Better estimation of evapotranspiration and transpiration using an improved modified Priestly-Taylor model based on a new parameter of leaf senescence in a rice field

Evapotranspiration (ET) is at the heart of the global water, energy, and carbon cycles. As ET is difficult and expensive to measure, it is crucial to develop estimation models that can be widely applied. Currently, an improved Priestley-Taylor (PT) model considers soil moisture stress, temperature constraints, and leaf senescence; however, its parameter (fs) for simulating crop senescence is based on empirical values, making it difficult to apply to different varieties and complex external conditions and thus challenging to generalize. We improved the parameters fs in the original model based on the chlorophyll decomposition that accompanies crop senescence through easily observable SPAD values (Soil-Plant Analysis Development readings) in the field. We validated the improved model by obtaining ET of different rice varieties in 2022 and 2023 using the energy balance residual method at the Free Air Concentration Enrichment Experimental (FACE) Facility located in Yangzhou City, China. The results showed that the simulation of leaf senescence using SPAD values was feasible and could be extended to different varieties. The new model using improved leaf senescence parameter for estimating ET and transpiration (T) in three plots (2022 and 2023) exhibited slightly enhanced accuracy, particularly at the later stages of crop growth. Moreover, the higher the T/ET ratio of the cropland, the more significant the improvement. This new development enhances the ability of PT models to estimate ET and T using readily available field observations and provides some suggestions for wider application in the field for other crop species.

Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress

Soil water balance is an essential element to consider for the management of droughts and agricultural land use. It is important to evaluate the water consumption of a crop in each of its phenological phases and the status of water reserves during critical hydrologic periods. This study developed an agricultural drought index (Standardized Soil Moisture Deficit Index - SMODI) conceptualized with a water balance model considering the vegetation stress caused by soil moisture deficit. This contribution was based on meteorological information, soil moisture from satellite images, hydrophysical properties of the soil and crop evapotranspiration. Information from 61 weather stations located in the dry zone of Tolima was used for estimating the water balance. SMODI was compared with the most common drought indexes: Standardized Precipitation - Evapotranspiration Index (SPEI), the Palmer Self-Calibrated Drought Index (scPDSI), and other eleven macroclimatic indexes. Pearson's correlation coefficients (r), Tukey's test, and analysis of variance were applied to analyze the degree of association between SMODI and the contrasting indexes on a quarterly basis. SMODI considers factors influencing soil moisture distribution and retention and the water stress thresholds that plants have evolved to withstand during drought periods. Consequently, this integrated approach enhances the assessment of agricultural drought by relying on pertinent physical processes. SMODI identified extremely dry, severe, moderate and normal drought 5 %, 3 %, 20 % and 72 % respectively conditions in areas characterized by Entisols, Inceptisols, and Andisols, where rice and fruit crops and pasturelands are cultivated. The SMODI has a good correlation with macroclimatic indexes (0.70 < r < 0.74).

Re-intensification of flash drought in western China over the past decade: Implications of fluctuating wetting trend

Climate changes and human activities have led to a rise of frequency and intensity of the global flash droughts, resulting in severe consequences for ecosystems, agriculture, and human societies. However, research dedicated to flash droughts in the dryland of western China is relatively limited, leaving their evolutionary characteristics and development processes of these phenomena unclear. To bridge this gap, this study analyzed the spatiotemporal characteristics of flash droughts in western China from 1981 to 2020, based on the standardized evapotranspiration stress index. Additionally, we investigated the development mechanisms by taking meteorological conditions and soil moisture into account. The findings revealed that the northern Qinghai-Tibet Plateau, western Qilian Mountains, and western and southern Loess Plateau are hotspots of flash droughts, characterized by rapid development rates. Across most of the study area, flash drought events persisted between 25 and 30 days. Adequate precipitation is necessary before the onset of flash droughts in western China, while water scarcity and high temperatures played crucial roles in driving the mid-stage of flash droughts. Within the context of the observed "warming and wetting" trend, the average flash droughts occurrence from 2011 to 2020 was approximately 16 % lower than that from 1981 to 1990, and there was a significant annual decrease in spatial coverage of 0.01 % per year. However, in the "wetting in west, drying in east" trend, the spatial coverage of flash droughts has shifted from a declining trend to an insignificant increasing trend since 2000 in the study area, with significant regional differences between the western and eastern regions. Over the past decade, flash droughts had once again intensified in the central Qinghai-Tibet Plateau and the Loess Plateau due to warming and fluctuating wetting trends, raising significant concerns for future ecosystem and agricultural water management in these regions.

Divergent trajectories of future global gross primary productivity and evapotranspiration of terrestrial vegetation in Shared Socioeconomic Pathways

Understanding the future trends of carbon and water fluxes between terrestrial ecosystems and the atmosphere is crucial for predicting Earth's climate dynamics. This study employs an advanced numerical approach to project global gross primary productivity (GPP) and evapotranspiration (ET) from 2001 to 2100 under various climate scenarios based on Shared Socioeconomic Pathways (SSPs). To improve predictions of vegetation dynamics, we introduce a novel model (CoLM-PVPM), an enhancement of the Common Land Model version 2014 (CoLM2014), incorporating a prognostic vegetation phenology model (PVPM). Compared to CoLM2014 that relies on satellite-based leaf area index (LAI) inputs, CoLM-PVPM predicts LAI time series using climate variables. Model validation using historical data from 2001 to 2010 demonstrates PVPM in capturing spatiotemporal variations in satellite LAI. Our modeling results indicate that annual averaged LAI and total GPP increase under SSP1-2.6 but decrease under SSP2-4.5, SSP3-7.0, and SSP5-8.5 by 2100. By comparison, annual total ET consistently increases under all SSP scenarios by 2100. Global annual averaged LAI is highly correlated with annual total GPP in all scenarios, while its correlation with annual total ET weakens in SSP2-4.5, SSP3-7.0, and SSP5-8.5. Global annual total vapor pressure deficit (VPD) and precipitation are highly correlated with annual total ET in all scenarios. As emission levels increase, the negative correlation between annual total VPD and GPP strengthens, while the correlation between annual total precipitation and GPP weakens. This research presents an improved model for predicting terrestrial vegetation processes and underscores the importance of low carbon emission scenarios in maintaining carbon-water balances in specific regions.

Do baseline assumptions alter the efficacy of green stormwater infrastructure to reduce combined sewer overflows?

Green stormwater infrastructure (GSI) is growing in popularity to reduce combined sewer overflows (CSOs) and hydrologic simulation models are a tool to assess their reduction potential. Given the numerous and interacting water flows that contribute to CSOs, such as evapotranspiration (ET) and groundwater (GW), these models should ideally account for them. However, due to the complexity, simplified models are often used, and it is currently unknown how these assumptions affect estimates of CSOs, GSI effectiveness, and ultimately planning guidance. This study evaluates the effect on estimates of CSOs and GSI effectiveness when different flows and hydrologic processes are neglected. We modified an existing EPA SWMM model of a combined sewer system in Switzerland to include ET, GW, and upstream inflows. Historical rainfall data over 30 years are used to assess volume and duration of CSOs with and without three types of GSI (bioretention basins, permeable pavements and green roofs). Results demonstrate that neglect of certain flows in modelling can alter CSO volumes from -15 % to 40 %. GSI effectiveness also varies considerably, resulting in differences in simulated percent of CSO volume reduced from 8 % to 35 %, depending on the GSI type and modeled flow or process. Representation of GW within models is particularly crucial when infiltrating GSI are present, as CSOs could increase in certain subcatchments due to higher GW levels from increased infiltration. When basing GSI planning decisions on modeled estimates of CSOs, all relevant hydrologic processes should be included to the extent possible, and uncertainty and assumptions should always be considered.

Assessment of Evaporative Demand Drought Index for drought analysis in Peninsular Malaysia

An effective drought monitoring tool is essential for the development of timely drought early warning system. This study evaluates Evaporative Demand Drought Index (EDDI) as a drought indicator in measuring spatiotemporal evolution of droughts over Peninsular Malaysia during 1989-2018. The modified Mann-Kendall and Sen's slope tests were performed to detect the presence of monotonic trends in EDDI, Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and their related climate variables. The performance of EDDI in capturing the drought onset, evolutions and demise of historical severe droughts was also compared with SPI and SPEI at multiple timescales. EDDI demonstrates strong spatiotemporal correlations with SPI and SPEI and comparable performance in historical drought events identification. At sub-monthly timescale, 2-week EDDI displays equivalent drought severities and durations for all historical severe droughts corresponding to the monthly EDDI. In the case when rainfall deficits are normalized in an otherwise warm and dry month, EDDI may serve as a great alternative to SPI and SPEI due to it being sensitive to the changes in prevalent atmospheric conditions. Collectively, the results fill in the knowledge gaps on drought evolutions from the evaporative perspective and highlight the efficacy of EDDI as a valuable drought early warning tool for Peninsular Malaysia. Future study should explore the physical mechanisms behind the development of flash drought and the role of evaporation in the drought propagation processes.

Effect of vegetation treatment and water stress on evapotranspiration in bioretention systems

Evapotranspiration is a key hydrological process for reducing stormwater runoff in bioretention systems, regardless of their physical configuration. Understanding the volumes of stormwater that can be returned to the atmosphere via evapotranspiration is, therefore, a key consideration in the design of any bioretention system. This study establishes the evapotranspiration dynamics of three common, structurally different, bioretention vegetation treatments (an Amenity Grass mix, and mono-cultures of Deschampsia cespitosa and Iris sibirica) compared with an un-vegetated control using lab-scale column experiments. Via continuous mass and moisture loss data, observed evapotranspiration rates were compared with those predicted by the FAO-56 Penman-Monteith model for five 14-day dry periods during Spring 2021, Summer 2021, and Spring 2022. Soil moisture reductions over the 14-day trials led to reduced rates of evapotranspiration. This necessitated the use of a soil moisture extraction function alongside a crop coefficient to represent actual evapotranspiration from FAO-56 Penman-Monteith reference evapotranspiration estimates. Crop coefficients (Kc) varied between 0.65 and 2.91, with a value of 1.0 identified as a recommended default value in the absence of treatment-specific empirical data. A continuous hydrological model with Kc=1.0 and a loading ratio of 10:1 showed that evapotranspiration could account for between 1 and 12% of the annual water budget for a bioretention system located in the UK and Ireland, increasing to a maximum of 35% when using the highest Kc observed (2.91).

