Variability Of Evapotranspiration Research Articles

Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe

Abstract. Evapotranspiration (ET) and gross primary production (GPP) are critical fluxes contributing to the energy, water, and carbon exchanges between the atmosphere and the land surface. Land surface models such as the Community Land Model v5 (CLM5) quantify these fluxes, estimate the state of carbon budgets and water resources, and contribute to a better understanding of climate change's impact on ecosystems. Past studies have shown the ability of CLM5 to model ET and GPP magnitudes well but emphasized systematic underestimations and lower variability than in the observations. Here, we evaluated CLM5's predictions of water and energy fluxes using observations from eddy covariance stations from the Integrated Carbon Observation System (ICOS), remote sensing, and reanalysis data sets. We assess simulated ET and GPP from the grid scale (CLM5grid) and the plant functional type (PFT) scale (CLM5PFT). CLM5PFT exhibited a low systematic error in simulating the ET at the ICOS sites (average bias of −4.68 %), indicating that PFT-specific ET closely matches the observations' magnitude. GPP was underestimated by CLM5PFT, especially in deciduous forests (bias of −43.76 %). The results showed an underestimation of the spatiotemporal variability in the simulated ET and GPP distribution moments across PFTs for both CLM setups compared to reanalysis data and remote-sensing products. These findings provide essential insights for improving land surface models, highlighting the need to enhance the CLM5's ability to capture the spatiotemporal variability in ET and GPP simulations across PFTs.

Primary roles of soil evaporation and vegetation in driving terrestrial evapotranspiration across global drylands.

Terrestrial evapotranspiration (ET) is a key variable in the global water cycle, notably affected by climate change and vegetation greening. However, its intrinsic driving modes and the ways through which driving factors influence it remain largely unexplored. Here, we quantified the internal and external drivers behind the spatiotemporal variability of ET across global drylands at seasonal and annual temporal scales and component levels based on pixel-by-pixel partial correlation and ridge regression analyses. The results show that vegetation predominantly drives changes in ET, plant canopy transpiration (Ec), evaporation of precipitation intercepted by vegetation (Ei), and soil evaporation (Es) in most regions of the global drylands. This pattern persisted on a seasonal scale. The contribution analysis revealed that vegetation had the strongest influence and the largest contribution to ET variation. Temperature was the climatic factor that contributed the most to the driving process of ET change and was significantly higher than the other climatic factors. Among the components of ET, Es was the predominant constituent part to ET, exerting a strong control on its variability. Its absolute contribution (60.3%) to this variability was approximately twice that of Ec. Our findings highlight the crucial role of Es and vegetation in driving ET across drylands and the potentially important moderating role of Es amid increasing water resource turbulence due to ongoing vegetation greening.

Relating spatial turfgrass quality to actual evapotranspiration for precision golf course irrigation

AbstractGolf courses are increasingly affected by water scarcity and climate change. An understanding of spatial variability of actual evapotranspiration (ETa) and turfgrass quality (TQ) site‐specific management zones (SSMZ) is important for the implementation of precision turfgrass management. Therefore, the main objectives of this study were to quantify the relationship between remotely sensed TQ and ETa estimates and to evaluate the spatial variations of TQ and ETa at a golf course in Utah. Ground‐based normalized difference vegetation index was collected using a TCM‐500 sensor, and aerial multispectral and thermal imagery data were acquired from unpiloted aircraft systems (UAS) in 2021, 2022, and 2023. A remote sensing TQ‐random forest (RF) model was developed using six datasets of UAS spectral indices and the RF algorithm. The spatial data were analyzed to determine the correlation between TQ and ETa estimates. The TQ and ETa SSMZ were created and integrated with irrigation heads on the golf course using the Thiessen polygons tool. Results demonstrated that TQ‐RF model was accurate within a root mean square error of 0.05. The correlation between TQ‐RF and ETa was stronger for fairways (R2 = 0.74), tees (R2 = 0.66), and roughs (R2 = 0.75) as compared to greens (R2 = 0.25) and the driving range (R2 = 0.36) on July 20, 2022. Actual evapotranspiration SSMZ, in combination with TQ‐RF SSMZ, is useful for irrigation scheduling, addressing the question of how much and where to irrigate. This study demonstrates the ability of TQ‐RF and ETa SSMZ to identify spatial variation for the purpose of landscape irrigation management in semi‐arid areas.

