Jazmin Campos Zeballos

In the context of participatory monitoring projects in hydrology, the collection of water level data by laypersons is used as a simple and cost-effective alternative to automatic water level sensors, especially in poorly gauged catchments in remote areas of countries in the Global South. Such data can be used for the development of hydrological models to support water resources management. However, a common problem with participatory monitoring approaches is the irregularity of data collection and its decreasing frequency over time. Determining the amount and timing of data collection required for satisfactory model calibration is critical. To investigate this further, we examined daily water levels from a four-year project in western Kenya. We set up scenarios that represented datasets of different lengths and seasonal starting points for measurements. These scenarios were then used to calibrate a simple rainfall-runoff model. The data were supplemented with satellite data on evapotranspiration to improve the simulated water balance. The model runs were filtered using a water balance filter, and the Kling-Gupta-Efficiency (KGE) was used to compare model efficiencies. While a single month of water level data collected during the rainy seasons was sufficient to achieve good model performance (KGE ≥ 0.75), several months of data were required for simulations starting in the dry seasons. Similar results were found for the validation period, with lower overall model performance (KGE ≥ 0.6). Water level data collected during high flows generally led to an improvement in model performance compared to data collected during low flows. However, after a certain threshold, more water level data did not lead to further substantial model improvement. Based on the analysis of different datasets, this study demonstrated that short-term participatory monitoring programs that collect water level data during the wet season have the potential to provide sufficient input to calibrate a hydrological model.

Digitalization is globally transforming the world with profound implications. It has enormous potential to foster progress toward sustainability. However, in its current form, digitalization also continues to enable and encourage practices with numerous unsustainable impacts affecting our environment, ingraining inequality, and degrading quality of life. There is an urgent need to identify such multifaceted impacts holistically. Impact assessment of digital interventions (DIs) leading to digitalization is essential specifically for Sustainable Development Goals (SDGs). Action is required to understand the pursuit of short-term gains toward achieving long-term value-driven sustainable development. We need to understand the impact of DIs on various actors and in diverse contexts. A holistic understanding of the impact will help us align the visions of sustainable development and identify potential measures to mitigate negative short and long-term impacts. The recently developed digitainability assessment framework (DAF) unveils the impact of DIs with an in-depth context-aware assessment and offers an evidence-based impact profile of SDGs at the indicator level. This paper demonstrates how DAF can be instrumental in guiding participatory action for the implementation of digitainability practices. This paper summarizes the insights developed during the Digitainable Spring School 2022 (DSS) on “Sustainability with Digitalization and Artificial Intelligence,” one of whose goals was to operationalize the DAF as a tool in the participatory action process with collaboration and active involvement of diverse professionals in the field of digitalization and sustainability. The DAF guides a holistic context-aware process formulation for a given DI. An evidence-based evaluation within the DAF protocol benchmarks a specific DI’s impact against the SDG indicators framework. The participating experts worked together to identify a DI and gather and analyze evidence by operationalizing the DAF. The four DIs identified in the process are as follows: smart home technology (SHT) for energy efficiency, the blockchain for food security, artificial intelligence (AI) for land use and cover change (LUCC), and Big Data for international law. Each of the four expert groups addresses different DIs for digitainability assessment using different techniques to gather and analyze data related to the criteria and indicators. The knowledge presented here could increase understanding of the challenges and opportunities related to digitainability and provide a structure for developing and implementing robust digitainability practices with data-driven insights.

Digitalization is globally transforming the world with profound implications. It has enormous potential to foster progress toward sustainability. However, in its current form, digitalization also continues to enable and encourage practices with numerous unsustainable impacts affecting our environment, ingraining inequality, and degrading quality of life. There is an urgent need to identify such multifaceted impacts holistically. Impact assessment of digital interventions (DIs) leading to digitalization is important specifically for Sustainable Development Goals(SDGs). Action is required to understand the pursuit of short-term gains toward achieving long-term value-driven sustainable development. We need to understand the impact of DIs on various actors and in diverse contexts. A holistic understanding of the impact it creates will help us align it with visions of sustainable development and identify potential measures to mitigate negative short and long-term impacts. The recently developed Digitainability Assessment Framework (DAF) unveils the impact of DIs with an in-depth context-aware assessment and offers an evidence-based impact profile of SDGs at the indicator level. We performed the impact assessment of diverse technologies using DAF. This paper summarizes the insights from the Digitainable Spring School 2022 on "Sustainability with Digitalization and Artificial Intelligence," one of whose goals was to operationalize the DAF as a tool in the action learning process with diverse professionals in the field of digitalization and sustainability. The DAF guides a holistic context-aware process formulation for a given DI. An evidence-based evaluation within the DAF protocol benchmarks a specific DI’s impact against the SDG indicators framework. The operationalization of the DAF was carried out by looking at four different DIs: smart home technologies (SHT) for energy efficiency, blockchain for food security, artificial intelligence for land use cover and changes (LUCC), and big data for international law. Each of the four studies addresses different DIs for digitainability assessment using different techniques for a diverse group of indicators, demonstrating the potential of the DAF but also outlining the existing data gaps that limit a comprehensive analysis.

Brazil has a large share of hydropower in its electricity matrix. Since hydropower depends on water availability, it is particularly vulnerable to drought events, making the Brazilian electricity matrix vulnerable to climate change. Starting in 2005, Brazil opened the matrix to new renewable sources, including sugarcane-based electricity. Sugarcane is known for its resilience to short dry spells. Over the last decades, its production area moved from the coastal plains of the Atlantic Forest biome to the savannahs of the Cerrado biome, which is characterised by a five- to six month-long dry season. The sugarcane-based electricity system is highly dynamic and complex due to the interlinkages, dependencies, and cascading impacts between its agricultural and industrial subsystems. This paper applies the risk framework proposed by the IPCC to assess climate-change-driven drought risks to sugarcane electricity generation systems to identify their strengths and weaknesses, considering the system dynamics and linkages. Our methodology aims to understand and characterize drought in the agriculture as well as industrial subsystems and offers a specific understanding of the system by using indicators tailored to sugarcane-based electricity generation. Our results underline the relevance of actions at different levels of management. Initiatives, such as regional weather forecasts specifically for agriculture, and measures to increase industrial water-use efficiency were identified to be essential to reduce the drought risk. Actions from farmers and mill owners, supported and guided by the government at different levels, have the potential to increase the resilience of the system. For example, the implementation of small dams was identified by local actors as a promising intervention to adapt to the long dry seasons; however, they need to be implemented based on a proper technical assessment in order to locate these dams in suitable places. Moreover, the results show that creating and maintaining small water reservoirs to enable the adoption of deficit-controlled irrigation technology contribute to reducing the overall drought risk of the sugarcane-based electricity generation system.