Future Rainfall Changes Research Articles

Sensitivity Analysis of Extreme Daily Rainfall Depth in Summer Season on Surface Air Temperature and Dew-Point Temperature

Looking at future data obtained from global climate models, it is expected that future extreme rainfall will increase in many parts of the world. The Clausius-Clapeyron equation provides a physical basis for understanding the sensitivity of rainfall in response to warming, but the relationship between rainfall and temperature is still uncertain. The purpose of this study is to analyze the sensitivity of extreme daily rainfall depth during the summer season (June–September) to climate change in Korea. The relationship between the observed extreme daily rainfall depth and the surface air temperature (SAT) and dew-point temperature (DPT), which were observed in the 60 sites of the Korea Meteorological Administration, were analyzed. The same analysis was also performed using future data provided in various climate models. In addition, the future trends of extreme rainfall, SAT, and DPT were analyzed using future data obtained from climate models, and the effects of increasing SAT and DPT on future extreme rainfall changes were investigated. Finally, it has been confirmed that using changes in SAT and DPT to look at changes in future extreme rainfall can give more consistent future projection results than using future rainfall data directly.

Climate change in the Congo Basin: processes related to wetting in the December\u2013February dry season

The Congo Basin is one of three key areas of tropical convection and contains the planet’s second largest rainforest. Understanding how global warming might change its climate is crucial, particularly during the dry seasons, when rainfall amounts currently bring the rainforest boundaries close to the threshold of viability. There is considerable uncertainty in projections of future rainfall change from the Coupled Model Intercomparison Project (CMIP5) under the high-emissions experiment (RCP8.5). Whilst there is a general trend towards wetting in most months, its magnitude varies considerably. In the December to February dry season, the projected change in seasonal rainfall varies from 2 to 160 mm across models. This study uses a regionally-focused process-based assessment to understand inter-model differences in rainfall projections, as a first step to assessing their plausibility. Models which produce the most wetting by the end of the century feature enhanced convection over the Congo Basin region, enhanced subsidence in the African subtropics, and decreased uplift over the Maritime Continent. In contrast, models with a small wetting response feature reduced convection over the Congo Basin. This indicates that wetting over the Congo Basin is related to a weakening of the Indian Ocean Walker circulation, reminiscent of a positive Indian Ocean Dipole state. Models with the highest magnitude wetting also feature greater low-to-mid-level moisture flux from the north and the east compared to models with less wetting. These results indicate that the future degree of wetting over the Congo Basin will be linked to changes in convection over the Maritime Continent.

Return period of extreme rainfall substantially decreases under 1.5 °C and 2.0 °C warming: a case study for Uttarakhand, India

In June 2013, Uttarakhand experienced a hydro-meteorological disaster due to a 4 d extreme precipitation event of return period more than 100 years, claiming thousands of lives and causing enormous damage to infrastructure. Using the weather@home climate modelling system and its Half a degree Additional warming, Prognosis and Projected Impacts simulations, this study investigates the change in the return period of similar events in a 1.5 °C and 2 °C warmer world, compared to current and pre-industrial levels. We find that the likelihood of such extreme precipitation events will significantly increase under both future scenarios. We also estimate the change in extreme river flow at the Ganges; finding a considerable increase in the risk of flood events. Our results also suggest that until now, anthropogenic aerosols may have effectively counterbalanced the otherwise increased meteorological flood risk due to greenhouse gas (GHG) induced warming. Disentangling the response due to GHGs and aerosols is required to analyses the changes in future rainfall in the South Asia monsoon region. More research with other climate models is also necessary to make sure these results are robust.

Projection of West African summer monsoon rainfall in dynamically downscaled CMIP5 models

West Africa faces an increasing challenge related to water resources in the coming decades; hence high-confidence climate projection is crucial for the region’s future. In this study, future changes in summer monsoon rainfall under the RCP8.5 scenario were investigated over West Africa for the period of 2074–2099 with respect to the present-day period of 1980–2005 using the Rossby Centre Regional Climate Model (RCA4), driven by six Coupled Model Intercomparison Project phase 5 (CMIP5) global circulation models (GCMs). Through evaluation of the present climate, the RCA4 simulations and their multi-model ensemble mean (EnsMean) reasonably reproduced the climatology of the summer rainfall and associated tropospheric circulation features. In a warmer climate, summer monsoon rainfall is projected to increase in most parts of West Africa, but a decrease is expected in the northwest, around Senegal. Changes in the monsoon circulation are opposite in the lower and upper troposphere, with the tipping point at 400 hPa. Specifically, a projected strengthening and northward shift is observed at the lower and mid-tropospheric level, while weakening is evident at the upper tropospheric level. A diagnosis based on a moisture budget equation reveals that the robust positive response of West African summer monsoon rainfall to global warming is largely explained by the enhancement of moisture convergence and surface evaporation.

