Future Precipitation Scenarios Research Articles

Pluvial flood risk assessment for 2021–2050 under climate change scenarios in the Metropolitan City of Venice

Pluvial flood is a natural hazard occurring from extreme rainfall events that affect millions of people around the world, causing damages to their properties and lives. The magnitude of projected climate risks indicates the urgency of putting in place actions to increase climate resilience. Through this study, we develop a Machine Learning (ML) model to predict pluvial flood risk under Representative Concentration Pathways (RCP) 4.5 and 8.5 for future scenarios of precipitation for the period 2021–2050, considering different triggering factors and precipitation patterns. The analysis is focused on the case study area of the Metropolitan City of Venice (MCV) and considers 212 historical pluvial flood events occurred in the timeframe 1995–2020. The methodology developed implements spatio-temporal constraints in the ML model to improve pluvial flood risk prediction under future scenarios of climate change. Accordingly, a cross-validation approach was applied to frame a model able to predict pluvial flood at any time and space. This was complemented with historical pluvial flood data and the selection of nine triggering factors representative of territorial features that contribute to pluvial flood events. Logistic Regression was the most reliable model, with the highest AUC score, providing robust result both in the validation and test set. Maximum cumulative rainfall of 14 days was the most important feature contributing to pluvial flood occurrence. The final output is represented by a suite of risk maps of the flood-prone areas in the MCV for each quarter of the year for the period 1995–2020 based on historical data, and risk maps for each quarter of the period 2021–2050 under RCP4.5 and 8.5 of future precipitation scenarios. Overall, the results underline a consistent increase in extreme events (i.e., very high and extremely high risk of pluvial flooding) under the more catastrophic scenario RCP8.5 for future decades compared to the baseline.

Spatial analysis of future climate risk to stormwater infrastructure.

Climate change is expected to result in more intense precipitation events that will affect the performance and design requirements of stormwater infrastructure. Such changes will vary spatially, and climate models provide a range of estimates of the effects on events of different intensities and recurrence. Infrastructure performance should be evaluated against the expected range of events, not just rare extremes. We present a national-scale, spatially detailed screening assessment of the potential effects of climatic change on precipitation, stormwater runoff, and associated design requirements. This is accomplished through adjustment relative to multiple future climate scenarios of precipitation intensity-duration-frequency analyses presented in NOAA Atlas 14, which are commonly used in infrastructure design. Future precipitation results are estimated for each Atlas 14 station (these currently omit the Pacific Northwest). Results are interpolated using a geographically conditioned regression kriging approach to provide information about potential climate change impacts in a format more directly useful to local stormwater managers. The intensity of 24-h events with 2-year or greater recurrence is likely to increase in most areas of the United States leading to increased runoff and potential need for increased storage volumes. Changes in more frequent events (e.g., the 90th percentile event) commonly used in design of green infrastructure are relatively less.

Moderate precipitation reduction enhances nitrogen cycling and soil nitrous oxide emissions in a semi-arid grassland.

The ongoing climate change is predicted to induce more weather extremes such as frequent drought and high-intensity precipitation events, causing more severe drying-rewetting cycles in soil. However, it remains largely unknown how these changes will affect soil nitrogen (N)-cycling microbes and the emissions of potent greenhouse gas nitrous oxide (N2 O). Utilizing a field precipitation manipulation in a semi-arid grassland on the Loess Plateau, we examined how precipitation reduction (ca. -30%) influenced soil N2 O and carbon dioxide (CO2 ) emissions in field, and in a complementary lab-incubation with simulated drying-rewetting cycles. Results obtained showed that precipitation reduction stimulated plant root turnover and N-cycling processes, enhancing soil N2 O and CO2 emissions in field, particularly after each rainfall event. Also, high-resolution isotopic analyses revealed that field soil N2 O emissions primarily originated from nitrification process. The incubation experiment further showed that in field soils under precipitation reduction, drying-rewetting stimulated N mineralization and ammonia-oxidizing bacteria in favor of genera Nitrosospira and Nitrosovibrio, increasing nitrification and N2 O emissions. These findings suggest that moderate precipitation reduction, accompanied with changes in drying-rewetting cycles under future precipitation scenarios, may enhance N cycling processes and soil N2 O emissions in semi-arid ecosystems, feeding positively back to the ongoing climate change.

