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Floodplains and other wetlands depend on seasonal river flooding and play an important role in the terrestrial water cycle. They influence evapotranspiration, water storage and river discharge dynamics, and they are the habitat of a large number of animals and plants. Thus, to assess the Earth’s system and its changes, a robust understanding of the dynamics of floodplain wetlands including inundated areas, water storages, and water flows is required.
This PhD thesis aims at improving the modeling of large floodplains and wetlands within the global-scale hydrological model WaterGAP, in order to better estimate water flows and water storage variations in different storage compartments. Within the scope of this thesis, I have developed a new approach to simulate dynamic floodplain inundation on a global-scale. This approach introduces an algorithm into WaterGAP, which has a spatial resolution of 0.5 degree (longitude and latitude) globally. The new approach uses subgrid-scale topography, based on high-resolution digital elevation models, to describe the floodplain elevation profile within each grid cell by applying a hypsographic curve. The approach comprises the modeling of a two-way river-floodplain interaction, the separate downstream water transport within the river and the floodplain – both with temporally and spatially different variable flow velocities – and the floodplain-groundwater interactions. The WaterGAP version that includes the floodplain algorithm, WaterGAP 2.2b_fpl, estimates floodplain and river water storage, inundated area and water table elevation, and also simulates backwater effects.
WaterGAP 2.2b_fpl was applied to model river discharge, river flow velocity, water storages, water heights and surface water extent on a global-scale. Model results were comprehensively validated against ground observations and remote sensing data. Overall, the modeled and observed data are in agreement. In comparison to the former version WaterGAP 2.2b, the model performance has improved significantly. The improvements are most remarkable in the Amazon River basin. However, the seasonal variation of surface water extent and total water storage anomalies are still too low in many regions on the globe when compared to observations. A detailed analysis of the simulated results suggests that in the Amazon River basin the introduction of backwater effects is important for realistically simulating water storages and surface water extent. Future efforts should focus on the simulation of water levels in order to better model the flow routing according to water slope. To further improve the model performance in specific regions, I recommend that the globally constant model parameters that affect inundation initiation, river-floodplain interaction, DEM correction for vegetation, and backwater amount at basin or subbasin-scale be adjusted.
Floodplains play an important role in the terrestrial water cycle and are very important for biodiversity. Therefore, an improved representation of the dynamics of floodplain water flows and storage in global hydrological and land surface models is required. To support model validation, we combined monthly time series of satellite-derived inundation areas (Papa et al., 2010) with data on irrigated rice areas (Portmann et al., 2010). In this way, we obtained global-scale time series of naturally inundated areas (NIA), with monthly values of inundation extent during 1993–2004 and a spatial resolution of 0.5°. For most grid cells (0.5°×0.5°), the mean annual maximum of NIA agrees well with the static open water extent of the Global Lakes and Wetlands database (GLWD) (Lehner and Döll, 2004), but in 16% of the cells NIA is larger than GLWD. In some regions, like Northwestern Europe, NIA clearly overestimates inundated areas, probably because of confounding very wet soils with inundated areas. In other areas, such as South Asia, it is likely that NIA can help to enhance GLWD. NIA data will be very useful for developing and validating a floodplain modeling algorithm for the global hydrological model WGHM. For example, we found that monthly NIAs correlate with observed river discharges.
