High Precipitation Variability Research Articles

Evaluation of WRF capability to detect dry and wet periods in Spain using drought indices

AbstractThe Weather Research and Forecasting (WRF) model has been used to show the benefits provided by downscaled fields to detect and analyze wet and dry periods over a region with high precipitation variability such as Spain. We have analyzed the spatiotemporal behavior of two widely used drought indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), computed at 3 and 12 month time scales, which provide important information in an agricultural and water‐resource context. These two indices were computed from WRF outputs and compared with those calculated from observational (monthly precipitation and temperature databases of Spain, MOPREDAS and MOTEDAS) and from the European Centre for Medium‐Range Weather Forecasts Interim Re‐Analysis (ERA‐Interim) data sets. This evaluation was made by using a regional scale and a multistep regionalization method and by comparison of individual grid points. In general, results indicate that the drought indices obtained by using WRF outputs provide a noticeable improvement regarding those computed by using ERA‐Interim, higher at longer time scales. Although results show no significant differences between drought indices analyzed, the improvement offered by WRF is greater for SPI than for SPEI. In terms of averaged duration, magnitude, and severity of drought, the benefits provided by WRF are not so evident, presenting better agreement with the observational data at 12 month time scale, being clearer for the intensity. These findings evidence the benefit of using WRF climate fields to monitor, analyze, and detect drought events, being a valuable source of knowledge for a suitable decision making, especially for water‐resource management.

The Advantage of Using International Multimodel Ensemble for Seasonal Precipitation Forecast over Israel

This study analyzes the results of monthly and seasonal precipitation forecasting from seven different global climate forecast models for major basins in Israel within October–April 1982–2010. The six National Multimodel Ensemble (NMME) models and the ECMWF seasonal model were used to calculate an International Multimodel Ensemble (IMME). The study presents the performance of both monthly and seasonal predictions of precipitation accumulated over three months, with respect to different lead times for the ensemble mean values, one per individual model. Additionally, we analyzed the performance of different combinations of models. We present verification of seasonal forecasting using real forecasts, focusing on a small domain characterized by complex terrain, high annual precipitation variability, and a sharp precipitation gradient from west to east as well as from south to north. The results in this study show that, in general, the monthly analysis does not provide very accurate results, even when using the IMME for one-month lead time. We found that the IMME outperformed any single model prediction. Our analysis indicates that the optimal combinations with the high correlation values contain at least three models. Moreover, prediction with larger number of models in the ensemble produces more robust predictions. The results obtained in this study highlight the advantages of using an ensemble of global models over single models for small domain.

Synthetic Scenarios from CMIP5 Model Simulations for Climate Change Impact Assessments in Managed Ecosystems and Water Resources: Case Study in South Asian Countries

Abstract . Increasing population, urbanization, and associated demand for food production compounded by climate change and variability have important implications for the managed ecosystems and water resources of a region. This is particularly true for south Asia, which supports one quarter of the global population, half of whom live below the poverty line. This region is largely dependent on monsoon precipitation for water. Given the limited resources of the developing countries in this region, the objective of our study was to empirically explore climate change in south Asia up to the year 2099 using monthly simulations from 35 global climate models (GCMs) participating in the fifth phase of the Climate Model Intercomparison Project (CMIP5) for two future emission scenarios (representative concentration pathways RCP4.5 and RCP8.5) and provide a wide range of potential climate change outcomes. This was carried out using a three-step procedure: calculating the mean annual, monsoon, and non-monsoon precipitation and temperatures; estimating the percent change from historical conditions; and developing scenario funnels and synthetic scenarios. This methodology was applied for the entire south Asia region; however, the percent change information generated at 1.5° grid scale can be used to generate scenarios at finer spatial scales. Our results showed a high variability in the future change in precipitation (-23% to 52%, maximum in the non-monsoon season) and temperature (0.8% to 2.1%) in the region. Temperatures in the region consistently increased, especially in the Himalayan region, which could have impacts including a faster retreat of glaciers and increased floods. It could also change rivers from perennial to seasonal, leading to significant challenges in water management. Increasing temperatures could further stress groundwater reservoirs, leading to withdrawal rates that become even more unsustainable. The high precipitation variability (with higher propensity for localized intense rainfall events) observed in the region can be a key factor for managed ecosystems and water management and could also lead to more incidence of severe urban flooding. The results could be used to assess both mitigation and adaptation alternatives to reduce vulnerabilities in managed ecosystems (agricultural and urban) and water resources.

