The hypothermic nature of fungi.

Fungi play essential roles in global health, ecology, and economy, but their thermal biology is relatively unexplored. Mushrooms, the fruiting body of mycelium, were previously noticed to be colder than surrounding air through evaporative cooling. Here, we confirm those observations using infrared thermography and report that this hypothermic state is also observed in mold and yeast colonies. The relatively colder temperature of yeasts and molds is also mediated via evaporative cooling and associated with the accumulation of condensed water droplets on plate lids above colonies. The colonies appear coldest at their center and the surrounding agar appears warmest near the colony edges. The analysis of cultivated Pleurotus ostreatus mushrooms revealed that the hypothermic feature of mushrooms can be observed throughout the whole fruiting process and at the level of mycelium. The mushroom's hymenium was coldest, and different areas of the mushroom appear to dissipate heat differently. We also constructed a mushroom-based air-cooling prototype system capable of passively reducing the temperature of a semiclosed compartment by approximately 10 °C in 25 min. These findings suggest that the fungal kingdom is characteristically cold. Since fungi make up approximately 2% of Earth's biomass, their evapotranspiration may contribute to cooler temperatures in local environments.

Planting pattern and nitrogen management strategies: positive effect on yield and quality attributes of Triticum aestivum L. crop.

Wheat (Triticum aestivum L.) is a staple food crop that plays a crucial role in global food security. A suitable planting pattern and optimum nitrogen (N) split management are efficient practices for improving wheat production. Therefore, an experiment was performed to explore the effect of N split management and sowing patterns on wheat at the Agronomy Research Farm, The University of Agriculture Peshawar, during rabi season 2020-21 and 2021-22. The treatments consisted of different nitrogen rates of 0, 80, 120, and 160 kg ha- 1 and planting patterns of W, M, broadcast and line sowing. The pooled analysis of both cropping seasons showed that application of 120 kg N ha- 1 increased spikelets spike- 1, grains spike- 1, 1000 grains weight, grain yield, grain N content, evapotranspiration and water use efficiency by 21.9, 16.7, 21.8, 70, 13, 19.9 and 40% as compared to control, respectively. In addition, W and M were observed the best management practices among all planting patterns. The M planting pattern enhanced chlorophyll a, b, carotenoids and evapotranspiration while W plating pattern improved yield components and yield of wheat as compared to broadcast planting patterns. The principal component analysis biplot showed a close association of M and W planting patterns with 120 kg N ha- 1 in most of the studied traits. Hence, it is concluded that split application of 120 kg N ha- 1 in W and M sowing patterns enhanced growth, biochemical traits and water use efficiency, reducing N fertilization from 160 to 120 kg ha- 1 while increasing grain yield of wheat. Hence, it is recommended that application of 120 kg N ha⁻¹ in combination with W and M planting patterns offer a sustainable approach to enhancing wheat production in the alkaline soil conditions of the Peshawar valley.

Inside-out: Synergising leaf biochemical traits with stomatal-regulated water fluxes to enhance transpiration modelling during abiotic stress.

As the global climate continues to change, plants will increasingly experience abiotic stress(es). Stomata on leaf surfaces are the gatekeepers to plant interiors, regulating gaseous exchanges that are crucial for both photosynthesis and outward water release. To optimise future crop productivity, accurate modelling of how stomata govern plant-environment interactions will be crucial. Here, we synergise optical and thermal imaging data to improve modelled transpiration estimates during water and/or nutrient stress (where leaf N is reduced). By utilising hyperspectral data and partial least squares regression analysis of six plant traits and fluxes in wheat (Triticum aestivum), we develop a new spectral vegetation index; the Combined Nitrogen and Drought Index (CNDI), which can be used to detect both water stress and/or nitrogen deficiency. Upon full stomatal closure during drought, CNDI shows a strong relationship with leaf water content (r2 = 0.70), with confounding changes in leaf biochemistry. By incorporating CNDI transformed with a sigmoid function into thermal-based transpiration modelling, we have increased the accuracy of modelling water fluxes during abiotic stress. These findings demonstrate the potential of using combined optical and thermal remote sensing-based modelling approaches to dynamically model water fluxes to improve both agricultural water usage and yields.

