Riparian Area Changes in Greenness and Water Use on the Lower Colorado River in the USA from 2000 to 2020

Limits of thermal and hydrological tolerance in a foundation tree species (Populus fremontii) in the desert southwestern United States

Populus fremontii is among the most dominant, and ecologically important riparian tree species in the western United States and can thrive in hyper-arid riparian corridors. Yet, P. fremontii forests have rapidly declined over the last decade, particularly in places where temperatures sometimes exceed 50°C. We evaluated high temperature tolerance of leaf metabolism, leaf thermoregulation, and leaf hydraulic function in eight P. fremontii populations spanning a 5.3°C mean annual temperature gradient in a well-watered common garden, and at source locations throughout the lower Colorado River Basin. Two major results emerged. First, despite having an exceptionally high Tcrit (the temperature at which Photosystem II is disrupted) relative to other tree taxa, recent heat waves exceeded Tcrit , requiring evaporative leaf cooling to maintain leaf-to-air thermal safety margins. Second, in midsummer, genotypes from the warmest locations maintained lower midday leaf temperatures, a higher midday stomatal conductance, and maintained turgor pressure at lower water potentials than genotypes from more temperate locations. Taken together, results suggest that under well-watered conditions, P. fremontii can regulate leaf temperature below Tcrit along the warm edge of its distribution. Nevertheless, reduced Colorado River flows threaten to lower water tables below levels needed for evaporative cooling during episodic heat waves.

A river basin spatial model to quantitively advance understanding of riverine tree response dynamics to water availability and hydrological management

Ecological condition continues to decline in arid and semi-arid river basins globally due to hydrological over-abstraction combined with changing climatic conditions. Whilst provision of water for the environment has been a primary approach to alleviate ecological decline, how to accurately monitor changes in riverine trees at fine spatial and temporal scales, remains a substantial challenge. This is further complicated by constantly changing water availability across expansive river basins with varying climatic zones. Within, we combine rare, fine-scale, high frequency temporal in-situ field collected data with machine learning and remote sensing, to provide a robust model that enables broadscale monitoring of physiological tree water stress response to environmental changes via actual evapotranspiration (ET). Physiological variation of Eucalyptus camaldulensis (River Red Gum) and E. largiflorens (Black Box) trees across 10 study locations in the southern Murray-Darling Basin, Australia, was captured instantaneously using sap flow sensors, substantially reducing tree response lags encountered by monitoring visual canopy changes. Actual ET measurement of both species was used to bias correct a national spatial ET product where a Random Forest model was trained using continuous timeseries of in-situ data of up to four years. Precise monthly AMLETT (Australia-wide Machine Learning ET for Trees) ET outputs in 30 m pixel resolution from 2012 to 2021, were derived by incorporating additional remote sensing layers such as soil moisture, land surface temperature, radiation and EVI and NDVI in the Random Forest model. Landsat and Sentinal-2 correlation results between in-situ ET and AMLETT ET returned R² of 0.94 (RMSE 6.63 mm period^-1) and 0.92 (RMSE 6.89 mm period^-1), respectively. In comparison, correlation between in-situ ET and a national ET product returned R² of 0.44 (RMSE 34.08 mm period^-1) highlighting the need for bias correction to generate accurate absolute ET values. The AMLETT method presented here, enhances environmental management in river basins worldwide. Such robust broadscale monitoring can inform water accounting and importantly, assist decisions on where to prioritize water for the environment to restore and protect key ecological assets and preserve floodplain and riparian ecological function.

Remote Sensing of Riparian Ecosystems

Riparian zones are dynamic ecosystems that form at the interface between the aquatic and terrestrial components of a landscape. They are shaped by complex interactions between the biophysical components of river systems, including hydrology, geomorphology, and vegetation. Remote sensing technology is a powerful tool useful for understanding riparian form, function, and change over time, as it allows for the continuous collection of geospatial data over large areas. This paper provides an overview of studies published from 1991 to 2021 that have used remote sensing techniques to map and understand the processes that shape riparian habitats and their ecological functions. In total, 257 articles were reviewed and organised into six main categories (physical channel properties; morphology and vegetation or field survey; canopy detection; application of vegetation and water indices; riparian vegetation; and fauna habitat assessment). The majority of studies used aerial RGB imagery for river reaches up to 100 km in length and Landsat satellite imagery for river reaches from 100 to 1000 km in length. During the recent decade, UAVs (unmanned aerial vehicles) have been widely used for low-cost monitoring and mapping of riverine and riparian environments. However, the transfer of RS data to managers and stakeholders for systematic monitoring as a source of decision making for and successful management of riparian zones remains one of the main challenges.

Quantifying the Spatiotemporal Variation of Evapotranspiration of Different Land Cover Types and the Contribution of Its Associated Factors in the Xiliao River Plain

Evapotranspiration (ET) is a vital constituent of the hydrologic cycle. Researching changes in ET is necessary for understanding variability in the hydrologic cycle. Although some studies have clarified the changes and influencing factors of ET on a regional or global scale, these variables are still unclear for different land cover types due to the range of possible water evaporation mechanisms and conditions. In this study, we first investigated spatiotemporal trends of ET in different land cover types in the Xiliao River Plain from 2000 to 2019. The correlation between meteorological, NDVI, groundwater depth, and topographic factors and ET was compared through spatial superposition analysis. We then applied the ridge regression model to calculate the contribution rate of each influencing factor to ET for different land cover types. The results revealed that ET in the Xiliao River Plain has shown a continuously increasing trend, most significantly in cropland (CRO). The correlation between ET and influencing factors differed considerably for different land cover types, even showing an opposite result between regions with and without vegetation. Only precipitation (PRCP) and NDVI had a positive impact on ET in all land cover types. In addition, we found that vegetation can deepen the limited depth of land absorbing groundwater, and the influence of topographic conditions may be mainly reflected in the water condition difference caused by surface runoff. The ridge regression model eliminates multicollinearity among influencing factors; R2 in all land cover types was over 0.6, indicating that it could be used to effectively quantify the contribution of various influencing factors to ET. According to the results of our model calculations, NDVI had the greatest impact on ET in grass (GRA), cropland (CRO), paddy (PAD), forest (FOR), and swamp (SWA), while PRCP was the main influencing factor in bare land (BAR) and sand (SAN). These findings imply that we should apply targeted measures for water resources management in different land cover types. This study emphasizes the importance of comprehensively considering differences among various hydrologic cycles according to land cover type in order to assess the contributions of influencing factors to ET.