Landscape heterogeneity enhances stability of wild bee abundance under highly varying temperature, but not under highly varying precipitation

The abundance of important providers of ecosystem services such as wild bees likely increases with landscape heterogeneity, but may also fluctuate across the flowering season following varying weather conditions. In the present study, we investigated the combined effect of landscape heterogeneity and intra-annual variability in temperature and precipitation on the spatial and temporal stability of wild bee abundance. We used bee monitoring data from six 4 km × 4 km sites in central Germany and 16 local communities per site. The data were collected six times per year from 2010 to 2013. Following a multimodel inference approach, we identified the importance of landscape heterogeneity, weather variability and their interaction to the stability of wild bee abundance. We found that the stability of wild bee abundance increased with landscape heterogeneity, but decreased with increasing intra-annual variability in both temperature and precipitation. However, our key finding was a buffering mechanism enabling high abundance stability in heterogeneous landscapes even under highly variable temperature conditions. Interestingly, the same mechanism did not apply for high variability in precipitation. Our findings suggest that increasing landscape heterogeneity is beneficial for protecting wild bees against the projected increase in temperature variability until the end of the twenty first century, although we cannot make inferences for extreme events such as heatwaves. Nevertheless, our results equally highlight that landscape heterogeneity should not be treated as a one-size-fits-all solution and the need remains for developing alternative strategies to mitigate the effect of increasing variability in precipitation.

Regional downscaling of Mediterranean droughts under past and future climatic conditions

The complexity of the Mediterranean climate with its high precipitation variability and its unequal seasonal distribution with a wet season from approximately October to April and a dry season in summer set general conditions for a high vulnerability of the Mediterranean area to droughts. In the last few decades the risk of drought episodes appears to be enhanced in the Mediterranean area due to temperature increases combined with precipitation decreases. This general change towards warmer and dryer conditions is expected to continue in the future. In the present study droughts are represented by the Standardized Precipitation Index (SPI), at 114 stations located across the Mediterranean area. The SPI is a normalized measure of drought severity relative to a specific location, obtained from rainfall totals aggregated over different time periods. This allows a comparison of different locations and the delineation of homogeneous regions with similar SPI variability. 13 regions have been identified. A downscaling approach using circulation types based on geopotential heights and relative humidity as predictors has been set up to downscale the SPI time series in the different regions. The downscaling approach has been validated using running 21years validation periods, in order to assess the skill of the method during different climatic conditions and to detect possible non-stationarities in the predictors-predictand relationships. Results show that the downscaling method provided satisfactory results, except for the most arid regions. Future projections, provided from a three member ensemble of the MPI-ESM-LR model under scenario RCP 8.5, indicate an increase in the drought severity and occurrence for the whole Mediterranean region for the period 2070–2100.

Using exogenous variables to improve precipitation predictions of ANNs in arid and hyper-arid climates

Because of scarcity and high variability of rainfall in arid areas, from one hand, reliable prediction of precipitation in such regions is considerably difficult. Furthermore, in some cases, shortage of observation data and several other limitations may intensify complexity of the forecasting. On the other hand, these regions highly suffer from low availability of water which necessitates development of an appropriate modeling approach to provide as precise as possible predictions of precipitation. Artificial neural networks (ANNs) are expected to be a powerful tool in capturing and analyzing high interannual variability of precipitation in arid climates and, subsequently, in proper prediction of precipitation fluctuations in the future. The end of this paper is to improve ANN predictions of precipitation in arid climates using better training of the network. To this end, two approaches were applied. In the first one, just the rainfall monthly data were considered as input. In the second approach, in addition to precipitation, several exogenous variables of precipitation are considered as input to predict precipitation. The chosen exogenous parameters are either effective on or relevant to the precipitation patterns. Then, several lag times, hidden layer sizes, and training algorithms for different running sums are used in order to produce best forecasts. It was shown that the performance of networks increases significantly by importing more external factors as inputs. The bigger time scales also exhibited better performances. In all the five time scales, smaller lag times (especially one month), bigger hidden layer sizes (especially between 31 and 40), and GDX training algorithm presented the best performance. The highest obtained performance was presented by the network with 10 inputs, 1 month lag, 36 hidden layers, and CGF training method in 18 months running sum with R 2 of 0.93.