Evaluating land use ımpact on evapotranspiration in Yellow River Basin China through a novel GSEBAL model: a remote sensing perspective

Evapotranspiration (ET) is critical to surface water dynamics. Effective water resource management necessitates an accurate ET estimation. In the Yellow River Basin China, a study area, cutting-edge technologies are needed to improve large-scale ET estimates. This study estimates ET using GSEBAL, an advanced ET estimation algorithm. Google Earth Engine integrates the surface energy balance model-based GSEBAL. The technique includes the collection, preparation, and calculation of ET using Landsat imagery and ERA5-Land meteorological data from 1990 to 2020. The study examined satellite LST, albedo, and NDVI data. The GSEBAL model calculates soil heat flow, net radiation, and sensible heat flux. The study tested the GSEBAL model utilizing essential ET datasets such as ECOSTRESS, MOD16, and SSEBop. The study showed that the model effectively predicted daily and seasonal ET variations in different climates. Root mean squared error, bias, and Pearson's correlation coefficient verified the model's reliability. The study also analyzed land use and land cover (LULC) over 30 years using Random Forest classifiers. In the 1990–2020 YRBC ET, land use changes affect ET rates annually and seasonally. The study area experiences changes in LST, NDVI, and LULC. Maximum ET values rose from 214.217 mm in 1990 to 234.891 mm in 2000. The pattern flipped in 2020, decreasing to 221.456 mm. In 2010, Summer had the highest ET, 484.455 mm. 2020 spring ET is 314.727 mm. Low ET decreased from 24.652 mm in 1990 to 18.2 mm in 2020, reducing water loss. Fall ET peaks at 24.9 mm in 2020; winter ET is 18.75 mm.

Calibration and validation of the CMADS-Driven SWAT model in the distributed hydrological simulation of the Baihe River Basin in Nanyang

ABSTRACT The China Meteorological Assimilation Driving Dataset for the SWAT model (CMADS) has gained widespread use for its accuracy. This study focuses on the Baihe River Basin in Nanyang, using the SWAT tool and CMADS dataset to construct two distributed hydrological models of different scales. The accuracy and effectiveness of these models were evaluated. Parameter uncertainty analysis on a monthly and annual scale was conducted usingSUFI-2 algorithm. The goal was to assess the applicability of the CMADS-driven SWAT model in theBasin by calibrating and validating flow data at both monthly and annual scales. The results form the calibration period (2009–2013) showedmonthly R2 and NSE values were 0.81 and 0.8, while the annual values were 0.91 and 0.9. During validation(2014–2016), the annual R2 and NSE values were 0.89 and 0.86, and the monthly values were 0.76 and 0.76, with reasonable values for the P-factor, R-factor, and percentage bias (PBIAS), indicating good model applicability. Seasonal variations in rainfall, surface runoff, water yield, evapotranspiration (ET), and potential evapotranspiration (PET) were analyzed. The results demonstrate that surface runoff and water yield is closely related to precipitation, while ET is largely controlled by land use types, providing insights for water resource management

The Evapotranspiration Characteristics and Evaporative Cooling Effects of Different Vegetation Types on an Intensive Green Roof: Dynamic Performance Under Different Weather Conditions

Previous research has demonstrated that the multiple environmental benefits of green roofs are primarily associated with their evaporative cooling effect. However, current studies on green roof evapotranspiration (ET) mainly focus on extensive green roofs, and the evaporative cooling effect of intensive green roofs is still unclear. Using the intensive green roof of AQUA City in Nanjing as a case study, this research employs the three-temperature (3T) model combined with high-resolution thermal infrared imagery obtained via an unmanned aerial vehicle (UAV) to estimate the ET of different vegetation types. The study aims to explore the spatiotemporal variations in surface temperature, evapotranspiration (ET) rate, and evaporative cooling rate for various vegetation types under typical seasonal (summer and winter) and weather conditions (sunny, cloudy, and rainy before and after rainy days). The results showed that: (1) the ET rates and evaporative cooling effects of different types of vegetation differed significantly, with shrubs having the fastest ET rates, followed by arbors, and grasslands having relatively low ET rates. (2) Solar radiation and air temperature are the most crucial meteorological parameters for inducing ET on green roofs. In this study, the evaporative cooling performance showed the patterns of summer > winter and sunny > cloudy > rainy days. (3) In the spatial distribution of tree and irrigation plant groups, some low-temperature diffusion phenomena to the adjacent small microenvironments were evident, while the diffusion effect in winter is smaller and mainly shows the opposite warming characteristics. This study offers a valuable reference for quantifying the ET and evaporative cooling effects of various vegetation types on intensive green roofs, facilitating the optimization of vegetation configuration and supporting sustainable urban development.

