Calculation Of Evapotranspiration Research Articles

Estimating Evapotranspiration in the Qilian Mountains Using GRACE/GRACE-FO Satellite Data

Evapotranspiration (ET) is the most significant constituent of the response to climate warming. It serves as a crucial link in the soil–vegetation–atmospheric continuum. Analyzing the driving forces and response of ET to regional-scale climate warming holds scientific significance in improving global water resource assessment methods and drought monitoring techniques. The innovation presented in this article is the calculation of ET by using GRACE/GRACE-FO satellite data through the water balance equation. The inter-annual and seasonal changes in ET in different regions of the Qilian Mountains were analyzed, along with quantifying the contribution of environmental meteorological factors to ET. The ETGRACE and ETMonitor products have good consistency, with a monthly correlation coefficient of 0.92, an NSE coefficient of 0.80, and a root mean square error of 10.38 mm. The results indicate that the increasing trend of ET in the Qilian Mountains region exhibits a “medium–high–low” distribution pattern. The rate of increase in ET is 5.2 mm/year in the central segment. In spring and summer, the overall trend of ET is an increasing one. However, the central and western segments exhibit a slight decreasing trend of ET in autumn. During winter, the southern part of the Qilian Mountains experiences a notable reduction in ET. The correlation between the changes in ET and soil moisture exhibited a strong association, with soil moisture change contributing significantly to ET: 57.8% for the eastern section, 52.8% for the middle section, and 46.9% for the western section. The thermal effect primarily controls ET variations within eastern sections, where temperature change accounts for approximately 6.7% of the total variation in ET levels. Conversely, the moisture factor dominates western sections, where precipitation change accounts for about 6.5% of the total variation in ET levels. Due to the distinct gradient characteristics of environmental meteorological factors in the central segment, the fluctuation of these factors collaboratively drives ET changes. This article provides a new approach for obtaining continuous and reliable actual evapotranspiration in high-altitude areas.

Agweather-qaqc: An Interactive Python Package for Quality Assurance and Quality Control of Daily Agricultural Weather Data and Calculation of Reference Evapotranspiration

agweather-qaqc: An Interactive Python Package for Quality Assurance and Quality Control of Daily Agricultural Weather Data and Calculation of Reference Evapotranspiration

Differences in sum-of-hourly and daily reference evapotranspiration for a rice–wheat cropping system in Ganga Basin, India

ABSTRACT Estimating reference evapotranspiration (ETo) at 24 h timesteps has been considered sufficiently accurate for a long time. However, recent advances in weather data acquisition have made it feasible to apply hourly procedures in ETo computation. Hourly timesteps can improve the accuracy of ETo estimates, as data averaged daily may misrepresent evaporative power during parts of the day. This study investigates the differences between daily ETo computations performed at 24 h (ETo,d) and sum of hourly (ETo,h) for rice–wheat cropping systems in the Ganga Basin, India. The meteorological data were collected from an automatic weather station located in an experimental plot at IIT Kanpur, India. Daily and sum-of-hourly ETo computations were performed according to the FAO-PM equation for rice and wheat cropping seasons. Diurnal variations of meteorological variables resulted in an underestimation of ETo when the daily timestep was considered. No significant difference was observed during wet periods. The sum-of-hourly estimates of ETo were able to capture the abrupt changes in climate variables, while the daily ETo failed to represent it as it considered the average values only. As a result, the sums of hourly ETo estimates are more reliable in the Ganga Plains.

Impact of Climate Change on the Development of Viticulture in Central Poland: Autoregression Modeling SAT Indicator

Ongoing climate change is having a profound impact on agriculture, which is attracting attention from the scientific community. One of its effects is an increase in average temperature, which is a key factor in grape cultivation. This may increase the popularity of viticulture in central Europe. The aim of this study was to assess the potential for the development of viticulture in central Poland based on SAT changes from 1975 to 2021, in addition to changes in evapotranspiration, occurrence of late spring and early autumn frosts and frosty days in selected years from this period as an important factors relating to climate change. The research utilized data obtained from the Institute of Meteorology and Water Management—National Research Institute. The Bai–Perron test was used to determine the direction of temperature changes. An AR(1) autoregression model was used to predict SAT changes in central Poland for the years 2022–2026, based on the results of the Bai–Perron test. As part of the in-depth research on the SAT index, reference evapotranspiration calculations were also made as a second factor that is considered an important indicator of climate change. The Sum of Active Temperatures from 1975 to 2021 in the provinces of central Poland showed an increasing trend of 0.07% per year. The average SAT in central Poland in 2022–2026 is expected to range from 2700 °C to 2760 °C. Considering the current thermal conditions in central Poland and the forecasts for the coming years, it can be expected that vineyard cultivation will develop in this region. However, the research shows that the observed increasing trend in evapotranspiration, both in total in individual years and in the period of the greatest vegetation, i.e., in the months from May to the end of August, will result in an increasing need in central Poland to ensure adequate irrigation in developing vineyards.

Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains

The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area in the United States, and the timely delivery and efficient use of irrigation water are critical to the sustainability and profitability of cotton production in this region. Current irrigation scheduling must be improved to reduce water consumption without compromising crop production. Presently, irrigation scheduling based on reference evapotranspiration (ETo) is limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, in-field variability in crop water demand is often overlooked, leading to lower irrigation efficiency. To address these challenges, we explored the potential use of an unmanned aerial vehicle (UAV)-based crop monitoring system to support irrigation management decisions. This study was conducted in Lubbock, Texas, in 2022, where high temporal and spatial resolution images were acquired using a UAV from a cotton field experiment with four irrigation levels. Soil moisture and canopy temperature sensors were deployed to monitor crop response to irrigation and rainfall. The results indicated a significant effect of water stress on crop growth (revealed by UAV-based canopy cover (CC) measurements), yield, and fiber quality. Strong correlations between multi-temporal CC and lint yield (R2 = 0.68 to 0.88) emphasized a clear trend: rainfed treatments with lower yields exhibited reduced CC, while irrigated plots with higher CC displayed increased yields. Furthermore, irrigated plots produced more mature and uniform fibers. This study also explored various evapotranspiration calculation approaches indicating that site-specific CC measurements obtained from a UAV could significantly reduce irrigation application. A regression model linking evapotranspiration to canopy cover demonstrated promising potential for estimating water demand in crops with an R2 as high as 0.68. The findings highlight the efficacy of UAV-based canopy features in assessing drought effects and managing irrigation water in water-limited production regions like the THP.

Modeling and prediction of high-precision global evapotranspiration: based on a different model of physical relationships

ABSTRACT The interchange of water vapor between the land and the atmosphere is influenced by actual evapotranspiration (AET). A nonlinear model (AET-SWC-PET-GPP, ASPG) was developed in this study to combine potential evapotranspiration (PET), soil water content (SWC), and gross primary productivity (GPP) in order to quantitatively estimate AET. The Fluxnet Network 2015 global flux station dataset was used to compare the AET models (AET-SWC, AS; AET-SWC-PET, ASP and AET-SWC-PET2, ASP2) with various combinations of influencing factors. The results show that the simulation accuracy of the ASPG model is higher than that of AS, ASP, and ASP2, with improvements in a coefficient of determination (R2) of 45.3, 8.1, and 5.7%, respectively.The ASPG performed well for various vegetation types, geographical regions, and time scales. It was also discovered that the fitting coefficients vary depending on the type of vegetation, each with its own range of values. The ASPG model put forth in this study can be used to more effectively estimate AET quantitatively on a global scale and can serve as a theoretical foundation for the accurate calculation of global evapotranspiration and the wise use of water resources.

Crop coefficients of natural wetlands and riparian vegetation to compute ecosystem evapotranspiration and the water balance

