Comparing estimates of actual evapotranspiration from satellites, hydrological models, and field data: a case study from Western Turkey

Evapotranspiration / Estimation / Remote sensing / Satellite surveys / Field tests / Measurement / Productivity / Crops / Water requirements / Water balance / Irrigation management / River basins / Hydrology / Models / Turkey / Gediz River

Remote sensing and hydrogeophysics give a new impetus to integrated hydrological models: a review

Integrated Hydrological Models (IHMs) dynamically couple surface and groundwater processes across the unsaturated zone domain. IHMs are data intensive and computationally demanding but can provide physically realistic output, particularly if sufficient input data of high quality is available. In-situ observations often have a small footprint and are time and cost-demanding. Satellite remote sensing observations, with their long time series archives and spatially semi-continuous gridded format, as well as hydrogeophysical observations with their flexible, ‘on-demand’ high-resolution data coverage, perfectly complement in-situ observations. We review the contribution of various satellite remote sensing products for IHM: (1) climate forcings, (2) parameters, (3) boundary conditions and (4) observations for constraining model calibration and data assimilation. Our review of hydrogeophysics focuses on the four mentioned IHM contributions, but we analyze them per data acquisition platform, i.e., surface, drone-borne and airborne hydrogeophysics. Finally, the review includes a discussion on the optimal use of satellite remote sensing and hydrogeophysical data in IHMs, as well as a vision for further improvements of data-driven, integrated hydrological modelling

The land surface water and energy budgets over the Tibetan Plateau

Tibetan Plateau plays an important role in the Asian Monsoon and global general circulation system. Due to the lack of quantitative observations and complicated cold season processes in high elevation terrain, however, the land surface water and energy budgets are still unexplored over this special region. In this study, the water and energy balances are analyzed in detail based on recently released land surface “reanalysis” data produced by NASA Global Land Data Assimilation System by three different land models, which first ingest all available ground and satellite data into the data assimilation system over the Tibetan Plateau. The major land surface energy and water components in the annual variability are compared. The model and data assimilation skills and deficiencies are also discussed. The total heat fluxes in the transition from heat source to heat sink is observed at the west edge of the TP during winter. But, the area and intensity is far less than the previous hypothesized. The Budyko curve for hydrology indicates that the TP is a typical dry and arid climate where evaporation is mainly controlled by precipitation

Multi-temporal evaluation of soil moisture and land surface temperature dynamics using in situ and satellite observations

Soil moisture (SM) is an important component of the Earth’s surface water balance and by extension the energy balance, regulating the land surface temperature (LST) and evapotranspiration (ET). Nowadays, there are two missions dedicated to monitoring the Earth’s surface SM using L-band radiometers: ESA’s Soil Moisture and Ocean Salinity (SMOS) and NASA’s Soil Moisture Active Passive (SMAP). LST is remotely sensed using thermal infrared (TIR) sensors on-board satellites, such as NASA’s Terra/Aqua MODIS or ESA & EUMETSAT’s MSG SEVIRI. This study provides an assessment of SM and LST dynamics at daily and seasonal scales, using 4 years (2011–2014) of in situ and satellite observations over the central part of the river Duero basin in Spain. Specifically, the agreement of instantaneous SM with a variety of LST-derived parameters is analyzed to better understand the fundamental link of the SM–LST relationship through ET and thermal inertia. Ground-based SM and LST measurements from the REMEDHUS network are compared to SMOS SM and MODIS LST spaceborne observations. ET is obtained from the HidroMORE regional hydrological model. At the daily scale, a strong anticorrelation is observed between in situ SM and maximum LST (R ˜ -0.6 to -0.8), and between SMOS SM and MODIS LST Terra/Aqua day (R ˜ - 0.7). At the seasonal scale, results show a stronger anticorrelation in autumn, spring and summer (in situ R ˜ -0.5 to -0.7; satellite R ˜ -0.4 to -0.7) indicating SM–LST coupling, than in winter (in situ R ˜ +0.3; satellite R ˜ -0.3) indicating SM–LST decoupling. These different behaviors evidence changes from water-limited to energy-limited moisture flux across seasons, which are confirmed by the observed ET evolution. In water-limited periods, SM is extracted from the soil through ET until critical SM is reached. A method to estimate the soil critical SM is proposed. For REMEDHUS, the critical SM is estimated to be ~0.12 m3/m3 , stable over the study period and consistent between in situ and satellite observations. A better understanding of the SM–LST link could not only help improving the representation of LST in current hydrological and climate prediction models, but also refining SM retrieval or microwave-optical disaggregation algorithms, related to ET and vegetation status.Peer ReviewedPostprint (published version

