scholarly journals Monitoring Drought through the Lens of Landsat: Drying of Rivers during the California Droughts

2021 ◽  
Vol 13 (17) ◽  
pp. 3423
Author(s):  
Shang Gao ◽  
Zhi Li ◽  
Mengye Chen ◽  
Daniel Allen ◽  
Thomas Neeson ◽  
...  
Keyword(s):  
Model Simulation ◽  
Indirect Comparison ◽  
Winter Precipitation ◽  
Ecological Impacts ◽  
Fine Spatial Resolution ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Hydroclimatic Variables ◽  
Global Surface

Water scarcity during severe droughts has profound hydrological and ecological impacts on rivers. However, the drying dynamics of river surface extent during droughts remains largely understudied. Satellite remote sensing enables surveys and analyses of rivers at fine spatial resolution by providing an alternative to in-situ observations. This study investigates the seasonal drying dynamics of river extent in California where severe droughts have been occurring more frequently in recent decades. Our methods combine the use of Landsat-based Global Surface Water (GSW) and global river bankful width databases. As an indirect comparison, we examine the monthly fractional river extent (FrcSA) in 2071 river reaches and its correlation with streamflow at co-located USGS gauges. We place the extreme 2012–2015 drought into a broader context of multi-decadal river extent history and illustrate the extraordinary change between during- and post-drought periods. In addition to river extent dynamics, we perform statistical analyses to relate FrcSA with the hydroclimatic variables obtained from the National Land Data Assimilation System (NLDAS) model simulation. Results show that Landsat provides consistent observation over 90% of area in rivers from March to October and is suitable for monitoring seasonal river drying in California. FrcSA reaches fair (>0.5) correlation with streamflow except for dry and mountainous areas. During the 2012–2015 drought, 332 river reaches experienced their lowest annual mean FrcSA in the 34 years of Landsat history. At a monthly scale, FrcSA is better correlated with soil water in more humid areas. At a yearly scale, summer mean FrcSA is increasingly sensitive to winter precipitation in a drier climate; and the elasticity is also reduced with deeper ground water table. Overall, our study demonstrates the detectability of Landsat on the river surface extent in an arid region with complex terrain. River extent in catchments of deficient water storage is likely subject to higher percent drop in a future climate with longer, more frequent droughts.

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 Blending SMAP, Noah and In Situ Soil Moisture Using Multiple Methods

2019 ◽  
Author(s):  
Ning Zhang ◽  
Steven M. Quiring ◽  
Trent W. Ford
Keyword(s):  
Soil Moisture ◽  
Error Variance ◽  
In Situ Measurements ◽  
The North ◽  
Sample Density ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Simple Average

Abstract. Soil moisture can be obtained from in-situ measurements, satellite observations, and model simulations. This study evaluates different methods of combining model, satellite, and in-situ soil moisture data to provide an accurate and spatially-continuous soil moisture product. Three independent soil moisture datasets are used, including an in situ-based product that uses regression kriging (RK) with precipitation, SMAP L4 soil moisture, and model-simulated soil moisture from the Noah model as part of the North American Land Data Assimilation System. Triple collocation (TC), relative error variance (REV), and RK were used to estimate the error variance of each parent dataset, based on which the least squares weighting (LSW) was applied to blend the parent datasets. These results were also compared with that using simple average (AVE). The results indicated no significant differences between blended soil moisture datasets using errors estimated from TC, REV or RK. Moreover, the LSW did not outperform AVE. The SMAP L4 data have a significant negative bias (−18 %) comparing with in-situ measurements, and in-situ measurements are valuable for improving the accuracy of hybrid results. In addition, datasets using anomalies and percentiles have smaller errors than using volumetric water content, mainly due to the reduced bias. Finally, the in situ-based soil moisture and the simple-averaged product from in situ-based and Noah soil moisture are the two optimal datasets for soil moisture mapping. The in situ-based product performs better when the sample density is high, while the simple-averaged product performs better when the station density is low, or measurement sites are less representative.

scholarly journals Temporal and Spatial Variations of Soil Moisture Over Xinjiang Based on Multiple GLDAS Datasets

2021 ◽  
Vol 9 ◽  
Author(s):  
Zengyun Hu ◽  
Xi Chen ◽  
Yaoming Li ◽  
Qiming Zhou ◽  
Gang Yin
Keyword(s):  
Soil Moisture ◽  
Temporal Variations ◽  
Limited Data ◽  
Spatial Characteristics ◽  
Land Data Assimilation ◽  
Temporal And Spatial ◽  
The Arid Regions ◽  
Land Data Assimilation System ◽  
Data Assimilation System

