Land subsidence is a global problem, but a global land subsidence map is not available yet. Such map is crucial to raise global awareness of land subsidence, as land subsidence causes extensive damage (probably in the order of billions of dollars annually). With the global land subsidence map relative sea level rise predictions may be improved, contributing to global flood risk calculations.
In this paper, we discuss the approach and progress we have made so far in making a global land subsidence map. Initial results will be presented and discussed, and we give an outlook on the work needed to derive a global land subsidence map.
Although its impact on flood risk is locally outranging the impact of absolute sea level rise, over the last decades land subsidence retrieved much less attention in terms of research. One of the reasons for this is the unknown extent of land subsidence around the world, in contrast to absolute sea level rise. For the latter, there are models that predict global absolute sea level rise (Church et al., 2013). For land subsidence there are many examples around the world, but a comprehensive picture of the global extent is currently unavailable. This originates from the heterogeneity of the land subsidence signal: an array of potential drivers and the heterogeneous subsurface hydrology and geology, makes land subsidence mainly a local phenomenon on a global scale.
A map showing the extent of land subsidence around the world, the global land subsidence map, would be very useful. It would raise awareness among scientist and policymakers alike. The land subsidence rates could be used – together with regional absolute sea level rise predictions – to estimate relative sea level rise in coastal areas. This serves as input for flood risk calculations. Lastly, a global land subsidence map would not only locate current land subsidence hotspots but also help to identify future sinking areas under predicted socio-economic development scenarios.
The maps that come close to a global land subsidence map are those based on
a collection of globally spread case studies. However, these have two major
limitations:
They are static and show the current land subsidence rates, but not necessarily at a single moment in time. They also have no predictive power. They are biased towards well-studied areas. Large cities or classic study sites in western countries show up most prominently.
Considering these limitations, we set out to produce a global land
subsidence map that is derived from numerical model calculations. In this
way, we are able to introduce a temporal component showing both historical
and predicted future land subsidence under different development scenarios.
In this paper, we discuss the approach and progress we have made so far. Initial results will be presented and discussed, and we give an outlook on the work needed to derive a global land subsidence map.
Land subsidence is caused my many different drivers, both natural and
anthropogenic-induced. Ideally, a global land subsidence map would cover all
known drivers, but for sake of simplicity, in first instance we restricted
ourselves to one driver. One of the most prominent causes for land
subsidence is aquifer compaction as a result of excessive groundwater
extraction for domestic, agricultural and industrial use (Galloway and
Burbey, 2011). For instance, the Vietnamese Mekong Delta sinks on average
1.6 cm yr
The proposed model suite to derive a global land subsidence map consists of three coupled models: the surface water model, the ground water model and a soil mechanics model.
Figure 1 illustrates different the model components used in this study. The integrated global hydrological and water resources model, PCR-GLOBWB (Van Beek et al., 2011; Sutanudjaja et al., 2015; Van Beek, 2008; Van Beek and Bierkens, 2009), serves as the starting point. The model is fed by global meteorological forcing data (i.e. precipitation, temperature and reference potential evaporation) and parameterized based on only global datasets (for an extensive list, see e.g. Sutanudjaja et al., 2011). PCR-GLOBWB simulates daily river discharge and groundwater recharge, as well as surface water and groundwater abstraction rates. The latter are estimated internally within the model based on the simulation of their availabilities and water demands for irrigation and other sectors (De Graaf et al., 2014; Wada et al., 2014). The daily output of PCR-GLOBWB would then be aggregated to the monthly resolution and used to force the MODFLOW groundwater model (McDonald and Harbaugh, 1988; Sutanudjaja et al., 2011, 2014) resolving spatio-temporal groundwater head dynamics, incorporating the simulated groundwater abstraction of PCR-GLOBWB. Subsequently, the simulated monthly groundwater head changes are fed into a land subsidence module, iMOD-SUB-CR (Bakr, 2015), which is an extension of the subsidence and aquifer-system compaction package (SUB-WT) of MODFLOW (Leake and Galloway, 2007). For this study all aforementioned models are simulated at the spatial resolution of 5 arc minutes (approximately 10 km at the equator).