Analysis of future changes in the trends and scaling coefficients for short-term rainfall in southwestern Slovakia

The study focuses on an analysis of the future changes in short-term rainfall in the southwestern region of Slovakia. The analysis was performed for three climatological stations, i.e. Bratislava-K...

Rainfall variation shifts habitat suitability for seedling establishment associated with tidal inundation in salt marshes

Growing concerns over the ecological consequences of global changes in rainfall regimes have motivated exploration into how variable rainfall influences the persistence of plant species. Understanding the influence of rainfall events on seedling establishment is critical for predicting the consequences of potential changes to the rainfall regime in coastal salt marshes. However, it is not clear whether seedling establishment of various species responses to rainfall are strongly regulated by tidal inundation. We conducted a series of rainfall manipulation experiments in salt marshes and controlled greenhouse experiments to test the effect of post-dry season rainfall on seedling establishment of plant species among different tidal elevations and during drought and large rainfall events. Our results showed that post-dry season rainfall created an opportunity for seedling establishment by strongly influencing the suitability of abiotic factors for species niches, but plant–soil–rainfall interactions were nonlinear across existing soil conditions, which were controlled by tidal inundation. Seedling establishment was highly dependent on post-dry season rainfall amount. The seedlings of species that were sensitive to saline soil or had low tolerance to soil moisture only established successfully during large rainfall events. Consequently, changes in the rainfall amount caused shifts in native species composition and opened invasion opportunities for new species. Our study highlighted that small changes in rainfall amount or distribution can trigger comparatively large and abrupt changes in different species depending on their ecological niches and local abiotic gradients. Within tidal marshes, exploration of rainfall–tide linkages advanced fundamental understanding of the effects of changing rainfall regimes and provided implications for coastal wetland management and restoration efforts to prepare for the future rainfall changes related to climate change.

Characterizing climate change risks by linking robust decision frameworks and uncertain probabilistic projections

There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning.

Contrasting effects of fog frequency on the radial growth of two tree species in a Mediterranean-temperate ecotone

The performance and persistence of rear-edge tree populations are relevant issues for conserving biodiversity because these stands harbor high intraspecific biodiversity and play a key role during periods of climate change. The occurrence of these populations is associated with the influence of heterogeneous topography, creating suitable refugia with regionally rare environmental conditions. Climate is changing at a global-scale, but little is known about the long-term impact on local climatic singularities and the associated taxa. We analyzed tree-ring growth chronologies of the two species (Fagus sylvatica and Quercus ilex) forming the evergreen-deciduous forest ecotone, constitutive of the rear-edge of F. sylvatica distribution. The study area is a coastal range with frequent fog immersion, which has been hypothesized to favor the persistence of F. sylvatica in Mediterranean peninsulas. We analyzed the long-term effect of fog on tree growth along a topographical gradient and the sensitivity of growth to rainfall and temperature. The annual number of foggy days has decreased by 62% over the last four decades, concomitant with increasing temperatures. Fog frequency was a relevant factor determining tree growth; fog during summer had positive effects on F. sylvatica growth mainly through a temperature buffering effect. The positive effect of fog on the growth of Q. ilex, however, was likely caused by a collinearity with rainfall. Q. ilex growth was less sensitive to climate than F. sylvatica, but growth of both species was enhanced by a positive early-summer water balance. Our results indicate that a decrease in fog frequency and an increase in temperature may generally benefit Q. ilex in this forest ecotone. Although future changes in rainfall and temperature matter most for the fate of rear-edge tree populations, local climatic singularities such as fog should also be considered. Those can have complementary effects that can swing the balance in ecotones and rear-edge tree populations such as those studied here.

