scholarly journals Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2083 ◽  
Author(s):  
Demirel ◽  
Özen ◽  
Orta ◽  
Toker ◽  
Demir ◽  
...  
Keyword(s):  
Soil Moisture ◽  
European Space Agency ◽  
Hydrologic Model ◽  
Distributed Models ◽  
Earth Observing System ◽  
Space Agency ◽  
Physically Based ◽  
Individual Source ◽  
Spatial Calibration ◽  
Additional Value

Although the complexity of physically-based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e., Hydrologiska Bryåns Vattenbalansavdelning (HBV). This has rarely been done for conceptual models, as satellite data are often used in the spatial calibration of the distributed models. Three different soil moisture products from the European Space Agency Climate Change Initiative Soil Measure (ESA CCI SM v04.4), The Advanced Microwave Scanning Radiometer on the Earth Observing System (EOS) Aqua satellite (AMSR-E), soil moisture active passive (SMAP), and total water storage anomalies from Gravity Recovery and Climate Experiment (GRACE) are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms, all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture, are used to analyze the contribution of each individual source of information.

scholarly journals Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration

2019 ◽  
Author(s):  
Mehmet Cüneyd Demirel ◽  
Alparslan Özen ◽  
Selen Orta ◽  
Emir Toker ◽  
Hatice Kübra Demir ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Model Calibration ◽  
Hydrologic Model ◽  
Substantial Contribution ◽  
Lumped Model ◽  
Groundwater Levels ◽  
Distributed Models ◽  
Physically Based ◽  
Spatial Calibration

Although the complexity of physically based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e. HBV. This has been rarely done for conceptual models as satellite data are often used in spatial calibration of the distributed models. Three different soil moisture products from ESA CCI SM v04.4, AMSR-E and SMAP, and total water storage anomalies from GRACE are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture are used to analyse the contribution of each individual source of information. Firstly, the most important parameters are selected using sensitivity analysis and then, these parameters are included in a subsequent model calibration. The results of our multi-objective calibration reveal substantial contribution of remote sensing products to the lumped model calibration even if their spatially distributed information is lost during the spatial aggregation. Inclusion of new observations such as groundwater levels from wells and remotely sensed soil moisture to the calibration improves the model’s physical behaviour while it keeps a reasonable water balance that is the key objective of every hydrologic model.

scholarly journals Evaluation of the Performance of SM2RAIN-Derived Rainfall Products over Brazil

2019 ◽  
Vol 11 (9) ◽  
pp. 1113 ◽  
Author(s):  
Franklin Paredes-Trejo ◽  
Humberto Barbosa ◽  
Carlos A. C. dos Santos
Keyword(s):  
Soil Moisture ◽  
Pearson Correlation ◽  
European Space Agency ◽  
Rainfall Events ◽  
Error Components ◽  
Space Agency ◽  
Daily Scale ◽  
Bioclimatic Conditions ◽  
Heavy Rainfall Events

Microwave-based satellite soil moisture products enable an innovative way of estimating rainfall using soil moisture observations with a bottom-up approach based on the inversion of the soil water balance Equation (SM2RAIN). In this work, the SM2RAIN-CCI (SM2RAIN-ASCAT) rainfall data obtained from the inversion of the microwave-based satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (CCI) (from the Advanced SCATterometer (ASCAT) soil moisture data) were evaluated against in situ rainfall observations under different bioclimatic conditions in Brazil. The research V7 version of the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TRMM TMPA) was also used as a state-of-the-art rainfall product with an up-bottom approach. Comparisons were made at daily and 0.25° scales, during the time-span of 2007–2015. The SM2RAIN-CCI, SM2RAIN-ASCAT, and TRMM TMPA products showed relatively good Pearson correlation values (R) with the gauge-based observations, mainly in the Caatinga (CAAT) and Cerrado (CER) biomes (R median > 0.55). SM2RAIN-ASCAT largely underestimated rainfall across the country, particularly over the CAAT and CER biomes (bias median < −16.05%), while SM2RAIN-CCI is characterized by providing rainfall estimates with only a slight bias (bias median: −0.20%), and TRMM TMPA tended to overestimate the amount of rainfall (bias median: 7.82%). All products exhibited the highest values of unbiased root mean square error (ubRMSE) in winter (DJF) when heavy rainfall events tend to occur more frequently, whereas the lowest values are observed in summer (JJA) with light rainfall events. The SM2RAIN-based products showed larger contribution of systematic error components than random error components, while the opposite was observed for TRMM TMPA. In general, both SM2RAIN-based rainfall products can be effectively used for some operational purposes on a daily scale, such as water resources management and agriculture, whether the bias is previously adjusted.

