Potential Evapotranspiration Estimates Research Articles

Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon

AbstractWhile deep learning (DL) models exhibit superior simulation accuracy over traditional distributed hydrological models (DHMs), their main limitations lie in opacity and the absence of underlying physical mechanisms. The pursuit of synergies between DL and DHMs is an engaging research domain, yet a definitive roadmap remains elusive. In this study, a novel framework that seamlessly integrates a process‐based hydrological model encoded as a neural network (NN), an additional NN for mapping spatially distributed and physically meaningful parameters from watershed attributes, and NN‐based replacement models representing inadequately understood processes is developed. Multi‐source observations are used as training data, and the framework is fully differentiable, enabling fast parameter tuning by backpropagation. A hybrid DL model of the Amazon Basin (∼6 × 106 km2) was established based on the framework, and HydroPy, a global‐scale DHM, was encoded as its physical backbone. Trained simultaneously with streamflow observations and Gravity Recovery and Climate Experiment satellite data, the hybrid model yielded median Nash‐Sutcliffe efficiencies of 0.83 and 0.77 for dynamic and distributed simulations of streamflow and total water storage, respectively, 41% and 35% higher than those of the original HydroPy model. Replacing the original Penman‒Monteith formulation in HydroPy with a replacement NN produces more plausible potential evapotranspiration (PET) estimates, and unravels the spatial pattern of PET in this giant basin. The NN used for parameterization was interpreted to identify the factors controlling the spatial variability in key parameters. Overall, this study lays out a feasible technical roadmap for distributed hydrological modeling in the big data era.

Applicability and improvement of different potential evapotranspiration models in different climate zones of China

BackgroundAccurate estimation of potential evapotranspiration (PET) is the key for studying land-air interaction hydrological processes. Several models are used to estimate the PET based on standardized meteorological data. Although combination-based models have the highest level performance estimation of PET, they require more meteorological data and may therefore be difficult to apply in areas lacking meteorological observation data.ResultsThe results showed significant differences in the spatial trends of PET calculated by different models in China, the Doorenbots–Pruitts model revealed the highest PET (1902.6 mm), and the Kuzmin model revealed the lowest PET (349.6 mm), with the largest difference being 5.5 times. The Romanenko and the Rohwer models were the recommended temperature-based and aerodynamic-based models. On the other hand, the Abtew model was more suitable for arid and semi-arid regions, while the Priestley–Taylor model was more suitable for humid regions. Combination-based models revealed ideal calculation accuracies, among which the Penman–Monteith model was the best option for PET calculation.ConclusionsThe accuracy range of Romanenko, Rohwer, Abten, Priestley Taylor, and Penman Monteith models improved in MPZ and TCZ is higher than that improved in TMZ and SMZ. This does not mean that the improved models have higher accuracy in MPZ and TCZ than in TMZ and SMZ. On the contrary, the original model performed poorly in MPZ and TCZ, so the improved accuracy was relatively large. The unimproved model was already more suitable in TMZ and SMZ, so the improved accuracy was relatively small. Therefore, regional calibration of the PET models can improve the accuracy and applicability of PET calculation, providing a reference for studying hydrological processes in different climatic zones.

Estimations of potential evapotranspiration from CMIP6 multi-model ensemble over Africa

