven irrigation estimation has limitations, also when based on improved soil moisture conditions obtained via data assimilationA new method based on an adaptive outlier detection improves the estimated irrigation in a synthetic backscatter data assimilation setupThe method performs best with frequent data assimilation in a temperate climate, reducing irrigation errors by 20% [ABSTRACT FROM AUTHOR]

Peatlands are poorly represented in global Earth system modeling frameworks. Here we add a peatland‐specific land surface hydrology module (PEAT‐CLSM) to the Catchment Land Surface Model (CLSM) of the NASA Goddard Earth Observing System (GEOS) framework. The amended TOPMODEL approach of the original CLSM that uses topography characteristics to model catchment processes is discarded, and a peatland‐specific model concept is realized in its place. To facilitate its utilization in operational GEOS efforts, PEAT‐CLSM uses the basic structure of CLSM and the same global input data. Parameters used in PEAT‐CLSM are based on literature data. A suite of CLSM and PEAT‐CLSM simulations for peatland areas between 40°N and 75°N is presented and evaluated against a newly compiled data set of groundwater table depth and eddy covariance observations of latent and sensible heat fluxes in natural and seminatural peatlands. CLSM's simulated groundwater tables are too deep and variable, whereas PEAT‐CLSM simulates a mean groundwater table depth of −0.20 m (snow‐free unfrozen period) with moderate temporal fluctuations (standard deviation of 0.10 m), in significantly better agreement with in situ observations. Relative to an operational CLSM version that simply includes peat as a soil class, the temporal correlation coefficient is increased on average by 0.16 and reaches 0.64 for bogs and 0.66 for fens when driven with global atmospheric forcing data. In PEAT‐CLSM, runoff is increased on average by 38% and evapotranspiration is reduced by 19%. The evapotranspiration reduction constitutes a significant improvement relative to eddy covariance measurements. Plain Language Summary: Peatlands are wetlands in which plant matter has accumulated over thousands of years under almost permanently water‐logged conditions. Alterations in these conditions as a result of global climate change can lead to the release of the huge peatland carbon pool as carbon dioxide over much shorter timescales than were required for accumulation. The additional emissions would amplify global warming. A better representation of the peatland hydrology in global Earth system models can help quantify how peatlands respond to a changing climate. In this paper, we add a peatland‐specific land surface hydrology module to the land surface model used in NASA's GEOS Earth system modeling framework. Comparisons of numerical simulations encompassing northern peatlands against field observations show that the new model version significantly improves our ability to capture the hydrological dynamics of peatlands. The new peatland representation in GEOS offers new opportunities, including the potential for merging model information and remote sensing observations in a way that improves our understanding of the overall role played by peatlands in the global water and carbon cycles. Key Points: A peatland‐specific land surface hydrology was added to an Earth system model and constrained by literature data, without parameter tuningSimulations were evaluated with a data set of groundwater table depth and evapotranspiration with unprecedented coverage in high latitudesThe peatland model version performs significantly better in terms of hydrological variables over peatlands than the operational model [ABSTRACT FROM AUTHOR]

There is an urgent need to include northern peatland hydrology in global Earth system models to better understand land-atmosphere interactions and sensitivities of peatland functions to climate change, and, ultimately, to improve climate change predictions. In this study, we introduced for the first time peatland-specific model physics into an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture Ocean Salinity (SMOS) mission to improve groundwater table estimates. We conducted two sets of model-only and data assimilation experiments using the Catchment Land Surface Model (CLSM), applying (over peatlands only) in one of them a peatland-specific adaptation (PEATCLSM). The evaluation against in-situ measurements of peatland groundwater table depth indicates the superiority of PEATCLSM model physics and additionally improved performance after assimilating SMOS Tb observations. The better performance of PEATCLSM over nearly all Northern Hemisphere peatlands is further supported by the better agreement between SMOS Tb observations and Tb estimates from the model-only and data assimilation runs. Within the data assimilation scheme, PEATCLSM reduces Tb observation-minus-forecast residuals and leads to reduced data assimilation updates of water storage components and, thus, reduced water budget imbalances in the assimilation system. • Peatland-specific model physics in an assimilation scheme for L-band brightness temperature (Tb) • Improved L-band brightness temperature (Tb) estimates over nearly all Northern Hemisphere peatlands • Groundwater estimates from Tb data assimilation correlate better with in situ measurements • Reduced water budget imbalances in assimilation system due to smaller required assimilation updates [ABSTRACT FROM AUTHOR]