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Abstract In most of the world, conditions conducive to wildfires are becoming more prevalent. Net carbon emissions from wildfires contribute to a positive climate feedback that needs to be monitored, quantified, and predicted. Here we use a causal inference approach to evaluate the influence of top-down weather and bottom-up fuel precursors on wildfires. The top-down dominance on wildfires is more widespread than bottom-up dominance, accounting for 73.3% and 26.7% of regions, respectively. The top-down precursors dominate in the tropical rainforests, mid-latitudes, and eastern Siberian boreal forests. The bottom-up precursors dominate in North American and European boreal forests, and African and Australian savannahs. Our study identifies areas where wildfires are governed by fuel conditions and hence where fuel management practices may be more effective. Moreover, our study also highlights that top-down and bottom-up precursors show complementary wildfire predictability across timescales. Seasonal or interannual predictions are feasible in regions where bottom-up precursors dominate.

European grasslands increase their water-use efficiency in summer through increased gross primary production and regulated transpiration, according to an analysis of three indices derived from remote sensing over the period 1995-2018

Deforestation has a wide range of effects on hydrological and geochemical processes. Dissolved organic carbon (DOC) dynamics, a sensitive environmental change indicator, is expected to be affected by deforestation, with changes in atmospheric sulfur (S) deposition compounding this. However, how precisely anthropogenic disturbance (deforestation) under a declining atmospheric S input scenario affects the underlying spatiotemporal dynamics and relationships of river DOC and sulfate with hydro-climatological variables e.g., stream water temperature, runoff, pH, total dissolved iron (Fetot), and calcium (Ca2+) remains unclear. We, therefore, examined this issue within the TERENO Wüstebach catchment (Eifel, Germany), where partial deforestation had taken place in 2013. Wavelet transform coherence (WTC) analysis was applied based on a 10-year time series (2010–2020) from three sampling stations, whose (sub) catchment areas have different proportions of deforested area (W10: 31%, W14: 25%, W17: 3%). We found that water temperature and DOC, sulfate, and Fetot concentrations showed distinct seasonal patterns, with DOC averaging concentrations ranging from 2.23 (W17) to 4.56 (W10) mg L–1 and sulfate concentration ranging from 8.04 (W10) to 10.58 (W17) mg L–1. After clear-cut, DOC significantly increased by 59, 58% in the mainstream (W10, W14), but only 26% in the reference stream. WTC results indicated that DOC was negatively correlated with runoff and sulfate, but positively correlated with temperature, Ca2+, and Fetot. The negative correlation between DOC with runoff and sulfate was apparent over the whole examined 10-year period in W17 but did end in W10 and W14 after the deforestation. Sulfate (SO4) was highly correlated with stream water temperature, runoff, and Fetot in W10 and W14 and with a longer lag time than W17. Additionally, pH was stronger correlated (higher R2) with sulfate and DOC in W17 than in W10 and W14. In conclusion, WTC analysis indicates that within this low mountainous forest catchment deforestation levels over 25% (W10 and W14) affected the coupling of S and C cycling substantially more strongly than “natural” environmental changes as observed in W17.

Soil moisture (SM) is an important land-surface parameter. Although microwave remote sensing is recognized as one of the most appropriate methods for retrieving SM, such retrievals often cannot meet the requirements of specific applications because of their coarse spatial resolution and spatiotemporal data gaps. A range of general models (GMs) for SM analysis topics (e.g., gap-filling, forecasting, and downscaling) have been introduced to address these shortcomings. This work presents a novel strategy (i.e., optimized wavelet-coupled fitting method (OWCM)) to enhance the fitting accuracy of GMs by introducing a wavelet transform (WT) technique. Four separate GMs are selected, i.e., elastic network regression, area-to-area regression kriging, random forest regression, and neural network regression. The fitting procedures are then tested within a downscaling analysis implemented between aggregated Global Land Surface Satellite products (i.e., LAI, FVC, albedo), Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST, and Random Forest Soil Moisture (RFSM) datasets in both the WT space and the regular space. Then, eight fine-resolution SM datasets mapped from the trained GMs and OWCMs are analyzed using direct comparisons with in situ SM measurements and indirect intercomparisons between the aggregated OWCM-/GM-derived SM and RFSM. The results demonstrate that OWCM-derived SM products are generally closer to the in situ SM observations, and better capture in situ SM dynamics during the unfrozen season, compared to the corresponding GM-derived SM product, which shows fewer time changes and more stable trends. Moreover, OWCM-derived SM products represent a significant improvement over corresponding GM-derived SM products in terms of their ability to spatially and temporally match RFSM. Although spatial heterogeneity still substantially impacts the fitting accuracies of both GM and OWCM SM products, the improvements of OWCMs over GMs are significant. This improvement can likely be attributed to the fitting procedure of OWCMs implemented in the WT space, which better captures high- and low-frequency image features than in the regular space.

