Drought prediction using in situ and remote sensing products with SVM over the Xiang River Basin, China

Drought is a natural hazard disaster that can deeply affect environments, economies, and societies around the world. Therefore, accurate monitoring of patterns in drought is important. Precipitation is the key variable to define the drought index. However, the spare and uneven distribution of rain gauges limit the access of long-term and reliable in situ observations. Remote sensing techniques enrich the precipitation data at different temporal–spatial resolutions. In this study, the climate prediction center morphing (CMORPH) technique (CMORPH-CRT), the tropical rainfall measuring mission (TRMM) multi-satellite precipitation analysis (TRMM 3B42V7), and the integrated multi-satellite retrievals for global precipitation measurement (IMERG V05) were evaluated and compared with in situ observations for the drought monitoring in the Xiang River Basin, a humid region in China. A widely-used drought index, the standardized precipitation index (SPI), was chosen to evaluate the drought monitoring utility. The atmospheric water deficit (AWD) was used for comparison of the drought estimation with SPI. The results were as follows: (1) IMERG V05 precipitation products showed the highest accuracy against grid-based precipitation, followed by CMORPH-CRT, which performed better than TRMM 3B42V7; (2) IMERG V05 showed the best performance in SPI-1 (one-month SPI) estimations compared with CMORPH-CRT and TRMM 3B42V7; (3) SPI-1 was more suitable for drought monitoring than AWD in the Xiang River Basin, because its high R-values and low root mean square error (RMSE) compared with the corresponding index based on in situ observations; (4) drought conditions in 2015 were apparently more severe than that in 2016 and 2017, with the driest area mainly distributed in the southwest part of the Xiang River Basin.

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Evapotranspiration estimate from water balance closure using satellite data for the Upper Yangtze River basin

Hydrology Research ◽

10.2166/nh.2013.016 ◽

2013 ◽

Vol 45 (4-5) ◽

pp. 603-614 ◽

Cited By ~ 10

Author(s):

C. Corbari ◽

M. Mancini ◽

Z. Su ◽

J. Li

Keyword(s):

Remote Sensing ◽

Water Balance ◽

River Basin ◽

Satellite Data ◽

Yangtze River ◽

Yangtze River Basin ◽

Regional Scale ◽

Upper Yangtze River ◽

The Upper Yangtze River

Application of hydrological models for water resources management at large continental river basins is often limited by the scarcity of in situ meteorological forcing data. Remote sensing information provides an alternative to in situ data, with observations that are, in some cases, at higher spatial and temporal resolutions than those available from traditional ground sources. In this work, the water balance equation is solved using precipitation retrieved from Tropical Rainfall Measuring Mission, water storage from Gravity Recovery and Climate Experiment satellite data and ground discharge. Evapotranspiration (ET) is then computed as a residual term of the water balance. Satellite data are compared with ground data to understand to what extent remote sensing observations can be used to improve estimates of the terrestrial water balance at regional scale. ET estimates are also compared with the ET computed from a detailed distributed energy water balance model and with the ET product from the Moderate Resolution Imaging Spectroradiometer Global Evapotranspiration Project. These analyses are performed for the Upper Yangtze River basin (China) in the framework of NRSCC-ESA DRAGON-2 Programme.

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Geospatial assessment of the Mokra Sura river ecological condition using remote sensing and in situ monitoring data

Journal of Geology Geography and Geoecology ◽

10.15421/111866 ◽

2019 ◽

Vol 27 (3) ◽

pp. 422-430

Author(s):

Mykola М. Kharytonov ◽

Andriy М. Pugach ◽

Sergey А. Stankevich ◽

Anna O. Кozlova

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Surface Water ◽

River Basin ◽

Ecological Status ◽

The State ◽

Water Bodies ◽

In Situ Monitoring ◽

Treated Wastewater

The use of remote sensing methods for environmental monitoring of the surface water quality is proved. Regression relationships are consistent with ground-based measurements at sampling sites in water bodies and are an effective tool for assessing the ecological status of water bodies. The state of the water bodies of the Mokra Sura river basin varies considerably. The best is the water quality in the upper part of the Mokra Sura river, the worst – in the middle and lower parts. The factors of water pollution are discharges of not enough treated wastewater of industrial enterprises of the Kamyans’koy and Dniprovs’koy industrial agglomeration. The purpose of our search included the following tasks: (a) calculation of integrated environmental water quality indices; b) obtaining satellite information, processing of multispectral satellite images of water bodies using appropriate applied software techniques; c) establishment of statistical dependencies between water quality indexes obtained for biotopically space images and data of actual in situ measurements. The results of systematic hydrochemical control of the Mokra Sura river basin from 2007 to 2011 years were initial data in 4 control areas located in the Dnipropetrovsk region: 1 – the Sursko-Litovske village; 2 – the Bratske village; 3 – the Novomykolayvka village; 4 – the Novooleksandryvka village. Environmental assessment of the water quality of the Mokra Sura river within the Dnipropetrovsk region was based on the calculation the integrated environmental index ( IEI ). Priority pollutants in this case are oil products and ions 2−SO 4, 2 + Mg , 2 + Zn , 6 + Cr . Two images with a difference in three years in April 2015 and May 2017 were used to determine the current changes in the land cover of the study area. Geomorphological assessment of the water network of the Morka Sura river was performed using satellite radar interferometry. Multispectral images of Landsat 5/TM (2007-2011) and Sentinel 2B/MSI (2017) satellite systems were used forremote assessment of water bodies in the study area of the Mokra Sura river basin. The multispectral index TCW (Tasseled Cap Wetness) was used to measure the spectral reflection of the aquatic environment along of the Mokra Sura river flow. The main advantage of the studies is a demonstration of remote sensing capabilities to estimate Mokra Sura river ecological status not only in individual sites, but also throughout the flow – from source to mouth. Follow the necessity to use water from the Mokra Sura river for irrigation, the level of soil water erosion can only increase and enhance the negative processes of eutrophication of reservoirs. Long term technogenic pollution requires information about the state of surface water of fishery, drinking and municipal water use facilities as an integral part of the aquatic ecosystem, the habitat of aquatic organisms and as a resource of drinking water supply. Over 80% of the Mokra Sura river basin surface (IEI 4-12) belong to the classes with the assessment of dirty, very and extremely dirty. The results of studies using remote sensing indicate the need to reduce the streams of not enough treated wastewater to the the Mokra Sura river. The obtained data can be used for ecological assessment of the current and retrospective state of water bodies, development of forecasts of rivers pollution.

