By Enrique Ortiz et Al.
Executive Secretary of the NGO GH2MF2 – Association for a Participated Global Hydrological Monitoring and Flood Forecasting System. Via Settimio Mobilio 16 Int.21 – 84127 (SA) Salerno – Italy. Fiscal Code: 95165880659
The development of GH2MF2 project was motivated from solidarity and from the need to dignify the most disadvantaged people living in the poorest countries in the world (Asia, Africa and South America), which are continually exposed to changes in the hydrologic cycle suffering events of large floods and/or long periods of droughts due to climate change. The key philosophy behind GH2MF2 is that anyone citizen in the world should deserve free access to hydrological information and forecasts, through a Web-GIS based platform in an interactive and bidirectional way. GH2MF2 is an open non-profit tool, designed to provide real time hydrological monitoring and forecasting at global scale, together with an assessment of predictive uncertainty, by combining hydrological to meteorological ensemble uncertainty, described as a function of the meteorological ensemble spread. To carry out the project, a Non-Profit association was established with legal headquarters in the Italian Republic in 2017, founded by three of the authors of this Conference Paper: ” GH2MF2 – Association for a Participated Global Hydrological Monitoring and Flood Forecasting System”. We present the development of a demonstrational pilot implementation of GH2MF2 in Transboundary Koshi River Basin (China, Nepal and India).
Koshi River (कोशी नदी in nepali or Kosi River in indian) is one of the biggest tributary of River Ganga originating from Tibet (China) and joins the Ganges in Bihar State (India) via Nepal. Total drainage area of the Koshi River is 88000 km2. Majority of the area comes from Tibet and Nepal (80%), and only 20% drainage area is in Indian Territory. The River Khosi originates at an altitude of over 7000 m above MSL in the Himalayas, 6 of the 14 eight-thousanders are in the basin (Mount Everest, Kangchenjunga, Lhotse, Makalu, Cho Oyu and Shishapangma).
In the north, the river is bound by the ridge separating it from the Tsangpo (Brahmaputra) River, while the River Ganga forms its southern boundary. The eastern and western boundaries are the ridge lines, separating it from the Mahananda and the Gandak/Burhi catchments respectively. The upper catchment of the river system lies in Nepal and Tibet. It enters the Indian Territory near Hanuman Nagar in Nepal. It joins the Ganga River near Kursela in Katihar district. In Nepal, this river is known as “Saptakoshi”. It is formed by the confluence of seven smaller streams, namely, the Sun Koshi, the Bhote Koshi, the Tama Koshi, the Dudh Koshi, the Barun Koshi, the Arun Koshi and the Tamor Koshi, meeting above Tribeni, about 10 km. upstream of Chatara. But for all practical purposes, the confluence at Tribeni in Nepal is considered to be formed by the three major tributaries out of the seven, the Arun Koshi from North, the Sun Koshi from West and the Tamor Koshi from East. Below the confluence at Tribeni, the Koshi flows in a narrow gorge for a length of about 10 km., till it debouches into plains, near Chatara in Nepal. Further down, the river runs in relatively flat plains of Nepal. The river flows through Nepal for 50 km below Chatara to Hanuman Nagar, before it enters the Indian Territory. Below Hanuman Nagar, the river Koshi runs about 100 km in a sandy tract and finds its way southward through several channels. After that, the river takes an eastward direction and has a single defined channel. The main channel joins the river Ganga near Kursela in Katihar district. In plains of Bihar, the river has two important right tributaries; these are the Bagmati and the Kamla Balan. The other tributaries worth mentioning on the right bank are the Trijuga and the Bhutahi Balan.
Socioeconomic conditions: Koshi River Basin has a population of 15.3 million (2009). The total annual GDP was about USD$ 10.4 billion in 2009 (i.e. less than USD$ 700 per capita/year). The region in India is mostly alluvial with subtropical climate and is very productive in agriculture. However, due to its large population (about 1000 people per km2), the average income in the region is below the national average of India (USD$ 1134 in 2009). The Nepalese in the region are about 6 million (1/4 of the country’s population). The population density is 200 km−2 varying from 32 (Solukhumbu) to 276 (Kavre) in the central part of the Koshi River. Forty percent of the residents are below Nepal’s poverty line (higher than the national average of 30%), but the GDP per capita in the region is near to Nepal’s national average (USD$ 427). The population in China’s territory is 94 thousand (with an average population density of 3.2 km−2 varying between 1.9 and 5.5 km−2). The average GDP per capita of Chinese in the region is USD$ 1970. Clearly, the region in the middle of the basin has the worst economic condition. The population density increases rapidly from the upstream to downstream.
The GH2MF2 System is integrated into a Decision Support System (DSS) platform, based on geographical data. The DSS is an innovative web application (for Pcs, Tablets, Mobile phones) not requiring installation (a web browser and an internet connection only) or updating (all upgrading being deployed on the remote server). The proposed platform is meant to be participative and interactive. To meet this objective, all information used in the system must be freely accessible to users, including the hydrological model, and allowed to set-up the models for the benefit of the community.
