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Gerhard Smiatek
and
Harald Kunstmann

Abstract

The pan-African Great Green Wall for the Sahara and the Sahel initiative (GGW) is a reforestation program to reverse the degradation of land. We investigate characteristics of mean precipitation due to proposed land-use changes to woody savannah with three hypothetical courses of the GGW, with an area between 0.8 and 1.25 million km2, and between the 100- and 400-mm isohyets. The global Model for Prediction Across Scales (MPAS) was applied for this investigation, employing ensembles with 40 members for the rainy season from June to September and 50 members for August when precipitation is at its peak. In comparison with the observational reference, the results show that a wet bias on the order of 33% in the eastern Sahel and a moderate dry bias of −41% in the western Sahel are present in the MPAS simulations. Our simulations do not provide any significant evidence for GGW-induced changes in the characteristics of the summer precipitation, for positive changes within the Sahel supporting the forestation activities, or for potentially adverse changes in the neighboring regions. Changes are present at the regional scale, but they are not significant at the 5% level. Also, changes simulated for further hydrometeorological variables such as temperature, radiation fluxes, or runoff are comparatively small.

Open access
Christof Lorenz
and
Harald Kunstmann

Abstract

The three state-of-the-art global atmospheric reanalysis models—namely, ECMWF Interim Re-Analysis (ERA-Interim), Modern-Era Retrospective Analysis for Research and Applications (MERRA; NASA), and Climate Forecast System Reanalysis (CFSR; NCEP)—are analyzed and compared with independent observations in the period between 1989 and 2006. Comparison of precipitation and temperature estimates from the three models with gridded observations reveals large differences between the reanalyses and also of the observation datasets. A major source of uncertainty in the observations is the spatial distribution and change of the number of gauges over time. In South America, active measuring stations were reduced from 4267 to 390. The quality of precipitation estimates from the reanalyses strongly depends on the geographic location, as there are significant differences especially in tropical regions. The closure of the water cycle in the three reanalyses is analyzed by estimating long-term mean values for precipitation, evapotranspiration, surface runoff, and moisture flux divergence. Major shortcomings in the moisture budgets of the datasets are mainly due to inconsistencies of the net precipitation minus evaporation and evapotranspiration, respectively, (PE) estimates over the oceans and landmasses. This imbalance largely originates from the assimilation of radiance sounding data from the NOAA-15 satellite, which results in an unrealistic increase of oceanic PE in the MERRA and CFSR budgets. Overall, ERA-Interim shows both a comparatively reasonable closure of the terrestrial and atmospheric water balance and a reasonable agreement with the observation datasets. The limited performance of the three state-of-the-art reanalyses in reproducing the hydrological cycle, however, puts the use of these models for climate trend analyses and long-term water budget studies into question.

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Gerhard Smiatek
and
Harald Kunstmann

Abstract

With large elevation gradients and high hydrometeorological variability, Alpine catchments pose special challenges to hydrological climate change impact assessment. Data from seven regional climate models run within the Coordinated Regional Climate Downscaling Experiments (CORDEX), each driven with a different boundary forcing, are used to exemplarily evaluate the reproduction of observed flow duration curves and access the future discharge of the Ammer River located in Alpine southern Germany applying the hydrological simulation model called the Water Flow and Balance Simulation Model (WaSiM). The results show that WaSiM reasonably reproduces the observed runoff for the entire catchment when driven with observed precipitation. When applied with CORDEX evaluation data (1989–2008) forced by ERA-Interim, the simulations underestimate the extreme runoff and reproduce the high percentile values with errors in the range from −37% to 55% with an ensemble mean of around 15%. Runs with historical data 1975–2005 reveal larger errors, up to 120%, with an ensemble mean of around 50% overestimation. Also, the results show a large spread between the simulations, primarily resulting from deficiencies in the precipitation data. Results indicate future changes for 2071–2100 in the 99.5th percentile runoff value of up to 9% compared to 1975–2005. An increase in high flows is also supported by flow return periods obtained from a larger sample of highest flows over 50 years, which reveals for 2051–2100 lower return periods for high runoff values compared to 1956–2005. Obtained results are associated with substantial uncertainties leading to the conclusion that CORDEX data at 0.11° resolution are likely inadequate for driving hydrologic analyses in mesoscale catchments that require a high standard of fidelity for hydrologic simulation performance.

