Sadiq Ibrahim Khan

Satellite Remote Sensing and Hydrologic Modeling for Flood Inundation Mapping in Lake Victoria Basin: Implications for Hydrologic Prediction in Ungauged Basins

Floods are among the most catastrophic natural disasters around the globe impacting human lives and infrastructure. Implementation of a flood prediction system can potentially help mitigate flood-induced hazards. Such a system typically requires implementation and calibration of a hydrologic model using in situ observations (i.e., rain and stream gauges). Recently, satellite remote sensing data have emerged as a viable alternative or supplement to in situ observations due to their availability over vast ungauged regions. The focus of this study is to integrate the best available satellite products within a distributed hydrologic model to characterize the spatial extent of flooding and associated hazards over sparsely gauged or ungauged basins. We present a methodology based entirely on satellite remote sensing data to set up and calibrate a hydrologic model, simulate the spatial extent of flooding, and evaluate the probability of detecting inundated areas. A raster-based distributed hydrologic model, Coupled Routing and Excess STorage (CREST), was implemented for the Nzoia basin, a subbasin of Lake Victoria in Africa. Moderate Resolution Imaging Spectroradiometer Terra-based and Advanced Spaceborne Thermal Emission and Reflection Radiometer-based flood inundation maps were produced over the region and used to benchmark the distributed hydrologic model simulations of inundation areas. The analysis showed the value of integrating satellite data such as precipitation, land cover type, topography, and other products along with space-based flood inundation extents as inputs to the distributed hydrologic model. We conclude that the quantification of flooding spatial extent through optical sensors can help to calibrate and evaluate hydrologic models and, hence, potentially improve hydrologic prediction and flood management strategies in ungauged catchments.

The coupled routing and excess storage (CREST) distributed hydrological model

Abstract The Coupled Routing and Excess STorage model (CREST, jointly developed by the University of Oklahoma and NASA SERVIR) is a distributed hydrological model developed to simulate the spatial and temporal variation of land surface, and subsurface water fluxes and storages by cell-to-cell simulation. CRESTs distinguishing characteristics include: (1) distributed rainfall–runoff generation and cell-to-cell routing; (2) coupled runoff generation and routing via three feedback mechanisms; and (3) representation of sub-grid cell variability of soil moisture storage capacity and sub-grid cell routing (via linear reservoirs). The coupling between the runoff generation and routing mechanisms allows detailed and realistic treatment of hydrological variables such as soil moisture. Furthermore, the representation of soil moisture variability and routing processes at the sub-grid scale enables the CREST model to be readily scalable to multi-scale modelling research. This paper presents the model development and demonstrates its applicability for a case study in the Nzoia basin located in Lake Victoria, Africa. Citation Wang, J., Yang, H., Li, L., Gourley, J. J., Sadiq, I. K., Yilmaz, K. K., Adler, R. F., Policelli, F. S., Habib, S., Irwn, D., Limaye, A. S., Korme, T. & Okello, L. (2011) The coupled routing and excess storage (CREST) distributed hydrological model. Hydrol. Sci. J. 56(1), 84–98.

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 Administrations 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.

A Cloud-Based Global Flood Disaster Community Cyber-Infrastructure: Development and Demonstration

Flood disasters have significant impacts on the development of communities globally. This study de- scribes a public cloud-based flood cyber-infrastructure (CyberFlood) that collects, organizes, visualizes, and manages several global flood databases for authorities and the public in real-time, providing location-based eventful visualization as well as statistical analysis and graphing capabilities. In order to expand and update the existing flood inventory, a crowdsourcing data collection methodology is employed for the public with smartphones or Internet to report new flood events, which is also intended to engage citizen-scientists so that they may become motivated and educated about the latest de- velopments in satellite remote sensing and hydrologic modeling technologies. Our shared vision is to better serve the global water community with comprehensive flood information, aided by the state-of- the-art cloud computing and crowd-sourcing technology. The CyberFlood presents an opportunity to eventually modernize the existing paradigm used to collect, manage, analyze, and visualize water-related disasters.