AbstractWetlands, namely the riparian ones, play a major role in landscape and water resources functionalities and provide enormous opportunities for ecosystems services. However, their area at globe scale is continuously decreasing due to appropriation by the riverain communities or by allocation of water resources to other uses, namely irrigation, in prejudice of natural wetlands. Due to the high competition for water, namely for agricultural irrigation, the calculation of the vegetation evapotranspiration (ETc), i.e. the consumptive water use of the wetland ecosystems, is mandatory for determining water supply–demand balance at various scales. Providing for the basin and local levels the reason for this review study on ETc to be presented in an irrigation focused Journal. The review also aims to make available adequate Kc values relative to these ecosystems in an ongoing update of FAO guidelines on evapotranspiration. The review on ETc of natural wetlands focused on its computation adopting the classical FAO method, thus the product of the FAO-PM grass reference ETo by the vegetation specific Kc, i.e., ETc = Kc ETo. This approach is not only the most common in agriculture but is also well used in natural wetlands studies, with Kc values fully related with vegetation ecosystems characteristics. A distinction was made between riparian and non-riparian wetland ecosystems due to differences between main types of water sources and main vegetation types. The Kc values are tabulated through grouping wetlands according to the climate since the variability of Kc with vegetation, soil, and water availability would require data not commonly available from the selected studies. Tabulated values appear to be coherent and appropriate to support field estimation of Kc and ETc for use in wetlands water balance when not measured but weather data may be available to compute the grass reference ETo. ETc and the water balance could then be estimated since they are definitely required to further characterization and monitoring of wetlands, defining measures for their protection, and assessing ecosystems’ services.

Revisiting reference evapotranspiration calculation under regional advection and its effect on single and dual crop coefficients: An empirical approach for quinoa crop

AbstractAdvection is a prevailing meteorological phenomenon in the arid and semi‐arid environments that directly affects the crop and soil hydrology. It could have a great impact on the rate of standard crop evapotranspiration (ETc) in the irrigated areas. Therefore, it is required to take it into consideration while computing crop water requirement through the physically based meteorological procedures. The objectives of this study are (1) simple modification of the Penman–Monteith (PM) equation in calculation of the grass reference evapotranspiration (ETo) to implement the local advection, (2) comparing the single (Kc) and dual crop coefficient (Kcb) of two quinoa cultivars (Titicaca, and Q5) with and without advection correction and (3) presenting a simple model to calculate the advection factor using the maximum air temperature and mean relative humidity for the future crop growth modelling studies. Both Q5 and Titicaca showed unexpectedly very high ETc and potential transpiration (Tp) as 1568 mm and 1003 mm, and 1156 mm and 829 mm, respectively, due to occurrence of regional advection, whereas very high unrealistic Kc and Kcb values for Q5 revealed the impact of strong local advection and external energy. Therefore, we modified ETo to implement the advection effect through introducing the advection factor, ETc/Rn (Rn is the net radiation), as a function of maximum air temperature and mean relative air humidity during the growing season [ETc/Rn = exp (0.025Tmax – 0.015RHavg)] which resulted in higher ETo values, and consequently lower and more realistic Kc and Kcb. As a result, modified maximum Kc values of 1.14 and 1.55 and the modified maximum Kcb of 0.94 and 1.0 were obtained for Titicaca and Q5 cultivars, respectively. This procedure leads to a more accurate site‐specific Kc estimation and indirectly reliable ETc estimation as a function of advection factor and its multiplication by the non‐modified ETo. Furthermore, for direct estimation of ETc through the PM equation, the coefficients of aerodynamic and canopy resistance components of PM equation were modified for estimation of ETc by the non‐modified Rn, which resulted in accurate estimation of ETc.

Analisis Evapotranspirasi Metode Penman Modifikasi Dan Thornthwaite Terhadap Pemodelan Debit Fj. Mock

Evapotranspiration is a key parameter used in run-off modelling. The Bulango Ulu Dam, located near the equator where sunlight is at its peak throughout the year, makes evapotranspiration a crucial factor in analysing water availability. This research aims to analyse the performance of two evapotranspiration calculation methods on the accuracy of run-off modelling to the Bulango Ulu dam. The study employs the modified Penman and Thornthwaite evapotranspiration methods as input to Mock's model. The Analysis indicates that the Mock-Penman method outperforms the Mock-Thornthwaite method, with statistical parameter results of MAE 0.963, NSE 0.891, R 0.949 and Kr 1.3%, Therefore, it concludes that the run-off modelling aligns with the observed flow value

Reference Evapotranspiration Evaluation Using Solar Radiation Estimated by ANN and Empirical Models

