Weather Time Series Research Articles

Assessing and modeling economic and environmental impact of wheat nitrogen management in Belgium

Future progress in wheat yield will rely on identifying genotypes and management practices better adapted to the fluctuating environment. Nitrogen (N) fertilization is probably the most important practice impacting crop growth. However, the adverse environmental impacts of inappropriate N management (e.g., lixiviation) must be considered in the decision-making process. A formal decisional algorithm was developed to tactically optimize the economic and environmental N fertilization in wheat. Climatic uncertainty analysis was performed using stochastic weather time-series (LARS-WG). Crop growth was simulated using STICS model. Experiments were conducted to support the algorithm recommendations: winter wheat was sown between 2008 and 2014 in a classic loamy soil of the Hesbaye Region, Belgium (temperate climate). Results indicated that, most of the time, the third N fertilization applied at flag-leaf stage by farmers could be reduced. Environmental decision criterion is most of the time the limiting factor in comparison to the revenues expected by farmers.

Selecting Stochastic Climate Realizations to Efficiently Explore a Wide Range of Climate Risk to Water Resource Systems

AbstractThere are significant computational requirements for assessing climate change impacts on water resource system reliability and vulnerability, particularly when analyzing a wide range of plausible scenarios. These requirements often deter analysts from exhaustively identifying climate hazards. This technical note investigates two approaches for generating a subset of stochastic climate realizations that efficiently explore a range of risk to water supply systems. In both methods, a large ensemble of stochastic weather time series is generated to simulate the natural variability of the local climate system, and a selected subset of these sequences is used in the impacts assessment. Method 1 selects the subset by first passing the entire ensemble through a rainfall-runoff model and then screening the hydrologic sequences using the sequent peak algorithm. Method 2 selects a subset of climate sequences based on climate statistics alone, prior to hydrological modeling. Both methods provide insight for i...

Detection of Precipitation and Temperature Trend Patterns for Mulanje District, Southern Part of Malawi

This paper reports on the recent trend of climate changes taking place in Mulanje, one of the major tea producing districts in Malawi, with a focus on precipitation and temperature. The study analyzed trends in weather time series (air temperature and rainfall) data from 1980 to 2011 using non-parametric Mann-Kendall test. The MK test gave a mix of positive and negative trends for the monthly and seasonal precipitation. Only the month of December showed a positive significant trend while February, April, May, June September, October and November had a significant negative trend. Cool and wet as well as hot and dry season showed a significant decreasing trend in precipitation. The data also indicated that annual rainfall variation is decreasing with time at the rate of about 4.29 mm per year while temperature is increasing by about 0.04°C every year. The MK test for temperature results showed a significant increasing trend for both mean annual minimum and maximum temperature. The results of the study do not deny climate change is happening, therefore, proper adaptation measures should be employed to reduce the vulnerability to climate change in the area because of its economic significance in tea production.

A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

Coupled atmosphere-ocean general circulation models (GCMs) simulate different realizations of possible future climates at global scale under contrasting scenarios of land-use and greenhouse gas emissions. Such data require several additional processing steps before it can be used to drive impact models. Spatial downscaling, typically by regional climate models (RCM), and bias-correction are two such steps that have already been addressed for Europe. Yet, the errors in resulting daily meteorological variables may be too large for specific model applications. Crop simulation models are particularly sensitive to these inconsistencies and thus require further processing of GCM-RCM outputs. Moreover, crop models are often run in a stochastic manner by using various plausible weather time series (often generated using stochastic weather generators) to represent climate time scale for a period of interest (e.g. 2000 ± 15 years), while GCM simulations typically provide a single time series for a given emission scenario. To inform agricultural policy-making, data on near- and medium-term decadal time scale is mostly requested, e.g. 2020 or 2030. Taking a sample of multiple years from these unique time series to represent time horizons in the near future is particularly problematic because selecting overlapping years may lead to spurious trends, creating artefacts in the results of the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data for Europe that addresses these problems. Input data consist of daily temperature and precipitation from three dynamically downscaled and bias-corrected regional climate simulations of the IPCC A1B emission scenario created within the ENSEMBLES project. Solar radiation is estimated from temperature based on an auto-calibration procedure. Wind speed and relative air humidity are collected from historical series. From these variables, reference evapotranspiration and vapour pressure deficit are estimated ensuring consistency within daily records. The weather generator ClimGen is then used to create 30 synthetic years of all variables to characterize the time horizons of 2000, 2020 and 2030, which can readily be used for crop modelling studies.