Evaluating the Two-Source Energy Balance Model Using MODIS Data for Estimating Evapotranspiration Time Series on a Regional Scale

Estimating daily continuous evapotranspiration (ET) can significantly enhance the monitoring of crop stress and drought on regional scales, as well as benefit the design of agricultural drought early warning systems. However, there is a need to verify the models’ performance in estimating the spatiotemporal continuity of long-term daily evapotranspiration (ETd) on regional scales due to uncertainties in satellite measurements. In this study, a thermal-based two-surface energy balance (TSEB) model was used concurrently with Terra/Aqua MODIS data and the ERA5 atmospheric reanalysis dataset to calculate the surface energy balance of the soil–canopy–atmosphere continuum and estimate ET at a 1 km spatial resolution from 2000 to 2022. The performance of the model was evaluated using 11 eddy covariance flux towers in various land cover types (i.e., savannas, woody savannas, croplands, evergreen broadleaf forests, and open shrublands), correcting for the energy balance closure (EBC). The Bowen ratio (BR) and residual (RES) methods were used for enforcing the EBC in the EC observations. The modeled ET was evaluated against unclosed ET and closed ET (ETBR and ETRES) under clear-sky and all-sky observations as well as gap-filled data. The results showed that the modeled ET presented a better agreement with closed ET compared to unclosed ET in both Terra and Aqua datasets. Additionally, although the model overestimated ETd across all different land cover types, it successfully captured the spatiotemporal variability in ET. After the gap-filling, the total number of days compared with flux measurements increased substantially, from 13,761 to 19,265 for Terra and from 13,329 to 19,265 for Aqua. The overall mean results including clear-sky and all-sky observations as well as gap-filled data with the Aqua dataset showed the lowest errors with ETRES, by a mean bias error (MBE) of 0.96 mm.day−1, an average mean root square (RMSE) of 1.47 mm.day−1, and a correlation (r) value of 0.51. The equivalent figures for Terra were about 1.06 mm.day−1, 1.60 mm.day−1, and 0.52. Additionally, the result from the gap-filling model indicated small changes compared with the all-sky observations, which demonstrated that the modeling framework remained robust, even with the expanded days. Hence, the presented modeling framework can serve as a pathway for estimating daily remote sensing-based ET on regional scales. Furthermore, in terms of temporal trends, the intra-annual and inter-annual variability in ET can be used as indicators for monitoring crop stress and drought.

Environmental Influences on Evapotranspiration in Wheat-Maize Rotation Systems under Diverse Hydrological Regimes in the Guanzhong Plain, China

Evapotranspiration (ET) is a crucial hydrological process in terrestrial and agricultural ecosystems, and is a key factor driving climate change dynamics. Understanding the variability of ET and how it responds to controlling factors helps describe the impact of environmental changes on agricultural ecosystems. This study analyzes long-term continuous measurements from an Eddy Covariance (EC) system in wheat-maize rotation fields in the Guanzhong Plain in China. Using structural equation modeling (SEM), we investigated the regulatory roles of environmental and biological factors on ET, and the mechanisms controlling ET for summer maize and winter wheat under varying hydrological and climatic conditions. The results indicate that the eight-year average ET during the summer maize season ranged from 259 to 348mm; during the winter wheat season, it ranged from 369 to 513mm. The adjusted SEM explained 64% of the variation in daily ET during the summer maize season and 80% during the winter wheat season. Photosynthetically active radiation (PAR) was the primary controlling factor for both seasons, with air temperature (Tair) also playing a significant role. Leaf area index (LAI) was an important mediator for ET, and had a greater effect during the winter wheat season than during the summer maize season. Vapor pressure deficit (VPD) and soil water content (SWC) had a more significant impact on ET during the summer maize season than during the winter wheat season. The ET response to environmental factors varied significantly under different hydrological and climatic conditions. Specifically, the total effect of PAR on ET increased with increased moisture availability and decreased temperature; the trend was opposite for Tair. This indicates that high temperatures and drought conditions may increase the sensitivity of ET to temperature changes, and decrease the sensitivity of ET to radiation changes. This study explains the variability of evapotranspiration in agricultural ecosystems and associated controlling factors, providing important insights about how agricultural evapotranspiration responds to vegetation phenology under future climate change scenarios. These investigations help optimize water resource utilization and allocation in agricultural fields.