Potential evapotranspiration (PET) plays a pivotal role in resource management and drought assessment. However, future PET estimates remain underexplored in the African region. This study employs twenty General Circulation Models (GCMs) to estimate past (1979–2014) and future PET changes across near-term (2021–2040), mid-term (2061–2080), and long-term (2081–2100) periods, considering four Shared Socioeconomic Pathways (SSPs) including SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. The research assesses the impact of various climatic factors on PET across Africa and its sub-regions using the Penman-Monteith model. The analysis reveals that Penman-Monteith estimates for PET during 1979–2014 exhibit an increase of 0.68 mm per year (mm/a) across Africa. Notably, the Northern region (NAF), Sahara (SAH), Southern region (SAF), and Eastern region (EAF) experience higher PET changes of 1.78 mm/a, 1.75 mm/a, 1.09 mm/a, and 0.12 mm/a, respectively. Conversely, the Western region (WAF) and Central region (CAF) exhibit negative trends of −0.03 mm/a, and − 0.28 mm/a. Future PET in whole Africa is projected to increase by 0.05 mm/a in SSP1–2.6 and SSP2–4.5, and 0.07 mm/a in higher emissions for 2021–2040, by 0.02 mm/an under SSP1–2.6, 0.07 in SSP2–4.5, 0.09 mm/a, and 0.16 mm/a, in SSP3–7.0 and SSP5–8.5 for 2061–2080, and by −0.01 mm/a in SSP1–2.6, 0.05 mm/a SSP2–4.5, 0.10 mm/a SSP3–7.0, and 0.18 mm/a SSP5–8.5 for 2081–2100. Furthermore, higher emissions are anticipated to drive PET increases in various regions during 2081–2100, with NAF, SAH, and SAF projected to rise by 0.17 mm/a, 0.16 mm/a, and 0.23 mm/a, respectively. WAF, CAF, and EAF are expected to experience increases of 0.20 mm/a, 0.19 mm/a, and 0.15 mm/a, respectively. Contribution analysis indicates that solar radiation played a major factor in PET over Africa as well as in WAF, CAF, and EAF. Maximum temperatures were pivotal in NAF, SAH, and SAF. In future periods (2021-2040, 2061-2080, and 2081-2100), maximum temperatures take precedence to Africa's PET, and at varying percentages to different sub-regions. The findings underscore the significance of PET estimation, particularly in the context of drought evaluation locally and regionally.

Evaluation of ERA5, ERA5-Land, GLDAS-2.1, and GLEAM potential evapotranspiration data over mainland China

Study regionChina. Study focusAccurate estimation of potential evapotranspiration (PET) is essential for understanding climate change. Using ground-based pan evaporation measurements over continental China, the monthly scale PET data during 2000–2017 of ERA5, ERA5-Land, GLDAS-2.1/Noah, and GLEAM V3.8a are evaluated, from the perspectives of their consistency in spatiotemporal variation, and performance measures. Factors controlling the data quality of the four datasets are investigated from the perspective of their PET calculation models and meteorologically input data. New hydrological insights for the regionPETERA5 performs the best in mainland China among four gridded PET datasets with higher correlation coefficients (r) and smaller biases, which can well capture the temporal variation of Epan. The outstanding performance of PETERA5 in China mainly results from the utilization of the Penman-Monteith (P-M) equation which performs the best among several competing formulas for PET computation, as well as its better meteorological inputs for computing PET than other datasets. Although the PETERA5-Land is a replay of the land component of the ERA5 climate reanalysis, it exhibits substantial overestimation of PET values and temporal trends, particularly in coastal areas of Southern China and the eastern side of Northeastern China, mainly caused by the overestimation of its net radiation. The PETGLDAS shows significant overestimation, partly due to its overestimation of wind speed, but mostly due to its modified P-M equation with its parameterization of land surface conditions for computing PETGLDAS. The PETGLEAM underestimated PET generally mainly due to the joint effect of the use of the Priestley-Taylor equation with small P-T parameter α, and the underestimation of the net radiation input from ERA-Interim, especially in Northwest and Qinghai Tibet.

Evaluation and bias correction of CRU TS4.05 potential evapotranspiration across vast environments with limited data