Asymmetric responses of soil bacterial community and soil respiration to precipitation changes: A global meta‐analysis

AbstractChanges in the frequency and intensity of precipitation altered the hydrological features, thereafter greatly affecting carbon (C) cycling in terrestrial ecosystems. Soil bacteria are pivotal drivers of the global C cycle in terrestrial ecosystems; however, the responses of soil bacterial communities and the C‐cycle they regulated to precipitation changes remain unclear. Here, we conducted a global meta‐analysis using 98 paired observations from 58 studies to explore the responses of soil bacteria (abundance and diversity) and their C‐related functions (soil respiration) to increased/decreased precipitation. We found that the response of soil bacterial abundance was linearly correlated with precipitation changes, while the response of soil respiration was negatively asymmetric (concave‐down). The relationship among soil bacterial abundance, alpha diversity, and soil respiration was weakened by decreased precipitation, indicating that decreased precipitation exhibited a stronger effect on soil bacterial community and soil respiration. Our study extends the understanding of soil bacteria and their functions in response to precipitation change and facilitates the prediction of soil carbon cycle‐related functions by using soil microorganisms in terrestrial ecosystem models under future precipitation scenarios.

Assessment of climate change and prospective analysis on shallow lakes. Las Encadenadas del Oeste watershed, Buenos Aires - Argentina

The earth’s ecosystem is fragile, and sometimes even small changes in the climate can have impacts on the environment and society. Changes in temperature and precipitation can cause numerous feedbacks that effect the ecosystem of the whole Earth. Many studies hold that the temperature will rise in some places, while other areas will experience a cooling in annual mean temperatures. The study area is famous for its many ponds. These ecosystems will be both physically, biologically, and chemically affected by climate change and its feedbacks. Las Encadenadas del Oeste consists of seven shallow lakes (Epecuen, La Paraguaya, Venado, Del Monte, Cochico, Alsina, and Inchauspe) of various depths and sizes is a closed river basin system aligned in an east-west direction. The objectives of this work are to demonstrate the change in shallow lake size over a period of 20 years and to relate these changes to temperature and precipitation over the basin area for the same period. It is also intended to examine future temperature and precipitation scenarios in the study area. Maximum and minimum temperature data and precipitation data was retrieved from a climate station in Carhue. A multiple regression analysis was performed and five models and the shallow lake area were compared. The water levels in the shallow lakes will continue to fluctuate in the future as precipitation and temperature varies. Temperatures will increase quickly in the area; and around a 3 ºC change is expected before 2099. Only small variations in the temperatures have previously caused the lake to change in size. Precipitation patterns show a high variation, but the change is very small. Minimum temperature, which is already the most significant factor according to the statistical analysis, will in the future be an even more important factor if changes occur.

Planting miscanthus instead of row crops may increase the productivity and economic performance of farmed potholes

AbstractClimate change projections indicate that precipitation events in the central United States are expected to become more intense, more frequent in the spring, and less frequent in the summer. Such a precipitation shift could adversely impact crop yields, especially in subfield areas known as farmed potholes, which are highly susceptible to flooding and ponding, and crop death is more likely to occur, particularly early in the growing season. This suggests that planting alternative crops, such as more flood tolerant perennials, in these areas may be a more profitable option. Using observations of crop growth and yield along with ponding depth of a specific field and farmed pothole in the central United States, we developed a spatially explicit version of the agroecosystem model Agro‐IBIS to estimate water depth and crop yield. After evaluating the model, we conducted a case study for a specific farmed pothole with a range of future precipitation scenarios with Agro‐IBIS to simulate the effects of contemporary (2002–2016) and future precipitation on a conventional corn/soybean (Zea maysL. andGlycine maxMerr.) rotation and an alternative perennial miscanthus (Miscanthus × giganteusGreef et Deu.) cropping system. The depth and frequency of ponding increased under most future precipitation scenarios. The corn/soybean rotation had greater total loss (i.e., no yield) on average (>30%) for all scenarios in comparison to miscanthus (<10%). Under one future precipitation scenario with increased spring precipitation, both the corn/soybean rotation and miscanthus simulations showed an increase in yield. A simple budget analysis indicated that it is more profitable to plant miscanthus instead of corn or soybeans where yields in farmed potholes are consistently poor. Our findings show that potholes can be individually modeled, and their influence on yield can be quantified for use in future management decisions dictated by change in climate.

