Overestimation Of Evapotranspiration Research Articles

Estimation and Validation of Actual Evapotranspiration (ETa) of Maize Wheat Cropping System Using SSEBop Model Over IARI Research Farm, New Delhi, India

Evapotranspiration (ET) is an indispensable component of earth surface energy exchange studies and forms basis for various environmental applications. The present study attempts to estimate Landsat-8 satellite-based spatial distribution of crop ET using Operational Simplified Surface Energy Balance (SSEBop) model and validate through ET estimated using multilevel micrometeorological tower-based Bowen Ratio Energy Balance (BREB). The study was conducted over Indian Agricultural Research Institute farm during the summer (kharif maize) and winter (wheat) season of 2017–2018. The result of BREB showed the intra-seasonal variations in actual ET estimation ranging from maximum ET of 3.84 mm day−1 and 2.64 mm day−1 for maize and wheat during mid-stage to minimum ET of 1.88 mm day−1 for maize in maturity and 1.14 mm day−1 in an initial stage of wheat. It was observed that SSEBop slightly overestimated the crop ET; however, it showed the good agreement with BREB estimated ET (R2 = 0.76) with d-index of 0.92 and root-mean-square error of 0.48 mm day−1. It can be concluded that despite slight overestimation of ET by SSEBop, satellite-based SSEBop model can be recommended for crop ET estimation over large area and can be used for various water management studies.

Penman and Thornwait Equations for Estimating Reference Evapotranspiration Under Semi-Arid Environment

The estimation of reference evapotranspiration (ETo) is required for effective development and management of agriculture water systems. In order to define the most accurate method to estimate ETo in semi-arid climatic environment of Faisalabad, Lahore and Peshawar. Penman and Thornwait ETo methods are compared with standard Penman-Monteith (PM) ETo method. The statistical results show that Penman ETo method overestimates PM ETo method in all the semi-arid climatic regions of Faisalabad, Lahore and Peshawar by 34.91%, 39.51% and 30.75%, respectively. The R2 were 0.98, 0.98 and 0.99 at Faisalabad, Lahore and Peshawar weather stations, respectively. The RMSE were 2.47 mm/day, 2.64 mm/day and 2.19 mm/day at Faisalabad, Lahore and Peshawar weather station, respectively. The MBE of -2.41 mm/day, -2.58 mm/day and -2.13 mm/day were noted at Faisalabad, Lahore and Peshawar weather stations, respectively. The statistical result of Thornwait (Th) ETo method as compared with PM ETo method indicates underestimation of ETo in winter season and overestimation of ETo in summer season by 13.81%, 22.43% and 14.54% at Faisalabad, Lahore and Peshawar stations, respectively. The R2 of 0.92, 0.89 and 0.95 were noted at Faisalabad, Lahore and Peshawar weather stations, respectively. The RMSE were 2.14 mm/day, 2.36 mm/day and 1.16 mm/day at Faisalabad, Lahore and Peshawar weather stations, respectively. The MBE were -0.68 mm/day, -1.12 mm/day and 0.61 mm/day at Faisalabad, Lahore and Peshawar weather stations, respectively. Overall, Thornwait method gave better estimation of ETo than Penman ETo method at all the weather stations.

Penman and Thornthwaite Equations for Estimating Reference Evapotranspiration Under Semi-Arid Environment

The estimation of reference evapotranspiration (ETo) is required for effective development and management of agriculture water systems. In order to define the most accurate method to estimate ETo in semi-arid climatic environment of Faisalabad, Lahore and Peshawar. Penman ETo method and Thornthwaite ETo method are compared with standard Penman-Monteith (PM) ETo method. The statistical results show that the Penman ETo method overestimate ETo as compared to the PM ETo method in all the semi-arid climatic regions of Faisalabad, Lahore and Peshawar by 34.91%, 39.51% and 30.75%, respectively. The coefficient of determination (R²) were 0.98, 0.98 and 0.99 at Faisalabad, Lahore and Peshawar weather stations, respectively. The root mean square error (RMSE) are 2.47 mm/day, 2.64 mm/day and 2.19 mm/day at Faisalabad, Lahore and Peshawar weather station, respectively. The mean bias error (MBE) of-2.41 mm/day,-2.58 mm/day and-2.13 mm/day are noted at Faisalabad, Lahore and Peshawar weather stations, respectively. The statistical results of Thornthwaite (Th) ETo method with PM ETo method indicate underestimation of ETo in winter season and overestimation of ETo in summer season by 13.81%, 22.43% and 14.54% at Faisalabad, Lahore and Peshawar stations, respectively. The coefficient of determination (R²) of Thornthwait ETo method when compared with PM ETo method 0.92, 0.89 and 0.95 are noted at Faisalabad, Lahore and Peshawar weather stations, respectively. The root mean square error (RMSE) are 2.14 mm/day, 2.36 mm/day and 1.16 mm/day at Faisalabad, Lahore and Peshawar weather stations, respectively. The mean bias error (MBE) are-0.68 mm/day,-1.12 mm/day and 0.61 mm/day at Faisalabad, Lahore and Peshawar weather stations, respectively. Overall, Thornthwaite method gave better estimation of ETo than Penman ETo method at all the Weather stations.