Long-term and reliable gridded estimates of potential evapotranspiration (PET) are often dearth. Being the longest available dataset with no validation, this work makes an effort to answer two questions: First, how well does the Climatic Research Unit (CRU) Time-Series (TS) Version 4.05 PET (CRU-PET) dataset capture FAO Penman-Monteith grass reference evapotranspiration (ETo) across a range of environments and, second, can a simple adjustment approach be devised to curb the error, if any? The CRU-PET dataset is evaluated against ETo in two data-scarce countries, namely Sudan and South Sudan. Monthly station data measured at 12 stations spread over hyper-arid, arid, semi-arid and dry sub-humid zones are used. It is shown generally that CRU-PET tends to perform better in dry than wet conditions, and in arid than in humid locations. In this regard, CRU-PET has a tendency to overestimate (underestimate) the stations' values in the arid (humid) zone, with best performance characteristics achieved north of latitude 15° N. Rainfall plays a key role in determining the bias and has a non-linear effect. This bias is, however, unclear in rainless months when both augmented overestimates and underestimates are observed. Incorporating geographical coordinates (latitude, longitude and altitude) as co-variables with monthly rainfall in a multiple linear regression explained 40.7% of the variations in the bias. In this data-scarce study area (>2.5 million km2), this simple adjustment significantly improves the stations' CRU-PET as indicated by eight performance metrics. Furthermore, validation analysis showed a reduction of the overall mean bias error based on the 12 stations from −0.255 to 0.086 mm/day. To apply this correction method on a spatial domain, gridded precipitation data are needed. We used the Global Precipitation Climatology Centre (GPCC 8) dataset, with which 41.9% of the CRU-PET bias is explained. In conclusion, this study cautions the use of CRU-PET dataset without prior evaluation in areas with similar geographical boundaries, climatic conditions and limited availability of data.

Bias-corrected NASA data for aridity index estimation over tropical climates in Ghana, West Africa

Study regionGhana, West Africa. Study focusNASA's Prediction of Worldwide Energy Resource (NASA POWER) satellite-based reanalysis products are used for estimating the aridity index (AI) in Ghana, West Africa. The NASA estimates are compared and bias-corrected with temperature-based potential evapotranspiration estimates and rainfall data from 22 synoptic climate stations. The cumulative distribution function (CDF) matching technique was used for bias correction New Hydrological Insights for the regionThe results indicated a previous 36% over-estimation of arid conditions in dryland climates and an under-estimation of wetland climate regions by the NASA POWER data compared with the station-based estimation. Post bias-correction, the satellite-based estimates showed substantial improvements, as evidenced by a correlation coefficient of R2 = 0.87. The rectified data suggests that with accurate interpretations and calibrations, satellite-based metrics can play a pivotal role in advancing hydrological studies and water resource management in West Africa Sub-region. This insight underscores the potential of satellite data in augmenting regional hydrological research, establishing a foundation for similar studies in analogous global environments.

Improving the quantification of climate change hazards by hydrological models: a simple ensemble approach for considering the uncertain effect of vegetation response to climate change on potential evapotranspiration

Abstract. Almost no hydrological model takes into account that changes in evapotranspiration are affected by how vegetation responds to changing CO2 and climate. This severely limits their ability to quantify the impact of climate change on evapotranspiration and, thus, water resources. As the simulation of vegetation responses is both complex and very uncertain, we recommend a simple approach to considering (in climate change impact studies with hydrological models) the uncertainty that the vegetation response causes with respect to the estimation of future potential evapotranspiration (PET). To quantify this uncertainty in a simple manner, we propose running the hydrological model in two variants: with its standard PET approach and with a modified approach to compute PET. In the case of PET equations containing stomatal conductance, the modified approach can be implemented by adjusting the conductance. We introduce a modified approach for hydrological models that computes PET as a function of net radiation and temperature only, i.e., with the Priestley–Taylor (PT) equation. The new PT-MA approach is based on the work of Milly and Dunne (2016) (MD), who compared the change in non-water-stressed actual evapotranspiration (NWSAET) as computed by an ensemble of global climate models (GCMs), which simulate vegetation response as well as interactions between the atmosphere and the land surface, with various methods to compute PET change. Based on this comparison, MD proposed estimating the impact of climate change on PET as a function of only the change in net energy input at the land surface. PT-MA retains the impact of temperature on daily to interannual as well as spatial PET variations but removes the impact of the long-term temperature trend on PET such that long-term changes in future PET are driven by changes in net radiation only. We implemented PT-MA in the global hydrological model WaterGAP 2.2d and computed daily time series of PET between 1901 and 2099 using the bias-adjusted output of four GCMs. Increases in GCM-derived NWSAET between the end of the 20th and the end of the 21st century for Representative Concentration Pathway 8.5 (RCP8.5) are simulated well by WaterGAP if PT-MA is applied but are severely overestimated with the standard PT method. Application of PT-MA in WaterGAP results in smaller future decreases or larger future increases in renewable water resources (expressed as the variable RWR) compared with the standard PT method, except in a small number of grid cells where increased inflow from upstream areas due to increased upstream runoff leads to enhanced evapotranspiration from surface water bodies or irrigated fields. On about 20 % of the global land area, PT-MA leads to an increase in RWR that is more than 20 % higher than in the case of standard PT, while on more than 10 % of the global land area, the projected RWR decrease is reduced by more than 20 %. While the modified approach to compute PET is likely to avoid the overestimation of future drying in many if not most regions, the vegetation response in other regions may be such that the application of the standard PET leads to more likely changes in PET. As these regions cannot be identified with certainty, the proposed ensemble approach with two hydrological model variants serves to represent the uncertainty in hydrological changes due to the vegetation response to climate change that is not represented in the model.

