The boreal zone consists of a mosaic of different land cover types, mainly forests and peatlands, both storing large amounts of carbon (Scharlemann et al., 2014;Turetsky et al., 2015). One of the natural features shaping the boreal landscape is wildfire (Bowman et al., 2009). Several studies indicate that lightning is the major source of ignition of wildfires in the boreal zone (Turetsky et al., 2015). It is proposed that lightning may increase due to global warming (Y.
<p>Recently, the FAO crop model AquaCrop v7.0 has been released as open-source code along with the standard graphical user interface for single field applications, and Linux, Windows, and Mac stand-alone executables for plugin into regional or climate simulations (https://www.fao.org/aquacrop/en/). In addition, AquaCrop v7.0 has been coupled as the first crop model into NASA&#8217;s Land Information System (LIS) to support regional modeling and data assimilation (DA) using spatially complete re-analysis meteorological forcings, and to produce spatio-temporally complete geolocated NetCDF output for the first time. This presentation explores the potential of soil moisture updating for improving crop growth model estimates of AquaCrop.</p> <p>Our DA setup uses the one-dimensional ensemble Kalman filter to assimilate the SMAP Level-2 surface soil moisture retrieval product from April 2015 through 2021 on a quarter-degree regular model grid over Europe. Prior to assimilation, a climatological rescaling is applied to remove the observation-minus-forecast bias. A preliminary evaluation against in-situ data of the International Soil Moisture Network indicates that topsoil (0-30 cm) soil moisture estimates of AquaCrop are improved through the DA compared to the model-only estimates. Our results show that the adjusted soil moisture strongly modulates biomass accumulation during the main growing period from April to June, particularly over moisture-limited areas. The impact on biomass will be further evaluated with the Copernicus Global Land Service dry matter productivity product as the observational reference.</p>
<p>The Fire Weather Index (FWI) is used worldwide to estimate the danger of wildfires. The FWI system integrates meteorological parameters and empirically combines them into several moisture codes, each representing a different fuel type. These moisture codes are then used in combination with wind speed to estimate a fire danger. Originally, the FWI system was developed for a standard jack pine forest, however, it is widely used by fire managers to assess the fire danger in different environments as well. Furthermore, it is often also used to assess the vulnerability of organic soils, such as peatlands, to ignition and depth of burn. The utility of which is often questioned.</p> <p>&#160;</p> <p>This research aims at improving the original FWI for northern peatlands by replacing parts of the original, purely weather-based FWI system with satellite-informed model estimates of peat moisture and water level. These come from a data assimilation output combining the NASA catchment model, including the peat modules PEATCLSM, and Soil Moisture and Ocean Salinity (SMOS) L-band brightness temperature observations. The predictive power of the new, peat-specific FWI (PEAT-FWI) is evaluated against the original FWI against fire data of the global fire atlas from 2010 through 2018 over the major northern peatlands areas. For the evaluation, the fires are split up in early and late season fires, as it is hypothesized that late fires are more hydrological driven, and the predictive power of the PEAT-FWI will thus differ between the two types of fires. Our results indeed indicate that the PEAT-FWI improves the predictive capability of estimating fire risk over northern peatlands in particular for late fires. By using a receiver operating characteristics (ROC) curve to evaluate the predictive power of the FWI against a random estimate, the area under the curve increases by up to 10% for the PEAT-FWI compared to the original FWI. The recent version 7 release of the operational Soil Moisture Active Passive (SMAP) Level-4 Soil Moisture Data Assimilation Product now includes PEATCLSM, thus, the proposed PEAT-FWI is straightforward to include in operational FWI products.</p>
<p>The boreal zone has experienced more severe fires over the last years, often coinciding with years of anomalously high lightning frequencies. These lightning frequencies might increase even further with global warming. Current lightning predictions are however highly uncertain, either relying on empirical relationships derived from present climate, or coarse-scale climate scenario simulations in which the critical process of deep convection is parameterized, and the detailed representation of land-atmosphere interactions is lacking.</p><p>&#160;</p><p>In this study, we used the NASA Unified-Weather Research and Forecasting (NU-WRF) modeling framework to simulate lightning over a 550,000 km<sup>2</sup> domain including the Great Slave Lake in Canada. Simulations were run for the six lightning seasons (June-August; 2015-2020) at both a convection-parameterized (9 km) and convection-permitting (3 km) spatial resolution. Additionally, two microphysics (MP) schemes (Goddard 4ICE and Thompson) were compared at both resolutions. From the simulation output, we derived four diagnostic lightning indices which were evaluated against observations from the Canadian Lightning Detection Network (CLDN). This evaluation was done in terms of the capability of the indices to match the observational spatial pattern (temporally averaged), spatiotemporal frequency distribution, daily and seasonal climatology (spatially averaged), and an event-based overall skill assessment. Our results show that the Thompson MP scheme better predicts the daily climatology than the Goddard 4ICE MP scheme. The Goddard 4ICE MP scheme, on the other hand, predicts the spatial pattern best. Both MP schemes predict the seasonality equally well. Concerning the spatial resolution, a clear improvement when simulating at convection-permitting resolution is only seen for the Goddard 4ICE MP scheme. Regarding the different lightning indices, no clear superior index is found as the relative performance of each index strongly depends on the evaluation criteria. Finally, the study shows that models are in particular poor in reproducing the long-term averaged observed spatial pattern of lightning occurrence. This might be related to an insufficient representation of the land surface heterogeneity in the study area.</p>
<p>Recent advances in gridded crop modeling and satellite observations help to improve the monitoring of crop growth and water requirements. In this contribution, we use AquaCrop v7 within the NASA Land Information System (i) to produce spatially distributed estimates of soil moisture, biomass and backscatter, and their uncertainty, and (ii) to assimilate backscatter observations from the Sentinel-1 satellite mission to improve soil moisture and biomass via state updating, at 1 km resolution over Europe. The results are evaluated against in situ observations of soil moisture and satellite-based vegetation products. We will discuss the opportunities and challenges of high-resolution gridded crop models and satellite-based active microwave data for agricultural applications.&#160;</p>
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