Impacts of Climate Change and Variability on Precipitation and Maximum Flows in Devil’s Creek, Tacna, Peru

Global projections of climate change indicate negative impacts on hydrological systems, with significant changes in precipitation and temperature in many parts of the world. As a result, floods and droughts are expected. This article discusses the potential effects of climate change and variability on the maximum precipitation, temperature, and hydrological regime in Devil’s Creek, Tacna, Peru. The outputs of precipitation and daily temperature of fifteen regional climate models were used for the RCP4.5 and RCP8.5 emission scenarios. The methodology used includes the bias correction and downscaling of meteorological variables using the quintiles mapping technique, hydrological modeling, the evaluation of two emission scenarios, and its effect on the maximum flows of the stream. The results of the multi-model ensemble show that the maximum annual precipitation will probably increase by more than 30% for the RCP4.5 and RCP8.5 scenarios for the 2021–2050 period relative to the 1981–2005 period. Likewise, as expected, the maximum flows could increase by 220% and 154% for the RCP4.5 scenarios for the 2021–2050 and 2051–2080 terms, respectively, and 234% and 484% for the RCP8.5 scenarios and for the 2021–2050 and 2051–2080 terms, respectively, concerning the recorded historical value, increasing the probability of flood events and damage in populations located downstream.

Run-Of-River Small Hydropower Plants as Hydro-Resilience Assets against Climate Change

Renewable energy sources, due to their direct (e.g., wind turbines) or indirect (e.g., hydropower, with precipitation being the generator of runoff) dependence on climatic variables, are foreseen to be affected by climate change. In this research, two run-of-river small hydropower plants (SHPPs) located at different water districts in Greece are being calibrated and validated, in order to be simulated in terms of future power production under climate change conditions. In doing so, future river discharges derived by the forcing of a hydrology model, by three Regional Climate Models under two Representative Concentration Pathways, are used as inputs for the simulation of the SHPPs. The research concludes, by comparing the outputs of short-term (2031–2060) and long-term (2071–2100) future periods to a reference period (1971–2000), that in the case of a significant projected decrease in river discharges (~25–30%), a relevant important decrease in the simulated future power generation is foreseen (~20–25%). On the other hand, in the decline projections of smaller discharges (up to ~15%) the generated energy depends on the intermonthly variations of the river runoff, establishing that runoff decreases in the wet months of the year have much lower impact on the produced energy than those occurring in the dry months. The latter is attributed to the non-existence of reservoirs that control the operation of run-of-river SHPPs; nevertheless, these types of hydropower plants can partially remediate the energy losses, since they are taking advantage of low flows for hydropower production. Hence, run-of-river SHPPs are designated as important hydro-resilience assets against the projected surface water availability decrease due to climate change.

Objective selection of model run from regional climate models ensemble

In this paper, a method was developed in relation to the north-western coast of the Black Sea in order to determine the optimal model run from regional climate models ensemble. As a result of climate change, which has been observed since the late 1980s in Ukraine, various natural objects changes have been also transformed. The study of such changes in the future is possible only by using runs of global or regional climate models. Moreover, the step of the spatial grid in the climate model must be comparative with the spatial size of a natural object under study. In the north-western coast of the Black Sea, climate change is characterized by increasing aridity of climate and a corresponding decrease in freshwater inflows into coastal lagoons from their catchments, making ecosystems of these lagoons sensitive and vulnerable to climate change. Using numerical models in order to study climate change impact on these natural objects requires input hydrometeorological information in the spatial grid points, the distance between which should correspond to the horizontal size of lagoons, i.e. several kilometers. In this paper, data from the scenarios RCP4.5 and RCP8.5 of the ensemble from 14 model runs with different regional climatic models of the CORDEX project were used to simulate the future changes of the temperature and precipitation regime. For each grid point and scenario, a single simulation was selected from the ensemble, which best reproduces the intra-annual changes of temperature, precipitation, and evaporation compared to the ensemble means. Despite the sufficiently large distance between the estuaries, the method allowed the selection of a single optimal model run, which shows the significant differences in spring and summer precipitation as well as year-around evaporation in the southern and northern parts of the northwestern coast of the region. This run well reproduces the relationship between temperature, precipitation, and evaporation in the southern and northern parts of the northwestern coast of the Black Sea.

