Thornthwaite Research Articles

A comparative study of crop evapotranspiration estimation in maize using empirical methods, pan evaporation and satellite-based remote sensing technique

A research study was conducted at the Agricultural College and Research Institute, Coimbatore to estimate the evapotranspiration (ET) of maize crop (Zea mays) over 2 consecutive seasons in 2022-2023. Among the different methods used to estimate crop evapotranspiration, the Food and Agricultural Organization Penman-Monteith model (FAO P-M) is widely recognized as the standard approach for ET estimation. This study aimed to compare the effectiveness of three alternative methods - Thornthwaite (TW), NDVI-based and pan methods against the FAO Penman-Monteith (P-M) model in estimating maize evapotranspiration. Meteorological data were collected from the TNAU weather station spanning the period from 2022 to 2023.The performance of the estimation methods was assessed using statistical metrics such as coefficient of determination (R2), root mean squared error (RMSE), percentage error and mean bias error. The findings revealed that the NDVI-based method, relying on satellite data, provided higher accuracy in estimating maize evapotranspiration compared to the FAO PM method. Specifically, the NDVI-based method achieved the highest coefficient of determination (R2) of 0.87 and 0.89, the lowest RMSE of 12.44 mm/month and 15.5 mm/month, the lowest percentage error of 4.8 % and 9.00 % and the lowest mean bias error of 5.5 and 7.85 for the first and second seasons respectively. This study highlights the effectiveness of the NDVI-based ET estimation method for accurately assessing maize evapotranspiration. While the FAO-56 Penman-Monteith method is highly regarded for its accuracy in both theoretical and practical contexts, the comparative evaluation presented in this paper offers valuable insights for selecting alternative methods that require less data, particularly in regions with limited data availability.

Method of generating potential evapotranspiration with high precision and resolution

Potential evapotranspiration (PET) is a key factor in hydrological cycle and energy balance and plays an important role in drought and global climate change response. Existing observational and modeling methods for PET retrieval have their limitations, such as low precision and poor spatial resolution, which becomes the focus of this study. A hybrid PET fusion (HPF) method is proposed by fusing station- and grid-based PET, in which the PET expression is determined by considering the factors of location, temperature, and zenith total delay (ZTD). In addition, an improved Helmert variance component estimation method is introduced to determine the optimal weights of the HPF model. Corresponding data, which include monthly Thornthwaite (TH)-derived PET data with a spatial resolution of 0.25° × 0.25° and Penman–Monteith (PM)-derived PET data at 704 meteorological stations, over the past 60 years from 1959 to 2018 in China are selected. The 10-fold cross-validation method is introduced to evaluate the internal and external accuracies of the proposed HPF method. Statistical result shows that the average root mean square (RMS) of the proposed HPF method is 13.98 mm, with an average RMS improvement rate (IR) of 46.71 % compared with TH-derived PET, when PM-derived PET is regarded as a reference. Moreover, the performance of the HPF-derived standardized precipitation evapotranspiration index (SPEI) is evaluated at different time scales, and the average RMS is 0.3, with an average RMS IR of 26.33 % compared with TH-derived SPEI. Such results verify the good performance of the proposed HPF model and enrich the methods for obtaining PET with high precision and resolution.

Sensitivity of Standardized Precipitation and Evapotranspiration Index (SPEI) to the choice of SPEI probability distribution and evapotranspiration method

Study regionThe state of Oklahoma located in the Southern Plains region of the United States. Study focusThe standardized precipitation evapotranspiration index (SPEI) is a widely used meteorological drought index that incorporates potential evapotranspiration (PET) into a precipitation-based index. However, the understanding of the appropriate PET method for SPEI across different temporal scales in non-arid climate conditions remains limited. We compared Thornthwaite (TW), Hargreaves (HG), and Penman-Monteith (PM) equations for SPEI at various accumulations, considering three temporal scales: 1) long-term (25 years), 2) event-based, and 3) monthly. Also, we examined the log-logistic and generalized extreme value distributions to test the normality of SPEI computed from the three PET methods. To do this, we utilized high-quality climate datasets measured at 107 stations across the state of Oklahoma, United States, which has a diverse climate ranging from semi-arid to humid subtropical. New hydrological insights for the regionThe log-logistic distribution was found to be suitable for SPEI. The SPEI-HG showed better agreement with SPEI-PM than SPEI-TW in this region for the analyses of three temporal scales. However, for accumulations of SPEI longer than one year, both TW and HG equations showed no significant differences with SPEI-PM. The findings provide practical guidance for selecting an appropriate PET equation in the Southern Plains region depending on the purpose of study without resorting to data-intensive methods for PET estimation.

