In the Netherlands, the Delta Programme aspires to adjust spatial planning climate-proof and water-resilient, in order to be prepared for extreme weather in 2050. To achieve this ambition, municipalities, provinces, regional water authorities and central governments conduct stress tests to map out the vulnerabilities in their areas of authority by no later than 2019. The stress tests comprise four themes: pluvial flooding, drought, heat and floods. In addition, the Delta Programme 2019 acknowledges the mitigation of and adaptation to land subsidence as an important tasking. The municipality of Rotterdam faces the challenge of adding land subsidence as stress test theme and assessing its influence on pluvial flooding. Contrary to the stress test pluvial flooding, no consended methodology exist on how to map out vulnerabilities concerning land subsidence. In addition, only few studies have numerically investigated the spatial-temporal effect of land subsidence on pluvial flooding in urban areas. However, the advent of techniques to measure ground level (LiDAR) and land subsidence (InSAR) and advances in high resolution flood modelling (3Di) enable the numerical modelling of urban pluvial flooding influenced by land subsidence. This research explores the investigation of the influence of land subsidence on pluvial flooding in Rotterdam by supplementing the conducted stress test pluvial flooding with a land subsidence assessment. The conducted stress test pluvial flooding in Rotterdam is based on a 3Di-simulation of standardised rain events, based on a DEM 2016.

To asses the current influence of land subsidence on pluvial flooding, a Digital Elevation Model (DEM) that approximates the sub-neighbourhood Tuinenhoven at design level is created and used as 3Di-input. When comparing 3Di-results based on this design DEM to the stress test pluvial flooding, it becomes clear that the total volume of water during extreme rainfall stored on the streets is not affected by land subsidence. The bathymetry of the DEM does affect the water's distribution however. The Tuinenhoven case-study demonstrates that currently land subsidence increases the severity of the impact of pluvial flooding but that the main cause of pluvial flooding during extreme rainfall is the limited capacity of the drainage system.

Land subsidence in Rotterdam complex. The conducted land subsidence analysis based on an InSAR data-set supports this complexity. It illustrates that the subsidence behaviour of Rotterdam is influenced by foundation type, land use classification, the presence of dredge in the anthropogenic layer and top soil type. This respectively indicates the occurrence of pole rot and shallow foundations, anthropogenic compression and compaction of shallow soft layers caused by loading, landfill subsidence as a result of land fillings that contain dredging spoil and consolidation of the Holocene clay layer as a result of drainage. However, the land subsidence analysis failed to identify location-specific dominant land subsidence processes. This failure was primarily caused by the limitation to only one linear subsidence rate between 2009 and 2014 per point. To demonstrate how land subsidence can be translated to pluvial flooding based on a land subsidence analysis, land use classification was selected as the most dominant influencing factor and used in a linear land subsidence prognosis until 2030. The linear assumptions largely obstructs results to be interpreted location-specific.

The IJsselmonde case-study shows that land subsidence is expected to decrease the passability of roads and decrease the risk per building in the future. These decreases are caused by the fact that roads relatively subside fast and buildings relatively slow. The biggest influence on the risk per building classification is the assumed threshold value per building. Simulated road maintenance results in an increase of the passability of roads and an increase of buildings at risk of water nuisance. The loss of the water-storing function of the road after reconstruction increases the water levels in gardens and puts buildings at an increasing risk.

In conclusion, the most challenging part of investigating the influence of land subsidence on pluvial flooding is the crucial identification of the different occurring land subsidence processes. It is demonstrated that the possibilities with InSAR-data are promising, when used with sufficient competency, although the available InSAR data should be divided in shorter intervals to detect the subsidence rate trends. Land subsidence rate trends are crucial in the identification of land subsidence processes and assessing influences like groundwater variations and increased loading due to maintenance or construction works. When the land subsidence analysis is improved, so will the land subsidence and threshold height per building prognosis. When the relative prognosed decrease of the threshold value per building is improved, it can be quickly assessed whether buildings classified at risk in the stress test pluvial flooding are at future increasing risk during extreme rainfall, without conducting a full 3Di-simulation.

IHE-Delft in cooperation with the Asian Development Bank (ADB) conducts a pilot project on assessing Crop Water Productivity in Asia, aiming to contribute to sustainable development in Asia’s irrigation sector, and create more value from scarce water resources. Indonesia is one of the 6 pilot countries where advanced technologies to measure Water Productivity (WP) from satellite data were introduced. Indonesia is the third largest rice producer of the world. Given the challenges such as growing population, degrading land and increasing water scarcity in upcoming decades, the Indonesian government aims to rehabilitate its irrigation systems. More insights in the spatial distribution of irrigation water and water productivity of rice paddies could contribute to decision-making in future rehabilitation investments.
This report describes the assessment of Water Productivity (WP) of paddy rice in Indonesia using the Surface Energy Balance Algorithm for Land (SEBAL). SEBAL is a tool that translates raw satellite measurements into maps of actual evapotranspiration and crop production, among others. The actual crop water consumption (i.e. actual evapotranspiration) and crop yield can now be estimated for every 30 m x 30 m, even if data on irrigation water application is not available. With this information, rice production per unit of land (kg/ha) as well as per unit of water consumed (kg/m3) can be computed.
Focus of this study are sites in Bali, West Java and Lombok. Fieldwork is conducted in Bali and West Java to support the maps with ‘ground truth’ data. Data is collected from local governmental institutes and farmers to verify the remote sensing outputs.
This research shows promising results linking SEBAL outputs with the ground truth even though the amount of fieldwork was limited. The inclusion of the new HANTS algorithm will create the technical opportunity to make daily WP reports for all rice fields in Indonesia, also under cloudy conditions. This could be a big information boost to support irrigation managers with their daily services of bringing water to farmers. Whereas some key explanatory reasons were detected (i.e. distance to canal, salt water intrusion, water quality, erosion), it is recommended to further explore relations between WP and influencing factors in the local context together with local irrigation officers. Even though the research revealed some limitations causing uncertainties, this new remote sensing technologies can support an efficient and effective investment purposes on modernization of irrigation. It is recommended that the Directorate of Irrigation and Lowlands recognize WP as a new policy instrument and implement it both at central level and irrigation district level.