The Effects of Spatial and Temporal Resolution of Gridded Meteorological Forcing on Watershed Hydrological Responses

Abstract. Meteorological forcing plays a critical role in accurately simulating the watershed hydrological cycle. With the advancement of high-performance computing and the development of integrated watershed models, simulating the watershed hydrological cycle at high temporal (hourly to daily) and spatial resolution (10s of meters) has become efficient and computationally affordable. These hyperresolution watershed models require high resolution of meteorological forcing as model input to ensure the fidelity and accuracy of simulated responses. In this study, we utilized the Advanced Terrestrial Simulator (ATS), an integrated watershed model, to simulate surface and subsurface flow and land surface processes using unstructured meshes at the Coal Creek Watershed near Crested Butte (Colorado). We compared simulated watershed hydrologic responses including streamflow, and distributed variables such as evapotranspiration, snow water equivalent (SWE), and groundwater table drivenby three publicly available, gridded meteorological forcing (GMF) – Daily Surface Weather and Climatological Summaries (Daymet), Parameter-elevation Regressions on Independent Slopes Model (PRISM), and North American Land Data Assimilation System (NLDAS). By comparing various spatial resolutions (ranging from 400 m to 4 km) of PRISM, the simulated streamflow only becomes marginally worse when spatial resolution of meteorological forcing is coarsened to 4 km (or 30 % of the watershed area). However, the 4 km resolution has much worse performance than finer resolution in spatially distributedvariables such as SWE. By comparing models forced by different temporal resolutions of NLDAS (hourly to daily), GMF in sub-daily resolution preserves the dynamic watershed responses (e.g., diurnal fluctuation of streamflow) that are absent in results forced by daily resolution. Conversely, the simulated streamflow shows better performance using daily resolution compared to that using sub-daily resolution. Our findings suggest that the choice of GMF and its spatiotemporal resolution depends on the quantity of interest and its spatial and temporal scale, which may have important implications on model calibration and watershed management decisions.

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Near–surface air temperature and snow skin temperature comparison from CREST-SAFE station data with MODIS land surface temperature data

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-7665-2015 ◽

2015 ◽

Vol 12 (8) ◽

pp. 7665-7687 ◽

Cited By ~ 2

Author(s):

C. L. Pérez Díaz ◽

T. Lakhankar ◽

P. Romanov ◽

J. Muñoz ◽

R. Khanbilvardi ◽

...

Keyword(s):

Surface Temperature ◽

Skin Temperature ◽

Air Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Hydrological Cycle ◽

Critical Role ◽

Surface Air Temperature ◽

Balance Model ◽

Near Surface

Abstract. Land Surface Temperature (LST) is a key variable (commonly studied to understand the hydrological cycle) that helps drive the energy balance and water exchange between the Earth's surface and its atmosphere. One observable constituent of much importance in the land surface water balance model is snow. Snow cover plays a critical role in the regional to global scale hydrological cycle because rain-on-snow with warm air temperatures accelerates rapid snow-melt, which is responsible for the majority of the spring floods. Accurate information on near-surface air temperature (T-air) and snow skin temperature (T-skin) helps us comprehend the energy and water balances in the Earth's hydrological cycle. T-skin is critical in estimating latent and sensible heat fluxes over snow covered areas because incoming and outgoing radiation fluxes from the snow mass and the air temperature above make it different from the average snowpack temperature. This study investigates the correlation between MODerate resolution Imaging Spectroradiometer (MODIS) LST data and observed T-air and T-skin data from NOAA-CREST-Snow Analysis and Field Experiment (CREST-SAFE) for the winters of 2013 and 2014. LST satellite validation is imperative because high-latitude regions are significantly affected by climate warming and there is a need to aid existing meteorological station networks with the spatially continuous measurements provided by satellites. Results indicate that near-surface air temperature correlates better than snow skin temperature with MODIS LST data. Additional findings show that there is a negative trend demonstrating that the air minus snow skin temperature difference is inversely proportional to cloud cover. To a lesser extent, it will be examined whether the surface properties at the site are representative for the LST properties within the instrument field of view.

