Estimation Of Daily Evaporation Research Articles

Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India

ABSTRACT Accurate prediction of pan evaporation and mean temperature is crucial for effective water resources management, influencing the hydrological cycle and impacting water availability. This study focused on New Delhi's semi-arid climate, data spanning 31 years (1990–2020) were used to predict these variables using advanced algorithms such as Bagging, Random Subspace (RSS), M5P, and REPTree. The models were rigorously evaluated using 10 performance metrics, including correlation coefficient, mean absolute error (MAE), and Nash–Sutcliffe Efficiency (NSE) model coefficient. The Bagging model emerged as the best model with performance indices values as r, MAE, RMSE, RAE, RRSE, MBE NSE, d, KGE, and MAPE as 0.86, 0.76, 1.43, 32.70, 49.44, 0.03, 0.85, 0.96, 0.90, and 22.0, respectively, during model testing phase for pan evaporation prediction. In predicting mean temperature, the Bagging model reported the best results with performance indices values as r, MAE, RMSE, RAE, RRSE, MBE NSE, d, KGE, and MAPE as 0.86, 0.76, 1.43, 32.70, 49.44, 0.03, 0.85, 0.96, 0.90 and 22.0, respectively, during the model testing phase. These findings offer valuable insights for enhancing relative humidity prediction models in diverse climatic conditions. The Bagging model's robust performance underscores its potential application in water resource management.

Developing a General Daily Lake Evaporation Model and Demonstrating Its Application in the State of Texas

AbstractOpen water evaporation, which often consumes a large fraction of annual storage (especially in arid and semi‐arid regions), is a controlling variable for modern water resource management. Developing a daily reservoir evaporation data set is necessary for reservoir operations to consider the influence of evaporation in a timely manner. However, over the past few decades, the quantification of reservoir evaporation has primarily relied on monthly Class A pan evaporation observations, which might lead to largely biased estimations because they do not incorporate the effects of heat storage and fetch. In this study, we developed a general Daily Lake Evaporation Model (DLEM) based on Penman's equation combined with daily atmospheric reanalysis data sets, to improve the validity and frequency of reservoir evaporation monitoring. Compared with daily evaporation estimates from eddy covariance systems, the DLEM showed good quality and reliability, with R2 values ranging from 0.50 to 0.73 and RMSE values ranging from 1.13 to 1.97 mm day−1 in four different sites. By applying DLEM to all 188 major reservoirs in Texas, we generated a long‐term data set of (1 January 1981–31 December 2021) daily evaporation rates. The results reveal a clear geographic distribution and strong seasonality of evaporation throughout Texas, where the mean evaporation rate is highest during July, with 6.85 mm day−1. Trend analysis indicates that the annual average evaporation rate has significantly increased since 1981 at a rate of 0.076 mm day−1 decade−1. This study provides sufficient data support for water resource operations and management, and regional water planning.

Estimation of Daily Evaporation from Shallow Groundwater Using Empirical Models with a Temperature Coefficient

Abstract Shallow groundwater evaporation (Eg) is a major component of the hydrological cycle, especially in semiarid and arid locations. Empirical methods are commonly used to estimate Eg. However, most of these methods can only weakly represent Eg variations along the soil depth and do not consider the energy driver. In this paper, a temperature coefficient was proposed and incorporated into two preferred empirical models to characterize the impacts of soil temperature and air temperature lags on Eg. The method was evaluated using in situ daily data obtained from nonweighing bare soil lysimeters. The results indicated that the models that considered the temperature gradient variable (T) conformed to the changes in the actual Eg values with depth more appropriately than the original models, accompanied by 4.3%–8.8% accuracy improvements overall. Shallow groundwater evaporation Eg was found to be influenced by the water table depth (H), T, and pan evaporation (E0) in descending order, and strong interactions were found between H and T. Moreover, the impact of precipitation on Eg was investigated; measurements from dry days without precipitation revealed the actual Eg process, the relative errors in the cumulative Eg values derived at different depths demonstrated a positive relationship with infiltration recharge, and the errors related to precipitation induced 6.7%–8.3% Eg underestimations. These results contribute to a better understanding of evaporative losses from shallow groundwater and the typical Eg situation that occurs simultaneously with recharge, and they provide promising perspectives for corresponding integrated hydrologic modeling research.

Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India

Precise quantification of evaporation has a vital role in effective crop modelling, irrigation scheduling, and agricultural water management. In recent years, the data-driven models using meta-heuristics algorithms have attracted the attention of researchers worldwide. In this investigation, we have examined the performance of models employing four meta-heuristic algorithms, namely, support vector machine (SVM), random tree (RT), reduced error pruning tree (REPTree), and random subspace (RSS) for simulating daily pan evaporation (EPd) at two different locations in north India representing semi-arid climate (New Delhi) and sub-humid climate (Ludhiana). The most suitable combinations of meteorological input variables as covariates to estimate EPd were ascertained through the subset regression technique followed by sensitivity analyses. The statistical indicators such as root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), Willmott index (WI), and correlation coefficient (r) followed by graphical interpretations, were utilized for model evaluation. The SVM algorithm successfully performed in reconstructing the EPd time series with acceptable statistical criteria (i.e., NSE = 0.937, 0.795; WI = 0.984, 0.943; r = 0.968, 0.902; MAE = 0.055, 0.993 mm/day; and RMSE = 0.092, 1.317 mm/day) compared with the other applied algorithms during the testing phase at the New Delhi and Ludhiana stations, respectively. This study also demonstrated and discussed the potential of meta-heuristic algorithms for producing reasonable estimates of daily evaporation using minimal meteorological input variables with applicability of the best candidate model vetted in two diverse agro-climatic settings.

Estimation of land surface evaporation using a generalized nonlinear complementary relationship

AbstractEvaporation is a key component of the hydrological cycle and affects regional water resources. Although the physics of evaporation is well understood, its estimation in practice remains a challenge. Among available methods for estimating it, the complementary principle of Bouchet has the potential to provide a practical tool for regional water resources assessment. In this study, the generalized nonlinear formulation of this principle by Brutsaert (2015) was tested against evaporation measurements from four flux stations in Australia under different climatic and vegetation conditions. The method was implemented using meteorological data and Class A pan evaporation measurements. After calibration the estimated daily evaporation values were in good agreement with flux station measurements with a mean correlation coefficient of 0.83 and a bias of 4% on average. More accurate estimates of daily evaporation were obtained when the evaporative demand or apparent potential evaporation was determined from the Penman equation instead of from pan evaporation. The obtained parameter values were found to lie well within the ranges of reported values in the literature. Advantages of the method are that only routine meteorological data are required and that it can be used to estimate long‐term evaporation trends.

Estimating total evaporation at the field scale using the SEBS model and data infilling procedures

The spatial representativeness of total evaporation estimates (ET) acquired from conventional approaches is limited, as these techniques generally provide site-specific values. The use of satellite earth observation has shown a great deal of potential in capturing spatially representative hydro-meteorological flux data and therefore represents a practical alternative for estimating ET. However, one of the challenges facing ET estimation using satellite earth observation data is the effect of clouds, which reduce the number of satellite images available for use. The objectives of this paper were firstly to validate satellite-derived ET estimates against estimates acquired from a surface renewal system and, secondly, to assess the feasibility of two infilling techniques to create a daily satellite-derived ET time series. The Surface Energy Balance System (SEBS) model was used to derive daily ET using MODIS imagery. Two infilling approaches, the K cact approach and a linear interpolation approach, were evaluated by comparing their respective values against in-situ ET measurements, as well as SEBS ET estimates derived using MODIS. The results showed that SEBS ET estimates were approximately 47% higher and produced R 2 and RMSE values of 0.33 and 2.19 mm∙d -1 , respectively, compared to in-situ ET values. The ET estimates obtained by applying the K cact approach and the linear interpolation approach compared favourably with the in-situ ET values, producing RMSE values of 0.9 mm∙d -1 and 0.6 mm∙d -1 , respectively. However, comparisons of ET estimates acquired by applying the K cact approach and the linear interpolation approach against the SEBS ET indicated a poor match, yielding RMSE values of 1.96 mm∙d -1 and 1.54 mm∙d -1 , respectively. Keywords: satellite earth observation, SEBS Model, ET, infilling, surface renewal system

Assessing the suitability of hybridizing the Cuckoo optimization algorithm with ANN and ANFIS techniques to predict daily evaporation

