Trends and drivers of recent summer drying in Switzerland DOI Creative Commons
Simon C. Scherrer, Martin Hirschi, Christoph Spirig

et al.

Environmental Research Communications, Journal Year: 2022, Volume and Issue: 4(2), P. 025004 - 025004

Published: Jan. 27, 2022

Abstract The Alpine region recently experienced several dry summers with important and adverse impacts on economy, society ecology. Here, we analyse drought indicators, evapotranspiration meteorological data from point observations, reanalyses regional climate model to assess trends drivers of summer in Switzerland the period 1981–2020. indicators station observations ERA5-Land ERA5 show a tendency towards drier half-years (climatic water balance: −39 mm decade −1 , 0–1 m integrated soil content: −5 −7 ) drying most months March October. Both, increasing (potential evapotranspiration: +21 or +7% K warming; actual +8 +15 non-significant precipitation decrease 17 are identified as roughly equivalent drivers. considerable differences for evapotranspiration, especially summers. is clearly than one ERA5-Land. smallest partly moisture-limited years while highest, still mainly energy-limited scales well temperature (+4% warming). seems better match situ measurements ERA5, but remain. Variability also investigated EURO-CORDEX ensemble. Most simulations considerably underestimate recent warming ensemble shows large possible range changes mean change near zero. precipitation-temperature scaling correlation between interannual time scale mostly overestimated. Our results highlight that analysis Central European evolution its remains challenging data, uncertainties exist reanalyses.

Language: Английский

ERA5-Land: a state-of-the-art global reanalysis dataset for land applications DOI Creative Commons
Joaquín Muñoz‐Sabater, Emanuel Dutra, Anna Agustí‐Panareda

et al.

Earth system science data, Journal Year: 2021, Volume and Issue: 13(9), P. 4349 - 4383

Published: Sept. 7, 2021

Abstract. Framed within the Copernicus Climate Change Service (C3S) of European Commission, Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset land component fifth generation ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, period covered will span from 1950 present, with continuous updates support monitoring applications. ERA5-Land describes evolution water and energy cycles over in a consistent manner production period, which, among others, could be used analyse trends anomalies. This achieved through high-resolution numerical integrations ECMWF surface model driven by downscaled meteorological forcing ERA5 climate reanalysis, including elevation correction thermodynamic near-surface state. shares most parameterizations that guarantees use state-of-the-art modelling applied weather prediction (NWP) models. A main advantage compared older ERA-Interim horizontal resolution, which globally 9 km 31 (ERA5) or 80 (ERA-Interim), whereas temporal resolution hourly ERA5. Evaluation against independent situ observations satellite-based reference datasets shows added value description hydrological cycle, particular soil moisture lake description, overall better agreement river discharge estimations available observations. However, snow depth fields present mixed performance when those ERA5, depending on geographical location altitude. The cycle comparable results Nevertheless, reduces averaged root mean square error skin temperature, taking MODIS data, mainly due contribution coastal points where spatial important. Since January 2020, has extended 1981 near 2- 3-month delay respect real time. segment prior production, aiming release whole summer/autumn 2021. high ERA5-Land, its consistency produced makes it valuable studies, initialize NWP models, diverse applications dealing resource, land, environmental management. full (Muñoz-Sabater, 2019a) monthly 2019b) presented this paper are C3S Data Store at https://doi.org/10.24381/cds.e2161bac https://doi.org/10.24381/cds.68d2bb30, respectively.

Language: Английский

Citations

2705
The International Soil Moisture Network: serving Earth system science for over a decade DOI Creative Commons
Wouter Dorigo,

Irene Himmelbauer,

Daniel Aberer

et al.