Aims: The reference evapotranspiration (ETo) estimation with Penman-Monteith or Priestley-Taylor methods requires measurements of temperature, radiation, humidity, and wind velocity. In this study, we evaluated the estimations of ETo by Penman-Monteith (ETo-PM) and Priestley-Taylor (ETo-PrT) methods using indirect methods of calculating solar radiation (Rs). Place and Duration of Study: Daily meteorological measurements from two stations in northern Greece were used for the development of solar radiation models and ETo calculation. Methodology: The indirect methods of calculating solar radiation (Rs) are based on Artificial Neural networks (ANN) technology and models using the multi-linear regression method (MLR). Three different ANN and MLR models were derived. The Hargreaves method is also used. The evaluation of the indirect Rs derived models and the ETo estimation by the two methods was performed with the use of correlation coefficients (r), root mean square error (RMSE), and efficiency (EF) indexes. Results: The statistics of ETo estimation at the two stations showed that the r and EF values, between the estimated ETo using the indirect Rs models and estimated ETo using Rs measured, were greater than 0.963 and 0.918, respectively, while the RMSE values were lower than 0.646 mm d-1. The statistics of Rs models, showed that the r and EF values were greater than 0.860 and 0.605, respectively, while the RMSE values were lower than 4.47 MJ m-2d-1. Conclusion: The results of ANN models in comparison to MLR models, when using the same input variables, are consistent between them. These findings indicate that the Penman-Monteith and Priestley-Taylor methods can accurately predict ETo using Rs values estimated indirectly through the examined methods and models.

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.

Estimation reference crop evapotranspiration (ET0) using artificial intelligence model in an arid climate with external data

Water resource management and crop growth control require the calculation of reference evapotranspiration (ET0), but meteorological data can often be incomplete, necessitating models with minimal inputs. This study was conducted in Iran’s arid synoptic stations of Sirjan and Kerman, where data scarcity is severe. Penman–Monteith FAO-56 was selected as the target data for modeling artificial neural network (ANN), fuzzy neural adaptive inference system (ANFIS), and ANN-gray wolf optimization (ANN-GWO). The performance of these models was evaluated using an input dataset consisting of the current station’s minimum and maximum temperatures, ET0, and the wind speed of the nearby station (external data) in three different combinations. The models’ accuracy was assessed using two widely used criteria: root mean square error (RMSE) and coefficient of determination (R2), as well as the empirical Hargreaves equation. In the absence of climatic data, the ANFIS, ANN, and ANN-GWO methods using minimum and maximum temperatures, which are relatively easier to estimate, outperformed the empirical Hargreaves equation method in both stations. The results demonstrate that the ANFIS method performed better than ANN and ANN-GWO in all three input combinations. All three methods showed improvement when external data (wind speed and ET0 of the adjacent station) were used. Ultimately, the ANFIS method using minimum and maximum temperatures and the adjacent station’s ET0 in Kerman and Sirjan yielded the best results, with an RMSE of 0.33 and 0.36, respectively.

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.

Improving evapotranspiration computation with electrical resistivity tomography in a maize field

AbstractHydrogeophysical methods have been increasingly used to study subsurface soil–water dynamics, yet their application beyond the soil compartment or the quantitative link to soil hydraulic properties remains limited. To examine how these methods can inform model‐based evapotranspiration (ET) calculation under varying soil water conditions, we conducted a pilot‐scale field study at an experimental maize plot with manipulated irrigation treatments. Our goal was to develop a workflow for (1) acquiring and inverting field electrical resistivity tomography (ERT) data, (2) correlating ERT to soil hydraulic properties, (3) spatially characterizing soil water stress that feeds into ET modeling (the FAO‐56 model), and (4) evaluating the performance of ERT‐based ET computation. Our results showed that ERT was able to capture decimeter‐scale soil water content (SWC) dynamics from root water uptake and irrigation manipulation and the contrast of soil water stress between deficiently and fully irrigated maize. We also demonstrated the flexibility of using ERT to spatially integrate soil water stress in the soil volume of interest, which could be adjusted based on different crops and plot layouts. The integration of the ERT datasets into ET modeling provided insights into the spatial heterogeneity of the subsurface that has been challenging for point‐based sensing, which can further our understanding of the hydraulic dynamics in the soil‐plant‐atmosphere continuum.

