scholarly journals Evaluations of Remote Sensing-Based Global Evapotranspiration Datasets at Catchment Scale in Mountain Regions

2021 ◽  
Vol 13 (24) ◽  
pp. 5096
Author(s):  
Yongshan Jiang ◽  
Zhaofei Liu
Keyword(s):  
Remote Sensing ◽  
Meteorological Data ◽  
River Basins ◽  
Temporal Variations ◽  
Semiarid Region ◽  
Efficiency Coefficient ◽  
Catchment Scale ◽  
Jinsha River ◽  
Lancang River ◽  
Nu River

Evapotranspiration (ET) is essential for connecting ecosystems and directly affects the water consumption of forests, grasslands, and farmlands. Eight global remote sensing-based ET (RS_ET) datasets generated using satellite imagery and ground-based observations were comprehensively assessed using monthly ET time series simulated by the water balance (WB) method at the catchment scale in the Hengduan Mountain (HDM) region, including the Nu River, Lancang River, and Jinsha River basins. The complementary relationship (CR) model, which derives ET from meteorological data, was also evaluated against WB-based ET (WB_ET). In addition, WB_ET, RS_ET, and CR-based ET (CR_ET) data were used to investigate ET spatial and temporal variations at the catchment, grid, and site scale, respectively. Most RS_ET datasets accurately simulated monthly ET with an average index of agreement ranging from 0.71–0.91. The Operational Simplified Surface Energy Balance dataset outperformed other RS_ET datasets, with Nash–Sutcliffe efficiency coefficient (NSE) and Kling–Gupta efficiency values of 0.80 and 0.90, respectively. RS_ET datasets generally performed better in northern semiarid areas than in humid southern areas. The monthly ET simulation by the CR model was consistent with that of the WB_ET in the HDM region, with mean values of correlation coefficient (cc) and NSE at each site of 0.89 and 0.68, respectively. The model showed better performance in simulating monthly ET in the Lancang River Basin than in the Nu River and Lancang River basins, with mean cc and NSE of 0.92 and 0.83, respectively. Generally, annual ET trends were consistent at the catchment, grid, and site scale, as estimated by the WB method, RS_ET datasets, and CR model. It showed a significant decreasing trend in the northern semiarid region of the HDM while exhibiting an increasing trend in the humid southern region.

Seasonal to sub-seasonal hydrological forecast in Central Asia to improve water management and mitigate hydrometeorological risks

2021 ◽  
Author(s):  
Abror Gafurov ◽  
Olga Kalashnikova ◽  
Uktam Adkhamov ◽  
Akmal Gafurov ◽  
Adkham Mamaraimov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Resources ◽  
Central Asia ◽  
Snow Cover ◽  
Water Availability ◽  
Meteorological Data ◽  
River Basins ◽  
Linear Regression Method ◽  
Operational Mode ◽  
Hydrological Forecast

<p>Central Asia is facing a water shortage due to the negative impacts of climate change and demographic development. Water resources in this region originate mainly in the mountains of Pamir and Tian-Shan due to snow-and glacier melt. However, a limited observation network is available in these mountain systems and many are malfunctioning. Thus, the region needs new innovative methods to forecast seasonal and sub-seasonal water availability to ensure better water resources management and mitigate hydro-meteorological risks.</p><p>In this study, we present the results of our efforts for many years to develop a forecasting tool and implementation in the region. Since the region has limited observed meteorological data, we use primarily remote sensing data on snow cover for this purpose. We apply the MODIS snow cover data that is processed, including cloud removal, using the MODSNOW-Tool. We have applied this tool, which can be used to monitor snow cover in an operational mode and forecast water availability for the vegetation period but also for the monthly scale using the multiple linear regression method.</p><p>Our results show that snow is important in most of the river basins and can also be used as a single predictor to forecast seasonal water availability. Especially, in remote areas with limited observations, this approach gives a possibility of forecasting water availability for different time period. Besides seasonal hydrological forecast, the MODSNOW-Tool was also used to forecast water availability for upcoming months. The validity of forecasts were tested against observed discharge for the last 20 years and mostly above 70 % verification was achieved. Additionally to remote sensing based snow cover data, observed meteorological information was also used as predictors and improved the validity of forecast models in some river basins.</p><p>The implementation of the MODSNOW-Tool to improve the hydrological forecast was done for 28 river basins in Central Asia that are located in the territories of five post-Soviet countries Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan.  The MODSNOW-Tool was also implemented at the National Hydrometeorological Services (NHMS) of each post-Soviet country.</p>