When assessing global water resources with hydrological models, it is essential to know about methodological uncertainties. The values of simulated water balance components may vary due to different spatial and temporal aggregations, reference periods, and applied climate forcings, as well as due to the consideration of human water use, or the lack thereof. We analyzed these variations over the period 1901–2010 by forcing the global hydrological model WaterGAP 2.2 (ISIMIP2a) with five state-of-the-art climate data sets, including a homogenized version of the concatenated WFD/WFDEI data set. Absolute values and temporal variations of global water balance components are strongly affected by the uncertainty in the climate forcing, and no temporal trends of the global water balance components are detected for the four homogeneous climate forcings considered (except for human water abstractions). The calibration of WaterGAP against observed long-term average river discharge Q significantly reduces the impact of climate forcing uncertainty on estimated Q and renewable water resources. For the homogeneous forcings, Q of the calibrated and non-calibrated regions of the globe varies by 1.6 and 18.5 %, respectively, for 1971–2000. On the continental scale, most differences for long-term average precipitation P and Q estimates occur in Africa and, due to snow undercatch of rain gauges, also in the data-rich continents Europe and North America. Variations of Q at the grid-cell scale are large, except in a few grid cells upstream and downstream of calibration stations, with an average variation of 37 and 74 % among the four homogeneous forcings in calibrated and non-calibrated regions, respectively. Considering only the forcings GSWP3 and WFDEI_hom, i.e., excluding the forcing without undercatch correction (PGFv2.1) and the one with a much lower shortwave downward radiation SWD than the others (WFD), Q variations are reduced to 16 and 31 % in calibrated and non-calibrated regions, respectively. These simulation results support the need for extended Q measurements and data sharing for better constraining global water balance assessments. Over the 20th century, the human footprint on natural water resources has become larger. For 11–18% of the global land area, the change of Q between 1941–1970 and 1971–2000 was driven more strongly by change of human water use including dam construction than by change in precipitation, while this was true for only 9–13 % of the land area from 1911–1940 to 1941–1970.
When assessing global water resources with hydrological models, it is essential to know the methodological uncertainties in the water resources estimates. The study presented here quantifies effects of the uncertainty in the spatial and temporal patterns of meteorological variables on water balance components at the global, continental and grid cell scale by forcing the global hydrological model WaterGAP 2.2 (ISI-MIP 2.1) with five state-of-the-art climate forcing input data-sets. While global precipitation over land during 1971–2000 varies between 103 500 and 111 000 km3 yr−1, global river discharge varies between 39 200 and 42 200 km3 yr−1. Temporal trends of global wa- ter balance components are strongly affected by the uncertainty in the climate forcing (except human water abstractions), and there is a need for temporal homogenization of climate forcings (in particular WFD/WFDEI). On about 10–20 % of the global land area, change of river discharge between two consecutive 30 year periods was driven more strongly by changes of human water use including dam construction than by changes in precipitation. This number increases towards the end of the 20th century due to intensified human water use and dam construction. The calibration approach of WaterGAP against observed long-term average river discharge reduces the impact of climate forcing uncertainty on estimated river discharge significantly. Different homgeneous climate forcings lead to a variation of Q of only 1.6 % for the 54 % of global land area that are constrained by discharge observations, while estimated renewable water resources in the remaining uncalibrated regions vary by 18.5 %. Uncertainties are especially high in Southeast Asia where Global Runoff Data Centre (GRDC) data availability is very sparse. By sharing already available discharge data, or installing new streamflow gauging stations in such regions, water balance uncertainties could be reduced which would lead to an improved assessment of the world’s water resources.
The assessment of water balance components using global hydrological models is subject to climate forcing uncertainty as well as to an increasing intensity of human water use within the 20th century. The uncertainty of five state-of-the-art climate forcings and the resulting range of cell runoff that is simulated by the global hydrological model WaterGAP is presented. On the global land surface, about 62 % of precipitation evapotranspires, whereas 38 % discharges into oceans and inland sinks. During 1971–2000, evapotranspiration due to human water use amounted to almost 1 % of precipitation, while this anthropogenic water flow increased by a factor of approximately 5 between 1901 and 2010. Deviation of estimated global discharge from the ensemble mean due to climate forcing uncertainty is approximately 4 %. Precipitation uncertainty is the most important reason for the uncertainty of discharge and evapotranspiration, followed by shortwave downward radiation. At continental levels, deviations of water balance components due to uncertain climate forcing are higher, with the highest discharge deviations occurring for river discharge in Africa (−6 to 11 % from the ensemble mean). Uncertain climate forcings also affect the estimation of irrigation water use and thus the estimated human impact of river discharge. The uncertainty range of global irrigation water consumption amounts to approximately 50 % of the global sum of water consumption in the other water use sector.