Assessment of Remotely Sensed Near-Surface Soil Moisture for Distributed Eco-Hydrological Model Implementation

Abstract

The aim of this study was to implement an eco-hydrological distributed model using only remotely sensed information (soil moisture and leaf area index) during the calibration phase. Four soil moisture-based metrics were assessed, and the best alternative was chosen, which was a metric based on the similarity between the principal components that explained at least 95% of the soil moisture variation and the Nash-Sutcliffe Efficiency (NSE) index between simulated and observed surface soil moisture. The selected alternative was compared with a streamflow-based calibration approach. The results showed that the streamflow-based calibration approach, even presenting satisfactory results in the calibration period (NSE = 0.91), performed poorly in the validation period (NSE = 0.47) and Leaf Area Index (LAI) and soil moisture were neither sensitive to the spatio-temporal pattern nor to the spatial correlation in both calibration and validation periods. Hence, the selected soil moisture-based approach showed an acceptable performance in terms of discharges, presenting a negligible decrease in the validation period (ΔNSE = 0.1) and greater sensitivity to the spatio-temporal variables’ spatial representation.

Evaluation of Water Quality in Ialomita River Basin in Relationship with Land Cover Patterns

“…The monitoring of surface waters should be based on a programmed process to collect samples and measure and record water parameters that will allow quality standards and the objectives proposed for the medium and long terms to be met. Water-quality monitoring must be performed at river basin scale, and numerical models should be used to extend the results of monitoring in time and space, taking into account the hydrological and geomorphological conditions of the riverbeds [15] to adequately account for processes such as dilution flow, transport capacity, dispersion and biodegradation capacities of pollutants, as well as by the oxygen intake from re-aeration and photosynthesis [16,17].With about 2500 springs and 60% of the mineral waters from Europe, Romania is considered one of Europe's richest states with respect to groundwater resources, but this fact does not eliminate the danger of a hydrological crisis in the future [18]. For example, desertification of Romania is possible under future climate change scenarios, while other changes such as massive deforestation, heavy water pollution, and aggressive industrial and unsustainable development also have the potential to affect the quantity and quality of available water.…”

“…The monitoring of surface waters should be based on a programmed process to collect samples and measure and record water parameters that will allow quality standards and the objectives proposed for the medium and long terms to be met. Water-quality monitoring must be performed at river basin scale, and numerical models should be used to extend the results of monitoring in time and space, taking into account the hydrological and geomorphological conditions of the riverbeds [15] to adequately account for processes such as dilution flow, transport capacity, dispersion and biodegradation capacities of pollutants, as well as by the oxygen intake from re-aeration and photosynthesis [16,17].…”

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

“…In recent years, some researchers have used different sources of remotely sensed information from satellites to improve the implementation of ecohydrological models. For example, actual evapotranspiration was used by Immerzeel and Droogers (2008) [150], Demirel et al (2018) [151], and Herman et al (2018) [152]; NDVI by Ruiz-Pérez et al (2017) [136]; LAI and actual evapotranspiration by Rajib et al (2018) [153]; land surface temperature by Silvestro et al (2013) [154] and Zink et al (2018) [155]; large-scale total water storage anomaly by Lo et al (2010) [156]; and near-surface soil moisture by Li et al (2018) [157], Yang et al (2019) [158], and Echeverría et al (2019) [159].…”