A Multi-temporal Analysis of AMSR-E Data for Flood and Discharge Monitoring during the 2008 Flood in Iowa

The objective of this work is to demonstrate the potential of using passive microwave data to monitor flood and discharge conditions and to infer watershed hydraulic and hydrologic parameters. The case study is the major flood in Iowa in summer 2008. A new Polarisation Ratio Variation Index (PRVI) was developed based on a multi-temporal analysis of 37 GHz satellite imagery from the Advanced Microwave Scanning Radiometer (AMSR-E) to calculate and detect anomalies in soil moisture and/or inundated areas. The Robust Satellite Technique (RST) which is a change detection approach based on the analysis of historical satellite records was adopted. A rating curve has been developed to assess the relationship between PRVI values and discharge observations downstream. A time-lag term has been introduced and adjusted to account for the changing delay between PRVI and streamflow. Moreover, the Kalman filter has been used to update the rating curve parameters in near real time. The temporal variability of the b exponent in the rating curve formula shows that it converges toward a constant value. A consistent 21-day time lag, very close to an estimate of the time of concentration, was obtained. The agreement between observed discharge downstream and estimated discharge with and without parameters adjustment was 65 and 95%, respectively. This demonstrates the interesting role that passive microwave can play in monitoring flooding and wetness conditions and estimating key hydrologic parameters

Evaluating Consistency of Snow Water Equivalent Retrievals from Passive Microwave Sensors over the North Central U. S.: SSM/I vs. SSMIS and AMSR-E vs. AMSR2

For four decades, satellite-based passive microwave sensors have provided valuable snow water equivalent (SWE) monitoring at a global scale. Before continuous long-term SWE records can be used for scientific or applied purposes, consistency of SWE measurements among different sensors is required. SWE retrievals from two passive sensors currently operating, the Special Sensor Microwave Imager Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), have not been fully evaluated in comparison to each other and previous instruments. Here, we evaluated consistency between the Special Sensor Microwave/Imager (SSM/I) onboard the F13 Defense Meteorological Satellite Program (DMSP) and SSMIS onboard the F17 DMSP, from November 2002 to April 2011 using the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) for continuity. Likewise, we evaluated consistency between AMSR-E and AMSR2 SWE retrievals from November 2007 to April 2016, using SSMIS for continuity. The analysis is conducted for 1176 watersheds in the North Central U.S. with consideration of difference among three snow classifications (Warm forest, Prairie, and Maritime). There are notable SWE differences between the SSM/I and SSMIS sensors in the Warm forest class, likely due to the different interpolation methods for brightness temperature (Tb) between the F13 SSM/I and F17 SSMIS sensors. The SWE differences between AMSR2 and AMSR-E are generally smaller than the differences between SSM/I and SSMIS SWE, based on time series comparisons and yearly mean bias. Finally, the spatial bias patterns between AMSR-E and AMSR2 versus SSMIS indicate sufficient spatial consistency to treat the AMSR-E and AMSR2 datasets as one continuous record. Our results provide useful information on systematic differences between recent satellite-based SWE retrievals and suggest subsequent studies to ensure reconciliation between different sensors in long-term SWE records

Toward the estimation of river discharge variations using MODIS data in ungauged basins

This study investigates the capability of the Moderate resolution Imaging Spectroradiometer (MODIS) to estimate river discharge, even for ungauged sites. Because of its frequent revisits (as little as every 3 h) and adequate spatial resolution (250 m), MODIS bands 1 and 2 have significant potential for mapping the extent of flooded areas and estimating river discharge even for medium-sized basins. Specifically, the different behaviour of water and land in the Near Infrared (NIR) portion of the electromagnetic spectrum is exploited by computing the ratio (C/M) of the MODIS channel 2 reflectance values between two pixels located within (M) and outside (C), but close to, the river. The values of C/M increase with the presence of water and, hence, with discharge. Moreover, in order to reduce the noise effects due to atmospheric contribution, an exponential smoothing filter is applied, thus obtaining C/M⁎. Time series of hourly mean flow velocity and discharge between 2005 and 2011 measured at four gauging stations located along the Po river (Northern Italy) are employed for testing the capability of C/M⁎ to estimate discharge/flow velocity. Specifically, the meanders and urban areas are considered the best locations for the position of the pixels M and C, respectively. Considering the optimal pixels, the agreement between C/M⁎ and discharge/flow velocity is fairly good with values in the range of 0.65–0.77. Additionally, the application to ungauged sites is tested by deriving a unique regional relationship between C/M⁎ and flow velocity valid for the whole Po river and providing only a slight deterioration of the performance. Finally, the sensitivity of the results to the selection of the C and M pixels is investigated by randomly changing their location. Also in this case, the agreement with in situ observations of velocity is fairly satisfactory (r ~ 0.6). The obtained results demonstrate the capability of MODIS to monitor discharge (and flow velocity). Therefore, its application for a larger number of sites worldwide will be the object of future studies