Ensemble seasonal forecast of extreme water inflow into a large reservoir

Abstract. An approach to seasonal ensemble forecast of unregulated water inflow into a large reservoir was developed. The approach is founded on a physically-based semi-distributed hydrological model ECOMAG driven by Monte-Carlo generated ensembles of weather scenarios for a specified lead-time of the forecast (3 months ahead in this study). Case study was carried out for the Cheboksary reservoir (catchment area is 374 000 km2) located on the middle Volga River. Initial watershed conditions on the forecast date (1 March for spring freshet and 1 June for summer low-water period) were simulated by the hydrological model forced by daily meteorological observations several months prior to the forecast date. A spatially distributed stochastic weather generator was used to produce time-series of daily weather scenarios for the forecast lead-time. Ensemble of daily water inflow into the reservoir was obtained by driving the ECOMAG model with the generated weather time-series. The proposed ensemble forecast technique was verified on the basis of the hindcast simulations for 29 spring and summer seasons beginning from 1982 (the year of the reservoir filling to capacity) to 2010. The verification criteria were used in order to evaluate an ability of the proposed technique to forecast freshet/low-water events of the pre-assigned severity categories.

Predictive clustering on non-successive observations for multi-step ahead chaotic time series prediction

Predictive clustering algorithm based upon modified Wishart clustering technique is applied to predict chaotic time series. Concept of predictable and non-predictable observations is introduced in order to distinguish between reliable and unreliable predictions and, consequently, to enhance an ability to predict up to considerable number of positions ahead. Non-predictable observations are easily ascertained in the frameworks of predictive clustering, regardless used clustering technique. Clustering vectors are composed from observations according to set of patterns of non-successive positions in order to reveal characteristic observations sequences, useful for multi-step ahead predictions. The employed clustering method is featured with an ability to generate just enough clusters (submodels) to cope with inherent complexity of the series in question. The methods demonstrate good prediction quality for Lorenz system time series and satisfactory results for weather, energy market and financial time series.

Projected Hydrologic Changes Under Mid-21st Century Climatic Conditions in a Sub-arctic Watershed

The potential effects of mid-21st century climate change on the hydrology of the Cook Inlet watershed in south-central Alaska was analyzed in this study. Climate datasets representing a set of potential change scenarios for the period 2041–2070 were developed from the North American Regional Climate Change Assessment Program (NARCCAP) archive of dynamically downscaled climate products. The NARCCAP 50-km scale regional climate output was converted to realistic daily weather time series using a “change factor” method in which observed meteorological time series used for model calibration are perturbed. The perturbations are based on statistical summaries of change for the different climate scenarios, by month, as calculated from the differences between the 1971–2000 and 2040–2070 climate model simulation periods. The downscaled climate datasets were then used to run the Soil and Water Assessment Tool (SWAT) for the Cook Inlet watershed. Generally, it was observed that increasing rainfall and warmer temperatures across the Cook Inlet watershed led to a predicted increase in the stream flow in the major rivers, increase in 7-day low flows, and considerable increase in 100-year peak flow. Furthermore, under future climatic conditions precipitation is expected to increase in the Cook Inlet watershed but the amount of snowfall is expected to decrease. Also, the amount of snowmelt is expected to increase due to warmer temperature thereby causing the average annual fraction of snowfall as precipitation to decrease leading to a reduction in the glacial mass balance in the watershed. Moreover, average annual water yield, runoff, baseflow, snowmelt across the basin is expected to increase. More specifically the different hydrologic components varied seasonally and monthly driven by the seasonal and monthly changes in precipitation and temperature. However, the overall hydrology of the watershed is projected to remain snowmelt dominated through the mid-21st century without a major shift in regime. These simulations provide a benchmark of hydrologic sensitivity to potential future climate change in this watershed useful for identifying vulnerabilities and informing the development of adaptation responses.