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Characterization of Northern Hemisphere Snow Water Equivalent Datasets, 1981–2010

Journal of Climate ◽

10.1175/jcli-d-15-0229.1 ◽

2015 ◽

Vol 28 (20) ◽

pp. 8037-8051 ◽

Cited By ~ 92

Author(s):

L. R. Mudryk ◽

C. Derksen ◽

P. J. Kushner ◽

R. Brown

Keyword(s):

Northern Hemisphere ◽

Land Surface ◽

Snow Water Equivalent ◽

European Space Agency ◽

Land Surface Model ◽

Surface Model ◽

Temporal Consistency ◽

Meteorological Forcing ◽

Temporal Correlations ◽

Snow Water

Abstract Five, daily, gridded, Northern Hemisphere snow water equivalent (SWE) datasets are analyzed over the 1981–2010 period in order to quantify the spatial and temporal consistency of satellite retrievals, land surface assimilation systems, physical snow models, and reanalyses. While the climatologies of total Northern Hemisphere snow water mass (SWM) vary among the datasets by as much as 50%, their interannual variability and daily anomalies are comparable, showing moderate to good temporal correlations (between 0.60 and 0.85) on both interannual and intraseasonal time scales. Wintertime trends of total Northern Hemisphere SWM are consistently negative over the 1981–2010 period among the five datasets but vary in strength by a factor of 2–3. Examining spatial patterns of SWE indicates that the datasets are most consistent with one another over boreal forest regions compared to Arctic and alpine regions. Additionally, the datasets derived using relatively recent reanalyses are strongly correlated with one another and show better correlations with the satellite product [the European Space Agency (ESA)’s Global Snow Monitoring for Climate Research (GlobSnow)] than do those using older reanalyses. Finally, a comparison of eight reanalysis datasets over the 2001–10 period shows that land surface model differences control the majority of spread in the climatological value of SWM, while meteorological forcing differences control the majority of the spread in temporal correlations of SWM anomalies.

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Bayesian Spatio-temporal Geostatistics-based Method for Multiple Satellite Products Fusion and Downscaling

10.5194/egusphere-egu2020-20814 ◽

2020 ◽

Author(s):

Yanchen Bo

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Satellite Remote Sensing ◽

Hydrological Cycle ◽

Microwave Remote Sensing ◽

Earth Surface ◽

Spatio Temporal ◽

High Level

High-level satellite remote sensing products of Earth surface play an irreplaceable role in global climate change, hydrological cycle modeling and water resources management, environment monitoring and assessment. Earth surface high-level remote sensing products released by NASA, ESA and other agencies are routinely derived from any single remote sensor. Due to the cloud contamination and limitations of retrieval algorithms, the remote sensing products derived from single remote senor are suspected to the incompleteness, low accuracy and less consistency in space and time. Some land surface remote sensing products, such as soil moisture products derived from passive microwave remote sensing data have too coarse spatial resolution to be applied at local scale. Fusion and downscaling is an effective way of improving the quality of satellite remote sensing products.We developed a Bayesian spatio-temporal geostatistics-based framework for multiple remote sensing products fusion and downscaling. Compared to the existing methods, the presented method has 2 major advantages. The first is that the method was developed in the Bayesian paradigm, so the uncertainties of the multiple remote sensing products being fused or downscaled could be quantified and explicitly expressed in the fusion and downscaling algorithms. The second advantage is that the spatio-temporal autocorrelation is exploited in the fusion approach so that more complete products could be produced by geostatistical estimation.This method has been applied to the fusion of multiple satellite AOD products, multiple satellite SST products, multiple satellite LST products and downscaling of 25 km spatial resolution soil moisture products. The results were evaluated in both spatio-temporal completeness and accuracy.

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Spatiotemporal Downscaling of GRACE Total Water Storage Using Land Surface Model Outputs

Remote Sensing ◽

10.3390/rs13050900 ◽

2021 ◽

Vol 13 (5) ◽

pp. 900

Author(s):

Detang Zhong ◽

Shusen Wang ◽

Junhua Li

Keyword(s):

