The impact of the SSM/I antenna gain function on land surface parameter retrieval

Abstract This paper introduces a new method to improve land surface model skill by merging different available precipitation datasets, given that an accurate land surface parameter ground truth is available. Precipitation datasets are merged with the objective of improving terrestrial water and energy cycle simulation skill, unlike most common methods in which the merging skills are evaluated by comparing the results with gauge data or selected reference data. The optimal merging method developed in this study minimizes the simulated land surface parameter (soil moisture, temperature, etc.) errors using the Noah land surface model with the Nelder–Mead (downhill simplex) method. In addition to improving the simulation skills, this method also impedes the adverse impacts of single-source precipitation data errors. Analysis has indicated that the results from the optimally merged precipitation product have fewer errors in other land surface states and fluxes such as evapotranspiration (ET), discharge R, and skin temperature T than do simulation results obtained by forcing the model using the precipitation products individually. It is also found that, using this method, the true knowledge of soil moisture information minimized land surface modeling errors better than the knowledge of other land surface parameters (ET, R, and T). Results have also shown that, although it does not have the true precipitation information, the method has associated heavier weights with the precipitation product that has intensity, amount, and frequency that are similar to those of the true precipitation.

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Contribution Analysis of the Spatial-Temporal Changes in Streamflow in a Typical Elevation Transitional Watershed of Southwest China over the Past Six Decades

Forests ◽

10.3390/f10060495 ◽

2019 ◽

Vol 10 (6) ◽

pp. 495 ◽

Cited By ~ 2

Author(s):

Chengcheng Meng ◽

Huilan Zhang ◽

Yujie Wang ◽

Yunqi Wang ◽

Jian Li ◽

...

Keyword(s):

Land Surface ◽

Southwest China ◽

Potential Evapotranspiration ◽

Influencing Factor ◽

Climate Impacts ◽

Spatial And Temporal Scales ◽

Attribution Analysis ◽

Land Surface Parameter ◽

The Impact

Attribution analyses on streamflow variation to changing climate and land surface characteristics are critical in studies of watershed hydrology. However, attribution results may differ greatly on different spatial and temporal scales, which has not been extensively studied previously. This study aims to investigate the spatial-temporal contributions of climate change and underlying surface variation to streamflow alteration using Budyko framework. Jiangling River Watershed (JRW), a typical landform transitional watershed in Southwest China, was chosen as the study area. The watershed was firstly divided into eight sub-basins by hydrologic stations, and hydrometeorological series (1954–2015) were divided into sub-intervals to discriminate spatial-temporal features. The results showed that long-term tendencies of hydrometeorological variables, i.e., precipitation (P), potential evapotranspiration (E0), and runoff depth (R), exhibited clear spatial patterns, which were highly related to topographic characteristics. Additionally, sensitivity analysis, which interpreted the effect of one driving factor by unit change, showed that climate factors P and E0, and catchment characteristics (land surface parameter n) played positive, negative, and negative roles in R, according to elastic coefficients (ε), respectively. The spatial distribution of ε illustrated a greater sensitivity and heterogeneity in the plateau and semi-humid regions (upstream). Moreover, the results from attribution analysis showed that the contribution of the land surface factor accounted for approximately 80% of the R change for the entire JRW, with an obvious spatial variation. Furthermore, tendencies of the contribution rates demonstrated regulations across different sub-regions: a decreasing trend of land surface impacts in trunk stream regions and increasing tendencies in tributary regions, and vice versa for climate impacts. Overall, both hydrometeorological variables and contributions of influencing factors presented regularities in long-term tendencies across different sub-regions. More particularly, the impact of the primary influencing factor on all sub-basins exhibited a decreasing trend over time. The evidence that climate and land surface change act on streamflow in a synergistic way, would complicate the attribution analysis and bring a new challenge to attribution analysis.

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European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset

International Journal of Remote Sensing ◽

10.1080/01431160310001618149 ◽

2004 ◽

Vol 25 (17) ◽

pp. 3303-3330 ◽

Cited By ~ 260

Author(s):

R. Stöckli ◽

P. L. Vidale

Keyword(s):

Land Surface ◽

Plant Phenology ◽

Surface Parameter ◽

Land Surface Parameter

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

Geoscientific Model Development Discussions ◽

10.5194/gmdd-8-2053-2015 ◽

2015 ◽

Vol 8 (2) ◽

pp. 2053-2100 ◽

Cited By ~ 1

Author(s):

S. Garrigues ◽

A. Olioso ◽

D. Carrer ◽

B. Decharme ◽

E. Martin ◽

...

Keyword(s):

Spatial Resolution ◽

Soil Texture ◽

Land Surface ◽

Large Scale ◽

Shortwave Radiation ◽

Soil Parameters ◽

Surface Model ◽

Land Surface Parameter ◽

The Mean ◽

The Impact

Abstract. Generic land surface models are generally driven by large-scale forcing datasets to describe the climate, the surface characteristics (soil texture, vegetation dynamic) and the cropland management (irrigation). This paper investigates the errors in these forcing variables 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 datasets used in the standard implementation of ISBA over France where the model is driven by the SAFRAN high spatial resolution atmospheric reanalysis, the Leaf Area Index (LAI) cycles 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 datasets which includes the ERA-Interim low spatial resolution reanalysis, the Global Precipitation Climatology Centre dataset (GPCC) and the MeteoSat Second Generation (MSG) satellite estimate of downwelling shortwave radiations. The methodology consists in comparing the simulation achieved using large-scale forcing datasets with the simulation achieved using local observations for each forcing variable. The relative impacts of the forcing variables on simulated ET are compared with each other and with the model uncertainties triggered by errors in soil parameters. LAI and the lack of irrigation in the simulation generate the largest mean deviations in ET between the large-scale and the local-scale simulations (equivalent to 24 and 19 months of ET over 12 yr). The climate induces smaller mean deviations equivalent to 7–8 months of ET over 12 yr. The soil texture has the lowest impact (equivalent to 3 months of ET). However, the impact of errors in the forcing variables is smaller than the impact triggered by errors in the soil parameters (equivalent to 27 months of ET). The absence of irrigation which represents 18% of cumulative rainfall over 12 years induces a deficit in ET of 14%. It generates much larger variations in incoming water for the model than the differences in rainfall between the reanalysis datasets. ET simulated with the Ecoclimap-II LAI climatology is overestimated by 18% over 12 years. This is related to the overestimation of the mean LAI over the crop cycle which reveals inaccurate representation of Mediterranean crop cycles. Compared to SAFRAN, the use of the ERA-I reanalysis, the GPCC rainfall and the downwelling shortwave radiation derived from the MSG satellite have little influence on the ET simulation performances. The error in yearly ET is mainly driven by the error in yearly rainfall and to a less extent by radiations. The SAFRAN and MSG satellite shortwave radiation estimates show similar negative biases (−9 and −11 W m−2). The ERA-I bias in shortwave radiations is 4 times smaller at daily time scale. Both SAFRAN and ERA-I underestimate longwave downwelling radiations by −12 and −16 W m−2, respectively. The biases in shortwave and longwave radiations show larger inter-annual variation for SAFRAN than for ERA-I. Regarding rainfall, SAFRAN and ERA-I/GPCC are slightly biased at daily and longer time scales (1 and 0.5% of the mean rainfall measurement). The SAFRAN rainfall estimates are more precise due to the use of the in situ daily rainfall measurements of the Avignon site in the reanalysis.

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