Meteorological and hydrological drought analysis of Sinop, Kastamonu, Bartın provinces in the Western Black Sea

In recent years, a significant change has been observed in the climate characteristics of Turkey. These changes, which are compatible with the general trend of global climate change, are also felt in the Western Black Sea Basin. This paper reported on the calculation of drought indices, with emphasis on two recently developed indices, the Reconnaissance Drought Index (RDI) and the Stream flow Drought Index (SDI) using specialized software package, named Drin C (Drought Indices Calculator). Additionally, Drin C includes a module for the estimation of potential evapo transpiration (PET) through temperature-based methods (Hargreaves, Blaney - Criddle, and Thornthwaite) that can be used for the calculation of RDI. Therefore, in this study carried out in the Western Black Sea region, meteorological drought analyzes for1-, 3-, 6- ve 12 months were made for 3 precipitation observation stations in the provinces of Sinop, Kastamonu, Bartın, and monthly total precipitation and monthly average temperature data were used to determine the meteorological drought.

Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiration

Reliable projection of evapotranspiration (ET) is important for planning sustainable water management for the agriculture field in the context of climate change. A global dataset of monthly climate variables was generated to estimate potential ET (PET) using 14 General Circulation Models (GCMs) for four main shared socioeconomic pathways (SSPs). The generated dataset has a spatial resolution of 0.5° × 0.5° and a period ranging from 1950 to 2100 and can estimate historical and future PET using the Penman-Monteith method. Furthermore, this dataset can be applied to various PET estimation methods based on climate variables. This paper presents that the dataset generated to estimate future PET could reflect the greenhouse gas concentration level of the SSP scenarios in latitude bands. Therefore, this dataset can provide vital information for users to select appropriate GCMs for estimating reasonable PETs and help determine bias correction methods to reduce between observation and model based on the scale of climate variables in each GCM.

A Thorough Evaluation of 127 Potential Evapotranspiration Models in Two Mediterranean Urban Green Sites

Potential evapotranspiration (PET) is a particularly important parameter for understanding water interactions and balance in ecosystems, while it is also crucial for assessing vegetation water requirements. The accurate estimation of PET is typically data demanding, while specific climatic, geographical and local factors may further complicate this task. Especially in city environments, where built-up structures may highly influence the micrometeorological conditions and urban green sites may occupy limited spaces, the selection of proper PET estimation approaches is critical, considering also data availability issues. In this study, a wide variety of empirical PET methods were evaluated against the FAO56 Penman–Monteith benchmark method in the environment of two Mediterranean urban green sites in Greece, aiming to investigate their accuracy and suitability under specific local conditions. The methods under evaluation cover all the range of empirical PET estimations: namely, mass transfer-based, temperature-based, radiation-based, and combination approaches, including 112 methods. Furthermore, 15 locally calibrated and adjusted models have been developed based on the general forms of the mass transfer, temperature, and radiation equations, improving the performance of the original models for local application. Among the 127 (112 original and 15 adjusted) evaluated methods, the radiation-based methods and adjusted models performed overall better than the temperature-based and the mass transfer methods, whereas the data-demanding combination methods received the highest ranking scores. The adjusted models seem to give accurate PET estimates for local use, while they might be applied in sites with similar conditions after proper validation.

