Towards a global land subsidence map

Abstract

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. <br><br> 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.

Proc. IAHS, 372, 8387, 2015 proc-iahs.net/372/83/2015/ doi:10.5194/piahs-372-83-2015 Author(s) 2015. CC Attribution 3.0 License.

G. Erkens1,2 and E. H. Sutanudjaja1,2

1Deltares Research Institute, Utrecht, the Netherlands

2Utrecht University, Utrecht, the Netherlands

Correspondence to: G. Erkens ([email protected])

Published: 12 November 2015

Abstract. 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 ood 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.

1 Introduction

Although its impact on ood 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 ood 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.

Preventionandmitigationofnaturalandanthropogenichazardsduetolandsubsidence

Open Access

Towards a global land subsidence map

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:

i. 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.

ii. 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.

2 The approach

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 rst instance we restricted ourselves

Published by Copernicus Publications on behalf of the International Association of Hydrological Sciences.

84 G. Erkens and E. H. Sutanudjaja: Towards a global land subsidence map

Figure 1. 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.

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 yr1, attributed to groundwater extraction (Erban et al., 2014). Crucially, in many coastal mega-cities, land subsidence is accelerated by ongoing urbanization and related increased demands for (ground) water. In Jakarta land subsidence is up to 20 cm yr1 (Abidin et al., 2008). With ongoing economic development in the near future the expectation is that ground-water abstraction and, consequently, land subsidence rates, will accelerate and areas affected will expand as well as. Therefore, in this paper, we chose to focus on aquifer abstraction induced land subsidence and present our numerical approach on constructing a global land subsidence map.

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 MOD-FLOW 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).

3 First results

In this study we perform all aforementioned model simulations for the period 19582010. 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 specically, we present and summarize the groundwater depletion rates in urban areas.

3.1 First attempt: depletion in urban area missed

Figure 2 shows rst 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 PCRGLOBWB 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.

This misconception is attributed to the previous assumption in the PCR-GLOBWB model that simplies that the fraction of water demands that is satised by groundwater resource (GWfrac) as a ratio between simulated baseow and river discharge. Agricultural irrigation results in a high water demand, and because the river discharge in those areas is dominated by baseow, the model assumes the use of groundwater. While in the urban area of Jakarta where river ows are not baseow dominant, water demands are lower and as a result, surface water availability as simulated for Jakarta is sufcient for the domestic and industrial water demand. However, in reality, the urban areas in Java rely on ground water as a clean and reliable source of water. In turn, it is foremost surface water that is used for irrigation in Java.

3.2 Second attempt: including water resources uses

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 satised by groundwater resource (GWfrac) is prescribed based on the

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G. Erkens and E. H. Sutanudjaja: Towards a global land subsidence map 85

Figure 2. Screen shot of initial depletion (m yr1) results as calculated by the global hydrology model for the island of Java, Indonesia. It shows the depletion of the groundwater as a result of abstraction for irrigation of agricultural areas, but misses known depleted urban areas, such as Jakarta.

Table 1. Calculated depletion (m yr1 for the period 20002010) for a selection of cities that are known to subside.

Country City Depletion (m yr1)

United States San Jose 3.27

Mexico Mexico City 1.30 United States Tucson 1.29 United States San Francisco 1.25 Iran Tehran 1.23 United States Sacramento 1.18

may be the consequence of the scale that we use: depletion rates are averaged over a 10 10 km gridcel. Groundwater

extraction and depletion, specically for industrial sites, may however be much more local than that. Another interesting outcome is the prominent position of the city of San Jose (USA) with 3.27 m yr1 depletion calculated for the 2000

2010 period (Table 1). In reality, depletion in the San Jose area has essentially stopped after dramatic subsidence forced the local government to reduce groundwater abstraction (Ingebritsen and Jones, 1999). Surface water is now being imported from elsewhere and the imported water is partly used for recharging the aquifer systems. The model however, still calculates the consumption of ground water in San Jose, as in the model local surface water availability is insufcient.

4 Towards a global land subsidence map

The PCR-GLOBWB outcomes will be used to force a two-layer global Modow model, consisting of an upper unit with low permeability and a deeper conned 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. Drr, 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 buildup.

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 nal 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 ood risk model. This

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aforementioned inventories. This outcome resembles much closer the expected outcome: dominant depletion in the urban areas.

In Fig. 4, the results of the same model run for the South China Sea area are depicted (depletion in m yr1). This matches known areas of subsidence and groundwater extraction, such as the western half of Taiwan, the cities of Hong Kong, Shanghai, Qingdao and Beijing. It also captured the North China Plain, which is an agricultural area with strongly depleted aquifers. These results give condence in the validity of the model.

3.3 A global city database of groundwater abstractions and discussion

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 20002010.

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

86 G. Erkens and E. H. Sutanudjaja: Towards a global land subsidence map

Figure 3. Screen shot of revised depletion (m yr1) results as calculated by the global hydrology model for the island of Java, Indonesia. It shows the depletion of the groundwater as a result of ground water abstraction in the urban area of Jakarta. The calculated depletion in the irrigation areas is now minor.

time. The predicted global land subsidence effects will be included in ood hazard and storm surge assessments within a global ood risk model.

Acknowledgements. 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.

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