Sensitivity of Streamflow to Changing Rainfall and Evapotranspiration in Catchments Across the Nile Basin

This research focuses on the complex dynamics governing the sensitivity of streamflow to variations in rainfall and potential evapotranspiration (PET) within the Nile basin. By employing a hydrological model, our study examines the interrelationships between meteorological variables and hydrological responses across six catchments (Blue Nile, El Diem, Kabalega, Malaba, Mpanga, and Ribb) and explores the intricate balance between rainfall, PET, and streamflow. Nash Sutcliffe Efficiency (NSE) for calibration of the hydrological model ranged from 0.636 (Ribb) to 0.831 (El Diem). For validation, NSE ranged from 0.608 (Ribb) to 0.811 (Blue Nile). With rainfall kept constant while PET was increased by 5%, the streamflows of the Blue Nile, El Diem, Kabalega, Malaba, Mpanga, and Ribb decreased by 7.00, 5.08, 2.49, 4.10, 1.84, and 7.67%, respectively. With the original PET data unchanged, increasing rainfall of the Blue Nile, El Diem, Kabalega, Malaba, Mpanga, and Ribb by 5% led to an increase in streamflow by 9.02, 9.87, 5.38, 4.34, 6.58, and 8.32%, respectively. The research reveals that the rate at which a catchment losing water to the atmosphere (determined by PET) substantially influences its drying rate. Utilizing linear models, we demonstrate that the surplus rainfall available for increasing streamflow (represented by model intercepts) amplifies with higher rainfall intensities. This highlights the pivotal role of rainfall in shaping catchment water balance dynamics. Moreover, our study stresses the varied sensitivities of catchments within the basin to changes in PET and rainfall. Catchments with lower PET exhibit heightened responsiveness to increasing rainfall, accentuating the influence of evaporative demand on streamflow patterns. Conversely, regions with higher PET rates necessitate refined management strategies due to their increased sensitivity to changes in evaporative demand. Understanding the intricate interplay between rainfall, PET, and streamflow is paramount for developing adaptive strategies amidst climate variability. By examining these relationships, our research contributes essential knowledge for sustainable water resource management practices at both the catchment and regional scales, especially in regions susceptible to varying sensitivities of catchments to climatic conditions.

Attributing the Variations in Evapotranspiration and Its Components of Alpine Grasslands Over the Tibetan Plateau

AbstractClarifying the controlling factors of annual variations in evapotranspiration (ET) and its components (transpiration (T) and evaporation (E)) over alpine grasslands of high‐cold regions is vital to understanding the hydrological processes of the terrestrial ecosystem. Therefore, this study investigated the variability of ET and its components over the alpine grasslands of the Tibetan Plateau (TP) and the driving factors underlying these changes during 1961–2013. The results showed that the annual ET over alpine grasslands was 339 mm, of which 59% and 41% were contributed by E and T, respectively. Annual ET, E, and T over the TP grasslands changed insignificantly before 1995, whereas increased dramatically during 1995–2013. Regarding different alpine grassland types, annual ET and its components in seasonal frost regions (SAG) were larger than in permafrost regions (PAG). The increase of ET and its components in PAG was profoundly larger than that in the SAG region during 1995–2013. Water and energy factors controlled the ET of approximately 65% and 31% area of the TP grasslands, respectively. Leaf area index was the major cause of T variability throughout 64% area of TP grasslands, while regions where energy factors were the major force of T change were mainly located in the eastern SAG region. Variability of E on entire TP grasslands (81%) was mainly regulated by available water supply. Our results indicate that as permafrost degradation has the potential to amplify climate warming and precipitation increase, ET over the PAG region was expected to continue increasing faster than the SAG region.

