AN INTERACTIVE WATER INDICATOR ASSESSMENT TOOL TO SUPPORT LAND USE PLANNING

This paper presents an interactive web-based rapid assessment tool that generates key water related indicators to support decision making by stakeholders in land use planning. The tool is built on a consistent science based method that combines remote sensing with hydrological and socioeconomic analyses. It generates transparent, impartial, and verifiable information regarding the impact of land use changes on water productivity, water consumption, water availability, and employment. The usefulness of the tool was demonstrated in the Inkomati River Basin in Southern Africa, where the tool was used to assess the impact of converting land use on the water resources to prioritize areas for conversion and to track required changes in land use to comply with tripartite water allocation agreements. This contributed to confidence building and to strengthening the process of conscientious land use planning, which is an extension of conventional work in this field.

[1]  P. Hellegers,et al.  Combining remote sensing and economic analysis to support decisions that affect water productivity , 2009, Irrigation Science.

[2]  Frank Veroustraete,et al.  Assessment of Evapotranspiration and Soil Moisture Content Across Different Scales of Observation , 2008, Sensors.

[3]  W. Bastiaanssen,et al.  SEBAL for detecting spatial variation of water productivity and scope for improvement in eight irrigated wheat systems , 2007 .

[4]  Charlotte de Fraiture,et al.  Comprehensive Assessment of Water Management in Agriculture , 2010 .

[5]  M. S. Moran,et al.  Assessing the Spatial Distribution of Evapotranspiration Using Remotely Sensed Inputs , 1991 .

[6]  Chris Kidd,et al.  Satellite rainfall climatology: a review , 2001 .

[7]  B. Séguin,et al.  Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches , 2005 .

[8]  R. Young.  Determining the Economic Value of Water: Concepts and Methods , 2005 .

[9]  W. Krajewski,et al.  Satellite estimation of precipitation over land , 1996 .

[10]  Prem S. Bindraban,et al.  Pathways for increasing agricultural water productivity , 2007 .

[11]  Wim G.M. Bastiaanssen,et al.  Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low - Middle Sao Francisco River basin, Brazil : part A : calibration and validation , 2009 .

[12]  V. M. Chowdary,et al.  Integrated Water Resource Development Plan for Sustainable Management of Mayurakshi Watershed, India using Remote Sensing and GIS , 2009 .

[13]  Wim G.M. Bastiaanssen,et al.  Satellite surveillance of evaporative depletion across the Indus Basin , 2002 .

[14]  Grant W. Petty,et al.  The status of satellite-based rainfall estimation over land☆ , 1995 .

[15]  Petra Hellegers,et al.  Combining remote sensing and economic analysis to assess water productivity; A demonstration project in the Inkomati Basin , 2006 .

[16]  Christian Kummerow,et al.  A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors , 1996, IEEE Trans. Geosci. Remote. Sens..

[17]  W. Bastiaanssen,et al.  A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan , 2003 .

[18]  Anthony Morse,et al.  A Landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning , 2005 .

[19]  M. Mccabe,et al.  Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data , 2008 .

[20]  E. C. Barrett,et al.  Satellite rainfall monitoring: An overview , 1994 .

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

[22]  Prem S. Bindraban,et al.  Improving agricultural water productivity: Between optimism and caution , 2010 .

[23]  Chris Perry,et al.  Efficient irrigation; inefficient communication; flawed recommendations , 2007 .

[24]  D. Yates,et al.  WEAP21—A Demand-, Priority-, and Preference-Driven Water Planning Model , 2005 .

[25]  Raffaele Casa,et al.  Assessing Crop Water Demand by Remote Sensing and GIS for the Pontina Plain, Central Italy , 2009 .

[26]  P. Hellegers,et al.  Determining the disaggregated economic value of irrigation water in the Musi sub-basin in India , 2010 .

[27]  P. Hellegers,et al.  Remote Sensing and Economic Indicators for Supporting Water Resources Management Decisions , 2010 .

[28]  William P. Kustas,et al.  Use of remote sensing for evapotranspiration monitoring over land surfaces , 1996 .

[29]  D. Kniveton,et al.  A statistical modelling approach to passive microwave rainfall retrieval , 1998 .

[30]  P. Hellegers,et al.  Can Irrigation Water Use Be Guided by Market Forces? Theory and Practice , 2006 .

[31]  James L. Wright,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Applications , 2007 .

[32]  E. Noordman,et al.  SEBAL model with remotely sensed data to improve water-resources management under actual field conditions , 2005 .

[33]  Wim G.M. Bastiaanssen,et al.  Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low-Middle São Francisco River basin, Brazil: Part B: Application to the regional scale , 2009 .

[34]  Edzer Pebesma,et al.  Automatic Prediction of High-Resolution Daily Rainfall Fields for Multiple Extents: The Potential of Operational Radar , 2007 .