A Comparative Study of Potential Evapotranspiration Estimation by Three Methods with FAO Penman–Monteith Method across Sri Lanka

Among numerous methods that have been developed to estimate potential evapotranspiration (PET), the Food and Agricultural Organization Penman–Monteith model (FAO P–M) is often recognized as a standard method to estimate PET. This study was conducted to evaluate the applicability of three other PET estimation methods, i.e., Shuttleworth–Wallace (S–W) model, Thornthwaite (TW) and pan methods, to estimate PET across Sri Lanka with respect to the FAO P–M model. The meteorological data, i.e., temperature, relative humidity, wind speed, net solar radiation, and pan evaporation, recorded at 14 meteorologic stations, representing all climate and topographic zones of Sri Lanka, were obtained from 2009 to 2019. The models’ performances were assessed based on three statistical indicators: root mean squared error (RMSE), bias, and Pearson correlation coefficient (R). In comparison with the FAO P–M model estimates, the seasonal and annual estimates of all three models show great differences. The results suggested that pan and S–W methods perform better in the dry zone of the country. Both S–W and pan methods underestimated PET over the entire county in all seasons. TW does not show consistent results over the country, thus being found as the least reliable alternative. Although S–W is highly correlated with the FAO P–M model, the application of the model in a data-scarce region is more constrained, as it requires more parameters than the FAO P–M model. Thus, the study suggests employing alternative methods based on the region of the country instead of one single method across the entire country.

Evaluation of Five Equations for Short-Term Reference Evapotranspiration Forecasting Using Public Temperature Forecasts for North China Plain

Accurate short-term forecasts of daily reference evapotranspiration (ET0) are essential for real-time irrigation scheduling. Many models rely on current and historical temperature data to estimate daily ET0. However, easily accessible temperature forecasts are relatively less reported in short-term ET0 forecasting. Furthermore, the accuracy of ET0 forecasting from different models varies locally and also across regions. We used five temperature-dependent models to forecast daily ET0 for a 7-day horizon in the North China Plain (NCP): the McCloud (MC), Hargreaves-Samani (HS), Blaney-Criddle (BC), Thornthwaite (TH), and reduced-set Penman–Monteith (RPM) models. Daily meteorological data collected between 1 January 2000 and 31 December 2014 at 17 weather stations in NCP to calibrate and validate the five ET0 models against the ASCE Penman–Monteith (ASCE-PM). Forecast temperatures for up to 7 d ahead for 1 January 2015–19 June 2021 were input to the five calibrated models to forecast ET0. The performance of the five models improved for forecasts at all stations after calibration. The calibrated RPM is the preferred choice for forecasting ET0 in NCP. In descending order of preference, the remaining models were ranked as HS, TH, BC, and MC. Sensitivity analysis showed that a change in maximum temperature influenced the accuracy of ET0 forecasting by the five models, especially RPM, HS, and TH, more than other variables. Meanwhile, the calibrated RPM and HS equations were better than the other models, and thus, these two equations were recommended for short-term ET0 forecasting in NCP.

Estimation of diurnal-provided potential evapotranspiration using GNSS and meteorological products

In the context of global warming, extreme drought has seriously endangered the safety of human lives and assets, indicating an urgent need for its accurate monitoring and early detection. Since various types of drought monitoring indices are calculated from the variable of potential evapotranspiration (PET), thus the selection of models to derive PET has attracted broad attention in recent decades. Generally, the common-used Thornthwaite (TH) model possesses the features of calculation simplicity and great universality, however, its accuracy and temporal resolution (monthly) are relatively low, which greatly hinders the further uses of this model. Considering these restrictions, a novel method for estimating diurnal-provided PET values (DTH-PET) using the TH model was first proposed, in which a 31-day sliding window was adopted to improve its temporal resolution. The new method takes both the diurnal and “monthly” variation features into account. Verification results showed that, compared with the monthly TH model, the improvements in the mean root mean square (RMS) errors made by the new method was 3.1 mm. In addition, to further improve the accuracy of the DTH-PETs, a new high-order calibration function with the use of GNSS and meteorological measurements including ZTD, temperature and pressure was then constructed and fully validated. In comparison with the 29.95 mm mean RMS from the conventional TH model without calibration, the new method led to a 4.37 mm result, i.e., a 25.57 mm improvement (over 85%) was made after a thorough calibration process. In addition, the comparison of the newly-proposed method with the common-used first-order equation also further illustrates its superiority during the whole calibration process. Therefore, all these results suggest that, by using the new strategies proposed in this study, DTH-PET can be easily estimated with high accuracy using limited meteorological variables.

