Haimanote Bayabil

Soil Water Content Sensor Response to Organic Matter Content under Laboratory Conditions

Studies show that the performance of soil water content monitoring (SWCM) sensors is affected by soil physical and chemical properties. However, the effect of organic matter on SWCM sensor responses remains less understood. Therefore, the objectives of this study are to (i) assess the effect of organic matter on the accuracy and precision of SWCM sensors using a commercially available soil water content monitoring sensor; and (ii) account for the organic matter effect on the sensor’s accuracy. Sand columns with seven rates of oven-dried sawdust (2%, 4%, 6%, 8%, 10%, 12% and 18% v/v, used as an organic matter amendment), thoroughly mixed with quartz sand, and a control without sawdust were prepared by packing quartz sand in two-liter glass containers. Sand was purposely chosen because of the absence of any organic matter or salinity, and also because sand has a relatively low cation exchange capacity that will not interfere with the treatment effect of the current work. Sensor readings (raw counts) were monitored at seven water content levels (0, 0.02, 0.04, 0.08, 0.12, 0.18, 0.24, and 0.30 cm3 cm−3) by uniformly adding the corresponding volumes of deionized water in addition to the oven-dry one. Sensor readings were significantly (p < 0.05) affected by the organic matter level and water content. Sensor readings were strongly correlated with the organic matter level (R2 = 0.92). In addition, the default calibration equation underestimated the water content readings at the lower water content range (<0.05 cm3 cm−3), while it overestimated the water content at the higher water content range (>0.05 cm3 cm−3). A new polynomial calibration equation that uses raw count and organic matter content as covariates improved the accuracy of the sensor (RMSE = 0.01 cm3 cm−3). Overall, findings of this study highlight the need to account for the effect of soil organic matter content to improve the accuracy and precision of the tested sensor under different soils and environmental conditions.

Remote Sensing Applications for Monitoring Water Resources in the UAE Using Lake Zakher as a Water Storage Gauge

The potential of remote sensing has been fully demonstrated in large scale and regional hydrological studies where in situ observations are limited. However, the use of satellite imagery to monitor water resources in small watersheds remains challenging, mainly due to coarse resolution satellite data. In this study, we assessed the efficacy of remotely sensed data to investigate changes in water storage in Al Ain watershed, in the United Arab Emirates (UAE). Lake Zakher, in the watershed, was used in this study as a gauge indicating changes in water storage. The area of the lake was monitored using Landsat 7 and 8 images, which were then used with a 15 m-Digital Elevation Model (DEM) to calculate time-series lake volumes. In addition, water storage anomalies over the watershed were estimated using Gravity Recovery and Climate Experiment (GRACE) images. Changes in water storage estimated from Landsat and GRACE were in agreement with water consumption and wastewater treatment reported by local agencies in the Emirate of Abu Dhabi. Discharged water from the wastewater treatment plant and volume of water in Lake Zakher showed similar patterns. The results from this study confirmed the reliability of remotely sensed data in monitoring water resources in arid and remote watersheds where ground-based observations are scarce.