Improved Remotely Sensed Total Basin Discharge and Its Seasonal Error Characterization in the Yangtze River Basin

Total basin discharge is a critical component for the understanding of surface water exchange at the land–ocean interface. A continuous decline in the number of global hydrological stations over the past fifteen years has promoted the estimation of total basin discharge using remote sensing. Previous remotely sensed total basin discharge of the Yangtze River basin, expressed in terms of runoff, was estimated via the water balance equation, using a combination of remote sensing and modeled data products of various qualities. Nevertheless, the modeled data products are presented with large uncertainties and the seasonal error characteristics of the remotely sensed total basin discharge have rarely been investigated. In this study, we conducted total basin discharge estimation of the Yangtze River Basin, based purely on remotely sensed data. This estimation considered the period between January 2003 and December 2012 at a monthly temporal scale and was based on precipitation data collected from the Tropical Rainfall Measuring Mission (TRMM) satellite, evapotranspiration data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite, and terrestrial water storage data collected from the Gravity Recovery and Climate Experiment (GRACE) satellite. A seasonal accuracy assessment was performed to detect poor performances and highlight any deficiencies in the modeled data products derived from the discharge estimation. Comparison of our estimated runoff results based purely on remotely sensed data, and the most accurate results of a previous study against the observed runoff revealed a Pearson correlation coefficient (PCC) of 0.89 and 0.74, and a root-mean-square error (RMSE) of 11.69 mm/month and 14.30 mm/month, respectively. We identified some deficiencies in capturing the maximum and the minimum of runoff rates during both summer and winter, due to an underestimation and overestimation of evapotranspiration, respectively.

Evaluation of Evapotranspiration from Eddy Covariance Using Large Weighing Lysimeters

Evapotranspiration (ET) is an important component in the water budget and used extensively in water resources management such as water planning and irrigation scheduling. In semi-arid regions, irrigation is used to supplement limited and erratic growing season rainfall to meet crop water demand. Although lysimetery is considered the most accurate method for crop water use measurements, high-precision weighing lysimeters are expensive to build and operate. Alternatively, other measurement systems such as eddy covariance (EC) are being used to estimate crop water use. However, due to numerous explicit and implicit assumptions in the EC method, an energy balance closure problem is widely acknowledged. In this study, three EC systems were installed in a field containing a large weighing lysimeter at heights of 2.5, 4.5, and 8.5 m. Sensible heat flux (H) and ET from each EC system were evaluated against the lysimeter. Energy balance closure ranged from 64% to 67% for the three sensor heights. Results showed that all three EC systems underestimated H and consequently overestimated ET; however, the underestimation of H was greater in magnitude than the overestimation of ET. Analysis showed accuracy of ET was greater than energy balance closure with error rates of 20%–30% for half-hourly values. Further analysis of error rates throughout the growing season showed that energy balance closure and ET accuracy were greatest early in the season and larger error was found after plants reached their maximum height. Therefore, large errors associated with increased biomass may indicate unaccounted-for energy stored in the plant canopy as one source of error. Summing the half-hourly data to a daily time-step drastically reduced error in ET to 10%–15%, indicating that EC has potential for use in agricultural water management.

