Evapotranspiration Inputs Research Articles

Revisiting evapotranspiration inputs in eco-hydrological modeling for climate change assessment

Evapotranspiration (ET) is an essential variable linking hydrological and ecological processes and is typically modeled as a function of potential evapotranspiration and soil moisture in traditional hydrological models. However, commonly used empirical ET models typically do not recognize the underlying vegetation dynamics. This can have implications when models are extrapolated under future climate change. In this study, the traditional HYMOD-BVM (THV) eco-hydrological model is adopted as a benchmark model. A modified HYMOD-BVM (MHV) is developed using an actual ET substitute for the traditional PET input to consider vegetation dynamics under climate change. The THV and MHV models are compared to evaluate how streamflow (Q) and leaf area index (LAI) vary under future climate in the Florentine River (FR, energy-limited) catchment and Murray River (MR, water-limited) catchment in Australia. Six global climate models (GCMs) from the latest Coupled Model Intercomparison Project (CMIP6) are bias-corrected and employed in the calibrated THV and MHV models, and ensemble mean results are analyzed. Results suggest that the streamflow projected by the MHV model tends to be higher than that from the THV model, while LAI presents an opposite trend. The energy-limited catchment appears more susceptible to climate change whereas larger vegetation effects are seen in the water-limited catchment. Overall, our research highlights the effects of evapotranspiration on future climate change scenario analysis and prompts the need for the development of spatial and temporal continuously accurate ET models for both current and future climates.

Estimating evapotranspiration using earth observation data: A comparison between hydrological and energy balance modelling approaches

• Earth observation data is a useful alternate data source to hydrological models. • The spatial resolution of satellite data affects the accuracy of evapotranspiration. • The Surface Energy Balance System model outperformed the ACRU model. • Hydrological models should be developed to allow actual evapotranspiration input. Hydrological models are often applied to acquire estimates of evapotranspiration ( ET a ) using quality inputs of reference evapotranspiration ( ET 0 ). However, their application may be limited by the need for good quality long-term data inputs. In data scarce regions, the use of satellite-earth observation data as inputs to these models may provide a potential solution to overcome data acquisition challenges associated with conventional data collection approaches. In this study, the Agricultural Catchments Research Unit (ACRU) hydrological model was applied to estimate the ET a using both in-situ and satellite-derived estimates of ET 0 as inputs to the model. The simulated ET a estimates were then evaluated through comparisons against field-based measurements of ET a acquired from an Eddy Covariance system ( EC ET ), as well as from the application of the Surface Energy Balance System (SEBS) model. The results of these investigations demonstrated that the meteorologically-based ACRU ET a simulations marginally outperformed the satellite-based ACRU ET a simulations. While these results highlight the influence which the quality of ET 0 inputs may have on the accuracy of the ACRU derived ET a estimates, the concurrent application and evaluation of SEBS demonstrated that alternate approaches to hydrological modelling, may be better suited for ET a estimation in certain instances.

Assessing future runoff changes with different potential evapotranspiration inputs based on multi-model ensemble of CMIP5 projections

• Xinanjiang model performed well in runoff simulation in North Johnstone catchment. • Runoff projected with different ETp inputs showed similar changes in the future. • Runoff in spring and winter would decrease in the future. • GCMs and RCPs were major factors resulting in uncertainty in runoff projections. Runoff projection under future climate scenarios has been widely studied to investigate the impacts of climate change on regional water availability. However, uncertainty in runoff projection due to different ETp inputs has not been fully assessed. This study firstly adopted the physically-based Penman model, temperature-based Hargreaves model, and radiation-based Abtew, Jensen-Haise, and modified Makkink models to drive Xinanjiang (XAJ) model, thus investigating the influence of different potential evapotranspiration (ETp) inputs on runoff simulation. Then, we used the validated XAJ model to project runoff in North Johnstone catchment, northeast Australia. Lastly, we quantified the uncertainty caused by 34 global climate models (GCMs), different representative concentrative pathway (RCP) scenarios (RCP4.5 & RCP8.5), and different ETp models with the technique of three-way analysis of variance (ANOVA). We found that XAJ model performed well (R 2 ≥ 0.88, NSE ≥ 0.86) and showed low sensitivity to different ETp inputs in runoff simulation and projection. Under future climate scenarios, spring and winter runoff had a large decrease, which was mainly caused by the decrease in rainfall. The mean decreases in spring and winter runoff were 14.6% – 20.1% and 10.3% – 15.2% respectively by 2090s under RCP8.5. GCMs (50.9% – 67.4%) and their interaction with RCPs (35.4% – 46.6%) were the dominant factors resulting in uncertainty in runoff projection. Our study not only advanced the understanding of the impacts of different ETp inputs on runoff projection but also offered insights on the understanding of the roles different factors played in the uncertainty in runoff projection. We expect such knowledge can provide a way forward to narrow down the uncertainty in runoff projection, thus providing more robust information for policy makers in water management.