Multi-datasets to monitor and assess meteorological and hydrological droughts in a typical basin of the Brazilian semiarid region

This study analyzed the meteorological and hydrological droughts in a typical basin of the Brazilian semiarid region from 1994 to 2016. In recent decades, this region has faced prolonged and severe droughts, leading to marked reductions in agricultural productivity and significant challenges to food security and water availability. The datasets employed included a digital elevation model, land use and cover data, soil characteristics, climatic data (temperature, wind speed, solar radiation, humidity, and precipitation), runoff data, images from the MODIS/TERRA and AQUA sensors (MOD09A1 and MODY09A1 products), and soil water content. A variety of methods and products were used to study these droughts: the meteorological drought was analyzed using the Standardized Precipitation Index (SPI) derived from observed precipitation data, while the hydrological drought was assessed using the Standardized Soil Index (SSI), the Nonparametric Multivariate Standardized Drought Index (NMSDI), and the Parametric Multivariate Standardized Drought Index (PMSDI). These indices were determined using water balance components, including streamflow and soil water content, from the Soil Water Assessment Tool (SWAT) model, and evapotranspiration data from the Surface Energy Balance Algorithm for Land (SEBAL). The findings indicate that the methodology effectively identified variations in water dynamics and drought periods in a headwater basin within Brazil's semiarid region, suggesting potential applicability in other semiarid areas. This study provides essential insights for water resource management and resilience building in the face of adverse climatic events, offering a valuable guide for decision-making processes.

Contrasting impact of extreme soil and atmospheric dryness on the functioning of trees and forests

Recent studies indicate an increase in the frequency of extreme compound dryness days (days with both extreme soil AND air dryness) across central Europe in the future, with little information on their impact on the functioning of trees and forests. This study aims to quantify and assess the impact of extreme soil dryness, extreme air dryness, and extreme compound dryness on the functioning of trees and forests. For this, >15 years of ecosystem-level (carbon dioxide and water vapor fluxes) and 6-10 years of tree-level measurements (transpiration and growth) each from a montane mixed deciduous forest (CH-Lae) and a subalpine evergreen coniferous forest (CH-Dav) in Switzerland, is used. The results showed extreme air dryness limitation on CO2 fluxes and extreme soil dryness limitations on water vapor fluxes. Additionally, CH-Dav was mainly affected by extreme air dryness whereas CH-Lae was affected by both extreme soil dryness and extreme air dryness. The impact of extreme compound dryness on net CO2 uptake (about 75 % decrease) was more due to higher increased ecosystem respiration (40 % and 70 % increase at CH-Dav and CH-Lae, respectively) than decreased gross primary productivity (10 % and 40 % decrease at CH-Dav and CH-Lae, respectively). A significant negative impact on evapotranspiration and transpiration was only observed at CH-Lae during extreme soil and compound dryness (about 25 % decrease). Furthermore, with some differences, the tree-level impact on tree water deficit, transpiration, and growth were consistent with the ecosystem-level impact on carbon uptake and evapotranspiration. Finally, the impact of extreme dryness showed no significant relationship with tree allometry (diameter and height) but across different tree species. The projected future is likely to expose these forest areas to more extreme and frequent dryness conditions, thus compromising the functioning of trees and forests, thereby calling for management interventions to increase the adaptive capacity and resistance of these forests.

Improvement of evapotranspiration simulation study in the Hailar River basin under the influence of vegetation dynamics

In arid and semi-arid areas with <400 mm of precipitation, evapotranspiration (ET) accounts for about 80% of precipitation and is the main water consumer in the watershed. However, vegetation greening in recent years will increase ET and exacerbate the aridity of the area by affecting soil moisture in the root system. Vegetation changes are regional and spatially heterogeneous, therefore, in order to characterize ET changes under vegetation dynamics, it is necessary to expand the spatial scale of ET simulation. However, widely used evapotranspiration simulation models, such as the Shuttleworth-Wallace model (SW model), are deficient in reflecting the direct and indirect effects of vertical (i.e., soil depths) and horizontal (i.e., vegetation dynamics) directions. Based on field sampling and constructed structural equation model (SEM), we found that vegetation dynamics affect evapotranspiration not only directly, but also indirectly by affecting soil moisture at different depths. On this basis, we defined the weighting coefficients of 0.85 and 0.15 for grassland vegetation zones, 0.3, 0.15, 0.20, 0.25, 0.10 for forest-grass interspersed zones, and 0.20, 0.55, 0.25 for forested zones, respectively, based on the SEM results. Different soil moisture weighting coefficients were defined within different vegetation type zones and the improved SW model is called S-W-α. Comparing the simulation results with the measured data, S-W-α improved the ET simulation accuracy in this region by 33.92% and the improved ET spatial trend can respond to the dynamic changes of vegetation. Replacing the ET module in the Block-wise use of TOPMODEL and Muskingum-Cunge method mode (BTOP model) with the modified S-W-α, the results show that the simulation accuracy of the improved model is increased by 25%, and the Nash is higher than 75% for both the rate period and the validation period, which realizes the extension of the model from the point scale to the basin scale. The modified model may provide technical support for simulation of evapotranspiration and management of ecosystem health in ecologically fragile areas.

Prediction of monthly evapotranspiration by artificial neural network model development with Levenberg-Marquardt method in Elazig, Turkey

The phenomenon of evapotranspiration (ET) is closely linked to the issue of water scarcity, as it involves water loss through both evaporation and plant transpiration. Accurate prediction of evapotranspiration is of utmost importance in the strategic planning of agricultural irrigation, effective management of water resources, and precise hydrological modeling. The current investigation aims to predict the monthly ET values in the Elazig province by developing an artificial neural network (ANN) model utilizing the Levenberg-Marquardt method. Consequently, the values of temperature, precipitation, relative humidity, solar hour, and mean wind speed were utilized in forecasting evapotranspiration values by implementing ANN algorithms. This research makes a valuable contribution to the existing body of literature by utilizing an ANN model developed with the Levenberg-Marquardt method to estimate evapotranspiration. It has been discovered that evapotranspiration values are impacted by various factors such as temperature (minimum, average, maximum), relative humidity (minimum, average, maximum), wind speed, solar hour, and precipitation values, which are taken into consideration for prediction. The findings indicated that Elazig, Keban, Baskil, and Agin sites had R values of 0.9995, 0.9948, 0.9898, and 0.9994 in the proposed model. It was found that Elazig's MAPE ranged from 0 to 0.2288, Keban's was 0.0001 to 0.3703, Baskil's was between 0 and 0.4453, and Agin's was both 0 and 0.2784. The findings obtained from the proposed model are compatible with evapotranspiration values computed from the Hargreaves method (R2 = 0.996). The study's findings provide significant insights for planners and decision-makers involved in the planning and managing water resources and agricultural irrigation.

Estimating maize evapotranspiration based on hybrid back-propagation neural network models and meteorological, soil, and crop data

Crop evapotranspiration is a key parameter influencing water-saving irrigation and water resources management of agriculture. However, current models for estimating maize evapotranspiration primarily rely on meteorological data and empirical coefficients, and the estimated evapotranspiration contains uncertainties. In this study, the evapotranspiration data of summer maize were collected from typical stations in Northern China (Yucheng Station), and a back-propagation neural network (BP) model for predicting maize evapotranspiration was constructed based on meteorological data, soil data, and crop data. To further improve its accuracy, the maize evapotranspiration model was optimized using three bionic optimization algorithms, namely the sand cat swarm optimization (SCSO) algorithms, hunter-prey optimizer (HPO) algorithm, and golden jackal optimization (GJO) algorithm. The results showed that the fusion of meteorological, soil moisture, and crop data can effectively improve the accuracy of the maize evapotranspiration model. The model showed higher accuracy with the hybrid optimization model SCSO-BP compared to the stand-alone BP neural network model, with improvements of 2.7-4.8%, 17.2-25.5%, 13.9-26.8%, and 3.3-5.6% in terms of R2, RMSE, MAE, and NSE, respectively. Comprehensively compared with existing maize evapotranspiration models, the SCSO-BP model presented the highest accuracy, with R2 = 0.842, RMSE = 0.433 mm/day, MAE = 0.316 mm/day, NSE = 0.840, and overall global evaluation index (GPI) ranking the first. The results have reference value for the calculation of daily evapotranspiration of maize in similar areas of northern China.

Geospatial patterns in runoff projections using random forest based forecasting of time-series data for the mid-Atlantic region of the United States

This research explores the geospatial patterns of historical runoff for the period 1958-2021 in the Mid-Atlantic region and uses these time-series data plus nine external climatic and hydrologic variables to predict future runoff for the period 2022-2031. Gridded, average monthly climatic water balance data were obtained from the TerraClimate dataset. A cluster analysis of the long term (1958-2021) historical runoff found 13 significant temporal trends, which tend to form large contiguous regions associated with climate gradients and topographic patterns. The runoff time-series clusters, and the associated time-series of 9 TerraClimate variables, were used to generate random forest based forecast models to predict future (2022-2031) runoff. The random forest-based forecast with the greatest accuracy included inputs of actual evapotranspiration, climate water deficit, minimum, average, and maximum temperature, and vapor pressure deficit. The final model predicted significantly increasing runoff in nine of the 13 clusters.