Spatial Resolution ◽

Land Surface ◽

High Spatial Resolution ◽

Water Storage ◽

Land Surface Model ◽

Surface Model ◽

Total Water ◽

Spatiotemporal Resolution ◽

Spherical Cap ◽

High Spatiotemporal Resolution

High spatiotemporal resolution of terrestrial total water storage plays a key role in assessing trends and availability of water resources. This study presents a two-step method for downscaling GRACE-derived total water storage anomaly (GRACE TWSA) from its original coarse spatiotemporal resolution (monthly, 3-degree spherical cap/~300 km) to a high resolution (daily, 5 km) through combining land surface model (LSM) simulated high spatiotemporal resolution terrestrial water storage anomaly (LSM TWSA). In the first step, an iterative adjustment method based on the self-calibration variance-component model (SCVCM) is used to spatially downscale the monthly GRACE TWSA to the high spatial resolution of the LSM TWSA. In the second step, the spatially downscaled monthly GRACE TWSA is further downscaled to the daily temporal resolution. By applying the method to downscale the coarse resolution GRACE TWSA from the Jet Propulsion Laboratory (JPL) mascon solution with the daily high spatial resolution (5 km) LSM TWSA from the Ecological Assimilation of Land and Climate Observations (EALCO) model, we evaluated the benefit and effectiveness of the proposed method. The results show that the proposed method is capable to downscale GRACE TWSA spatiotemporally with reduced uncertainty. The downscaled GRACE TWSA are also evaluated through in-situ groundwater monitoring well observations and the results show a certain level agreement between the estimated and observed trends.

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An integrated model for the assessment of global water resources – Part 1: Input meteorological forcing and natural hydrological cycle modules

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-4-3535-2007 ◽

2007 ◽

Vol 4 (5) ◽

pp. 3535-3582 ◽

Cited By ~ 5

Author(s):

N. Hanasaki ◽

S. Kanae ◽

T. Oki ◽

K. Masuda ◽

K. Motoya ◽

...

Keyword(s):

Water Resources ◽

Land Surface ◽

Hydrological Cycle ◽

River Basins ◽

Integrated Model ◽

Surface Hydrology ◽

Meteorological Forcing ◽

Land Surface Hydrology ◽

Global Water ◽

River Routing

Abstract. An integrated global water resources model was developed consisting of six modules: land surface hydrology, river routing, crop growth, reservoir operation, environmental flow requirement estimation, and anthropogenic water withdrawal. It simulates both natural and anthropogenic water flow globally (excluding Antarctica) on a daily basis at a spatial resolution of 1°×1° (longitude and latitude). The simulation period is 10 years, from 1986 to 1995. This first part of the two-feature report describes the input meteorological forcing and natural hydrological cycle modules of the integrated model, namely the land surface hydrology module and the river routing module. The input meteorological forcing was provided by the second Global Soil Wetness Project (GSWP2), an international land surface modeling project. Several reported shortcomings of the forcing component were improved. The land surface hydrology module was developed based on a bucket type model that simulates energy and water balance on land surfaces. Simulated runoff was compared and validated with observation-based global runoff data sets and observed streamflow records at 32 major river gauging stations around the world. Mean annual runoff agreed well with earlier studies at global, continental, and continental zonal mean scales, indicating the validity of the input meteorological data and land surface hydrology module. In individual basins, the mean bias was less than ±20% in 14 of the 32 river basins and less than ±50% in 24 of the basins. The performance was similar to the best available precedent studies with closure of energy and water. The timing of the peak in streamflow and the shape of monthly hydrographs were well simulated in most of the river basins when large lakes or reservoirs did not affect them. The results indicate that the input meteorological forcing component and the land surface hydrology module provide a framework with which to assess global water resources, with the potential application to investigate the subannual variability in water resources. GSWP2 participants are encouraged to re-run their model using this newly developed meteorological forcing input, which is in identical format to the original GSWP2 forcing input.

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A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset

10.5194/essd-2016-55 ◽

2016 ◽

Cited By ~ 2

Author(s):

Jaap Schellekens ◽

Emanuel Dutra ◽

Alberto Martínez-de la Torre ◽

Gianpaolo Balsamo ◽

Albert van Dijk ◽

...

Keyword(s):