Estimating daily potential evapotranspiration using GNSS-based precipitable water vapor

Potential evapotranspiration (PET) is a crucial component of the hydrological cycle and energy balance. Although the Penman-Monteith (PM) model is the most widely used method to estimate daily PET, it requires temperature, relative humidity, solar radiation, and wind speed. In Thailand, the number of potential weather stations to provide the required data is limited, which resulted in the absence of some input variables in many locations. The objective of this study is to develop the revised potential evapotranspiration (RPET) model to estimate daily PET using Global Navigation Satellite System-derived Precipitable Water Vapor (GNSS-PWV) and temperature data. The multiple linear regression analysis was used to develop and validate the RPET model. The performance of the RPET model along with the Global Land Evaporation Amsterdam Model (GLEAM v3.2 b) and the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5-Land) products was investigated using the PM model. The results revealed that the RPET model showed a strong correlation with the PM model (r = 0.85, RMSE = 0.97 mm day−1, RSR = 0.53, NSE = 0.72) under limited meteorological inputs. The RPET model performance was superior when compared to GLEAM and ERA5-Land (r = 0.80, RMSE = 1.06 mm day−1). Therefore, the proposed model is greatly suitable for daily PET estimation with only required GNSS-PWV and temperature data, and this can be implemented for drought assessment and water resources management.

Deep learning for daily potential evapotranspiration using a HS-LSTM approach

Accurate estimation of potential evapotranspiration (ET0) is important for the sound design of irrigation schedules, management of water resources, assessment of hydrological drought, and research on atmospheric variations. The present study proposed a novel deep learning (DL) approach for daily ET0 estimations with limited daily climate data: HS- LSTM. This approach was constructed based on a classic ET0 model and a long short-term memory neural network (LSTM). Specifically, the Hargreaves-Samani (HS) model was employed as the classic model, and the predictors were restricted to the daily maximum and minimum air temperature data. Ground truth data for ET0 were employed to train, validate, and test the models. Traditional machine learning (ML) algorithms comprising adaptive neuro-fuzzy inference system (ANFIS), genetic programming (GP), multi-gene genetic programming (MGGP), and one-dimensional CNN (1D-CNN), as well as the HS-ML models (HS-ANFIS, HS-GP, HS-MGGP, HS-1D-CNN), were also established and assessed for daily ET0 estimations. Compared to the other tested approaches, the errors of the HS-LSTM technique significantly decreased, demonstrating that the novel HS-LSTM approach significantly outperformed the other techniques beyond the study area (in Songliao Basin, Northeast China, which is a semi-humid zone with temperate continental climate). The developed models can then be used to estimate future ET0 with only air temperature forecasts, which can be readily obtained from public weather forecasts. The present study provides a new and promising strategy that can provide more accurate estimations of daily ET0 with limited meteorological data, along with significant implications for enhancing atmospheric research.

A modified temperature based model for estimation of potential evapotranspiration over Ghataprabha river basin, south India

A modified temperature based model for estimation of potential evapotranspiration over Ghataprabha river basin, south India

Tracer‐aided ecohydrological modelling across climate, land cover, and topographical gradients in the tropics