Using Artificial Neural Network to Model Water Discharge and Chemistry in a River Impacted by Acid Mine Drainage

In southeast Ohio, Raccoon Creek Watershed (RC) has an extensive mining history resulting in acid mine drainage (AMD) and subsequent environmental problems. Modeling of the discharge and chemistry of AMD impacted Hewett Fork, a tributary of Raccoon Creek, is the focus of this paper. Discharge measurements are collected by the United States Geological Survey (USGS) gage station at the Bolin Mills (BM) station on the main stem of RC. This data for the period 2011-2019 has been analyzed to develop a prediction model for BM discharge and subsequently use the model to predict flow and water chemistry in Hewett Fork (HF). The Neural Network Model using group method of data handling (GMDH) and generalized regression neural network (GRNN) shows a variation of prediction models for BM due to parameters such as decay factor in API and ATI, as well as in the evapotranspiration input variables of the model. However, the study reveals that the GRNN model for BM is the most suitable for BM prediction based on its performance, with an r-value greater than 0.90, and its ease in predicting discharge by specifying a data set to be added to the data set for training and calibrating the network. The result of the water chemistry model using GMDH, with r values greater than 0.80 for each model, shows the input variables have a good capacity to predict chemical concentration/load in the HF stream. This study shows that Artificial Neural Network (ANN) modeling can help to model successfully and predict flow and chemical evolution of rivers in Ohio and other parts of the world.

Assessing the impact of PET estimation methods on hydrologic model performance

Abstract Evapotranspiration is a necessary input and one of the most uncertain hydrologic variables for quantifying the water balance. Key to accurately predicting hydrologic processes, particularly under data scarcity, is the development of an understanding of the regional variation of the impact of potential evapotranspiration (PET) data inputs on model performance and parametrization. This study explores this impact using four different potential evapotranspiration products (of varying quality). For each data product, a lumped conceptual rainfall–runoff model (GR4J) is tested on a sample of 57 catchments included in the MOPEX data set. Monte Carlo sampling is performed, and the resulting parameter sets are analyzed to understand how the model responds to differences in the forcings. Test catchments are classified as energy- or water-limited using the Budyko framework and by eco-region, and the results are further analyzed. While model performance (and parameterization) in water-limited sites was found to be largely unaffected by the differences in the evapotranspiration inputs, in energy-limited sites model performance was impacted as model parameterizations were clearly sensitive to evapotranspiration inputs. The quality/reliability of PET data required to avoid negatively impacting rainfall–runoff model performance was found to vary primarily based on the water and energy availability of catchments.

Vegetation optical depth at L-band and above ground biomass in the tropical range: Evaluating their relationships at continental and regional scales

The relationship between vegetation optical depth (VOD) retrieved by L-band SMOS radiometer and forest above ground biomass (AGB) was investigated in tropical areas of Africa and South America. VOD was retrieved from the latest version of level 2 SMOS algorithm, while reference AGB was obtained from a pantropical database, encompassing a large number of ground plot data derived from field surveys conducted on both continents. In Africa and South-America, VOD increased with AGB, reaching saturation at about 350 Mg ha−1. The strength of the relation was improved selecting VOD data in appropriate seasons, characterized by a higher dynamic range of values. The capability of VOD data to estimate AGB was further investigated using Random Forest decision trees, adding to VOD selected climate variables from the Climatic Research Unit (temperature, potential evapotranspiration, and precipitation) and water deficit data, and validating regression tests with ground data from the reference AGB database. The results for the five analyzed years indicate that the best estimates of AGB are obtained by the joined use of VOD and potential evapotranspiration input data, but all climate variables brought an improvement in AGB estimates. AGB estimates were relatively stable for the considered period, with limited variations possibly due to changes in biomass and to data quality of VOD and of climate variables. The VOD signal and estimated AGB were also analyzed according to ecological homogeneous units (ecoregions), evidencing data clusters, partially overlapped to each other, in the VOD - AGB plane.