Aerodynamic effects cause higher forest evapotranspiration and water yield reductions after wildfires in tall forests.

Wildfires are increasing in frequency, intensity, and extent globally due to climate change and they can alter forest composition, structure, and function. The destruction and subsequent regrowth of young vegetation can modify the ecosystem evapotranspiration and downstream water availability. However, the response of forest recovery on hydrology is not well known with even the sign of evapotranspiration and water yield changes following forest fires being uncertain across the globe. Here, we quantify the effects of forest regrowth after catastrophic wildfires on evapotranspiration and runoff in the world's tallest angiosperm forest (Eucalyptus regnans) in Australia. We combine eddy covariance measurements including pre- and post-fire periods, mechanistic ecohydrological modeling and then extend the analysis spatially to multiple fires in eucalypt-dominated forests in south-eastern Australia by utilizing remote sensing. We find a fast recovery of evapotranspiration which reaches and exceeds pre-fire values within 2 years after the bushfire, a result confirmed by eddy covariance data, remote sensing, and modeling. Such a fast evapotranspiration recovery is likely generalizable to tall eucalypt forests in south-eastern Australia as shown by remote sensing. Once climate variability is discounted, ecohydrological modeling shows evapotranspiration rates from the recovering forest which reach peak values of +20% evapotranspiration 3 years post-fire. As a result, modeled runoff decreases substantially. Contrary to previous research, we find that the increase in modeled evapotranspiration is largely caused by the aerodynamic effects of a much shorter forest height leading to higher surface temperature, higher humidity gradients and therefore increased transpiration. However, increases in evapotranspiration as well as decreases in runoff caused by the young forest are constrained by energy and water limitations. Our result of an increase in evapotranspiration due to aerodynamic warming in a shorter forest after wildfires could occur in many parts of the world experiencing forest disturbances.

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.

Non-linear response of Norway spruce to climate variation along elevational and age gradients in the Carpathians.

Climate change, namely increased warming coupled with a rise in extreme events (e.g., droughts, storms, heatwaves), is negatively affecting forest ecosystems worldwide. In these ecosystems, growth dynamics and biomass accumulation are driven mainly by environmental constraints, inter-tree competition, and disturbance regimes. Usually, climate-growth relationships are assessed by linear correlation due to the simplicity and straightforwardness of modeling. However, applying this method may bias results, since the ecological and physiological responses of trees to environmental factors are non-linear, and usually bell-shaped. In the Eastern Carpathian, Norway spruce is at the southeasternmost edge of its natural occurrence; this region is thus potentially vulnerable to climate change. A non-linear assessment of climate-growth relationships using machine-learning techniques for Norway spruce in this area had not been conducted prior to this study. To address this knowledge gap, we analyzed a large tree-ring network from 158 stands, with over 3000 trees of varying age distributed along an elevational gradient. Our results showed that non-linearity in the growth-climate response of spruce was season-specific: temperatures from the previous autumn and current growing season, along with water availability during winter, induced a bell-shaped response. Moreover, we found that at low elevations, spruce growth was mainly limited by water availability in the growing season, while winter temperatures are likely to have had a slight influence along the entire elevational gradient. Furthermore, at elevations lower than 1400 m, spruce trees were also found to be sensitive to previous autumn water availability. Overall, our results shed new light on the response of Norway spruce to climate in the Carpathians, which may aid in management decisions.

Lanthanum interferes with the fundamental rhythms of stomatal opening, expression of related genes, and evapotranspiration in Arabidopsis thaliana.