Evaluating Precipitation Estimates from Eta, TRMM and CHRIPS Data in the South-Southeast Region of Minas Gerais State—Brazil

Precipitation estimates derived from the Eta model and from TRMM (Tropical Rainfall Measuring Mission) and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) remotely sensed data were compared to the precipitation data of the INMET (National Institute of Meteorology) meteorological stations in the south-southeast region of Minas Gerais state, Brazil, in the period between July 2009 and June 2015. Then, information about evapotranspiration (ETR), water deficit (DEF), and water surplus (EXC) was obtained from the precipitation data, using the sequential water balance (SWB) separately for each type of precipitation data (INMET, TRMM, Eta, and CHIRPS). Subsequently, the components of the SWB were comparatively analyzed. The results indicate that all three products overestimate rainfall. The strongest relationships between the INMET data and the estimated data were observed for the TRMM, in terms of precipitation estimates, as well as DEF, EXC, and ETR components. The Eta precipitation estimates are overestimated relative to those from INMET, resulting in underestimation of the water deficit (DEFETA) and overestimation of evapotranspiration (ETRETA). In general, the CHIRPS data presented a pattern similar to the station data, though statistical analyses were lower than those of the TRMM data.

Evaluating the Community Land Model (CLM4.5) at a coniferous forest site in northwestern United States using flux and carbon-isotope measurements

Abstract. Droughts in the western United States are expected to intensify with climate change. Thus, an adequate representation of ecosystem response to water stress in land models is critical for predicting carbon dynamics. The goal of this study was to evaluate the performance of the Community Land Model (CLM) version 4.5 against observations at an old-growth coniferous forest site in the Pacific Northwest region of the United States (Wind River AmeriFlux site), characterized by a Mediterranean climate that subjects trees to water stress each summer. CLM was driven by site-observed meteorology and calibrated primarily using parameter values observed at the site or at similar stands in the region. Key model adjustments included parameters controlling specific leaf area and stomatal conductance. Default values of these parameters led to significant underestimation of gross primary production, overestimation of evapotranspiration, and consequently overestimation of photosynthetic 13C discrimination, reflected in reduced 13C : 12C ratios of carbon fluxes and pools. Adjustments in soil hydraulic parameters within CLM were also critical, preventing significant underestimation of soil water content and unrealistic soil moisture stress during summer. After calibration, CLM was able to simulate energy and carbon fluxes, leaf area index, biomass stocks, and carbon isotope ratios of carbon fluxes and pools in reasonable agreement with site observations. Overall, the calibrated CLM was able to simulate the observed response of canopy conductance to atmospheric vapor pressure deficit (VPD) and soil water content, reasonably capturing the impact of water stress on ecosystem functioning. Both simulations and observations indicate that stomatal response from water stress at Wind River was primarily driven by VPD and not soil moisture. The calibration of the Ball–Berry stomatal conductance slope (mbb) at Wind River aligned with findings from recent CLM experiments at sites characterized by the same plant functional type (needleleaf evergreen temperate forest), despite significant differences in stand composition and age and climatology, suggesting that CLM could benefit from a revised mbb value of 6, rather than the default value of 9, for this plant functional type. Conversely, Wind River required a unique calibration of the hydrology submodel to simulate soil moisture, suggesting that the default hydrology has a more limited applicability. This study demonstrates that carbon isotope data can be used to constrain stomatal conductance and intrinsic water use efficiency in CLM, as an alternative to eddy covariance flux measurements. It also demonstrates that carbon isotopes can expose structural weaknesses in the model and provide a key constraint that may guide future model development.

Simulating Evapotranspiration and Yield Response of Selected Corn Varieties under Full and Limited Irrigation in the Texas High Plains Using DSSAT-CERES-Maize

Abstract. Water scarcity due to drought and groundwater depletion has led to increased interest in deficit irrigation strategies that reduce irrigation requirements while maintaining profitable yields. This has resulted in an increase in the number of modeling studies aimed at evaluating crop response to limited irrigation strategies. However, the ability of widely used crop simulation models to accurately represent responses to limited irrigation has not been thoroughly evaluated. The primary objective of this study was to determine the efficacy of DSSAT-CERES-Maize (ver. 4.6.1.0) to simulate leaf area index (LAI), crop evapotranspiration (ET), and yield response to full (100%) and limited (75% and 50%) irrigation regimes for two corn varieties. Comparisons of simulated and measured data from full and limited irrigation treatments of` two drought-tolerant corn hybrids (DuPont Pioneer AQUAmax P1151HR and Pioneer 33D49) grown in the Texas Panhandle in 2013 and 2014 were evaluated. Simulated in-season daily crop ET values for P1151HR grown in 2013 were also compared to those measured by precision large weighing lysimeters at Bushland, Texas. Additionally, a comparison of simulated and measured soil water content (SWC) within the root zone was performed for P1151HR grown in 2013. Simulated LAI for fully irrigated treatments approximated measured values reasonably well, although manipulation of plant genetic parameters failed to match measured LAI during the period between maximum LAI and the beginning of crop senescence in the 50% irrigation treatments. Similarly, simulated yield values approximated measured values for the fully irrigated treatments, while considerable overestimation of yield occurred in the limited irrigation treatments for both varieties. However, consistent overestimation of both LAI and yield for the limited irrigation treatments suggests a functional relationship between LAI and yield. Further, DSSAT overestimated crop ET by 16% for fully irrigated P1151HR and by 40% for limited irrigation treatments in 2013 as compared to measured lysimeter values. Corresponding underestimations of SWC were also observed in neutron probe measurements for both treatments. Overestimation of ET and yield and corresponding underestimation of SWC in limited irrigation treatments were mainly due to overestimation of LAI in those treatments, indicating a potential deficiency in the water stress algorithms. Additional comparisons of agronomic and lysimeter-based water balance data are needed to corroborate the findings of this study. Further investigations into the calculation of reference evapotranspiration (ETo), crop coefficients, and water stress functions in DSSAT are needed in order to provide suggestions for model improvement. Keywords: TCERES-Maize, Crop modeling, DSSAT, Evapotranspiration, Limited irrigation, Lysimeters, Maize, Semi-arid, Sprinkler irrigation, Weighing lysimeters.

