From hillslope to catchment scale hydrologic prediction in a semi-arid region with in-situ observations, satellite soil moisture products, and a distributed catchment model

2020 ◽  
Author(s):  
In-Young Yeo ◽  
Ali Binesh ◽  
Garry Willgoose ◽  
Greg Hancock ◽  
Omer Yeteman
Keyword(s):  
Soil Moisture ◽  
Arid Region ◽  
Remotely Sensed ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Limited Region ◽  
Semi Arid Region ◽  
Catchment Model ◽  
Semi Arid

<p>The water-limited region frequently experiences extreme climate variability.  This region, however, has relatively little hydrological information to characterize the catchment dynamics and its feedback to the climate system. This study assesses the relative benefits of using remotely sensed soil moisture, in addition to sparsely available in-situ soil moisture and stream flow observations, to improve the hydrologic understanding and prediction.  We propose a multi-variable approach to calibrate a hydrologic model, Soil and Water Assessment Tool (SWAT), a semi-distributed, continuous catchment model, with observed streamflow and in-situ soil moisture.  The satellite<span> soil moisture products (~ 5 cm top soil) from the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) are then used to evaluate the model estimates of soil moisture over the spatial scales through time.  The results show the model calibrated against streamflow only could provide misleading prediction for soil moisture.  Long term in-situ soil moisture observations, albeit limited availability, are crucial to constrain model parameters leading to improved soil moisture prediction at the given site.  </span><span>Satellite soil moisture products </span><span>provide useful information to assess simulated soil moisture results across the spatial domains, filling the gap on the soil moisture information at landscape scales.</span> <span>The preliminary results from this study suggest the potential to produce robust soil moisture and streamflow estimates across scales for a semi-arid region, using a distributed catchment model with in-situ soil network and remotely sensed observations and enhance the overall water budget estimations for multiple hydrologic variables across scales.  </span>This research is conducted on Merriwa catchment, a semi-arid region located in the Upper Hunter Region of NSW, Australia.</p>

scholarly journals Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1953 ◽  
Author(s):  
Safa Bousbih ◽  
Mehrez Zribi ◽  
Mohammad El Hajj ◽  
Nicolas Baghdadi ◽  
Zohra Lili-Chabaane ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Arid Region ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Remotely Sensed Data ◽  
Semi Arid Region ◽  
Semi Arid ◽  
Sentinel 2 ◽  
Reference Fields

This paper presents a technique for the mapping of soil moisture and irrigation, at the scale of agricultural fields, based on the synergistic interpretation of multi-temporal optical and Synthetic Aperture Radar (SAR) data (Sentinel-2 and Sentinel-1). The Kairouan plain, a semi-arid region in central Tunisia (North Africa), was selected as a test area for this study. Firstly, an algorithm for the direct inversion of the Water Cloud Model (WCM) was developed for the spatialization of the soil water content between 2015 and 2017. The soil moisture retrieved from these observations was first validated using ground measurements, recorded over 20 reference fields of cereal crops. A second method, based on the use of neural networks, was also used to confirm the initial validation. The results reported here show that the soil moisture products retrieved from remotely sensed data are accurate, with a Root Mean Square Error (RMSE) of less than 5% between the two moisture products. In addition, the analysis of soil moisture and Normalized Difference Vegetation Index (NDVI) products over cultivated fields, as a function of time, led to the classification of irrigated and rainfed areas on the Kairouan plain, and to the production of irrigation maps at the scale of individual fields. This classification is based on a decision tree approach, using a combination of various statistical indices of soil moisture and NDVI time series. The resulting irrigation maps were validated using reference fields within the study site. The best results were obtained with classifications based on soil moisture indices only, with an accuracy of 77%.

Spatial and temporal dynamics of soil moisture for surfaces with a change in land use in the semi-arid region of Brazil

CATENA ◽  
2020 ◽  
Vol 188 ◽  
pp. 104457 ◽  
Author(s):  
Maria Gabriela de Queiroz ◽  
Thieres George Freire da Silva ◽  
Sérgio Zolnier ◽  
Alexandre Maniçoba da Rosa Ferraz Jardim ◽  
Carlos André Alves de Souza ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Arid Region ◽  
Temporal Dynamics ◽  
Spatial And Temporal Dynamics ◽  
Semi Arid Region ◽  
Semi Arid

Quantification of irrigation water using remote sensing of soil moisture in a semi-arid region

2019 ◽  
Vol 231 ◽  
pp. 111226 ◽  
Author(s):  
Ehsan Jalilvand ◽  
Masoud Tajrishy ◽  
Sedigheh Alsadat Ghazi Zadeh Hashemi ◽  
Luca Brocca
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Irrigation Water ◽  
Arid Region ◽  
Semi Arid Region ◽  
Semi Arid

scholarly journals Semi-Arid Region Soil Moisture Prediction using Multivariate Regression

2019 ◽  
Vol 8 (4) ◽  
pp. 12457-12460
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Properties ◽  
Arid Region ◽  
Soil Moisture Content ◽  
Linear Regression Analysis ◽  
Organic Content ◽  
Dry Density ◽  
Semi Arid Region ◽  
Semi Arid

The Water Scarcity is a prominent feature in Arid and Semi-Arid region. Soil moisture content is significant factor in deciding vegetation growth and also affects the performance of any water harvesting system in place. This paper evaluates the interrelationship of Soil properties with Soil Moisture content. The study covers about 13 soil Samples from Single Watershed. The soil properties covered in the study are Conductivity, pH, Bulk Density, Dry Density, Specific gravity, organic content, void ratio, and Moisture Content. Multiple linear regression analysis was done to determine significance of each soil properties for soil moisture content as individual and as whole. Modelling was done based on soil characteristics to predict Soil Moisture. Principal Component Analysis was performed to identify most significant soil properties responsible for variation of prediction of Soil Moisture content. The Correlation between location topography and Moisture Content was obtained through Cluster Analysis.

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