In this study we perform all aforementioned model simulations for the period 1958–2010. Our results are focused on the simulation results of PCR-GLOBWB that are fed in the MODFLOW model, particularly the rates of groundwater depletion, i.e. the abstraction of groundwater stores that are not being replenished by groundwater recharge. More specifically, we present and summarize the groundwater depletion rates in urban areas.
Figure 2 shows first results for the island of Java, Indonesia. This is an area that is well known for its extensive land subsidence as a result ground water extraction. The map shows the depletion of ground water as calculated by the PCR-GLOBWB hydrology model. Most of the areas that show depletion are irrigation areas, where the model suggests that groundwater is excessively used. Large urban areas, such as Jakarta, with well-known depletion of groundwater and related land subsidence, are completely missed.
Screen shot of initial depletion (m yr
This misconception is attributed to the previous assumption in the
PCR-GLOBWB model that simplifies that the fraction of water demands that is
satisfied by groundwater resource (
To improve the model, we included (global) databases on the relative
contribution of ground water and surface water to the total water usage, for
both urban agglomerations (McDonald et al., 2014: Dataset of water
infrastructure) and agricultural areas (Siebert et al., 2010: Groundwater
use for irrigation). Figure 3 shows the result of the second attempt in
which the contribution of actual groundwater and surface water is included.
The fraction of water demands that is satisfied by groundwater resource
(
Screen shot of revised depletion (m yr
In Fig. 4, the results of the same model run for the South China Sea area
are depicted (depletion in m yr
Screen shot of depletion (m yr
Based on the model calculations, we are now able provide depletion per year for different urban areas. Table 1 lists some examples of calculated depletion rates for a selected number of cities for the period 2000–2010.
Calculated depletion (m yr
Most of the cities that are known to have depletion of the aquifers are
found on the list. Some cities, such as Ho-Chi-Minh City (Vietnam) or
Albuquerque (New Mexico, USA) are, however, missed. One of the possible
explanations for this is the unknown contribution of irrigation. This may be
overestimated in urban areas, which suffer from land surface sealing as a
result of building. The rates as calculated by the model are on the lower
end of reported depletion rates. This may be the consequence of the scale
that we use: depletion rates are averaged over a
The PCR-GLOBWB outcomes will be used to force a two-layer global Modflow model, consisting of an upper unit with low permeability and a deeper confined aquifer system. The groundwater model will yield hydraulic heads. A decrease in hydraulic head will not automatically imply that subsidence will occur. If the deposits consist of sand, or are (over)consolidated, the hydraulic pressure decrease and effective stress increase will not lead to inelastic volume reduction. There is enough global information on the surface geology (e.g. Dürr, et al., 2005) to be able to focus only on those areas that are susceptible to subside. The required subsurface information to parameterize the geotechnical model is however unavailable on a global scale at this time, but will be approached by using different scenarios of subsurface build-up.
The outcomes will be compared to measured or modeled land level lowering in well-known case study areas, such as Jakarta and the Vietnamese Mekong Delta. The final map will include also future land subsidence rates under different development scenarios for the entire earth, and includes a sensitivity test for different subsurface build-up. The entire map will be used as input for a global flood risk model. This will be done within the concept of a dynamic DEM (digital elevation model). Within this concept, the currently available height information (DEM) for a certain area is updated with the cumulative subsidence (or uplift) over a certain period of time. The predicted global land subsidence effects will be included in flood hazard and storm surge assessments within a global flood risk model.
The authors thank their colleagues Marijn Kuijper (Deltares Research Institute), Hessel Winsemius (Deltares Research Institute), Inge de Graaf (Utrecht University) and Marc Bierkens (Utrecht University, Deltares Research Institute) for data, support and discussion. This paper is a part of the World Resources Institute project Flood Risk and Intervention Assessment for Global Cities funded by the Netherlands Ministry of Infrastructure and Environment and the Netherlands Ministry of Foreign Affairs.