Interrelated impacts of climate and land-use change on a widespread waterbird.

Together climate and land-use change play a crucial role in determining species distribution and abundance, but measuring the simultaneous impacts of these processes on current and future population trajectories is challenging due to time lags, interactive effects and data limitations. Most approaches that relate multiple global change drivers to population changes have been based on occurrence or count data alone. We leveraged three long-term (1995-2019) datasets to develop a coupled integrated population model-Bayesian population viability analysis (IPM-BPVA) to project future survival and reproductive success for common loons Gavia immer in northern Wisconsin, USA, by explicitly linking vital rates to changes in climate and land use. The winter North Atlantic Oscillation (NAO), a broad-scale climate index, immediately preceding the breeding season and annual changes in developed land cover within breeding areas both had strongly negative influences on adult survival. Local summer rainfall was negatively related to fecundity, though this relationship was mediated by a lagged interaction with the winter NAO, suggesting a compensatory population-level response to climate variability. We compared population viability under 12 future scenarios of annual land-use change, precipitation and NAO conditions. Under all scenarios, the loon population was expected to decline, yet the steepest declines were projected under positive NAO trends, as anticipated with ongoing climate change. Thus, loons breeding in the northern United States are likely to remain affected by climatic processes occurring thousands of miles away in the North Atlantic during the non-breeding period of the annual cycle. Our results reveal that climate and land-use changes are differentially contributing to loon population declines along the southern edge of their breeding range and will continue to do so despite natural compensatory responses. We also demonstrate that concurrent analysis of multiple data types facilitates deeper understanding of the ecological implications of anthropogenic-induced change occurring at multiple spatial scales. Our modelling approach can be used to project demographic responses of populations to varying environmental conditions while accounting for multiple sources of uncertainty, an increasingly pressing need in the face of unprecedented global change.

Design of the tundra rainfall experiment (TRainEx) to simulate future summer precipitation scenarios

The majority of climate models predict severe increases in future temperature and precipitation in the Arctic. Increases in temperature and precipitation can lead to an intensification of the hydrologic cycle that strongly impacts Arctic environmental conditions. In order to investigate effects of future precipitation scenarios on ecosystems, precipitation manipulation experiments are being performed to simulate drought and extreme precipitation conditions. However, most of the existing research so far has been unevenly distributed, primarily focusing on temperate grasslands and woodlands. Despite large changes in the predicted precipitation and potentially high sensitivity of the Arctic tundra ecosystem to these changes, it is among the most understudied ecosystems for precipitation manipulation experiments.Gherardi and Sala (2013) presented a design for precipitation manipulation experiments that, relative to other methods at the time, was cheap, simplistic, and easily reproducible. In this study, we:•Present modifications to the original Gherardi and Sala (2013) design that are adapted to cold, harsh conditions, such as those present in the Siberian Arctic tundra.•Provide a detailed documentation of the improved design.•Validate our modified experimental design based on the first two years of our experiment.