An estimation of evapotranspiration by MHBJHHHsimple models in the semi arid Mediterranean zone of the North-West of Algeria

Four temperature-based evapotranspiration models were applied and compared to daily measurements obtained by a Piche evaporimeter in a Mediterranean climate zone of the semi arid type in the North-West of Algeria. Direct measurements of evaporation obtained by Piche on five stations were corrected by the Bouchet model. Evapotranspiration was calculated according to the following models: Blaney-Criddle, Blaney-Criddle (SCS), Linacre and Romanenko. The results showed that the best model is the Blaney-Criddle (SCS) one which gave excellent results for almost all of the stations studied, presenting the lowest statistical parameters [root mean squared error (RMSE), mean bias error (MBE) and t-statistic and a higher index of agreement (d). The Blaney-Criddle (SCS) also showed the lowest overestimation of evapotranspiration compared with other models. Therefore, the Blaney-Criddle (SCS) model is the most suitable model for estimating the daily evapotranspiration when direct evaporation measurements are not available.

Evaluation of GLDAS-1 and GLDAS-2 Forcing Data and Noah Model Simulations over China at the Monthly Scale

Abstract The Global Land Data Assimilation System (GLDAS) is an important data source for global water cycle research. Using ground-based measurements over continental China, the monthly scale forcing data (precipitation and air temperature) during 1979–2010 and model outputs (runoff, water storage, and evapotranspiration) during 2002–10 of GLDAS models [focusing on GLDAS, version 1 (GLDAS-1)/Noah and GLDAS, version 2 (GLDAS-2)/Noah] are evaluated. Results show that GLDAS-1 has serious discontinuity issues in its forcing data, with large precipitation errors in 1996 and large temperature errors during 2000–05. While the bias correction of the GLDAS-2 precipitation data greatly improves temporal continuity and reduces the biases, it makes GLDAS-2 precipitation less correlated with observed precipitation and makes it have larger mean absolute errors than GLDAS-1 precipitation for most months over the year. GLDAS-2 temperature data are superior to GLDAS-1 temperature data temporally and spatially. The results also show that the change rates of terrestrial water storage (TWS) data by GLDAS and the Gravity Recovery and Climate Experiment (GRACE) do not match well in most areas of China, and both GLDAS-1 and GLDAS-2 are not very capable of capturing the seasonal variation in monthly TWS change observed by GRACE. Runoff is underestimated in the exorheic basins over China, and runoff simulations of GLDAS-2 are much more accurate than those of GLDAS-1 for two of the three major river basins of China investigated in this study. Evapotranspiration is overestimated in the exorheic basins in China by both GLDAS-1 and GLDAS-2, whereas the overestimation of evapotranspiration by GLDAS-2 is less than that by GLDAS-1.

Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach

Abstract California's Central Valley grows a significant fraction of grapes used for wine production in the United States. With increasing vineyard acreage, reduced water availability in much of California, and competing water use interests, it is critical to be able to monitor regional water use and evapotranspiration (ET) over large areas, but also in detail at individual field scales to improve water management within these viticulture production systems. This can be achieved by integrating remote sensing data from multiple satellite systems with different spatiotemporal characteristics. In this research, we evaluate the utility of a multi-scale system for monitoring ET as applied over two vineyard sites near Lodi, California during the 2013 growing season, leading into the drought in early 2014. The system employs a multi-sensor satellite data fusion methodology (STARFM: Spatial and Temporal Adaptive Reflective Fusion Model) combined with a multi-scale ET retrieval algorithm based on the Two-Source Energy Balance (TSEB) land-surface representation to compute daily ET at 30 m resolution. In this system, TSEB is run using thermal band imagery from the Geostationary Environmental Operational Satellites (GOES; 4-km spatial resolution, hourly temporal sampling), the Moderate Resolution Imaging Spectroradiometer (MODIS) data (1 km resolution, daily acquisition) and the new Landsat 8 satellite (sharpened to 30 m resolution, ~ 16 day acquisition). Estimates of daily ET generated in two neighboring fields of Pinot noir vines of different age agree with ground-based flux measurements acquired in-field during most of the 2013 season with relative mean absolute errors on the order of 19–23% (root mean square errors of approximately 1 mm d − 1 ), reducing to 14–20% at the weekly timestep relevant for irrigation management (~ 5 mm wk − 1 ). A model overestimation of ET in the early season was detected in the younger vineyard, perhaps relating to an inter-row grass cover crop. Spatial patterns of cumulative ET generally correspond to measured yield maps and indicate areas of variable crop moisture, soil condition, and yield within the vineyards that could require adaptive management. The results suggest that multi-sensor remote sensing observations provide a unique means for monitoring crop water use and soil moisture status at field-scales over extended growing regions, and may have value in supporting operational water management decisions in vineyards and other high value crops.

Impact of climate, vegetation, soil and crop management variables on multi-year ISBA-A-gs simulations of evapotranspiration over a Mediterranean crop site

Abstract. Generic land surface models are generally driven by large-scale data sets to describe the climate, the soil properties, the vegetation dynamic and the cropland management (irrigation). This paper investigates the uncertainties in these drivers and their impacts on the evapotranspiration (ET) simulated from the Interactions between Soil, Biosphere, and Atmosphere (ISBA-A-gs) land surface model over a 12-year Mediterranean crop succession. We evaluate the forcing data sets used in the standard implementation of ISBA over France where the model is driven by the SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie) high spatial resolution atmospheric reanalysis, the leaf area index (LAI) time courses derived from the ECOCLIMAP-II land surface parameter database and the soil texture derived from the French soil database. For climate, we focus on the radiations and rainfall variables and we test additional data sets which include the ERA-Interim (ERA-I) low spatial resolution reanalysis, the Global Precipitation Climatology Centre data set (GPCC) and the MeteoSat Second Generation (MSG) satellite estimate of downwelling shortwave radiations. The evaluation of the drivers indicates very low bias in daily downwelling shortwave radiation for ERA-I (2.5 W m−2) compared to the negative biases found for SAFRAN (−10 W m−2) and the MSG satellite (−12 W m−2). Both SAFRAN and ERA-I underestimate downwelling longwave radiations by −12 and −16 W m−2, respectively. The SAFRAN and ERA-I/GPCC rainfall are slightly biased at daily and longer timescales (1 and 0.5 % of the mean rainfall measurement). The SAFRAN rainfall is more precise than the ERA-I/GPCC estimate which shows larger inter-annual variability in yearly rainfall error (up to 100 mm). The ECOCLIMAP-II LAI climatology does not properly resolve Mediterranean crop phenology and underestimates the bare soil period which leads to an overall overestimation of LAI over the crop succession. The simulation of irrigation by the model provides an accurate irrigation amount over the crop cycle but the timing of irrigation occurrences is frequently unrealistic. Errors in the soil hydrodynamic parameters and the lack of irrigation in the simulation have the largest influence on ET compared to uncertainties in the large-scale climate reanalysis and the LAI climatology. Among climate variables, the errors in yearly ET are mainly related to the errors in yearly rainfall. The underestimation of the available water capacity and the soil hydraulic diffusivity induce a large underestimation of ET over 12 years. The underestimation of radiations by the reanalyses and the absence of irrigation in the simulation lead to the underestimation of ET while the overall overestimation of LAI by the ECOCLIMAP-II climatology induces an overestimation of ET over 12 years. This work shows that the key challenges to monitor the water balance of cropland at regional scale concern the representation of the spatial distribution of the soil hydrodynamic parameters, the variability of the irrigation practices, the seasonal and inter-annual dynamics of vegetation and the spatiotemporal heterogeneity of rainfall.