AbstractQuantitative estimations of ecohydrological water partitioning into evaporation and transpiration remains mostly based on plot‐scale investigations that use well‐instrumented, small‐scale experimental catchments in temperate regions. Here, we attempted to upscale and adapt the conceptual tracer‐aided ecohydrology model STARRtropics to simulate water partitioning, tracer, and storage dynamics over daily time steps and a 1‐km grid larger‐scale (2565 km2) in a sparsely instrumented tropical catchment in Costa Rica. The model was driven by bias‐corrected regional climate model outputs and was simultaneously calibrated against daily discharge observations from 2 to 30 years at four discharge gauging stations and a 1‐year, monthly streamwater isotope record of 46 streams. The overall model performance for the best discharge simulations ranged in KGE values from 0.4 to 0.6 and correlation coefficients for streamflow isotopes from 0.3 to 0.45. More importantly, independent model‐derived transpiration estimates, point‐scale residence time estimates, and measured groundwater isotopes showed reasonable model performance and simulated spatial and temporal patterns pointing towards an overall model realism at the catchment scale over reduced performance in the headwaters. The simulated catchment system was dominated by low‐seasonality and high precipitation inputs and a marked topographical gradient. Climatic drivers overrode smaller, landcover‐dependent transpiration fluxes giving a seemingly homogeneous rainfall‐runoff dominance likely related to model input bias of rainfall isotopes, oversimplistic Potential Evapotranspiration (PET) estimates and averaged Leaf Area Index (LAI). Topographic influences resulted in more dynamic water and tracer fluxes in the headwaters that averaged further downstream at aggregated catchment scales. Modelled headwaters showed greater storage capacity by nearly an order of magnitude compared to the lowlands, which also favoured slightly longer residence times (>250 days) compared to superficially well‐connected groundwater contributing to shorter streamflow residence times (<150 days) in the lowlands. Our findings confirm that tracer‐aided ecohydrological modelling, even in the data‐scarce Tropics, can help gain a first, but crucial approximation of spatio‐temporal dynamics of how water is partitioned, stored and transported beyond the experimental catchment scale of only a few km2.

Spatiotemporal variation of potential evapotranspiration and meteorological drought based on multi-source data in Nepal

Quantifying the spatiotemporal dynamic of potential evapotranspiration (PET) in topographically complex country are often limited by the scarcity and difficulty in obtaining ground-based measured climate data. Remote sensing products have multiple advantage, which allows rapid acquisition of information and status over large areas and long time series. This study compared and explored the spatiotemporal variation of PET across Nepal using three widely-used remote sensing and reanalysis data PET products (CRU_PET, MODIS_PET and PML_PET), and PET estimation using Hargreaves-Samani method based on CHIRTS datasets. On the basis of this, the spatiotemporal dynamic at seasonal and annual level and the periodical change of PET and meteorological aridity index AI (precipitation P/PET) were identified using Mann-Kendall test and Morlet Wavelet method. These PET products showed general spatial consistency over most areas in Nepal with higher PET in the south and less in the north. However, great differences were also found in PET values for four different products. Compared with the three remote sensing and reanalysis data products, CHIRTS_PET performs better in Nepal. The AI based on CHIRTS_PET showed an insignificant increasing (wetting) trend in the western and southern regions, while an insignificant decreasing (drying) trend in the central and northeastern region, which is generally consistent with the spatial pattern of precipitation. In the western and southern regions, the positive impact of increasing precipitation on AI masked the negative effect of increasing PET, and thus making this region getting humid. However, in the central and northeastern region, the increase in PET aggravates the impact of reduced precipitation on the drying trend. Our investigation have broad implications for sustainable water resources management under climate change in a topographically complex country like Nepal.

Evaluation of different methods to estimate monthly reference evapotranspiration at Raichur region, India

The potential evapotranspiration (PET) is an important parameter to address water needs for growing crops. Estimation of potential evapotranspiration (PET) requires knowledge of the values of many climatic variables, some of which require special equipment and careful observations. Although potential evapotranspiration is an important component of water balance, the data required for its accurate estimation are commonly available only at widely spaced measurement stations. Numerous potential evapotranspiration models have been developed and are being used, depending upon the availability of weather data. For the proposed study, the daily data of all weather parameters viz., maximum and minimum air temperature, maximum and minimum relative humidity, wind speed, actual sunshine hours and rainfall for estimating potential evapotranspiration for the duration of 31 years (1991-2021) were collected from the weather station at Main Agricultural Research Station (MARS), Raichur. A number of empirical formulae or approaches have been used for the determination of potential evapotranspiration (PET) from meteorological data. Availability of climatic data and accurately converting them in terms of water requirement are of great constraints. Judging the accuracy of different PET estimation models is a difficult task. The FAO 56-Penman Monteith model was considered as a standard reference for PET estimation in this investigation over the study location. The objective was to compare various PET models with standard reference model (FAO 56 Penman-Monteith model) and choose the best suitable model for estimating PET for the study area.

