Amelia Fernández Rodríguez

I-DIP builds on InfoSequia, an advanced toolbox that integrates satellite data, local observations, and machine learning to monitor and forecast droughts. A new flash drought indicator, tailored to Pakistan’s climate, will be developed and embedded within NDMC’s existing system. The project will enhance early warning capacities, safeguard food and water security, and contribute to national climate adaptation efforts, paving the way for I-DIP’s upscaling across Pakistan.

Beyond monitoring, I-DIP will connect its forecasts to decision-making tools. Impact information will be disseminated through advisory bulletins and the inFarmer app (developed by WaterSprint), already widely used among farming communities. Field facilitators deployed by the Better Cotton Initiative will translate these insights into actionable guidance for farmers, enabling them to adapt irrigation practices, adjust cropping calendars, and mitigate potential losses. This integration of cutting-edge drought science with established communication networks ensures that early warnings are transformed into practical actions at the field level.

By embedding I-DIP within NDMC’s operational system, the project directly strengthens Pakistan’s early warning capacity for droughts, aligns with national climate policies, and supports the country’s commitments under the Sustainable Development Goals. In the longer term, the pilot is expected to catalyse scaling of I-DIP across Pakistan, offering authorities a state-of-the-art tool to anticipate and manage such extreme events.

We invite you to the SPHY Hydrological Model Webinar on 9 October 2025, from 10:00 to 11:00 CET. This event will showcase the latest developments in SPHY, including new tools, features, and workflows, and provide an interactive platform to ask your questions.

A New Era for SPHY

SPHY is undergoing a major transformation to become more powerful and user-friendly. Recent milestones include:

QGIS plugin: a redesigned interface that allows users to set up, run, and visualize SPHY entirely within QGIS.
SPHY version 3.1: new features such as bias correction for meteorological data, improved snowmelt routines, and more flexible simulation options.
New website and resources: updated manuals, tutorials, and datasets are now available at sphymodel.com.

These innovations make SPHY more accessible and relevant for a wide range of hydrological and water resources applications.

What to Expect at the Webinar

Tijmen Schults will introduce SPHY and its latest features.
Amelia Fernández Rodríguez will give a live demonstration of the new QGIS plugin.

This is the perfect opportunity to see SPHY in action and get direct input from the experts.

Who Should Attend

The webinar is open to hydrologists, water managers, GIS practitioners, researchers, students, and anyone interested in open-source hydrological modelling.

Practical Info & Registration

Date & Time: 9 October 2025, 10:00–11:00 CET
Format: Online webinar
Cost: Free
Register here

To further advance our hydrological model SPHY, we are proud to announce four major milestones for the SPHY modelling community. Since SPHY is widely used by FutureWater in capacity building programs, our goal has always been to make the model and its data as accessible and user-friendly as possible.

Until now, Graphical User Interfaces (GUIs) for SPHY were only available in QGIS for version 2.0. This project has upgraded those plugins to ensure full compatibility with the latest versions of SPHY, QGIS, and Python. The updated plugins also integrate new functionalities to handle cutting-edge data sources as model inputs. With these new QGIS plugins, running SPHY no longer requires programming skills—opening the door for a much broader audience to set up, run, and analyze hydrological simulations with ease.

1. Introducing the SPHY QGIS plugin

SPHY is now directly integrated into QGIS, allowing users to set up, run, and visualize SPHY simulations through an intuitive graphical interface. This plugin streamlines workflows, improves accessibility for new users, and enhances integration with geospatial datasets. Features include

Complete SPHY workflow integrated in QGIS
Streamlined preprocessing of model inputs
Intuitive, no-code interface
Results visualized directly in QGIS
Modular setup for diverse applications
Open-source and fully reproducible

2. New SPHY website

The new SPHY website offers easier navigation, updated documentation, and a central hub for resources, downloads, and training materials. Visit www.sphymodel.com to explore a fresh, modern platform for all SPHY matters.

3. SPHY model version 3.1 released

The latest SPHY release on Github introduces new features, performance improvements, and enhanced capabilities for hydrological and cryospheric modelling. This version builds on the robust foundations of previous releases, integrating feedback from the community and advancing the model’s flexibility and accuracy. It can be downloaded completely free and is open source. New in this version are:

Added bias-correction procedure for meteorological forcing
Increased flexibility and options for defining simulation periods
Enhanced snow melt calculation

4. Updated manuals, tutorials and datasets

Manuals of the new SPHY version and QGIS plugin are now available, as well as new datasets to make your model work. For the new QGIS plugin a video tutorial was made to explain all the ins and outs of the tool.

More to come soon!

These developments mark a significant step forward in making SPHY more accessible, powerful, and user-friendly for researchers, practitioners, and decision-makers worldwide. Soon we will organize a webinar to explain all the new features and later this year we plan on hosting a user day to discuss future model developments with the SPHY community.