Actual evapotranspiration estimation for different land use and land cover in urban regions using Landsat 5 data

Evapotranspiration (ET) is deemed critical for water resources management. Even in the same climatic and meteorological conditions, actual ET (ET a) may exhibit remarkable spatial variability across different vegetation covers, agricultural land use practices, and differing types of urban land development. The main objectives of this study are (1) to evaluate the possible closure of the heat balance equation using Oklahomas unique environmental monitoring network; and (2) to estimate ET a and determine the variation with regards to varying types of land use and land cover in urban settings. In this study, a Surface-Energy-Balance ET algorithm was implemented to estimate ET a at a higher spatial resolution using Landsat 5 satellite images while the Oklahoma Mesonet observations can be used as our ground truth data. Accuracy of the estimated ET a was assessed using latent heat flux measurements provided by AmeriFlux towers. The associated bias ratios of daily mean ET a with respect to both burn and control sites are -0.92%, and -8.86% with a correlation of 0.83 and 0.81, respectively. Additionally, estimated ET a from a water balance budget analysis and the remotely sensed ET a are cross-validated with a low bias ratio of 5.2%, and a correlation coefficient of 0.7 at the catchment scale. The lowest ET a was observed for developed urban areas and highest for open water bodies. The ET a difference is also demonstrated from two contrasting counties. The results show Garfield County (agricultural) has higher ET a values than Oklahoma County (urban) for all land cover types except open water bodies.

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.

Developmentevaluation of an actual evapotranspiration estimation algorithm using satellite remote sensingmeteorological observational network in Oklahoma

Actual evapotranspiration (AET) estimation using satellite sensors can improve water resources planningwater regulation in irrigated areas. This paper evaluates an AET estimation algorithm developed by integrating satellite remote sensingan environmental monitoring network in Oklahoma, USA for operational daily water management purpose. First, a surface energy balance evapotranspiration (ET) algorithm is implemented to estimate actual ET by integrating the twice-daily overpass of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor dataOklahomas world-class environmental monitoring network—Mesonet—with 5-minute data acquisition in real time. Second, accuracy of the estimated ET is evaluated at the site scale using Ameriflux tower latent heat fluxMesonet site crop ET on daily, 8-dayseasonal basis. The results showed that MODIS/Mesonet-AET (MM-AET) estimation showed agreement with ground observations, with daily ET bias less than 15%seasonal bias less than 8%. Additionally, actual ET modelled from a water balance budget analysis in a heavily instrumented basin compares favourably (bias < 3%) with the MM-AET at catchment scales with an order of several hundreds square kilometres. This study demonstrates that (1) the MM-AET estimation is acceptable for daily actual ET estimation(2) it is feasible to implement the proposed MM-AET algorithm in real time for irrigational water resources management at the scale of irrigation projects in Oklahoma.

Hydrologic Remote Sensing : Capacity Building for Sustainability and Resilience

Environmental remote sensing plays a critical role in observing key hydrological components such as precipitation, soil moisture, evapotranspiration and total water storage on a global scale. As water security is one of the most critical issues in the world, satellite remote sensing techniques are of particular importance for emerging regions which have inadequate in-situ gauge observations. This book reviews multiple remote sensing observations, the application of remote sensing in hydrological modeling, data assimilation and hydrological capacity building in emerging regions.

Spatial and Diurnal Variability of Monsoon Systems Assessed by TRMM Rain Rate Over Indus Basin

The spatiotemporal characteristics of the Asian monsoon over the Indus River basin are studied using the latest near real-time satellite precipitation estimates from the tropical rainfall measuring mission (TRMM) multisatellite precipitation analysis (TMPA). The TMPA data product (3B42RT V7) is used to analyze diurnal variability of the Asian monsoon over the study domain during January 2005 to December 2010. First, the spatiotemporal uncertainty of satellite estimates is systematically characterized by comparison to rain gauge observations using four standard error metrics, i.e., the Pearson correlation coefficient (CC), root-mean-square error (RMSE), mean absolute error (MAE), and relative bias (BIAS). Second, diurnal rainfall variability over selected regions is investigated by comparing rainfall patterns during premonsoon and monsoon seasons. The comparison and evaluation of satellite-based estimates and rain gauge data revealed significant correlation of 0.87 for the stations in the southwest and 0.63 in the northeast monsoon region. The results indicated TMPA underestimates over the intense monsoon region from -8% to -25%, while there is an overestimation over the southern region from 7% to 35%. This study improves understanding on the rainfall diurnal variations captured by the three hourly TMPA products during the April-June (premonsoon) and June-August (monsoon) over the extreme monsoon year 2010 versus the regular periods of 2005-2009 by investigating precipitation mean, frequency, and intensity, as well as the diurnal and semidiurnal cycles. A noticeable bimodal variation during the 2010 season showed an increase in rainfall associated with anomalous atmospheric conditions, causing catastrophic floods in Pakistan.