scholarly journals Biodiversity of non-Saccharomyces yeasts associated with spontaneous fermentation of Cabernet Sauvignon wines from Shangri-La wine region, China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yue Zhao ◽  
Qingyang Sun ◽  
Shusheng Zhu ◽  
Fei Du ◽  
Ruzhi Mao ◽  
...  
Keyword(s):  
Yeast Species ◽  
Its Region ◽  
Cabernet Sauvignon ◽  
Jinsha River ◽  
Spontaneous Fermentation ◽  
Lancang River ◽  
5.8S Rrna ◽  
Colonial Morphology ◽  
26S Rrna ◽  
Wine Region

AbstractShangri-La is a wine region that has the highest altitude vineyards in China. This is the first study investigated the biodiversity of non-Saccharomyces yeasts associated with spontaneous fermentation of Cabernet Sauvignon wines produced from two sub-regions (Lancang River and Jinsha River) of Shangri-La. The culturable yeasts were preliminarily classified based on their colonial morphology on the Wallerstein Laboratory nutrient agar plates. Yeast species were identified by the sequencing of the 26S rRNA D1/D2 region and the 5.8S rRNA ITS region. Twenty-five non-Saccharomyces yeast species belonging to sixteen genera were isolated and identified in Shangri-La wine region. Candida, Hanseniaspora, Pichia, and Starmerella were found in both sub-regions, but the Lancang River showed more diverse yeast species than the Jinsha River. Shangri-La not only exhibited high diversity of non-Saccharomyces yeasts, and furthermore, seven species of non-Saccharomyces yeasts were exclusively found in this region, including B. bruxellensis, D. hansenii, M. guilliermondii, S. vini, S. diversa, T. delbrueckii and W. anomalus, which might play an important role in distinctive regional wine characteristics. This study provide a relatively comprehensive analysis of indigenous non-Saccharomyces yeasts associated with Cabernet Sauvignon from Shangri-La, and has significance for exploring ‘microbial terroir’ of wine regions in China.

scholarly journals The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

2021 ◽  
Author(s):  
Leijin Long ◽  
Feng He ◽  
Hongjiang Liu
Keyword(s):  
Remote Sensing ◽  
Southwest China ◽  
Influence Factors ◽  
Classification Error ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Jinsha River ◽  
Detection Model ◽  
High Level

AbstractIn order to monitor the high-level landslides frequently occurring in Jinsha River area of Southwest China, and protect the lives and property safety of people in mountainous areas, the data of satellite remote sensing images are combined with various factors inducing landslides and transformed into landslide influence factors, which provides data basis for the establishment of landslide detection model. Then, based on the deep belief networks (DBN) and convolutional neural network (CNN) algorithm, two landslide detection models DBN and convolutional neural-deep belief network (CDN) are established to monitor the high-level landslide in Jinsha River. The influence of the model parameters on the landslide detection results is analyzed, and the accuracy of DBN and CDN models in dealing with actual landslide problems is compared. The results show that when the number of neurons in the DBN is 100, the overall error is the minimum, and when the number of learning layers is 3, the classification error is the minimum. The detection accuracy of DBN and CDN is 97.56% and 97.63%, respectively, which indicates that both DBN and CDN models are feasible in dealing with landslides from remote sensing images. This exploration provides a reference for the study of high-level landslide disasters in Jinsha River.

scholarly journals Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products

2021 ◽  
Vol 13 (5) ◽  
pp. 853
Author(s):  
Mohsen Soltani ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Principal Component ◽  
Small Scale ◽  
Dependence Structure ◽  
Test Case ◽  
Catchment Scale ◽  
Balance Approach