Comparison of the Distribution of Evapotranspiration on Shady and Sunny Slopes in Southwest China

Evapotranspiration (ET) plays a significant role in the surface water cycle, particularly within the unique geographical context of Southwest China. The region’s different topography, driven by mountain uplift and variations in slope direction, alters regional hydrothermal conditions, thereby affecting local ecoclimatic patterns. ET characteristics, shaped by slope orientation, can also serve as important indicators of climate variability in the study area. While most existing ET research on shady and sunny slopes has been conducted at the point scale, this study employed Global Land Surface Satellite (GLASS) ET products to estimate the average ET for shady and sunny slopes across five provinces in Southwest China between 2003 and 2018. The driving factors behind the variation in ET across different regions were also explored. Key results include the following: (1) Annual ET in Southwest China ranges between 200 mm and 800 mm, with Tibet exhibiting the lowest values and Yunnan the highest. (2) ET decreases gradually with increasing altitude in the altitude range of 0 m to 5000 m. The ET is higher on the sunny slopes than on the shady slopes. Notably, when the altitude is higher than 5000 m, ET on shady slopes in Tibet is greater than that on sunny slopes as the altitude increases. (3) ET initially increases with slope inclination before decreasing. Notably, in areas with slopes exceeding 35° in Yunnan, the ET value is found to be significantly higher on shady slopes compared to sunny slopes. (4) The effects of soil moisture, the Normalized Difference Vegetation Index, relative humidity, and land surface temperature on ET are more substantial on shady slopes than sunny slopes, whereas air temperature has a stronger impact on ET on sunny slopes. These results provide valuable data for research on regional climate change and contribute to strategies for ecological and environmental protection.

Spatiotemporal variations in potential evapotranspiration in the Yellow River and Yangtze River Basins under changing environments: impacts on runoff and their causes

Spatiotemporal variations in potential evapotranspiration in the Yellow River and Yangtze River Basins under changing environments: impacts on runoff and their causes

Disentangling the contributions of water vapor, albedo and evapotranspiration variations to the temperature effect of vegetation greening over the Arctic

Vegetation greening is observed over the Arctic, and its feedback to Arctic amplification has attracted increasing attention. Previous studies have primarily focused on the temperature effect of a single environmental variable (e.g., albedo), while the separate contributions of land surface albedo, evapotranspiration (ET) and water vapor remain underexamined. In this study, we develop knowledge-based data-driven models (i.e., path analysis and machine learning) to estimate the temperature effect of vegetation greening and quantify the separate contributions of albedo, ET and water vapor in July and August from 1982 to 2015. The results show a wide range of temperature sensitivity to the NDVI (Normalized Difference Vegetation Index), and vegetation greening has led to Arctic warming of 0.76 °C, 0.68 °C, 0.83 °C in July and August and the average of the two months, respectively. Path analysis suggested that vegetation greening affects Arctic air temperature mainly by regulating albedo and water vapor. In July, changes in water vapor contributed the most to the temperature effect of vegetation greening with a contribution of 0.25 ± 0.08 °C, while in August, changes in albedo and water vapor had similar effects with a contribution of 0.21 ± 0.08 °C. In contrast, changes in ET have generated a negligible cooling effect due to small changes in ET. Further analysis shows similar positive contributions of albedo and water vapor in barren, graminoid tundra, prostrate-shrub tundra and erect-shrub, with contributions ranging from 0.18 ± 0.05°C to 0.30 ± 0.11°C, while changes in water vapor dominate vegetation’s temperature effect in wetlands, with contributions ranging from 0.26 ± 0.11°C to 0.32 ± 0.16°C. This study emphasizes the importance of considering multiple driving factors to assess the temperature effect of vegetation greening in a consistent framework and highlights the critical role of water vapor change in addition to the widely examined albedo in explaining Arctic warming.