A Long-Term Hydrologically-Based Data Set of Land Surface Fluxes and States for the Conterminous United States

A frequently encountered difficulty in assessing model-predicted land–atmosphere exchanges of moisture and energy is the absence of comprehensive observations to which model predictions can be compared at the spatial and temporal resolutions at which the models operate. Various methods have been used to evaluate the land surface schemes in coupled models, including comparisons of model-predicted evapotranspiration with values derived from atmospheric water balances, comparison of model-predicted energy and radiative fluxes with tower measurements during periods of intensive observations, comparison of model-predicted runoff with observed streamflow, and comparison of model predictions of soil moisture with spatial averages of point observations. While these approaches have provided useful model diagnostic information, the observation-based products used in the comparisons typically are inconsistent with the model variables with which they are compared—for example, observations are for points or areas much smaller than the model spatial resolution, comparisons are restricted to temporal averages, or the spatial scale is large compared to that resolved by the model. Furthermore, none of the datasets available at present allow an evaluation of the interaction of the water balance components over large regions for long periods. In this study, a model-derived dataset of land surface states and fluxes is presented for the conterminous United States and portions of Canada and Mexico. The dataset spans the period 1950–2000, and is at a 3-h time step with a spatial resolution of ⅛ degree. The data are distinct from reanalysis products in that precipitation is a gridded product derived directly from observations, and both the land surface water and energy budgets balance at every time step. The surface forcings include precipitation and air temperature (both gridded from observations), and derived downward solar and longwave radiation, vapor pressure deficit, and wind. Simulated runoff is shown to match observations quite well over large river basins. On this basis, and given the physically based model parameterizations, it is argued that other terms in the surface water balance (e.g., soil moisture and evapotranspiration) are well represented, at least for the purposes of diagnostic studies such as those in which atmospheric model reanalysis products have been widely used. These characteristics make this dataset useful for a variety of studies, especially where ground observations are lacking

Assessment and formulation of data assimilation techniques for a 3D Richards equation-based hydrological model

The main objectives of de DAUFIN project are: to develop a unifying modeling framework applicable at the catchment scale and based on rigorous conservation equations for the study of hydrological processes in the stream channel, land surface, soil, and groundwater components of a river basin; to implement data assimilation methodologies within this modeling framework and for other control models to enable the optimal use of remote sensing, ground-based, and simulation data; to test and apply the models and the data assimilation methods at various catchment scales, including hillslopes and subcatchment of the Ourthe water shed in Belgium and the entire Meuse river basin, one of the major basins in Europe with a drainage area of 33000 km² that comprises the Ourthe

Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI

[EN] Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatiotemporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment-the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and datascarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.The research leading to these results has received funding from the Spanish Ministry of Economy and Competitiveness and FEDER funds, through the research projects ECOTETIS (CGL2011-28776-C02-014) and TETISMED (CGL2014-58127-C3-3-R). The collaboration between Universitat Politecnica de Valencia, Universita degli studi della Basilicata and Princeton University was funded by the Spanish Ministry of Economy and Competitiveness through the EEBB-I-15-10262 fellowship.Ruiz Perez, G.; Koch, J.; Manfreda, S.; Caylor, KK.; Francés, F. (2017). Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI. HYDROLOGY AND EARTH SYSTEM SCIENCES. 21(12):6235-6251. https://doi.org/10.5194/hess-21-6235-2017S623562512112Allen, R. G., Pruitt, W. O., Wright, J. L., Howell, T. A., Ventura, F., Snyder, R., Itenfisu, D., Steduto, P., Berengena, J., Yrisarry, J. B., Smith, M., Pereira, L. S., Raes, D., Perrier, A., Alves, I., Walter, I., Elliott, R.: A recommendation on standardized surface resistance for hourly calculation of reference ET0 by the FAO56 Penman-Monteith method, Agr. 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Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions on how to capitalize on state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world