ENSO effects on the relationship between aerosols and evapotranspiration in the south of the Amazon biome

The effects of the El Nino-Southern Oscillation (ENSO) events have local, regional, and global consequences for water regimes, causing floods or extreme drought events. Tropical forests are strongly affected by ENSO, and in the case of the Amazon, its territorial extension allows for a wide variation of these effects. The prolongation of drought events in the Amazon basin contributes to an increase in gas and aerosol particle emissions mainly caused by biomass burning, which in turn alter radiative fluxes and evapotranspiration rates, cyclically interfering with the hydrological regime. The ENSO effects on the interactions between aerosol particles and evapotranspiration is a critical aspect to be systematically investigated. Therefore, this study aimed to evaluate the ENSO effect on a site located on the southern portion of the Amazonian region. In addition to quantifying and testing possible differences between aerosols and evapotranspiration under different ENSO classes (El Niño, La Niña and Neutrality), this study also evaluated possible variations in evapotranspiration as a function of the aerosol load. A highly significant difference was found for air temperature, relative humidity and aerosol load between the El Niño and La Niña classes. For evapotranspiration, significant differences were found for the El Niño and La Niña classes and for El Niño and Neutrality classes. Under the Neutrality class, the aerosol load correlated significantly with evapotranspiration, explaining 20% of the phenomenon. Under the El Niño and La Niña classes, no significant linear correlation was found between aerosol load and evapotranspiration. However, the results showed that for the total data set, there is a positive and significant correlation between aerosol and evapotranspiration. It increases with a quadratic fit, i.e., the aerosol favors evapotranspiration rates up to a certain concentration threshold. The results obtained in this study can help to understand the effects of ENSO events on atmospheric conditions in the southern Amazon basin, in addition to elucidating the role of aerosols in feedback to the water cycle in the region.

A review of hydroclimate variability and changes in the Blue Nile Basin, Ethiopia

Understanding the factors that influence hydroclimate variability is crucial for developing sustainable water management strategies in dynamic environments. The Blue Nile Basin, a significant freshwater resource in Africa, is facing challenges related to hydroclimate changes that impact sustainable development. Since the 1970s, the hydroclimate patterns of the region have undergone notable changes, prompting the need for a review of the literature on hydroclimate variability of the basin. Therefore, this study aims to offer a brief overview of the latest literature on hydroclimate variability and changes in the Blue Nile Basin. Based on the review of hydroclimate studies in the basin, it is evident that there have been significant advancements in our understanding of this complex system. However, the review also highlights that there are still areas of research that require further development to provide more comprehensive knowledge of the basin's hydroclimate. The projected intensification of hydroclimate change throughout the 21st century underscores the urgency for continued research efforts. The observed warming trend in the temperature of the basin and the discrepancies amongst research outputs on precipitation changes are important areas that require further investigation. Additionally, the inconsistency in reported changes in the watershed's hydrology and streamflow across the basin emphasizes the need for continued research to understand the factors behind these changes. Overall, this review provides valuable insights into the current state of hydroclimate studies in the basin and highlights the key areas for future research efforts to enhance our understanding of this vital system.

Water sources for street trees in mesic urban environments

Street trees support climate resiliency through a variety of pathways, such as offsetting urban heat and attenuating storm water runoff. While urban trees in arid and semiarid ecosystems have been shown to take up water from irrigation, it is unknown where street trees in mesic cities obtain their water. In this study, we use natural abundance stable isotopes to estimate the proportional sources of water taken up by Acer platanoides street trees in Boston, Massachusetts, United States, including precipitation, irrigation, groundwater, and wastewater. We use Bayesian multisource mixing models to estimate water sources by comparing the natural abundance isotopic ratios of hydrogen and oxygen across potential water sources with water extracted from tree stem samples. We find that during the summer of 2021, characterized by anomalously high rainfall, street trees predominantly utilized water from precipitation. Precipitation accounted for 72.3 % of water extracted from trees sampled in August and 65.6 % from trees sampled in September. Of the precipitation taken up by street trees, most water was traced back to large storm events in July, with July rainfall alone accounting for up to 84.0 % of water found within street trees. We find strong relationships between canopy cover fractions and the proportion of precipitation lost to evapotranspiration across the study domain, supporting the conclusion that tree planting initiatives result in storm water mitigation benefits due to utilization of water from precipitation by urban vegetation. However, while the mature trees studied here currently support their water demand from precipitation, the dependency of street trees on precipitation in mesic cities may lead to increased water stress in a changing climate characterized by a higher frequency and severity of drought.

Threshold response of ecosystem water use efficiency to soil water in an alpine meadow

Ecosystem water use efficiency (WUE) is a coupled index of carbon (gross ecosystem productivity, GEP) and water fluxes (transpiration, Tr or evapotranspiration, ET), reflecting how ecosystem uses water efficiently to increase its carbon uptake. Though ecosystem WUE is generally considered to decrease with increasing precipitation levels, it remains elusive whether and how it nonlinearly responds to extreme water changes. Here, we performed a 5-year precipitation halving experiment in an alpine meadow, combined with extremely interannual precipitation fluctuations, to create a large range of soil water variations. Our results showed that WUETr and WUEET consistently showed a quadratic pattern in response to soil water. Such quadratic patterns were steadily held at different stages of growing seasons, with minor changes in the optimal water thresholds (25.0-28.4 %). Below the water threshold, more soil water stimulated GEP but reduced Tr and ET by lowering soil temperature, resulting in a positive response of ecosystem WUE to soil water. Above the threshold, soil water stimulated GEP less than Tr (ET), leading to a negative response of ecosystem WUE to soil water. However, biological processes, including plant cover and belowground biomass as well as vertical root biomass distribution, had less effect on ecosystem WUE. Overall, this work is among the first to reveal the nonlinearity and optimal water thresholds of ecosystem WUE across a broad range of soil water, suggesting that future extreme precipitation events will more frequently surpass the water threshold and differently change the coupling relationships of carbon and water fluxes in alpine grasslands.

Regulation of biophysical drivers on carbon and water fluxes over a warm-temperate plantation in northern China

Plantations have great potential for carbon sequestration and play a vital role in the water cycle. However, it is challenging to accurately estimate the carbon and water fluxes of plantations, and the impact of biophysical drivers on the coupling of carbon and water fluxes is not well understood. Thus, we modified the phenology module of the Biome-BGC model and optimized the parameters with the aim of simulating the gross primary productivity (GPP), evapotranspiration (ET) and water use efficiency (WUE) of a warm-temperate plantation in northern China from 2009 to 2020. Photosynthetically active radiation (PAR) showed significant positive correlations on GPP and WUE during the first stage of the growing season (S1: from early April to late July). Active accumulated temperature (Taa) mainly controlled the changes in GPP and ET during the second stage (S2: between the end of July and early November). Throughout the growing season, soil water content dominated daily GPP and WUE, whereas Taa regulated ET. The optimized Biome-BGC model performed better than the original model in simulating GPP and ET. Compared with the values simulated by the original model, root mean square error decreased by 7.89 % and 15.97 % for the simulated GPP and ET, respectively, while the determination coefficient increased from 0.77 to 0.81 for simulated GPP and from 0.51 to 0.62 for simulated ET. The results of this study demonstrated that the optimized model more accurately assessed carbon sequestration and water consumption in plantations.

Monitoring agricultural drought in Peshawar Valley, Pakistan using long -term satellite and meteorological data

Monitoring agricultural drought across a large area is challenging, especially in regions with limited data availability, like the Peshawar Valley, which holds great agricultural significance in Pakistan. Although remote sensing provides biophysical variables such as precipitation (P), land surface temperature (LST), normalized difference vegetation index (NDVI), and relative soil moisture (RSM) to assess drought conditions at various spatiotemporal scales, these variables have limited capacity to capture the complex nature of agricultural drought and associated crop responses. Here, we developed a composite drought index named "Temperature Vegetation ET Dryness Index" (TVEDI) by modifying the Temperature Vegetation Precipitation Dryness Index (TVPDI) and integrating NDVI, LST, and remotely sensed evapotranspiration (ET) using 3D space and Euclidean distance. Several statistical techniques were employed to examine TVPDI and TVEDI trends and relationships with other commonly used drought indices such as the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and standardized soil moisture index (SSI), as well as crop yield, to better understand how these indices captured the spatial and temporal distribution of agricultural drought in the Peshawar valley between 1986 and 2018. Results indicated that while the temporal patterns of the 3-month SPI, SPEI, and SSI generally align with those of TVEDI and TVPDI, TVEDI was more strongly correlated with these indices (e.g., correlation coefficient, r = 0.78-0.84 from TVEDI and r = 0.73-0.79 from TVPDI). Moreover, the crop yield, a measure of crop response to agricultural drought, demonstrated a significant positive correlation with TVEDI (r = 0.60-0.80), much higher than its correlation with TVPDI (r = 0.30-0.48). These outcomes indicate that the inclusion of ET in TVEDI effectively captured changes in soil moisture, crop water status, and their impact on crop yield. Overall, TVEDI exhibited enhanced capability to identify drought impacts compared to TVPDI, showing its potential for characterizing agricultural drought in regions with limited data availability.

