An Evaporation Correction Approach and Its Characteristics

AbstractEvaporation is a principal factor in the hydrological cycle and energy exchange; however, estimations of evaporation include large uncertainties. In this study, a modified estimation of evaporation based on empirical linearly simplified Penman evaporation (PES) is proposed, soil moisture and precipitation are used to correct the land surface evaporation estimation, and the temporal and spatial characteristics of the corrected evaporation (CE) are investigated globally. The results show that CE is strong at low latitudes and weak at high latitudes. CE has obvious seasonal variation, ranging from 0.2 to 4.0 mm day−1; CE is prominent in summer but feeble in winter. Compared to PES, CE is generally weaker in most regions, especially in arid regions, with differences of more than 9 mm day−1. CE agrees well with evaporation derived from FLUXNET-Model Tree Ensemble (FLUXNET-MTE), MERRA, and GLDAS. In general, the root-mean-square error (RMSE) between annual CE and FLUXNET-MTE is less than 0.2 mm day−1, and CE is about 5%–10% less than the evaporation of FLUXNET-MTE. In the arid regions, the maximum CE almost occurs in the month with the strongest precipitation; in the tropical regions, soil moisture enhances CE only when precipitation is less. In the context of global temperature rise, PES always shows an apparent increasing trend due to the water supply is not considered; however, CE decreases in western Asia, the western United States, the Amazon basin, and Central Africa, but weakly increases in the other study regions from 1984 to 2013. This study provides a method for estimating evaporation considering more restrictive factors on evaporation.

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Multi-source hydrological soil moisture state estimation using data fusion optimisation

Hydrology and Earth System Sciences ◽

10.5194/hess-21-3267-2017 ◽

2017 ◽

Vol 21 (7) ◽

pp. 3267-3285 ◽

Cited By ~ 5

Author(s):

Lu Zhuo ◽

Dawei Han

Keyword(s):

Soil Moisture ◽

Data Fusion ◽

Land Surface ◽

Hydrological Cycle ◽

Local Linear Regression ◽

Data Sources ◽

Superior Performance ◽

Flood Prediction ◽

Time To Build ◽

Moisture State

Abstract. Reliable estimation of hydrological soil moisture state is of critical importance in operational hydrology to improve the flood prediction and hydrological cycle description. Although there have been a number of soil moisture products, they cannot be directly used in hydrological modelling. This paper attempts for the first time to build a soil moisture product directly applicable to hydrology using multiple data sources retrieved from SAC-SMA (soil moisture), MODIS (land surface temperature), and SMOS (multi-angle brightness temperatures in H–V polarisations). The simple yet effective local linear regression model is applied for the data fusion purpose in the Pontiac catchment. Four schemes according to temporal availabilities of the data sources are developed, which are pre-assessed and best selected by using the well-proven feature selection algorithm gamma test. The hydrological accuracy of the produced soil moisture data is evaluated against the Xinanjiang hydrological model's soil moisture deficit simulation. The result shows that a superior performance is obtained from the scheme with the data inputs from all sources (NSE = 0.912, r = 0.960, RMSE = 0.007 m). Additionally, the final daily-available hydrological soil moisture product significantly increases the Nash–Sutcliffe efficiency by almost 50 % in comparison with the two most popular soil moisture products. The proposed method could be easily applied to other catchments and fields with high confidence. The misconception between the hydrological soil moisture state variable and the real-world soil moisture content, and the potential to build a global routine hydrological soil moisture product are discussed.