Estimation of evaporation is of indispensable significance for management and development of water resources. This study aims to identify the suitability of hybridizing the Cuckoo optimization algorithm (COA) with two well-known approaches of artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for prediction of daily pan evaporation. For this aim, two hybrid models of ANN–COA and ANFIS–COA are developed and their performances are compared with single ANN and ANFIS. As case study, the daily climate parameters including the average air temperature (Tavg), sunshine hours (S), relative humidity (Rh), wind speed (W) and pan evaporation (E) measured and collected for three Iranian stations of Zabol, Iranshahr and Shiraz have been utilized. The used data sets are divided into three parts so that 60, 20 and 20 % of the data are applied for training, testing and prediction phases, respectively. The achieved results prove that the models’ performances are variable among cities. It is found that combining the COA with ANN and ANFIS techniques does not enhance the precision of the developed ANN and ANFIS models noticeably in all considered stations. In fact, the results demonstrate that hybridizing the COA with ANN and ANFIS cannot be a viable option for estimation of daily evaporation. Overall, the study results indicate that further accuracy can generally be achieved by the ANN model; consequently, the ANN model can be sufficiently used in the prediction of daily evaporation.

A three-source SVAT modeling of evaporation: Application to the seasonal dynamics of a grassed vineyard

A parsimonious and versatile Soil–Vegetation–Atmosphere Transfer (SVAT) model is proposed for three component vineyards, which includes vine foliage, grassed soil and bare soil. A three-source energy balance approach describes the energy and mass transfer between the soil–plant continuum and the lower atmosphere with an hourly time step. It is coupled with a soil water balance module running with a daily time step. The model makes use of standard meteorological data together with parameters describing foliage development, grass and soil characteristics. The model is calibrated by means of the Multi-objective Calibration Iterative Process (MCIP) algorithm and next validated for evaporation and soil moisture over a dataset collected in a Southern France grassed vineyard. The validation exercise is twofold. It focuses first on the daily course of evaporation derived from the surface energy balance module only, forced with meteorological variables, net radiation and soil moisture. The comparison against Eddy Covariance measurements shows a good agreement (R2=0.96 and RMSE=14.0Wm−2). Next, a simulation coupling the surface energy balance module with the soil water balance module is validated over Eddy Covariance and soil moisture measurements. Simulations throughout two contrasting growing seasons provide good estimates of daily evaporation (R2=0.90 and RMSE=0.43mmd−1) and soil water content (R2=0.98 and RMSE=6.95mm). Model inaccuracies arise mainly under conditions of strong surface runoff. Results also suggest that the parameterizations relating the surface-atmosphere module with the soil module (i.e. stomatal resistance) should be carefully examined under water stress conditions. Finally, the model versatility is addressed through a set of simulations. It appears that the modeling approach allows assessing the seasonal water balance of vineyards with different structure (grass fraction or distance between rows) and that it could be applied to similar cropping systems.

The effect of data quality on model performance with application to daily evaporation estimation

The model performance is usually influenced by the quality of the data used in model construction. If the model performance is less affected by data quality, the resulting estimates will be more reliable. In this paper, the variation in model performance due to different data quality is explored in a field-scale application. Hence, two models, the proposed support vector machine (SVM) based model and the Stephen and Stewart (SS) model, are employed for daily estimation of evaporation at an experiment station. Five scenarios corresponding to different data qualities are designed to evaluate the effect of data quality on model performance. Additionally, the most effective meteorological variables influencing evaporation are obtained by a systematic input determination process. These most effective meteorological variables are used as inputs to the SVM-based model. The results show that the model performance decreases as the data quality decreases (i.e. the percentage of missing data increases). However, the estimation accuracy of SVM-based models is still better than that of the SS model. Moreover, the variation of model performance of the SVM-based model is smaller than that of the SS model. That is, the negative impact of different data quality is effectively decreased by using the SVM-based model instead of the SS model.

Measurement and modelling of evaporation from a coastal wetland in Maputaland, South Africa

Abstract. The surface renewal (SR) method was used to determine the long-term (12 months) total evaporation (ET) from the Mfabeni Mire with calibration using eddy covariance during two window periods of approximately one week each. The SR method was found to be inexpensive, reliable and with low power requirements for unattended operation. Despite maximum ET rates of up to 6.0 mm day−1, the average summer (October to March) ET was lower (3.2 mm day−1) due to early morning cloud cover that persisted until nearly midday at times. This reduced the daily available energy, and the ET was lower than expected despite the available water and high average wind speeds. In winter (May to September), there was less cloud cover but the average ET was only 1.8 mm day−1 due to plant senescence. In general ET was suppressed by the inflow of humid air (low vapour pressure deficit) and the comparatively low leaf area index of the wetland vegetation. The accumulated ET over 12 months was 900 mm. Daily ET estimates were compared to the Priestley-Taylor model results and a calibration α = 1.0 (R2 = 0.96) was obtained for the site. A monthly crop factor (Kc) was determined for the standardised FAO-56 Penman-Monteith. However, Kc was variable in some months and should be used with caution for daily ET modelling. These results represent not only some of the first long-term measurements of ET from a wetland in southern Africa, but also one of the few studies of actual ET in a subtropical peatland in the Southern Hemisphere. The study provides wetland ecologists and hydrologists with guidelines for the use of two internationally applied models for the estimation of wetland ET within a coastal, subtropical environment and shows that wetlands are not necessarily high water users.