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Gerhard Smiatek
and
Harald Kunstmann

Abstract

Data from five different RCMs run in two experiments from the Coordinated Regional Climate Downscaling Experiment (CORDEX) are applied together with the Water Flow and Balance Simulation Model (WaSiM) to assess the future availability of water in the upper Jordan River. Simulation results for 1976–2000 show that the modeling system was able to reasonably reproduce the observed discharge rates in the partially karstic complex terrain without bias correction of the precipitation input. For the future climate in the area, the applied CORDEX models indicate an increasing annual mean temperature for 2031–60 by 1.8 K above the 1971–2000 mean and by 2.6 K for 2071–2100. The simulated ensemble mean precipitation is predicted to decrease by 16.3% in the first period and 22.1% at the end of the century. In relation to the mean for 1976–2000, the discharge of the upper Jordan River is simulated to decrease by 7.4% until 2060 and by 17.5% until 2100, together with a reduction of high river flow years.

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Dominikus Heinzeller
,
Wolfgang Junkermann
, and
Harald Kunstmann

Abstract

It is commonly understood that the observed decline in precipitation in southwestern Australia during the twentieth century is caused by anthropogenic factors. Candidates therefore are changes to large-scale atmospheric circulations due to global warming, extensive deforestation, and anthropogenic aerosol emissions—all of which are effective on different spatial and temporal scales. This contribution focuses on the role of rapidly rising aerosol emissions from anthropogenic sources in southwestern Australia around 1970. An analysis of historical long-term rainfall data of the Bureau of Meteorology shows that southwestern Australia as a whole experienced a gradual decline in precipitation over the twentieth century. However, on smaller scales and for the particular example of the Perth catchment area, a sudden drop in precipitation around 1970 is apparent. Modeling experiments at a convection-resolving resolution of 3.3 km using the Weather Research and Forecasting (WRF) Model version 3.6.1 with the aerosol-aware Thompson–Eidhammer microphysics scheme are conducted for the period 1970–74. A comparison of four runs with different prescribed aerosol emissions and without aerosol effects demonstrates that tripling the pre-1960s atmospheric CCN and IN concentrations can suppress precipitation by 2%–9%, depending on the area and the season. This suggests that a combination of all three processes is required to account for the gradual decline in rainfall seen for greater southwestern Australia and for the sudden drop observed in areas along the west coast in the 1970s: changing atmospheric circulations, deforestation, and anthropogenic aerosol emissions.

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Gerhard Smiatek
,
Harald Kunstmann
, and
Andreas Heckl

Abstract

The impact of climate change on the future water availability of the upper Jordan River (UJR) and its tributaries Dan, Snir, and Hermon located in the eastern Mediterranean is evaluated by a highly resolved distributed approach with the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) run at 18.6- and 6.2-km resolution offline coupled with the Water Flow and Balance Simulation Model (WaSiM). The MM5 was driven with NCEP reanalysis for 1971–2000 and with Hadley Centre Coupled Model, version 3 (HadCM3), GCM forcings for 1971–2099. Because only one regional–global climate model combination was applied, the results may not give the full range of possible future projections. To describe the Dan spring behavior, the hydrological model was extended by a bypass approach to allow the fast discharge components of the Snir to enter the Dan catchment. Simulation results for the period 1976–2000 reveal that the coupled system was able to reproduce the observed discharge rates in the partially karstic complex terrain to a reasonable extent with the high-resolution 6.2-km meteorological input only. The performed future climate simulations show steadily rising temperatures with 2.2 K above the 1976–2000 mean for the period 2031–60 and 3.5 K for the period 2070–99. Precipitation trends are insignificant until the middle of the century, although a decrease of approximately 12% is simulated. For the end of the century, a reduction in rainfall ranging between 10% and 35% can be expected. Discharge in the UJR is simulated to decrease by 12% until 2060 and by 26% until 2099, both related to the 1976–2000 mean. The discharge decrease is associated with a lower number of high river flow years.