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Contrasting roles of interception and transpiration in the hydrological cycle – Part 1: Temporal characteristics over land

Earth System Dynamics ◽

10.5194/esd-5-441-2014 ◽

2014 ◽

Vol 5 (2) ◽

pp. 441-469 ◽

Cited By ~ 51

Author(s):

L. Wang-Erlandsson ◽

R. J. van der Ent ◽

L. J. Gordon ◽

H. H. G. Savenije

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Hydrological Cycle ◽

Land Surface Model ◽

Open Water ◽

Water Evaporation ◽

Surface Model ◽

Rain Event ◽

Temporal Characteristics ◽

Moisture Recycling

Abstract. Moisture recycling, the contribution of terrestrial evaporation to precipitation, has important implications for both water and land management. Although terrestrial evaporation consists of different fluxes (i.e. transpiration, vegetation interception, floor interception, soil moisture evaporation, and open-water evaporation), moisture recycling (terrestrial evaporation–precipitation feedback) studies have up to now only analysed their combined total. This paper constitutes the first of two companion papers that investigate the characteristics and roles of different evaporation fluxes for land–atmosphere interactions. Here, we investigate the temporal characteristics of partitioned evaporation on land and present STEAM (Simple Terrestrial Evaporation to Atmosphere Model) – a hydrological land-surface model developed to provide inputs to moisture tracking. STEAM estimates a mean global terrestrial evaporation of 73 900 km3 year-1, of which 59% is transpiration. Despite a relatively simple model structure, validation shows that STEAM produces realistic evaporative partitioning and hydrological fluxes that compare well with other global estimates over different locations, seasons, and land-use types. Using STEAM output, we show that the terrestrial residence timescale of transpiration (days to months) has larger inter-seasonal variation and is substantially longer than that of interception (hours). Most transpiration occurs several hours or days after a rain event, whereas interception is immediate. In agreement with previous research, our simulations suggest that the vegetation's ability to transpire by retaining and accessing soil moisture at greater depth is critical for sustained evaporation during the dry season. We conclude that the differences in temporal characteristics between evaporation fluxes are substantial and reasonably can cause differences in moisture recycling, which is investigated more in the companion paper (van der Ent et al., 2014, hereafter Part 2).

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Evaluation of global terrestrial evapotranspiration by state-of-the-art approaches in remote sensing, machine learning, and land surface models

10.5194/hess-2019-409 ◽

2019 ◽

Author(s):

Shufen Pan ◽

Naiqing Pan ◽

Hanqin Tian ◽

Pierre Friedlingstein ◽

Stephen Sitch ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Land Surface ◽

Amazon Basin ◽

State Of The Art ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Physical Models ◽

Tropical Regions ◽

Surface Models

Abstract. Evapotranspiration (ET) is a critical component in global water cycle and links terrestrial water, carbon and energy cycles. Accurate estimate of terrestrial ET is important for hydrological, meteorological, and agricultural research and applications, such as quantifying surface energy and water budgets, weather forecasting, and scheduling of irrigation. However, direct measurement of global terrestrial ET is not feasible. Here, we first gave a retrospective introduction to the basic theory and recent developments of state-of-the-art approaches for estimating global terrestrial ET, including remote sensing-based physical models, machine learning algorithms and land surface models (LSMs). Then, we utilized six remote sensing-based models (including four physical models and two machine learning algorithms) and fourteen LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the mean annual global terrestrial ET ranged from 50.7 × 103 km3 yr−1（454 mm yr−1）to 75.7 × 103 km3 yr−1 (6977 mm yr−1), with the average being 65.5 × 103 km3 yr−1 (588 mm yr−1), during 1982–2011. LSMs had significant uncertainty in the ET magnitude in tropical regions especially the Amazon Basin, while remote sensing-based ET products showed larger inter-model range in arid and semi-arid regions than LSMs. LSMs and remote sensing-based physical models presented much larger inter-annual variability (IAV) of ET than machine learning algorithms in southwestern U.S. and the Southern Hemisphere, particularly in Australia. LSMs suggested stronger control of precipitation on ET IAV than remote sensing-based models. The ensemble remote sensing-based physical models and machine-learning algorithm suggested significant increasing trends in global terrestrial ET at the rate of 0.62 mm yr−2 (p 0.05), even though most of the individual LSMs reproduced the increasing trend. Moreover, all models suggested a positive effect of vegetation greening on ET intensification. Spatially, all methods showed that ET significantly increased in western and southern Africa, western India and northeastern Australia, but decreased severely in southwestern U.S., southern South America and Mongolia. Discrepancies in ET trend mainly appeared in tropical regions like the Amazon Basin. The ensemble means of the three ET categories showed generally good consistency, however, considerable uncertainties still exist in both the temporal and spatial variations in global ET estimates. The uncertainties were induced by multiple factors, including parameterization of land processes, meteorological forcing, lack of in situ measurements, remote sensing acquisition and scaling effects. Improvements in the representation of water stress and canopy dynamics are essentially needed to reduce uncertainty in LSM-simulated ET. Utilization of latest satellite sensors and deep learning methods, theoretical advancements in nonequilibrium thermodynamics, and application of integrated methods that fuse different ET estimates or relevant key biophysical variables will improve the accuracy of remote sensing-based models.