This study aims to improve the standard water balance evapotranspiration (WB ET) estimate, which is typically used as benchmark data for catchment-scale ET estimation, by accounting for net intercatchment groundwater flow in the ET calculation. Using the modified WB ET approach, we examine errors and shortcomings associated with the long-term annual mean (2002–2014) spatial patterns of three remote-sensing (RS) MODIS-based ET products from MODIS16, PML_V2, and TSEB algorithms at 1 km spatial resolution over Denmark, as a test case for small-scale, energy-limited regions. Our results indicate that the novel approach of adding groundwater net in water balance ET calculation results in a more trustworthy ET spatial pattern. This is especially relevant for smaller catchments where groundwater net can be a significant component of the catchment water balance. Nevertheless, large discrepancies are observed both amongst RS ET datasets and compared to modified water balance ET spatial pattern at the national scale; however, catchment-scale analysis highlights that difference in RS ET and WB ET decreases with increasing catchment size and that 90%, 87%, and 93% of all catchments have ∆ET < ±150 mm/year for MODIS16, PML_V2, and TSEB, respectively. In addition, Copula approach captures a nonlinear structure of the joint relationship with multiple densities amongst the RS/WB ET products, showing a complex dependence structure (correlation); however, among the three RS ET datasets, MODIS16 ET shows a closer spatial pattern to the modified WB ET, as identified by a principal component analysis also. This study will help improve the water balance approach by the addition of groundwater net in the ET estimation and contribute to better understand the true correlations amongst RS/WB ET products especially over energy-limited environments.

Snow particle sizes and their distributions in Dronning Maud Land, Antarctica, at sample, local and regional scales

2016 ◽  
Vol 28 (3) ◽  
pp. 219-231
Author(s):  
Susanne Ingvander ◽  
Peter Jansson ◽  
Ian A. Brown ◽  
Shuji Fujita ◽  
Shin Sugyama ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Particle Size ◽  
Temporal Variations ◽  
Spatial And Temporal Variations ◽  
Coupled Processes ◽  
Sample Type ◽  
Surface Snow ◽  
Dronning Maud Land ◽  
Size Variability ◽  
Snow Particle

AbstractIn this study, snow particle size variability was investigated along a transect in Dronning Maud Land from the coast to the polar plateau. The aim of the study was to better understand the spatial and temporal variations in surface snow properties. Samples were collected twice daily during a traverse in 2007–08 to capture regional variability. Local variability was assessed by sampling in 10×10 m grids (5 m spacing) at selected locations. The particle size and shape distributions for each site were analysed through digital image analysis. Snow particle size variability is complex at different scales, and shows an internal variability of 0.18–3.31 mm depending on the sample type (surface, grid or pit). Relationships were verified between particle size and both elevation and distance to the coast (moisture source). Regional seasonal changes were also identified, particularly on the lower elevations of the polar plateau. This dataset may be used to quantitatively analyse the optical properties of surface snow for remote sensing. The details of the spatial and temporal variations observed in our data provide a basis for further studies of the complex and coupled processes affecting snow particle size and the interpretation of remote sensing of snow covered areas.

Improving Seasonal Predictions of Climate Variability and Water Availability at the Catchment Scale

2009 ◽  
Vol 10 (6) ◽  
pp. 1521-1533 ◽  
Author(s):  
Matthew B. Switanek ◽  
Peter A. Troch ◽  
Christopher L. Castro
Keyword(s):  
United States ◽  
Water Availability ◽  
Colorado River ◽  
River Basins ◽  
Historical Record ◽  
Sea Surface ◽  
Climate Indices ◽  
Time Lags ◽  
Catchment Scale ◽  
Stressed Region

Abstract In a water-stressed region, such as the southwestern United States, it is essential to improve current seasonal hydroclimatic predictions. Typically, seasonal hydroclimatic predictions have been conditioned by standard climate indices, for example, Niño-3 and Pacific decadal oscillation (PDO). In this work, the statistically unique relationships between sea surface temperatures (SSTs) and particular basins’ hydroclimates are explored. The regions where global SSTs are most correlated with the Little Colorado River and Gunnison River basins’ hydroclimates are located throughout the year and at varying time lags. The SSTs, from these regions of highest correlation, are subsequently used as hydroclimatic predictors for the two basins. This methodology, named basin-specific climate prediction (BSCP), is further used to perform hindcasts. The hydroclimatic hindcasts obtained using BSCP are shown to be closer to the historical record, for both basins, than using the standard climate indices as predictors.

ASSESSMENT OF IRRIGATION EFFICIENCIES OF AN AGRICULTURAL VALLEY WITH AN OVEREXPLOITED AQUIFER USING REMOTE SENSING AND METEOROLOGICAL DATA

2018 ◽  
Author(s):  
Orlando Ramirez-Valle ◽  
Hugo A. Gutierrez-Jurado ◽  
Suzan Aranda-Luna ◽  
Jaime Garatuza-Payán ◽  
Jose Cruz Jimenez-Galindo
Keyword(s):  
Remote Sensing ◽  
Meteorological Data ◽  
Overexploited Aquifer
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