Application of a grid-scale lateral discharge model in the BALTEX region

In this study, a hydrological discharge model is presented which may be applied as a tool to validate the simulation of the hydrologic cycle of atmospheric models that are used in climate change studies. It can also be applied in studies of global climate change to investigate how changes in climate may affect the discharge of large rivers. The model was developed for the application with the climate models used at the Max-Planck- Institute for Meteorology. It describes the translation and retention of the lateral waterflows on the global scale as a function of the spatially distributed land surface characteristics which are globally available. Here, global scale refers to the resolution of 0.5° and lower, corresponding to a typical average gridbox area of about 2500 km2. The hydrological discharge model separates between the flow processes of overland flow, baseflow and overflow. The model parameters are mainly functions of the gridbox characteristics of topography and gridbox length. The hydrological discharge model is applied to the BALTEX (Baltic Sea Experiment) region using input from an atmospheric general circulation model (ECHAM4) as well as from a regional climate model (REMO). The simulated inflows into the Baltic Sea and its sub- catchments are compared to observed and naturalized discharges. The results of this comparison are discussed and the simulated values of precipitation, surface air temperature and accumulated snowpack are compared to both observed data and surrogate data

Estimating the characteristics of runoff inflow into Lake Gojal in ungauged, highly glacierized upper Hunza River Basin, Pakistan

Motivated by the potential flood outburst of Lake Gojal in the ungauged highly glacierized (27%) upper Hunza River Basin (HRB) in Pakistan that was dammed by a massive landslide on 4 January 2010, we attempt to analyze the characteristics of water inflow to the lake employing remote sensing data, two hydrological models, and sparsely observed data. One of the models (Model I) is a monthly degree-day model, while another (Model II) is the variable infiltration capacity (VIC) model. The mixture of glacier runoff output from Model I and runoff over unglacierized areas calculated by Model II has a similar seasonal variation pattern as that estimated from data recorded at a downstream station. This suggests that glacier runoff is the main source (87%) of runoff inflow into the lake. A sensitivity analysis suggests that the water inflow to the lake is highly sensitive to an increase in air temperature. Runoff in May is predicted to sharply increase by 15% to more than two-fold if the air temperature increases by 1 to 7, but it is predicted to increase only from 9% to 34% if the precipitation increases by 10% to 40%. The results suggested that the water inflow into Lake Gojal will not sharply rise even if there is heavy rain, and it needs to be in caution if the air temperature sharply increases. Analysis on long-term air temperature record indicates that the water inflow into the lake in May 2010 was probably less than average owing to the relatively low air temperature. Consequently, the flood outburst did not occur before the completion of the spillway on 29 May 2010. © 2013 China University of Geosciences and Springer-Verlag Berlin Heidelberg

On the role of soil moisture in the generation of heavy rainfall during the Oder flood event in July 1997

Soil moisture-atmosphere feedbacks play an important role in the regional climate over many regions worldwide, not only for the mean climate but also for extreme events. Several studies have shown that the extent and severity of droughts and heat waves can be significantly impacted by dry or wet soil moisture conditions. To date, the impact of soil moisture on heavy rainfall events has been less frequently investigated. Thus, we consider the role of soil moisture in the formation of heavy rainfall using the Oder flood event in July 1997 as an example. Here, we used the regional climate model CCLM as an uncoupled stand alone model and the coupled COSTRICE system, where CCLM is coupled with an ocean and a sea ice model over the Baltic and North Sea regions. The results from climate simulations over Europe show that the coupled model can capture the second phase (18-20 July) of heavy rainfall that led to the Oder flood, while the uncoupled model does not. Sensitivity experiments demonstrate that the better performance of the coupled model can be attributed to the simulated soil moisture conditions in July 1997 in Central Europe, which were wetter for the coupled model than for the uncoupled model. This finding indicates that the soil moisture preceding the event significantly impacted the generation of heavy rainfall in this second phase. The better simulation in the coupled model also implies the added value that the atmosphere-ocean coupling has on the simulation of this specific extreme event. As none of the model versions captured the first phase (4-8 July), despite the differences in soil moisture, it can be concluded that the importance of soil moisture for the generation of heavy rainfall events strongly depends on the event and the general circulation pattern associated with it