Future Predictions of Rainfall and Temperature Using GCM and ANN for Arid Regions: A Case Study for the Qassim Region, Saudi Arabia

Future predictions of rainfall patterns in water-scarce regions are highly important for effective water resource management. Global circulation models (GCMs) are commonly used to make such predictions, but these models are highly complex and expensive. Furthermore, their results are associated with uncertainties and variations for different GCMs for various greenhouse gas emission scenarios. Data-driven models including artificial neural networks (ANNs) and adaptive neuro fuzzy inference systems (ANFISs) can be used to predict long-term future changes in rainfall and temperature, which is a challenging task and has limitations including the impact of greenhouse gas emission scenarios. Therefore, in this research, results from various GCMs and data-driven models were investigated to study the changes in temperature and rainfall of the Qassim region in Saudi Arabia. Thirty years of monthly climatic data were used for trend analysis using Mann–Kendall test and simulating the changes in temperature and rainfall using three GCMs (namely, HADCM3, INCM3, and MPEH5) for the A1B, A2, and B1 emissions scenarios as well as two data-driven models (ANN: feed-forward-multilayer, perceptron and ANFIS) without the impact of any emissions scenario. The results of the GCM were downscaled for the Qassim region using the Long Ashton Research Station’s Weather Generator 5.5. The coefficient of determination (R2) and Akaike’s information criterion (AIC) were used to compare the performance of the models. Results showed that the ANNs could outperform the ANFIS for predicting long-term future temperature and rainfall with acceptable accuracy. All nine GCM predictions (three models with three emissions scenarios) differed significantly from one another. Overall, the future predictions showed that the temperatures of the Qassim region will increase with a specified pattern from 2011 to 2099, whereas the changes in rainfall will differ over various spans of the future.

Differences in climate change impacts between weather patterns: possible effects on spatial heterogeneous changes in future extreme rainfall

The impacts of global warming/climate change on extreme rainfall events during the Baiu period in Japan and their dependency on weather patterns (WPs) were examined using the self-organizing map (SOM) method. To investigate the differences in climate change impacts on daily rainfall among the WPs, a SOM was applied to surface atmospheric circulation data from the Database for Policy Decision Making for Future Climate Change (d4PDF) to determine the dominant heavy rainfall WPs. The obtained SOM shows that different WPs are associated with regional variations in extreme rainfall events. Projected future changes in the occurrence of heavy rainfall displayed a non-uniform spatial distribution. To understand the spatial heterogeneous rainfall changes, the sensitivity of heavy rainfall WPs to climate forcing was evaluated. Results of the SOM analysis suggest that this regional variation in the future changes in extreme rainfall events could be attributed to sensitivity differences between WPs to the climate changes. These sensitivity differences can be attributed to the non-uniform spatial changes in the large-scale climatological background state in East Asia via modulations in the moist air intrusion into Japan.

Future changes in rainfall associated with ENSO, IOD and changes in the mean state over Eastern Africa

This study examines the projected changes in the characteristics of the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) in terms of mean state, intensity and frequency, and associated rainfall anomalies over eastern Africa. Two regional climate models driven by the same four global climate models (GCMs) and the corresponding GCM simulations are used to investigate projected changes in teleconnection patterns and East African rainfall. The period 1976–2005 is taken as the reference for present climate and the far-future climate (2070–2099) under Representative Concentration Pathway 8.5 (RCP8.5) is analyzed for projected change. Analyses of projections based on GCMs indicate an El Niño-like (positive IOD-like) warming pattern over the tropical Pacific (Indian) Ocean. However, large uncertainties remain in the projected future changes in ENSO/IOD frequency and intensity with some GCMs show increase of ENSO/IOD frequency and intensity, and others a decrease or no/small change. Projected changes in mean rainfall over eastern Africa based on the GCM and RCM data indicate a decrease in rainfall over most parts of the region during JJAS and MAM seasons, and an increase in rainfall over equatorial and southern part of the region during OND, with the greatest changes in equatorial region. During ENSO and IOD years, important changes in the strength of the teleconnections are found. During JJAS, when ENSO is an important driver of rainfall variability over the region, both GCM and RCM projections show an enhanced La Niña-related rainfall anomaly compared to the present period. Although the long rains (MAM) have little association with ENSO in the reference period, both GCMs and RCMs project stronger ENSO teleconnections in the future. On the other hand, during the short rains (OND), a dipole future change in rainfall teleconnection associated with ENSO and IOD is found, with a stronger ENSO/IOD related rainfall anomaly over the eastern part of the domain, but a weaker ENSO/IOD signal over the southern part of the region. This signal is consistent and robust in all global and regional model simulations. The projected increase in OND rainfall over the eastern horn of Africa might be linked with the mean changes in SST over Indian and Pacific Ocean basins and the associated Walker circulations.