Climate Change and Its Possible Impact in Groundwater Resource of the Kankai River Basin, East Nepal Himalaya

Increasing evidence of changing climate patterns is being observed, and the impact of this change on groundwater has a direct impact on the livelihood and economy of the region. The research focuses on the impacts of global temperature increase and changing precipitation on the groundwater resources of part of the Himalayan river system. The spatial and temporal variations of the hydro-meteorological data of the Kankai River Basin in East Nepal were analyzed using non-parametric Mann–Kendall tests and Sen’s Slope methods, whereas CanESM2 was used to predict the future precipitation scenarios, and an attempt has been made to evaluate the possible impacts on groundwater systems in the region. The temperature shows a significant warming trend (0.14–0.64 °C/decade); however, the precipitation trends suggest remarkable variation mostly at higher elevation. The average annual precipitation suggests a decrease of 1.82 mm/year and a similar decrement has been projected for the future. The groundwater in the region has been influenced by the changing climate and the condition may further be exaggerated by reduced recharge and increased evapotranspiration. This understanding of the impacts and climate scenarios will help the planners with better adaptation strategies, plans, and programs for a better society.

Global Phosphorus Losses from Croplands under Future Precipitation Scenarios

Phosphorus (P) losses from fertilized croplands to inland water bodies cause serious environmental problems. During wet years, high precipitation disproportionately contributes to P losses. We combine simulations of a gridded crop model and outputs from a number of hydrological and climate models to assess global impacts of changes in precipitation regimes on P losses during the 21st century. Under the baseline climate during 1991-2010, median P losses are 2.7 ± 0.5 kg P ha-1 year-1 over global croplands of four major crops, while during wet years, P losses are 3.6 ± 0.7 kg P ha-1 year-1. By the end of this century, P losses in wet years would reach 4.2 ± 1.0 (RCP2.6) and 4.7 ± 1.3 (RCP8.5) kg P ha-1 year-1 due to increases in high annual precipitation alone. The increases in P losses are the highest (up to 200%) in the arid regions of Middle East, Central Asia, and northern Africa. Consequently, in three quarters of the world's river basins, representing about 40% of total global runoff and home up to 7 billion people, P dilution capacity of freshwater could be exceeded due to P losses from croplands by the end of this century.

Numerical simulations of precipitation and streamflow in current climate and future projections to drainage areas of Brazilian hydroelectric plants

Hydroelectric sources are a major contributor to power generation in Brazil. The constant evaluation of climate change impacts is relevant for guiding Brazilian energy policy. This research presents a methodological framework composed of the calibration of a hydrological model and verification of a climate model in the ‘present’ climate (1961-1990), in addition to future scenarios (2011-2100) of precipitation and streamflow for 4 hydroelectric plants in Brazil. For future projections, data from the Eta regional climate model (20 km horizontal resolution) nested within the HadGEM2-ES and MIROC5 global climate models were used. Monthly linear bias correction was applied to the simulations. Future projections were based on IPCC RCP4.5 and 8.5 scenarios. The SMAP hydrological model was adopted on a monthly scale with the addition of a translation parameter that examines the level of dependence of the present streamflow on the previous month's streamflow. The climate and hydrological models satisfactorily capture the distribution of precipitation and streamflow in different Brazilian regions, and effectively represent seasonal variability. Future projections point to a reduction in rainfall and natural streamflow in central-northern Brazil and a slight increase in the southern region. These scenarios should be carefully considered and require constant improvement and research since there are uncertainties associated with atmospheric dynamics and the hydrological cycle.

Cropping system productivity and evapotranspiration in the semiarid Loess Plateau of China under future temperature and precipitation changes: An APSIM-based analysis of rotational vs. continuous systems