Video

On June 25, we celebrated the successful closure of the RoSPro project with a national workshop held in Nepal. The event brought together key stakeholders to reflect on the project’s positive outcomes, including improved spring water access, road resilience, and community engagement in water management.

A key highlight of the project was the Decision Support System (DSS) developed by FutureWater. This system integrated hydrological (as an outcome of the SPHY hydrological model), geospatial, and socioeconomic data into a user-friendly platform, enabling data-driven decisions for sustainable water management. Moreover, the system provides a simple Cost&Benefit Calculator to assist stakeholders in evaluating the potential of proposed management measures for mountain springs.

The workshop underscored the potential for scaling up the approach in other regions, using the DSS to guide future spring protection and water resource management efforts. We look forward to continue this work in the future!

An open access version of the DSS is available here

The approach of FutureWater and Galayr is designed to be both scientifically rigorous and contextually grounded, ensuring that the developed drought model is locally relevant, sustainable, and fully integrated into existing national systems such as those of SODMA and NADFOR. The model will merge top-down (data-driven, machine learning-enabled) and bottom-up (stakeholder-informed) approaches, combining satellite data, climate indices, and indigenous knowledge to co-develop impact-based forecasts and consensus-based triggers for anticipatory action.

For the development of the drought forecasting model and the knowledge transfer we will focus on the following pillars:

A phased work plan that spans institutional capacity assessments, model development, validation, stakeholder consultations, and hands-on capacity building.
Application of state-of-the-art forecasting models ranging from ARIMA and regression to more advanced machine learning techniques, while maintaining focus on usability and institutional adoption.
A strong emphasis on knowledge transfer, including training programs and the establishment of a collaborative knowledge-sharing platform using the FutureWater Academy platform
A robust risk management plan, including mitigation strategies for data scarcity, stakeholder disengagement, and institutional turnover.

The BUCRA (Building Unity for Climate Resilient Agriculture) project focuses on enhancing agricultural resilience in Qahbunah, a farming community in Egypt’s Nile Delta. Facing challenges like water scarcity, climate change, and land fragmentation, local farmers require innovative approaches to sustain their livelihoods.

At the heart of BUCRA are two cutting-edge tools developed by FutureWater: Croptimal and SOSIA, which combine advanced technology with local insights to transform traditional farming practices.

Croptimal is a climate suitability analysis tool that leverages climate projections, geospatial data, and agricultural insights to assess the suitability of various crops under current and future climate scenarios. By identifying areas and crops that are most resilient to climate stressors like heat, salinity, and water scarcity, Croptimal empowers farmers with data-driven recommendations to optimize their crop choices and planting strategies. This tool provides highly detailed maps and actionable advice, enabling farmers to adapt their practices to the challenges of climate change while enhancing productivity.

SOSIA (Satellite-based Open-source Irrigation Advisory) is an irrigation management tool designed to improve water use efficiency. It uses open-source satellite data, real-time weather information, and local soil conditions to provide precise daily irrigation advice. Farmers receive recommendations on how long to irrigate their crops each day via WhatsApp, making the service both accessible and cost-effective. This innovative approach not only reduces water usage but also improves crop yields and energy efficiency, addressing the increasing pressures on water resources in the Nile Delta.

In addition to these tools, BUCRA includes demonstration plots showcasing climate-smart techniques such as efficient irrigation, soil management, and crop rotation. Farmers will also participate in a blended learning program that combines field-based training with easy-to-use digital applications to improve their technical skills and knowledge.
BUCRA emphasizes empowering youth and women in agriculture, strengthening market linkages, and promoting sustainable land-use practices. By aligning Dutch expertise with local needs, the project aims to boost productivity, stabilize incomes, and build a sustainable agricultural future in Qahbunah.

The long-term vision is to inspire broader adoption of these tools and practices, ensuring food and water security in the region while addressing the challenges posed by climate change.

Graphical User Interfaces are available for QGIS but only for SPHY v2.0 at the moment. This project will upgrade these plugins in order to make them compatible with the latest versions of SPHY (v3.0 and v3.1), QGIS and Python available. The updated plugins will also incorporate the additional functionalities to process state of the art new data sources as inputs.

As SPHY is used by FutureWater in several capacity building programs, our aim is to make the access to the data and the model as easy and intuitive as possible. With updated QGIS Plugins, no programming skills will be required to run the model, so a broader audience can use SPHY for their own purposes.

More information can be found at the SPHY website.

Most recent research has focused on identifying historical megadroughts based on paleo-records and understanding their climatic causes, or on the study of “modern” events and their impacts, generally in lowland and plain regions. However, high-mountain regions and snow-dependent catchments have been little studied, and little is known about the impact of megadroughts on the state and dynamics of the cryosphere in mountain water towers.