Future climate change enhances rainfall seasonality in a regional model of western Maritime Continent

In this study, future changes in rainfall due to global climate change are investigated over the western Maritime Continent based on dynamically downscaled climate projections using the MIT Regional Climate Model (MRCM) with 12 km horizontal resolution. A total of nine 30-year regional climate projections driven by multi-GCMs projections (CCSM4, MPI-ESM-MR and ACCESS1.0) under multi-scenarios of greenhouse gases emissions (Historical: 1976–2005, RCP4.5 and RCP8.5: 2071–2100) from phase 5 of the Coupled Model Inter-comparison Project (CMIP5) are analyzed. Focusing on dynamically downscaled rainfall fields, the associated systematic biases originating from GCM and MRCM are removed based on observations using Parametric Quantile Mapping method in order to enhance the reliability of future projections. The MRCM simulations with bias correction capture the spatial patterns of seasonal rainfall as well as the frequency distribution of daily rainfall. Based on projected rainfall changes under both RCP4.5 and RCP8.5 scenarios, the ensemble of MRCM simulations project a significant decrease in rainfall over the western Maritime Continent during the inter-monsoon periods while the change in rainfall is not relevant during wet season. The main mechanism behind the simulated decrease in rainfall is rooted in asymmetries of the projected changes in seasonal dynamics of the meridional circulation along different latitudes. The sinking motion, which is marginally positioned in the reference simulation, is enhanced and expanded under global climate change, particularly in RCP8.5 scenario during boreal fall season. The projected enhancement of rainfall seasonality over the western Maritime Continent suggests increased risk of water stress for natural ecosystems as well as man-made water resources reservoirs.

Evaluation of the subtropical westerly jet and its effects on the projected summer rainfall over central Asia using multi‐CMIP5 models

ABSTRACTBased on historical and RCP8.5 experiments, along with 25 Coupled Model Intercomparison Project Phase 5 (CMIP5) models, we evaluate the skills of the CMIP5 models in simulating the subtropical westerly jet (SWJ) and its effect on the projected summer rainfall over central Asia (CA). The historical experiments show that 23 models can well simulate the changes in the position and strength of the SWJ. Further analysis finds that 21 models can capture the relationship between the SWJ position change and summer rainfall over the domain of 55–85°E and 35–50°N and 17 models can capture the relationship between the SWJ strength change and summer rainfall over the domain of 55–75°E and 45–55°N, where 15 models can capture the relationships between both SWJ strength and position change with summer rainfall. Based on the SWJ change among the 15 models, except for the HadGEM2‐CC model, the 500 hPa winds of the rest 14 models well match the summer rainfall over CA. So the 14 models are used to do next analysis. In the last 50 years of the 21st century, only the inmcm4 model presents the change in the SWJ strength, and the rest 13 models present the changes in the SWJ position. Among above 13 models, 10 models maintain the relationships between the SWJ position change and summer rainfall over the domain of 55–85°E and 35–50°N into the future. Under the background of global warming, the changes in the SWJ position are well related to the sea surface temperatures (SSTs) over the Indian Ocean in the future. The SST warming over the Indian Ocean results in the South Asian summer monsoon weakening, corresponding to an anomalous anticyclone over the low‐level troposphere of the monsoon regions; this situation results in an anomalous cyclone at the upper level of the troposphere of the northwestern flank of the monsoon heating regions due to Matsuno–Gill‐type responses, which favour the SWJ axis shifting further south. In addition, we also compare the current projected summer rainfall over CA from our selected models with other selected previous models using different methods. The ensemble results show that more summer rainfall occurring in most of Kazakhstan and Xinjiang Province of northwestern China is more likely in the future; in the rest of the countries of CA, there are uncertainties regarding future rainfall changes.