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Operational reservoir inflow forecasting with radar altimetry: the Zambezi case study

Hydrology and Earth System Sciences ◽

10.5194/hess-18-997-2014 ◽

2014 ◽

Vol 18 (3) ◽

pp. 997-1007 ◽

Cited By ~ 14

Author(s):

C. I. Michailovsky ◽

P. Bauer-Gottwein

Keyword(s):

Remote Sensing ◽

Data Assimilation ◽

River Basin ◽

River Discharge ◽

Remote Sensing Data ◽

Hydrological Models ◽

Data Set ◽

Radar Altimetry ◽

Sensing Data

Abstract. River basin management can greatly benefit from short-term river discharge predictions. In order to improve model produced discharge forecasts, data assimilation allows for the integration of current observations of the hydrological system to produce improved forecasts and reduce prediction uncertainty. Data assimilation is widely used in operational applications to update hydrological models with in situ discharge or level measurements. In areas where timely access to in situ data is not possible, remote sensing data products can be used in assimilation schemes. While river discharge itself cannot be measured from space, radar altimetry can track surface water level variations at crossing locations between the satellite ground track and the river system called virtual stations (VS). Use of radar altimetry versus traditional monitoring in operational settings is complicated by the low temporal resolution of the data (between 10 and 35 days revisit time at a VS depending on the satellite) as well as the fact that the location of the measurements is not necessarily at the point of interest. However, combining radar altimetry from multiple VS with hydrological models can help overcome these limitations. In this study, a rainfall runoff model of the Zambezi River basin is built using remote sensing data sets and used to drive a routing scheme coupled to a simple floodplain model. The extended Kalman filter is used to update the states in the routing model with data from 9 Envisat VS. Model fit was improved through assimilation with the Nash–Sutcliffe model efficiencies increasing from 0.19 to 0.62 and from 0.82 to 0.88 at the outlets of two distinct watersheds, the initial NSE (Nash–Sutcliffe efficiency) being low at one outlet due to large errors in the precipitation data set. However, model reliability was poor in one watershed with only 58 and 44% of observations falling in the 90% confidence bounds, for the open loop and assimilation runs respectively, pointing to problems with the simple approach used to represent model error.

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Operational reservoir inflow forecasting with radar altimetry: the Zambezi case study

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-9615-2013 ◽

2013 ◽

Vol 10 (7) ◽

pp. 9615-9644 ◽

Cited By ~ 2

Author(s):

C. I. Michailovsky ◽

P. Bauer-Gottwein

Keyword(s):

Remote Sensing ◽

Data Assimilation ◽

River Basin ◽

River Discharge ◽

Remote Sensing Data ◽

River System ◽

River Basin Management ◽

Hydrological Models ◽

Radar Altimetry

Abstract. River basin management can greatly benefit from short-term river discharge predictions. In order to improve model produced discharge forecasts, data assimilation allows for the integration of current observations of the hydrological system to produce optimal forecasts and reduce prediction uncertainty. Data assimilation is widely used in operational applications to update hydrological models with in situ discharge or level measurements. In areas where timely access to in situ data is not possible, remote sensing data products can be used in assimilation schemes. While river discharge itself cannot be measured from space, radar altimetry can track surface water level variations at crossing locations between the satellite ground track and the river system called virtual stations (VS). Use of radar altimetry in operational settings is complicated by the low temporal resolution of the data (between 10 and 35 days revisit time at a VS depending on the satellite) as well as the fact that the location of the measurements is not necessarily at the point of interest. Combining radar altimetry from multiple VS with hydrological models could overcome these limitations. In this study, a rainfall runoff model of the Zambezi River Basin is built using remote sensing datasets and used to drive a routing scheme coupled to a simple floodplain model. The Extended Kalman filter is used to update the states in the routing model with data from 9 Envisat VS. Model fit was improved through assimilation with Nash-Sutcliffe model efficiencies increasing from 0.21 to 0.63 and from 0.82 to 0.87 at the outlets of two distinct watersheds. However, model reliability was poor in one watershed with only 54% and 55% of observations falling in the 90% confidence bounds, for the deterministic and assimilation runs respectively, pointing to problems with the simple approach used to represent model error.

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