Evaluation of Reference Evapotranspiration Estimation Methods for the Assessment of Hydrological Impacts of Photovoltaic Power Plants in Mediterranean Climates

Large-scale photovoltaic (PV) power plants may affect the hydrological cycle in all its components. Among the various components, evapotranspiration is one of the most important. As a preliminary step for assessing the impacts of PV plants on evapotranspiration, in this study, we performed an evaluation study of methods for estimating reference evapotranspiration (ETo). FAO and ASCE recommend the Penman–Monteith (PM) method for the estimation of ETo when the data for all involved variables are available. However, this is often not the case, and different empirical methods to estimate ETo, requiring mainly temperature data, need to be used. This study aimed at assessing the performance of different temperature- and radiation-based empirical ETo estimation methods against the standardized PM ETo method in an experimental photovoltaic power plant in Piazza Armerina, Sicily, Italy, where a meteorological station and a set of sensors for soil moisture were installed. The meteorological data were obtained from the Lab from July 2019 to end of January 2022. By taking the ETo estimations from the PM method as a benchmark, the study assessed the performance of various empirical methods. In particular, the following methods were considered: Hargreaves and Samani (HS), Baier and Robertson (BR), Priestley and Taylor (PT), Makkink (MKK), Turc (TUR), Thornthwaite (THN), Blaney and Criddle (BG), Ritchie (RT), and Jensen and Haise (JH) methods, using several performance metrics. The result showed that the PT is the best method, with a Nash–Sutcliffe efficiency (NSE) of 0.91. The second method in order of performance is HS, which, however, performs significantly worse than PT (NSE = 0.51); nevertheless, this is the best among methods using only temperature data. BG, TUR, and THN underestimate ETo, while MKK, BG, RT, and JH showed overestimation of ETo against the PM ETo estimation method. The PT and HS methods are thus the most reliable in the studied site.

Banana growing and its climate suitability in the municipality of Recife-Pe, Brazil

The banana tree is a plant characteristic of the tropical region, demanding in temperatures and rainfall with good regional distribution, due to its low cost and high nutritional value, it is an integral part of the diet of populations. The objective is to analyze the climatic oscillations, from the calculation of the water balance and its climatic classification, according to the methods of Thorn Thwaite and Köppen, for the city of Recife, verifying the occurrence of the aptitude of the banana cultivar. The average rainfall and temperature data were acquired from the National Institute of Meteorology, for the period between 1962 and 2016. Banana plantations are moderate because they have a water deficit of 287.4 mm. The evaporative power registered in the municipal area is high, but the demand for irrigation complements the scarcity of water to cultivate it. The local altitude is in the range of planting of the cultivar as well as the variability of the relative humidity of the air. Wind is one of the limiting factors for commercial exploitation of banana cultivation, if the cultivars are tall and planted in sandy soils.

Intercomparison of Estimates of Reference Evapotranspiration of Surat Region of Gujarat, India

Evapotranspiration is a vital parameter for irrigation planning and management, and for hydrological and climatological studies. This can be estimated by using Blaney-Criddle (BC), Thornthwaite (TW), Hargreaves (HG) and Penman-Monteith (PM) methods. Out of these methods, BC and TW are known as temperature based whereas HG is radiation based and PM is based on Food and Agriculture Organization (FAO) report on crop water requirements. This paper illustrates a study on comparison on estimates of reference evapotranspiration (ETo) obtained from temperature, radiation and FAO based methods for Surat region. The meteorological data such as air temperature, relative humidity and sunshine hours observed at Surat region for the period 2001 to 2016 is used. In addition, the wind speed and solar radiation data is downloaded from the International Water Management Institute website and used in estimation of ETo using PM. The performance of the methods used in estimation of ETo is evaluated through regression analysis by developing a linear regression between the observed and estimated values of ETo using BC, TW and HG, and FAO based PM. The results of linear regression analysis indicated that there is a good line of agreement between the observed and estimated ETo using PM with CC of 0.968 when compared with those values of BC, TW and HG. The study show that the PM is better suited amongst four methods applied in estimating the ETo for Surat region, Gujarat.