The accumulation of rare earth elements (REEs) in the global environment poses a threat to plant health and ecosystem stability. Stomata located on leaves serve as the primary site for plant responses to REE-related threats. This study focused on lanthanum [La(III)], a prevalent REE in the atmospheric environment. Using interdisciplinary techniques, it was found that La(III) (≤80 µM) interfered with the fundamental rhythms of stomatal opening, related gene expression, and evapotranspiration in plants. Specifically, when exposed to low concentrations of La(III) (15 and 30 µM), the expression levels of six genes were increased, stomatal opening was enhanced, and the evapotranspiration rate was accelerated. The interference on stomatal rhythms was enhanced with higher concentrations of La(III) (60 and 80 µM), increasing the expression levels of six genes, stomatal opening, and evapotranspiration rate. To counter the interference of low concentrations of La(III) (15 and 30 µM), plants accelerated nutrient replenishment through La(III)-induced endocytosis, which the redundant nutrients enhanced photosynthesis. However, replenished nutrients failed to counter the disruption of plant biological rhythms at higher concentrations of La(III) (60 and 80 µM), thus inhibiting photosynthesis due to nutrient deficit. The interference of La(III) on these biological rhythms negatively affected plant health and ecosystem stability.

Machine learning-based estimation of evapotranspiration under adaptation conditions: a case study in Heilongjiang Province, China.

The prediction of evapotranspiration (ET0) is crucial for agricultural ecosystems, irrigation management, and environmental climate regulation. Traditional methods for predicting ET0 require a variety of meteorological parameters. However, obtaining data for these multiple parameters can be challenging, leading to inaccuracies or inability to predict ET0 using traditional methods. This affects decision-making in critical applications such as agricultural irrigation scheduling and water management, consequently impacting the development of agricultural ecosystems. This issue is particularly pronounced in economically underdeveloped regions. Therefore, this paper proposes a machine learning-based evapotranspiration estimation method adapted to evapotranspiration conditions. Compared to traditional methods, our approach relies less on the variety of meteorological parameters and yields higher prediction accuracy. Additionally, we introduce a 'region of evapotranspiration adaptability' division method, which takes into account geographical differences in ET0 prediction. This effectively mitigates the negative impact of anomalies or missing data from individual meteorological stations, making our method more suitable for practical agricultural irrigation and ecosystem water resource management. We validated our approach using meteorological data from 25 stations in Heilongjiang, China. Our results indicate that non-adjacent geographical areas, despite different climatic conditions, can have similar impacts on ET0 prediction. In summary, our method facilitates accurate ET0 prediction, offering new insights for the development of agricultural irrigation and ecosystems, and further contributes to agricultural food supply.

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.

A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models.

Evapotranspiration (ETo) is a complex and non-linear hydrological process with a significant impact on efficient water resource planning and long-term management. The Penman-Monteith (PM) equation method, developed by the Food and Agriculture Organization of the United Nations (FAO), represents an advancement over earlier approaches for estimating ETo. Eto though reliable, faces limitations due to the requirement for climatological data not always available at specific locations. To address this, researchers have explored soft computing (SC) models as alternatives to conventional methods, known for their exceptional accuracy across disciplines. This critical review aims to enhance understanding of cutting-edge SC frameworks for ETo estimation, highlighting advancements in evolutionary models, hybrid and ensemble approaches, and optimization strategies. Recent applications of SC in various climatic zones in Bangladesh are evaluated, with the order of preference being ANFIS > Bi-LSTM > RT > DENFIS > SVR-PSOGWO > PSO-HFS due to their consistently high accuracy (RMSE and R2). This review introduces a benchmark for incorporating evolutionary computation algorithms (EC) into ETo modeling. Each subsection addresses the strengths and weaknesses of known SC models, offering valuable insights. The review serves as a valuable resource for experienced water resource engineers and hydrologists, both domestically and internationally, providing comprehensive SC modeling studies for ETo forecasting. Furthermore, it provides an improved water resources monitoring and management plans.

Assessing the cooling potential of climate change adaptation measures in rural areas.