Agriculture in the semiarid region is undergoing radical changes driven by global warming and increasing incidences of extreme weather events. Predicting and evaluating the responses of crop yield and water use patterns of rainfed cropping systems according to future temperature and precipitation changes could provide important information regarding adoption of climate-smart farming systems that could offer great resilience and sustainability. The objective of this study was to evaluate the potential changes in agronomic productivity, hydrological balance and economic profitability of cropping systems with perennial legumes and rotation on the Loess Plateau of China affected by different future temperature and precipitation scenarios using APSIM-based modeling. Five different cropping systems were investigated: (i) continuous maize (Zea mays) (M), (ii) continuous winter wheat (Triticum aestivum) (W), (iii) continuous lucerne (Medicago sativa) (L), (iv) maize-wheat-soybean (Gyleine max) rotation (MWS) and (v) lucerne (4-yr)-winter wheat (2-yr) rotation (LW) in Xifeng, Gansu, China. Five series of temperature and precipitation changes scenarios (no changes in atmospheric CO2 concentrations were considered) based on RCPs (representative concentration pathways) were integrated into scenario simulations, including the baseline scenario (1980–2010), mid-century scenarios of RCP4.5 (M45) and RCP8.5 (M85), and end-century scenarios of RCP4.5 (E45) and RCP8.5 (E85). The results showed that, compared with baseline simulations, mean maize yield under RCPs decreased by 6.7 %–37.7 %, and the mean wheat yield decreased by 1.7 %–23.6 %. Lucerne yield consistently increased by 7.2 %–12.3 % under M45 and E45. Although different cropping systems affected the yield to a certain extent, only the difference between the wheat yields of W and MWS was significant (P < 0.05). On a cropping system level of every 6-yr (totally five phases for a scenario of 30-yr), the temperature and precipitation changes did not significantly affect plant transpiration (Tc) per phase. For all systems except L [which received its highest soil evaporation (Es) per phase in baseline], the greatest Es per phase was found in the E85. L tended to provide greater Tc per phase and evapotranspiration (ET) per phase (ranging from 1219 to 1332 mm and 2910–3034 mm, respectively), followed by LW. L and MWS presented the highest and lowest Es per phase (1636–1724 and 1592–1677 mm), respectively. The gross profit (GP per 6-yr phase) and water productivity (WP per 6-yr phase) of L were the greatest among all cropping systems (11.5–13.7 thousand US$ ha−1 per phase and 3.72–4.41 US$ ha-1 mm-1 per phase, respectively), followed by LW and MWS. For M, W and MWS, the GP per phase and the WP per phase were predicted to be greater under the baseline than under any of the RCPs, whereas M45 and E45 scenarios improved the GP per phase and WP per phase of L and LW compared with those of the baseline. In general, we advocate that LW-based systems have the greatest potential for producing acceptable yield and economic profit under future temperature and precipitation scenarios for this local environment.

Drought Event Analysis and Projection of Future Precipitation Scenario in Abaya Chamo Sub-Basin, Ethiopia

Monthly observed and future precipitation magnitudes were subjected to statistical trend analysis to examine possible time series behavior. Future precipitation was downscaled from large-scale output through statistical downscaling. The observed and downscaled future precipitation was analyzed for drought events using the Standardized Precipitation Index (SPI) method. In the Abaya Chamo sub-basin, Ethiopia precipitation is explained by below average magnitudes in most of the low land area, characterized by moderate to extreme drought episodes. Nine drought events were discerned during the period of 1988 to 2015, i.e. once in three years, resulting in harvest failure and subsequent food insecurity. The NCEP-NCAR and CanESM2 model predictors were used to statistically downscale the precipitation data. The monthly observed and downscaled precipitation magnitudes were in good agreement. The RCP-2.6, RCP-4.5 and RCP-8.5 long-term future scenarios were computed to evaluate future drought patterns. The mean annual precipitation scenario decreased by 0.2% to 13.7%, 0.5% to 6.4% and 0.1% to 1.3% for the period from 2016 to 2040, 2050s and 2080s respectively. The increase in mean precipitation was projected to be 0.7% to 12.2%, 0.2% to 11.7% and 0.1% to 17.8% for the period from 2016 to 2040, 2050s and 2080s respectively. The present analysis may provide useful information associated to drought events to decision makers and can be used as a basis for future research in this area.

Projection of future precipitation extremes across the Bangkok Metropolitan Region