Foraminiferal proxy response to ocean temperature variability and acidification over the last 150 years in the Santa Barbara Basin (California)

The last 150 years on the California margin have been marked by high variability in precipitation, temperature, sea surface temperature, and ocean chemistry, in response to both natural and anthropogenic changes. Deciphering climate history from ocean sediments is dependent on our understanding of the various influences on marine proxies. Sediment from Santa Barbara Basin (SBB), California (box core SPR0901-10BC) provides an opportunity to compare multiple marine proxies with instrumental records of climate of the past 150 years and extend them beyond the historical record. Here we use three planktonic foraminiferal proxies, δ18Ocalcite, Mg/Ca, and size-normalized shell weight, to reconstruct the surface temperature and δ18Owater in SBB California at near-annual resolution from AD 1850–2000. Results indicate that Globigerina bulloides Mg/Ca reflects the instrumental temperature trends over the last 150 years, with average Mg/Ca temperatures approximately 2 °C lower than instrumental values, due to the slightly deeper habitat preference of G. bulloides. Similarly, temperature is the dominant control on G. bulloides δ18Ocalcite, and both Mg/Ca and δ18Ocalcite reflect variability consistent with changes in the intensification of ENSO over the past 150 years.Shell weights of the planktic foraminifer G. bulloides are inversely correlated with historical sea surface temperatures over the last 150 years in SBB sediments. Greater shell weight associated with cooler surface ocean temperatures does not support the anticipated relationship between coastal upwelling of cool, low pH water and shell thinning due to CO32− undersaturation. However, in contrast to the previous 130 years, G. bulloides shells deposited on the sea floor since the late 1970s exhibit a distinctive smooth shell structure indicative of partial dissolution. We hypothesize that a mid-1970s shift in intermediate water flow from predominantly equatorward to anomalously poleward led to the influx of relatively corrosive bottom water into SBB with subsequent carbonate dissolution at the sediment–water interface.

Evaluation of Satellite Based Precipitations and Their Applicability for Rainfall Runoff Modelling in Narayani Basin of Nepal

High spatial variability of precipitation over Nepal demands dense network of rain-gauge stations. But to set-up a dense rain gauge network is almost impossible due to mountainous topography of Nepal. Also the dense rain gauge network will be very expensive and some time impossible for timely maintenance. Satellite precipitation products are an alternative way to collect precipitation data with high temporal and spatial resolution over Nepal. In this study, the satellite precipitation products TRMM and GSMaP were analyzed. Precipitation was compared with ground based gauge precipitation in the Narayani basin, while the applicability of these rainfall products for runoff simulation were tested using the LANDPINE model for Trishuli basin which is a sub-basin within Narayani catchment. The Nash-Sutcliffe efficiency calculated for TRMM and GSMaP from point to pixel comparison is negative for most of stations. Also the estimation bias for both the products is negative indicating under estimation of precipitation by satellite products, with least under estimation for the GSMaP precipitation product. After point to pixel comparison, satellite precipitation estimates were used for runoff simulation in the Trishuli catchment with and without bias correction for each product. Among the two products, TRMM shows good simulation result without any bias correction for calibration and validation period with scaling factor of 2.24 for precipitation which is higher than that for gauge precipitation. This suggests, it could be used for runoff simulation to the catchments where there is no precipitation station. But it is too early to conclude by just looking into one catchment. So extensive study need to be done to make such conclusion.Journal of Hydrology and Meteorology, Vol. 8(1) p.22-31

VALIDATION OF SATELLITE PRECIPITATION (TRMM 3B43) IN ECUADORIAN COASTAL PLAINS, ANDEAN HIGHLANDS AND AMAZONIAN RAINFOREST

Precipitation monitoring is of utmost importance for water resource management. However, in regions of complex terrain such as Ecuador, the high spatio-temporal precipitation variability and the scarcity of rain gauges, make difficult to obtain accurate estimations of precipitation. Remotely sensed estimated precipitation, such as the Multi-satellite Precipitation Analysis TRMM, can cope with this problem after a validation process, which must be representative in space and time. In this work we validate monthly estimates from TRMM 3B43 satellite precipitation (0.25° x 0.25° resolution), by using ground data from 14 rain gauges in Ecuador. The stations are located in the 3 most differentiated regions of the country: the Pacific coastal plains, the Andean highlands, and the Amazon rainforest. Time series, between 1998 – 2010, of imagery and rain gauges were compared using statistical error metrics such as bias, root mean square error, and Pearson correlation; and with detection indexes such as probability of detection, equitable threat score, false alarm rate and frequency bias index. The results showed that precipitation seasonality is well represented and TRMM 3B43 acceptably estimates the monthly precipitation in the three regions of the country. According to both, statistical error metrics and detection indexes, the coastal and Amazon regions are better estimated quantitatively than the Andean highlands. Additionally, it was found that there are better estimations for light precipitation rates. The present validation of TRMM 3B43 provides important results to support further studies on calibration and bias correction of precipitation in ungagged watershed basins.