The novel aspect of the proposed project and its platform is that any user will be allowed to set-up, calibrate and validate hydrological models, by downloading from the platform the base maps (e.g. digital elevation model) and the hydro-meteorological forcing data. Validated models (according to provided standards) will be then uploaded on the platform to be part of the daily run of the system and to be available to other users, who can view and interrogate the platform for forecasts in the basins. The hydrological simulations will also be processed by the platform for their visualization and for diffusion of warnings when extreme conditions (e.g. floods and droughts) are reached. The platform comprises the following components:
- Web portal with a dashboard to share, download and upload hydrological datasets;
- Blog and social media pages where users can exchange impressions, suggestions and comments on the use of the platform and their simulations, and the possibility of upload geo-referenced observed water levels and other relevant information useful for other users;
- Decision Support System based on the latest generation Web-GIS, which enables the user to view and query on any 1×1 km2 cell the evolution of modeled variables in the form of Multi-Temporal output raster maps: rainfall, temperature, soil moisture (surface layer and deep layer), evapotranspiration, snow water equivalent (SWE), percolation, discharge flow in the channel network and surface flow.
To meet the requirements of availability, stability, interoperability and portability, a systematic architecture of three tiers, including the presentation, application and database and model, is considered. The Web GIS DSS will be built upon a platform of proven open source component including Geoserver, Open Layers, ExtJs, PostgreSql with PostGis extension, GDAL libraries. It implements Open Geospatial Consortium (OGC) standards, including Web Map Server (WMS), and Web Feature Service (WFS). The main available functions of the platform are:
- WebGis-portal with user-oriented interface, designed for facilitating remote access to the key operational tools;
- Pre-processing of historical and real-time hydrological data freely available, such as those provided by the WMO Hydrological Observing System (WHOS);
- Pre-processing of observed/historical and near real time Precipitation/Temperature derived by Satellite and Reanalysis datasets, such as NASA TRMM 3B42RT, PERSIANN-CCS, MSWEP, Reanalysis ERA5-ECMWF and CFSR-NCEP-NOAA;
- Pre-processing of ensemble numerical weather predictions (NWP) such us GFS 0.25º;
- Parameterization of a continuous and distributed hydrological model with reference base maps at global resolution of 1×1 km2;
- Customization of model parameters based on user expert knowledge (e.g. based on other data sources or after calibration for specific catchments);
- Batch simulations with historical hydro-meteorological data;
- Forecasts with real-time data and NWP;
- Visualization and download of model outputs, including both catchment distributed state variables (e.g. soil water content, snow cover, SWE, evapotranspiration, flow discharge in every cell of fluvial network grid) and integrated catchment response (e.g. flow discharge), at the user-required time-resolution (from one hour to one year);
- Simulation performance is evaluated by comparing simulated and observed discharge at selected gauged catchment outlets, e.g. those provided by WMO Hydrological Observing System (WHOS). The performance will be evaluated with the common statistics such as: correlation, root mean square error, Nash-Sutcliffe efficiency index;
- Predictive uncertainty of the catchment discharge is evaluated with the Model Conditional Processor approach (MCP model);
- Alerts and warnings for low flows and floods, based on user-assigned probabilities that the simulated discharge is below or above user defined thresholds.
The hydrological simulations are generated by distributed hydrological model TOPKAPI-X (TOPographic Kinematic APproximation and Integration – extended). TOPKAPI-X has already accumulated a long history in the literature as well in operational experiences all over the world. Thanks to its computational efficiency, global hydrological simulations can be performed with hourly time-step and a 1×1 km2 spatial resolution.
TOPKAPI-X is a fully distributed and continuous hydrologic model, with a simple and parsimonious parameterization. The model is based on the idea of combining the kinematic approach and the topography of the basin. Spatial distribution of catchment parameters, precipitation input and hydrologic response is achieved horizontally by an orthogonal grid network and vertically by soil layers at each grid pixel. Four ‘structurally similar’ non-linear reservoir differential equations characterize the TOPKAPI-X approach and are used to describe subsurface flow (superficial and deep layers), overland flow and channel flow. Moreover, the TOPKAPI-X model includes components representing the processes of the hydrologic cycle: infiltration, percolation, evapotranspiration and snowmelt, plus a lake/reservoir component, a parabolic routing component and a groundwater component. Being a physically based model, the values of the model parameters can be easily derived from digital elevation maps, soil type and land use maps in terms of topology, slope, soil permeability, soil depth and superficial roughness. A calibration based on observed streamflow data is then necessary for ‘fine tuning’ the model to reproduce the behavior of the catchment. Thanks to its physically based parameters, the TOPKAPI-X model can be successfully implemented also in un-gauged catchments where the model cannot be calibrated using measured data. In this case the model parameters can be derived from thematic maps. The reference base maps used for hydrological model in Koshi River Basin implementation are:
HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales) provides hydrographic information in a consistent and comprehensive format for regional and global-scale applications. HydroSHEDS offers a suite of geo-referenced data sets (vector and raster), including stream networks, watershed boundaries, drainage directions, and ancillary data layers such as flow accumulations, distances, and river topology information. HydroSHEDS is derived from elevation data of the Shuttle Radar Topography Mission (SRTM) at 3 arc-second resolution.
The parameters of hydraulic properties of soils have been obtained from the combination of three datasets at 1km2 resolution: The Harmonized World Soil Database Version 1.1. HWSD, SoilGrids 1Km and the HiHydroSoil database.
Land Use Maps Data: The Global Land Cover-SHARE (GLC-SHARE) is a new land cover database at the global level created by FAO, Land and Water Division in partnership and with contribution from various partners and institutions. It provides a set of eleven major thematic land cover layers resulting by a combination of “best available” high resolution national, regional and/or sub-national land cover databases. The database is produced with a resolution of 30 arc-second (~1×1 km2).