Development and Application of ETCalc, a Unique Online Tool for Estimation of Daily Evapotranspiration

ABSTRACT Various empirical methods that use meteorological data have been developed for estimating evapotranspiration. However, there are currently no online tools available for the estimation of daily evapotranspiration based on user-provided daily data. Here, we introduce ETCalc (https://etcalc.hydrotools.tech), a free, unique online tool that integrates eight methods (i.e. Penman-Monteith, Thornthwaite, Blaney – Criddle, Turc, Priestley – Taylor, Hargreaves, Jensen – Haise and Abtew) for estimation of daily potential evapotranspiration, reference evapotranspiration and, by employing user-defined crop (or cover) coefficients, daily actual evapotranspiration, based on user-provided daily meteorological data. ETCalc has been developed in response to the effort of the Canadian federal government to encourage easier and open access to science and is applicable to any area for which basic meteorological data are available and hence, its suitability is not restricted to particular geographical areas. Through a streamlined interface, ETCalc allows for uploading of user-provided data, tabular and graphical inspection of the input and output data, as well as export of the output data and of the associated metadata. The use of ETCalc is exemplified using 10-year daily meteorological data from Charlottetown, Prince Edward Island, Canada for comparing the output from each of the ETCalc methods and for the calculation of the precipitation deficit.

Hydrological and water resources investigations of the Mereb-Gash river basin in Eritrea

Introduction. Adaptation and mitigation of climate change and its consequences through effective water resources management, among others, is essential for achieving sustainable development goals. The construction of water infrastructure facilities and the maintenance of existing ones which can be done with up-to-date hydrological information are required. Therefore, this study aims at obtaining unique information and choosing effective tools for stream flow simulations in Mereb-Gash river basin.
 
 Materials and methods. To this end, physically-based semi-distributed and conceptual models are investigated using climate reanalysis datasets and geomorphologic catchment characteristics. Prior to feeding model forcing variables, their statistical and spatial patterns, methods of potential evapotranspiration estimations, and basin drought conditions are studied. Two or more approaches have been employed for each of these tasks.
 
 Results. GIUH-Nash model has the potential to produce acceptable values in most cases irrespective of sources and resolutions of digital elevation models, but it is found to be sensitive to the type of algorithms selected for stream network generations. The continuous simulation models reveal considerable overestimation of most of the precipitation in the reanalysis datasets, which in turn has a significant effect on other variables, such as potential evapotranspiration and thereby leading to a substantial discrepancy between simulated and observed.
 
 Conclusions. Despite the fact that reanalysis dataset has a great advantage over ground-based observations in terms of their simplicity and accessibility, the research results have shown the need for a critical attitude to both sources of initial data. To minimize the uncertainties of mathematical models and thereby to improve modeling capabilities, there is no qualitative alternative to restoring existing as well as new ground-based observation stations.

A robust ensemble drought index: construction and assessment

Drought indices have been widely used in drought monitoring and assessments, but it is difficult to select a uniform index suitable for different geographical and climatic conditions. In addition, the statistical parameters of drought index also vary with fitting reference period, which brings more challenges to the practicality of drought index under climate change. In this study, a new multitimescale and more robust integrated drought index, the EDI was constructed by integrating the common components of seven single drought indices (including SPI, PDSI_th, PDSI_pm, scPDSI_th, scPDSI_pm, SPEI_th and SPEI_pm). The EDI was compared with the single drought index over China in terms of the occurrence ability of historical drought events, the dependence of statistical parameters on reference periods, the goodness-of-fit of standard normal distribution and the correlation with soil moisture and runoff. We find that the EDI could more accurately reproduce the location and intensity of historical drought events, reduce the uncertainty caused by both potential evapotranspiration estimation and the reference period, has higher goodness-of-fit of standard normal distribution, higher correlation with soil moisture, more robust performance in hydrologic drought monitoring and less affected by the difference of geographical and climatic conditions. Moreover, the EDI improved the accuracy of drought monitoring in arid and semiarid regions of China, where assessment is always complex. Furthermore, the available evidence supports that a long-term stationary climate variables series could provide more accurate drought assessment.

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.