VALIDATION OF SATELLITE PRECIPITATION (TRMM 3B43) IN ECUADORIAN COASTAL PLAINS, ANDEAN HIGHLANDS AND AMAZONIAN RAINFOREST

Abstract. Precipitation monitoring is of utmost importance for water resource management. However, in regions of complex terrain such as Ecuador, the high spatio-temporal precipitation variability and the scarcity of rain gauges, make difficult to obtain accurate estimations of precipitation. Remotely sensed estimated precipitation, such as the Multi-satellite Precipitation Analysis TRMM, can cope with this problem after a validation process, which must be representative in space and time. In this work we validate monthly estimates from TRMM 3B43 satellite precipitation (0.25° x 0.25° resolution), by using ground data from 14 rain gauges in Ecuador. The stations are located in the 3 most differentiated regions of the country: the Pacific coastal plains, the Andean highlands, and the Amazon rainforest. Time series, between 1998 – 2010, of imagery and rain gauges were compared using statistical error metrics such as bias, root mean square error, and Pearson correlation; and with detection indexes such as probability of detection, equitable threat score, false alarm rate and frequency bias index. The results showed that precipitation seasonality is well represented and TRMM 3B43 acceptably estimates the monthly precipitation in the three regions of the country. According to both, statistical error metrics and detection indexes, the coastal and Amazon regions are better estimated quantitatively than the Andean highlands. Additionally, it was found that there are better estimations for light precipitation rates. The present validation of TRMM 3B43 provides important results to support further studies on calibration and bias correction of precipitation in ungagged watershed basins.

Understanding Drought Dynamics during Dry Season in Eastern Northeast Brazil

Eastern Northeast Brazil (ENEB) generally experiences a high variability in precipitation in the dry season, with amplitudes that can overcome 500 mm. The understanding of this variability can help in mitigating the socio-economic issues related to the planning and management of water resources this region, which is highly vulnerable to drought. This work aims to assess spatio-temporal variability of precipitation during the dry season and investigate the relationships between climate phenomena and drought events in the ENEB, using univariate (Spearman correlation) and multivariate statistical techniques, such as Principal Component Analysis, Cluster Analysis and Maximum Covariance Analysis. The results indicate that the variability of precipitation in the dry season can be explained mainly (62%) by local physical conditions and climate conditions have a secondary contribution. Further analysis of the larger anomalous events suggests that the state of Atlantic and Pacific oceans can govern the occurrence of those events, and the conditions of Atlantic Ocean can be considered a potential modulator of anomalous phenomena of precipitation in ENEB.

Precipitation gradient and crop management affect N2O emissions: Simulation of mitigation strategies in rainfed Mediterranean conditions

In Mediterranean areas high precipitation variability and crop dependence on soil water availability make the interaction between climate and agricultural management a key issue for mitigating N2O emissions. In this study we used the STICS model to capture the effect of a water deficit gradient and precipitation variability on N2O emissions and mitigation strategies (i.e. N fertilizer type, grain legumes introduction in crop rotations and crop residues management) in a rainfed Mediterranean transect (HWD-Senés, MWD-Selvanera and LWD-Auzeville, i.e. high, medium and low water deficit, respectively). The model was first tested against a database of daily N2O fluxes measured during twelve growing seasons of winter crops at the LWD site. Several scenarios were then run on each site, always over 9 successive growing seasons to take into account precipitation variability. STICS showed a good ability to simulate the driving variables of N2O fluxes at the daily time scale. The mean observed and simulated cumulative emissions during the growing season were 0.71 and 0.82kgN2O–Nha−1, respectively. The simulated N2O emissions (mean of all scenarios) decreased with increasing water deficit being 2.51, 0.65 and 0.26kgN2O–Nha−1yr−1 for LWD-Auzeville, MWD-Selvanera and HWD-Senés, respectively, which is consistent with published results. The lower N2O emissions in the driest sites were not only related to lower fertilization rates but also to other factors associated with the Mediterranean characteristics, particularly, the drier water regime. Simulated N2O emissions were highly sensitive to the interannual variability of the climatic conditions. According to the simulations, urea fertilizer would lead to slightly higher N2O emissions (+6 and +8%) than ammonium- and calcium nitrate, respectively. The incorporation of winter pea in the traditional cereal-based Mediterranean rotations would reduce by ca. 22% the N2O emissions in HWD-Senés without changing wheat yields. Differently, in MWD-Selvanera and LWD-Auzeville, N2O emissions would remain unchanged since the emissions associated to the decomposition of low C:N ratio pea residues would counteract the lower application of N fertilizer. The systematic removal of crop residues at LWD-Auzeville would decrease the N2O emissions by 20%. However, this practice seems not recommendable if tillage is practiced due to the concomitant decrease of soil organic matter, fact that would worsen the C footprint of the system and increase the susceptibility to soil erosion. Our work highlights the interest of combining experimental and modelling approaches to account for climatic variability and evaluate long-term effects of N2O mitigation practices under Mediterranean conditions.