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A Triple Collocation-Based Comparison of Three L-Band Soil Moisture Datasets, SMAP, SMOS-IC, and SMOS, Over Varied Climates and Land Covers

Frontiers in Water ◽

10.3389/frwa.2021.693172 ◽

2021 ◽

Vol 3 ◽

Author(s):

Seokhyeon Kim ◽

Jianzhi Dong ◽

Ashish Sharma

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Arid Regions ◽

Water Cycle ◽

Northern China ◽

Global Scale ◽

Retrieval Algorithm ◽

Western Africa ◽

L Band ◽

Land Covers

Soil moisture plays an important role in the hydrologic water cycle. Relative to in-situ soil moisture measurements, remote sensing has been the only means of monitoring global scale soil moisture in near real-time over the past 40 years. Among these, soil moisture products from radiometry sensors operating at L-band, e.g., SMAP, SMOS, and SMOS-IC, are theoretically established to be more advantageous than previous C/X-band products. However, little effort has been made to investigate the inter-product differences of L-band soil moisture retrievals and provide insights into the optimal use of these products. In this regard, this study aims to identify the relative strengths and weaknesses of three L-band soil moisture products across diverse climate zones and land covers at the global scale using triple collocation analysis. Results show that SMOS-IC exhibits significantly improved soil moisture estimation skills, relative to the original SMOS product. This demonstrates the paramount importance of retrieval algorithm development in improving global soil moisture estimates—given both SMOS-IC and SMOS are using the same L-band brightness temperature information. Relative to SMOS-IC, SMAP is superior across 69% of global land surface in terms of error variances. However, SMOS-IC tends to outperform SMAP over temperate/arid regions including in the east of North America, South America, western Africa, northern China, and central Australia. Additionally, considerable performance degradation of all the L-band data products is observed over unvegetated areas. This may suggest that improving soil moisture retrieval accuracy over arid and semi-arid regions should be a key priority for future L-band soil moisture development, and model-based (e.g., GLDAS) soil moisture products appear to provide more accurate soil moisture estimates over these regions.

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Land-surface feedbacks on temperature and precipitation in CMIP6-LS3MIP projections

10.5194/egusphere-egu21-12175 ◽

2021 ◽

Author(s):

Franco Catalano ◽

Andrea Alessandri ◽

Wilhelm May ◽

Thomas Reerink

Keyword(s):