Comparisons of sequential water balances between 1920-2018 in the municipality of Serra Talhada and São Bento do Una – Pe, Brazil

A sequential water balance was performed using the Thorn Thwaite and Mather method in order to verify the contribution of water storage and damming in the municipality of São Bento do Una in relation to the water crisis. The monthly average climatological precipitation data were grouped into 95 years, characterizing a period of climatological normal, where the software was used in electronic spreadsheets to extract the values ​​of the monthly, annual averages of precipitation for the period from 1920 to 2018. Were estimated by the multiple regression line methods applied to the T-estima software, taking into account the geographic coordinates. Climatic impacts have caused changes in the region's water balance in the last decade. Environmental degradation, the local effect of human action, has by itself accelerated the process of modifying the regional climate, thus directly affecting the conditions of the rainfall regime and the availability of water in the soil. It is extremely necessary to use rainwater harvesting and other storage sources for human, animal and plant survival, thus contributing to the agricultural and poultry sector in the region. Climatic impacts may cause changes in the region's water balance, as heavy and short-lived rains are expected, leading to a drop in poultry and agricultural production.

Evolution of potential evapotranspiration and its sensitivity to climate change based on the Thornthwaite, Hargreaves, and Penman–Monteith equation in environmental sensitive areas of China

Global warming intensifies the water cycle and regional water condition differences by changing the surface environment. The ecological system in environmental sensitive areas of China (ESAC) is stressed by water resources, and the ecological effect of dry–wet evolution is even more profound. However, the applicability of different potential evapotranspiration (ET0) assessment methods and the response mechanism of ET0 to climate change remain unknown. In this study, we evaluated the reliability of ETTH, ETHarg, and ETPM derived from Thornthwaite (TH), Hargreaves (Harg), and Penman–Monteith (PM) formulas using the monthly average air temperature (Tavg), maximum air temperature (Tmax), minimum air temperature, sunshine duration (SSD), wind speed (WIN), and relative humidity (RHU) from 1961 to 2018. Further, non-parametric trend analysis, correlation analysis, multiple regression analysis, wavelet analysis, and seasonal and trend decomposition using Loess were used to analyze the spatial–temporal evolution of ET0 and the moisture index (MI) and its relationship with climate change in ESAC from 1961 to 2018. The results are as follows: (1) The P-M formula has excellent applicability in the research area. Under the background of average temperature rising at 0.035 °C yr−1, ETPM decreases with a rate of −0.59 mm yr−1, which indicates the “pan evaporation paradox” phenomenon. Using 1993 as the node, ETPM changed from a significant decrease to an increase. (2) WIN, SSD, Tavg, and Tmax significantly promoted changes in ETPM, and RHU and precipitation (Pr) were mainly reduced. The determinant factor of ETPM change was Tavg from 1961 to 2018. (3) MI change was mainly influenced by Pr, SSD, and ETPM. Pr promoted the change, whereas SSD and ETPM reduced it. The linear combination of ETPM and SSD could explain the change in MI. Our findings verified the applicability of PM in areas with severe interannual precipitation fluctuations, revealing the evolution of dry–wet pattern changes and their driving mechanisms in ESAC, which can deepen the cognition of the law of long-term land surface water cycle and effectively guide the formulation and implementation of regional drought and flood disaster response measures.

Uncertainties in evapotranspiration projections associated with estimation methods and CMIP6 GCMs for South Korea

This study compared the performance capabilities of three potential evapotranspiration (PET) methods, Thornthwaite (TW), Hargreaves and Samani (HS), and Penman-Monteith (PM), to simulate historical and future daily PET levels in South Korea using climate variables from Coupled Model Intercomparison Project 6 (CMIP6) Global Climate Models (GCMs). Five evaluation metrics were used to quantify the reproducibility of the climate variables and PETs at ten stations in South Korea for the historical period used here (1985–2014). The changes and uncertainty associated with the changes in PET in the near (2031–2060) and far (2071–2100) futures were calculated for two shared socioeconomic pathways (SSPs) of 2–4.5 and 5–8.5. As a result, PETs estimated using the three methods for the historical period showed high performance in terms of five evaluation metrics. Overall, PETs showed an increase for both the future periods and the SSPs. The PET estimated using the PM method showed the greatest increase, while that estimated using HS showed the most modest increase in the future. The PM method also showed the highest reliability and lowest uncertainty in the PET estimations, while the opposite was true for HS. This study contributes to our understanding of rational PET methods by which to calculate hydrological factors such as drought indexes for future periods via GCM simulations.