In general, catchments dependent on high mountain systems have an intrinsic capacity to buffer the lack of precipitation and excess evapotranspiration that depends on the water reserves stored in the cryosphere (snow, glaciers and permafrost). It is presumed that the this buffer capacity is limited until a tipping point is reached from which the impacts of water shortages and temperature extremes may be amplified and jeopardize the functioning of ecosystems and water resource systems.

Megawat has a double objective: 1) to address the knowledge gaps around the hydro-climatic causes of extreme droughts and their impact on the water balance of Europe’s mountain water towers, with special emphasis on the concurrence of compound events and cascading and multi-scale effects and 2) to develop and propose new adaptation strategies to cope with the duration, extent and severity of future megadroughts and their potential impacts on environmental and socio-economic assets.

For its implementation, MegaWat focuses on Europe’s high mountain regions and their dependent-catchments. MegaWat aims to develop three products:

Product 1. A methodological framework for the identification and characterization of historical megadroughts during the instrumental period, and the assessment of the role of the cryosphere in supporting the landscape development of downstream areas, or in buffering climate change impacts. Product 1 relies on a combination of climate regionalization, surface energy balance modelling, hydrological simulation, and water evaluation and allocation analysis at the catchment level (figure below).
Product 2. A high-resolution, open-access regionalized climate database.
Product 3. A list of potential adaptation strategies useful for the prevention and mitigation of drought impacts, and the enhancement of the water security and resilience of high mountain regions and dependent catchments. These scenarios will be agreed with regional and local actors and stakeholders, and their effectiveness will be evaluated under extreme drought scenarios in three pilot regions in Europe. These pilot regions will be previously selected following criteria of representativeness, strategic importance and vulnerability to droughts.

Schematic representation of a high mountain basin, including the main components, processes and impacts related to droughts.

FutureWater plays an important role in MegaWat by coordinating the Work Package which aims to develop and test simulation tools that help to adapt to megadroughts and support the decision making process. Two specific objectives are pursued in this Work Package: a) the development of a methodological prototype for quantifying impacts and identifying tipping points for water security in snow-dependent downstream catchments, and b) the generation and the integration of snow drought indicators in the FutureWater’s Drought Early Warning System called InfoSequia (figure below).

Workflow of the InfoSequia Early Warning System developed by FutureWater and adapted for the detection of tipping-points of water scarcity in snow-dependent catchments. More information about InfoSequia.

A one-pager can be downloaded here.

Acknowledgements

This project has received funding from the Water4All programme with co-funding from CDTI (Spanish Office for Science and Technology) and the EU’s Horizon Europe Framework Programme for Research and Innovation.

This week marked a significant milestone for the MAGDA Project as the Mid-term Review Meeting was held in the city of Beaune, France. Over the course of two days, the consortium gathered to conduct a comprehensive assessment of the progress achieved during the first half of the project. Reflecting on past achievements, the gathering also served as an opportunity to outline the roadmap for the project’s successful completion.

A highlight of the event was a field visit to one of the project’s pilot areas, offering firsthand insights into the practical implementation of MAGDA equipment. The field visit provided a unique opportunity to witness the innovative solutions in action, including the cutting-edge metodrone developed by MeteoMatics and state-of-the-art meteorological stations provided by CAP2020.

As the MAGDA Project enters its second phase, the momentum generated in Beaune sets a promising roadmap for continued success.

The MAGDA project aims at providing an integrated – but modular – system to provide severe weather forecasts and irrigation advisories enhanced by means of various satellite-borne, drone-borne and ground-based weather-observing technologies. The main applications will be in providing both warnings about severe weather that could affect crops and irrigation advisories based on enhanced rain forecasts. These warnings and advisories will be channelled through a Farm Management System to ensure the capability to effectively reach farmers and agricultural operators.

Meteodrone before performing demo flight

This week, the second part of the Water Accounting Training for the Agriculture, Climate and Water Sector Organizations in Pakistan has been successfully completed at the Food and Agriculture Organization of the United Nations (FAO) office in Islamabad, Pakistan.

As an agrarian economy that heavily depends on water, it is crucial for Pakistan to adopt a more integrated water management approach and formulate data-driven strategies to avert from the deepening water crisis.

This training has been designed by FutureWater and FAO as part of the Green Climate Fund funded project titled: ‘Transforming the Indus Basin with Climate Resilient Agriculture and Water Management’. Component 1 of this project focuses on enhancing information services for climate change adaptation in the water and agriculture sectors.

This second part of the training is comprised in seven modules and the aim is to enable stakeholders to develop water accounts at different scales. Given the growing issues of water scarcity, climate change impacts and unmet irrigation demands, this water accounting system can aid decision-makers to design evidence-based policies and achieve sustainable water resources management.

In this in-person training of one week, participants further extended their knowledge on how to compute inflows and outflows of a system at using remote sensing and assessing global datasets.

More information about the project can be found here.