Comparison of Runoff Components, Water Balance, and the Parameters of Conceptual Models HBV and GR4J: Case Study of the Upper Ussuri Basin (South of Primorsky Region, Pacific Russia)

The efficiency of runoff simulation, the values of parameters, and the dynamics of the estimated runoff components were analyzed for two conceptual hydrological models GR4J and HBV for 17 watersheds in the Upper Ussuri River (Primorsky Krai, Russia) with areas from 138 to 24 400 km2. Both models demonstrate an increase in the simulation efficiency (based on NSE criterion) with an increase in the catchment area up to 1–2 thousand km2, after which they stabilize within the interval of 0.75–0.85 for the calibration period and 0.70–0.80 for the verification period. The estimates obtained for the HBV model were 5–10% higher than those for the GR4J model. Analysis of the measured and calculated annual runoff maximums over the warm season suggests the conclusion that GR4J model is on the average 5–6% more efficient than the HBV model in calculating the maximal values of rain flood discharges. At the same time, the obtained values of the relative error BIAS demonstrate a more accurate reproduction of the annual average runoff by the HBV model. The main distinctions determining the efficiency of simulation in the study region are as follows: the method of considering the precipitation height increments within altitude belts, the specific features of the calculation of model evapotranspiration, the method for calculating the outflow from conceptual runoff-forming storages in the GR4J and HBV models.

Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate

The Food and Agriculture Organization recommends that the Penman–Monteith Method contains Equation 56 (PMF) as a widely accepted standard for reference evapotranspiration (ETo) calculation. Despite this, the PMF cannot be employed when meteorological variables are constrained; therefore, alternative models for ETo estimation requiring fewer variables must be chosen, which means that they perform at least as well as, if not better than, the PMF in terms of accuracy and efficiency. This study evaluated five machine learning (ML) algorithms to estimate ETo and compared their results with the standardized PMF. For this purpose, ML models were trained using monthly time series climatic data. The created ML models underwent testing to determine ETo under varying meteorological input combinations. The results of ML models were compared to assess their accuracy and validate their performance using several statistical indicators, errors (root-mean-square (RMSE), mean absolute error (MAE)), model efficiency (NSE), and determination coefficient (R2). The process of evaluating ML models involved the utilization of radar charts, Smith graphs, heatmaps, and bullet charts. Based on our findings, satisfactory results have been obtained using RBFFNN based on M12 input combinations (mean temperature (Tmean), mean relative humidity (RHmean), sunshine hours (Sh)) for ETo estimation. The RBFFNN model exhibited the most precise estimation as RMSE obtained values of 0.30 and 0.22 during the training and testing phases, respectively. In addition, during training and testing, the MAE values for this model were recorded as 0.15 and 0.17, respectively. The highest R2 and NSE values were noted as 0.98 and 0.99 for the RBFNN during performance analysis, respectively. The scatter plots and spatial variations of the RBFNN and PMF in the studied region indicated that the RBFNN had the highest efficacy (R2, NSE) and lowest errors (RMSE, MAE) as compared with the other four ML models. Overall, our study highlights the potential of ML models for ETo estimation in the arid region (Jacobabad), providing vital insights for improving water resource management, helping climate change research, and optimizing irrigation scheduling for optimal agricultural water usage in the region.

Hybrid Algorithm of Backpropagation and Relevance Vector Machine with Radial Basis Function Kernel for Hydro-Climatological Data Prediction

Hydro-climatological data serves a pivotal role in monitoring climatic alterations and facilitating agricultural planning, inclusive of evapotranspiration estimation, water management, and crop pattern design. The necessity to accurately and expeditiously model and forecast this data underscores the need for effective methodologies. This paper introduces a hybrid algorithm, integrating backpropagation and relevance vector machine (BP-RVM) with a radial basis function (RBF) kernel. A comparative analysis was conducted between RBF and Logsig activation functions in conjunction with resilient backpropagation (trainrp) and Levenberg-Marquardt backpropagation (trainlm). The algorithm was employed to predict and categorize rainfall, temperature, wind speed, humidity, and sunshine duration data. Through extensive trials, the architecture parameters in the training-testing process of the BP-RVM algorithm were meticulously determined. Mean squared error (MSE) and mean absolute percentage error (MAPE) values were classified as indicating high forecast accuracy (<10%). Despite the RBF-trainlm kernel function combination exhibiting a faster epoch completion rate, the BP-RVM algorithm with the RBF-trainrp kernel function combination is recommended for future data prediction stages due to its lower error generation. The BP-RVM-RBF-trainrp algorithm outperformed BP-RVM-RBF-trainlm, with an average error difference of 1.39% in the training process and 2.28% in the testing process. The identified algorithms and architectures present potential for future applications in evapotranspiration calculation and crop pattern planning based on hydro-climatological data.