High resolution discharge simulations over Europe and the Baltic Sea catchment

Regional coupled system models require a high-resolution discharge component to couple their atmosphere/land components to the ocean component and to adequately resolve smaller catchments and the day-to-day variability of discharge. As the currently coupled discharge models usually do not fulfill this requirement, we improved a well-established discharge model, the Hydrological Discharge (HD) model, to be globally applicable at 5 Min. resolution. As the first coupled high-resolution discharge simulations are planned over Europe and the Baltic Sea catchment, we focus on the respective regions in the present study. As no river specific parameter adjustments were conducted and since the HD model parameters depend on globally available gridded characteristics, the model is, in principle, applicable for climate change studies and over ungauged catchments. For the validation of the 5 Min. HD (HD5) model, we force it with prescribed fields of surface and subsurface runoff. As no large-scale observations of these variables exist, they need to be calculated by a land surface scheme or hydrology model using observed or re-analyzed meteorological data. In order to pay regard to uncertainties introduced by these calculations, three different methods and datasets were used to derive the required fields of surface and subsurface runoff for the forcing of the HD5 model. However, the evaluation of the model performance itself is hampered by biases in these fields as they impose an upper limit on the accuracy of simulated discharge. 10-years simulations (2000–2009) show that for many European rivers, where daily discharge observations were available for comparison, the HD5 model captures the main discharge characteristics reasonably well. Deficiencies of the simulated discharge could often be traced back to deficits in the various forcing datasets. As direct anthropogenic impact on the discharge, such as by regulation or dams, is not regarded in the HD model, those effects can generally not be simulated. Thus, discharges for many heavily regulated rivers in Scandinavia or for the rivers Volga and Don are not well represented by the model. The comparison of the three sets of simulated discharges indicates that the HD5 model is suitable to evaluate the terrestrial hydrological cycle of climate models or land surface models, especially with regard to the separation of throughfall (rain or snow melt) into surface and subsurface runoff

Life time of soil moisture perturbations in a coupled land-atmosphere simulation

This study evaluates the lifetime of soil moisture perturbations using an atmosphere-land GCM. We find memory of up to 9 months for root zone soil moisture. Interactions with other surface states result in significant but short-lived anomalies in surface temperature and more stable anomalies in leaf carbon content. As these anomalies can recur repeatedly, e.g. due to interactions with a deep-soil moisture reservoir, we conclude that soil moisture initialization may impact climate predictions

Characterizing uncertainties in the ESA-CCI land cover map of the epoch 2010 and their impacts on MPI-ESM climate simulations

Limitations of mapping land surface properties and their conversion into climate model boundary conditions are major sources of uncertainty in climate simulations. In this paper, the range of the largest possible uncertainty in satellite-derived land cover (LC) map is estimated and its impact on climate simulations is quantified with the Earth System Model of the Max-Planck Institute for Meteorology utilizing prescribed sea surface temperature and sea ice. Two types of uncertainty in the LC map are addressed: (i) uncertainty due to classification algorithm of spectral reflectance into LC classes, and (ii) uncertainty due to conversion of LC classes into the climate model vegetation distribution. For forest cover, each of them is about the same order of magnitude as the uncertainty range in recent observations (∼± 700 Mha). Superposing two sources of uncertainty results in LC maps that feature the range of vegetation deviation that is about the same order of magnitude as the recent (since year 1700) forest loss due to agriculture (forest cover uncertainty range ∼± 1700 Mha). These uncertainties in vegetation distribution lead to noticeable variations in near-surface climate variables, local, regional, and global climate forcing. Temperature does not show significant uncertainty in global mean, but rather exhibits regional deviations with an opposite response to LC uncertainty that compensate each other in the global mean (e.g., albedo feedback controls temperature in boreal North America resulting in cooling (warming) with decrease (increase) of vegetation while evaporative cooling controls temperature in South America and sub-Saharan Africa resulting in cooling (warming) with increase (decrease) of vegetation). Large-scale circulation is also affected by the LC uncertainty, and consequently precipitation pattern as well. It is demonstrated that precipitation uncertainty in the monsoonal regions are about the same order of magnitude as in previous studies with idealized perturbations of vegetation. These findings indicate that the range of uncertainty in satellite-derived vegetation maps for climate models is about the same order of magnitude as the uncertainty in recent observations of forest cover or as the forest lost due to agriculture. Consequently, climate simulations have a similar range of uncertainty in variables representing near-surface climate as the observed climate change due to land use. Hence, more accurate methods are needed for mapping and converting LC properties into model vegetation in order to increase reliability of climate model simulations. © 2018, The Author(s)

HydroPy (v1.0): a new global hydrology model written in Python

Global hydrological models (GHMs) are a useful tool in the assessment of the land surface water balance. They are used to further the understanding of interactions between water balance components and their past evolution as well as potential future development under various scenarios. While GHMs have been part of the hydrologist's toolbox for several decades, the models are continuously being developed. In our study, we present the HydroPy model, a revised version of an established GHM, the Max Planck Institute for Meteorology's Hydrology Model (MPI-HM). Being rewritten in Python, the new model requires much less effort in maintenance, and due to its flexible infrastructure, new processes can be easily implemented. Besides providing a thorough documentation of the processes currently implemented in HydroPy, we demonstrate the skill of the model in simulating the land surface water balance. We find that evapotranspiration is reproduced realistically for the majority of the land surface but is underestimated in the tropics. The simulated river discharge correlates well with observations. Biases are evident for the annual accumulated discharge; however, they can - at least to some extent - be attributed to discrepancies between the meteorological model forcing data and the observations. Finally, we show that HydroPy performs very similarly to MPI-HM and thus conclude the successful transition from MPI-HM to HydroPy