Water budget, performance of evapotranspiration formulations, and their impact on hydrological modeling of a small boreal peatland-dominated watershed

Peatlands occupy around 13% of the land cover of Canada, and thus they play a key role in the water balance at high latitudes. They are well known for having substantial water loss due to evapotranspiration. Since measurements of evapotranspiration are scarce over these environments, hydrologists generally rely on models of varying complexity to evaluate these water exchanges in the global watershed balance. This study quantifies the water budget of a small boreal peatland-dominated watershed. We assess the performance of three evapotranspiration models in comparison with in situ observations and the impact of using these models in the hydrological modeling of the watershed. The study site (∼1 km2) is located in the eastern James Bay lowlands, Québec, Canada. During summer 2012, an eddy flux tower measured evapotranspiration continuously, while a trapezoidal flume monitored streamflow at the watershed outlet. We estimated evapotranspiration with a combinational model (Penman), a radiation-based model (Priestley–Taylor), and a temperature-based model (Hydro-Québec), and performed the hydrological modeling of the watershed with HYDROTEL, a physically based semi-distributed model. Our results show that the Penman and Priestley–Taylor models reproduce the observations with the highest precision, while a substantial drop in performance occurs with the Hydro-Québec model. However, these discrepancies did not appear to reduce the hydrological model efficiency, at least from what can be concluded from a 3-month modeling period. HYDROTEL appears sensitive to evapotranspiration inputs, but calibration of model parameters can compensate for the differences. These findings still need to be confirmed with longer modeling periods.

Assessment of the Influences of Different Potential Evapotranspiration Inputs on the Performance of Monthly Hydrological Models under Different Climatic Conditions

AbstractPotential evapotranspiration (PET), which determines the upper limit of actual evapotranspiration (AET), is a necessary input in monthly hydrological models. In this study, the sensitivities of monthly hydrological models to different PET inputs are investigated in 37 catchments under different climatic conditions. Four types of PET estimation methods (i.e., Penman–Monteith, Hargreaves–Samani, Jensen–Haise, and Hamon) give significantly different PET values in the 37 catchments. However, similar runoff simulations are produced based on different PET inputs in both nonhumid and humid regions. It is found that parameter calibration of the hydrological model can eliminate the influences of different PET inputs on runoff simulations in both nonhumid and humid regions. However, the influences of parameter calibration on the simulated water balance components, including AET and water storage change (WSC), are different in nonhumid and humid regions. In nonhumid regions, simulated runoff, AET, and WSC are similar using different PET inputs. In humid regions, simulated AET and WSC using different PET inputs are significantly different, and simulated runoff and the sums of AET and WSC are similar to each other. It is suggested that the choice of PET inputs for monthly hydrological models be based on the selected region and the relevant hydrological practices. In runoff modeling, different PET inputs can produce similar runoff simulations in both nonhumid and humid regions. However, when estimating AET and WSC in humid regions, different PET inputs will result in significantly different AET and WSC simulations, which should be noted by model users.

Comparison of an artificial neural network and a conceptual rainfall–runoff model in the simulation of ephemeral streamflow

ABSTRACTThe rainfall–runoff process is governed by parameters that can seldom be measured directly for use with distributed models, but are rather inferred by expert judgment and calibrated against historical records. Here, a comparison is made between a conceptual model (CM) and an artificial neural network (ANN) for their ability to efficiently model complex hydrological processes. The Sacramento soil moisture accounting model (SAC-SMA) is calibrated using a scheme based on genetic algorithms and an input delay neural network (IDNN) is trained for variable delays and hidden layer neurons which are thoroughly discussed. The models are tested for 15 ephemeral catchments in Crete, Greece, using monthly rainfall, streamflow and potential evapotranspiration input. SAC-SMA performs well for most basins and acceptably for the entire sample with R2 of 0.59–0.92, while scoring better for high than low flows. For the entire dataset, the IDNN improves simulation fit to R2 of 0.70–0.96 and performs better for high flows while being outmatched in low flows. Results show that the ANN models can be superior to the conventional CMs, as parameter sensitivity is unclear, but CMs may be more robust in extrapolating beyond historical record limits and scenario building.EDITOR M.C. Acreman; ASSOCIATE EDITOR not assigned

