The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.

Global impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.

The effects of land use and land cover (LULC) on groundwater recharge and surface runoff and how these are affected by LULC changes are of interest for sustainable water resources management. However, there is limited quantitative evidence on how changes to LULC in semi-arid tropical and subtropical regions affect the subsurface components of the hydrologic cycle, particularly groundwater recharge. Effective water resource management in these regions requires conclusive evidence and understanding of the effects of LULC changes on groundwater recharge and surface runoff. We reviewed a total of 27 studies (2 modeling and 25 experimental), which reported on pre- and post land use change groundwater recharge or surface runoff magnitude, and thus allowed to quantify the response of groundwater recharge rates and runoff to LULC. Comparisons between initial and subsequent LULC indicate that forests have lower groundwater recharge rates and runoff than the other investigated land uses in semi-arid tropical/ subtropical regions. Restoration of bare land induces a decrease in groundwater recharge from 42% of precipitation to between 6 and 12% depending on the final LULC. If forests are cleared for rangelands, groundwater recharge increases by 7.8 ± 12.6%, while conversion to cropland or grassland results in increases of 3.4 ± 2.5 and 4.4 ± 3.3%, respectively. Rehabilitation of bare land to cropland results in surface runoff reductions of between 5.2 and 7.3%. The conversion of forest vegetation to managed LULC shows an increase in surface runoff from 1 to 14.1% depending on the final LULC. Surface runoff was reduced from 2.5 to 1.1% when grassland is converted to forest vegetation. While there is general consistency in the results from the selected case studies, we conclude that there are few experimental studies that have been conducted in tropical and subtropical semi-arid regions, despite that many people rely heavily on groundwater for their livelihoods. Therefore, there is an urgent need to increase the body of quantitative evidence given the pressure of growing human population and climate change on water resources in the region.