Evaluation of Water Use Efficiency Derived from MODIS Products against Eddy Variance Measurements in China

Water use efficiency (WUE) is a useful indicator to illustrate the interaction of carbon and water cycles in terrestrial ecosystems. MODIS gross primary production (GPP) and evapotranspiration (ET) products have been used to analyze the spatial and temporal patterns of WUE and their relationships with environmental factors at regional and global scales. Although MODIS GPP and ET products have been evaluated using eddy covariance flux measurements, the accuracy of WUE estimated from MODIS products has not been well quantified. In this paper, we evaluated WUE estimated from MODIS GPP and ET products against eddy covariance measurements of GPP and ET during 2003–2008 at eight sites of the Chinese flux observation and research network (ChinaFLUX) and conducted sensitivity analysis to investigate the possible key contributors to the bias of MODIS products. Results show that MODIS products underestimate eight-day water use efficiency in four forest ecosystems and one cropland ecosystem with the bias from −0.36–−2.28 g·C·kg−1 H2O, while overestimating it in three grassland ecosystems with the bias from 0.26–1.11 g·C·kg−1 H2O. Mean annual WUE was underestimated by 14%–54% at four forest sites, 45% at one cropland site and 7% at an alpine grassland site, but overestimated by 66% and 9% at a temperate grassland site and an alpine meadow site, respectively. The underestimation of WUE by MODIS data results from underestimated GPP and overestimated ET at four forest sites, while MODIS WUE values are significantly overvalued mainly due to underestimated ET in the three grassland ecosystems. The maximum light use efficiency and fraction of photosynthetically-active radiation (FPAR) were the two most sensitive factors to the estimation of WUE derived from the MODIS GPP and ET algorithms. The error in meteorological data partly caused the overestimation of ET and accordingly underestimation in WUE in subtropical and tropical forests. The bias of MODIS-produced WUE was also derived from the uncertainties in eddy flux data due to gap-filling processes and unbalanced surface energy issue. Their contributions to the uncertainty in estimated WUE at both eight-day and annual scales still need to be further quantified.

Assessment of simulated water balance from Noah, Noah‐MP, CLM, and VIC over CONUS using the NLDAS test bed

AbstractThis study assesses the hydrologic performance of four land surface models (LSMs) for the conterminous United States using the North American Land Data Assimilation System (NLDAS) test bed. The four LSMs are the baseline community Noah LSM (Noah, version 2.8), the Variable Infiltration Capacity (VIC, version 4.0.5) model, the substantially augmented Noah LSM with multiparameterization options (hence Noah‐MP), and the Community Land Model version 4 (CLM4). All four models are driven by the same NLDAS‐2 atmospheric forcing. Modeled terrestrial water storage (TWS), streamflow, evapotranspiration (ET), and soil moisture are compared with each other and evaluated against the identical observations. Relative to Noah, the other three models offer significant improvements in simulating TWS and streamflow and moderate improvements in simulating ET and soil moisture. Noah‐MP provides the best performance in simulating soil moisture and is among the best in simulating TWS, CLM4 shows the best performance in simulating ET, and VIC ranks the highest in performing the streamflow simulations. Despite these improvements, CLM4, Noah‐MP, and VIC exhibit deficiencies, such as the low variability of soil moisture in CLM4, the fast growth of spring ET in Noah‐MP, and the constant overestimation of ET in VIC.

Assessment of Alternative Methods for Statistically Downscaling Daily GCM Precipitation Outputs to Simulate Regional Streamflow

AbstractThis study applied three statistical downscaling methods: (1) bias correction and spatial disaggregation at daily time scale (BCSD_daily); (2) a modified version ofBCSDwhich reverses the order of spatial disaggregation and bias correction (SDBC), and (3) the bias correction and stochastic analog method (BCSA) to downscale general circulation model daily precipitation outputs to the subbasin scale for west‐centralFlorida. Each downscaled climate input dataset was then used in an integrated hydrologic model to examine differences in ability to simulate retrospective streamflow characteristics. Results showed theBCSD_daily method consistently underestimated mean streamflow because the highly spatially correlated small precipitation events produced by this method resulted in overestimation of evapotranspiration. Highly spatially correlated large precipitation events produced by theSDBCmethod resulted in overestimation of the standard deviation of wet season daily streamflow and the magnitude/frequency of high streamflow events.BCSAshowed better performance than the other methods in reproducing spatiotemporal statistics of daily precipitation and streamflow. This study demonstrated differences in statistical downscaling techniques propagate into significant differences in streamflow predictions, and underscores the need to carefully select a downscaling method that reproduces precipitation characteristics important for the hydrologic system under consideration.