Hydrology and earth system sciences, Journal Year: 2021, Volume and Issue: 25(11), P. 5749 - 5804

Published: Nov. 9, 2021

Abstract. In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by European Space Agency, to serve centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together collected and freely shared multitude of organisations, harmonises them terms units sampling rates, applies advanced quality control, stores database. Users can retrieve from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, has evolved into primary reference worldwide, evidenced more than 3000 active users over 1000 scientific publications referencing sets provided network. As July 2021, now contains 71 networks 2842 stations located all globe, with time period spanning 1952 present. number covered is still growing, approximately 70 % contained continue be updated on regular or irregular basis. main scope paper inform readers about evolution past decade, including description network set updates control procedures. A comprehensive review existing literature making use also order identify current limitations functionality usage shape priorities next decade operations unique community-based repository.

Language: Английский

Citations

278
Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning DOI
Qingliang Li,

Ziyu Wang,

Wei Shangguan

et al.

Journal of Hydrology, Journal Year: 2021, Volume and Issue: 600, P. 126698 - 126698

Published: July 16, 2021

Language: Английский

Citations

128
Assessment of 24 soil moisture datasets using a new in situ network in the Shandian River Basin of China DOI Creative Commons

Jingyao Zheng,

Tianjie Zhao,

Haishen Lü

et al.

Remote Sensing of Environment, Journal Year: 2022, Volume and Issue: 271, P. 112891 - 112891

Published: Jan. 13, 2022

A new soil moisture and temperature wireless sensor network (the SMN-SDR) consisting of 34 sites was established within the Shandian River Basin in 2018, located a semi-arid area northern China. In this study, situ measurements SMN-SDR were used to evaluate 24 different datasets grouped according three categories: (1) single-sensor satellite-based products, (2) multi-sensor merged (3) model-based products. Triple collocation analysis (TCA) applied all possible triplets verify reliability robustness results. Impacts factors on accuracy products also investigated, including local acquisition time, physical surface temperature, vegetation optical depth (VOD). The results reveal that latest Climate Change Initiative (CCI) -combined product (v06.1, merging extra low-frequency passive microwave data) had best agreement with from SMN-SDR, lowest ubRMSE (< 0.04 m3/m3) highest R (> 0.6). Among retrieved Soil Moisture Active Passive (SMAP) performed terms 0.6) (close m3/m3), SMAP-MDCA (Modified Dual Channel Algorithm) being slightly better than baseline SCA-V (Single Algorithm-Vertical polarization). Importantly, newly developed SMAP-IB product, which does not use auxiliary data, delivered bias statistics higher VOD values compared drier SMAP retrievals, suggesting low (underestimated effects) may be major factor causing dry study area. It found TCA systematically overestimate correlation underestimate as ground-based metrics. TCA-based metrics vary considerably when using triplets, due assumptions violated even most conservative (in case an active product). Redundant multiple independent could averaged increase final estimates. This is first conduct comprehensive evaluation commonly used, multi-source These are expected further promote improvement satellite-

Language: Английский

Citations

112
An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018 DOI Creative Commons
Yongzhe Chen, Xiaoming Feng, Bojie Fu

et al.

Earth system science data, Journal Year: 2021, Volume and Issue: 13(1), P. 1 - 31

Published: Jan. 5, 2021

Abstract. Soil moisture is an important variable linking the atmosphere and terrestrial ecosystems. However, long-term satellite monitoring of surface soil at global scale needs improvement. In this study, we conducted data calibration fusion 11 well-acknowledged microwave remote-sensing products since 2003 through a neural network approach, with Moisture Active Passive (SMAP) applied as primary training target. The efficiency was high (R2=0.95) due to selection nine quality impact factors complicated organizational structure multiple networks (five rounds iterative simulations, eight substeps, 67 independent networks, more than 1 million localized subnetworks). Then, developed remote-sensing-based dataset (RSSSM) covering 2003–2018 0.1∘ resolution. temporal resolution approximately 10 d, meaning that three records are obtained within month, for days 1–10, 11–20, from 21st last day month. RSSSM proven comparable in situ measurements International Network sites (overall R2 RMSE values 0.42 0.087 m3 m−3), while overall existing popular similar usually ranges 0.31–0.41 0.095–0.142 respectively. generally presents advantages over other arid relatively cold areas, which probably because difficulty simulating impacts thawing transient precipitation on moisture, during growing seasons. Moreover, persistent well complete spatial coverage ensure applicability studies both patterns (e.g. trend). suggest increase mean moisture. without considering deserts rainforests, loss consecutive rainless highest summer low latitudes (30∘ S–30∘ N) but mostly winter mid-latitudes (30–60∘ N, 30–60∘ S). Notably, error propagation controlled extension simulation period past, indicating algorithm proposed here will be meaningful future when advanced sensors become operational. can accessed https://doi.org/10.1594/PANGAEA.912597 (Chen, 2020).

