LEADER 04322nam 2201105z- 450 001 9910557426703321 005 20220111 035 $a(CKB)5400000000043457 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76951 035 $a(oapen)doab76951 035 $a(EXLCZ)995400000000043457 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aUsing Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (216 p.) 311 08$a3-0365-2331-6 311 08$a3-0365-2332-4 330 $aRemotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions on how to capitalize on state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world. 606 $aResearch & information: general$2bicssc 610 $a5G 610 $aarid ungauged regions 610 $aassimilation frequency 610 $acalibration 610 $aclimate change 610 $acoefficient of variability 610 $acoupled atmospheric-hydrologic system 610 $adata assimilation 610 $adesign rainfall 610 $adouble-mass analysis 610 $aE-band 610 $aecological water transfer 610 $aEphemeral rivers 610 $aevaporation 610 $aevapotranspiration 610 $aflash flood 610 $aflood peak discharge 610 $aflux tower 610 $agrid-based Hebei model 610 $ahydrological prediction 610 $aIDF formula 610 $aincipient motion 610 $aIntegrated Multi-Satellite Retrievals for Global Precipitation Measurement 610 $aLAI 610 $aland use change 610 $aLOS-MIMO 610 $alumped Hebei model 610 $amillimeter-wave 610 $aMK-S trend analysis 610 $amodel 610 $aNDVI 610 $anorthwestern China 610 $aPenman-Monteith equation 610 $aPML-V2 610 $aradar reflectivity 610 $arain rate estimation 610 $arainfall forecast 610 $arainfall monitoring 610 $aRainfall Triggering Index 610 $arainfall-runoff prediction 610 $aRainyDay 610 $aregression 610 $aremote sensing 610 $aseasonal ARIMA 610 $aSentinel-2 610 $aSierra Nevada 610 $asponge city 610 $asurface and groundwater interaction 610 $aSWAT 610 $aUAV remote sensing 610 $aungauged drainage basin 610 $aurban ecosystem 610 $aurban flood 610 $avapor pressure deficit 610 $awater limitation 610 $awetland vegetation ecosystem 610 $aWRF-3DAVR 610 $aWRF-3DVar data assimilation 610 $aWRF-Hydro modeling system 610 $aYunnan 615 7$aResearch & information: general 700 $aZhang$b Yongqiang$4edt$01297609 702 $aRyu$b Dongryeol$4edt 702 $aZheng$b Donghai$4edt 702 $aZhang$b Yongqiang$4oth 702 $aRyu$b Dongryeol$4oth 702 $aZheng$b Donghai$4oth 906 $aBOOK 912 $a9910557426703321 996 $aUsing Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World$93024609 997 $aUNINA