There is a pressing need to develop local-scale climate projection profiles for supporting climate impact assessments. This study contributes plausible future precipitation scenarios for the Bangkok Metropolitan Region (BMR), which builds on the existing evidence base that projects increasing future precipitation. Meteorological data sets from 16 stations located within the BMR and nearby provinces were used for bias correcting five regional climate model scenarios, and future extreme indices were graphed and spatially interpolated to interpret how precipitation extremes may develop to the end of the 21st century. Results indicate that over the coming century, total annual rainfall will increase, with the volume and number of days with heavy/very heavy rainfall also increasing. Total monthly and monthly heavy/very heavy rainfall are projected to increase in the late monsoon, and monthly five-day cumulative and one-day maxima project higher amounts of late monsoonal rains. Spatial interpolation of selected indices indicate substantial projected increases in extreme rainfall across the BMR, with its northern part receiving the heaviest amounts of precipitation. In comparison to the past period (1980–2009), over the long-term (2070–2098) the total monthly heavy/very heavy precipitation during October is projected to increase by 100–120% over Pathum Thani province and 80–100% over the remainder of the BMR. Together with the study's associated R and Python scripts, this study aims to provide an open and reproducible approach to deriving plausible future projections of climate variables at the city scale.

Temperature and precipitation change in Malawi: Evaluation of CORDEX-Africa climate simulations for climate change impact assessments and adaptation planning

Malawi is highlighted as one of the most vulnerable countries in the world to the effects of climate change. The large uncertainty around future climate change in the region remains a barrier to adaptation planning. Despite this high potential vulnerability, relatively little research has gone into determining how well available models represent this country's climate. This work therefore evaluates the ability of existing General Circulation Models (GCMs) and Regional Climate Models (RCMs) to hindcast climatic variables in Malawi at a resolution appropriate for climate change impact assessment and adaptation planning. We focus on monthly precipitation rate, and mean, maximum and minimum surface air temperature. This assessment compares available observed datasets against the outputs of six ERA-interim driven RCMs and 21 GCM-driven RCMs from the Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative, and the 11 GCMs which form their boundary conditions. It was found that the performance of the RCMs is highly influenced by their boundary conditions. None of the individual or ensemble RCMs or GCMs assessed in this paper correlate well with the observed datasets for any of the assessed climatic variables. While, they do simulate the trending change in temperature variables well, the simulated outputs for precipitation are highly divergent. Based on these findings we suggest that either the ensemble RCMs or ensemble GCMs would be suitable for understanding projected temperature trends, with the RCMs providing better spatial resolution. However, none of the assessed models provide certainty over future precipitation trends in Malawi. As such we suggest that impact assessments and adaptation plans in Malawi will need to be designed and tested against a range of future precipitation scenarios. To improve modelling for Malawi it is recommended that regional climate models be improved for higher spatial resolution and inclusion of the impacts from large water bodies, including Lake Malawi.

Analyzing the future climate change of Upper Blue Nile River basin using statistical downscaling techniques

Abstract. Climate change is becoming one of the most threatening issues for the world today in terms of its global context and its response to environmental and socioeconomic drivers. However, large uncertainties between different general circulation models (GCMs) and coarse spatial resolutions make it difficult to use the outputs of GCMs directly, especially for sustainable water management at regional scale, which introduces the need for downscaling techniques using a multimodel approach. This study aims (i) to evaluate the comparative performance of two widely used statistical downscaling techniques, namely the Long Ashton Research Station Weather Generator (LARS-WG) and the Statistical Downscaling Model (SDSM), and (ii) to downscale future climate scenarios of precipitation, maximum temperature (Tmax) and minimum temperature (Tmin) of the Upper Blue Nile River basin at finer spatial and temporal scales to suit further hydrological impact studies. The calibration and validation result illustrates that both downscaling techniques (LARS-WG and SDSM) have shown comparable and good ability to simulate the current local climate variables. Further quantitative and qualitative comparative performance evaluation was done by equally weighted and varying weights of statistical indexes for precipitation only. The evaluation result showed that SDSM using the canESM2 CMIP5 GCM was able to reproduce more accurate long-term mean monthly precipitation but LARS-WG performed best in capturing the extreme events and distribution of daily precipitation in the whole data range. Six selected multimodel CMIP3 GCMs, namely HadCM3, GFDL-CM2.1, ECHAM5-OM, CCSM3, MRI-CGCM2.3.2 and CSIRO-MK3 GCMs, were used for downscaling climate scenarios by the LARS-WG model. The result from the ensemble mean of the six GCM showed an increasing trend for precipitation, Tmax and Tmin. The relative change in precipitation ranged from 1.0 to 14.4 % while the change for mean annual Tmax may increase from 0.4 to 4.3 ∘C and the change for mean annual Tmin may increase from 0.3 to 4.1 ∘C. The individual result of the HadCM3 GCM has a good agreement with the ensemble mean result. HadCM3 from CMIP3 using A2a and B2a scenarios and canESM2 from CMIP5 GCMs under RCP2.6, RCP4.5 and RCP8.5 scenarios were downscaled by SDSM. The result from the two GCMs under five different scenarios agrees with the increasing direction of three climate variables (precipitation, Tmax and Tmin). The relative change of the downscaled mean annual precipitation ranges from 2.1 to 43.8 % while the change for mean annual Tmax and Tmin may increase in the range from 0.4 to 2.9 ∘C and from 0.3 to 1.6 ∘C respectively.

