Pakistan and other regions facing alternation of droughts and floods (as described in this challenge) are usually arid and semi-arid that are mainly dependent on rain-fed agriculture and are facing water scarcity issues, rainwater harvesting is critically important for these areas.

Outline steps for solution

For determining optimum sites for rainwater harvesting and the potential of rainwater harvesting structures, data on land cover/land use, elevation and topography, geo-chemical formation of soils, stream runoff, and various hydro-meteorological variables are required. High-quality data on land cover/land use, elevation, stream identification, water potential of individual watersheds, and slope with fine spatial resolution can be derived from space-based satellite imagery. Although stream runoff and hydro-meteorological variable statistics with sufficient accuracy can only be obtained through ground-based in-situ sensors, these measurements also need space-based location services to make them input in the spatial analysis along with the satellite-derived products.

In many cases, however, satellite remote sensing represents a critical source of information, especially in regions with limited sensor networks and where information on hydrologic conditions is inaccessible. Remote sensing and geographical information technologies can play a compelling role in addressing major challenges since spatial patterns of aridity, climatic uncertainty or rapid climatic variability are not vividly understood or considered by local farmers or municipal authorities while planning for agriculture or domestic water use. Robust modeling is possible when space technologies are applied to identify suitable locations and harvesting potentials for ponds and pans, check dams, terracing, percolation tanks, and Nala-bunds; with a very small amount of time, effort, and overhead assessment cost.

1. Identify satellite data sources for

1. Elevation/ Digital Elevation Model (DEM)  
2. Soil data  
3. Meteorological data  
4. Sentinel satellite data: Slope, elevation, drainage density, annual rainfall, NDWI, NDVI, LULC, curve number  
5. Landcover/land-use (LULC) 
6. Stream identification 
7. Slope (with fine spatial resolution)  
8. Water potential of individual watersheds (kPA) 

All these datasets are available as open-source datasets, which are used to derive parameters such as slope elevation, drainage density, annual rainfall, land use/land cover, curve number, and distance from streams. Further, a model that suggests the suitability of sites for rainwater harvesting will be developed.  

2. Modeling

The model includes four fundamental parameters that are readily accessible globally (DEM, soil, rainfall and satellite data, Fig.1). It addresses the simplicity of running the model but highlights the longer process involved in calculating drainage density and suggests its development as an open-source tool. Similarly, it mentions the complexity of the SCS-CN method and proposes its development as a single-click tool. The need for developing reclassification tools for influencing factors in open-source GIS software is also emphasized. The presentation contrasts the affordability and accessibility of ArcGIS with open-source alternatives like QGIS for tasks such as slope calculation. It acknowledges the availability of tools for slope calculation but emphasizes the lack of readily available tools for drainage density, indicating a gap in open-source resources. 

Rainwater harvesting suitability model development  

To develop the model the datasets are run in ArcGIS. Slope, elevation profile, NDVI, NDWI, and distance from stream datasets are run by a single step. However, drainage density, annual rainfall, curve number, and the LULC data need to be run by various complicated steps.  

1. Drainage density 

After calculating the slope and elevation data the drainage density needs to be developed. To calculate this there is a multi-step process (10 steps) involved. This calculation includes stream generation, stream links, grid indexing, clipping, the intersection between stream links and clipped grid index, dissolution, attribute assignment, conversion of polygon features into points features, and finally interpolation. This currently requires expertise to navigate.  

2. Annual rainfall  

NetCDF format data for annual rainfall needs to be converted into geographical raster layers for implementation in a geographical scenario. This calculation is done in six steps, which include the conversion of NetCDF to raster, the exportation of raster to the destination folder, the calculation of annual rainfall of a certain year, the calculation of cell statistics to sum all band values and interpolation of data. 

3. Curve Number (CN) 

The curve number (CN) for rainwater harvesting has three major steps involved. Firstly, the land use/land cover data (LULC) needs to be prepared, which involves transforming categorical data into numerical scores. Secondly, the soil data with LULC data needs to be merged, highlighting the necessity of transforming soil nomenclature into a usable format and assigning scores based on research papers. Lastly, the classification of reclassified raster data into polygons can be simplified and optimized. 

4. Merging soil and LULC data  

Further, merging soil and land use/land cover data needs to automatically generate tables that assign scores based on specific combinations of soil types and land use categories.  Merging soil and LULC data undergoes the process of reclassification and weight assignment, highlighting the simplicity of linear equations in ArcGIS. This proposes the integration of pairwise comparison methods for assigning weights. Additionally, online tools for pairwise comparison and analytical hierarchy process (AHP) are available online, which can streamline the weight assignment process. Finally, after the weights for each layer have been assigned and all raster layers have been reclassified, the site suitability analysis can be done. This analysis suggests the optimum sites where rainwater can be harvested (Fig.2). 

3. Future steps

There are 55 individual tools in this model slated for separate development, to consolidate these into one tool capable of integrating all five steps. This consolidation represents the primary objective to facilitate the model's transition to open-source status, enabling users to access the comprehensive solution through a single menu interface. The following processes need a single-click tool to simplify the process for users without specialized knowledge.  

• Drainage density: This 10-step process needs to be streamlined into a single tool for ease of use, particularly in open-source software like QGIS.  
• Annual rainfall: A separate open-source tool needs to be developed for the conversion process of annual rainfall data into a geographical raster, outlining steps such as raster calculation expressions to combine multiple years of data into a single value. 
• Curve number: Automation of the transformation process of LULC data into numerical score is needed. 
• Merge of soil data and LULC data: A dedicated tool for CN grid calculation in open-source software like QGIS is needed.