시공간구조를 가지는 확률적 강우 모형

일반화 선형모형(GLM)에 기초한 확률적 날씨 발생기(Stochastic weather generator)는 일일 날씨를 생성하는데 가장 일반적으로 사용되는 방법인다. 본 논문에서는 다층구조를 이용하여 기존의 GLM weather generator에 공간구조를 소개하였다. 계절별 총강우량의 overdispersion 현상을 효과적으로 제거하기 위해서 smoothing된 계절별 총강우량을 모형에 포함하였고 공간구조를 소개하기 위해서 Stochastic weather generator의 모형계수에 공간구조를 가지는 다변량 정규분포를 가정하였다. 그리고 제안된 공간구조를 가지는 GLM weather generator 모형을 우리나라 76개 지역에서 39년간 측정된 일별 강우량 관측자료에 적용하였다. A stochastic weather generator based on a generalized linear model (GLM) approach is a commonly used tools to simulate a time series of daily weather. In this paper, we propose a multi-site weather generator with applications to historical data in South Korea. The proposed method extends the approach of Kim et al. (2012) by considering spatial dependence in the model. To reduce this phenomenon, we also incorporate a time series of seasonal mean precipitations of South Korea in the GLM weather generator as a covariate. Spatial dependence was incorporated into the model through a latent Gaussian process. We apply the proposed model to precipitation data provided by 62 stations in Korea from 1973{2011.

Application of a stochastic weather generator to assess climate change impacts in a semi-arid climate: The Upper Indus Basin

Summary Assessing local climate change impacts requires downscaling from Global Climate Model simulations. Here, a stochastic rainfall model (RainSim) combined with a rainfall conditioned weather generator (CRU WG) have been successfully applied in a semi-arid mountain climate, for part of the Upper Indus Basin (UIB), for point stations at a daily time-step to explore climate change impacts. Validation of the simulated time-series against observations (1961–1990) demonstrated the models’ skill in reproducing climatological means of core variables with monthly RMSE of Differences between a control (1961–1990) and future (2071–2100) regional climate model (RCM) time-slice experiment were then used to provide change factors which could be applied within the rainfall and weather models to produce perturbed ‘future’ weather time-series. These project year-round increases in precipitation (maximum seasonal mean change:+27%, annual mean change: +18%) with increased intensity in the wettest months (February, March, April) and year-round increases in mean temperature (annual mean +4.8 °C). Climatic constraints on the productivity of natural resource-dependent systems were also assessed using relevant indices from the European Climate Assessment (ECA) and indicate potential future risk to water resources and local agriculture. However, the uniformity of projected temperature increases is in stark contrast to recent seasonally asymmetrical trends in observations, so an alternative scenario of extrapolated trends was also explored. We conclude that interannual variability in climate will continue to have the dominant impact on water resources management whichever trajectory is followed. This demonstrates the need for sophisticated downscaling methods which can evaluate changes in variability and sequencing of events to explore climate change impacts in this region.

Comparison of five stochastic weather generators in simulating daily precipitation and temperature for the Loess Plateau of China

Stochastic weather generators are computer models used to simulate synthetic weather time series based on statistical characteristics of observed weather time series for a location of interest. The performance of weather generators should always be evaluated when applied over a new region, especially in a different climate zone. The Loess Plateau of China suffers from extensive soil erosion problems, mostly resulting from extreme precipitation events in an otherwise dry climate. The generation of accurate synthetic climate data (including the extremes) is the key to adequately assess the effect of soil and water conservation measures, especially in a changing climate. Accordingly, this work compares the ability of five commonly used stochastic weather generators (WGEN, CLIMGEN, CLIGEN, WeaGETS and LARSWG) to simulate daily precipitation and temperature for the Loess Plateau. The results show that the first-order Markov chain-based model satisfactorily simulates the precipitation occurrence, even though slightly better results can be achieved by using the second- and third-order Markov chain-based models. The semi-empirical distribution-based model shows a slightly better performance than the higher-order Markov chain-based models at generating dry spells, while it gives a worse performance in generating wet spells. The semi-empirical distribution-based LARSWG is consistently better than the parametric distribution-based weather generators at simulating daily precipitation amounts, and the three-parameter distribution-based models are consistently better than two-parameter distribution-based models, especially for generating extreme precipitation events. Overall, the performance of WeaGETS is generally superior to that of the other weather generators; it can be used for simulating daily precipitation, Tmax and Tmin for the Loess Plateau. While CLIGEN is not consistently a top performer, its reasonable representation of extreme precipitation events makes it suitable for soil erosion studies.

