scholarly journals Influence of Precipitation Forcing Uncertainty on Hydrological Simulations with the NASA South Asia Land Data Assimilation System

Hydrology ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 57 ◽  
Author(s):  
Debjani Ghatak ◽  
Benjamin Zaitchik ◽  
Sujay Kumar ◽  
Mir A. Matin ◽  
Birendra Bajracharya ◽  
...  
Keyword(s):  
South Asia ◽  
Hydrological Simulation ◽  
Meteorological Forcing ◽  
In Situ Data ◽  
Precipitation Estimates ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

: Accurate meteorological estimates are critical for process-based hydrological simulation and prediction. This presents a significant challenge in mountainous Asia where in situ meteorological stations are limited and major river basins cross international borders. In this context, remotely sensed and model-derived meteorological estimates are often necessary inputs for distributed hydrological analysis. However, these datasets are difficult to evaluate on account of limited access to ground data. In this case, the implications of uncertainty associated with precipitation forcing for hydrological simulations is explored by driving the South Asia Land Data Assimilation System (South Asia LDAS) using a range of meteorological forcing products. MERRA2, GDAS, and CHIRPS produce a wide range of estimates for rainfall, which causes a widespread simulated streamflow and evapotranspiration. A combination of satellite-derived and limited in situ data are applied to evaluate model simulations and, by extension, to constrain the estimates of precipitation. The results show that available gridded precipitation estimates based on in situ data may systematically underestimate precipitation in mountainous regions and that performance of gridded satellite-derived or modeled precipitation estimates varies systematically across the region. Since no station-based data or product including station data is satisfactory everywhere, our results suggest that the evaluation of the hydrological simulation of streamflow and ET can be used as an indirect evaluation of precipitation forcing based on ground-based products or in-situ data. South Asia LDAS produces reasonable evapotranspiration and streamflow when forced with appropriate meteorological forcing and the choice of meteorological forcing should be made based on the geographical location as well as on the purpose of the simulations.

scholarly journals An all-sky 1 km daily surface air temperature product over mainland China for 2003–2019 from MODIS and ancillary data

2021 ◽  
Author(s):  
Yan Chen ◽  
Shunlin Liang ◽  
Han Ma ◽  
Bing Li ◽  
Tao He ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Meteorological Forcing ◽  
Clear Sky ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Sky Conditions ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. Surface air temperature (Ta), as an important climate variable, has been used in a wide range of fields such as ecology, hydrology, climatology, epidemiology, and environmental science. However, ground measurements are limited by poor spatial representation and inconsistency, while reanalysis and meteorological forcing datasets suffer from coarse spatial resolution and inaccuracy. Previous studies using satellite data have mainly estimated Ta under clear-sky conditions, or with limited temporal and spatial coverage. In this study, an all-sky daily mean Ta product at 1 km spatial resolution over mainland China for 2003–2019 has been generated mainly from the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Global Land Data Assimilation System (GLDAS) dataset. Three Ta estimation models based on random forest were trained using ground measurements from 2384 stations for three different clear-sky and cloudy-sky conditions. The validation results showed that R2 and root mean square error (RMSE) values of the three models ranged from 0.984 to 0.986 and 1.342 K to 1.440 K, respectively. We examined the spatiotemporal patterns and land cover type dependences of model accuracy. The relative contributions of different features to models were also quantitatively analysed. Finally, values of our Ta product in 2010 were validated and compared with the China Land Data Assimilation System (CLDAS) dataset at 0.0625° spatial resolution, China Meteorological Forcing Data (CMFD) dataset at 0.1° spatial resolution and GLDAS dataset at 0.25° spatial resolution. The R2 and RMSE values of our product were 0.992 and 1.010 K, respectively, indicating this high-resolution satellite product has significantly higher accuracy. In summary, the all-sky daily mean Ta dataset developed in this study has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in the studies of climate change and hydrological cycle. This dataset is freely available at http://doi.org/10.5281/zenodo.4399453 (Chen et al., 2021b) and the University of Maryland (http://glass.umd.edu/Ta_China/) currently. A sub-dataset that covers Beijing generated from this dataset is publicly available at http://doi.org/10.5281/zenodo.4405123 (Chen et al., 2021a).

scholarly journals An all-sky 1 km daily land surface air temperature product over mainland China for 2003–2019 from MODIS and ancillary data

2021 ◽  
Vol 13 (8) ◽  
pp. 4241-4261
Author(s):  
Yan Chen ◽  
Shunlin Liang ◽  
Han Ma ◽  
Bing Li ◽  
Tao He ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Meteorological Forcing ◽  
Clear Sky ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Sky Conditions ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. Surface air temperature (Ta), as an important climate variable, has been used in a wide range of fields such as ecology, hydrology, climatology, epidemiology, and environmental science. However, ground measurements are limited by poor spatial representation and inconsistency, and reanalysis and meteorological forcing datasets suffer from coarse spatial resolution and inaccuracy. Previous studies using satellite data have mainly estimated Ta under clear-sky conditions or with limited temporal and spatial coverage. In this study, an all-sky daily mean land Ta product at a 1 km spatial resolution over mainland China for 2003–2019 has been generated mainly from the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Global Land Data Assimilation System (GLDAS) dataset. Three Ta estimation models based on random forest were trained using ground measurements from 2384 stations for three different clear-sky and cloudy-sky conditions. The random sample validation results showed that the R2 and root-mean-square error (RMSE) values of the three models ranged from 0.984 to 0.986 and from 1.342 to 1.440 K, respectively. We examined the spatiotemporal patterns and land cover type dependences of model accuracy. Two cross-validation (CV) strategies of leave-time-out (LTO) CV and leave-location-out (LLO) CV were also used to evaluate the models. Finally, we developed the all-sky Ta dataset from 2003 to 2009 and compared it with the China Land Data Assimilation System (CLDAS) dataset at a 0.0625∘ spatial resolution, the China Meteorological Forcing Data (CMFD) dataset at a 0.1∘ spatial resolution, and the GLDAS dataset at a 0.25∘ spatial resolution. Validation accuracy of our product in 2010 was significantly better than other datasets, with R2 and RMSE values of 0.992 and 1.010 K, respectively. In summary, the developed all-sky daily mean land Ta dataset has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in the studies of climate change and the hydrological cycle. This dataset is currently freely available at https://doi.org/10.5281/zenodo.4399453 (Chen et al., 2021b) and the University of Maryland (http://glass.umd.edu/Ta_China/, last access: 24 August 2021). A sub-dataset that covers Beijing generated from this dataset is also publicly available at https://doi.org/10.5281/zenodo.4405123 (Chen et al., 2021a).

