scholarly journals Decreasing water resources in Southeastern U.S. as observed by the GRACE satellites

Water Policy ◽  
2021 ◽  
Author(s):  
Johanna Engström ◽  
Sarah Praskievicz ◽  
Bennett Bearden ◽  
Hamid Moradkhani
Keyword(s):  
Monthly Data ◽  
Climate Assessment ◽  
Terrestrial Water ◽  
Grace Satellites ◽  
Land Data Assimilation ◽  
National Climate Assessment ◽  
Land Data Assimilation System ◽  
Gravity Recovery ◽  
Data Assimilation System ◽  
Assimilation System

Abstract Changing water quantities and location can be estimated using the Gravity Recovery and Climate Experiment (GRACE) satellites. By measuring differences in the Earth's gravity, the satellites provide monthly data on regional changes in the Earth's mass resulting from the movement of water. Studying the Southeast U.S., using the full record of the original GRACE satellites (2002–2016), a significant trend of declining water quantities appears in west-central Alabama, extending into eastern Mississippi. These findings confirm earlier research which indicates declining streamflow levels but develops this research further by estimating the amount lost as 11.6 km3. Considering the different terrestrial water storages by analyzing data from the National Climate Assessment – Land Data Assimilation System Noah 3.3 Version 2 (NCA-LDAS) indicates that the majority of this loss can be attributed to groundwater losses, a finding that is further confirmed by well records throughout the region.

scholarly journals NCA-LDAS Land Analysis: Development and Performance of a Multisensor, Multivariate Land Data Assimilation System for the National Climate Assessment

2019 ◽  
Vol 20 (8) ◽  
pp. 1571-1593 ◽  
Author(s):  
Sujay V. Kumar ◽  
Michael Jasinski ◽  
David M. Mocko ◽  
Matthew Rodell ◽  
Jordan Borak ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Snow Depth ◽  
Climate Assessment ◽  
Land Data Assimilation ◽  
National Climate Assessment ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
The U.S ◽  
Assimilation System

Abstract This article describes one of the first successful examples of multisensor, multivariate land data assimilation, encompassing a large suite of soil moisture, snow depth, snow cover, and irrigation intensity environmental data records (EDRs) from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), Advanced Scatterometer (ASCAT), Moderate-Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), Soil Moisture Ocean Salinity (SMOS) mission, and Soil Moisture Active Passive (SMAP) mission. The analysis is performed using the NASA Land Information System (LIS) as an enabling tool for the U.S. National Climate Assessment (NCA). The performance of the NCA Land Data Assimilation System (NCA-LDAS) is evaluated by comparing it to a number of hydrological reference data products. Results indicate that multivariate assimilation provides systematic improvements in simulated soil moisture and snow depth, with marginal effects on the accuracy of simulated streamflow and evapotranspiration. An important conclusion is that across all evaluated variables, assimilation of data from increasingly more modern sensors (e.g., SMOS, SMAP, AMSR2, ASCAT) produces more skillful results than assimilation of data from older sensors (e.g., SMMR, SSM/I, AMSR-E). The evaluation also indicates the high skill of NCA-LDAS when compared with other LSM products. Further, drought indicators based on NCA-LDAS output suggest a trend of longer and more severe droughts over parts of the western United States during 1979–2015, particularly in the southwestern United States, consistent with the trends from the U.S. Drought Monitor, albeit for a shorter 2000–15 time period.

scholarly journals Bridging Terrestrial Water Storage Anomaly During GRACE/GRACE-FO Gap Using SSA Method: A Case Study in China

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4144 ◽  
Author(s):  
Li ◽  
Wang ◽  
Zhang ◽  
Wen ◽  
Zhong ◽  
...  
Keyword(s):  
Water Storage ◽  
Terrestrial Water Storage ◽  
Global Land ◽  
Terrestrial Water ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Better Than

The terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) is now a significant issue for scientific research in high-resolution time-variable gravity fields. This paper proposes the use of singular spectrum analysis (SSA) to predict the TWSA derived from GRACE. We designed a case study in six regions in China (North China Plain (NCP), Southwest China (SWC), Three-River Headwaters Region (TRHR), Tianshan Mountains Region (TSMR), Heihe River Basin (HRB), and Lishui and Wenzhou area (LSWZ)) using GRACE RL06 data from January 2003 to August 2016 for inversion, which were compared with Center for Space Research (CSR), Helmholtz-Centre Potsdam-German Research Centre for Geosciences (GFZ), Jet Propulsion Laboratory (JPL)’s Mascon (Mass Concentration) RL05, and JPL’s Mascon RL06. We evaluated the accuracy of SSA prediction on different temporal scales based on the correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE), which were compared with that of an auto-regressive and moving average (ARMA) model. The TWSA from September 2016 to May 2019 were predicted using SSA, which was verified using Mascon RL06, the Global Land Data Assimilation System model, and GRACE-FO results. The results show that: (1) TWSA derived from GRACE agreed well with Mascon in most regions, with the highest consistency with Mascon RL06 and (2) prediction accuracy of GRACE in TRHR and SWC was higher. SSA reconstruction improved R, NSE, and RMSE compared with those of ARMA. The R values for predicting TWS in the six regions using the SSA method were 0.34–0.98, which was better than those for ARMA (0.26–0.97), and the RMSE values were 0.03–5.55 cm, which were better than the 2.29–5.11 cm RMSE for ARMA as a whole. (3) The SSA method produced better predictions for obvious periodic and trending characteristics in the TWSA in most regions, whereas the detailed signal could not be effectively predicted. (4) The predicted TWSA from September 2016 to May 2019 were basically consistent with Global Land Data Assimilation System (GLDAS) results, and the predicted TWSA during June 2018 to May 2019 agreed well with GRACE-FO results. The research method in this paper provides a reference for bridging the gap in the TWSA between GRACE and GRACE-FO.

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 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 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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