Atmospheric heat has become a major public concern in a rapidly warming world. Evapotranspiration, however, provides effective land surface cooling during the vegetation period. Adversely, modern cultural landscapes - due to both water and potential evapotranspiration pathways lacking - are increasingly incapable of offering this important benefit. We hypothesised that concerted measures for a revived landscape water retention can fuel plant transpiration, especially during dry periods, and thus contribute to climate change adaptation by stabilising the regional climate. Seeking nature-based ways to an improved landscape water retention, we used the land surface temperature (LST) as a proxy for landscape mesoclimate. For our drought-prone rural study area, we identified potential candidate environmental predictors for which we established statistical relationships to LST. We then, from a set of potential climate change adaptation measures, mapped selected items to potential locations of implementation. Building on that, we evaluated a certain measures' probable cooling effect using (i) the fitted model and (ii) the expected expression of predictors before and after a hypothetical measure implementation. In the modelling, we took into account the spatial and temporal autocorrelation of the LST data and thus achieved realistic parameter estimates. Using the candidate predictor set and the model, we were able to establish a ranking of the effectiveness of climate adaptation measures. However, due to the spatial variability of the predictors, the modelled LST is site-specific. This results in a spatial differentiation of a measure's benefit. Furthermore, seasonal variations occur, such as those caused by plant growth. On average, the afforestation of arable land or urban brownfields, and the rewetting of former wet meadows have the largest cooling capacities of up to 3.5 K. We conclude that heat countermeasures based on fostering both evapotranspiration and landscape water retention, even in rural regions, offer promising adaptation ways to atmospheric warming.

Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region: A comparative study.

Accurate and reliable estimation of Reference Evapotranspiration (ETo) is crucial for water resources management, hydrological processes, and agricultural production. The FAO-56 Penman-Monteith (FAO-56PM) approach is recommended as the standard model for ETo estimation; nevertheless, the absence of comprehensive meteorological variables at many global locations frequently restricts its implementation. This study compares shallow learning (SL) and deep learning (DL) models for estimating daily ETo against the FAO-56PM approach based on various statistic metrics and graphic tool over a coastal Red Sea region, Sudan. A novel approach of the SL model, the Catboost Regressor (CBR) and three DL models: 1D-Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were adopted and coupled with a semi-supervised pseudo-labeling (PL) technique. Six scenarios were developed regarding different input combinations of meteorological variables such as air temperature (Tmin, Tmax, and Tmean), wind speed (U2), relative humidity (RH), sunshine hours duration (SSH), net radiation (Rn), and saturation vapor pressure deficit (es-ea). The results showed that the PL technique reduced the systematic error of SL and DL models during training for all the scenarios. The input combination of Tmin, Tmax, Tmean, and RH reflected higher performance than other combinations for all employed models. The CBR-PL model demonstrated good generalization abilities to predict daily ETo and was the overall superior model in the testing phase according to prediction accuracy, stability analysis, and less computation cost compared to DL models. Thus, the relatively simple CBR-PL model is highly recommended as a promising tool for predicting daily ETo in coastal regions worldwide which have limited climate data.

Climate risk zoning for wheat crops in the southeastern region of Brazil.

BACKGROUND: Wheat (Triticum aestivum L.) is the second most consumed food in the world. One way to meet this demand is the expansion of wheat cultivation to the Brazilian Cerrado in the southeastern region. However, one of the major limitations is that there are few studies related to wheat climate risk zoning. Thus, this study aimed to determine the agroclimatic zoning of wheat by estimating the water needs satisfaction index (ISNA) in the southeastern region of Brazil. For this purpose, a 60-year historical series of meteorological data was used to calculate the potential evapotranspiration, crop evapotranspiration, and climatological water balance values. To define the agroclimatic zones of wheat and sowing date, the ISNA method was used. The data were analyzed using descriptive statistics to determine the variations. To obtain the agroclimatic zoning of wheat, the geostatistical method of kriging interpolation was used. RESULTS: The regions with the highest rainfall are the south of Minas Gerais and the coast of São Paulo. The sowing period directly impacts the development of the crop, the available water capacity and the ISNA values indicated the spring and summer had better cultivation conditions, and the best window for wheat cultivation is concentrated in the fall due to the limitation of biotic factors. CONCLUSION: In terms of altitude (>700 m), Minas Gerais has 39.4% of the area suitable for wheat cultivation. Thus, climatic variations within and between the states of the southeastern region should be considered for the positioning of wheat cultivars in these regions to obtain the maximum yield. © 2023 Society of Chemical Industry.