Inverse relationship between present-day tropical precipitation and its sensitivity to greenhouse warming

Future changes in rainfall have serious impacts on human adaptation to climate change, but quantification of these changes is subject to large uncertainties in climate model projections. To narrow these uncertainties, significant efforts have been made to understand the intermodel differences in future rainfall changes. Here, we show a strong inverse relationship between present-day precipitation and its future change to possibly calibrate future precipitation change by removing the present-day bias in climate models. The results of the models with less tropical (40° S–40° N) present-day precipitation are closely linked to the dryness over the equatorial central-eastern Pacific, and project weaker regional precipitation increase due to the anthropogenic greenhouse forcing 1–6 with stronger zonal Walker circulation. This induces Indo-western Pacific warming through Bjerknes feedback, which reduces relative humidity by the enhanced atmospheric boundary-layer mixing in the future projection. This increases the air–sea humidity difference to enhance tropical evaporation and the resultant precipitation. Our estimation of the sensitivity of the tropical precipitation per 1 K warming, after removing a common bias in the present-day simulation, is about 50% greater than the original future multi-model projection. A present-day bias identified in climate projections means that future tropical rainfall may be underestimated. This bias can be addressed by constraining projections with observations.

The influence of ENSO, PDO and PNA on secular rainfall variations in Hawai‘i

Over the last century, significant declines in rainfall across the state of Hawai‘i have been observed, and it is unknown whether these declines are due to natural variations in climate, or manifestations of human-induced climate change. Here, a statistical analysis of the observed rainfall variability was applied as first step towards better understanding causes for these long-term trends. Gridded seasonal rainfall from 1920 to 2012 is used to perform an empirical orthogonal function (EOF) analysis. The leading EOF components are correlated with three indices of natural climate variations (El Nino-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Pacific North American (PNA)), and multiple linear regression (MLR) is used to model the leading components with climate indices. PNA is the dominant mode of wet season (November–April) variability, while ENSO is most significant in the dry season (May–October). To assess whether there is an anthropogenic influence on rainfall, two methods are used: a linear trend term is included in the MLR, and pattern correlation coefficients (PCC) are calculated between recent rainfall trends and future changes in rainfall projected by downscaling methods. PCC results indicate that recent observed rainfall trends in the wet season are positively correlated with future expected changes in rainfall, while dry season PCC results do not show a clear pattern. The MLR results, however, show that the trend term adds significantly to model skill only in the dry season. Overall, MLR and PCC results give weak and inconclusive evidence for detection of anthropogenic signals in the observed rainfall trends.

Environmental drivers of vascular and non‐vascular epiphyte abundance in tropical pre‐montane cloud forests in Northern Peru

AbstractQuestionsWhat is the role of microclimate relative to easily obtainable measures of forest structure in explaining epiphyte abundance? Do these roles differ between epiphytic plant groups?LocationTropical pre‐montane cloud forests, Alto Mayo watershed, northern Peru.MethodsWe recorded vascular epiphyte abundance, epiphytic bryophyte cover and forest structural features in 36 plots (20 m × 20 m), and measured air temperature and humidity in a subset of 17 plots. We modelled bryophyte cover, total vascular epiphyte abundance and the abundances of the main vascular epiphyte groups separately (bromeliads, aroids, ferns), as a function of forest structure and microclimate using spatial autoregressive models. Three forest structural variables (basal area, tree height and canopy openness) and two microclimatic variables (minimum humidity and maximum temperature) were considered. We constructed all possible combinations of maximum two‐variable models from the five explanatory variables and carried out AIC‐based model selection and variable importance tests with these as input models.ResultsCanopy openness was the most important variable explaining the abundance of the main epiphytic plant groups. It was also strongly correlated with stand microclimate. Therefore, predictions of epiphyte abundance did not improve with the inclusion of microclimatic data in the models. There were some differences among the epiphytic plant groups in their response to microclimate and forest structural features.ConclusionsForest stand microclimate, reflected through canopy openness in particular, was a main determinant of the distributions of all epiphytic plant groups. This implies that easily measurable forest structural variables alone can be used as good predictors of epiphyte abundance. Taxon‐specific differences in responses to microclimate imply that these taxa may also differ in their sensitivity to predicted future changes in temperature and rainfall.

Future extreme rainfall change projections in the north of Iran

ABSTRACTFuture changes in extreme rainfall arising from climate change may have a significant influence on flood and water erosion control and management strategies to a great extent. The maximum daily rainfall time series were projected for 2020–2049 using six general climate models and two scenarios through artificial neural networks for 22 stations across the north of Iran. The results indicate a reduction of between −3.0 and −0.2% in maximum rainfall for the selected stations and five out of six of the general climate models. The changes in the frequency and magnitude of extreme rainfall were then investigated by fitting a generalized extreme value distribution to the historical (from 1981 to 2010) and projected maximum rainfall. The location parameter of the generalized extreme value distribution fitted to the projected maximum rainfall does not show a significant change while the scale and shape parameters exhibit significant changes compared to the historical period. Estimating the 2, 50 and 100 year return periods showed that the maximum rainfall will have a reduction in the probability of large amounts across the region compared with the base period while the number of extraordinary extreme events may show growth. As a region vulnerable to flash floods and water erosion due to rainfall characteristics and land use change from forest to agriculture, the results may send an alarm to define long term and effective strategies for future flood control management in the region.