High-Precision Potential Evapotranspiration Model Using GNSS Observation

Potential evapotranspiration (PET) can reflect the characteristics of drought change in different time scales and is the key parameter for calculating the standardized precipitation evapotranspiration index (SPEI). The Thornthwaite (TH) and Penman–Monteith (PM) models are generally used to calculate PET, but the precision of PET derived from the TH model is poor, and a large number of meteorological parameters are required to evaluate the PM model. To obtain high-precision PET with fewer meteorological parameters, a high-precision PET (HPET) model is proposed to calculate PET by introducing precipitable water vapor (PWV) from Global Navigation Satellite System (GNSS) observation. The PET difference (DPET) between TH- and PM-derived PET was calculated first. Then, the relationship between the DPET and GNSS-derived PWV/temperature was analysed, and a piecewise linear regression model was calculated to fit the DPET. Finally, the HPET model was established by adding the fitted DPET to the initial PET derived from the TH model. The Loess Plateau (LP) was selected as the experiment area, and the statistical results show the satisfactory performance of the proposed HPET model. The averaged root mean square (RMS) of the HPET model over the whole LP area is 8.00 mm, whereas the values for the TH and revised TH (RTH) models are 34.25 and 12.55 mm, respectively, when the PM-derived PET is regarded as the reference. Compared with the TH and RTH models, the average improvement rates of the HPET model over the whole LP area are 77.5 and 40.5%, respectively. In addition, the HPET-derived SPEI is better than that of the TH and RTH models at different month scales, with average improvement rates of 49.8 and 23.1%, respectively, over the whole LP area. Such results show the superiority of the proposed HPET model to the existing PET models.

A conjugate application of MODIS/Terra data and empirical method to assess reference evapotranspiration for the southwest region of Bangladesh

Availability of water resources, especially irrigation water in the Southwest (SW) region of Bangladesh, is in threat due to climate change impacts, reduction of upstream flow, and excessive groundwater consumption. It is essential to assess the evapotranspiration ( $$\mathrm{ET}$$ ), especially reference evapotranspiration ( $${\mathrm{ET}}_{0}$$ ) to ensure efficient irrigation demand for this region. However, direct measurement of $${\mathrm{ET}}_{0}$$ is a difficult task as empirical equation-based assessment requires a complex set of input data. As remote sensing (RS) technology has evolved as a viable alternative to the ground-based estimation of ET0, this study investigated whether it is possible to attain a reliable assessment of $${\mathrm{ET}}_{0}$$ for the SW region of Bangladesh using satellite data. Land surface temperature (LST) data retrieved from the moderate-resolution imaging spectroradiometer (MODIS) satellite have been evaluated as an alternative of air temperature in three empirical $${\mathrm{ET}}_{0}$$ assessment methods, namely the Hargreaves–Samani (HS), Thornthwaite (TH), and Blaney–Criddle (BC) methods. $${\mathrm{ET}}_{0}$$ assessed from these methods have been calibrated and validated against the FAO-56 Penman–Monteith (PM) method. Several statistical indices, viz. root mean square error (RMSE), mean absolute error (MAE), mean relative error (MRE), and coefficient of determination ( $${R}^{2}$$ ) have been used to evaluate the performance of $${\mathrm{ET}}_{0}$$ assessment from the above-mentioned methods. MODIS-LST data have been found strongly correlated with air temperature ( $${R}^{2}$$ = 0.95). A good correlation in $${\mathrm{ET}}_{0}$$ assessment has been found between the PM method and HS ( $${R}^{2}$$ = 0.85), TH ( $${R}^{2}$$ = 0.81), and BC ( $${R}^{2}$$ = 0.79) methods. With the calibrated result, the calibrated HS method provides the most accurate results compared to the other methods, with an average RMSE of 0.44 mm/day, MAE of 0.39 mm/day, and MRE of 12.34%. Considering the consistency of the findings with the previous studies, this method has been recommended for assessing $${\mathrm{ET}}_{0}$$ under conditions of limited data availability.