Vertical flow constructed wetlands as green facades and gardens for on-site greywater treatment in buildings: Two-year mesocosm study on removal performance

This study focuses on the performance and clogging of vertical flow constructed wetlands (VFCWs) planted with climbing ornamentals and ornamental plants for greywater treatment, after two years of operation at mesocosm level. Different substrate (sand, vermiculite) and vegetation (Trachelospermum jasminoides, Lonicera japonica, Callistemon laevis) types were evaluated to determine the optimal removal of pollutants. Results revealed that, during the second year of operation, removal efficiencies of turbidity and COD were significantly higher (1st year: 54-94 %; 71-89 %, 2nd year: 82-98 %; 86-95 %, respectively) for both studied planted substrates, compared to the first year. Moreover, it was found that sand systems from each studied plant as well as from the unplanted systems, were more effective compared to vermiculite for most of the studied parameters (turbidity, TSS, COD, anionic surfactants, pathogens). Sand systems were also quite effective in removing total coliforms (5 log reduction) and Escherichia coli (4 log reduction). At the end of the two-year experiment, all planted systems with sand had significantly higher hydraulic conductivity than the unplanted ones. With reference to evapotranspiration, even though planted systems had significantly higher losses, C. laevis systems demonstrated less water losses than the other vegetated systems. According to the findings, the studied plants managed to continue growing without facing added stress. Therefore, the application of climbing and ornamental plants in VFCWs for greywater treatment in buildings seems a promising option for developing green infrastructures in urban areas and enhancing the removal efficiency of such systems.

Evaluation of flash drought under the impact of heat wave events in southwestern Germany

Flash droughts are a recently recognised type of extreme drought defined by the rapid onset and strong intensification of drought conditions. Our understanding of flash drought processes under the influence of heat waves needs to be improved in the context of global warming. Here, we applied a physically based hydrological model, i.e., TRAnspiration and INterception (TRAIN) model to simulate root zone soil moisture (RZSM) and evapotranspiration (ET) with daily time steps and at a 1 × 1 km resolution to identify and assess flash droughts. Two states, Baden-Württemberg (BW) and Rhineland-Palatinate (RP), located in southwestern Germany, were selected as the study areas. Three datasets, the Global Land Evaporation Amsterdam Model (GLEAM) dataset, ERA5-Land (land component of the fifth generation of European ReAnalysis) dataset, and SMAP-L4 (Soil Moisture Active Passive Level-4) dataset, were selected to evaluate the TRAIN simulated RZSM and ET from 1961 to 2016. The results show that the simulated RZSM had the highest correlation with the ERA5-Land products, followed by SMAP-L4 and GLEAM, with regional average correlation coefficients (CC) of 0.765, 0.762, and 0.746, respectively. The CC of the TRAIN simulated ET with ERA5-Land and GLEAM ET were 0.828 and 0.803, respectively. The results of the trend analyses showed a significant increase (p < 0.05) in the number of flash droughts and heat waves in both the BW and RP states. A comparative analysis revealed that the mean duration and onset speed of flash droughts in BW (RP) without heat waves were 10.42 (10.67) pentads and 19.69th percentile/pentad (17.16th percentile/pentad), respectively, while associated with heat waves they were 8.95 (9.53) pentads and 21.77th percentile/pentad (19.91th percentile/pentad), respectively. This indicates that flash droughts under the influence of heat waves are generally shorter in duration but faster in occurrence. The findings of this study have important implications for flash drought assessment, monitoring, and mitigation under the impact of heat waves.

Ecohydrologic model with satellite-based data for predicting streamflow in ungauged basins

Information on water availability in basins can be crucial for making decisions for effective water resource management in basins. As the operation of hydrometric stations in Korea is mainly focused on flood season and large rivers, most basins have lack or no observed data. Consequently, this complicates water resource planning and management. Remote sensing data is emerging as a powerful alternative to hydrological information in ungauged basins. This study investigated the applicability of Satellite-Remote Sensed Data (SRSD) as a source for model calibration in Prediction in Ungauged Basins (PUB) through modeling. Remote sensed leaf area index (LAI), actual evapotranspiration, and soil moisture data were used. Each SRSD was used alone to calibrate a hydrologic model to predict the daily streamflow for 28 basins in Korea. A vegetation module was added to the existing hydrologic model to use LAI. Among the SRSDs tested, the model calibrated with LAI had the most robust performance, predicting streamflow with acceptable accuracy compared to the traditional calibration based on streamflow. In particular, since the model account for vegetation actively interacting with evapotranspiration and soil moisture in the season of low flow, the LAI-calibrated model showed an advantage in improving the flow prediction performance. Although further research is required to utilize evapotranspiration and soil moisture data, the overall results of the LAI-based calibration were promising for predicting streamflow in ungauged basins where observations are scarce or absent, given that the satellite-derived LAI data were used alone without any preprocessing such as a bias correction. However, the prediction performance of the LAI-calibrated model was found to have a statistically significant relationship with local conditions. Therefore, by evaluating and improving the potential of SRSD in different region and climatic conditions, it is expected that the application of the SRSD-only calibration method can be extended to various ungauged basins.

Quantifying evapotranspiration fluxes on green roofs: A comparative analysis of observational methods

Evapotranspiration (ET) is an important process in green stormwater infrastructure (GSI) aiming to reduce urban drainage, to promote cooling and/or to contribute to an urban hydrological balance restoration closer to the natural one. However, on these structures and particularly on green roofs (GR), its evaluation remains challenging and subject to discussion. Estimates of ET by water balance, energy balance, and an ET chamber were performed on five different plots of a full-scale experimental green roof in Trappes (France). Compared to both water balance (90th percentile range of daily ET values: 0.8 mm/d to 3 mm/d) and chamber methods (90th percentile range of daily ET values: 1 mm/d to 1.5 mm/d), the energy balance (90th percentile range of daily ET values is between 1.8 mm and 3.7 mm) produces higher values, 1 to 2 times higher in cumulative values during common periods. The chamber ET displays a similar trend to the energy balance on an hourly basis, and its values remain within the same range as the water balance evaluations on a daily time-step. All three assessments consistently fell below the potential ET values estimated with the Penman-Monteith formula. Critical issues in ET estimation through experimentation have arisen. Sensible heat flux (H) significantly increases ET values when using the energy balance approach compared to the other two methods. The Water Balance method is practical, but on days following rainfall events, the Chamber method may prove more reliable, albeit more time and labour-intensive. The three methods indicated that the substrate thickness was the main contributing factor to increase ET, with well-maintained herbaceous plants providing higher ET values than sedums in thick (15 cm) substrates. In addition, the substrate's nature, especially its organic content, is another factor that promotes ET in green roofs.

Impacts of longleaf pine (Pinus palustris Mill.) on long-term hydrology at the watershed scale

Threats from climate change and growing populations require innovative solutions for restoring streamflow in many regions. In the arid western U.S., attempts to increase streamflow (Q) through forest management have had mixed results, but these approaches may be more successful in the eastern U.S. where greater precipitation (P) and lower evapotranspiration (ET) offer greater potential to increase Q by reducing ET. Longleaf pine (Pinus palustris Mill.) (LLP) woodlands, once the dominant land cover in the southeastern United States, often have lower ET than other forest types but it is unclear how longleaf pine cover impacts watershed-scale hydrology. To address this question, we analyzed 21 gaged rural watersheds. We estimated annual water balance ET (ETwb) as the difference between precipitation (P) and streamflow (Q) between 1989 and 2021 and quantified low flow rates (7Q10) among watersheds with high and low LLP cover. To control for climate variability among watersheds, we compared variation in hydrology metrics with biotic and abiotic variables using the Budyko equation (ETBudyko) to understand the differences between the two ET estimates (∆ET). Watersheds with 15-72 % LLP cover had 17 % greater mean annual Q, 7 % lower annual ETwb, and 92 % greater 7Q10 low flow rates than watersheds with <3 % LLP. LLP cover decreased ET and increased Q by 2.4 mm or 0.15 % Q/P per 1 % of watershed area, but only when LLP was managed as open woodlands. Our results demonstrate that ecological forest restoration in these systems, which entails mechanical thinning and re-introduction of low-intensity prescribed fire to maintain open woodlands, and enhance understory diversity, can contribute to decreases in ET and increases in Q in eastern forests.

Estimating cucumber crop coefficients under different greenhouse microclimatic conditions

This study aimed to determine cucumber crop coefficients under different greenhouse microclimatic conditions, parameterizing the Priestley-Taylor reference evapotranspiration model. Crop evapotranspiration was directly measured with the use of lysimeters, and crop coefficients were computed following the two-step climate FAO 56 methodology. Greenhouse compartments (i.e., cooled or uncooled) showed reference evapotranspiration differences of up to 12% in an autumn-winter crop. The results presented cucumber crop coefficient values from the initial to the late-season growth stages from 0.45 to 0.94 depending on the greenhouse climate. Based on the greenhouse hourly microclimatic variation of KC, it is recommended not to apply a KC as a constant for transpiration estimation even at greenhouses located within the same region Regression analysis relating crop coefficients with leaf area revealed very high correlation coefficients for the equations tested. The results indicated that evapotranspiration can be modeled satisfactory based on a significant relationship between crop coefficient and simple measurements of the leaf area index (i.e., KC = 0.447 × LAI).

Lagged feedback of peak season photosynthetic activities on local surface temperature in Inner Mongolia, China

Increased vegetation peak growth and phenological shifts toward spring have been observed in response to climate warming in the temperate regions. Such changes have the potential to modify warming by perturbing land‒atmosphere energy exchanges; however, the signs and magnitudes of biophysical feedback on surface temperature in different biomes are largely unknown. Here, we synthesized information from vegetation growth proxies, land surface temperature (LST), and surface energy balance factors (surface evapotranspiration (ET), albedo, and broadband emissivity (BBE)) to investigate the variations in timing (PPT) and productivity (PPmax) of seasonal peak photosynthesis and their time-lagged biophysical feedbacks to the post-season LST in Inner Mongolia (IM) during 2001-2020. We found that increased PPmax, rather than advanced PPT, exhibited a significant impact on LST, with divergent signs and magnitudes across diurnal periods and among different biomes. In the grassland biome, increased PPmax cooled both LST during daytime (LSTday) and nighttime (LSTnight) throughout the post-season period, with a more pronounced response during daytime and diminishing gradually from July to September. This cooling effect on LST was primarily attributed to enhanced ET, as evidenced by the greater effect of ET cooling than that of albedo warming and BBE cooling based on a structural equation model (SEM). In the forest biome, increased PPmax led to a symmetrical warming effect on LSTday and LSTnight, and none of the surface energy balance factors were identified as significant intermediate explanatory factors for the observed warming effect. Moreover, the responses of average LST (LSTmean) and diurnal temperature range of LST (LSTDTR) to variations in PPmax were consistent with those of LSTday at two biomes. The observations above elucidate the divergent feedback mechanisms of vegetation peak growth on LST among different biomes and diurnal cycles, which could facilitate the improvement of the realistic parameterization of surface processes in global climate models.

