Analysis of Sensor Imaging and Field-Validation for Monitoring, Evaluation and Control Future Flood Prone Areas along River Niger and Benue Confluence Ecology, Lokoja, Nigeria

The study area often suffered from flood for the last two year resulting to ecological damages including farmlands, infrastructures, property damage, loss of life and degradation of land-cover. Flood prone areas assessment is conducted using sensor data from space-borne optical sensors with cross-validation by ground-truthing the study area along the two major rivers that converge at Lokoja, otherwise called river-confluence. Maximum likelihood classification (MLC) and ISO-clustering unsupervised classification method of Arcmap-10.1 using NigeriaSat-1 data is applied to the regimes of up-stream and down-stream of River Niger and River Benue respectively. Based on ground truthing of the study areas, classification of inundated areas closely connected with actual flood prone area was developed. The results of the classifications of flood prone areas were displayed on satellite imagery, of which the percentage differences of change detected from variations of 16 class of land-use (LU) and land-cover (LC) using optical sensor shows that wetland flood plain comprising of runoffs-routes and lowland areas recorded the highest of 14.42% using MLC and 16.02% using ISO-DATA. In the final analysis, the classification accuracy conducted shows that the ecology of flood prone areas can be adequately classified using MLC (54.89%) and ISO-clustering unsupervised classification (45.11%). In the same vein, the result of regression function shows high correlation coefficient of 0.6242 (62%) and high strength in their relationship of which the potential flood runoff-route did correlate with the state of the location of the study area. It is anticipated that remote-sensing data integrated from optical sensors could be used to supplement up-stream, down-stream and runoffs-route to monitor, evaluate and detect floods prone areas. It is therefore significant that government and relevant agencies adopts these findings to help in the monitoring, evaluating and control of future ecological disasters. Keywords:Analysis, lokoja,river niger, river benue, confluence, monitor, evaluate, control, ecology, flood, spatial, tempora

Multi-Sensor Imaging and Space-Ground Cross-Validation for 2010 Flood along Indus River, Pakistan

Flood monitoring was conducted using multi-sensor data from space-borne optical, and microwave sensors; with cross-validation by ground-based rain gauges and streamflow stations along the Indus River; Pakistan. First; the optical imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) was processed to delineate the extent of the 2010 flood along Indus River; Pakistan. Moreover; the all-weather all-time capability of higher resolution imagery from the Advanced Synthetic Aperture Radar (ASAR) is used to monitor flooding in the lower Indus river basin. Then a proxy for river discharge from the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA’s Aqua satellite and rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) are used to study streamflow time series and precipitation patterns. The AMSR-E detected water surface signal was cross-validated with ground-based river discharge observations at multiple streamflow stations along the main Indus River. A high correlation was found; as indicated by a Pearson correlation coefficient of above 0.8 for the discharge gauge stations located in the southwest of Indus River basin. It is concluded that remote-sensing data integrated from multispectral and microwave sensors could be used to supplement stream gauges in sparsely gauged large basins to monitor and detect floods.JRC.G.2-Global security and crisis managemen

Evaluation of the satellite-based Global Flood Detection System for measuring river discharge: Influence of local factors

One of the main challenges for global hydrological modelling is the limited availability of observational data for calibration and model verification. This is particularly the case for real time applications. This problem could potentially be overcome if discharge measurements based on satellite data were sufficiently accurate to substitute for ground-based measurements. The aim of this study is to test the potentials and constraints of the remote sensing signal of the Global Flood Detection System for converting the flood detection signal into river discharge values. The study uses data for 322 river measurement locations in Africa, Asia, Europe, North America and South America. Satellite discharge measurements were calibrated for these sites and a validation analysis with in situ discharge was performed. The locations with very good performance will be used in a future project where satellite discharge measurements are obtained on a daily basis to fill the gaps where real time ground observations are not available. These include several international river locations in Africa: Niger, Volta and Zambezi rivers. Analysis of the potential factors affecting the satellite signal was based on a classification decision tree (Random Forest) and showed that mean discharge, climatic region, land cover and upstream catchment area are the dominant variables which determine good or poor performance of the measurement sites. In general terms, higher skill scores were obtained for locations with one or more of the following characteristics: a river width higher than 1km; a large floodplain area and in flooded forest; with a potential flooded area greater than 40%; sparse vegetation, croplands or grasslands and closed to open and open forest; Leaf Area Index > 2; tropical climatic area; and without hydraulic infrastructures. Also, locations where river ice cover is seasonally present obtained higher skill scores. The work provides guidance on the best locations and limitations for estimating discharge values from these daily satellite signals.JRC.H.7-Climate Risk Managemen

Using modelled discharge to develop satellite-based river gauging: a case study for the Amazon Basin

River discharge measurements have proven invaluable to monitor the global water cycle, assess flood risk, and guide water resource management. However, there is a delay, and ongoing decline, in the availability of gauging data and stations are highly unevenly distributed globally. While not a substitute for river discharge measurement, remote sensing is a cost-effective technology to acquire information on river dynamics in situations where ground-based measurements are unavailable. The general approach has been to relate satellite observation to discharge measured in situ, which prevents its use for ungauged rivers. Alternatively, hydrological models are now available that can be used to estimate river discharge globally. While subject to greater errors and biases than measurements, model estimates of river discharge do expand the options for applying satellite-based discharge monitoring in ungauged rivers. Our aim was to test whether satellite gauging reaches (SGRs), similar to virtual stations in satellite altimetry, can be constructed based on Moderate Resolution Imaging Spectroradiometer (MODIS) optical or Global Flood Detection System (GFDS) passive microwave-derived surface water extent fraction and simulated discharge from the World-Wide Water (W3) model version 2. We designed and tested two methods to develop SGRs across the Amazon Basin and found that the optimal grid cell selection method performed best for relating MODIS and GFDS water extent to simulated discharge. The number of potential river reaches to develop SGRs increases from upstream to downstream reaches as rivers widen. MODIS SGRs are feasible for more river reaches than GFDS SGRs due to its higher spatial resolution. However, where they could be constructed, GFDS SGRs predicted discharge more accurately as observations were less affected by cloud and vegetation. We conclude that SGRs are suitable for automated large-scale application and offer a possibility to predict river discharge variations from satellite observations alone, for both gauged and ungauged rivers.</p

