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Evaporation is a principal factor in the hydrological cycle and energy exchange; however, estimations of evaporation include large uncertainties. In this study, a modified estimation of evaporation based on empirical linearly simplified Penman evaporation (PES) is proposed, soil moisture and precipitation are used to correct the land surface evaporation estimation, and the temporal and spatial characteristics of the corrected evaporation (CE) are investigated globally. The results show that CE is strong at low latitudes and weak at high latitudes. CE has obvious seasonal variation, ranging from 0.2 to 4.0 mm day−1; CE is prominent in summer but feeble in winter. Compared to PES, CE is generally weaker in most regions, especially in arid regions, with differences of more than 9 mm day−1. CE agrees well with evaporation derived from FLUXNET-Model Tree Ensemble (FLUXNET-MTE), MERRA, and GLDAS. In general, the root-mean-square error (RMSE) between annual CE and FLUXNET-MTE is less than 0.2 mm day−1, and CE is about 5%–10% less than the evaporation of FLUXNET-MTE. In the arid regions, the maximum CE almost occurs in the month with the strongest precipitation; in the tropical regions, soil moisture enhances CE only when precipitation is less. In the context of global temperature rise, PES always shows an apparent increasing trend due to the water supply is not considered; however, CE decreases in western Asia, the western United States, the Amazon basin, and Central Africa, but weakly increases in the other study regions from 1984 to 2013. This study provides a method for estimating evaporation considering more restrictive factors on evaporation. [ABSTRACT FROM AUTHOR]

The maximum entropy production (MEP) model based on nonequilibrium thermodynamics and the theory of Bayesian probabilities was recently developed to model land surface fluxes, including soil evaporation and vegetation transpiration. This model requires few input data and ensures the closure of the surface energy balance. This study aims to test the capability of such a model to realistically simulate evapotranspiration (ET) over a wide range of climates and vegetation covers. A weighting coefficient is introduced to calculate total ET from soil evaporation and vegetation transpiration over partially vegetated land surfaces, resulting in the MEP-ET model. Using this coefficient, the model outputs are compared with in situ observations of ET at eight FLUXNET sites across the continental United States. Results confirm the close agreement between the MEP-ET predicted daily ET and the corresponding observations at sites characterized by moderately limited water availability. Poor ET results were obtained under high water stress conditions. A regulation parameter was therefore introduced in the MEP-ET model to properly take into account the effects of soil water stress on stomata, yielding the generalized MEP-ET model. This parameter considerably reduced model biases under water stress conditions for various heterogeneous land surface sites. The generalized MEP-ET model outperforms several popular ET models, including Penman-Monteith (PM), modified Priestley-Taylor-Jet Propulsion Laboratory (PT-JPL), and air-relative-humidity-based two-source model (ARTS) at all test sites. [ABSTRACT FROM AUTHOR]

Precipitation values estimated by radar are assumed to be the amount of precipitation that occurred at the surface, yet this notion is inaccurate. Numerous atmospheric and microphysical processes can alter the precipitation rate between the radar beam elevation and the surface. One such process is evaporation. This study determines the applicability of integrating an evaporation correction scheme for real-time radar-derived mosaicked precipitation rates to reduce quantitative precipitation estimate (QPE) overestimation and to reduce the coverage of false surface precipitation. An evaporation technique previously developed for largescale numerical modeling is applied to Multi-Radar Multi-Sensor (MRMS) precipitation rates through the use of 2D and 3D numerical weather prediction (NWP) atmospheric parameters as well as basic radar properties. Hourly accumulated QPE with evaporation adjustment compared against gauge observations saw an average reduction of the overestimation bias by 57%-76% for rain events and 42%-49% for primarily snow events. The removal of false surface precipitation also reduced the number of hourly gauge observations that were considered as "false zero" observations by 52.1% for rain and 38.2% for snow. Optimum computational efficiency was achieved through the use of simplified equations and hourly 10-km horizontal resolution NWP data. The run time for the evaporation correction algorithm is 6-7 s. [ABSTRACT FROM AUTHOR]