Divergent response to abiotic factor determines the decoupling of water and carbon fluxes over an artificial C4 shrub in desert

Knowledge on relationship and determinants of water and carbon dioxide (CO2) exchange is crucial to land managers and policy makers especially for the desertified land restoration. However, there remains highly uncertain in terms of water use and carbon sequestration for artificial plantation in desert. Here, continuous water and carbon fluxes were measured using eddy covariance (EC) in conjunction with hydrometeorological measurements over an artificial C4 shrub, Haloxylon ammodendron (C. A. Mey.) Bunge, from July 2020 to 2021 in Tengger Desert, China. Throughout 2021, evapotranspiration (ET) was 189.5 mm, of which 85% (150 mm) occurred during growing season, that was comparable with the summation of precipitation (132.2 mm), dew (33.5 mm) and potential other sources (e.g. deep subsoil water). This ecosystem was a strong carbon sink with net ecosystem production (NEP) up to 446.4 g C m-2 yr-1, much higher than surrounding sites. Gross primary production (GPP, 598.7 g C m-2 yr-1) in this shrubland was comparable with that of other shrublands, whereas ecosystem respiration (Re, 152.3 g C m-2 yr-1) was lower. Random Forest showed that environmental factors can explain 71.56% and 80.07% variation of GPP and ET, respectively. Interestingly, environmental factors have divergent effect on water and carbon exchange, i.e., soil hydrothermic factors (soil moisture content and soil temperature) determine the magnitude and seasonal pattern of ET and Re, while aerodynamics factors (net radiation, atmospheric temperature and wind speed) determine GPP and NEP. As such, divergent response of abiotic factors resulted in the decoupling of water and carbon exchange. Our results suggest that H. ammodendron is a suitable species for large-scale afforestation in dryland given its low water use but high carbon sequestration. Therefore, we infer that artificial planting H. ammodendron in dryland could provide an opportunity for climate change mitigation, and the long-term time series data is needed to confirm its sustainable role of carbon sequestration in the future.

Vegetation as the catalyst for water circulation on global terrestrial ecosystem

Global climate change is expected to further intensify the global water cycle, leading to more rapid evaporation and more intense precipitation. At the same time, the growth and expansion of natural vegetation caused by climate change and human activities create potential conflicts between ecosystems and humans over available water resources. Clarifying how terrestrial ecosystem evapotranspiration responds to global precipitation and vegetation facilitates a better understanding of and prediction for the responses of global ecosystem energy, water, and carbon budgets under climate change. Relying on the spatial and temporal distribution of evapotranspiration, precipitation, and solar-induced chlorophyll fluorescence (SIF) from remote sensing platforms, we decouple the interaction mechanism of evapotranspiration, precipitation, and vegetation in linear and nonlinear scenarios using correlation and partial correlation analysis, multiple linear regression analysis, and binning. Major conclusions are as follows: (1) As a natural catalyst of the global water cycle, vegetation plays a crucial role in regulating the relationship between climate change and the water‑carbon-energy cycle. (2) Vegetation, a key parameter affecting the water cycle, participates in the entire water cycle process. (3) The increase in vegetation productivity and photosynthesis plays a dominant role in promoting evapotranspiration in vegetated areas, while the increase in precipitation dominates the promotion of evapotranspiration in non-vegetated areas.

Assessing moisture contributions from precipitation, waste, and leachate for active municipal solid waste landfills

Precipitation, evapotranspiration (ET), waste tonnage, landfill gas (LFG), and leachate data were aggregated from public sources to perform a 5-10 year water balance and estimate the contributions of three water sources (precipitation, incoming waste, and leachate recycling) for 36 active municipal solid waste (MSW) landfills in Ohio, USA. Uniquely, the water balance incorporated waste decomposition, using gas collection data to inform mass loss from biodegradation. Moisture contents of 20-30% for incoming waste indicate that entrained water is the largest source of landfill moisture. Infiltration of precipitation into the landfill after ET was the second largest source. Even at facilities where a majority of the leachate generated was recirculated, it did not significantly affect the moisture content in that year. Using the water balance approach, it appears leachate recirculation is unlikely to increase moisture content above 40% by mass, a regulatory threshold in the US, which would impose stricter air pollution control requirements. However, poor stormwater management could easily allow for "bioreactor" conditions to develop. The calculated landfill moisture content was significantly affected by the assumed runoff coefficient (CRO) parameter. CRO values below 20% and above 50% produced unrealistically high or low moisture contents, respectively. This approach can assist operators and regulators in understanding the contribution of different sources to a landfill's moisture profile and avoid future operational problems.

Seasonal correlation of aerosols with soil moisture, evapotranspiration, and vegetation over Pakistan using remote sensing

Aerosols have a severe impact on the Earth's climate, human health, and ecosystem. To understand the impacts of aerosols on climate, human health, and the ecosystem we must need to understand the variability of aerosols and their optical properties. Therefore, we used Aqua-MODIS retrieved aerosol optical depth (AOD) (550 nm) and Angstrom exponent (AE) (440/870) data to analyze the Spatio-temporal seasonal variability of aerosols and their relationship with different meteorological parameters over Pakistan from 2002 to 2021. High (>0.5) AOD values were observed during the summer season and low (<0.8) in the spring season. AE values were observed to be high (>1) in the northern regions of Pakistan indicating the dominance of fine mode particles during the winter season. Moreover, AOD showed a positive correlation with Relative Humidity (RH), Evapotranspiration, Wind speed (WS), and Temperature. On the other hand, it showed a negative correlation with Soil moisture (SM), Normalized difference vegetation index (NDVI), and precipitation over Pakistan. Therefore, considering the outcomes of this study will help policymakers to understand the spatiotemporal variability of aerosols and their seasonal correlation with different meteorological parameters.

Data quantifying interseeded cover crops effects on soil water and corn productivity in corn-soybean-wheat no-till cropping systems

The data described support the research article entitled "Interseeded cover crop mixtures influence soil water storage during the corn phase of corn-soybean-wheat no-till cropping systems". Data were collected during the corn (Zea mays L.) phase from rotations with four different cover crop (CC) treatments. The study was conducted at the USDA research facility in Beltsville, MD from 2017 through 2020. The data are available from a repository at Ag Data Commons. Descriptions of crop rotations, soil water and temperature sensors, placement, and frequency of measurements are provided in the manuscript and repository. Hourly volumetric soil water content (m3 m-3) (VWC) and soil temperature (°C) data for each soil depth (0-12, 25-35, 50-60, 75-85 cm) are available from the repository. In the manuscript, daily values of soil water storage were used to estimate daily evapotranspiration (ET) and infiltration. A text file of meta information is provided in the repository describing data collection procedures, estimation of ET and infiltration, and methods used to replace sensor data having errors. Daily precipitation, maximum and minimum temperatures, net solar radiation, and windspeed collected at a nearby weather station are provided for estimating growing degree days and potential ET. Cover crop biomass (kg ha-1) prior to corn planting and corn yields are provided by replication and cover crop system treatment for the four years.

Direct relationship between evapotranspiration rate (ETO) and vertebrate decomposition rate

When vertebrate scavenging is excluded, the Evapotranspiration Rate (ETo) of a given geographic region directly regulates the decomposition rate of unclothed vertebrate carrion, with any deviation attributed to insect activity. We conducted four decomposition experiments using pig carrion (Sus scrofa domesticus) over the span of two years (2018-2020) at a location in Davis, California. We used ETo, a variable that accounts for five climatic parameters (wind, temperature, humidity, solar radiation, and altitude) as the rate-determining variable of the decomposition process. We found ETo to have a strong (R2 = 0.98) predictive relationship with the decomposition rate. To account for maggot activity decomposing the carrion, we measured maggot weight in 2019 and 2020 using a novel method, and in 2020 we used FLIR imagery to measure maggot mass temperatures as a surrogate measurement of total maggot activity. Maggot activity was a significant predictor (p < 0.0001) of the decomposition rate, while maggot weight was not (p > 0.1). We hope to show the forensic entomology community the potential of using ETo. Future projects can incorporate ETo as a baseline to decomposition studies to determine if ETo remains the most accurate descriptor of decomposition and ultimately increase certainty in the Postmortem Interval (PMI).