Climate Change ◽

Soil Moisture ◽

Land Surface ◽

Hydrological Cycle ◽

Climate Change Signal ◽

Moisture Change ◽

Climate Conditions ◽

Temperature And Precipitation ◽

Systematic Biases ◽

The Impact

The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) aims at diagnosing systematic biases in the land models of CMIP6 Earth System Models and assessing the role of land-atmosphere feedbacks on climate change. Two components of experiments have been designed: the first is devoted to the assessment of the systematic land biases in offline mode (LMIP) while the second component is dedicated to the analysis of the land feedbacks in coupled mode (LFMIP). Here we focus on the LFMIP experiments. In the LFMIP protocol (van den Hurk et al. 2016), which builds upon the GLACE-CMIP configuration, two sets of climate-sensitivity projections have been carried out in amip mode: in the first set (amip-lfmip-pdLC) the land feedbacks to climate change have been disabled by prescribing the soil-moisture states from a climatology derived from &#8220;present climate conditions&#8221; (1980-2014) while in the second set (amip-lfmip-rmLC) 30-year running mean of land-surface state from the corresponding ScenarioMIP experiment (O&#8217;Neill et al., 2016) is prescribed. The two sensitivity simulations span the period 1980-2100 with sea surface temperature and sea-ice conditions prescribed from the first member of historical and ScenarioMIP experiments. Two different scenarios are considered: SSP1-2.6 (f1) and SSP5-8.5 (f2).In this analysis, we focus on the differences between amip-lfmip-rmLC and amip-lfmip-pdLC at the end of the 21st Century (2071&#8211;2100) in order to isolate the impact of the soil moisture changes on surface climate change. The (2071-2100) minus (1985-2014) temperature change is positive everywhere over land and the climate change signal of precipitation displays a clear intensification of the hydrological cycle in the Northern Hemisphere. Warming and hydrological cycle intensification are larger in SSP5-8.5 scenario. Results show large differences in the feedbacks between wet, transition and semi-arid climates. In particular, over the regions with negative soil moisture change, the 2m-temperature increases significantly while the cooling signal is not significant over all the regions getting wetter. In agreement with Catalano et al. (2016), the larger effects on precipitation due to soil moisture forcing occur mostly over transition zones between dry and wet climates, where evaporation is highly sensitive to soil moisture. The sensitivity of both 2m-temperature and precipitation to soil moisture change is much stronger in the SSP5-8.5 scenario.

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Bayesian Spatio-temporal Geostatistics-based Method for Multiple Satellite Products Fusion and Downscaling

10.5194/egusphere-egu2020-20814 ◽

2020 ◽

Author(s):

Yanchen Bo

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Satellite Remote Sensing ◽

Hydrological Cycle ◽

Microwave Remote Sensing ◽

Earth Surface ◽

Spatio Temporal ◽

High Level

High-level satellite remote sensing products of Earth surface play an irreplaceable role in global climate change, hydrological cycle modeling and water resources management, environment monitoring and assessment. Earth surface high-level remote sensing products released by NASA, ESA and other agencies are routinely derived from any single remote sensor. Due to the cloud contamination and limitations of retrieval algorithms, the remote sensing products derived from single remote senor are suspected to the incompleteness, low accuracy and less consistency in space and time. Some land surface remote sensing products, such as soil moisture products derived from passive microwave remote sensing data have too coarse spatial resolution to be applied at local scale. Fusion and downscaling is an effective way of improving the quality of satellite remote sensing products.We developed a Bayesian spatio-temporal geostatistics-based framework for multiple remote sensing products fusion and downscaling. Compared to the existing methods, the presented method has 2 major advantages. The first is that the method was developed in the Bayesian paradigm, so the uncertainties of the multiple remote sensing products being fused or downscaled could be quantified and explicitly expressed in the fusion and downscaling algorithms. The second advantage is that the spatio-temporal autocorrelation is exploited in the fusion approach so that more complete products could be produced by geostatistical estimation.This method has been applied to the fusion of multiple satellite AOD products, multiple satellite SST products, multiple satellite LST products and downscaling of 25 km spatial resolution soil moisture products. The results were evaluated in both spatio-temporal completeness and accuracy.

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Evaluation of a NOAH-MP Land Surface Model Ensemble over the Mississippi Basin

10.5194/egusphere-egu2020-21890 ◽

2020 ◽

Author(s):

Jonas Rothermel ◽

Maike Schumacher

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Numerical Weather Prediction ◽

