Continuum: a distributed hydrological model for water management and flood forecasting

Abstract. Full process description and distributed hydrological models are very useful tools in hydrology as they can be applied in different contexts and for a wide range of aims such as flood and drought forecasting, water management, and prediction of impact on the hydrologic cycle due to natural and human-induced changes. Since they must mimic a variety of physical processes, they can be very complex and with a high degree of parameterization. This complexity can be increased by necessity of augmenting the number of observable state variables in order to improve model validation or to allow data assimilation. In this work a model, aiming at balancing the need to reproduce the physical processes with the practical goal of avoiding over-parameterization, is presented. The model is designed to be implemented in different contexts with a special focus on data-scarce environments, e.g. with no streamflow data. All the main hydrological phenomena are modelled in a distributed way. Mass and energy balance are solved explicitly. Land surface temperature (LST), which is particularly suited to being extensively observed and assimilated, is an explicit state variable. A performance evaluation, based on both traditional and satellite derived data, is presented with a specific reference to the application in an Italian catchment. The model has been firstly calibrated and validated following a standard approach based on streamflow data. The capability of the model in reproducing both the streamflow measurements and the land surface temperature from satellites has been investigated. The model has been then calibrated using satellite data and geomorphologic characteristics of the basin in order to test its application on a basin where standard hydrologic observations (e.g. streamflow data) are not available. The results have been compared with those obtained by the standard calibration strategy based on streamflow data.

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An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data

10.5194/hess-2019-105 ◽

2019 ◽

Cited By ~ 3

Author(s):

Bouchra Ait Hssaine ◽

Olivier Merlin ◽

Jamal Ezzahar ◽

Nitu Ojha ◽

Salah Er-raki ◽

...

Keyword(s):

Soil Moisture ◽

Energy Balance ◽

Surface Temperature ◽

Land Surface Temperature ◽

Vegetation Cover ◽

Land Surface ◽

Surface Soil ◽

Surface Soil Moisture ◽

Wide Range ◽

The Mean

Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1 km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014–2018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014–2018). The field was seeded for the 2014–2015 (S1), 2016–2017 (S2) and 2017–2018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015–2016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated αPT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved αPT remains at a mostly constant value (∼ 0.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181 W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62 W/m2 for S1, S2, S3 and B1 respectively.

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A Physically-Constrained Calibration Database for Land Surface Temperature Using Infrared Retrieval Algorithms

10.20944/preprints201608.0073.v2 ◽

2016 ◽

Author(s):

João P. A. Martins ◽

Isabel F. Trigo ◽

Virgílio A. Bento ◽

Carlos da Camara

Keyword(s):

Water Vapor ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Dynamic Range ◽

Single Channel ◽

Level Structure ◽

Full Range ◽

Wide Range ◽

Total Column

Land Surface Temperature (LST) is routinely retrieved from remote sensing instruments using semi-empirical relationships between top of atmosphere (TOA) radiances and LST, using ancillary data such as total column water vapor or emissivity. These algorithms are calibrated using a set of forward radiative transfer simulations that return the TOA radiances given the LST and the thermodynamic profiles. The simulations are done in order to cover a wide range of surface and atmospheric conditions and viewing geometries. This work analyses calibration strategies, considering some of the most critical factors that need to be taken into account when building a calibration dataset, covering the full dynamic range of relevant variables. A sensitivity analysis of split-windows and single channel algorithms revealed that selecting a set of atmospheric profiles that spans the full range of surface temperatures and total column water vapor combinations that are physically possible seems beneficial for the quality of the regression model. However, the calibration is extremely sensitive to the low-level structure of the atmosphere indicating that the presence of atmospheric boundary layer features such as temperature inversions or strong vertical gradients of thermodynamic properties may affect LST retrievals in a non-trivial way. This article describes the criteria established in the EUMETSAT Land Surface Analysis – Satellite Application Facility to calibrate its LST algorithms applied both for current and forthcoming sensors.

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Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations

Journal of Climate ◽

10.1175/2009jcli2900.1 ◽

2010 ◽

Vol 23 (3) ◽

pp. 618-633 ◽

Cited By ~ 332

Author(s):

Arnon Karnieli ◽

Nurit Agam ◽

Rachel T. Pinker ◽

Martha Anderson ◽

Marc L. Imhoff ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Growing Season ◽