High-quality and long time-series soil moisture (SM) data are increasingly required for the Qinghai-Tibet Plateau (QTP) to more accurately and effectively assess climate change. In this study, to evaluate the accuracy and effectiveness of SM data, five passive microwave remotely sensed SM products are collected over the QTP, including those from the soil moisture active passive (SMAP), soil moisture and ocean salinity INRA-CESBIO (SMOS-IC), Fengyun-3B microwave radiation image (FY3B), and two SM products derived from the advanced microwave scanning radiometer 2 (AMSR2). The two AMSR2 products are generated by the land parameter retrieval model (LPRM) and the Japan Aerospace Exploration Agency (JAXA) algorithm, respectively. The SM products are evaluated through a two-stage data comparison method. The first stage is direct validation at the grid scale. Five SM products are compared with corresponding in situ measurements at five in situ networks, including Heihe, Naqu, Pali, Maqu, and Ngari. Another stage is indirect validation at the regional scale, where the uncertainties of the data are quantified by using a three-cornered hat (TCH) method. The results at the regional scale indicate that soil moisture is underestimated by JAXA and overestimated by LPRM, some noise is contained in temporal variations in SMOS-IC, and FY3B has relatively low absolute accuracy. The uncertainty of SMAP is the lowest among the five products over the entire QTP. In the SM map composed by five SM products with the lowest pixel-level uncertainty, 66.64% of the area is covered by SMAP (JAXA: 19.39%, FY3B: 10.83%, LPRM: 2.11%, and SMOS-IC: 1.03%). This study reveals some of the reasons for the different performances of these five SM products, mainly from the perspective of the parameterization schemes of their corresponding retrieval algorithms. Specifically, the parameterization configurations and corresponding input datasets, including the land-surface temperature, the vegetation optical depth, and the soil dielectric mixing model are analyzed and discussed. This study provides quantitative evidence to better understand the uncertainties of SM products and explain errors that originate from the retrieval algorithms.

Time series of soil moisture (SM) data in the Qinghai–Tibet plateau (QTP) covering a period longer than one decade are important for understanding the dynamics of land surface–atmosphere feedbacks in the global climate system. However, most existing SM products have a relatively short time series or show low performance over the challenging terrain of the QTP. In order to improve the spaceborne monitoring in this area, this study presents a random forest (RF) method to rebuild a high-accuracy SM product over the QTP from 19 June 2002 to 31 March 2015 by adopting the advanced microwave scanning radiometer for earth observing system (AMSR-E), and the advanced microwave scanning radiometer 2 (AMSR2), and tracking brightness temperatures with latitude and longitude using the International Geosphere–Biospheres Programme (IGBP) classification data, the digital elevation model (DEM) and the day of the year (DOY) as spatial predictors. Brightness temperature products (from frequencies 10.7 GHz, 18.7 GHz and 36.5 GHz) of AMSR2 were used to train the random forest model on two years of Soil Moisture Active Passive (SMAP) SM data. The simulated SM values were compared with third year SMAP data and in situ stations. The results show that the RF model has high reliability as compared to SMAP, with a high correlation (R = 0.95) and low values of root mean square error (RMSE = 0.03 m3/m3) and mean absolute percent error (MAPE = 19%). Moreover, the random forest soil moisture (RFSM) results agree well with the data from five in situ networks, with mean values of R = 0.75, RMSE = 0.06 m3/m3, and bias = −0.03 m3/m3 over the whole year and R = 0.70, RMSE = 0.07 m3/m3, and bias = −0.05 m3/m3 during the unfrozen seasons. In order to test its performance throughout the whole region of QTP, the three-cornered hat (TCH) method based on removing common signals from observations and then calculating the uncertainties is applied. The results indicate that RFSM has the smallest relative error in 56% of the region, and it performs best relative to the Japan Aerospace Exploration Agency (JAXA), Global Land Data Assimilation System (GLDAS), and European Space Agency’s Climate Change Initiative (ESA CCI) project. The spatial distribution shows that RFSM has a similar spatial trend as GLDAS and ESA CCI, but RFSM exhibits a more distinct spatial distribution and responds to precipitation more effectively than GLDAS and ESA CCI. Moreover, a trend analysis shows that the temporal variation of RFSM agrees well with precipitation and LST (land surface temperature), with a dry trend in most regions of QTP and a wet trend in few north, southeast and southwest regions of QTP. In conclusion, a spatiotemporally continuous SM product with a high accuracy over the QTP was obtained.

Main Conclusion: Meta-QTL (MQTL) analysis was done for yield-related traits in wheat. Candidate genes were identified within the refined MQTL and further validated by genotype-phenotype association analysis. Extensive studies have been undertaken on quantitative trait locus/loci (QTL) for wheat yield and its component traits. This study conducted a meta-analysis of 381 QTL related to wheat yield under various environments, including irrigated, drought- and/or heat-stressed conditions. Markers flanking meta-QTL (MQTL) were mapped on the wheat reference genome for their physical positions. Putative candidate genes were examined for MQTL with a physical interval of less than 20 Mbp. A total of 86 MQTL were identified as responsible for yield, of which 34 were for irrigated environments, 39 for drought-stressed environments, 36 for heat-stressed environments, and 23 for both drought- and heat-stressed environments. The high-confidence genes within the physical positions of the MQTL flanking markers were screened in the reference genome RefSeq V1.0, which identified 210 putative candidate genes. The phenotypic data for 14 contrasting genotypes with either high or low yield performance-according to the Australian National Variety Trials-were associated with their genotypic data obtained through ddRAD sequencing, which validated 18 genes or gene clusters associated with MQTL that had important roles for wheat yield. The detected and refined MQTL and candidate genes will be useful for marker-assisted selection of high yield in wheat breeding.