Long-term analysis of reference evapotranspiration variations in the lower Bhavani basin

Evapotranspiration is a crucial component of the hydrological cycle and is profoundly influenced by climate change. Assessing regional evapotranspiration changes is essential for effective water resource management. The study investigated the dynamics of reference evapotranspiration in the Lower Bhavani Basin, a region with diverse landscapes increasingly vulnerable to climatic variability. Reference evapotranspiration data from the Food and Agriculture Organization using the AgERA5 dataset was analyzed to assess annual and seasonal trends from 2000 to 2022. The result showed that the mean yearly reference evapotranspiration in the basin is 1672.87 mm, with a significant decline of 3.53 mm per year. Seasonal analysis revealed a consistent decreasing trend across all seasons, with the southwest monsoon season showing the most significant reduction in evapotranspiration rates. The notable shift in the annual evapotranspiration rate was observed in 2016, highlighting the impact of climate change. Further, temperature, solar radiation and relative humidity were identified as the dominant factors influencing evapotranspiration rates in the basin. The relative moisture index indicated prevalent dry conditions, making the region susceptible to water stress. The findings provide critical insights for regional water resources management and accentuate the need for sustainable water management strategies to mitigate the impacts of climate change.

Assessing the effect of climate change on spatiotemporal variations in reference evapotranspiration across Iraq

Assessing the effect of climate change on spatiotemporal variations in reference evapotranspiration across Iraq

Spatiotemporal variations and driving factors of evapotranspiration in the Yunnan-Guizhou Plateau from 2003 to 2020

ABSTRACT Evapotranspiration (ET) is vital for the Earth's energy and water balance, particularly influenced by global climate change. The Yunnan-Guizhou Plateau (YGP), characterized by abundant water resources and intricate terrain, has been a subject of study. However, previous research often overlooked intra-annual climate variations in ET. This study employed high-spatiotemporal-resolution ET data from 2003 to 2020 to quantitatively analyze the spatiotemporal characteristics of ET on the YGP. The annual ET showed an increasing trend of 0.18 mm/year, with monthly ET increases in January, March, November, and December, mainly influenced by vegetation transpiration, which accounts for 56% of ET. Breakpoints in ET trends and seasonal components occurred in January 2007 and June 2018. The geodetector model assessed the impact of 15 driving factors on ET, with net radiation and vegetation index playing dominant roles with q-values of 0.29 and 0.24. Factor impacts varied seasonally, with greater influence in the dry season (q-value of 0.53 for net radiation in January) and less in the rainy season (q-value of 0.08 in August). Pearson correlation analysis indicated that different driving factors influenced ET in different months. These findings enhance understanding of plateau ET responses to climate-change mechanisms.

The Dynamics of Vegetation Evapotranspiration and Its Response to Surface Meteorological Factors in the Altay Mountains, Northwest China

The Altay Mountains’ forests are vital to Xinjiang’s terrestrial ecosystem, especially water regulation and conservation. This study evaluates vegetation evapotranspiration (ET) from 2000 to 2017 using temperature, precipitation, and ET data from the China Meteorological Data Sharing Service. The dataset underwent quality control and was interpolated using the inverse distance weighted (IDW) method. Correlation analysis and climate trend methodologies were applied to assess the impacts of temperature, precipitation, drought, and extreme weather events on ET. The results indicate that air temperature had a minimal effect on ET, with 68.34% of the region showing weak correlations (coefficients between −0.2 and 0.2). Conversely, precipitation exhibited a strong positive correlation with ET across 98.91% of the area. Drought analysis, using the standardized precipitation evapotranspiration index (SPEI) and the Temperature Vegetation Dryness Index (TVDI), showed that ET was significantly correlated with the SPEI in 96.47% of the region, while the TVDI displayed both positive and negative correlations. Extreme weather events also significantly influenced ET, with reductions in the Simple Daily Intensity Index (SDII), heavy precipitation days (R95p, R10), and increases in indicators like growing season length (GSL) and warm spell duration index (WSDI) leading to variations in ET. Based on the correlation coefficients and their significance, it was confirmed that the SII (precipitation intensity) and R95p (heavy precipitation) are the main factors causing vegetation ET increases. These findings offer crucial insights into the interactions between meteorological variables and ET, essential information for sustainable forest management, by highlighting the importance of optimizing water regulation strategies, such as adjusting species composition and forest density to enhance resilience against drought and extreme weather, thereby ensuring long-term forest health and productivity in response to climate change.