Hydro-PE: gridded datasets of historical and future Penman–Monteith potential evaporation for the United Kingdom

Abstract. We present two new potential evaporation datasets for the United Kingdom: a historical dataset, Hydro-PE HadUK-Grid, which is derived from the HadUK-Grid gridded observed meteorology (1969–2021), and a future dataset, Hydro-PE UKCP18 RCM, which is derived from UKCP18 regional climate projections (1980–2080). Both datasets are suitable for hydrological modelling and provide Penman–Monteith potential evapotranspiration parameterised for short grass, with and without a correction for interception on days with rainfall. The potential evapotranspiration calculations have been formulated to closely follow the methodology of the existing Meteorological Office Rainfall and Evaporation Calculation System (MORECS) potential evapotranspiration, which has historically been widely used by hydrological modellers in the United Kingdom. The two datasets have been created using the same methodology to allow seamless modelling from past to future. Hydro-PE HadUK-Grid shows good agreement with MORECS in much of the United Kingdom, although Hydro-PE HadUK-Grid is higher in the mountainous regions of Scotland and Wales. This is due to differences in the underlying meteorology, in particular the wind speed, which are themselves due to the different spatial scales of the data. Hydro-PE HadUK-Grid can be downloaded from https://doi.org/10.5285/9275ab7e-6e93-42bc-8e72-59c98d409deb (Brown et al., 2022) and Hydro-PE UKCP18 RCM can be downloaded from https://doi.org/10.5285/eb5d9dc4-13bb-44c7-9bf8-c5980fcf52a4 (Robinson et al., 2021).

Hubungan Evapotranspirasi, Hujan dan Elevasi Muka Air Tanah pada Lahan Gambut Tropis Sebagai Awal Penentuan Kondisi Lahan Basah

The relationship between parameters in wetlands, especially peatlands, as a part of the analysis is interesting to study. The analysis is expected to show the condition of the peatland or the state of the wetlands in general. The water table elevation (WTE) is measured daily, so to compare it with evapotranspiration and rain, daily values for these two parameters are also required. The study was conducted between June-July and August 2022 on shallow peatlands in Gambut District, Banjar Regency, South Kalimantan Province. Evapotranspiration in this study was calculated using the Hargreaves and Modified Hargreaves method which presents a daily evapotranspiration calculation. The methods only depend on the maximum and minimum temperature, the number of outer space radiation whose magnitude depends on its location on the earth's surface, and the time when the research is conducted. The results showed that the highest daily evapotranspiration value generally occurred in August, which was in accordance with previous studies, but the magnitudes were different. The daily evapotranspiration value in this study ranged from 0.5 mm to 1.8 mm, while previously, the values were greater. WTE values show a strong relationship with rainfall, where rainfall increases the WTE value, and conversely, the absence causes the WTEs to decrease gradually. In general, the condition of the peatlands in the study area based on the groundwater elevation conditions still looks quite good with a fast response to rain. The relationship of evapotranspiration with rain and water table elevation cannot be clearly seen in this study because alterations in rainfall and WTE do not directly indicate changes in evapotranspiration values, so a more extended study covering other months and more in-depth covering parameters and other methods is needed to draw valid conclusions more accurate.

Rainwater Retention Test of Various Green Roofs: Influence on Membrane Temperatures and Evapotranspiration

Green structures such as roofs or facades are great examples of ways to mitigate climate change, and they have multidisciplinary benefits. One of them is water retention during storms and high rainfalls. In this paper, the impact on the temperature of a membrane after excessive watering was investigated. The results of watered samples were compared to the non-watered ones. This test was carried out on five equal samples. The results showed that the actual foliage cover is very important. The worst results were obtained by the samples that did not have sufficient plant (foliage) cover. Notable differences in courses after watering reached up to 7 °C and lasted up to five days. Visible differences after watering were also exhibited by other samples. The type of drainage/retention layer also influenced the results, whereas the hard-profiled plastic board had high practical retention, which impacted temperatures over a long time period. The actual water content influenced and reduced the daily amplitude from 13 to 7 °C for at least five sunny days. This confirms the theoretical evapotranspiration calculations and actual water content in green roofs. In this case, the water content in the samples proves the cooling effect on the waterproofing membrane.