The simultaneous prediction of yield and maturity date for wheat-maize by combining satellite images with crop model.

BACKGROUND: The simultaneous prediction of yield and maturity date has an important impact on ensuring food security. However, few studies have focused on simultaneous prediction of yield and maturity date for wheat-maize in the North China Plain (NCP). In this study, we developed the prediction model of maturity date and yield (PMMY) for wheat-maize using multi-source satellite images, an Agricultural Production Systems sIMulator (APSIM) model and a random forest (RF) algorithm. RESULTS: The results showed that the PMMY model using peak leaf area index (LAI) and accumulated evapotranspiration (ET) has the optimal performance in the prediction of maturity date and yield. The accuracy of the PMMY model using peak LAI and accumulated ET was higher than that of the PMMY model using only peak LAI or accumulated ET. In a single year, the PMMY model had good performance in the prediction of maturity date and yield. The latitude variation in spatial distribution of maturity date for WM was obvious. The spatial heterogeneity for yield of wheat-maize was not prominent. Compared with 2001-2005, the maturity date of the two crops in 2016-2020 advanced 1-2 days, while yield increased 659-706 kg ha-1. The increase in minimum temperature was the main meteorological factor for advance in the maturity date for wheat-maize. Precipitation was mainly positively correlated with maize yield, while the increase in minimum temperature and solar radiation was crucial to the increase in yield. CONCLUSION: The simultaneous prediction of yield and maturity can be used to guide agricultural production and ensure food security. © 2024 Society of Chemical Industry.

The banana tree circle as a nature-based solution for sustainable greywater management: A new design model.

The banana tree circle (BTC) is a low-cost system for local greywater management, using a natural treatment and disposal process, providing additional resource recovery benefits. However, there are no standard design criteria for BTC that would allow for quality control of its efficiency and sustainability, and little is currently known about the full-scale performance of BTC. Based on the scoping literature review of 31 documents in the scientific database and eight documents from grey literature, a standard design model was proposed for the BTC technology based on the concept of water balance, greywater flows, rain, infiltration, and evapotranspiration. The first two steps of the BTC design were determining the areas required for infiltration and evapotranspiration. A cylindrical form trench, the soil percolation rate, and the hydraulic loading rate were considered for the infiltration area. The banana trees' evapotranspiration rate was taken into consideration for the evapotranspiration area. The proposed model was applied in a case study where we used a trench with 0.8 m depth and 1.5 m diameter. This study proposes a standard design criterion for the BTC based on environmental factors, and the scoping of the literature provides the basis for future studies to evaluate its environmental sustainability.

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.

Comparative evaluation of multiscaler drought indices under different climatic conditions in Gujarat state of India.

The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), two drought indices, have been compared for interchangeability and reliability under various climatic conditions in Gujarat, India. As the quality of the input is crucial for the accuracy of the index, weather records from surface observatories are preferable over grid and reanalysis data. This study found that on a short timescale (1-3 months), SPEI diagnosed mild and moderate droughts more frequently than SPI, particularly in stations with relatively heavy rainfall. The indices in all timescales at all sites throughout the monsoon months displayed strong positive associations (r > 0.8). The correlation decreases but remains positive as the temporal scale is extended up to 8 months. On a 9-months or longer scales that encompassed active monsoon rainfall months at all stations, correlation coefficients were between 0.8 and 0.9 for all months of the year. During monsoon months, high fractional matches were observed on a short scale. The months after the monsoon show a generalized diagonal pattern of high fractional match with the timescale for all stations. The kappa statistic followed a broad pattern comparable to the match fractions. The instances with poor agreements (R, Match and kappa < 0.3) had proportional bias between the indices. SPEI recognized more drought events at all stations in the short time periods, while the agreements increased with longer time scales. However, SPI detects high intensities in the subhumid.

Groundwater recharge estimation using WetSpass-M and MTBS leveraging from HydroOffice and WHAT tools for baseflow in Weyib watershed, Ethiopia.