Predicting Climate Change Impacts on Sugarcane Production at Sites in Australia, Brazil and South Africa Using the Canegro Model

Reliable predictions of sugarcane response to climate change are necessary to plan adaptation strategies. The objective of this study was to assess the use of global climate models (GCMs) and a crop simulation model for predicting climate change impacts on sugarcane production. The Canegro model was used to simulate growth and development of sugarcane crops under typical management conditions at three sites (irrigated crops at Ayr, Australia; rainfed crops at Piracicaba, Brazil and La Mercy, South Africa) for current and three future climate scenarios. The baseline scenario consisted of a 30-year time series of historical weather records and atmospheric CO2 concentration ([CO2]) set at 360 ppm. Future climate scenarios were derived from three GCMs and [CO2] set at 734 ppm. Future cane yields are expected to increase at all three sites, ranging from +4 % for Ayr, to +9 and +20 % for Piracicaba and La Mercy. Canopy development was accelerated at all three sites by increased temperature, which led to increased interception of radiation, increased transpiration, and slight increases in drought stress at rainfed sites. For the high potential sites (Ayr and Piracicaba), yield increases were limited by large increases in maintenance respiration which consumed most of the daily assimilate when high biomass was achieved. A weakness of the climate data used was the assumption of no change in rainfall distribution, solar radiation and relative humidity. Crop model aspects that need refinement include improved simulation of (1) elevated [CO2] effects on crop photosynthesis and transpiration, and (2) high temperature effects on crop development, photosynthesis and respiration.

Multisite stochastic weather generation using cluster analysis and k-nearest neighbor time series resampling

We offer a multisite stochastic weather generator which is an enhancement to the traditional K-nearest neighbor resampling approach. The proposed weather generator consists of three main components: (i) Clustering of spatial locations into homogeneous regions based on a selected attribute (precipitation), (ii) Markov transition probabilities (either on individual clusters or) over all eight wet/dry states of the three-cluster system to model the spatial precipitation occurrence, and (iii) the traditional K-NN weather generator applied to each cluster-averaged weather time series to generate weather sequences at all the desired locations. The weather generator is also adapted to conditional simulation based on seasonal forecasts involving modification of the third component. We demonstrate the utility of this approach by simulating daily weather sequences at 66 locations in the 25,000 sq. mile San Juan River watershed, a tributary of the Colorado River, USA. As the classic K-NN approach involves sampling from a domain-averaged feature vector, all daily weather is simulated across all locations simultaneously. While this preserves the joint statistics, it tends to be biased to the extremes on any given day. Our cluster-based approach offers the ability to account for regional persistence and spatial non-stationarities. In our comparison of the methods, the cluster-based approach demonstrates some improvement over the classic approach, particularly when modeling winter precipitation, reproducing spells, and in dry years. While this particular application shows only marginal improvement, we offer cluster-based resampling as a novel methodological contribution.

GLM 날씨 발생기를 이용한 서울지역 일일 기온 모형

확률적 날씨 발생기(Stochastic weather generator)는 일일 날씨를 생성하는데 일반적으로 사용되는 방법으로 최근에는 일반화선형모형에 기초한 확률적 날씨 발생 방법이 제안되었다. 본 논문에서는 서울지역의 일일 기온을 모형화하하기 위해서 일반화선형모형에 기초한 확률적 날씨 발생기를 고려하였다. 이 모형에서는 계절성을 나타내는 변수와 강우발생 유무가 공변수로 사용되었다. 일반적으로 확률적 날씨 발생기에서는 생성된 일일 날씨가 월별 또는 계절별 총강우량이나 평균온도에 충분한 변동을 만들어 내지 못하는 과대산포 현상이 발생하는데, 이러한 한계를 극복하기 위해 본 연구에서는 평활된 계절별 평균 온도를 일반화선형모형의 공변수로 추가하였다. 그리고 제안된 모형을 1961년부터 2011년까지 51년 동안의 서울지역 일일 평균 기온자료에 적용하였다. Stochastic weather generator is a commonly used tool to simulate daily weather time series. Recently, a generalized linear model(GLM) has been proposed as a convenient approach to tting these weather generators. In the present paper, a stochastic weather generator is considered to model the time series of daily temperatures for Seoul South Korea. As a covariate, precipitation occurrence is introduced to a relate short-term predictor to short-term predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate a time series of seasonal mean temperatures in the GLM weather generator as a covariate.