DEVELOPMENT AND EVALUATION OF AN ADVANCED REGIONAL AND GLOBAL HYDROLOGICAL PREDICTION SYSTEM ENABLED BY SATELLITE REMOTE SENSING, NUMERICAL WEATHER FORECASTING, AND ENSEMBLE DATA ASSIMILATION

This dissertation advanced the traditional hydrological prediction via multi-sensor satellite remote sensing products, numerical weather forecasts and advanced data assimilation approach in sparsely gauged or even ungauged regions and then extend this approach to global scale with enhanced efficiency for prototyping a flood early warning system on a global basis. This dissertation consists of six chapters: the first chapter is the introductive chapter which describes the problem and raises the hypotheses, Chapters 2 to 5 are the four main Chapters followed by Chapter 6 which is an overall summary of this dissertation. For regional hydrological prediction in Chapter 2 and 3, two rainfall – runoff hydrological models: the HyMOD (Hydrological MODel) and the simplified version of CREST (Coupled Routing and Excess Storage) Model were set up and tested in Cubango River basin, Africa. In Chapter 2, first, the AMSR-E (Advanced Microwave Scanning Radiometer for Earth observing system) signal/TMI (TRMM Microwave Imager) passive microwave streamflow signals are converted into actual streamflow domain with the unit of m3/s by adopting the algorithm from Brakenridge et al. (2007); then the HyMOD was coupled with Ensemble Square Root Filter (EnSRF) to account for uncertainty in both forcing data and model initial conditions and thus improve the flood prediction accuracy by assimilating the signal converted streamflow, in comparison to the benchmark assimilation of in-situ streamflow observations in actual streamflow domain with the unit of m3/s. In Chapter 3, the remote-sensing streamflow signals, without conventional in-situ hydrological measurements, was applied to force, calibrate and update the hydrologic model coupled with EnSRF data assimilation approach in the same research region, but resulting in exceedance probability-based flood prediction. For global hydrological predictions in Chapter 4 and 5, a physical based distributed hydrological model CREST is set up at 1/8 degree from 50°N to 50°S and forms the Real Time Hydrological Prediction System (http://eos.ou.edu) which was co-developed by HyDROS (Hydrometeorology and Remote Sensing Laboratory) lab at the University of Oklahoma and NASA Goddard center. In Chapter 4, the CREST model is described with details and then the Real Time Global Hydrological Monitoring System will be comprehensively evaluated on basis of gauge based streamflow observation and gridded global runoff data from GRDC (Global Runoff Data Center, http://www.bafg.de/GRDC/EN/Home/homepage_node.html). In order to extend the hydrological forecast horizon for the Real Time Global Hydrological Prediction System, the deterministic precipitation forecast fields from a numerical meteorological model GFS (Global Forecasting System) as well as the ensemble precipitation forecast fields are introduced as the forcing data to be coupled into the global CREST model in order to generate the global hydrological forecasting up to around 7 days lead time in Chapter 5. The July 21, 2012 Beijing extreme flooding event is selected to evaluate the hydrological prediction skills for extremes of both the deterministic and the ensemble GFS products

HUMAN AND CLIMATE IMPACTS ON FLOODING VIA REMOTE SENSING, BIG DATA ANALYTICS, AND MODELING

Over the last 20 years, the amount of streamflow has greatly increased and spring snowmelt floods have occurred more frequently in the north-central U.S. In the Red River of the North Basin (RRB) overlying portions of North Dakota and Minnesota, six of the 13 major floods over the past 100 years have occurred since the late 1990s. Based on numerous previous studies as well as senior flood forecasters’ experiences, recent hydrological changes related to human modifications [e.g. artificial subsurface drainage (SSD) expansion] and climate change are potential causes of notable forecasting failures over the past decade. My dissertation focuses on the operational and scientific gaps in current forecasting models and observational data and provides insights and value to both the practitioner and the research community. First, the current flood forecasting model needs both the location and installation timing of SSD and SSD physics. SSD maps were developed using satellite “big” data and a machine learning technique. Next, using the maps with a land surface model, the impacts of SSD expansion on regional hydrological changes were quantified. In combination with model physics, the inherent uncertainty in the airborne gamma snow survey observations hinders the accurate flood forecasting model. The operational airborne gamma snow water equivalent (SWE) measurements were improved by updating antecedent surface moisture conditions using satellite observations on soil moisture. From a long-term perspective, flood forecasters and state governments need knowledge of historical changes in snowpack and snowmelt to help flood management and to develop strategies to adapt to climate changes. However, historical snowmelt trends have not been quantified in the north-central U.S. due to the limited historical snow data. To overcome this, the current available historical long-term SWE products were evaluated across diverse regions and conditions. Using the most reliable SWE product, a trend analysis quantified the magnitude of change extreme snowpack and melt events over the past 36 years. Collectively, this body of research demonstrates that human and climate impacts, as well as limited and noisy data, cause uncertainties in flood prediction in the great plains, but integrated approaches using remote sensing, big data analytics, and modeling can quantify the hydrological changes and reduce the uncertainties. This dissertation improves the practice of flood forecasting in Red River of the North Basin and advances research in hydrology and snow science

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne