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Estimation of the Total Water Entering the Soil (Precipitation and Irrigation) Using in Situ and Satellite Soil Moisture Data

Jalilvand, Ehsan | 2019

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 51829 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Tajrishy, Masoud; Brocca, Luca
  7. Abstract:
  8. Precipitation is the most important term in the world water balance and irrigation is the major consumer of fresh water resources. However, most of the precipitation products are unable to accurately estimate the amount of water that is reaching the ground. Moreover limited information are available in terms of irrigated area and irrigation water use (IWU) at the global scale. On the other hand the confounding effect of the climate change and the world population growth has accelerated the decline in the world fresh water resources. Thereby, the precise estimates of these two parameters (i.e. Precipitation and irrigation) is essential for food and water security. In this study the soil water balance is inverted through SM2RAIN algorithm to estimate the total water entering the soil using the measured soil moisture variation The SM2RAIN model that is developed by Brocca et al (2014) can estimate precipitation from the satellite soil moisture data at global scale. But one of the major drawbacks of this model is the need for calibration against ground truth precipitation data to estimate the model parameters (drainage parameters), in other words SM2RAIN needs precipitation to estimate precipitation. So in this study a new approach named Drainage from Drydown (DfD) is proposed to estimate the coefficients of drainage using soil moisture observations which can be later used in SM2RAIN and relax the need for the calibration in this model. DfD firstly selects multiple drydown events when surface runoff and evapotranspiration rates are negligible compared to the drainage rate. Secondly, by inverting the soil water balance equation, the drainage coefficients are obtained. Synthetic experiments are carried out in order to tune the overall procedure. DfD is then tested with in situ observations at 8 different sites worldwide characterized by different climates and soil types. The reliability of the DfD is evaluated by using the DfD drainage coefficients in a physically based soil water balance model (SWB) for simulating soil moisture and a rainfall estimation model (SM2RAIN). The results indicate that the climate and the soil conditions exert an important role in the occurrence and magnitude of drainage rate. DfD is found capable of correctly identify periods in which drainage rate is the dominant process. Drainage coefficients obtained from DfD are consistent with the expected soil hydraulic properties based on the soil texture and land cover at each site. By using DfD drainage coefficients to estimate rainfall and soil moisture via SM2RAIN and SWB, promising results are obtained with median correlation of 0.83 and 0.91 between estimated and in situ data. However, in sites characterized by high rate of evapotranspiration (>700 mm/year) and low permeable soil (e.g., clay) the DfD performance is reduced. Overall, DfD demonstrates the ability to decouple drainage and evapotranspiration processes and to estimate the drainage coefficients from in situ observations.In the second part of the study the SM2RAIN model is used to estimate the IWU. The satellite soil moisture observations obtained from Advanced Microwave Scanning Radiometer 2 (AMSR2) along with different rainfall and evapotranspiration (ET) products in the period 2012- 2015 are used as the input to the model. The methodology is tested in the agricultural plains of southern Urmia Lake, which is one of the main agricultural plains in Iran for which actual irrigation data is available.The results reveal that the proposed approach can capture the overall irrigation pattern, although; it is systematically overestimating irrigation volume compared to observed irrigation data. Thus the bias is calculated over largely non-irrigated pixels and used to modify the model estimates. The bias-corrected results show good agreement with the in situ irrigation data. In particular, the average model performance in the irrigated pixels in terms of R and RMSE (mm/month) are (0.86 and 12.895) respectively. Accuracy varied depending on the inputs, with improvement in order of 11% and 42% in R and RMSE depending on the inputs chosen. The method is also applied to less irrigated areas that result in obtaining significantly lower irrigation rates.The low spatial resolution of soil moisture products (i.e. ~50 km) makes it difficult to capture the irrigation water of small irrigated croplands. Unreliable rainfall and ET data can also lead to the over/underestimation of irrigation. In spite of the above limitations (particularly lack of reliable ET dataset), the proposed model can still capture the irrigation pattern, given that strong soil moisture signal from irrigation is detected by the satellite
  9. Keywords:
  10. Satellite Soil Moisture Data ; Soil Moisture to Rain (SM2RAIN) ; Drainage from Drydown (DFD) ; Irrigation ; Miandoab Plain ; Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) ; Soil Moisture

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