Intermittency is a core characteristic of precipitation, not well described by data and very poorly modeled. Detailed analyses are made of near-global gridded (about 1°) hourly or 3-hourly precipitation rates from two updated observational datasets [3-hourly TRMM 3B42, version 7, and hourly CMORPH, version 1.0, bias corrected (CRT)] and from special runs of CESM from January 1998 to December 2013 to obtain hourly values. The analyses explore the intermittency of precipitation: the frequency, intensity, duration, and amounts. A comparison is made for all products using several metrics with a focus on the duration of events, and a new metric is proposed based on the ratio of the frequency of precipitation at certain rates (0.2-2 mm h−1) for hourly versus 3-hourly versus daily data. For all seasons and rain rates, TRMM values are similar in pattern to CMORPH, but durations are about 80%-85%. It is mainly over land in the monsoons that CMORPH exceeds TRMM rain durations. Observed duration of precipitation events in CMORPH over oceans are 12-15 h in the tropics and subtropics, much less than the ~20 h for CESM. Hence, the observational results differ somewhat but both are considerably different from the model, which has too much precipitation overall, and it precipitates far too often at low rates and not enough for intense rates, with the divide about 1-2 mm h−1. There is a need to properly represent precipitation phenomena and processes either explicitly or implicitly (parameterized). [ABSTRACT FROM AUTHOR]

Atmospheric evaporative demand plays a pivotal role in global water and energy budgets, and its change is very important for drought monitoring, irrigation scheduling, and water resource management under a changing environment. Here, future changes of pan evaporation Epan, a measurable indicator for atmospheric evaporative demand, are first projected and attributed over China through a physically based approach, namely, the PenPan model, forced with outputs from 12 state-of-the-art climate models from phase 5 of the Coupled Model Intercomparison Project. An equidistant quantile mapping method was also used to correct the biases in GCMs outputs to reduce uncertainty in Epan projection. The results indicated that Epan would increase during the periods 2021-50 and 2071-2100 relative to the baseline period 1971-2000 under the representative concentration pathway (RCP) 4.5 and 8.5 scenarios, which can mainly be attributed to the projected increase in air temperature and vapor pressure deficit over China. The percentage increase of Epan is relatively larger in eastern China than in western China, which is due to the spatially inconsistent increases in air temperature, net radiation, wind speed, and vapor pressure deficit over China. The widely reported 'pan evaporation paradox' was not well reproduced for the period 1961-2000 in the climate models, before or after bias correction, suggesting discrepancy between observed and modeled trends. With that caveat, it was found that the pan evaporation has been projected to increase at a rate of 117-167 mm yr−1 K−1 (72-80 mm yr−1 K−1) over China using the multiple GCMs under the RCP 4.5 (RCP 8.5) scenario with increased greenhouse gases and the associated warming of the climate system. [ABSTRACT FROM AUTHOR]

NASA's Soil Moisture Active Passive (SMAP) mission provides global surface soil moisture retrievals with a revisit time of 2-3 days and a latency of 24 h. Here, to enhance the utility of the SMAP data, an approach is presented for improving real-time soil moisture estimates (nowcasts) and for forecasting soil moisture several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAPretrievals themselves. The nowcast accuracy over the continental United States is shown to be markedly higher than that achieved with the simple yet common persistence approach. The accuracy of soil moisture forecasts, which rely on precipitation forecasts rather than on precipitation measurements, is reduced relative to nowcast accuracy but is still significantly higher than that obtained through persistence. [ABSTRACT FROM AUTHOR]