Land Surface ◽

Snow Water Equivalent ◽

Water Cycle ◽

Signal To Noise Ratio ◽

Model Performance ◽

Meteorological Forcing ◽

Unconstrained Model ◽

Total Runoff ◽

Ensemble Mean ◽

Benchmark Datasets

Abstract. The dataset presented here consists of an ensemble of ten global hydrological and land surface models for the period 1979–2012 using a reanalysis-based meteorological forcing dataset (0.5° resolution). The current dataset serves as a state-of-the-art in current global hydrological modelling and as a benchmark for further improvements in the coming years. A signal-to-noise ratio analysis revealed low inter-model agreement over (i) snow-dominate regions and (ii) tropical rainforest and monsoon areas. The large uncertainty of precipitation in the tropics is not being reflected in the ensemble runoff. Verification of the results against benchmark datasets for evapotranspiration, snow cover, snow water equivalent, soil moisture anomaly and total water storage anomaly using the tools from The International Land Model Benchmarking Project (ILAMB) showed overall useful model performance, while the ensemble mean generally outperformed the single model estimates. The results also show that there is currently no single best model for all variables and that model performance is spatially variable. In our unconstrained model runs the ensemble mean of total runoff into the ocean was 46 268 km3/yr (334 kg/m2/yr) while the ensemble mean of total evaporation was 537 kg/m2/yr. All data are made available openly through a Water Cycle Integrator portal (WCI, wci.earth2observe.eu), and via a direct http and ftp download. The portal follows the protocols of the open geospatial consortium such as OPeNDAP, WCS and WMS. The doi for the data is: doi:10.5281/zenodo.167070

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High spatiotemporal resolution and low photo-toxicity fluorescence imaging in live cells and in vivo

Biochemical Society Transactions ◽

10.1042/bst20190020 ◽

2019 ◽

Vol 47 (6) ◽

pp. 1635-1650 ◽

Cited By ~ 4

Author(s):

Xiaohong Peng ◽

Xiaoshuai Huang ◽

Ke Du ◽

Huisheng Liu ◽

Liangyi Chen

Keyword(s):

Fluorescence Microscopy ◽

Spatial Resolution ◽

Cell Biology ◽

Critical Role ◽

Super Resolution ◽

Light Sheet ◽

Live Cells ◽

Spatiotemporal Resolution ◽

And Function

Taking advantage of high contrast and molecular specificity, fluorescence microscopy has played a critical role in the visualization of subcellular structures and function, enabling unprecedented exploration from cell biology to neuroscience in living animals. To record and quantitatively analyse complex and dynamic biological processes in real time, fluorescence microscopes must be capable of rapid, targeted access deep within samples at high spatial resolutions, using techniques including super-resolution fluorescence microscopy, light sheet fluorescence microscopy, and multiple photon microscopy. In recent years, tremendous breakthroughs have improved the performance of these fluorescence microscopies in spatial resolution, imaging speed, and penetration. Here, we will review recent advancements of these microscopies in terms of the trade-off among spatial resolution, sampling speed and penetration depth and provide a view of their possible applications.

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A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions

Journal of Climate ◽

10.1175/jcli-d-12-00508.1 ◽

2013 ◽

Vol 26 (23) ◽

pp. 9384-9392 ◽

Cited By ~ 336

Author(s):

Ben Livneh ◽

Eric A. Rosenberg ◽

Chiyu Lin ◽

Bart Nijssen ◽

Vimal Mishra ◽

...

Keyword(s):

United States ◽

Spatial Resolution ◽

Land Surface ◽

Snow Water Equivalent ◽

Meteorological Data ◽

Surface Fluxes ◽

Heat Fluxes ◽

Infiltration Capacity ◽

Surface Heat Fluxes

This paper describes a publicly available, long-term (1915–2011), hydrologically consistent dataset for the conterminous United States, intended to aid in studies of water and energy exchanges at the land surface. These data are gridded at a spatial resolution of [Formula: see text] latitude/longitude and are derived from daily temperature and precipitation observations from approximately 20 000 NOAA Cooperative Observer (COOP) stations. The available meteorological data include temperature, precipitation, and wind, as well as derived humidity and downwelling solar and infrared radiation estimated via algorithms that index these quantities to the daily mean temperature, temperature range, and precipitation, and disaggregate them to 3-hourly time steps. Furthermore, the authors employ the variable infiltration capacity (VIC) model to produce 3-hourly estimates of soil moisture, snow water equivalent, discharge, and surface heat fluxes. Relative to an earlier similar dataset by Maurer and others, the improved dataset has 1) extended the period of analysis (1915–2011 versus 1950–2000), 2) increased the spatial resolution from ⅛° to [Formula: see text], and 3) used an updated version of VIC. The previous dataset has been widely used in water and energy budget studies, climate change assessments, drought reconstructions, and for many other purposes. It is anticipated that the spatial refinement and temporal extension will be of interest to a wide cross section of the scientific community.

