Analyzing the Suitability of Remotely Sensed ET for Calibrating a Watershed Model of a Mediterranean Montane Forest

Identification and Prediction of Crop Waterlogging Risk Areas under the Impact of Climate Change

Waterlogging refers to the damage to plants by water stress due to excess soil water in the crop’s root zone that exceeds the maximum water holding capacity of the field. It is one of the major disasters affecting agricultural production. This study aims to add a crop waterlogging identification module to the coupled SWAT (Soil and Water Assessment Tools)-MODFLOW (Modular Finite Difference Groundwater Flow Model) model and to accurately identify and predict crop waterlogging risk areas under the CMIP6 (Coupled Model Intercomparison Project 6) climate scenarios. The result showed that: (1) The SWAT-MODFLOW model, which coupled with a crop waterlogging identification module, had good simulation results for LAI (Leaf Area Index), ET (Evapotranspiration), spring wheat yield, and groundwater level in the middle and lower reaches of the Bayin River; (2) The precipitation showed an overall increasing trend in the Bayin River watersheds over the next 80 years under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios. The temperature showed a clear increasing trend over the next 80 years under the SSP2-4.5 and SSP5-8.5 scenarios; (3) Under the SSP1-2.6 scenario, the mountain runoff from the upper reaches of the Bayin River was substantially higher than in other scenarios after 2041. The mountain runoff in the next 80 years will decrease substantially under the SSP2-4.5 scenario. The mountain runoff over the next 80 years showed an initial decrease and then an increasing trend under the SSP5-8.5 scenario; (4) During the historical period, the crop waterlogging risk area was 10.9 km2. In the next 80 years, the maximum crop waterlogging area will occur in 2055 under the SSP1-2.6 scenario. The minimum crop waterlogging area, 9.49 km2, occurred in 2042 under the SSP2-4.5 scenario. The changes in the area at risk of crop waterlogging under each scenario are mainly influenced by the mountain runoff from the upper reaches of the Bayin River.

Investigating Relationships between Runoff–Erosion Processes and Land Use and Land Cover Using Remote Sensing Multiple Gridded Datasets

Climate variability, land use and land cover changes (LULCC) have a considerable impact on runoff–erosion processes. This study analyzed the relationships between climate variability and spatiotemporal LULCC on runoff–erosion processes in different scenarios of land use and land cover (LULC) for the Almas River basin, located in the Cerrado biome in Brazil. Landsat images from 1991, 2006, and 2017 were used to analyze changes and the LULC scenarios. Two simulations based on the Soil and Water Assessment Tool (SWAT) were compared: (1) default application using the standard model database (SWATd), and (2) application using remote sensing multiple gridded datasets (albedo and leaf area index) downloaded using the Google Earth Engine (SWATrs). In addition, the SWAT model was applied to analyze the impacts of streamflow and erosion in two hypothetical scenarios of LULC. The first scenario was the optimistic scenario (OS), which represents the sustainable use and preservation of natural vegetation, emphasizing the recovery of permanent preservation areas close to watercourses, hilltops, and mountains, based on the Brazilian forest code. The second scenario was the pessimistic scenario (PS), which presents increased deforestation and expansion of farming activities. The results of the LULC changes show that between 1991 and 2017, the area occupied by agriculture and livestock increased by 75.38%. These results confirmed an increase in the sugarcane plantation and the number of cattle in the basin. The SWAT results showed that the difference between the simulated streamflow for the PS was 26.42%, compared with the OS. The sediment yield average estimation in the PS was 0.035 ton/ha/year, whereas in the OS, it was 0.025 ton/ha/year (i.e., a decrease of 21.88%). The results demonstrated that the basin has a greater predisposition for increased streamflow and sediment yield due to the LULC changes. In addition, measures to contain the increase in agriculture should be analyzed by regional managers to reduce soil erosion in this biome.

Assessing Variations in Water Use Efficiency and Linkages with Land-Use Changes Using Three Different Data Sources: A Case Study of the Yellow River, China

The dependence of water use efficiency (WUE) on changes in land cover types is crucial for understanding of long-term water availability and assessment of water-saving strategies. Investigating the impact of land cover types on ecosystem WUE has important implications when revealing water dynamics and land management. However, the determination of WUE and its dominant factors have always been subject to high data dependency and large calculation consumption within large basins. This paper proposes a framework for processing actual evapotranspiration (AET) and WUE calculation by coupling the Maximum Entropy Production (MEP) method with the Google Earth Engine (GEE). By employing the proposed framework and three data sources available in the GEE platform, results for actual ET and WUE from 2001 to 2020 were obtained in the Yellow River Basin (YRB). The results show that the proposed framework provides an acceptable estimation of actual ET via validation with Eddy Covariance flux sites in the YRB. The calculated WUE values varied greatly in different sub-basins within the YRB, indicating a cumulative growth rate of about 56% during the past 20 years. The dominant factor that led to these changes was the transition from Grasslands into other land-use types. Our results suggest that the use of the GEE platform coupled with the MEP method offers new possibilities for advancing understanding of water exchange and water resource management.

Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions, on how to make the most out of the state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world.