Partitioning evapotranspiration (ET) into soil evaporation E and plant transpiration T is important, but it is still a theoretical and technical challenge. The isotopic technique is considered to be an effective method, but it is difficult to quantify the isotopic composition of transpiration δT and evaporation δE directly and continuously; few previous studies determined δT successfully under a non-steady state (NSS). Here, multiple methods were used to partition ET in a maize field and a new flow-through chamber system was refined to provide direct and continuous measurement of 67- and δE. An eddy covariance and lysimeter (EC-L)-based method and two isotope-based methods [isotope combined with the Craig-Gordon model (Iso-CG) and isotope using chamber measurement (Iso-M)] were applied to partition δT. Results showed the transpiration fraction FT in Iso-CG was consistent with EC-L at both diurnal and growing season time scales, but FT calculated by Iso-M was less than Iso-CG and EC-L. The chamber system method presented here to determine ST under NSS and isotope steady state (ISS) was robust, but there could be some deviation in measuring δE. The FT varied from 52% to 91%, with a mean of 78% during the entire growing season, and it was well described by a function of LAI, with a nonlinear relationship of FT = 0.71LAI0.14. The results demonstrated the feasibility of the isotope-based chamber system to partition ET. This technique and its further development may enable field ET partitioning accurately and continuously and improve understanding of water cycling through the soil-plant-atmosphere continuum. [ABSTRACT FROM AUTHOR]

The deuterium excess is a second-order parameter linking water-stable oxygen and hydrogen isotopes and has been widely used in hydrological studies. The deuterium excess in precipitation is greatly influenced by below-cloud evaporation through unsaturated air, especially in an arid climate. Based on an observation network of isotopes in precipitation of arid central Asia, the difference in deuterium excess from cloud base to ground was calculated for each sampling site. The difference on the southern slope of the Tian Shan is generally larger than that on the northern slope, and the difference during the summer months is greater than that during the winter months. Generally, an increase of 1% in evaporation of raindrops causes deuterium excess to decrease by approximately 1‰. Under conditions of low air temperature, high relative humidity, heavy precipitation, and large raindrop diameter, a good linear correlation is exhibited between evaporation proportion and difference in deuterium excess, and a linear regression slope of <1‰ %−1 can be seen; in contrast, under conditions of high air temperature, low relative humidity, light precipitation, and small raindrop diameter, the linear relationship is relatively weak, and the slope is much larger than 1‰ %−1. A sensitivity analysis under different climate scenarios indicates that, if air temperature has increased by 5°C, deuterium excess difference decreases by 0.3‰-4.0‰ for each site; if relative humidity increases by 10%, deuterium excess difference increases by 1.1‰-10.3‰. [ABSTRACT FROM AUTHOR]

The widespread influence of reservoirs on global rivers makes representations of reservoir outflow and storage essential components of large-scale hydrology and climate simulations across the land surface and atmosphere. Yet, reservoirs have yet to be commonly integrated into earth system models. This deficiency influences model processes such as evaporation and runoff, which are critical for accurate simulations of the coupled climate system. This study describes the development of a generalized reservoir model capable of reproducing realistic reservoir behavior for future integration in a global land surface model (LSM). Equations of increasing complexity relating reservoir inflow, outflow, and storage were tested for 14 California reservoirs that span a range of spatial and climate regimes. Temperature was employed in model equations to modulate seasonal changes in reservoir management behavior and to allow for the evolution of management seasonality as future climate varies. Optimized parameter values for the best-performing model were generalized based on the ratio of winter inflow to storage capacity so a future LSM user can generate reservoirs in any grid location by specifying the given storage capacity. Model performance statistics show good agreement between observed and simulated reservoir storage and outflow for both calibration (mean normalized RMSE = 0.48; mean coefficient of determination = 0.53) and validation reservoirs (mean normalized RMSE = 0.15; mean coefficient of determination = 0.67). The low complexity of model equations that include climate-adaptive operation features combined with robust model performance show promise for simulations of reservoir impacts on hydrology and climate within an LSM. [ABSTRACT FROM AUTHOR]