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Testing gridded land precipitation data and precipitation and runoff reanalyses (1982–2010) between 45° S and 45° N with normalised difference vegetation index data

Hydrology and Earth System Sciences ◽

10.5194/hess-19-1713-2015 ◽

2015 ◽

Vol 19 (4) ◽

pp. 1713-1725 ◽

Cited By ~ 9

Author(s):

S. O. Los

Keyword(s):

Spatial Resolution ◽

Land Surface ◽

General Circulation ◽

Vegetation Index ◽

Hydrological Cycle ◽

General Circulation Models ◽

Precipitation Data ◽

Index Data ◽

Normalised Difference Vegetation Index ◽

Adverse Weather

Abstract. The realistic simulation of key components of the land-surface hydrological cycle – precipitation, runoff, evaporation and transpiration, in general circulation models of the atmosphere – is crucial to assess adverse weather impacts on environment and society. Here, gridded precipitation data from observations and precipitation and runoff fields from reanalyses were tested with satellite derived global vegetation index data for 1982–2010 and latitudes between 45° S and 45° N. Data were obtained from the Climate Research Unit (CRU), the Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Monitoring Mission (TRMM; analysed for 1998–2010 only) and precipitation and runoff reanalyses were obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the NASA Global Modelling and Assimilation Office (GMAO). Annual land-surface precipitation was converted to annual potential vegetation net primary productivity (NPP) and was compared to mean annual normalised difference vegetation index (NDVI) data measured by the Advanced Very High Resolution Radiometer (AVHRR; 1982–1999) and Moderate Resolution Imaging Spectroradiometer (MODIS; 2001–2010). The effect of spatial resolution on the agreement between NPP and NDVI was investigated as well. The CRU and TRMM derived NPP agreed most closely with the NDVI data. The GPCP data showed weaker spatial agreement, largely because of their lower spatial resolution, but similar temporal agreement. MERRA Land and ERA Interim precipitation reanalyses showed similar spatial agreement to the GPCP data and good temporal agreement in semi-arid regions of the Americas, Asia, Australia and southern Africa. The NCEP/NCAR reanalysis showed the lowest spatial agreement, which could only in part be explained by its lower spatial resolution. No reanalysis showed realistic interannual precipitation variations for northern tropical Africa. Inclusion of runoff in the NPP prediction resulted only in marginally better agreement for the MERRA Land reanalysis and slightly worse agreement for the NCEP/NCAR and ERA Interim reanalyses.

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Testing gridded land precipitation data and precipitation and runoff reanalyses (1982–2010) between 45° S and 45° N with Normalized Difference Vegetation Index data

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-13175-2014 ◽

2014 ◽

Vol 11 (12) ◽

pp. 13175-13205 ◽

Cited By ~ 1

Author(s):

S. O. Los

Keyword(s):

Spatial Resolution ◽

Land Surface ◽

General Circulation ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Hydrological Cycle ◽

General Circulation Models ◽

Precipitation Data ◽

Index Data ◽

Adverse Weather

Abstract. The realistic simulation of key components of the land-surface hydrological cycle – precipitation, runoff, evaporation and transpiration – in general circulation models of the atmosphere is crucial to assess adverse weather impacts on environment and society. Here, gridded precipitation data from observations and precipitation and runoff fields from reanalyses were tested with satellite-derived global vegetation index data for 1982–2010 and latitudes between 45° S and 45° N. Data were obtained from the Climate Research Unit (CRU), the Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Monitoring Mission (TRMM; analysed for 1998–2010 only) and (precipitation and runoff) reanalyses were obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the NASA Global Modelling and Assimilation Office (GMAO). Annual land-surface precipitation was converted to annual potential vegetation net primary productivity (NPP) and was compared to mean annual Normalized Difference Vegetation Index data measured by the Advanced Very High Resolution Radiometer (1982–1999) and MODIS (2001–2010). The effect of spatial resolution on the agreement between NPP and NDVI was investigated as well. The CRU and TRMM derived NPP agreed most closely with the NDVI data. The GPCP data showed weaker spatial agreement, largely because of their lower spatial resolution, but similar temporal agreement. MERRA Land and ERA Interim precipitation reanalyses showed similar spatial agreement as the GPCP data and good temporal agreement in semi-arid regions of the Americas, Asia, Australia and southern Africa. The NCEP/NCAR reanalysis showed the lowest spatial agreement which could only in part be explained by its lower spatial resolution. No reanalysis showed realistic interannual precipitation variations for northern tropical Africa. Inclusion of runoff in the NPP prediction resulted only in (marginally) better agreement for the MERRA Land reanalysis and worse agreement for the NCEP/NCAR and ERA Interim reanalyses.

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