A Spatial and Temporal Continuum Remotely Sensed Soil Moisture Dataset of the Tibet Plateau From 2002 to 2015

2020 ◽  
Author(s):  
Yaokui Cui ◽  
Chao Zeng ◽  
Jie Zhou ◽  
Xi Chen
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Land Surface ◽  
European Space Agency ◽  
Remotely Sensed ◽  
General Regression Neural Network ◽  
Tibet Plateau ◽  
The Tibetan Plateau ◽  
Space Agency ◽  
Spatio Temporal

&lt;p&gt;&lt;strong&gt;Abstract&lt;/strong&gt;:&lt;/p&gt;&lt;p&gt;Surface soil moisture plays an important role in the exchange of water and energy between the land surface and the atmosphere, and critical to climate change study. The Tibetan Plateau (TP), known as &amp;#8220;The third pole of the world&amp;#8221; and &amp;#8220;Asia&amp;#8217;s water towers&amp;#8221;, exerts huge influences on and sensitive to global climates. Long time series of and spatio-temporal continuum soil moisture is helpful to understand the role of TP in this situation. In this study, a dataset of 14-year (2002&amp;#8211;2015) Spatio-temporal continuum remotely sensed soil moisture of the TP at 0.25&amp;#176; resolution is obtained, combining MODIS optical products and ESA (European Space Agency) ECV (Essential Climate Variable) combined soil moisture products based on General Regression Neural Network (GRNN). The validation of the dataset shows that the soil moisture is well reconstructed with R&lt;sup&gt;2&lt;/sup&gt; larger than 0.65, and RMSE less than 0.08 cm&lt;sup&gt;3&lt;/sup&gt; cm&lt;sup&gt;-3&lt;/sup&gt; and Bias less than 0.07 cm&lt;sup&gt;3&lt;/sup&gt; cm&lt;sup&gt;-3 &lt;/sup&gt;at 0.25&amp;#176; and 1&amp;#176; spatial scale, compared with the in-situ measurements in the central of TP. And then, spatial&amp;#160;and&amp;#160;temporal&amp;#160;characteristics and trend of SM over TP were analyzed based on this dataset.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords: &lt;/strong&gt;Soil moisture; Remote Sensing; Dataset; GRNN; ECV; Tibetan Plateau&lt;/p&gt;

scholarly journals Physically based modeling of rainfall-triggered landslides: a case study in the Luquillo forest, Puerto Rico

2013 ◽  
Vol 17 (9) ◽  
pp. 3371-3387 ◽  
Author(s):  
C. Lepore ◽  
E. Arnone ◽  
L. V. Noto ◽  
G. Sivandran ◽  
R. L. Bras
Keyword(s):  
Soil Moisture ◽  
Soil Column ◽  
Hydrologic Model ◽  
Richards Equation ◽  
Landslide Risk ◽  
Modeling Framework ◽  
Soil Moisture Dynamics ◽  
Physically Based ◽  
Spatially Distributed ◽  
Moisture Dynamics

Abstract. This paper presents the development of a rainfall-triggered landslide module within an existing physically based spatially distributed ecohydrologic model. The model, tRIBS-VEGGIE (Triangulated Irregular Networks-based Real-time Integrated Basin Simulator and Vegetation Generator for Interactive Evolution), is capable of a sophisticated description of many hydrological processes; in particular, the soil moisture dynamics are resolved at a temporal and spatial resolution required to examine the triggering mechanisms of rainfall-induced landslides. The validity of the tRIBS-VEGGIE model to a tropical environment is shown with an evaluation of its performance against direct observations made within the study area of Luquillo Forest. The newly developed landslide module builds upon the previous version of the tRIBS landslide component. This new module utilizes a numerical solution to the Richards' equation (present in tRIBS-VEGGIE but not in tRIBS), which better represents the time evolution of soil moisture transport through the soil column. Moreover, the new landslide module utilizes an extended formulation of the factor of safety (FS) to correctly quantify the role of matric suction in slope stability and to account for unsaturated conditions in the evaluation of FS. The new modeling framework couples the capabilities of the detailed hydrologic model to describe soil moisture dynamics with the infinite slope model, creating a powerful tool for the assessment of rainfall-triggered landslide risk.

scholarly journals Modeling Influence of Soil Properties in Different Gradients of Soil Moisture: The Case of the Valencia Anchor Station Validation Site, Spain

2021 ◽  
Vol 13 (24) ◽  
pp. 5155
Author(s):  
Ester Carbó ◽  
Pablo Juan ◽  
Carlos Añó ◽  
Somnath Chaudhuri ◽  
Carlos Diaz-Avalos ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temporal Variation ◽  
European Space Agency ◽  
Spatial And Temporal Variation ◽  
Anchor Station ◽  
Space Agency ◽  
Ocean Salinity ◽  
Individual Covariates ◽  
Predicted Values ◽  
The Relationship