PENERAPAN MACHINE LEARNING BERBASIS DATA GEOSPASIAL UNTUK OPTIMALISASI LAHAN PERTANIAN PADA MASA PANDEMI DAN PASCA PANDEMI

2021 ◽  
pp. 161
Author(s):  
Royyannuur Kurniawan Endrayanto ◽  
Adharul Muttaqin
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Early Warning Systems ◽  
Google Earth ◽  
Warning Systems ◽  
Land Data Assimilation ◽  
Google Earth Engine ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

Pertanian merupakan salah satu sektor penting karena dapat memenuhi kebutuhan pangan sebagai kebutuhan pokok. Kebutuhan pangan masih menjadi salah satu isu hangat terlebih di masa pandemi COVID- 19 seperti saat ini. Pemenuhan kebutuhan pangan juga berkaitan erat dengan jumlah bahan pangan yang diproduksi oleh petani. Lingkungan merupakan salah satu faktor keberhasilan dalam kegiatan pertanian. Kondisi lingkungan Indonesia yang beragam seperti suhu dan tingkat presipitasi menyebabkan adanya perbedaan jenis tanaman pangan potensial setiap daerah di Indonesia. Oleh karena itu perlu upaya untuk mengoptimalkan produksi lahan pertanian berdasarkan faktor lingkungan di setiap daerah. Upaya ini diharapkan dapat membantu menjaga ketahanan pangan baik di masa pandemi dan pasca pandemi. Pada penelitian ini diperkenalkan pemanfaatan data geospasial untuk klasifikasi jenis tanaman pangan menggunakan algoritma machine learning sebagai upaya optimalisasi lahan pertanian. Data yang digunakan adalah Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). Algoritma machine learning yang digunakan adalah algoritma klasifikasi Random Forest. Teknologi yang digunakan adalah Google Colab, Google Earth Engine dan Python. Tujuan dari penelitian ini adalah untuk mengklasifikasikan tanaman pangan yang memiliki potensi paling baik untuk ditanam di suatu daerah berdasarkan kondisi lingkungan yang ada.

scholarly journals Evaluation of multiple forcing data sets for precipitation and shortwave radiation over major land areas of China

2017 ◽  
Vol 21 (11) ◽  
pp. 5805-5821 ◽  
Author(s):  
Fan Yang ◽  
Hui Lu ◽  
Kun Yang ◽  
Jie He ◽  
Wei Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Variations ◽  
Spatiotemporal Variability ◽  
Shortwave Radiation ◽  
Data Sets ◽  
Precipitation Data ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. Precipitation and shortwave radiation play important roles in climatic, hydrological and biogeochemical cycles. Several global and regional forcing data sets currently provide historical estimates of these two variables over China, including the Global Land Data Assimilation System (GLDAS), the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) and the China Meteorological Forcing Dataset (CMFD). The CN05.1 precipitation data set, a gridded analysis based on CMA gauge observations, also provides high-resolution historical precipitation data for China. In this study, we present an intercomparison of precipitation and shortwave radiation data from CN05.1, CMFD, CLDAS and GLDAS during 2008–2014. We also validate all four data sets against independent ground station observations. All four forcing data sets capture the spatial distribution of precipitation over major land areas of China, although CLDAS indicates smaller annual-mean precipitation amounts than CN05.1, CMFD or GLDAS. Time series of precipitation anomalies are largely consistent among the data sets, except for a sudden decrease in CMFD after August 2014. All forcing data indicate greater temporal variations relative to the mean in dry regions than in wet regions. Validation against independent precipitation observations provided by the Ministry of Water Resources (MWR) in the middle and lower reaches of the Yangtze River indicates that CLDAS provides the most realistic estimates of spatiotemporal variability in precipitation in this region. CMFD also performs well with respect to annual mean precipitation, while GLDAS fails to accurately capture much of the spatiotemporal variability and CN05.1 contains significant high biases relative to the MWR observations. Estimates of shortwave radiation from CMFD are largely consistent with station observations, while CLDAS and GLDAS greatly overestimate shortwave radiation. All three forcing data sets capture the key features of the spatial distribution, but estimates from CLDAS and GLDAS are systematically higher than those from CMFD over most of mainland China. Based on our evaluation metrics, CLDAS slightly outperforms GLDAS. CLDAS is also closer than GLDAS to CMFD with respect to temporal variations in shortwave radiation anomalies, with substantial differences among the time series. Differences in temporal variations are especially pronounced south of 34° N. Our findings provide valuable guidance for a variety of stakeholders, including land-surface modelers and data providers.

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