Primary Factor ◽

Drought Monitoring ◽

Negative Slope ◽

Limiting Factor ◽

Vegetation Growth ◽

Wide Range

Abstract A large number of water- and climate-related applications, such as drought monitoring, are based on spaceborne-derived relationships between land surface temperature (LST) and the normalized difference vegetation index (NDVI). The majority of these applications rely on the existence of a negative slope between the two variables, as identified in site- and time-specific studies. The current paper investigates the generality of the LST–NDVI relationship over a wide range of moisture and climatic/radiation regimes encountered over the North American continent (up to 60°N) during the summer growing season (April–September). Information on LST and NDVI was obtained from long-term (21 years) datasets acquired with the Advanced Very High Resolution Radiometer (AVHRR). It was found that when water is the limiting factor for vegetation growth (the typical situation for low latitudes of the study area and during the midseason), the LST–NDVI correlation is negative. However, when energy is the limiting factor for vegetation growth (in higher latitudes and elevations, especially at the beginning of the growing season), a positive correlation exists between LST and NDVI. Multiple regression analysis revealed that during the beginning and the end of the growing season, solar radiation is the predominant factor driving the correlation between LST and NDVI, whereas other biophysical variables play a lesser role. Air temperature is the primary factor in midsummer. It is concluded that there is a need to use empirical LST–NDVI relationships with caution and to restrict their application to drought monitoring to areas and periods where negative correlations are observed, namely, to conditions when water—not energy—is the primary factor limiting vegetation growth.

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VALIDATION AND UPSCALING OF SOIL MOISTURE SATELLITE PRODUCTS IN ROMANIA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b2-313-2016 ◽

2016 ◽

Vol XLI-B2 ◽

pp. 313-317 ◽

Cited By ~ 1

Author(s):

I. Sandric ◽

A. Diamandi ◽

N. Oana ◽

D. Saizu ◽

C. Vasile ◽

...

Keyword(s):

Soil Moisture ◽

Surface Temperature ◽

Land Surface Temperature ◽

Soil Texture ◽

Soil Type ◽

Land Surface ◽

Vegetation Type ◽

In Situ Measurements ◽

Wide Range

The study presents the validation of SMOS soil moisture satellite products for Romania. The validation was performed with in-situ measurements spatially distributed over the country and with in-situ measurements concentrated in on small area. For country level a number of 20 stations from the national meteorological observations network in Romania were selected. These stations have in-situ measurements for soil moisture in the first 5 cm of the soil surface. The stations are more or less distributed in one pixel of SMOS, but it has the advantage that covers almost all the country with a wide range of environmental conditions. Additionally 10 mobile soil moisture measurements stations were acquired and installed. These are spatially concentrated in one SMOS pixel in order to have a more detailed validation against the soil type, soil texture, land surface temperature and vegetation type inside one pixel. The results were compared and analyzed for each day, week, season, soil type, and soil texture and vegetation type. Minimum, maximum, mean and standard deviation were extracted and analyzed for each validation criteria and a hierarchy of those were performed. An upscaling method based on the relations between soil moisture, land surface temperature and vegetation indices was tested and implemented. The study was financed by the Romanian Space Agency within the framework of ASSIMO project <a href="http://assimo.meteoromania.ro"target="_blank">http://assimo.meteoromania.ro</a>.

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A Global Analysis of Land Surface Temperature Diurnal Cycle Using MODIS Observations

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-18-0256.1 ◽

2019 ◽

Vol 58 (6) ◽

pp. 1279-1291 ◽

Cited By ~ 6

Author(s):

Zahra Sharifnezhadazizi ◽

Hamid Norouzi ◽

Satya Prakash ◽

Christopher Beale ◽

Reza Khanbilvardi

Keyword(s):

Land Cover ◽

Surface Temperature ◽

Land Surface Temperature ◽

Diurnal Cycle ◽

Land Surface ◽

Interpolation Method ◽

Diurnal Variations ◽

Maximum Temperature ◽

Land Cover Types ◽

Wide Range

AbstractDiurnal variations of land surface temperature (LST) play a vital role in a wide range of applications such as climate change assessment, land–atmosphere interactions, and heat-related health issues in urban regions. This study uses 15 years (2003–17) of daily observations of LST Collection 6 from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on board the Aqua and the Terra satellites. A spline interpolation method is used to estimate half-hourly global LST from the MODIS measurements. A preliminary assessment of interpolated LST with hourly ground-based observations over selected stations of North America shows bias and an error of less than 1 K. Results suggest that the present interpolation method is capable of capturing the diurnal variations of LST reasonably well for different land-cover types. The diurnal cycle of LST and time of occurrence of maximum temperature are computed from the spatially and temporally consistent interpolated diurnal LST data at a global scale. Regions with higher variability in the timing of maximum LST hours and diurnal amplitude are identified in this study. The global desert regions show generally small variability of the monthly mean diurnal LST range, whereas larger areas of the global land exhibit rather higher variability in the diurnal LST range during the study period. Moreover, the changes in diurnal temperature range for the study period are examined for distinct land-cover types. Analysis of the 15-yr time series of the diurnal LST record shows an overall decrease of 0.5 K in amplitude over the Northern Hemisphere. However, the diurnal LST range shows variant changes in the Southern Hemisphere.