Explaining patterns of species dominance in the shrub steppe systems of the Junggar Basin (China) and Great Basin (USA)

Natural scientists have long recognized that regions with similar climate tend to have similar vegetation. Preliminary observations suggest that shrub steppe communities of the western US and western China may be two such regions with similar annual precipitation, temperature, land use, and vegetation. These cold dry shrub steppes have traditionally been grazed. Despite these similarities, patterns of species dominance are different. Annual species that are rare in China become dominant when introduced to the United States. The objective of this study was to investigate how climate, land use and community structure may explain these patterns of species dominance. Community structure and grazing intensity were measured at 5 sites in each region. This information was combined with a broader review of the literature describing the history of grazing in both basins. Climate was analyzed based on a spatially-gridded, interpolated weather time series (monthly records) and climatological summary (1961–1990 mean conditions) data set from the Climate Research Unit. We found that differences in summer precipitation and winter minimum temperature, land use intensity, and shrub size may all contribute to the dominance of annual species in the Great Basin, particularly Bromus tectorum. In particular, previous work indicates that summer precipitation and winter temperature drive the distribution of Bromus tectorum in the Great Basin. As a result, sites with wet summers and cold springs, similar to the Chinese sites, would not be expected to be dominated by Bromus tectorum. A history of more intense grazing of the Chinese sites, as described in the literature, also is likely to decrease fire frequency, and decreases litter and shrub dominance, all of which have been demonstrated to be important in Bromus tectorum establishment and ultimate dominance. Further research is necessary to determine if other annuals that follow the same pattern of scarcity in the Junggar Basin and dominance in the Great Basin are responding to the same influences.

Wind energy analysis based on turbine and developed site power curves: A case-study of Darling City

The observed wind at a given site varies continuously as a function of time and season, increasing hub heights, topography of the terrain, prevailing weather condition etc. The quality of wind resource is one of the important site factors to be considered when assessing the wind potential of any location for any energy project. In this study, two wind energy analysis techniques are presented: the use of direct technique where the electrical power outputs of the wind turbines at a time t are estimated using the turbine power curve(s) and the use of statistical-based technique where the power outputs are estimated based on the developed site power curve(s). The wind resource assessment at Darling site is conducted using a 5-min time series weather data collected on a 10 m height over a period of 24 months. Because of the non-linearity of the site's wind speed and its corresponding power output, the wind resources are modeled and the developed site power curve(s) are used to estimate the long term energy outputs of the wind turbines for changing weather conditions. Three wind turbines rating of 1.3 MW, 1.3 MW and 1.0 MW were selected for the energy generation based on the gauged wind resource(s) at 50, 60 and 70 m heights, respectively. The energy outputs at 50 m height using the 1.3 MW WT were compared to the energy outputs at 60 m to determine the standard height for utility scale energy generation at this site. An additional energy generation of 190.71 MWh was available by deploying the same rated turbine at a 60 m height. Furthermore, comparisons were made between the use of turbine and site power curve for wind energy analysis at the considered heights. The results show that the analysis of the energy outputs of the WTs based on the site power curve is an accurate technique for wind energy analysis as compared to the turbine power curve. Conclusions are drawn on the suitability of this site for utility scale generation based on the wind resources evaluation at different heights.

Addressing the Challenge of Interpreting Microclimatic Weather Data Collected from Urban Sites

This paper presents some installation and data analysis issues from an ongoing urban air temperature and humidity measurement campaign in Hangzhou and Ningbo, China. The location of the measurement sites, the positioning of the sensors and the harsh conditions in an urban environment can result in missing values and observations that are unrepresentative of the local urban microclimate. Missing data and erroneous values in micro-scale weather time series can produce bias in the data analysis, false correlations and wrong conclusions when deriving the specific local weather patterns. A methodology is presented for the identification of values that could be false and for determining whether these are “noise”. Seven statistical methods were evaluated in their performance for replacing missing and erroneous values in urban weather time series. The two methods that proposed replacement with the mean values from sensors in locations with a Sky View Factor similar to that of the target sensor and the sensors closest to the target’s location performed well for all Day-Night and Cold-Warm days scenarios. However, during night time in warm weather the replacement with the mean values for air temperature of the nearest locations outperformed all other methods. The results give some initial evidence of the distinctive urban microclimate development in time and space under different regional weather forcings.

