45
4 Conclusion and Outlook
46 Chapter 4. Conclusion and Outlook
additional information about the reflective surface of the river. The altimetry obser-vations classified as water were then used to estimate the water level. However, the classification was not able to distinguish between water reflections originating from the river and reflections from surrounding wetlands. Thus, in regions with surrounding (seasonal) wetland, the classification failed to identify the observations which should be used for the river water level estimation.
• What is the size of a river that can be observed with satellite altimetry?
The answer to this question depends on the altimeter technique used for the water level observation and the method applied for the water level estimation. The hooking ap-proach from P-1 allowed for pulse limited altimetry to observe water levels of rivers with a width as small as 90 m, although most of the observed rivers had a width be-tween 200 m to 500 m. Theoretically, the hooking approach would allow to observe even smaller rivers but such rivers can have too limited reflective surfaces in the al-timeter footprint to observe their reflection in the waveform. This depended on the surrounding land as well: the less well it reflected the radar signal the more likely it was to observe the river. The topography had an influence as well: Mountainous ter-rain can shadow part of the radar signal and thus, complicated the observation of small rivers.
SAR altimetry has a major advantage for observing small rivers: the smaller along-track footprint size compared to pulse limited altimetry. With this, it was possible in P-3 to observe rivers as narrow as 20 m, but most of the observed rivers were only as small as 100 m. SAR altimetry can also be influenced by the surrounding topography similar to pulse limited altimetry. However, as only nadir observations were used in SAR altimetry for the water level estimation, the shadowing of mountainous terrain had less influence.
• Do novel altimeter techniques improve the derived water levels?
This question aimed at both the SAR altimetry, on CryoSat-2, and Ka band altimetry, onSatellite with Argos and AltiKa (SARAL). In P-3, levels derived from CryoSat-2 SAR altimetry have been compared to pulse limited altimetry observed by Envir-onmental Satellite (Envisat). It turned out that under the chosen comparison, the SAR water levels had a higher quality than those observed by Envisat. The smaller along-track footprint was improving the quality of the water levels. Also the number of usable water level observations over small rivers was larger for CryoSat-2 SAR than for Envisat, which could also be seen in the size of the data sets used in P-4. Thus, the SAR altimetry technique is improving the observation’s quality of small rivers.
4.1. Conclusion 47
The Ka band pulse limited altimeter on SARAL has a smaller footprint compared to the Ku band pulse limited altimeters of all other missions. Thus, it should be more suit-able for inland water observations. The potential of the Ka band altimeter on SARAL was investigated in Schwatke et al. (2015a). It was found that the higher sensitivity of the Ka band towards atmospheric water content deteriorates the resulting water lev-els, especially during the rain and high flood season. Not only individual water level observations were missing in the SARAL data but sometimes even a whole Virtual Station (VS) could not be used in the data sets of both P-2 and P-4. Therefore, Ka band altimetry does not improve the water levels of rivers, at least not in a region with a (seasonal) high atmospheric water content.
Combination methods for multi-mission altimetry
• What is an appropriate method for the combination?
In this thesis, two kriging methods were presented for the combination of altimetry data along the Mekong River. Kriging in general produces a statistically optimal estimation with respect to the mean-squared prediction error. It was suitable for the combination of altimetry data as it was flexible enough to combine data with different accuracies both across the time and the spatial domain. The two kriging approaches of this thesis, Ordinary Kriging (OK) presented in P-2 and Universal Kriging (UK) in P-4, have different advantages and disadvantages:
In P-2, the OK method was applied to combine data of theshort-repeatorbit (SRO) missions Envisat, SARAL, and Jason-2. Only these missions, as well as any other SRO mission, could be used with OK as the method required a constant mean value in the whole data set. This implied that the mean water level of each time series had to be reduced before the multi-mission combination. As this was only feasible for SRO missions which allow the VS concept,long-repeatorbit (LRO) and non-repeatorbit (NRO) missions could not be combined with the OK approach. On the other hand, the approach was numerically stable.
The UK approach was introduced in P-4 and allowed combining data of all altimeter missions, regardless of their orbit constellation. In this kriging approach the mean water level did not need to be reduced but was modelled in the approach itself with an estimated combination of basis functions. The advantage of this was its flexibility to incorporate all kind of altimetry data, but to be numerically stable the data needed to be more equally distributed in the study area. UK with only data of SRO missions was not possible as the topography, or mean water level, along the river could not be successfully modelled with only the sparse spatial data coverage. Also the setup of the basis function—B-splines in P-4—along a river network was challenging. In
48 Chapter 4. Conclusion and Outlook
conclusion, the answer to the question which of the two methods, OK or UK, is more appropriate for the multi-mission combinations strongly depends on the data that are used.
• How can the flow of the river be statistically modelled?
The kriging methods for the multi-mission combinations required a statistical mod-elling of the river. In this thesis, this modmod-elling was done by means of covariances.
In P-2, two spatio-temporal covariance models were introduced. In both models the temporal part was assumed to be stationary and thus, the temporal covariance between two observations only depended on the time distance between the two. In the first model the spatial part was also assumed stationary, therefore it was termed station-ary model. The second, more advanced model, was non-stationstation-ary in space, termed the non-stationary model. The stationary model was far easier to implement as fewer parameters needed to be estimated from empirical covariances. At the same time, it showed comparably good results in P-2 despite ignoring the changing flow along the course of the river.
The non-stationary covariance model included the non-stationary covariance along the river between two observations but also the relation between their sub-catchments.
However, for this model more parameter needed to be estimated. Its big advantage over the stationary model was the twofold ability to incorporate tributaries as well. As long as only the main stream of the Mekong River was involved, the flow behaviour did not change significantly. Thus, ignoring the changing flow of the river in the covariance model could be acceptable. However, the tributaries showed significantly changing flood behaviour. Therefore, for the inclusion of tributary data a non-stationary model was needed that can represent the flow connection, too. Thus, in P-4, only the non-stationary model was applied.
Hence, the choice of the covariance model depends again on the data to combine.
As long as only main river data are combined and the river has no large changes in the flow, the stationary covariance model can be sufficient. Including tributaries or rivers with complicated flow behaviour, a non-stationary covariance model is more appropriate.
• Is multi-mission altimetry sufficient to observe extreme river flood events?
This question was addressed in P-4 for the years 2008 to 2016. The multi-mission time series was able to observe the interannual flood behaviour with exceptionally high and low floods. This was not possible with only single-mission altimetry. Especially the floodings in 2008 and 2011 were well observed by the multi-mission time series basin wide, whereas the low floods in 2015 and 2016 were less well observed. However,