Impact of potential and (scintillometer-based) actual evapotranspiration estimates on the performance of a lumped rainfall–runoff model

Abstract. Evapotranspiration (ET) plays a key role in hydrological impact studies and operational flood forecasting models as ET represents a loss of water from a catchment. Although ET is a major component of the catchment water balance, the evapotranspiration input for rainfall–runoff models is often simplified in contrast to the detailed estimates of catchment averaged precipitation. In this study, an existing conceptual rainfall–runoff model calibrated for and operational in the Bellebeek catchment in Belgium firstly has been validated and its sensitivity to different available potential ET input has been studied. It has been shown that when applying a calibrated rainfall–runoff model, the model input should be consistent with the input used for the calibration process, not only on the volume of ET, but also on the seasonal pattern. Secondly, estimates of the actual evapotranspiration based on measurements of a large aperture scintillometer (LAS) have been used as model forcing in the rainfall–runoff model. From this analysis, it has been shown that the actual evapotranspiration is a crucial factor in simulating the catchment water balance and the resulting stream flow. Regarding the actual evapotranspiration estimates from the LAS, it has been concluded that they can be considered realistic in summer months. In the months where stable conditions prevail (autumn, winter and (early) spring), an underestimation of the actual evapotranspiration is made, which has an important impact on the catchment's water balance.

Recharge estimation methodologies employed by the Environment Agency of England and Wales for the purposes of regional groundwater resource modelling

Abstract Recharge calculations based on daily soil moisture balance models define the resource available for most regional-scale groundwater models used by the environmental regulator in England and Wales. A switch in recent years from the Penman–Grindley methodology to the Food and Agricultural Organization approach has improved the representation of soil properties in these calculations. A new Meteorological Office algorithm for calculating potential evapotranspiration inputs has also been adopted and rain-gauge data processing on individual models are currently being streamlined towards the use of nationally derived grids. A range of infiltration, bypass and runoff–recharge mechanisms have been conceptualized and modelled incorporating simple representation of unsaturated zone storage and flow processes. This paper reviews the recent changes adopted and considers future challenges.

Evaluation of Climate Input Biases and Water Balance Issues Using a Coupled Surface–Subsurface Model

A physically based, coupled and distributed hydrologic model has been set up for the Ringkøbing Fjord catchment, Denmark. This transient model, built with the MIKE SHE/MIKE 11 code, comprises all major components of the terrestrial water cycle, including a three‐dimensional finite‐difference groundwater model. The dynamic coupling of the hydrologic processes secures physically sound feedback and makes the model an ideal tool for evaluating the overall water balance and quantifying potential water balance issues. Historically, failure to obtain water balance closure has been a persistent and much debated problem in Denmark, presumably arising mainly from uncertainties in precipitation, actual evapotranspiration, and groundwater flow to the sea. In this study, the water balance issues were addressed within the modeling framework through analysis of different rain gauge catch corrections and potential evapotranspiration input. The analysis focused on the effect of different rain gauge catch corrections on the model performance, the optimized parameter sets, and state variables not included in the model calibration. The results suggest that water balance problems can be reduced by using a dynamic rain gauge catch correction based on daily wind speed and temperature fields. The model optimization and performance evaluation revealed, however, that several parameter sets gave similar performance compared with the observed groundwater head and river discharge data but still resulted in significant differences with respect to internal water fluxes such as evapotranspiration, groundwater recharge, and stream flow components. New observational data are needed to constrain the model further and thus reduce the water balance uncertainties convincingly.

Which potential evapotranspiration input for a lumped rainfall–runoff model?: Part 2—Towards a simple and efficient potential evapotranspiration model for rainfall–runoff modelling

This research sought to identify the most relevant approach to calculate potential evapotranspiration (PE) for use in a daily rainfall–runoff model, while answering the following question: How can we use available atmospheric variables to represent the evaporative demand at the basin scale? The value of 27 PE models was assessed in terms of streamflow simulation efficiency over a large sample of 308 catchments located in France, Australia and the United States. While trying to identify which atmospheric variables were the most relevant to compute PE as input to rainfall–runoff models, we showed that the formulae based on temperature and radiation tend to provide the best streamflow simulations. Surprisingly, PE approaches based on the Penman approach seem less advantageous to feed rainfall–runoff models. This investigation has resulted in a proposal for a temperature-based PE model, combining simplicity and efficiency, and adapted to four rainfall–runoff models. This PE model only requires mean air temperature (derived from long-term averages) and leads to a slight but steady improvement in rainfall–runoff model efficiency.

Which potential evapotranspiration input for a lumped rainfall-runoff model?: Part 1—Can rainfall-runoff models effectively handle detailed potential evapotranspiration inputs?

Evapotranspiration is a major component of the catchment water balance and potential evapotranspiration (PE) data should therefore be a key input to rainfall-runoff models. Nevertheless, it is common to use mean PE (i.e. the same seasonally variable PE, identically repeated each year) instead of temporally varying PE as input to rainfall-runoff models, due to the scarcity of meteorological data. This article investigates the validity of using mean PE instead of temporally varying PE as input to four different daily rainfall-runoff models. The investigation focuses on Penman PE estimates. The value of PE inputs for rainfall-runoff modelling is assessed in terms of streamflow simulation efficiency over a large sample of 308 catchments located in France, Australia and the United States. We found no systematic improvements in the rainfall-runoff model efficiencies when using temporally varying PE instead of mean PE, and we conclude that the insensitivity of rainfall-runoff models to detailed PE knowledge may bring into question the very concept of PE.

Climatic variability of soil water in the American Midwest: Part 1. Hydrologic modeling

A conceptual hydrologic rainfall-runoff model that is an adaptation of the operational US National Weather Service hydrologic model is used to simulate the hydrologic processes in large basins of the US upper Mississippi region. In particular, the conceptual rainfall-runoff model is used to produce daily streamflow from daily rainfall, temperature and potential evapotranspiration input over three neighboring headwater basins that span 2° longitude by 2° latitude. When used for simulation of historical flows, the model provides a means of inference of the 40 year time series of unrecorded mesoscale soil water and actual evapo-transpiration for climate studies. In this paper we discuss issues associated with parameter estimation, the reliability and stability of parameter estimates, and the interpretation of soil water estimates. It is concluded that the conceptual hydrologic model may be used to estimate the variability of aggregate soil water over large areas of the Midwestern US provided that: (a) all significant basin inflows and outflows are accounted for; (b) model verification yields good agreement between observed and simulated flows on a daily basis. Parameter sensitivity studies showed that estimating the soil's capacity to hold water is most important for flood event prediction and flow simulation, and, for such parameters, underestimation is more critical than overestimation. Also, uncertainty associated with the parametrization of evapo-transpiration may introduce local errors in the time series of soil water estimates produced by the model. In a companion paper we present a spatio-temporal analysis of the estimated time series of soil water.

Land-use changes in the Balquhidder catchments simulated by a daily streamflow model

The development of a model for the simulation of streamflow using only rainfall and evapotranspiration inputs at daily intervals presents special difficulties when using the data from the Kirkton and Monachyle catchments. The complex vegetation distribution in the Monachyle and the upper areas of the Kirkton, and the problems associated with the winter dormant grass made it necessary to modify the conceptual model which has been developed at the Institute of Hydrology for estimating the effects of land-use changes. Despite these difficulties, simulation of the streamflow in the calibration period produced volume agreement with observed values within 1% on the Monachyle and 4% on the Kirkton, and close agreement in the time distribution except during the few major snow accumulation episodes. Cumulative water use predicted by the model was in reasonable agreement with the catchment water balance estimates. A comparison of model estimates of soil moisture deficits with those observed by soil moisture neutron probe during plot studies produced a good correlation, providing further evidence of the validity of the model concepts. During the subsequent period, when the land use was being changed, the model also performed well. Simulating these changes in the model produced streamflow volume agreement with observations within 0.2% in the Monachyle and 0.8% in the Kirkton. This performance gave confidence in the use of the model to generate extended data sets for use in assessing the effects on water resources of various levels of afforestation in this environment.