The prediction of spatial and temporal variation of soil water content brings numerous benefits in the studies of soil. However, it requires a considerable number of covariates to be included in the study, complicating the analysis. Integrated nested Laplace approximations (INLA) with stochastic partial differential equation (SPDE) methodology is a possible approach that allows the inclusion of covariates in an easy way. The current study has been conducted using INLA-SPDE to study soil moisture in the area of the Valencia Anchor Station (VAS), soil moisture validation site for the European Space Agency SMOS (Soil Moisture and Ocean Salinity). The data used were collected in a typical ecosystem of the semiarid Mediterranean conditions, subdivided into physio-hydrological units (SMOS units) which presents a certain degree of internal uniformity with respect to hydrological parameters and capture the spatial and temporal variation of soil moisture at the local fine scale. The paper advances the knowledge of the influence of hydrodynamic properties on VAS soil moisture (texture, porosity/bulk density and soil organic matter and land use). With the goal of understanding the factors that affect the variability of soil moisture in the SMOS pixel (50 km × 50 km), five states of soil moisture are proposed. We observed that the model with all covariates and spatial effect has the lowest DIC value. In addition, the correlation coefficient was close to 1 for the relationship between observed and predicted values. The methodology applied presents the possibility to analyze the significance of different covariates having spatial and temporal effects. This process is substantially faster and more effective than traditional kriging. The findings of this study demonstrate an advancement in that framework, demonstrating that it is faster than previous methodologies, provides significance of individual covariates, is reproducible, and is easy to compare with models.

scholarly journals Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery

2018 ◽  
Author(s):  
Katarzyna Dabrowska-Zielinska ◽  
Jan Musial ◽  
Alicja Malinska ◽  
Maria Budzynska ◽  
Radoslaw Gurdak ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Incidence Angle ◽  
Temporal Frequency ◽  
European Space Agency ◽  
Wetland Ecosystems ◽  
Space Agency ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

Soil moisture (SM) plays an essential role in environmental studies related to wetlands, an ecosystem sensitive to climate change. Hence, there is the need for its constant monitoring. SAR (Synthetic Aperture Radar) satellite imagery is the only mean to fulfill this objective regardless of the weather. The objective of the study was to develop the methodology for SM retrieval under wetland vegetation using Sentinel-1 (S-1) satellite data. The study was carried out during the years 2015&ndash;2017 in the Biebrza Wetlands, situated in northeastern Poland. At the Biebrza Wetlands, two Sentinel-1 validation sites were established, covering grassland and marshland biomes, where a network of 18 stations for soil moisture measurement was deployed. The sites were funded by the European Space Agency (ESA), and the collected measurements are available through the International Soil Moisture Network (ISMN). The NDVI (Normalized Difference Vegetation Index) was derived from the optical imagery of a MODIS (Moderate Resolution Imaging Spectroradiometer) sensor onboard the Terra satellite. The SAR data of the Sentinel-1 satellite with VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization were applied to soil moisture retrieval for a broad range of NDVI values and soil moisture conditions. The new methodology is based on research into the effect of vegetation on backscatter () changes under different soil moisture and vegetation (NDVI) conditions. It was found that the state of the vegetation may be described by the difference between  VH and  VV, or the ratio of  VV/VH, as calculated from the Sentinel-1 images. The most significant correlation coefficient for soil moisture was found for data that was acquired from the ascending tracks of the Sentinel-1 satellite, characterized by the lowest incidence angle, and SM at a depth of 5 cm. The study demonstrated that the use of the inversion approach, which was applied to the new developed models and includes the derived indices based on S-1, allowed the estimation of SM for peatlands with reasonable accuracy (RMSE ~ 10 vol. %). Due to the temporal frequency of the two S-1 satellites&rsquo; (S-1A and S-1B) acquisitions, it is possible to monitor SM changes every six days. The conclusion drawn from the study emphasizes a demand for the derivation of specific soil moisture retrieval algorithms that are suited for wetland ecosystems, where soil moisture is several times higher than in agricultural areas.

scholarly journals The International Soil Moisture Network: serving Earth system science for over a decade

2021 ◽  
Author(s):  
Wouter Dorigo ◽  
Irene Himmelbauer ◽  
Daniel Aberer ◽  
Lukas Schremmer ◽  
Ivana Petrakovic ◽  
...  
Keyword(s):  
Quality Control ◽  
Soil Moisture ◽  
European Space Agency ◽  
Data Repository ◽  
Reference Database ◽  
Data Sets ◽  
Scientific Publications ◽  
Data Set ◽  
Space Agency

