Climate change and interaction with water resources

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MODIS data

MODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument NASA’s Terra satellite (launched in 1999) and Aqua satellite (launched in 2002) as part of the Earth Observing System (EOS) Programme (Barnes et al., 1998; Justice et al., 1998). The MODIS instrument acquires data in 36 spectral bands ranging in wavelength from 0.4 µm to 14.4 µm. The spatial resolution of the products ranges from 250 m to 500 m and to 1 km, depending on the bands used, with daily time coverage.
In this study, the 250-m spatial resolution, 16-day composites of MODIS NDVI data (MOD13Q1, collection 5) were used to calculated NDVI with equation 3.1 for soil moisture study (see Chapter 4). This product is retrieved from atmosphere-corrected, daily bidirectional surface re ectance observations, using a compositing technique based on product quality assurance metrics to re-move low-quality pixels. The 250-m spatial resolution is the nest available from the MODIS NDVI dataset, and the 16-day composite was selected in order to ensure a high probability of having the best quality pixel (reduced cloud e ects) representing the NDVI within every 16 days.

DEM data

A DEM was used as an ancillary dataset for improving the accuracy of water level retrieval (see Chapter 6). The Shuttle Radar Topography Mission (SRTM) is an international research e ort that obtains DEM data on a near-global scale from 56◦S to 60◦N (Farr et al., 2007). The SRTM provides global data at two resolutions: 1 arc-second ( 30 m) and 3 arc-seconds ( 90 m). In this thesis study, Version 2.1 of the 1 arc-second global elevation data, o ering worldwide coverage of void- lled data at a resolution of approximately 30 m, is used. The vertical accuracy of SRTM DEM is about ±16 m (absolute) and ±6 m (relative) (Farr et al., 2007; Mukul et al., 2015, 2017; Rabus et al., 2003; Elkhrachy, 2017). However, the vertical accuracy of the data decreases with the increase in slope and elevation due to the presence of large outliers and voids (Mukul et al., 2017).

SMOS data

SMOS (Soil Moisture and Ocean Salinity) mission, launched in November 2009, is to observe the soil moisture over Earth’s landmasses and the salinity over Earth’s oceans. The SMOS satellite carries a new type of instrument called Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), which measures Earth surface in protected L-band at a 1.4 GHz frequency (Kerr et al., 2001; Kerr et al., 2010). SMOS has global coverage and a 3-day equatorial revisiting period. The top soil layer (5 cm) moisture is retrieved with an accuracy of 0.04 m3/m3 (Kerr et al., 2012). The spatial resolution ranges from 35 to 55 km, depending on the incidence angle. SMOS low-resolution moisture products (SMOS Level 3 daily product), corresponding to the two-year period of Sentinel-1 acquisitions, were used as ancillary data for soil moisture estimation (see Chapter 4).

In situ data

Ground soil moisture measurements

In-situ soil moisture measurements were acquired continuously over a period of several months, in two demonstration elds belonging to the new irrigation SG system: Foradada and Agramunt (Figure 3.4). Each measurement point was analysed at di erent depths. Table 3.6 lists the measured soil moisture and texture characteristics of the two test elds.
The ground soil moisture measurements are done by capacitive EC-5 sensors (METER Group, Pullman, WA, USA) at a di erent depth. The EC-5 determines volumetric water content (VWC) by measuring the dielectric constant of the media using capacitance/frequency domain technol-ogy at a 5-minute sampling frequency. The di erent irrigation methods between the two sites, along with the di erent pedological properties, make their ranges of soil moisture variation very di erent.
The in situ soil moisture measurements serve as a validation dataset for the satellite retrieved soil moisture products (see Chapter 4). Since the electromagnetic (EM) waves can only measure the properties of the surface soil, the in situ moisture measurements with the depth nearest to the surface are considered. The soil moisture measurements obtained at a depth of 3 cm in the Foradada eld, and at a depth of 5 cm in the Agramunt eld, are used for comparison with the satellite estimations.

Meteorological data

The meteorological data were collected from Ruralcat website (Ruralcat, 2018). The precipi-tation along with other meteorological data including temperature and humidity were downloaded with hourly or daily resolution. The nearest meteorological station to the demonstration eld is chosen as auxillary data. For the Foradada eld, the Baldomar station, which is about 6 km away, is taken, while for Agramunt eld, the Tornabous station, which is about 11 km, is taken. Meteorological data were used as supplementary data for the analysis of the satellite retrieved soil moisture (see Chapter 4).