Retrieval of a High-Precision Drought Monitoring Index by Using GNSS-Derived ZTD and Temperature

Potential evapotranspiration (PET) is a key parameter for calculating drought monitoring index that is generally difficult to obtain. In addition, PET has low spatial resolution and can only be obtained at a site-based point. Therefore, retrieving PET with high precision, high spatial resolution, and less meteorological data becomes the focus of this paper. In this paper, a high-precision and high-spatial-resolution drought monitoring (HDM) model was established to accurately calculate PET by using the zenith troposphere delay (ZTD) derived from global navigation satellite system (GNSS) and temperature (T). The initial PET value was calculated by using the PET periodical model based on PenmanMonteith (PM)-derived PET. The PET difference (DET) between the PM and periodic model was then calculated, and a multiple linear regression model was established to fit DET by using the ZTD and T differences at meteorological stations. To improve the spatial resolution of the calculated PET, a spherical harmonic function was applied to fit the coefficients of these stations. The HDM-derived PET at grid points was eventually obtained by using the fitted coefficients and ZTD/T. The HDM-derived PET and standardized precipitation evapotransporation index (SPEI) were compared with those from the Thornthwaite (TH) and revised TH (RTH) models over the loess plateau (LP) area with the PM-derived PET and SPEI as references. Comparison results highlight the excellent performance of the proposed HDM model and the Pearsons correlations of SPEI between the HDM and PM models all exceeded 0.96 under different month scales.

A comparison of empirical equations of Estimating Potential Evapotranspiration (PET) from climatological data in Erbil city

Potential evapotranspiration (PET) is an important index of hydrologic budgets at different spatial scales and it is a critical variable for understanding regional hydrological processes. The objective of this study is to find the most suitable method for estimating PET in Erbil city which was done by comparing seven commonly PET methods. Radiation based method: Pristley –Taylor(PT), Turc(TU), Makkink (MK), and temperature based method: Ivanov (IV),  Penman–Monteith method (1965) , FAO Penman–Monteith method, and Thornthwaite(TW). The data were collected and used in the models to find PET for the period (1992-2015). The performance indicators were applied by using statistical parameters such as: the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), person correlation coefficient (R2), MBE Mean bias error. The monthly PET results from the models were compared with the actual evaporation. Current work shows that Makkink (MK) model is better than the other models for estimating the potential Evapotranspiration in Erbil depending on the values of statistical parameters.

Assessment of the effect of PET calculation method on the Standardized Precipitation Evapotranspiration Index (SPEI)

Standardized Precipitation Evapotranspiration Index (SPEI) is one of the more applied indices to evaluate drought characteristics. SPEI is based on precipitation (P) and potential evapotranspiration (PET) parameters. In PET calculation, many different equations have been introduced. In this research, monthly and seasonal SPEI values based on five more applied equations of PET calculation methods and Food and Agriculture Organization Penman-Monteith equation (FPM) (that is the standard equation of PET calculation and used as reference method) were compared. In this paper, meteorological data series of 24 meteorological stations (with suitable geographic distribution and different climate condition) in Iran from 1967 to 2017 were used to calculate PET values and SPEI in monthly and seasonal time scales. Results showed that according to normalized root mean square error index (NRMSE), similarities of estimated SPEI using Blaney-Criddle (BC) method with FPM method in monthly (at the 79.1% of evaluated stations) and seasonal (at the 75% of evaluated stations) time scale were more than other methods. According to the results of clustering analysis, the average of similarity of calculated SPEI between FPM equation and Jensen-Haise (JH), Modified Jensen-Haise (MJH), Thornthwaite (TW), Hargreaves-Samani (HS), BC equations were 85.46, 83.08, 82.17, 81.38, and 83.46% in monthly time scale and 84.08, 82.04, 80.38, 81.93, and 82.79% in seasonal time scale. Therefore, in areas with the least available meteorological data, it is suggested to replace the FPM equation with JH or BC equations.