Land Surface ◽

Hydrological Cycle ◽

Weather Prediction ◽

Surface Model ◽

Empirical Parameter ◽

Numerical Weather ◽

Parameter Values

Physical-based Land Surface Models (LSMs) have deepened the understanding of the hydrological cycle and serve as the lower boundary layer in atmospheric models for numerical weather prediction. As any numerical model, they are subject to various sources of uncertainty, including simplified model physics, unknown empirical parameter values and forcing errors, particularly precipitation. Quantifying these uncertainties is important for assessing the predictive power of the model, especially in applications for environmental hazard warning. Data assimilation systems also benefit from realistic model error estimates.In this study, the LSM NOAH-MP is evaluated over the Mississippi basin by running a large ensemble of model configurations with suitably perturbed forcing data and parameter values. For this, sensible parameter distributions are obtained by performing a thorough sensitivity analysis, identifying the most informative parameters beforehand by a screening approach. The ensemble of model outputs is compared against various hydrologic and atmospheric feedback observations, including SCAN soil moisture data, GRACE TWS anomaly data and AmeriFlux evapotranspiration measurements. The long-term aim of this study is to improve land-surface states via data assimilation and to investigate their influence on short- to midterm numerical weather prediction. Thus, the uncertainty of the simulated model states, such as snow, soil moisture in various layers, and groundwater are thoroughly studied to estimate the relative impact of possible hydrologic data sets in the assimilation.

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The detection of irrigation through remote sensing soil moisture and a land surface model: a case study in Spain

10.5194/egusphere-egu2020-3654 ◽

2020 ◽

Author(s):

Jacopo Dari ◽

Pere Quintana-Seguí ◽

María José Escorihuela ◽

Luca Brocca ◽

Renato Morbidelli ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Land Surface ◽

Hydrological Cycle ◽

Land Surface Model ◽

Surface Model ◽

Data Sets ◽

Data Set ◽

Agricultural Areas ◽

Irrigation Practices

Irrigation practices introduce imbalances in the natural hydrological cycle at different spatial scales and put pressure on water resources, especially under climate changing and population increasing scenarios. Despite the implications of irrigation on food production and on the rational management of the available freshwater, detailed information about the areas where irrigation actually occurs is still lacking. For this reason, the comprehensive knowledge of the dynamics of the hydrological cycle over agricultural areas is often tricky.The first aim of this study is to evaluate the capability of five remote sensing soil moisture data sets to detect the irrigation signal over an intensely irrigated area located within the Ebro river basin, in the North of Spain, during the biennium 2016-2017. As a second objective, a methodology to map the irrigated areas through the K-means clustering algorithm is proposed. The remotely sensed soil moisture products used in this study are: SMOS (Soil Moisture and Ocean Salinity) at 1 km, SMAP (Soil Moisture Active Passive) at 1 km and 9 km, Sentinel-1 at 1 km and ASCAT (Advanced SCATterometer) at 12.5 km. The 1 km versions of SMOS and SMAP are DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled versions of the corresponding coarser resolution products. An additional data set of soil moisture simulated by the SURFEX-ISBA (Surface Externalis&#233;e - Interaction Sol Biosph&#232;re Atmosph&#232;re) land surface model is used as a support for the performed analyses.The capability of soil moisture products to detect irrigation has been investigated by exploiting indices representing the spatial and temporal dynamics of soil moisture. The L-band passive microwave downscaled products, especially SMAP at 1 km, result the best performing ones in detecting the irrigation signal over the pilot area; on the basis of these data sets, the K-means algorithm has been employed to classify three kinds of surfaces within the study area: the dryland, the forest or natural areas, and the actually irrigated areas. The resulting maps have been validated by exploiting maps of crops in Catalonia as ground truth data set. The percentage of irrigated areas well classified by the proposed method reaches the value of 78%; this result is obtained for the period May - September 2017. In addition, the method performs well in distinguishing the irrigated areas from rainfed agricultural areas, which are dry during summer, thus representing a useful tool to obtain explicit spatial information about where irrigation practices actually occur over agricultural areas equipped for this purpose.