Under the global warming, as the typical arid region of Central Asia, the Xinjiang Uygur Autonomous Region (Xinjiang) has been experienced the remarkable warming and increased precipitation based on large previous studies. The arid and semiarid ecosystem of Xinjiang is very sensitive and vulnerable to climate change and water resource variations. However, the sparse and highly unevenly distributed in-situ stations in this region provide limited data for understanding of the soil moisture variations. In this study, the spatial and temporal changes and variations of soil moisture were explored at annual and seasonal time scales during the period of 2000–2017. The soil moisture data are from the Global Land Data Assimilation System (GLDAS) models, including four GLDAS 1 models: CLM, Mosaic, VIC and Noah 2.7 and one GLDAS 2.1 model: Noah 3.3. Major results show that 1) Noah 3.3 and VIC have the significant positive trends of annual soil moisture with the values of 2.64°mm/a and 0.98°mm/a. The trend of CLM is significant negative. The other two models Mosaic and Noah 2.7 have the weak positive trends. The temporal variations of seasonal soil moisutre are similar the annual soil moisture for each of the model. 2) For the spatial characteristics of the soil mositure variations, CLM displays the negative trends over large part of Xinjiang. Mosaic and VIC have the similar spatial characteristics of the linear trends. Noah 3.3 has the significant positive trends over almost Xinjiang which is different with Noah 2.7. All the five models have the positive trends over KLM. Our results have a better understanding of the soil moisture variations across Xinjiang, and they also enhance the reconginzing of the complex hydrological circulation in the arid regions.

scholarly journals Assimilation of Passive L-band Microwave Brightness Temperatures in the Canadian Land Data Assimilation System: Impacts on Short-Range Warm Season Numerical Weather Prediction

2019 ◽  
Vol 20 (6) ◽  
pp. 1053-1079 ◽  
Author(s):  
Marco L. Carrera ◽  
Bernard Bilodeau ◽  
Stéphane Bélair ◽  
Maria Abrahamowicz ◽  
Albert Russell ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Weather Prediction ◽  
Warm Season ◽  
Brightness Temperatures ◽  
Screen Level ◽  
Land Data Assimilation ◽  
L Band ◽  
Land Data Assimilation System ◽  
Data Assimilation System

Abstract This study examines the impacts of assimilating Soil Moisture Active Passive (SMAP) L-band brightness temperatures (TBs) on warm season short-range numerical weather prediction (NWP) forecasts. Focusing upon the summer 2015 period over North America, offline assimilation cycles are run with the Canadian Land Data Assimilation System (CaLDAS) to compare the impacts of assimilating SMAP TB versus screen-level observations to analyze soil moisture. The analyzed soil moistures are quantitatively compared against a set of in situ sparse soil moisture networks and a set of SMAP core validation sites. These surface analyses are used to initialize a series of 48-h forecasts where near-surface temperature and precipitation are evaluated against in situ observations. Assimilation of SMAP TBs leads to soil moisture that is markedly improved in terms of correlation and standard deviation of the errors (STDE) compared to the use of screen-level observations. NWP forecasts initialized with SMAP-derived soil moistures exhibit a general dry bias in 2-m dewpoint temperatures (TD2m), while displaying a relative warm bias in 2-m temperatures (TT2m), when compared to those forecasts initialized with soil moistures analyzed with screen-level temperature errors. Largest impacts with SMAP are seen for TD2m, where the use of screen-level observations leads to a daytime wet bias that is reduced with SMAP. The overall drier soil moisture leads to improved precipitation bias scores with SMAP. A notable deterioration in TD2m STDE scores was found in the SMAP experiments during the daytime over the Northern Great Plains. A reduction in the daytime TD2m wet bias was found when the observation errors for the screen-level observations were increased.

scholarly journals Long-term O<sub>3</sub>–precursor relationships in Hong Kong: field observation and model simulation

2017 ◽  
Vol 17 (18) ◽  
pp. 10919-10935 ◽  
Author(s):  
Yu Wang ◽  
Hao Wang ◽  
Hai Guo ◽  
Xiaopu Lyu ◽  
Hairong Cheng ◽  
...  
Keyword(s):  
Hong Kong ◽  
Model Simulation ◽  
Ground Level ◽  
Effective Control ◽  
Photochemical Pollution ◽  
Mixing Ratios ◽  
The Past ◽  
Total Volatile Organic Compounds

Abstract. Over the past 10 years (2005–2014), ground-level O3 in Hong Kong has consistently increased in all seasons except winter, despite the yearly reduction of its precursors, i.e. nitrogen oxides (NOx =  NO + NO2), total volatile organic compounds (TVOCs), and carbon monoxide (CO). To explain the contradictory phenomena, an observation-based box model (OBM) coupled with CB05 mechanism was applied in order to understand the influence of both locally produced O3 and regional transport. The simulation of locally produced O3 showed an increasing trend in spring, a decreasing trend in autumn, and no changes in summer and winter. The O3 increase in spring was caused by the net effect of more rapid decrease in NO titration and unchanged TVOC reactivity despite decreased TVOC mixing ratios, while the decreased local O3 formation in autumn was mainly due to the reduction of aromatic VOC mixing ratios and the TVOC reactivity and much slower decrease in NO titration. However, the decreased in situ O3 formation in autumn was overridden by the regional contribution, resulting in elevated O3 observations. Furthermore, the OBM-derived relative incremental reactivity indicated that the O3 formation was VOC-limited in all seasons, and that the long-term O3 formation was more sensitive to VOCs and less to NOx and CO in the past 10 years. In addition, the OBM results found that the contributions of aromatics to O3 formation decreased in all seasons of these years, particularly in autumn, probably due to the effective control of solvent-related sources. In contrast, the contributions of alkenes increased, suggesting a continuing need to reduce traffic emissions. The findings provide updated information on photochemical pollution and its impact in Hong Kong.

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.

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