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Gerhard Smiatek
,
Severin Kaspar
, and
Harald Kunstmann

Abstract

A set of downscaled climate change data from transient experiments with regional climate models has been used to access the future climate change signal in the area of the Figeh spring system in Syria and its potential effects on future water availability. The data ensemble at a spatial resolution of 0.25° has been investigated for the period 1961–90 for present-day climate and the periods 2021–50 and 2070–99 for future climate. The focus is on changes to annual, seasonal, and monthly surface air temperature and precipitation. For the first time, the Figeh spring discharge has been assessed with a hydrological runoff model based on an artificial neural network (ANN) approach. The ANN model was formulated and validated for the years 1987–2007, applying daily meteorological driving data. The investigations show that water supply from the spring might face serious problems under changed climate conditions. An expected, a precipitation decrease of about −11% in winter and −8% in spring, together with increased temperatures of up to +1.6°C and a significant decrease in snow mass, can substantially limit the water recharge potential already in the near future until 2050. In the period 2070–99, the annual precipitation amount is simulated to decrease by −22% and the annual mean temperature to increase by +4°C, relative to the 1961–90 mean. The ensemble mean of the relative change in mean discharge reveals a decrease during the peak flow from March to May, with values up to −20% in 2021–50 and almost −50% in the period 2069–98, both related to the 1961–90 mean.

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Gerhard Smiatek
,
Severin Kaspar
, and
Harald Kunstmann
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Subhasmita Dash
,
Rajib Maity
, and
Harald Kunstmann

Abstract

This study explores the population exposure to an increasing number of hydroclimatic extreme events owing to the warming climate. It is well agreed that the extreme events are increasing in terms of frequency as well as intensity due to climate change and that the exposure to compound extreme events (concurrent occurrence of two or more extreme phenomena) affects population, ecosystems, and a variety of socioeconomic aspects more adversely. Specifically, the compound precipitation–temperature extremes (hot-dry and hot-wet) are considered, and the entire Indian mainland is regarded as the study region that spans over a wide variety of climatic regimes and wide variation of population density. The developed copula-based statistical method evaluates the change in population exposure to the compound extremes across the past (1981–2020) and future (near future: 2021–60 and far future: 2061–2100) due to climate change. The results indicate an increase of more than 10 million person-year exposure from the compound extremes across many regions of the country, considering both near and far future periods. Densely populated regions have experienced more significant changes in hot-wet extremes as compared with the hot-dry extremes in the past, and the same is projected to continue in the future. The increase is as much as sixfold in many parts of the country, including the Indo-Gangetic Plain and southernmost coastal regions, identified as the future hotspots with the maximum increase in exposure under all the projected warming and population scenarios. The study helps to identify the regions that may need greater attention based on the risks of population exposure to compound extremes in a warmer future.

Significance Statement

How is the growing population being affected now, and in the future, how will it be affected due to climate change induced compound extreme events? This study explores this societal consequence in terms of population exposure for the most populous country, India. An increase of more than 10 million person-year exposure from the precipitation–temperature compound extremes across many regions is indicated. Densely populated regions are expected to experience enhanced population exposure to hot-wet extremes as compared with the hot-dry extremes. Furthermore, the maximum increase in population exposure to compound extremes is expected across the Indo-Gangetic Plain and southern coastal regions of India. The outcome of the study will be helpful for adopting socioeconomic decisions toward the welfare of society.

Restricted access
Rajib Maity
,
Mayank Suman
,
Patrick Laux
, and
Harald Kunstmann

Abstract

Changes in extreme precipitation due to climate change often require the application of methods to bias correct simulated atmospheric fields, including extremes. Most existing bias correction techniques (i) only focus on the bias in the mean value or on the extreme values separately, and (ii) exclude zero values from analysis, even though their presence is significant in daily precipitation. We developed a copula-based bias correction scheme that is suitable for zero-inflated daily precipitation data to correct the bias in mean as well as in extreme precipitation at any specific statistical quantile. In considering the whole of Germany as a test bed, the proposed scheme is found to work well across the entire study area, including the German Alpine regions. The joint distribution between observed and regional climate model (RCM)-derived precipitation is developed through copulas. In particular, the joint distribution is modified to make it discrete at zero in order to account for zero values. The benefit of considering zero precipitation values is revealed through the improved performance of bias correction both in the mean and extreme values. Second, the quantile that best captures the bias (whether in the mean or any extreme value) is determined for a specific location and varies spatially and seasonally. This relaxation in selecting the location-specific optimal quantile renders the proposed methodology spatially transferable. By acknowledging possible changes in extreme precipitation due to climate change, the proposed scheme is expected to be suitable for climate change impact assessments for extreme events worldwide.

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