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The Geographic Information System for Agricultural Parcels (SIGPAC) is a public registry of an administrative nature that contains information on the parcels, which is updated every year (Direccio General de Desenvolupament Rural, 2018). The supplied SIGPAC plotter contains both graphical and alphanumeric information. The graphics information includes the referenced geographic boundary of each plot of land. Alphanumeric information contains information about each one of the enclosures such as identi cation codes, surface area, land usage, irrigation coef-cient, average slope, and others. The interface is shown in Figure 3.6, and further information can be found on the website: (DARP, 2018). In this study, the SIGPAC data were used as ground truth to validate irrigation mapping results with information regarding the land usage and irrigation coe cient (see Chapter 5).


In situ data of water levels and river ows for the Ebro River basin are available in the Automatic Hydrological Information System (in Spanish Sistema Automatico de Informacion Hidrologica – SAIH) Ebro data hub, with examples shown in Figure 3.7. SAIH Ebro is an online system providing hourly and daily hydrological information, including river gauge data, reservoir levels, rainfall amounts and temperatures, over the Ebro River basin (SAIH Ebro, 2018). The in situ data were collected from June 2016 to June 2018, used as validation for water level retrieval (see Chapter 6) and hydrological simulation involving the reservoirs (see Chapter 7).


This chapter presented the study area for water resources estimation, where the Ebro river basin is considered for water estimation in reservoirs and the analysis of dam in uence (see Chapter 6), and the Urgell site within the Ebro river basin is considered for irrigation mapping and soil moisture study(see Chapter 4 and 5). The Ebro River basin is within the main Hot-Spot of climate change, and irrigation is well developed there for agriculture, making it a typical area for the study of anthropogenic e ects for the water cycle.
The dataset including the remote sensing data and the in situ data were presented as well. The Sentinel-1, 2, and 3 are the main satellite data used for methodology development, while DEM and SMOS data are used as supplementary data. The validation data for soil moisture retrieval comes from two demonstrative elds, which are located in Foradada and Agramunt, whilst for irrigation mapping, the SIGPAC system o ers the irrigation coe cient indicating whether the eld is irrigated or not. The SAIH Ebro system, which provides the water level, river ow and characteristics of plenty of reservoirs, serves as the validation data for water level retrieval and river ow simulation for the analysis of dams in uences.
With all the valuable datasets, the methodology can be developed with con dence, the re-trieved results can be validated, and the accuracy can be assessed.

Table of contents :

1 Introduction 
1.1 Context
1.1.1 Climate change and interaction with water resources
1.1.2 Water resources in global scale
1.1.3 Water resources in regional scale
1.1.4 Water cycle and anthropogenic interventions
1.2 Water resources estimation
1.2.1 Hydrological models and land surface models
1.2.2 Measurements and remote sensing techniques
1.2.3 Challenges
1.3 About the thesis
1.3.1 Objectives
1.3.2 Structure of the thesis
2 Remote Sensing Principles 
2.1 Introduction
2.1.1 Electromagnetic waves
2.1.2 Characteristics of electromagnetic waves
2.2 Microwave remote sensing
2.2.1 Radar
2.2.2 Synthetic Aperture Radar
2.2.3 Imaging systems Scattering mechanisms Topographic eects Speckle eect Soil moisture principle
2.2.4 Ranging system and principle of altimetry Trackers and Retrackers Pulse-limited altimetry SAR/Delay-Doppler altimetry
2.3 Conclusion
3 Study Area and Datasets 
3.1 Study area
3.1.1 Ebro basin
3.1.2 Urgell site
3.2 Remote sensing dataset
3.2.1 Sentinel data
3.2.2 MODIS data
3.2.3 DEM data
3.2.4 SMOS data
3.3 In situ data
3.3.1 Ground soil moisture measurements
3.3.2 Meteorological data
3.3.3 SIGPAC
3.3.4 SAIH Ebro
3.4 Conclusion
4 Soil Moisture Retrieval 
4.1 Introduction
4.2 Methodologies
4.2.1 Backscatter dierence with the driest date
4.2.2 Backscatter dierence between two consecutive dates
4.3 Soil moisture at 1 km resolution
4.4 Soil moisture at 100 m resolution
4.5 Published paper
4.6 Summary and Conclusions
5 Irrigation Mapping 
5.1 Introduction
5.2 Methodology
5.3 Published paper
5.4 Summary and Conclusions
6 Water Level Retrieval 
6.1 Introduction
6.2 Methodologies
6.3 Published paper
6.4 Summary and Conclusions
7 Dam Simulation 
7.1 Introduction
7.2 Models
7.2.1 SURFEX land surface model
7.2.2 SAFRAN meteorological analysis system
7.2.3 SASER hydrological model
7.3 Methodology
7.3.1 Forcing model
7.3.2 Conversion from water level to volume
7.4 Results
7.4.1 Simulation
7.4.2 Satellite data forcing
7.5 Conclusion
8 Conclusions and Perspectives 
8.1 Main conclusions
8.2 Future research lines


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