Impacts of Climate Change on Rainfall Erosivity in the Huai Luang Watershed, Thailand

This study focuses on the impacts of climate change on rainfall erosivity in the Huai Luang watershed, Thailand. The multivariate climate models (IPCC AR5) consisting of CCSM4, CSIRO-MK3.6.0 and MRI-CGCM3 under RCP4.5 and RCP8.5 emission scenarios are analyzed. The Quantile mapping method is used as a downscaling technique to generate future precipitation scenarios which enable the estimation of future rainfall erosivity under possible changes in climatic conditions. The relationship between monthly precipitation and rainfall erosivity is used to estimate monthly rainfall erosivity under future climate scenarios. The assessment compared values of rainfall erosivity during 1982–2005 with future timescales (i.e., the 2030s, 2050s, 2070s and 2090s). The results indicate that the average of each General Circulation Model (GCM) combination shows a rise in the average annual rainfall erosivity for all four future time scales, as compared to the baseline of 8302 MJ mm ha−1 h−1 year−1, by 12% in 2030s, 24% in 2050s, 43% in 2070s and 41% in 2090s. The magnitude of change varies, depending on the GCMs (CCSM4, CSIRO-MK3.6.0, and MRI-CGCM3) and RCPs with the largest change being 82.6% (15,159 MJ mm ha−1 h−1 year−1) occurring under the MRI-CGCM3 RCP8.5 scenario in 2090s. A decrease in rainfall erosivity has been found, in comparison to the baseline by 2.3% (8114 MJ mm ha−1 h−1 year−1) for the CCSM4 RCP4.5 scenario in 2030s and 2.6% (8088 MJ mm ha−1 h−1 year−1) for the 2050s period. However, this could be considered uncertain for future rainfall erosivity estimation due to different GCMs. The results of this study are expected to help development planners and decision makers while planning and implementing suitable soil erosion and deposition control plans to adapt climate change in the Huai Luang watershed.

DRI-Grass: A New Experimental Platform for Addressing Grassland Ecosystem Responses to Future Precipitation Scenarios in South-East Australia.

Climate models predict shifts in the amount, frequency and seasonality of rainfall. Given close links between grassland productivity and rainfall, such changes are likely to have profound effects on the functioning of grassland ecosystems and modify species interactions. Here, we introduce a unique, new experimental platform – DRI-Grass (Drought and Root Herbivore Interactions in a Grassland) – that exposes a south-eastern Australian grassland to five rainfall regimes [Ambient (AMB), increased amount (IA, +50%), reduced amount (RA, -50%), reduced frequency (RF, single rainfall event every 21 days, with total amount unchanged) and summer drought (SD, 12–14 weeks without water, December–March)], and contrasting levels of root herbivory. Incorporation of a belowground herbivore (root-feeding scarabs) addition treatment allows novel investigation of ecological responses to the twin stresses of altered rainfall and root herbivory. We quantified effects of permanently installed rain shelters on microclimate by comparison with outside plots, identifying small shelter effects on air temperature (-0.19°C day, +0.26°C night), soil water content (SWC; -8%) and photosynthetically active radiation (PAR; -16%). Shelters were associated with modest increases in net primary productivity (NPP), particularly during the cool season. Rainfall treatments generated substantial differences in SWC, with the exception of IA; the latter is likely due to a combination of higher transpiration rates associated with greater plant biomass in IA and the low water-holding capacity of the well-drained, sandy soil. Growing season NPP was strongly reduced by SD, but did not respond to the other rainfall treatments. Addition of root herbivores did not affect plant biomass and there were no interactions between herbivory and rainfall treatments in the 1st year of study. Root herbivory did, however, induce foliar silicon-based defenses in Cynodon dactylon and Eragrostis curvula. Rapid recovery of NPP following resumption of watering in SD plots indicates high functional resilience at the site, and may reflect adaptation of the vegetation to historically high variability in rainfall, both within- and between years. DRI-Grass provides a unique platform for understanding how ecological interactions will be affected by changing rainfall regimes and, specifically, how belowground herbivory modifies grassland resistance and resilience to climate extremes.