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Canopy-scale biophysical controls of transpiration and evaporation in the Amazon Basin

Hydrology and Earth System Sciences ◽

10.5194/hess-20-4237-2016 ◽

2016 ◽

Vol 20 (10) ◽

pp. 4237-4264 ◽

Cited By ~ 20

Author(s):

Kaniska Mallick ◽

Ivonne Trebs ◽

Eva Boegh ◽

Laura Giustarini ◽

Martin Schlerf ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Water Resource Management ◽

Large Scale ◽

Amazon Basin ◽

Climate Models ◽

Spatial Scales ◽

Rooting Depth ◽

Combining Data ◽

Biophysical Controls

Abstract. Canopy and aerodynamic conductances (gC and gA) are two of the key land surface biophysical variables that control the land surface response of land surface schemes in climate models. Their representation is crucial for predicting transpiration (λET) and evaporation (λEE) flux components of the terrestrial latent heat flux (λE), which has important implications for global climate change and water resource management. By physical integration of radiometric surface temperature (TR) into an integrated framework of the Penman–Monteith and Shuttleworth–Wallace models, we present a novel approach to directly quantify the canopy-scale biophysical controls on λET and λEE over multiple plant functional types (PFTs) in the Amazon Basin. Combining data from six LBA (Large-scale Biosphere-Atmosphere Experiment in Amazonia) eddy covariance tower sites and a TR-driven physically based modeling approach, we identified the canopy-scale feedback-response mechanism between gC, λET, and atmospheric vapor pressure deficit (DA), without using any leaf-scale empirical parameterizations for the modeling. The TR-based model shows minor biophysical control on λET during the wet (rainy) seasons where λET becomes predominantly radiation driven and net radiation (RN) determines 75 to 80 % of the variances of λET. However, biophysical control on λET is dramatically increased during the dry seasons, and particularly the 2005 drought year, explaining 50 to 65 % of the variances of λET, and indicates λET to be substantially soil moisture driven during the rainfall deficit phase. Despite substantial differences in gA between forests and pastures, very similar canopy–atmosphere "coupling" was found in these two biomes due to soil moisture-induced decrease in gC in the pasture. This revealed the pragmatic aspect of the TR-driven model behavior that exhibits a high sensitivity of gC to per unit change in wetness as opposed to gA that is marginally sensitive to surface wetness variability. Our results reveal the occurrence of a significant hysteresis between λET and gC during the dry season for the pasture sites, which is attributed to relatively low soil water availability as compared to the rainforests, likely due to differences in rooting depth between the two systems. Evaporation was significantly influenced by gA for all the PFTs and across all wetness conditions. Our analytical framework logically captures the responses of gC and gA to changes in atmospheric radiation, DA, and surface radiometric temperature, and thus appears to be promising for the improvement of existing land–surface–atmosphere exchange parameterizations across a range of spatial scales.

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The effect of chalk representation in land surface modelling

10.5194/hess-2016-244 ◽

2016 ◽

Author(s):

Mostaquimur Rahman ◽

Rafael Rosolem

Keyword(s):

Heat Flux ◽

Soil Moisture ◽

Latent Heat ◽

Latent Heat Flux ◽

Land Surface ◽

Unsaturated Zone ◽

Water Movement ◽

Hydrological Cycle ◽

Surface Model ◽

Surface Mass

Abstract. Modelling and monitoring of hydrological processes in the unsaturated zone of the chalk, which is a porous medium with fractures, is important to optimize water resources assessment and management practices in the United Kingdom (UK). However, efficient simulations of water movement through chalk unsaturated zone is difficult mainly due to the fractured nature of chalk, which creates high-velocity preferential flow paths in the subsurface. Complex hydrology in the chalk aquifers may also influence land surface mass and energy fluxes because processes in the hydrological cycle are connected via non-linear feedback mechanisms. In this study, it is hypothesized that explicit representation of chalk hydrology in a land surface model influences land surface processes by affecting water movement through the shallow subsurface. In order to substantiate this hypothesis, a macroporosity parameterization is implemented in the Joint UK Land Environment Simulator (JULES), which is applied on a study area encompassing the Kennet catchment in the Southern UK. The simulation results are evaluated using field measurements and satellite remote sensing observations of various fluxes and states in the hydrological cycle (e.g., soil moisture, runoff, latent heat flux) at two distinct spatial scales (i.e., point and catchment). The results reveal the influence of representing chalk hydrology on land surface mass and energy balance components such as surface runoff and latent heat flux via subsurface processes (i.e., soil moisture dynamics) in JULES, which corroborates the proposed hypothesis.

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