Abstract. In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011a, b). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonizes them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of December 2020, the ISMN now contains data of 65 networks and 2678 stations located all over the globe, with a time period spanning from 1952 to present.The number of networks and stations covered by the ISMN is still growing and many of the data sets contained in the database continue to be updated. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade,including a description of network and data set updates and quality control procedures. A comprehensive review of existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage, and to shape priorities for the next decade of operations of this unique community-based data repository.

scholarly journals Irrigation and Precipitation Hydrological Consistency with SMOS, SMAP, ESA-CCI, Copernicus SSM1km, and AMSR-2 Remotely Sensed Soil Moisture Products

2020 ◽  
Vol 12 (22) ◽  
pp. 3737
Author(s):  
Nicola Paciolla ◽  
Chiara Corbari ◽  
Ahmad Al Bitar ◽  
Yann Kerr ◽  
Marco Mancini
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
European Space Agency ◽  
Irrigation Season ◽  
Surface Soil Moisture ◽  
Irrigation Period ◽  
Space Agency ◽  
Ocean Salinity ◽  
Passive Data ◽  
Active Data

Numerous Surface Soil Moisture (SSM) products are available from remote sensing, encompassing different spatial, temporal, and radiometric resolutions and retrieval techniques. Notwithstanding this variety, all products should be coherent with water inputs. In this work, we have cross-compared precipitation and irrigation with different SSM products: Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), European Space Agency (ESA) Climate Change Initiative (ESA-CCI) products, Copernicus SSM1km, and Advanced Microwave Scanning Radiometer 2 (AMSR2). The products have been analyzed over two agricultural sites in Italy (Chiese and Capitanata Irrigation Consortia). A Hydrological Consistency Index (HCI) is proposed as a means to measure the coherency between SSM and precipitation/irrigation. Any time SSM is available, a positive or negative consistency is recorded, according to the rainfall registered since the previous measurement and the increase/decrease of SSM. During the irrigation season, some agreements are labeled as “irrigation-driven”. No SSM dataset stands out for a systematic hydrological coherence with the rainfall. Negative consistencies cluster just below 50% in the non-irrigation period and lose 20–30% in the irrigation period. Hybrid datasets perform better (+15–20%) than single-technology measurements, among which active data provide slightly better results (+5–10%) than passive data.

scholarly journals The AQUI Soil Moisture Network for Satellite Microwave Remote Sensing Validation in South-Western France

2018 ◽  
Vol 10 (11) ◽  
pp. 1839 ◽  
Author(s):  
A. Al-Yaari ◽  
S. Dayau ◽  
C. Chipeaux ◽  
C. Aluome ◽  
A. Kruszewski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Agricultural Research ◽  
Pearson Correlation ◽  
European Space Agency ◽  
Microwave Remote Sensing ◽  
Climate Conditions ◽  
Space Agency ◽  
Satellite Microwave

Global soil moisture (SM) products are currently available thanks to microwave remote sensing techniques. Validation of these satellite-based SM products over different vegetation and climate conditions is a crucial step. INRA (National Institute of Agricultural Research) has set up the AQUI SM and soil temperature in situ network (composed of three main sites Bouron, Bilos, and Hermitage), over a flat area of dense pine forests, in South-Western France (the Bordeaux–Aquitaine region) to validate the Soil Moisture and Ocean salinity (SMOS) satellite SM products. SMOS was launched in 2009 by the European Space Agency (ESA). The aims of this study are to present the AQUI network and to evaluate the SMOS SM product (in the new SMOS-IC version) along with other microwave SM products such as the active ASCAT (Advanced Scatterometer) and the ESA combined (passive and active) CCI (Climate Change Initiative) SM retrievals. A first comparison, using Pearson correlation, Bias, RMSE (Root Mean Square Error), and Un biased RMSE (ubRMSE) scores, between the 0–5 cm AQUI network and ASCAT, CCI, and SMOS SM products was conducted. In general all the three products were able to reproduce the annual cycle of the AQUI in situ observations. CCI and ASCAT had best and similar correlations (R~0.72) over the Bouron and Bilos sites. All had comparable correlations over the Hermitage sites with overall average values of 0.74, 0.68, and 0.69 for CCI, SMOS-IC, and ASCAT, respectively. Considering anomalies, correlation values decreased for all products with best ability to capture day to day variations obtained by ASCAT. CCI (followed by SMOS-IC) had the best ubRMSE values (mostly < 0.04 m3/m3) over most of the stations. Although the region is highly impacted by radio frequency interferences, SMOS-IC followed correctly the in situ SM dynamics. All the three remotely-sensed SM products (except SMOS-IC over some stations) overestimated the AQUI in situ SM observations. These results demonstrate that the AQUI network is likely to be well-suited for satellite microwave remote sensing evaluations/validations.

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