Improved Drought Monitoring Index Using GNSS-Derived Precipitable Water Vapor over the Loess Plateau Area

Standardized precipitation evapotranspiration index (SPEI) is an acknowledged drought monitoring index, and the evapotranspiration (ET) used to calculated SPEI is obtained based on the Thornthwaite (TH) model. However, the SPEI calculated based on the TH model is overestimated globally, whereas the more accurate ET derived from the Penman–Monteith (PM) model recommended by the Food and Agriculture Organization of the United Nations is unavailable due to the lack of a large amount of meteorological data at most places. Therefore, how to improve the accuracy of ET calculated by the TH model becomes the focus of this study. Here, a revised TH (RTH) model is proposed using the temperature (T) and precipitable water vapor (PWV) data. The T and PWV data are derived from the reanalysis data and the global navigation satellite system (GNSS) observation, respectively. The initial value of ET for the RTH model is calculated based on the TH model, and the time series of ET residual between the TH and PM models is then obtained. Analyzed results reveal that ET residual is highly correlated with PWV and T, and the correlate coefficient between PWV and ET is −0.66, while that between T and ET for cases of T larger or less than 0 °C are −0.54 and 0.59, respectively. Therefore, a linear model between ET residual and PWV/T is established, and the ET value of the RTH model can be obtained by combining the TH-derived ET and estimated ET residual. Finally, the SPEI calculated based on the RTH model can be obtained and compared with that derived using PM and TH models. Result in the Loess Plateau (LP) region reveals the good performance of the RTH-based SPEI when compared with the TH-based SPEI over the period of 1979–2016. A case analysis in April 2013 over the LP region also indicates the superiority of the RTH-based SPEI at 88 meteorological and 31 GNSS stations when the PM-based SPEI is considered as the reference.

Projected Spatiotemporal Dynamics of Drought under Global Warming in Central Asia

Drought, one of the most common natural disasters that have the greatest impact on human social life, has been extremely challenging to accurately assess and predict. With global warming, it has become more important to make accurate drought predictions and assessments. In this study, based on climate model data provided by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), we used the Palmer Drought Severity Index (PDSI) to analyze and project drought characteristics and their trends under two global warming scenarios—1.5 °C and 2.0 °C—in Central Asia. The results showed a marked decline in the PDSI in Central Asia under the influence of global warming, indicating that the drought situation in Central Asia would further worsen under both warming scenarios. Under the 1.5 °C warming scenario, the PDSI in Central Asia decreased first and then increased, and the change time was around 2080, while the PDSI values showed a continuous decline after 2025 in the 2.0 °C warming scenario. Under the two warming scenarios, the spatial characteristics of dry and wet areas in Central Asia are projected to change significantly in the future. In the 1.5 °C warming scenario, the frequency of drought and the proportion of arid areas in Central Asia were significantly higher than those under the 2.0 °C warming scenario. Using the Thornthwaite (TH) formula to calculate the PDSI produced an overestimation of drought, and the Penman–Monteith (PM) formula is therefore recommended to calculate the index.

Analysis of Dry/Wet Variations in the Poyang Lake Basin Using Standardized Precipitation Evapotranspiration Index Based on Two Potential Evapotranspiration Algorithms

Global warming has resulted in unevenly distributed changes in precipitation and evapotranspiration, which has some influence on dry/wet conditions, thus exerting a tremendous impact on national life and the social economy, especially agricultural production. In order to characterize the dry/wet variations in the Poyang Lake basin during 1958–2013, based on the potential evapotranspiration (PET) estimated by the Thornthwaite (TH) and Penman–Monteith (PM) formulas, two types of Standardized Precipitation Evapotranspiration Index (SPEI), namely SPEI_th and SPEI_pm, were calculated in this study. The characteristic of dry/wet variations in the Poyang Lake basin was analyzed and a comparative analysis of two SPEIs was conducted. The results indicate that both SPEI series showed a wet trend in the Poyang Lake basin on an annual scale as well as seasonal scales during 1958–2013, except for spring and autumn. A drying trend was observed in spring, while in autumn, the dry and wet conditions in two SPEIs had opposite trends. However, all trends from two SPEIs were not significant, except for summer SPEI_pm. Meanwhile, significant positive correlations were detected between precipitation and two SPEIs, with the correlation coefficients above 0.95, whereas negative correlations were detected between PET and two SPEIs, with the correlation coefficients ranging from −0.17 to −0.85. This indicates that precipitation was the main climatic factor to determine change in dry/wet conditions in the Poyang Lake basin. Although there were obvious differences between the accumulated values of the Penman–Monteith-based PET (ET_pm) and Thornthwaite-based PET (ET_th), trends in the SPEI_pm series were generally consistent with those in the SPEI_th series, revealing that the method for PET calculation was not critical to the change in dry/wet conditions. Moreover, the results of the conditional probability of SPEI_pm and SPEI_th show that both SPEI_pm and SPEI_th could detect wet or dry events that were identified by SPEI_pm or SPEI_th.