Accounting for Weather Probabilities in Crop Insurance Rating

The US crop insurance program previously used a simple average of equally weighted historical loss cost data to serve as the backbone for estimating crop insurance premium rates. This article develops a procedure for weighting the historical loss cost experience based on longer time-series weather information and improve statistical validity of estimated premium rates. It was determined that the best weather data to account for weather probabilities in crop insurance premium rating is the National Climatic Data Center’s Time Bias Corrected Divisional Temperature-Precipitation-Drought Index data, also called the Climate Division Data. Using fractional logit and out-of-sample competitions, weather variables can be selected to construct an index that would allow proper assessment of the relative probability of weather events that drive production losses and to construct proper “weather weights” that can be applied when averaging historical loss cost data to calculate rates. A variable width binning approach with equal probabilities was determined as the best approach for classifying each year in the shorter historical loss cost data used for rating. When the weather weighting approach described above is applied, we find that for apples, barley, cotton, potatoes, rice, and spring/winter wheat, the weather weighted average loss costs at the national level tend to be smaller than the calculated average loss costs without weather weighting. However, for corn, cotton, sorghum, and soybeans, the weather weighted average loss costs at the national level tend to be larger. Around 51% of the counties have weather weighted average loss costs lower than the average loss costs without weather weighting

The effects of climate variability and the color of weather time series on agricultural diseases and pests, and on decisions for their management

If climate change scenarios include higher variance in weather variables, this can have important effects on pest and disease risk beyond changes in mean weather conditions. We developed a theoretical model of yield loss to diseases and pests as a function of weather, and used this model to evaluate the effects of variance in conduciveness to loss and the effects of the color of time series of weather conduciveness to loss. There were two qualitatively different results for changes in system variance. If median conditions are conducive to loss, increasing system variance decreases mean yield loss. On the other hand, if median conditions are intermediate or poor for disease or pest development, such that conditions are conducive to yield loss no more than half the time, increasing system variance increases mean yield loss. Time series for weather conduciveness that are darker pink (have higher levels of temporal autocorrelation) produce intermediate levels of yield loss less commonly. A linked model of decision-making based on either past or current information about yield loss also shows changes in the performance of decision rules as a function of system variance. Understanding patterns of variance can improve scenario analysis for climate change and help make adaptation strategies such as decision support systems and insurance programs more effective.

Reducing overdispersion in stochastic weather generators using a generalized linear modeling approach

Stochastic weather generators are commonly used to simulate time series of daily weather, especially minimum (Tmin) and maximum (Tmax) temperature and amount of precipitation. Recently, generalized linear models (GLM) have been proposed as a convenient approach to fit- ting weather generators. One limitation of weather generators is a marked tendency to underesti- mate the observed interannual variance in monthly, seasonal, or annual total precipitation and mean temperature, termed the 'overdispersion' phenomenon. In this study, aggregated statistics, consisting of seasonal total precipitation and mean Tmin and Tmax, are introduced as additional covariates into the GLM weather generator. With an appropriate degree of smoothing of these aggregated statistics, this approach is shown to virtually eliminate overdispersion when applied to 2 sites, Pergamino and Pilar, in the Argentine Pampas. The addition of these covariates does not distort the performance of the weather generator in other respects, such as annual cycles in the probability of precipitation and in the mean Tmin and Tmax. For seasonal total precipitation, the reduction in overdispersion is partially attributable to a corresponding reduction in the overdisper- sion of the frequency of precipitation occurrence, as well as to apparent temporal trends or 'regime' shifts. For seasonal mean Tmin and Tmax, the reduction in overdispersion is largely due to temporal trends on an interannual time scale.

Standardization of Reference Evapotranspiration Models for a Subhumid Valley Rangeland in the Eastern Himalayas

AbstractFor efficient irrigation water management and hydro-meteorological studies at both the field and catchment scales, a need exists to evaluate the existing evapotranspiration (ET) estimation methods under varied physiographical and data availability conditions. In this study, using the FAO-56 Penman-Monteith model as the benchmark model, a total of 16 various ET estimation methods were evaluated using both the continuous daily time series and average time series weather data of a subhumid valley cultivated rangeland in the eastern Indian Himalayas. The results revealed that some pan evaporation models have restricted performances. Consequently, for an improved performance, all of these models were standardized using a genetic algorithm–based linear corrector transformation model. Almost all of the models performed reasonably well when using the average daily time series data. Furthermore, the trend analysis of reference ET showed an increasing trend during the months of February to July, including O...