Comparative analysis of FAO dual Kc, Priestley‐Taylor and flux variance similarity methods in partitioning evapotranspiration from flood‐irrigated rice fields

AbstractAccurate quantification of evapotranspiration (ET) and its bifurcation into productive transpiration (T) and unproductive evaporation (E) are essential for the successful implementation of sustainable irrigation practices. This study compares the performances of three distinct methods for partitioning ET into E and T fluxes from flood‐irrigated rice fields. These methods include: i) the FAO dual Kc‐ETo model, which utilizes Penman–Monteith (PM) estimates of reference ET and crop‐specific scaling factors; ii) the Priestley‐Taylor (PT) method, which relies on radiation‐driven energy balance at the canopy surface; and iii) the flux variance similarity (FVS) method, which utilizes high‐frequency eddy covariance (EC) measurements of carbon and water vapour fluxes. Meteorological, phenological and hydrological parameters were monitored over two winter seasons in a paddy field, replicating site‐specific management strategies. The cumulative ETs obtained from the PM, PT and FVS methods were 445 mm, 300 mm and 356 mm, respectively, for season 1 and 428 mm, 321 mm and 375 mm, respectively, for season 2. The accuracy of the ET estimation and partition methods was assessed against independent measurements of ET and E using a lysimeter and a microlysimeter. The results demonstrated that the EC‐based FVS method offers superior accuracy in both quantifying (R2 = 0.67, RMSE = 0.70 mm) and partitioning (R2 = 0.51, RMSE = 0.76 mm) ET fluxes, followed by the PM‐based FAO dual Kc‐ETo and PT methods. Evaporation accounted for 29 ± 5% of consumptive water use, highlighting an opportunity for the implementation of water‐saving strategies, particularly during vegetative and transplantation stages. The findings of the path analysis indicate that vegetative and radiation factors influence ET variation, while meteorological and soil factors significantly impact T variation.

Water budgets in an arid and alpine permafrost basin: Observations from the High Mountain Asia

Ground freeze‒thaw processes have significant impacts on infiltration, runoff and evapotranspiration. However, there are still critical knowledge gaps in understanding of hydrological processes in permafrost regions, especially of the interactions among permafrost, ecology, and hydrology. In this study, an alpine permafrost basin on the northeastern Qinghai‒Tibet Plateau was selected to conduct hydrological and meteorological observations. We analyzed the annual variations in runoff, precipitation, evapotranspiration, and changes in water storage, as well as the mechanisms for runoff generation in the basin from May 2014 to December 2015. The annual flow curve in the basin exhibited peaks both in spring and autumn floods. The high ratio of evapotranspiration to annual precipitation (>1.0) in the investigated wetland is mainly due to the considerably underestimated ‘observed’ precipitation caused by the wind-induced instrumental error and the neglect of snow sublimation. The stream flow from early May to late October probably came from the lateral discharge of subsurface flow in alpine wetlands. This study can provide data support and validation for hydrological model simulation and prediction, as well as water resource assessment, in the upper Yellow River Basin, especially for the headwater area. The results also provide case support for permafrost hydrology modeling in ungauged or poorly gauged watersheds in the High Mountain Asia.

Effects of hydrological variability on the sustainable use of water in a regional economy. An application to Tuscany

Existing input-output (IO) models have mainly focused on water demand. Some studies have incorporated water supply (availability), but do not take into account its natural variability, an essential element when performing a water stress analysis. The present study integrates the hydrological variability of water availability into a hydroeconomic IO model, considering its exogenous effects on water supply and its exogenous effects on water demand. Two endogenous effects are considered: i) changes in blue water requirements in the agricultural industry due to variations in precipitation and evapotranspiration, and ii) changes in grey water requirements in all discharging industries due to variations in runoff and groundwater recharge. By means of a T-years hydrological series and Monte Carlo simulations, the model allows estimating T values of the Extended Water Exploitation Index (EWEI), obtaining its empirical probability distribution and confronting it with scarcity thresholds. Additionally, the model includes a methodology to incorporate intra-annual variation, obtaining the critical month EWEI and defining a more transparent and endogenous scarcity threshold. Empirically tested for the Italian region of Tuscany considering a multivariate hydrological model for the generation of a 100-year hydrological series, our results allow a more in-depth analysis of water scarcity in the region.