The effect of spatial rainfall variability on water balance modelling for south-eastern Australian catchments

Summary Spatial rainfall variability is considered an important factor affecting the accuracy of streamflow prediction. This study evaluated the effect of spatial rainfall variability on water balance modelling by conducting a series of virtual experiments. The three-layer Variable Infiltration Capacity model (VIC-3L) was applied using both uniform and spatially variable rainfall for 60 catchments in south-eastern Australia. The spatially variable rainfall was generated from the 0.05° gridded SILO daily rainfall with different degrees of variability. The VIC-3L model was calibrated against observed daily streamflow using gridded SILO daily rainfall to generate reference parameter values. Then the model was applied using the generated spatially variable rainfall and reference parameter values to produce virtual water balance components associated with different spatially variable rainfall. Differences between the lumped and distributed modelling (i.e. virtual experiments) represent the effects of spatial rainfall variability on water balance modelling. The results showed that spatial rainfall variability interacts with catchments characteristics to influence hydrological processes and the effects are not uniform on different water balance components. For a given rainfall total, ignoring spatial rainfall variability will result in underestimation of the total streamflow volume and overestimation of evapotranspiration. Effect of spatial rainfall variability on water balance modelling is more pronounced in catchments with larger rainfall variability. In most cases, information on spatial rainfall variability will help to improve accuracy of water balance modelling. However, in some cases, lumped water balance modelling (i.e. ignoring spatial rainfall variability) may outperform distributed modelling due to catchment filtering effect. The results from this study suggest that more accurate information of rainfall spatial distribution in a catchment can help to improve water balance modelling.

Representation of Terrestrial Hydrology and Large-Scale Drought of the Continental United States from the North American Regional Reanalysis

Abstract The North American Regional Reanalysis (NARR) is a state-of-the-art land–atmosphere reanalysis product that provides improved representation of the terrestrial hydrologic cycle compared to previous global reanalyses, having the potential to provide an enhanced picture of hydrologic extremes such as floods and droughts and their driving mechanisms. This is partly because of the novel assimilation of observed precipitation, state-of-the-art land surface scheme, and higher spatial resolution. NARR is evaluated in terms of the terrestrial water budget and its depiction of drought at monthly to annual time scales against two offline land surface model [Noah v2.7.1 and Variable Infiltration Capacity (VIC)] simulations and observation-based runoff estimates over the continental United States for 1979–2003. An earlier version of the Noah model forms the land component of NARR and so the offline simulation provides an opportunity to diagnose NARR land surface variables independently of atmospheric feedbacks. The VIC model has been calibrated against measured streamflow and so provides a reasonable estimate of large-scale evapotranspiration. Despite similar precipitation, there are large differences in the partitioning of precipitation into evapotranspiration and runoff. Relative to VIC, NARR and Noah annual evapotranspiration is biased high by 28% and 24%, respectively, and the runoff ratios are 50% and 40% lower. This is confirmed by comparison with observation-based runoff estimates from 1130 small, relatively unmanaged basins across the continental United States. The overestimation of evapotranspiration by NARR is largely attributed to the evapotranspiration component of the Noah model, whereas other factors such as atmospheric forcings or biases induced by precipitation assimilation into NARR play only a minor role. A combination of differences in the parameterization of evapotranspiration and in particular low stomatal resistance values in NARR, the seasonality of vegetation characteristics, the near-surface radiation and meteorology, and the representation of soil moisture dynamics, including high infiltration rates and the relative coupling of soil moisture with baseflow in NARR, are responsible for the differences in the water budgets. Large-scale drought as quantified by soil moisture percentiles covaries closely over the continental United States between the three datasets, despite large differences in the seasonal water budgets. However, there are large regional differences, especially in the eastern United States where the VIC model shows higher variability in drought dynamics. This is mostly due to increased frequency of completely dry conditions in NARR that result from differences in soil depth, higher evapotranspiration, early snowmelt, and early peak runoff. In the western United States, differences in the precipitation forcing contribute to large discrepancies between NARR and Noah/VIC simulations in the representation of the early 2000s drought.