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

Microwave Satellite Data for Hydrologic Modeling in Ungauged Basins

An innovative flood-prediction framework is developed using Tropical Rainfall Measuring Mission precipitation forcing and a proxy for river discharge from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) onboard the National Aeronautics and Space Administration’s Aqua satellite. The AMSR-E-detected water surface signal was correlated with in situ measurements of streamflow in the Okavango Basin in Southern Africa as indicated by a Pearson correlation coefficient of 0.90. A distributed hydrologic model, with structural data sets derived from remote-sensing data, was calibrated to yield simulations matching the flood frequencies from the AMSR-E-detected water surface signal. Model performance during a validation period yielded a Nash–Sutcliffe efficiency of 0.84. We concluded that remote-sensing data from microwave sensors could be used to supplement stream gauges in large sparsely gauged or ungauged basins to calibrate hydrologic models. Given the global availability of all required data sets, this approach can be potentially expanded to improve flood monitoring and prediction in sparsely gauged basins throughout the world.JRC.G.2-Global security and crisis managemen

Improvements and Applications of Satellite-Derived Soil Moisture Data for Flood Forecasting

Accurate knowledge of the spatiotemporal behavior of soil moisture can greatly improve hydrological forecasting capability. While ground-based soil moisture measurements are ideal, they tend to be sparse in space and only available for limited periods. To overcome this, a viable alternative is space-borne microwave remote sensing because of the observational capability it offers for retrieving soil moisture in near real-time at the global scale. However, its direct applications have been limited due to the uncertainty associated, and the coarse spatial resolution these are available at. Therefore, this thesis aims to use satellite soil moisture products for assessing flood risk by redressing their drawbacks in terms of accuracy and spatial resolution. The research consists of three inter-dependent focal areas; evaluation, improvement and application of the soil moisture products. For the first objective, this thesis compared two alternate soil moisture products using spatiotemporally identical passive microwave observations but different retrieval algorithms. Complementarity in the performance of the products was identified and accordingly provided the basis for the improvement in soil moisture. For the second objective, based on the identified complementarity, different formulations of weighted linear combination were proposed as a means of reducing the structural uncertainty associated with each retrieval algorithm. To address the limitation of resulting retrievals existing over coarse grid resolutions, an approach was presented to spatially disaggregate coarse soil moisture by only using a remotely sensed vegetation index product. The method provides a continuous timeseries of disaggregated soil moisture with a persistence structure closer to what is observed. Lastly, for the third objective, a fully remote sensing based flood warning method using readily available soil moisture and rainfall data, open-access topographic and soil data, was developed. This method was applied over a number of anthropogenically unaffected river basins and was shown to have promise for flood warning in ungauged watersheds. Ongoing and future research will form an integrated pathway for producing an improved soil moisture product available at finer spatial resolution, which can be used for various regional applications, along with using this to provide real-time flood warnings using freely available information to rural and remote communities worldwide

Decadal development of CREST hydrological model family: review, improvements, applications, and outlook

A hydrological model is an indispensable tool in Earth system science and engineering operations to understand, predict, and manage water resources on Earth. The Coupled Routing and Excess Storage (CREST) model, released in 2011, is one such to simulate distributed hydrologic states and fluxes at variable scales. Over the last decade, CREST model has been actively under development and applied by different sectors to tackle water-related problems worldwide. This dissertation is dedicated to expanding the capacity of CREST model from three main fronts: (1) hydrologic data, (2) model development, and (3) applications. To start, the decadal development and applications of CREST model family were reviewed to lay the foundation for my contribution (Chapter 1). First, uncertainties in hydrologic input data were evaluated comprehensively for three state-of-the-science precipitation datasets derived from in-situ instruments, ground weather radar, and satellites during extreme events (Chapter 2); then a 120-year CONUS-wide flood database was compiled into a unified format as a validation source for models and hydroclimatic research (Chapter 3). From the model development front, a Hydrologic&Hydraulic (H&H) framework was developed to empower flood inundation mapping capacity for CREST (Chapter 4); furthermore, the re-infiltration, an important yet often ignored hydrologic process during the flooding period, was incorporated to improve the more realistic rainfall-runoff modeling representation (Chapter 5). To further improve the model efficiency, a vector-based CREST model was developed that can achieve 10x speedup for a continental-scale simulation, as well as improved model accuracy (Chapter 6). Finally on the model application, the high-resolution CREST model was applied in quantifying future US floods in a warmer climate: flood flashiness is becoming 7.9% higher for the continent (Chapter 7); and extreme rainfall and floods are becoming more frequent, widespread, and less seasonal (Chapter 8). The final Chapter 9 summarizes the contributions to the CREST model family development, outlooks, and general remarks for advancing our understanding of hydrologic science and engineering