Development of a support‐vector‐machine‐based model for daily pan evaporation estimation

AbstractEvaporation estimation is an important issue in water resources management. In this article, a four‐season model with optimal input combination is proposed to estimate the daily evaporation. First, the model based on support vector machine (SVM) coupled with an input determination process is used to determine the optimal combination of input variables. Second, a comparison of the SVM‐based model with the model based on back‐propagation network (BPN) is made to demonstrate the superiority of the SVM‐based model. In addition, season data are used to construct the SVM‐based four‐season model to further improve the daily evaporation estimation. An application is conducted to demonstrate the performance of the proposed model. Results show that the SVM‐based model can select the optimal input combination with physical mechanism. The SVM‐based model is more appropriate than the BPN‐based model because of its higher accuracy, robustness and efficiency. Moreover, the improvement due to the use of the four‐season model increases from 3.22% to 15.30% for RMSE and from 4.84% to 91.16% for CE, respectively. In conclusion, the SVM‐based model coupled with the proposed input determination process should be used to select input variables. The proposed four‐season SVM‐based model with optimal input combination is recommended as an alternative to the existing models. The proposed modelling technique is expected to be useful to improve the daily evaporation estimation. Copyright © 2012 John Wiley & Sons, Ltd.

Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes

This study follows three aims; firstly to develop and examine three different Artificial Neural Networks (ANNs) viz.: Multi-Layer Perceptron (MLP), Radial Basis Neural Network (RBNN) and Elman network for estimating daily evaporation rate of Tabriz and Urmia cities using measured hydro-meteorological data; second to compare the results of ANN models with three physically-based models include, Energy balance, Aerodynamic, and Penman models and also black-box Multiple Linear Regression (MLR) model; and finally to perform a sensitivity analysis to investigate the effect of each input parameter on the output in terms of magnitude and direction. The used meteorological data set to develop the models for estimation of daily evaporation includes daily air temperature, evaporation, solar radiation, air pressure, relative humidity, and wind speed measured at synoptic stations of Tabriz and Urmia cities which have almost distinct climatologic conditions. The obtained results denote to the superiority of the ANN models on the classic models. Also based on the comparisons, the MLP network performs better than the RBNN and Elman network so that in the next step, sensitivity analysis is performed by the Partial Derivation (PaD) and Weights methods on the MLP outputs. Sensitivity analysis results show although air temperature, solar radiation and the amount of evaporation at previous time step are the effective parameters in estimation of daily evaporation at both regions, due to the climatologic condition wind speed and relative humidity are other predominant parameters in Tabriz and Urmia, respectively.

Application of Artificial Intelligence to Estimate Daily Pan Evaporation Using Available and Estimated Climatic Data in the Khozestan Province (South Western Iran)

Estimation of evaporation, a major component of the hydrologic cycle, is required for a variety of purposes in water resources development and management. This paper investigates the abilities of genetic programming (GP) to improve the accuracy of daily evaporation estimation. In the first part of the study, different GP models, comprising various combinations of daily climatic variables, namely, air temperature, sunshine hours, wind speed, and relative humidity, were developed to evaluate the degree of the effect of each variable on daily pan evaporation. A dynamic modeling of evaporation was also performed, with the current climatic variables and one of the previous variables, to evaluate the effect of their time series on evaporation. In the second part of the study, the estimated solar radiation data were used as input vectors instead of recorded sunshine values. Statistics such as correlation coefficient (R), root mean square error (RMSE), coefficient of residual mass (CRM) and scatter index (SI) were used to measure the performance of models. Tthe dynamic model approach was shown to give the best results with relatively fewer errors and higher correlations. To assess the ability of GP relative to the neuro-fuzzy (NF) and artificial neural networks (ANN), several NF and ANN models were developed by using the same data set. The obtained results showed the superiority of GP to the NF and ANN approaches. The Stephen-Stewart and Christiansen methods were also considered for comparison. The results indicated that the proposed GP model performed quite well in modeling evaporation processes from the available climatic data. The results also showed that the estimated solar radiation data can be applied successfully instead of the recorded sunshine data.

Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression

Measurement of evaporation (E) rate from various natural surfaces is known as the key element in any hydrological cycle and hydrometeorological studies. Due to the shortage of pan evaporation (EP) data, the estimation of EP for such studies seems necessary. The main aim of this paper was to estimate daily EP using artificial neural network (ANN) and multivariate non-linear regression (MNLR) methods in semi-arid region of Iran. Five different ANN and MNLR models comprising various combinations of daily meteorological variables, that is, relative humidity (RH), air temperature (T), solar radiation (SR), wind speed (U) and precipitation (P) were developed to evaluate degree of effect of each of these variables on EP. The comparison of models estimates showed that the ANN 5 model characterized by Delta-Bar-Delta learning algorithm and Sigmoid activation function which uses all input parameters (T, U, SR, RH, P) performed best in prediction of daily EP. The sensitivity analysis revealed that the estimated EP data are more sensitive to T and U, respectively. A comparison of the model performance between ANN and MNLR models indicated that ANN method presents the best estimates of daily EP.

Daily pan evaporation modelling using a neuro-fuzzy computing technique

Summary Evaporation, as a major component of the hydrologic cycle, is important in water resources development and management. This paper investigates the abilities of neuro-fuzzy (NF) technique to improve the accuracy of daily evaporation estimation. Five different NF models comprising various combinations of daily climatic variables, that is, air temperature, solar radiation, wind speed, pressure and humidity are developed to evaluate degree of effect of each of these variables on evaporation. A comparison is made between the estimates provided by the NF model and the artificial neural networks (ANNs). The Stephens–Stewart (SS) method is also considered for the comparison. Various statistic measures are used to evaluate the performance of the models. Based on the comparisons, it was found that the NF computing technique could be employed successfully in modelling evaporation process from the available climatic data. The ANN also found to perform better than the SS method.

Evaporation estimation models for Lake Eğirdir, Turkey

Lake Eğirdir is located in the Lakes District in southwestern Turkey and it is the second largest freshwater resource lake. Evaporation is an important parameter in hydrological and meteorological practical studies. This study has three objectives: (1) to develop models for the estimation of daily evaporation using measured data from the automated GroWeather meteorological station located near Lake Eğirdir; (2) to compare the evaporation models with the classical Penman approach; (3) to evaluate the potential of each model. The comparisons are based on daily and monthly available data from 2001 and 2002. The evaporation estimation models (EEMs) developed in this paper have lower mean absolute errors and higher coefficient of determination R2 values than the Penman method. In order to evaluate the potential of the EEMs, daily evaporation values are calculated by the Priestley–Taylor, Brutsaert–Stricker, de Bruin, Makkink and Hamon methods. The EEMs are statistically indistinguishable from the classical methods on the basis of the parameters of mean, standard deviation, etc. In the evaluation of daily and monthly values, the relative error percentage for daily evaporation has lower values than for monthly evaporation. It can be seen that the EEMs help in calculating daily evaporation rather than monthly. Final evaluation and comparison indicate that there is a good agreement between the results of EEMs and the Penman approach than with the classical methods. Copyright © 2006 John Wiley & Sons, Ltd.

An Experiment on the Spatial Variability of Water Balance Terms in a Small Grass-Covered Area

By analyzing the time series of measurements of soil moisture at a point along with precipitation and potential evaporation using the bucket with a bottom hole (BBH) model, we evaluated water balance terms at the point. The estimates of those terms made at nine points on a 1.5m grid in a small grass-covered area showed their own spatial variations. The results obtained are summarized as follows.(a) Ranking of the spatial variability of the water balance terms involved in the BBH model was W (soil moisture) < E (evaporation) < Rs (surface runoff) < Gd (gravity drainage).(b) Clear patterns showing the spatial variation in surface runoff and gravity drainage in a 3×3m patch were obtained, which suggests that subsurface water below the ASSL (30cm thick) has flow systems on spatial scales of a few meters.(c) The spatial variation patterns of the water balance terms obtained by the parameterization of the BBH model did not necessarily agree with that of soil moisture but showed ones peculiar to the terms. This means that the parameterization takes the effects of other factors than soil moisture such as the micro topographic relief, vegetation cover, soil texture and structure into account appropriately, because such factors should by nature exhibit spatially not random but organized variations.(d) Daily evaporation estimates made by the BBH model were in good agreement with the measurements taken for reference by the Bowen ratio method, though it was conducted by the expedient of disregarding an insufficiency of fetch and neglecting the heat flux into the soil.

Observation and estimation of daily actual evapotranspiration and evaporation on a glacierized watershed at the headwater of the Urumqi River, Tianshan, China

In order to develop an effective approach to estimate evaporation and evapotranspiration (ET) in the glacierized watershed for hydrological process studies, the meteorological and energy budget data obtained in 1986 from a small watershed at the headwater of Urumqi River Basin, Tianshan, China were utilized. The field observation of ET and surface soil water content was briefly described. The performance of several existing approaches in estimating the daily actual ET and evaporation for the two kinds of predominant ground surface in the basin (i.e. the alpine tundra and glacier surface) was examined. The estimated daily ET from the alpine tundra for the summer season by Morton's equation correlates fairly well with the lysimeter-observed data, and the proposed wetness index by means of the principle of Morton's complementary relationship, provided reliable information of the surface soil wetness condition for the same period. For the other seasons, Kojima's equation was adopted for estimation of daily evaporation on both alpine tundra and the glacier surface when most parts of the watershed were snow covered. The daily evaporation on the glacier surface for the melting season was ultimately estimated by Ohno's equation for the non-melting season. The monthly mean evaporation estimated for the glacier surface was close to the field observation data obtained in 1987–1988. The annual evaporation obtained on both sites by this approach was in good agreement with that estimated with the water balance method for the same sites by Zhang et al. (1992). Copyright © 1999 John Wiley & Sons, Ltd.

Evaporation from bare soil in a temperate humid climate—measurement using micro-lysimeters and time domain reflectometry

In humid areas soil evaporation is an important factor in relation to soil tillage and timing of irrigation early in the growing season. Direct measurements of evaporation from a loamy sand were made using micro-lysimeters. The accuracy of daily measurement was about ± 0.5 mm H 2O day −1. The micro-lysimeter method was not valid in periods with high precipitation. The use of time domain reflectometry (TDR) for measuring soil water content was investigated using a manual interpretation of the trace. The precision of changes in soil water content calculated from daily measurements with TDR was about 1.3 mm H 2O, when using probes of 50 cm length. However, improved precision may be obtained by the use of an automatic interpretation of the trace. Estimates of daily evaporation from bare soil calculated from the water balance equation and measurements of soil water content with TDR were compared with measurements with micro-lysimeters. The TDR technique was suitable for estimating bare soil evaporation when the soil water content was integrated over a 0–50 cm soil profile and drainage had ceased at the lower depths of the profile. Evaporation during a 13 day drying period in spring, just after the soil had been fully rewetted, was about 26 mm. During a 23 day drying period later in the season the evaporation from the bare soil was about 30 mm. In both periods the accumulated evaporation was rather high and equivalent to about 65% and 50% of the accumulated potential evapotranspiration in the first and second drying period, respectively, even though the soil water content in the 0–50 cm profile was well below field capacity at the beginning of the second drying period.

乾燥表土層(DSL)法によるHEIFE沙漠観測点の砂地面からの蒸発量評価

Most methods of measuring soil-surface evaporation originate from those of measuring water-surface evaporation, and hence they are not necessarily suitable for making measurements of evaporation from dry land surfaces. The major reason is that, as observation shows, when the dry surface layer (DSL) forms on the soil, the constant-flux layer of water vapor is barely developed in the surface air layer. However, during most of the day, the upward water vapor flux in the DSL is nearly constant with depth. The principle of the DSL method is to estimate this upward near-constant flux in the DSL from the depth profiles of temperature and soil water content in the layer. The DSL method was applied to measurements of evaporation from a sand surface at the HEIFE desert station and they were compared to the decreases in soil moisture in the top 1 m of sand, from which the following results were obtained. (a) Estimates of daily evaporation about a week after rainfall of 15 mm or so, which were made on the basis of seven measurements taken by the DSL method during the day, were in good agreement with the daily decreases in soil moisture in the top 30 cm of sand. (b) Daily evaporation from dry soil can be approximately estimated from one measurement of evaporation rate made by the DSL method in the afternoon and the time lapsed since the last rainfall.