Hydrological Modeling and Capacity Building in the Republic of Namibia

AbstractThe Republic of Namibia, located along the arid and semiarid coast of southwest Africa, is highly dependent on reliable forecasts of surface and groundwater storage and fluxes. Since 2009, the University of Oklahoma (OU) and National Aeronautics and Space Administration (NASA) have engaged in a series of exercises with the Namibian Ministry of Agriculture, Water, and Forestry to build the capacity to improve the water information available to local decision-makers. These activities have included the calibration and implementation of NASA and OU’s jointly developed Coupled Routing and Excess Storage (CREST) hydrological model as well as the Ensemble Framework for Flash Flood Forecasting (EF5). Hydrological model output is used to produce forecasts of river stage height, discharge, and soil moisture.To enable broad access to this suite of environmental decision support information, a website, the Namibia Flood Dashboard, hosted on the infrastructure of the Open Science Data Cloud, has been developed...

[1]  Robert L. Grossman,et al.  Use of the Earth Observing One (EO-1) Satellite for the Namibia SensorWeb Flood Early Warning Pilot , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  M. Ek,et al.  Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water , 2011 .

[3]  Florian Pappenberger,et al.  Continental and global scale flood forecasting systems , 2016 .

[4]  Jeff W. Brogden,et al.  Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities , 2016 .

[5]  M. Watkins,et al.  The gravity recovery and climate experiment: Mission overview and early results , 2004 .

[6]  Florian Pappenberger,et al.  Ensemble flood forecasting: a review. , 2009 .

[7]  Florian Pappenberger,et al.  Do probabilistic forecasts lead to better decisions , 2012 .

[8]  K. Pearson VII. Note on regression and inheritance in the case of two parents , 1895, Proceedings of the Royal Society of London.

[9]  農業土木学会応用水文研究部会,et al.  応用水文 = Applied hydrology , 1991 .

[10]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[11]  G. Hornberger,et al.  A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils , 1984 .

[12]  P. Bauer‐Gottwein,et al.  Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for hydrological model calibration in a large poorly gauged catchment , 2011 .

[13]  Sadiq I. Khan,et al.  The coupled routing and excess storage (CREST) distributed hydrological model , 2011 .

[14]  Rodrigo Proença de Oliveira,et al.  Evaluation of extreme precipitation estimates from TRMM in Angola , 2015 .

[15]  David T. Sandwell,et al.  Accuracy and resolution of shuttle radar topography mission data , 2003 .

[16]  Faisal Hossain,et al.  Crossing the “Valley of Death”: Lessons Learned from Implementing an Operational Satellite-Based Flood Forecasting System , 2014 .

[17]  Yang Hong,et al.  Estimating a-priori kinematic wave model parameters based on regionalization for flash flood forecasting in the Conterminous United States , 2016 .

[18]  F. Pappenberger,et al.  Forecasting droughts in East Africa , 2013 .

[19]  E. Wood,et al.  A Drought Monitoring and Forecasting System for Sub-Sahara African Water Resources and Food Security , 2014 .

[20]  D. Gesch,et al.  Global multi-resolution terrain elevation data 2010 (GMTED2010) , 2011 .

[21]  Yang Hong,et al.  The FLASH Project: Improving the Tools for Flash Flood Monitoring and Prediction across the United States , 2017 .

[22]  Cajo J. F. ter Braak,et al.  Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation , 2008 .

[23]  J. E. Nash,et al.  The form of the instantaneous unit hydrograph , 1957 .

[24]  Yang Hong,et al.  Statistical and hydrological evaluation of TRMM-based Multi-satellite Precipitation Analysis over the Wangchu Basin of Bhutan: Are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins? , 2013 .

[25]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[26]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[27]  M. Seely,et al.  The surface geology and geomorphology around gobabeb, namib desert, namibia , 2013 .

[28]  F. Wetterhall,et al.  Seasonal predictions of agro-meteorological drought indicators for the Limpopo basin , 2014 .

[29]  Stephen G. Ungar,et al.  Overview of the Earth Observing One (EO-1) mission , 2003, IEEE Trans. Geosci. Remote. Sens..

[30]  Massimiliano Zappa,et al.  Visualizing flood forecasting uncertainty: some current European EPS platforms—COST731 working group 3 , 2010 .

[31]  S. K. Jenson,et al.  Extracting topographic structure from digital elevation data for geographic information-system analysis , 1988 .

[32]  The impact of flooding on people living with HIV: a case study from the Ohangwena Region, Namibia , 2015, Global health action.

[33]  G. Huffman,et al.  The TRMM Multi-Satellite Precipitation Analysis (TMPA) , 2010 .

[34]  G. Heuvelink,et al.  SoilGrids1km — Global Soil Information Based on Automated Mapping , 2014, PloS one.

[35]  Bellie Sivakumar,et al.  Droughts in a warming climate: A global assessment of Standardized precipitation index (SPI) and Reconnaissance drought index (RDI) , 2015 .

[36]  Andrew Newsham,et al.  Knowing, farming and climate change adaptation in North-Central Namibia , 2011 .

[37]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[38]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[39]  T. Sturm,et al.  Open Channel Hydraulics , 2001 .

[40]  K. Verdin,et al.  New Global Hydrography Derived From Spaceborne Elevation Data , 2008 .

[41]  Yu Zhang,et al.  Hydrometeorological Analysis and Remote Sensing of Extremes: Was the July 2012 Beijing Flood Event Detectable and Predictable by Global Satellite Observing and Global Weather Modeling Systems? , 2015 .

[42]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[43]  Walter J. Rawls,et al.  Green‐ampt Infiltration Parameters from Soils Data , 1983 .

[44]  R. Nemani,et al.  Global Distribution and Density of Constructed Impervious Surfaces , 2007, Sensors.

[45]  Peter Bauer-Gottwein,et al.  Regional review: the hydrology of the Okavango Delta, Botswana—processes, data and modelling , 2009 .

[46]  Y. Hong,et al.  SERVIR-Africa: Developing an Integrated Platform for Floods Disaster Management in Africa , 2010 .

[47]  X. R. Liu,et al.  The Xinanjiang model. , 1995 .

[48]  Chaowei Yang,et al.  Enabling Big Geoscience Data Analytics with a Cloud-Based, MapReduce-Enabled and Service-Oriented Workflow Framework , 2015, PloS one.

[49]  Wolfgang Kinzelbach,et al.  Hydrological real-time modelling in the Zambezi river basin using satellite-based soil moisture and rainfall data , 2011 .