ERRATUM “Estimating Evapotranspiration and Seepage for a Sinkhole Wetland from Diurnal Surface‐Water Cycles”1,2

: This study used measured diurnal surface-water cycles to estimate daily evapotranspiration (ET) and seepage for a seasonally flooded sinkhole wetland. Diurnal surface-water cycles were classified into five categories based on the relationship between the surface-water body and the surrounding ground-water system (i.e., recharge/discharge). Only one class of diurnal cycles was found to be suitable for application of this method. This subset of diurnal cycles was used to estimate ET and seepage and the relative importance of each transfer process to the overall water budget. The method has limited utility for wetlands with erratic hydrologic regimes (e.g., wetlands in urban environments). This is due to violation of the critical assumption that the inflow/outflow rate remains constant throughout the day. For application to surface-water systems, the method is typically applied with an assumed specific yield of 1.0. This assumption was found to be invalid for application to surface-water systems with a noncylindrical pond geometry. An overestimation of ET by as much as 60% was found to occur under conditions of low pond stage and high water loss. The results demonstrate the high ET rates that can occur in isolated wetlands due to contrasting roughness and moisture conditions (oasis and clothesline effects). Estimated ET rates ranged from 4.1 to 18.7 mm/day during the growing season. Despite these large ET rates, seepage (recharge) was found to be the dominant water loss mechanism for the wetland.

Estimating Evapotranspiration and Seepage for a Sinkhole Wetland From Diurnal Surface‐Water Cycles1

Abstract: This study used measured diurnal surface‐water cycles to estimate daily evapotranspiration (ET) and seepage for a seasonally flooded sinkhole wetland. Diurnal surface‐water cycles were classified into five categories based on the relationship between the surface‐water body and the surrounding ground‐water system (i.e., recharge/discharge). Only one class of diurnal cycles was found to be suitable for application of this method. This subset of diurnal cycles was used to estimate ET and seepage and the relative importance of each transfer process to the overall water budget. The method has limited utility for wetlands with erratic hydrologic regimes (e.g., wetlands in urban environments). This is due to violation of the critical assumption that the inflow/outflow rate remains constant throughout the day. For application to surface‐water systems, the method is typically applied with an assumed specific yield of 1.0. This assumption was found to be invalid for application to surface‐water systems with a noncylindrical pond geometry. An overestimation of ET by as much as 60% was found to occur under conditions of low pond stage and high water loss. The results demonstrate the high ET rates that can occur in isolated wetlands due to contrasting roughness and moisture conditions (oasis and clothesline effects). Estimated ET rates ranged from 4.1 to 18.7 mm/day during the growing season. Despite these large ET rates, seepage (recharge) was found to be the dominant water loss mechanism for the wetland.

STANDARDIZED ASCE PENMAN-MONTEITH: IMPACT OF SUM-OF-HOURLY VS. 24-HOUR TIMESTEP COMPUTATIONS AT REFERENCE WEATHER STATION SITES

The standardized ASCE Penman-Monteith (ASCE-PM) model was used to estimate grass-reference evapotranspiration(ETo) over a range of climates at seven locations based on hourly and 24 h weather data. Hourly ETo computationswere summed over 24 h periods and reported as sum-of-hourly (SOH). The SOH ASCE-PM ETo values (ETo,h,ASCE) werecompared with the 24 h timestep ASCE-PM ETo values (ETo,d) and SOH ETo values using the FAO Paper 56 Penman-Monteith(FAO56-PM) method (ETo,h,FAO). The ETo,h,ASCE values were used as the basis for comparison. The ETo,d estimated higherthan ETo,h,ASCE at all locations except one, and agreement between the computational timesteps was best in humid regions.The greatest differences between ETo,d and ETo,h,ASCE were in locations where strong, dry, hot winds cause advective increasesin ETo. Three locations showed considerable signs of advection. Some of the differences between the timesteps was attributedto uncertainties in predicting soil heat flux and to the difficulty of ETo,d to effectively account for abrupt diurnal changes inwind speed, air temperature, and vapor pressure deficit. The ETo,h,FAO values correlated well with ETo,h,ASCE values (r2 >0.997), but estimated lower than ETo,h,ASCE at all locations by 5% to 8%. This was due to the impact of higher surfaceresistance during daytime periods. Summing the ETo values over a weekly, monthly, or annual basis generally reduced thedifferences between ETo,d and ETo,h,ASCE. The differences suggest that using ETo,d rather than ETo,h,ASCE would result inunderestimation or overestimation of ETo. Summing the ETo,d values over multiple days and longer periods for peak ETomonths resulted in inconsistent differences between the two timesteps. The results suggest a potential improvement in accuracywhen using the standardized ASCE-PM procedure applied hourly rather than daily. The hourly application helps to accountfor abrupt changes in atmospheric conditions on ETo estimation in advective and other environments when hourly climatedata are available.