Quantification of rainfall, temperature, and reference evapotranspiration trend and their interrelationship in sub-climatic zones of Bangladesh

Rainfall, temperature, and reference evapotranspiration (ET0) have a significant influence on irrigation, aridity, flooding, and crop water requirements. The primary aims of this study were to analyze the trends in rainfall, temperature, and ET0 in seven sub-climatic zones of Bangladesh from 1989 to 2020, as well as examine their interrelationships. The Modified Mann-Kendall method was employed to assess trends, while linear regression was used for trend validation. ET0 was calculated using the FAO-56 Penman-Monteith method, and Sen's slope was utilized to quantify the magnitude. Spatial analysis was conducted using Inverse Distance Weighting techniques. The findings revealed that annual rainfall increased only in the south-eastern zone, while the other zones experienced a decline. No significant changes were observed in annual maximum temperature, except in the south-eastern, north-eastern, and south-central zones, which showed variations ranging from 0.02 to 0.05 (°C/year). However, the yearly minimum temperature increased in all zones. Additionally, negative changes were observed in the annual magnitude of ET0 for all zones and seasons, except for the south-eastern and north-eastern zones, with a range of 0.01-0.02 mm/year. It was also noted that rainfall and ET0 displayed a strong decreasing relationship, except during the pre-monsoon season. Regarding regional variation, the northern regions exhibited a significant decreasing trend in both rainfall and ET0. The study identified key challenges, including water scarcity and irrigation difficulties due to declining rainfall and evapotranspiration, increased aridity, changing flood patterns, temperature-related impacts on crop growth, regional disparities in climate trends, and the need for effective climate change adaptation measures. Therefore, the study's findings can contribute to knowledge in areas such as irrigation scheduling, promoting climate-smart agricultural practices, encouraging crop diversification to reduce dependence on water-intensive crops cultivation, and planning resilient water resource management to minimize the effects of environmental shifts, regulate human operations, and implement disaster remedial actions in Bangladesh.

Predicting rainfall and irrigation requirements of corn in Ecuador

This work is a case study whose objective is prediction of irrigation needs of corn crops in different regions of Ecuador; being this a fundamental basic food for the country's economy, as in the remaining countries of the Andean area. The proposed methodology seeks to help improving the quality of corn crop. Specifically, we propose the application of regression models, within the framework of Functional Data Analysis (FDA), to predict the amount of rainfall (scalar response variable) in the places with the highest production of corn in Ecuador, as a function of functional covariates such as temperature and wind speed. From the estimation of the amount of rainfall, effective precipitation is calculated. This is the fraction of water used by the crops, from which the value of real evapotranspiration or ETc is obtained and, more importantly, the irrigation requirements at each stage of the corn crop, for its adequate physiological development. Application of regression models based on functional basis, Functional Principal Components (FPC) or Functional Partial Least Squares (FPLS) for scalar response variable, allows us to use the information of variables such as wind speed and temperature (of functional nature) in a better way than using multivariate models, for predicting the amount of rainfall, obtaining, as a result, very explicative models, defined by a high goodness of fit (R 2 = 0.97 , with 6 significant parameters and an error of 0.14) and practical utility. The model has been also applied to North Peru regions, obtaining rainfall prediction errors between 9% and 22%. Thus, the geographical limitations of the model could be the Andean regions with similar climate. In addition, this study proposes the application of FDA exploratory analysis and FDA outlier detection techniques as a common and useful practice in the specific domain of rainfall prediction studies, prior to applying the regression models.

Effects of stand age, tree species, and climate on water table fluctuations and estimated evapotranspiration in managed peatland forests

Lowland conifer forests dominated by black spruce (Picea mariana) and tamarack (Larix laricina) typically occur in peatlands in the boreal North American forest with near-surface water tables throughout the year. These forests are ecologically and economically important resources that may be impacted by climate change. However, information characterizing effects of forest disturbance, such as even-aged harvest on water table dynamics is needed to evaluate which forest tree species cover types are most hydrologically susceptible to even-aged harvest and changes in precipitation. We used a chronosequence approach to evaluate water table fluctuations and evapotranspiration across four stand age classes (<10, 15-30, 40-80, and >100-years old) and three distinct forest cover types (productive black spruce, stagnant black spruce, and tamarack) for a period of three years in Minnesota, USA. In general, there is limited evidence for elevated water tables in the younger age classes; the <10-year age class had no significant difference in mean weekly water table depth compared to the older age classes across all cover types. Estimated actual daily evapotranspiration (ET) generally agreed with the water table observations, with the exception of the tamarack cover type where ET was significantly lower in the <10-year age class. Productive black spruce sites that are 40-80-years old had higher evapotranspiration, and lower water table, possibly reflecting increased transpiration associated with the stem exclusion stage of stand development. Tamarack in the 40-80-year age class had higher water tables but no difference in ET compared to all other age classes, indicating that other external factors are driving higher water tables in that age class. To evaluate susceptibility to changing climate, we also assessed the sensitivity and response of water table dynamics to pronounced differences in growing season precipitation that occurred across study years. In general, tamarack forests are more sensitive to changes in precipitation compared to the two black spruce forest cover types. These findings can inform expected responses of site hydrology for a range of precipitation scenarios that may occur under future climate and be used by forest managers to evaluate hydrologic impacts of forest management activities across lowland conifer forest cover types.

Remote sensing environmental indicators for monitoring spatial and temporal dynamics of weather and vegetation conditions: applications for Brazilian biomes

The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm and the radiation use efficiency (RUE) model were coupled to test large-scale remote sensing environmental indicators in Brazilian biomes. MODIS MOD13Q1 reflectance product and gridded weather data for the year 2016 were used to demonstrate the suitability of the algorithm to monitor the dynamics of environmental remote sensing indicators along a year in the Brazilian biomes, Amazon, Caatinga, Cerrado, Pantanal, Atlantic Forest, and Pampa. Significant spatial and temporal variations in precipitation (P), actual evapotranspiration (ET), and biomass production (BIO) yielded differences on water balance (WB = P-ET) and water productivity (WP = ET/BIO). The highest WB and WP differences were detected in the wettest biomes, Amazon, Atlantic Forest, and Pampa, when compared with the driest biome, Caatinga. Rainfall distribution along the year affected the magnitude of the evaporative fraction (ETf), i.e., the ET to reference evapotranspiration (ET0) ratio. However, there was a gap between ETf and WB, which may be related to the time needed for recovering good soil moisture conditions after rainfalls. For some biomes, BIO related most to the levels of absorbed photosynthetically active radiation (Amazon and Atlantic Forest), while for others, BIO followed most the soil moisture levels, depicted by ETf (Caatinga, Cerrado, Pantanal, and Pampa). The large-scale modeling showed suitability for monitoring the water and vegetation conditions, making way to detect anomalies for specific periods along the year by using historical images and weather data, with strong potential to support public policies for management and conservation of natural resources and with possibilities for replication of the methods in other countries.

Apparent fractionation of hydrogen isotope from precipitation to leaf wax n-alkanes from natural environments and manipulation experiments

Knowledge of hydrogen isotopic fractionation (ε) of plant leaf waxes is the foundation for applying hydrogen isotope values (δ²H) in environmental reconstructions. In this work, we systematically investigated plant ε values (ε_{alk/precipitation}, ε_{alk/soil water}, ε_{alk/leaf water} and ε_{alk/lake water}, representing the isotopic fractionation between plant n-alkane δ²H and precipitation δ²H, soil water δ²H, leaf water δ²H and lake water δ²H) from the natural environments and manipulation experiments. The results show that the ε_{alk/precipitation} values of terrestrial plants have large variations (from -190 ‰ to -20 ‰) and become more negative with increasing aridity index. This phenomenon is possibly caused by the δ²H changes in source water (from precipitation to soil water and then to leaf water) during plant leaf wax synthesis under various evapotranspiration conditions in different climatic zones. The rainfall manipulation experiments show that leaf water δ²H values are generally higher than soil water δ²H values, and the latter are higher than precipitation δ²H values. This finding further demonstrates that the evapotranspiration effect on source water δ²H affects the quantification of the leaf wax apparent ε values (ε_{alk/leaf water} < ε_{alk/soil water} < ε_{alk/precipitation}). The ε_{alk/lake water} values of submerged plants display a smaller range (-153 ± 5 ‰) than the ε_{alk/precipitation} values of terrestrial plants, which is close to the terrestrial ε_{alk/precipitation} values in humid areas. Therefore, the biosynthetic ε value of terrestrial plant leaf waxes is relatively constant (ca. -153 ± 5 ‰), and the observed variable apparent ε_{alk/precipitation} values are possibly caused by the varied degree of evapotranspiration effect on the water that plants used in different climatic conditions. This effect should be considered when applying δ²H values of leaf waxes to trace environmental changes.

Water use strategy determines the effectiveness of internal water storage for trees growing in biofilters subject to repeated droughts

Impervious surfaces create large volumes of stormwater which degrades receiving waterways. Incorporating trees into biofilters can increase evapotranspiration and therefore reduce stormwater runoff. Tree species with i) high water use, ii) drought tolerance and iii) rapid and full recovery after drought have been suggested for biofilters to maximise runoff reduction while minimising drought stress. Moisture availability fluctuates greatly in biofilter substrates and trees growing in biofilters will likely experience multiple, extended drought events that increase trade-offs between these traits. Providing an internal water storage has the potential to reduce tree drought stress and increase evapotranspiration. Two urban tree species (Agonis flexuosa and Callistemon viminalis) were grown in plastic drums with biofilter profiles. Three irrigation treatments were used: well-watered, drought with an internal water storage and drought without an internal water storage. Transpiration, leaf water potential and biomass were measured to determine the effect of biofilter internal water storage and repeated drought events on tree water use, drought stress and growth. Biofilter internal water storage improved water use and reduced drought stress for A. flexuosa, whereas C. viminalis reduced leaf loss but saw no change in water use or drought stress. A. flexuosa with biofilter internal water storage was able to recover transpiration to well-watered levels after repeated droughts, while C. viminalis experienced reduced recovery ability. It is recommended all biofilters planted with trees should have internal water storage. In systems with lower moisture availability a species with more stomatal control, such as A. flexuosa, is recommended. If selecting a species with less stomatal control, such as C. viminalis, the internal water storage volume needs to be increased to avoid drought stress.