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A Physically-Constrained Calibration Database for Land Surface Temperature Using Infrared Retrieval Algorithms

10.20944/preprints201608.0073.v1 ◽

2016 ◽

Author(s):

João P. A. Martins ◽

Isabel F. Trigo ◽

Virgílio A. Bento ◽

Carlos da Camara

Keyword(s):

Water Vapor ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Dynamic Range ◽

Single Channel ◽

Level Structure ◽

Full Range ◽

Wide Range ◽

Total Column

Land Surface Temperature (LST) is routinely retrieved from remote sensing instruments using semi-empirical relationships between top of atmosphere (TOA) radiances and LST, using ancillary data such as total column water vapor or emissivity. These algorithms are calibrated using a set of forward radiative transfer simulations that return the TOA radiances given the LST and the thermodynamic profiles. The simulations are done in order to cover a wide range of surface and atmospheric conditions and viewing geometries. This work analyses calibration strategies, considering some of the most critical factors that need to be taken into account when building a calibration dataset, covering the full dynamic range of relevant variables. A sensitivity analysis of split-windows and single channel algorithms revealed that selecting a set of atmospheric profiles that spans the full range of surface temperatures and total column water vapor combinations that are physically possible seems beneficial for the quality of the regression model. However, the calibration is extremely sensitive to the low-level structure of the atmosphere indicating that the presence of atmospheric boundary layer features such as temperature inversions or strong vertical gradients of thermodynamic properties may affect LST retrievals in a non-trivial way.

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VALIDATION AND UPSCALING OF SOIL MOISTURE SATELLITE PRODUCTS IN ROMANIA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b2-313-2016 ◽

2016 ◽

Vol XLI-B2 ◽

pp. 313-317

Author(s):

I. Sandric ◽

A. Diamandi ◽

N. Oana ◽

D. Saizu ◽

C. Vasile ◽

...

Keyword(s):

Soil Moisture ◽

Surface Temperature ◽

Land Surface Temperature ◽

Soil Texture ◽

Soil Type ◽

Land Surface ◽

Vegetation Type ◽

In Situ Measurements ◽

Wide Range

The study presents the validation of SMOS soil moisture satellite products for Romania. The validation was performed with in-situ measurements spatially distributed over the country and with in-situ measurements concentrated in on small area. For country level a number of 20 stations from the national meteorological observations network in Romania were selected. These stations have in-situ measurements for soil moisture in the first 5 cm of the soil surface. The stations are more or less distributed in one pixel of SMOS, but it has the advantage that covers almost all the country with a wide range of environmental conditions. Additionally 10 mobile soil moisture measurements stations were acquired and installed. These are spatially concentrated in one SMOS pixel in order to have a more detailed validation against the soil type, soil texture, land surface temperature and vegetation type inside one pixel. The results were compared and analyzed for each day, week, season, soil type, and soil texture and vegetation type. Minimum, maximum, mean and standard deviation were extracted and analyzed for each validation criteria and a hierarchy of those were performed. An upscaling method based on the relations between soil moisture, land surface temperature and vegetation indices was tested and implemented. The study was financed by the Romanian Space Agency within the framework of ASSIMO project <a href="http://assimo.meteoromania.ro"target="_blank">http://assimo.meteoromania.ro</a>.

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Spatial Hotspot Analysis of Bucharest’s Urban Heat Island (UHI) Using Modis Data

Annals of Valahia University of Targoviste Geographical Series ◽

10.2478/avutgs-2018-0002 ◽

2018 ◽

Vol 18 (1) ◽

pp. 14-22 ◽

Cited By ~ 1

Author(s):

Georgiana Grigoraș ◽

Bogdan Urițescu

Keyword(s):

Surface Temperature ◽

Air Temperature ◽

Land Surface Temperature ◽

Hot Spots ◽

Land Surface ◽

Hot Spot ◽

Monitoring Network ◽

Residential Areas ◽

Hotspot Analysis ◽

The City

Abstract The aim of the study is to find the relationship between the land surface temperature and air temperature and to determine the hot spots in the urban area of Bucharest, the capital of Romania. The analysis was based on images from both moderate-resolution imaging spectroradiometer (MODIS), located on both Terra and Aqua platforms, as well as on data recorded by the four automatic weather stations existing in the endowment of The National Air Quality Monitoring Network, from the summer of 2017. Correlation coefficients between land surface temperature and air temperature were higher at night (0.8-0.87) and slightly lower during the day (0.71-0.77). After the validation of satellite data with in-situ temperature measurements, the hot spots in the metropolitan area of Bucharest were identified using Getis-Ord spatial statistics analysis. It has been achieved that the “very hot” areas are grouped in the center of the city and along the main traffic streets and dense residential areas. During the day the "very hot spots” represent 33.2% of the city's surface, and during the night 31.6%. The area where the mentioned spots persist, falls into the "very hot spot" category both day and night, it represents 27.1% of the city’s surface and it is mainly represented by the city center.

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