The Mesoscale Response to Global Warming over the Pacific Northwest Evaluated Using a Regional Climate Model Ensemble

This paper describes the downscaling of an ensemble of twelve GCMs using the WRF model at 12-km grid spacing over the period 1970-2099, examining the mesoscale impacts of global warming as well as the uncertainties in its mesoscale expression. The RCP 8.5 emissions scenario was used to drive both global and regional climate models. The regional climate modeling system reduced bias and improved realism for a historical period, in contrast to substantial errors for the GCM simulations driven by lack of resolution. The regional climate ensemble indicated several mesoscale responses to global warming that were not apparent in the global model simulations, such as enhanced continental interior warming during both winter and summer as well as increasing winter precipitation trends over the windward slopes of regional terrain, with declining trends to the lee of major barriers. During summer there is general drying, except to the east of the Cascades. April 1 snowpack declines are large over the lower to middle slopes of regional terrain, with small snowpack increases over the lower elevations of the interior. Snow-albedo feedbacks are very different between GCM and RCM projections, with the GCM’s producing large, unphysical areas of snowpack loss and enhanced warming. Daily average winds change little under global warming, but maximum easterly winds decline modestly, driven by a preferential sea level pressure decline over the continental interior. Although temperatures warm continuously over the domain after approximately 2010, with slight acceleration over time, occurrences of temperature extremes increase rapidly during the second half of the 21st century.

Simulation performance of selected global and regional climate models for temperature and rainfall in some locations in India

Global and regional climate models are reported to have inherent bias in simulating the observed climatology of a region. This bias of climate models is the major source of uncertainties in climate change impact assessments. Therefore, use of bias corrected simulated climate data is important. In this study, the bias corrected climate data for 30 years’ period (1976-2005) from selected common fourGCMs and RCMs for six Indian locations are compared with the respective observed data of India Meteorological Department. The analysis indicated that the RCMs performance is much better than GCMs after bias correction for minimum and maximum temperatures. Also, RCMs performance is better than GCMs in simulating extreme temperatures. However, the selected RCMs and GCMs are found to either over estimate or under estimate the rainfall despite bias correction and also overestimated the rainfall extremes for selected Indian locations. Based on the overall performance of four models for the six locations, it was found that the GFDL_ESM2M and NORESM1-M RCMs performed comparatively better than CSIRO and IPSL models. After bias correction, the RCMs could represent the observed climatology better than the GCMs. And these RCMs viz., GFDL_ESM2M and NORESM1-M can be usedindividually after bias correction in the climate change assessment studies for the selected regions.

Using high-resolution regional climate models to estimate return levels of daily extreme precipitation over Bavaria

Extreme daily rainfall is an important trigger for floods in Bavaria. The dimensioning of water management structures as well as building codes is based on observational rainfall return levels. In this study, three high-resolution regional climate models (RCMs) are employed to produce 10- and 100-year daily rainfall return levels and their performance is evaluated by comparison to observational return levels. The study area is governed by different types of precipitation (stratiform, orographic, convectional) and a complex terrain, with convective precipitation also contributing to daily rainfall levels. The Canadian Regional Climate Model version 5 (CRCM5) at a 12 km spatial resolution and the Weather and Forecasting Research (WRF) model at a 5 km resolution both driven by ERA-Interim reanalysis data use parametrization schemes to simulate convection. WRF at a 1.5 km resolution driven by ERA5 reanalysis data explicitly resolves convectional processes. Applying the generalized extreme value (GEV) distribution, the CRCM5 setup can reproduce the observational 10-year return levels with an areal average bias of +6.6 % and a spatial Spearman rank correlation of ρ=0.72. The higher-resolution 5 km WRF setup is found to improve the performance in terms of bias (+4.7 %) and spatial correlation (ρ=0.82). However, the finer topographic details of the WRF-ERA5 return levels cannot be evaluated with the observation data because their spatial resolution is too low. Hence, this comparison shows no further improvement in the spatial correlation (ρ=0.82) but a small improvement in the bias (2.7 %) compared to the 5 km resolution setup. Uncertainties due to extreme value theory are explored by employing three further approaches. Applied to the WRF-ERA5 data, the GEV distributions with a fixed shape parameter (bias is +2.5 %; ρ=0.79) and the generalized Pareto (GP) distributions (bias is +2.9 %; ρ=0.81) show almost equivalent results for the 10-year return period, whereas the metastatistical extreme value (MEV) distribution leads to a slight underestimation (bias is −7.8 %; ρ=0.84). For the 100-year return level, however, the MEV distribution (bias is +2.7 %; ρ=0.73) outperforms the GEV distribution (bias is +13.3 %; ρ=0.66), the GEV distribution with fixed shape parameter (bias is +12.9 %; ρ=0.70), and the GP distribution (bias is +11.9 %; ρ=0.63). Hence, for applications where the return period is extrapolated, the MEV framework is recommended. From these results, it follows that high-resolution regional climate models are suitable for generating spatially homogeneous rainfall return level products. In regions with a sparse rain gauge density or low spatial representativeness of the stations due to complex topography, RCMs can support the observational data. Further, RCMs driven by global climate models with emission scenarios can project climate-change-induced alterations in rainfall return levels at regional to local scales. This can allow adjustment of structural design and, therefore, adaption to future precipitation conditions.