The role of continental evapotranspiration on water vapour isotopic variability in the troposphere

Rainforests play an important role in hydrological and carbon cycles, both at regional and global scales. They pump large quantities of moisture from the soil to the atmosphere and are major rainfall hotspots of the world. Satellite-observed stable water isotope ratios have played an essential role in determining sources of moisture in the atmosphere. Satellites provide information about the processes involving vapour transport in different zones of the world, identifying sources of rainfall and distinguishing moisture transport in monsoonal systems. This paper focuses on major rainforests of the world (Southern Amazon, Congo and Northeast India) to understand the role of continental evapotranspiration in influencing tropospheric water vapour. We have used satellite measurements of 1H2H16O/1H216O from Atmospheric InfraRed Sounder (AIRS), evapotranspiration (ET), solar-induced fluorescence (SIF), precipitation (P), atmospheric reanalysis-derived moisture flux convergence (MFC) and wind to discern the role of ET in influencing water vapour isotopes. A global map of the correlation between δ2Hv and ET-P flux indicates that densely vegetated regions in the tropics show the highest positive correlation (r > 0.5). Using mixing models and observations of specific humidity and isotopic ratio over these forested regions, we discern the source of moisture in pre-wet and wet seasons.

Influence of green roof plant density and redirecting rainfall via runoff zones on rainfall retention and plant drought stress

Green roofs are a promising engineered ecosystem designed to reduce stormwater runoff and restore vegetation cover in cities. Plants can contribute to rainfall retention by rapidly depleting water in the substrate, however, this increases the risk of plant drought stress. This study determined whether lower plant density or preferentially redirecting rainfall to plants on green roofs could reduce drought stress without reducing rainfall retention. Plant density was manipulated, and metal structures were installed above the substrate surfaces to redirect the flow of rainwater towards plants (runoff zones). Green roof modules were used to test three plant density treatments: unplanted, half-planted (10 plants/m²) and fully-planted (18 plants/m²), and two runoff zone treatments which were installed in unplanted and half-planted modules. It was expected that 1) green roofs with greater plant density would experience more drought stress (i.e., lower leaf water status), and 2) green roofs with runoff zones would show higher ET and hence retention compared with those without runoff zones, as water will be directed to plants (run-on zones), facilitating growth. Contrary to the hypothesis, evapotranspiration (ET) and rainfall retention were similar for half-planted and fully-planted modules, such that ~82 % of applied rainfall was retained. While both vegetation treatments dried out the substrates before rainfall was applied, the fully-planted modules dried out quicker and showed significantly lower leaf water status than half-planted modules. This indicates that planting at lower density may reduce plant drought stress, without reducing rainfall retention. Installing runoff zones marginally reduced ET and rainfall retention, likely due to shading by the runoff zone structures reducing evaporation from the substrate. However, runoff also occurred earlier where runoff zones were installed as they likely created preferential flow paths that reduced soil moisture and therefore ET and retention. Despite reduced rainfall retention, plants in modules with runoff zones showed significantly higher leaf water status. Reducing plant density therefore represents a simple means of reducing plant stress on green roofs without reducing rainfall retention. Installing runoff zones on green roofs is a novel approach that could reduce plant drought stress, particularly in hot and dry climates, albeit at a small cost of reduced rainfall retention.

Parameter variability across different timescales in the energy balance-based model and its effect on evapotranspiration estimation

Evapotranspiration is a key consideration for addressing a number of scientific and engineering issues. There are considerable errors in current evapotranspiration models due to the high uncertainty in model parameters. Considering that evapotranspiration models maintain the same mathematical form when run on different timescales, we argue that the uncertainty in model parameters can be reduced by considering the parameter variability across different timescales. Here, the four key parameters in the energy balance-based evapotranspiration model, including aerodynamic roughness length, thermodynamic roughness length, surface conductance, and energy balance ratio, are retrieved and evaluated on instantaneous and daily timescales based on the observations from 113 sites in the FLUXNET2015 dataset. Then data-driven instantaneous and daily parameter models are built to estimate evapotranspiration. The results show that strong multi-timescale variability occurs in all four parameters. The coefficients of variation of the four instantaneous parameters range from 0.32 to 1.70. The links of parameters on different timescales are weak. The correlation coefficients of the daily mean value of instantaneous parameter values and daily parameter values vary from 0.44 to 0.83. By considering the multi-timescale variability of the parameters, the accuracy of evapotranspiration estimation can be largely improved, with RMSE of the instantaneous and daily evapotranspiration estimation decreasing from 35.76 to 9.52 W m^-2 and from 12.01 to 3.01 W m^-2, respectively. We also find that the parameter models perform well on their inherent timescales but degrade significantly when transferring to other timescales. This study proves the necessity of defining parameter variability across different timescales in evapotranspiration models and provides new insight into the model parameters.

Evapotranspiration responses to CO2 and its driving mechanisms in four ecosystems based on CMIP6 simulations: Forest, shrub, farm and grass

Evapotranspiration (ET) is an essential process of the water cycle through which water is transferred from terrestrial ecosystems to atmosphere. However, in the climate context of increasing CO₂ concentration (also called as a CO₂-enriched climate), the variation of ET and its main drivers among different ecosystems remain unclear. This study analyzed the output data of the CMCC ESM2 model with a ridge regression method, and proposed the trends and drivers of ET in different ecosystems in a CO₂-enriched climate. In particular, the temporal - spatial characteristics of ET and its primary drivers for different periods and wetness levels were revealed. With the rising of CO₂ concentration, the atmospheric evapotranspiration demand increases, and the vegetation grows more luxuriantly. ET shows an overall upward trend, especially in the shrub ecosystems (7.41 mm decade^-1). Our results show that the thermal conditions are the main driving factors for humid forest and shrub ecosystems whereas relative humidity (RH) is the main driving factor for arid farm and grass ecosystems. In terms of the average contribution in all periods, surface solar radiation contributes 26% and 41% to ET variation in forest and shrub ecosystems, and RH contributes 49% and 32% to ET variation in farm and grass ecosystems, respectively. Notably, with the increase of wetness levels, the contribution of water conditions on ET becomes smaller, while that of thermal conditions becomes larger. Correlation analysis shows that LAI impacts on ET are regulated by environmental factors, which reflects the complexity of ET change mechanism. Overall, these findings further provide a reference for rational planning of ecosystems and efficient utilization of water resources.

Impacts of vegetation restoration on water resources and carbon sequestration in the mountainous area of Haihe River basin, China

The mountainous region of the Haihe River basin (MHRB) plays an important role in the water resource supply of its nearby mega-cities, including Beijing and Tianjin, and large areas of cropland. With the implementation of afforestation projects in recent decades, vegetation and carbon (C) uptake have greatly increased in the MHRB. In addition, the annual runoff has significantly declined, threatening regional water security. The trade-off relationship between water yield and C uptake in the MHRB remains unknown. This study employed a biogeochemical model (Biome-BGC) to simulate the natural vegetation dynamics and gross primary productivity (GPP) during 1982-2019 driven by climate forcing. A distributed hydrological model (geomorphology-based hydrological model, GBHM) was adopted to assess the impact of vegetation restoration on the hydrological processes. The results indicated that the leaf area index in the MHRB increased significantly (P < 0.01) during 1982-2019, which led to evapotranspiration increase and runoff (R) reduction. Under the influence of vegetation restoration, both the GPP and the water use efficiency (WUE) increased significantly in the MHRB during 2000-2019, however, the improvement of WUE decreased with the aridity index increasing. Our results showed that vegetation restoration can improve C sequestration efficiency in the MHRB and that the trade-off between water yield and C sequestration should be considered in planning ecological projects to achieve C neutrality.

Detecting patterns of post-fire pine regeneration in a Madrean Sky Island with field surveys and remote sensing

The American Southwest is experiencing drastic increases in aridity and wildfire incidence, triggering conversion of some frequent surface forests to non-forest. Extensive research has focused on these dynamics in regional ponderosa pine forests, but we know much less about Madrean pine-oak forests, which are broadly distributed from the Sierra Madre in Mexico to the Sky Island mountain ranges in the U.S. Increased fire incidence and drought in these forests are limiting pine regeneration and driving conversion of biodiverse forests to oak shrublands. We investigated regeneration patterns in Pinus engelmannii and P. leiophylla during severe drought 10 years after the Horseshoe Two Megafire in the Chiricahua Mountains, Arizona-a follow-up to an assessment five years post-fire. In long-term plots, we examined changes in pine seedling and resprout recruitment. Past research demonstrated that topography and fire severity influenced pine recruitment across environmental gradients. We investigated here whether Landsat-8 normalized difference vegetation index (NDVI) and evapotranspiration estimated by the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) added explanatory value to our understanding of these patterns. Conversion of Madrean pine-oak forest to oak shrublands continued 6-10 years post-fire. A dense, low oak canopy continued to coalesce in sites subject to severe fire. The importance of resprouts in P. leiophylla regeneration accelerated because these plants outgrew competing oak resprouts. Topography and fire severity (dNBR) were important predictors of 2021 patterns of pine recruitment. NDVI added explanatory value to these models, suggesting its potential in tracking forest dynamics. Evapotranspiration did not add value, likely because ECOSTRESS' larger pixel sizes and moving pixel locations created excessive subpixel heterogeneity in this highly dissected landscape. These models suggest that P. engelmannii is more drought sensitive, was more negatively affected by drought and fire, and is more at risk to shifts in climate and wildfires than P. leiophylla.