Language: Английский

Citations

106
A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data DOI
Jiangtao Liu, Farshid Rahmani, Kathryn Lawson

et al.

Geophysical Research Letters, Journal Year: 2022, Volume and Issue: 49(7)

Published: March 15, 2022

Abstract Deep learning (DL) models trained on hydrologic observations can perform extraordinarily well, but they inherit deficiencies of the training data, such as limited coverage in situ data or low resolution/accuracy satellite data. Here we propose a novel multiscale DL scheme simultaneously from and to predict 9 km daily soil moisture (5 cm depth). Based spatial cross‐validation over sites conterminous United States, obtained median correlation 0.901 root‐mean‐square error 0.034 m 3 /m . It outperformed Soil Moisture Active Passive mission's product, alone, land surface models. Our product showed better accuracy than previous 1 downscaling products, highlighting impacts improving resolution. Not only is our useful for planning against floods, droughts, pests, generically applicable geoscientific domains with multiple scales, breaking confines individual sets.

Language: Английский

Citations

73
Soil moisture content retrieval from Landsat 8 data using ensemble learning DOI
Yufang Zhang, Shunlin Liang, Zhiliang Zhu

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2022, Volume and Issue: 185, P. 32 - 47

Published: Jan. 19, 2022

Language: Английский

Citations

72
A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution DOI Creative Commons
Chaolei Zheng, Jia Li, Tianjie Zhao

et al.

Scientific Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: March 15, 2023

Abstract Global soil moisture estimates from current satellite missions are suffering inherent discontinuous observations and coarse spatial resolution, which limit applications especially at the fine scale. This study developed a dataset of global gap-free surface (SSM) daily 1-km resolution 2000 to 2020. is achieved based on European Space Agency - Climate Change Initiative (ESA-CCI) SSM combined product 0.25° resolution. Firstly, an operational gap-filling method was fill missing data in ESA-CCI using ERA5 reanalysis dataset. Random Forest algorithm then adopted disaggregate coarse-resolution 1-km, with help International Soil Moisture Network in-situ other optical remote sensing datasets. The generated had good accuracy, high correlation coefficent (0.89) low unbiased Root Mean Square Error (0.045 m 3 /m ) by cross-validation. To best our knowledge, this currently only long-term far.

Language: Английский

Citations

69
A review of recent developments on drought characterization, propagation, and influential factors DOI
Vinícius de Matos Brandão Raposo, Veber Afonso Figueiredo Costa, André Ferreira Rodrigues

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 898, P. 165550 - 165550

Published: July 17, 2023

Language: Английский

Citations

66
Soil moisture-evaporation coupling shifts into new gears under increasing CO2 DOI Creative Commons
Hsin Hsu, Paul A. Dirmeyer

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: March 1, 2023

When soil moisture (SM) content falls within a transitional regime between dry and wet conditions, it controls evaporation, affecting atmospheric heat humidity. Accordingly, different SM regimes correspond to gears of land-atmosphere coupling, climate. Determining patterns their future evolution is imperative. Here, we examine global distributions from ten climate models. Under increasing CO2, the range extends into unprecedented coupling in many locations. Solely areas decline globally by 15.9%, while emerge currently humid tropics high latitudes. Many semiarid regions spend more days fewer regime. These imply that larger fraction world will evolve experience multiple with strongly coupled expanding most. This could amplify sensitivity feedbacks land management.

Language: Английский

Citations

56