Estimating Actual Evapotranspiration over Croplands Using Vegetation Index Methods and Dynamic Harvested Area

Advances in estimating actual evapotranspiration (ETa) with remote sensing (RS) have contributed to improving hydrological, agricultural, and climatological studies. In this study, we evaluated the applicability of Vegetation-Index (VI) -based ETa (ET-VI) for mapping and monitoring drought in arid agricultural systems in a region where a lack of ground data hampers ETa work. To map ETa (2000–2019), ET-VIs were translated and localized using Landsat-derived 3- and 2-band Enhanced Vegetation Indices (EVI and EVI2) over croplands in the Zayandehrud River Basin (ZRB) in Iran. Since EVI and EVI2 were optimized for the MODerate Imaging Spectroradiometer (MODIS), using these VIs with Landsat sensors required a cross-sensor transformation to allow for their use in the ET-VI algorithm. The before- and after- impact of applying these empirical translation methods on the ETa estimations was examined. We also compared the effect of cropping patterns’ interannual change on the annual ETa rate using the maximum Normalized Difference Vegetation Index (NDVI) time series. The performance of the different ET-VIs products was then evaluated. Our results show that ETa estimates agreed well with each other and are all suitable to monitor ETa in the ZRB. Compared to ETc values, ETa estimations from MODIS-based continuity corrected Landsat-EVI (EVI2) (EVIMccL and EVI2MccL) performed slightly better across croplands than those of Landsat-EVI (EVI2) without transformation. The analysis of harvested areas and ET-VIs anomalies revealed a decline in the extent of cultivated areas and a loss of corresponding water resources downstream. The findings show the importance of continuity correction across sensors when using empirical algorithms designed and optimized for specific sensors. Our comprehensive ETa estimation of agricultural water use at 30 m spatial resolution provides an inexpensive monitoring tool for cropping areas and their water consumption.

Evaporation in Brazilian dryland reservoirs: Spatial variability and impact of riparian vegetation

Evaporation is a major factor controlling the hydrological dynamics of surface water reservoirs in dry environments, therefore quantification with minimal uncertainties is desired. The aim of this paper is to assess the spatial variability and impact of riparian vegetation on reservoir evaporation by remote sensing. Eight reservoirs located in subhumid and semi-arid climates in the Brazilian Drylands were studied. Scenes from Landsat 5 and Landsat 8 satellites (1985 and 2018) supplied the data for four evaporation models. For reference evaporation, the Class A Pan and Piché Evaporimeter closest to the reservoirs were considered. The occurrence/density of riparian vegetation was associated with the Normalized Difference Vegetation Index (NDVI) and its influence on evaporation was assessed. The Surface Energy Balance System for Water (AquaSEBS) model presented the best average performance (Nash-Sutcliffe Efficiency coefficient 0.40 ± 0.19). Evaporation was observed to be higher at the reservoirs' margins and near the dams, due to the contact of exposed soil and rock/concrete, respectively, which transfer heat to the water. Marginal areas near the riparian forest presented low evaporation rates with decreases between 18% and 31% in relation to the average. This interdependence was evidenced by the high negative correlation (R² 0.87-0.96) between NDVI and evaporation; vegetation reduces radiation because of the shading of the reservoir margin and changes local aerodynamics, reducing evaporation. Depending on the spatial variability of evaporation, it was found that the volumes transferred to the atmosphere may have variations of up to 30%. On average, the evaporated volume in all the studied reservoirs is 450,000 m³/day, a quantity enough to supply more than two million people. Overall, the results of this study contribute not only to a better understanding of the spatial variability of evaporation in surface reservoirs, but also of the interdependence between riparian vegetation and evaporation rates.

Recent Advances in Remote Sensing of Evapotranspiration

Evapotranspiration (ET) plays an important role in coupling the global energy, water, and biogeochemical cycles and explains ecosystem responses to global environmental change. However, quantifying and mapping the spatiotemporal distribution of ET across a large area is still a challenge, which limits our understanding of how a given ecosystem functions under a changing climate. This also poses a challenge to water managers, farmers, and ranchers who often rely on accurate estimates of ET to make important irrigation and management decisions. Over the last three decades, remote sensing-based ET modeling tools have played a significant role in managing water resources and understanding land-atmosphere interactions. However, several challenges, including limited applicability under all conditions, scarcity of calibration and validation datasets, and spectral and spatiotemporal constraints of available satellite sensors, exist in the current state-of-the-art remote sensing-based ET models and products. The special issue on “Remote Sensing of Evapotranspiration II” was launched to attract studies focusing on recent advances in remote sensing-based ET models to help address some of these challenges and find novel ways of applying and/or integrating remotely sensed ET products with other datasets to answer key questions related to water and environmental sustainability. The 13 articles published in this special issue cover a wide range of topics ranging from field- to global-scale analysis, individual model to multi-model evaluation, single sensor to multi-sensor fusion, and highlight recent advances and applications of remote sensing-based ET modeling tools and products.