GCMs Derived Projection of Precipitation and Analysis of Spatio-Temporal Variation over N-W Himalayan Region

The ensembles of two Global Climate Models (GCMs) namely, third generation Canadian Coupled Global Climate Model (CGCM3) and Hadley Center Coupled Model, version 3 (HadCM3) are used to project future precipitation in a part of North-Western (N-W) Himalayan region, India. Statistical downscaling method is used to downscale and generate future scenarios of precipitation at station scale from large scale climate variables obtained from GCMs. The observed historical precipitation data has been collected for three metrological stations, namely, Rampur, Sunni and Kasol falling in the basin for further analysis. The future trends and patterns in precipitation under scenarios A2 and A1B for CGCM3 model, and A2 and B2 for HadCM3 model are analyzed for these stations under three different time periods: 2020’s, 2050’s and 2080’s. An overall rise in mean annual precipitation under scenarios A2 and A1B for CGCM3 model have been noticed for future periods: 2020’s, 2050’s and 2080’s. Decrease, in precipitation has been found under A2 and B2 scenarios of HadCM3 model for 2050’s and slight increase for 2080’s periods. Based on the analysis of results, CGCM3 model has been found better for simulation of precipitation in comparison to HadCM3 model.Journal of Hydrology and Meteorology, Vol. 9(1) 2015, p.1-14

Northern Hemisphere hydroclimate variability over the past twelve centuries.

Accurate modelling and prediction of the local to continental-scale hydroclimate response to global warming is essential given the strong impact of hydroclimate on ecosystem functioning, crop yields, water resources, and economic security. However, uncertainty in hydroclimate projections remains large, in part due to the short length of instrumental measurements available with which to assess climate models. Here we present a spatial reconstruction of hydroclimate variability over the past twelve centuries across the Northern Hemisphere derived from a network of 196 at least millennium-long proxy records. We use this reconstruction to place recent hydrological changes and future precipitation scenarios in a long-term context of spatially resolved and temporally persistent hydroclimate patterns. We find a larger percentage of land area with relatively wetter conditions in the ninth to eleventh and the twentieth centuries, whereas drier conditions are more widespread between the twelfth and nineteenth centuries. Our reconstruction reveals that prominent seesaw patterns of alternating moisture regimes observed in instrumental data across the Mediterranean, western USA, and China have operated consistently over the past twelve centuries. Using an updated compilation of 128 temperature proxy records, we assess the relationship between the reconstructed centennial-scale Northern Hemisphere hydroclimate and temperature variability. Even though dry and wet conditions occurred over extensive areas under both warm and cold climate regimes, a statistically significant co-variability of hydroclimate and temperature is evident for particular regions. We compare the reconstructed hydroclimate anomalies with coupled atmosphere-ocean general circulation model simulations and find reasonable agreement during pre-industrial times. However, the intensification of the twentieth-century-mean hydroclimate anomalies in the simulations, as compared to previous centuries, is not supported by our new multi-proxy reconstruction. This finding suggests that much work remains before we can model hydroclimate variability accurately, and highlights the importance of using palaeoclimate data to place recent and predicted hydroclimate changes in a millennium-long context.