Potential Impact of Climate Change on Residential Energy Consumption in Dhaka City

This study aimed to assess the impacts of climate change on residential energy consumption in Dhaka city of Bangladesh. The monthly electricity consumption data for the period 2011–2014 and long-term climate variables namely monthly rainfall and temperature records (1961–2010) were used in the study. An ensemble of six global circulation models (GCMs) of coupled model intercomparison project phase 5 (CMIP5) namely, BCCCSM1-1, CanESM2, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, and NorESM1-M under four representative concentration pathway (RCP) scenarios were used to project future changes in rainfall and temperature. The regression models describing the relationship between historical energy consumption and climate variables were developed to project future changes in energy consumptions. The results revealed that daily energy consumption in Dhaka city increases in the range of 6.46–11.97 and 2.37–6.25 MkWh at 95% level of confidence for every increase of temperature by 1 °C and daily average rainfall by 1 mm, respectively. This study concluded that daily total residential energy demand and peak demand in Dhaka city can increase up to 5.9–15.6 and 5.1–16.7%, respectively, by the end of this century under different climate change scenarios.

Rain-induced changes in soil CO2 flux and microbial community composition in a tropical forest of China

Rain-induced soil CO2 pulse, a rapid excitation in soil CO2 flux after rain, is ubiquitously observed in terrestrial ecosystems, yet the underlying mechanisms in tropical forests are still not clear. We conducted a rain simulation experiment to quantify rain-induced changes in soil CO2 flux and microbial community composition in a tropical forest. Soil CO2 flux rapidly increased by ~83% after rains, accompanied by increases in both bacterial (~51%) and fungal (~58%) Phospholipid Fatty Acids (PLFA) biomass. However, soil CO2 flux and microbial community in the plots without litters showed limited response to rains. Direct releases of CO2 from litter layer only accounted for ~19% increases in soil CO2 flux, suggesting that the leaching of dissolved organic carbon (DOC) from litter layer to the topsoil is the major cause of rain-induced soil CO2 pulse. In addition, rain-induced changes in soil CO2 flux and microbial PLFA biomass decreased with increasing rain sizes, but they were positively correlated with litter-leached DOC concentration rather than total DOC flux. Our findings reveal an important role of litter-leached DOC input in regulating rain-induced soil CO2 pulses and microbial community composition, and may have significant implications for CO2 losses from tropical forest soils under future rainfall changes.

Estimating impact of rainfall change on hydrological processes in Jianfengling rainforest watershed, China using BASINS-HSPF-CAT modeling system

Climate change over the past several decades has resulted in shifting rainfall pattern and modifying rainfall intensity, which has exacerbated hydrological processes and added the uncertainty and instability to these processes. This study ascertained impacts of potential future rainfall change on hydrological processes at the Jianfengling (JFL) tropical mountain rainforest watershed in Hainan Island, China using the BASINS (Better Assessment Science Integrating Point and Nonpoint Sources)-HSPF (Hydrological Simulation Program-FORTRAN)-CAT (Climate Assessment Tool) modeling system. The HSPF model was calibrated and validated with available measured data prior to its applications. Three simulation scenarios were then performed to gain a better understanding of the impacts of different rainfall rates and storm intensities on stream discharge, surface water runoff from forest land, and water outflow from the JFL watershed outlet. Results showed that a 10% increase in rainfall rate could result in 1.3 times increase in stream discharge, surface runoff, and water outflow. A potential future wet climate could have profound impacts on hydrological processes at the JFL watershed, whereas a potential future dry climate could result less impacts on stream discharge, surface runoff, and water outflow at the same watershed. Our simulation further revealed that climate change driven by extreme rain storms had greater impacts on annual surface runoff than on annual stream discharge. The coupled CAT-HSPF model is a useful tool to modify historical rainfall data for projecting future rainfall variation impacts on forest hydrological processes due to climate change. This approach would be able to extend to other regions around the world.