Performance study of an innovative concept of hybrid constructed wetland-extensive green roof with growing media amended with recycled materials

Implementation of green roofs requires a large amount of primary material, especially for constructing the growing media layer. In addition, irrigation of green roofs with potable water is uneconomical and unsustainable. The novel hybrid green roof system proposed in this paper is in line with the principles of circular economy as it incorporates recycled materials into green roof growing media and greywater for irrigation. Two experimental beds were built to evaluate the concept of treating greywater in a constructed wetland prior to using it to irrigate a dual-layer extensive green roof. The growing media in both two extensive green roof beds contained ca. 37.5% by volume of recycled crushed building rubble containing a large proportion of brick. One of the two beds additionally contained 9.5% by volume of sewage sludge-based biochar. The concept of the hybrid roof and novel growing media was evaluated based on laboratory analysis of the growing media and on onsite measurements of hydraulic and thermal performance. The growing media amended with recycled materials developed in this study had hydrophysical properties comparable to commercially available growing media without recycled materials. Observations made during one vegetation season from June to October and a ten day-intensive water quality monitoring campaign during September 2020 showed that the constructed wetland significantly reduced total nitrogen and orthophosphate concentrations in pre-treated greywater. Due to the irrigation method employed, in which water flowed predominantly through drainage mats below the growing media, nutrient-leaching by the irrigation water was avoided. Concentrations of nutrients in the effluent were observed to increase only in response to precipitation. The temperature peak of the bottom green roof layer was shifted by almost 9 h from the peak in air temperature, and temperature fluctuations were significantly reduced. Vegetation on the bed amended with biochar demonstrated more vigorous growth due to available nutrients in the biochar which increased the rate of temperature-reducing evapotranspiration. More water evapotranspirated more water, which provided more water retention capacity confirmed by a lower runoff coefficient. Simple storage routing hydraulic modeling of hybrid green roof runoff using a nonlinear reservoir was performed.

Quantifying heterogeneity in ecohydrological partitioning in urban green spaces through the integration of empirical and modelling approaches

Urban green spaces (UGS) can help mitigate hydrological impacts of urbanisation and climate change through precipitation infiltration, evapotranspiration and groundwater recharge. However, there is a need to understand how precipitation is partitioned by contrasting vegetation types in order to target UGS management for specific ecosystem services. We monitored, over one growing season, hydrometeorology, soil moisture, sapflux and isotopic variability of soil water under contrasting vegetation (evergreen shrub, evergreen conifer, grassland, larger and smaller deciduous trees), focussed around a 150-m transect of UGS in northern Scotland. We further used the data to develop a one-dimensional model, calibrated to soil moisture observations (KGE's generally > 0.65), to estimate evapotranspiration and groundwater recharge. Our results evidenced clear inter-site differences, with grassland soils experiencing rapid drying at the start of summer, resulting in more fractionated soil water isotopes. Contrastingly, the larger deciduous site saw gradual drying, whilst deeper sandy upslope soils beneath the evergreen shrub drained rapidly. Soils beneath the denser canopied evergreen conifer were overall least responsive to precipitation. Modelled ecohydrological fluxes showed similar diversity, with median evapotranspiration estimates increasing in the order grassland (193 mm) < evergreen shrub (214 mm) < larger deciduous tree (224 mm) < evergreen conifer tree (265 mm). The evergreen shrub had similar estimated median transpiration totals as the larger deciduous tree (155 mm and 128 mm, respectively), though timing of water uptake was different. Median groundwater recharge was greatest beneath grassland (232 mm) and lowest beneath the evergreen conifer (128 mm). The study showed how integrating observational data and simple modelling can quantify heterogeneities in ecohydrological partitioning and help guide UGS management.

Combining discrete wavelet decomposition with soft computing techniques to predict monthly evapotranspiration in semi-arid Hakkâri province, Türkiye

Accurate prediction of evapotranspiration values is important in planning agricultural irrigation, crop growth research, and hydrological modeling. This study is aimed at estimating monthly evapotranspiration (ET) values in Hakkâri province by combining support vector regression, bagged tree, and boosted tree methods with wavelet transform. For this purpose, precipitation, runoff, surface net solar radiation, air temperatures, and previous ET values were divided into sub-signals with various mother wavelets such as Daubechies 4, Meyer, and Symlet 2 and presented as input to machine learning (ML) algorithms. The study's main contribution to the literature is to reveal which wavelet-based machine learning model, mother wavelet type, and combination of meteorological data show the most realistic results in ET estimation. While establishing the models, the data were divided into 80% training and 20% testing. The models' performances were based on the widely used root mean square error, mean absolute error, determination coefficient, and Taylor diagrams. As a result of the study, it was revealed that the hybrid wavelet ML, which is established with input combinations separated into subcomponents by wavelet transform, generally produces more successful predictions than the stand-alone ML model. In addition, it was revealed that the optimum ET forecasting model was obtained with the wavelet bagged tree algorithm with Symlet 2 mother wavelet. Even though the best model established is based on the precipitation and temperature inputs, it was revealed that past ET, solar radiation, and runoff values are also effective inputs in ET prediction. The results can also be used in other regions of the world with semi-arid climates, such as Hakkâri. The study's outputs provide essential resources to decision-makers and planners to manage water resources and plan agricultural irrigation.

Evaluating the contribution of satellite-derived evapotranspiration in the calibration of numerical groundwater models in remote zones using the EEFlux tool

Assessment of groundwater resources is crucial for developing water management practices for its sustainable exploitation, both for current and future needs. Numerical models are useful tools for such purpose. However, the lack of continuous monitoring networks, mainly due to difficult access to some remote locations, poses a challenge in developing and calibrating groundwater models. Remote sensing offers an alternative for acquiring information on hydrological and climatic variables at multiple spatiotemporal scales that has the potential to strengthen groundwater modeling. The aim of this study is to develop a methodology that uses remote sensing products to support model calibration. With this aim, we used the Parameter Estimation software (PEST) to calibrate a hydrogeological model of an unexploited basin located in the arid Chilean Altiplano using observed groundwater levels and evapotranspiration (ET) derived from the Earth Engine Evapotranspiration Flux (EEFlux) tool as observations. Our results show that the best model calibration is achieved using both EEFlux-ET and heads as observations to calibrate the hydraulic properties (normalized root mean square error = 4.1 %). We analyzed the effect of EEFlux-ET on the calibration of these properties and found a direct effect on specific yield parameters, which regulate the fluctuations of the water table over time. Incorporating EEFlux-ET estimates in the calibration resulted in lower values of specific yield across the aquifer. Therefore, incorporating remotely sensed ET as observations in the calibration of the groundwater model contributes to a better simulation of the spatiotemporal head variations in the basin.

Multi-scale spatial analysis of satellite-retrieved surface evapotranspiration in Beijing, a rapidly urbanizing region under continental monsoon climate

As one of the important components of hydrological cycle, evapotranspiration spatial distribution is of great significance to regional water resources planning and rational utilization. This research used Surface Energy Balance System model to estimate the daily evapotranspiration (ET) in Beijing based on Landsat 8 observations. Results showed that the daily ET in Beijing ranged from 3.469 to 5.474 mm/day. ET is known to decrease with the increase of land surface temperature (LST) and to increase with the increase of Normalized Difference Vegetation Index (NDVI). NDVI primarily decreased from the northwest to the southeast. When the NDVI value was 0.4-0.6, the average ET peaked at 4.88 mm/day, and then slightly decreased by 3.7%. The coefficient of determination of NDVI (0.95) was much greater than that of LST (0.30) upon linear fitting, showing LST was not the main factor controlling ET in Beijing. In contrast to the linear fitting results, the spatial correlation between LST and ET is more significant than that between NDVI and ET in the global bivariate spatial analysis, where the absolute value of global bivariate Moran's I of LST (0.51) was higher than that of NDVI (0.21) at a resolution of 150 m. And the univariate spatial autocorrelation indices of LST, ET, and NDVI equaled 0.84, 0.65, and 0.51, respectively. Furthermore, the complex spatial distribution pattern of variables could significantly affect the correlation analysis results. Local bivariate spatial analysis showed that over 60% of the Beijing area had a significant correlation, of which the negative correlation area of LST accounted for about 85%, and the positive correlation area of NDVI accounted for 74%. By improving the correlation analysis accuracy, the regional conditions for the establishment of correlation analysis results were clarified from the overall correlation analysis results.

Improved drought monitoring in teleconnection to the climatic escalations: A hydrological modeling based approach

Catchment-scale efficient agricultural drought monitoring and irrigation planning mostly depend on the accuracy of evapotranspiration (ET) estimates under different crop-growth phases. As indirect ET estimation under limited data availability scenario and complex paddy-field dynamics could not be sufficiently addressed by the conventional curve number (CN) based SWAT model, SWAT incorporates an add-in pothole module (SWAT-P) that conceptualizes the paddy-field hydrology with the empirical coefficient or equations resulting in poor ET estimates. To address these limitations, this study integrates an improved pothole methodology in SWAT (SWAT-EP) accounting the vadose-zone soil water dynamics under alternate ponding and drying conditions, ET variation under moisture abundant and stress conditions, and role of irrigation return flow generated in the paddy fields. The proposed approach was validated in the Kangsabati River integrated reservoir-catchment-command (12,014.70 km²) of the eastern India, and the results reveal that SWAT-EP outperformed the existing SWAT-P in reproducing the catchment-scale streamflow and ET flux with respect to the FAO-56 Penman-Monteith (PM) based ET estimates in all the four cropping seasons. The SWAT-EP derived Standardized Evapotranspiration Drought Index (SEDI) based drought severity and duration well-corroborate with the benchmark MOD16A2 derived drought estimates; whereas, the SWAT-P tends to overestimate the drought severity in the command area. The predictive uncertainty in drought monitoring was the lowest by SWAT-EP with relatively lower uncertainty was observed in the crop-growing locations of Kharagpur and Mohanpur. Moreover, the teleconnection between drought and climatic escalations corresponds to a better reproducibility of El Niño and La Niña phases by the SWAT-EP, while the SWAT-P performed un-satisfactorily across different spatiotemporal domains. This study endorses to adopt the proposed SWAT-EP model for river basin-scale drought monitoring and irrigation planning with prior validation in the diversified climatic and topographic conditions.