WetSpass-M model and multi-technique baseflow separation (MTBS) were applied to estimate spatio-temporal groundwater recharge (GWR) to be used to comprehend and enhance sustainable water resource development in the data-scarce region. Identification of unit Hydrographs And Component flows from Rainfall, Evaporation, and Streamflow (IHACRES) techniques outperform the existing 13 MTBS techniques to separate baseflow depending on the correlation matrix; mean baseflow was 5.128 m3/s. The WetSpass-M model performance evaluated by Nash-Sutcliff Efficiency (NSE) was 0.95 and 0.89; R2 was 0.90 and 0.85 in comparison to observed and simulated mean monthly baseflow and runoff (m3/s), respectively. The estimated mean annual water balance was 608.2 mm for actual evapotranspiration, 221.42 mm for the surface runoff, 87.42 mm for interception rate, and 177.66 mm for GWR, with an error of - 3.29 mm/year. The highest annual actual evapotranspiration was depicted in areas covered by vegetation, whereas lower in the settlement. The peak annual interception rates have been noticed in areas covered with forests and shrublands, whereas the lowest in settlement and bare land. The maximum annual runoff was depicted in settlement and bare land, while the lowest was in forest-covered areas. The annual recharge rates were low in bare land due to high runoff and maximum in forest-covered areas due to low surface runoff. The watershed's downstream areas receive scanty annual rainfall, which causes low recharge and drought. The findings point the way ahead in terms of selecting the best approach across multi-technique baseflow separations.

Influence of management practices on water-related grain yield determinants.

Adequate management of N supply, plant density, row spacing, and soil cover has proved useful for increasing grain yields and/or grain yield stability of rainfed crops over the years. We review the impact of these management practices on grain yield water-related determinants: seasonal crop evapotranspiration (ET) and water use efficiency for grain production per unit of evapotranspired water during the growing season (WUEG,ET,s). We highlight a large number of conflicting results for the impact of management on ET and expose the complexity of the ET response to environmental factors. We analyse the influence of management practices on WUEG,ET,s in terms of the three main processes controlling it: (i) the proportion of transpiration in ET (T/ET), (ii) transpiration efficiency for shoot biomass production (TEB), and (iii) the harvest index. We directly relate the impact of management practices on T/ET to their effect on crop light interception and provide evidence that management practices significantly influence TEB. To optimize WUEG,ET,s, management practices should favor soil water availability during critical periods for seed set, thereby improving the harvest index. The need to improve the performance of existing crop growth models for the prediction of water-related grain yield determinants under different management practices is also discussed.

Contribution of Tibetan Plateau ecosystems to local and remote precipitation through moisture recycling.

The ecosystems of the Tibetan Plateau (TP) provide multiple important ecosystem services that benefit both local populations and those beyond, such as through climate regulation services on precipitation for East Asia and China. However, the precipitation regulation service of the TP ecosystems for supplying moisture and maintaining precipitation is yet to be evaluated. In this study, we used the moisture recycling framework and a moisture tracking model to quantify the precipitation regulation services of TP ecosystems for their contribution to precipitation. We found TP ecosystems contributed substantially to local and downwind precipitation, with a contribution of 221 mm/year for the TP and neighboring areas through evapotranspiration (ET) (104 mm/year through transpiration), declined to <10 mm/year for eastern China and other surrounding countries. Among ecosystem types, grassland contributed most to precipitation, followed by barren and snow lands, forests, and shrublands. In terms of seasonality, precipitation contribution from TP ecosystems was greater in summer months than in non-summer months for western China, while the opposite was true for eastern China-although the magnitude was much smaller. Over the past two decades, the significant ET increases in TP translated to a widespread increase in precipitation contribution for TP and downwind beneficiary regions from 2000 to 2020. Our study provides a quantitative way to understand the precipitation regulation services of TP ecosystems through moisture recycling, substantiating their key role to maintain precipitation and the water cycle for downwind regions-effectively acting as an ecological safeguard that could be perceived by the public.