Woody Biomass Estimation in a Southwestern U.S. Juniper Savanna Using LiDAR-Derived Clumped Tree Segmentation and Existing Allometries

The rapid and accurate assessment of above ground biomass (AGB) of woody vegetation is a critical component of climate mitigation strategies, land management practices and process-based models of ecosystem function. This is especially true of semi-arid ecosystems, where the high variability in precipitation and disturbance regimes can have dramatic impacts on the global carbon budget by rapidly transitioning AGB between live and dead pools. Measuring regional AGB requires scaling ground-based measurements using remote sensing, an inherently challenging task in the sparsely-vegetated, spatially-heterogeneous landscapes characteristic of semi-arid regions. Here, we test the ability of canopy segmentation and statistic generation based on aerial LiDAR (light detection and ranging)-derived 3D point clouds to derive AGB in clumps of vegetation in a juniper savanna in central New Mexico. We show that single crown segmentation, often an error-prone and challenging task, is not required to produce accurate estimates of AGB. We leveraged the relationship between the volume of the segmented vegetation clumps and the equivalent stem diameter of the corresponding trees (R2 = 0.83, p < 0.001) to drive the allometry for J. monosperma on a per segment basis. Further, we showed that making use of the full 3D point cloud from LiDAR for the generation of canopy object statistics improved that relationship by including canopy segment point density as a covariate (R2 = 0.91). This work suggests the potential for LiDAR-derived estimates of AGB in spatially-heterogeneous and highly-clumped ecosystems.

Stalagmite based high resolution precipitation variability for past four centuries in the Indian Central Himalaya: Chulerasim cave re-visited and data re-interpretation

Although the variations in δ18O and δ13C and the U/Th dating in the speleothems are considered as key proxies, improved dating with better quality resolution as well as composition of stalagmites and growth rate along with the cave monitoring are equally important for understanding the high resolution precipitation variability in the past. With a total of six dates on a 11.5 cm long stalagmite, we re-interpret the decadal to century scale climatic changes with multi-year droughts from the Indian Central Himalaya between ca. 1622 and 1950 AD. The sample is composed of aragonite (both compact sub-layers and porous sub-layers). Although, the age model of this young speleothem may be within age uncertainty owing to the high 230Th/232Th isotope ratios, yet the distinction of this study lies in recording various historical drought events which are otherwise never reported from the Himalayan foothills. Additionally, the sample consists of reasonable amount of U (>2 ppm), thus the age correction requirement may be minimum. The higher growth rate and comparatively lower values of δ18O and δ13C are observed during the Little Ice Age (LIA) until ca. 1820 AD, indicating its being wet in the Himalayan foothills in contrast to the Peninsular India and other regions which are solely influenced by the Indian Summer Monsoon (ISM). This is mainly because the monsoon trough moves from the plains to the Himalayan foothills during break-monsoon conditions and provides more orographic precipitation in form of the Westerlies in the south facing Himalayan slopes. The post-LIA period from ca. 1820 AD onwards is interpreted as comparatively drier than the LIA.

Tree‐ring‐based precipitation reconstruction in southern Kazakhstan, reveals drought variability since A.D. 1770

ABSTRACTIn this article, we developed a tree‐ring‐width chronology of Schrenk spruce (Picea schrenkiana Fisch. et Mey) in southern Kazakhstan. Climate‐growth response result showed that the precipitation from the previous June to current May was the principal limited factor of radial growth. We also obtained a 246‐year reconstruction of June–May precipitation in southern Kazakhstan. The reconstruction explains 39.7% of the variance in precipitation records during the 1902–2013 calibration periods. In addition, the precipitation over the past 246 years in southern Kazakhstan has experienced six wetter periods and seven drier ones, and the extreme drought years were 1837, 1879, 1917 and 1945. From 1985 to 2004, precipitation has experienced longer and more rapid wetting, but the trend of precipitation has decreased in the past few years. The reconstructed series of precipitation is consistent with the historic precipitation/Palmer Drought Severity Index of the western Tianshan Mountains. Here, high precipitation variability occurred during 1770–1800 and 1900‐present, while 1800–1900 was relatively calm. Meanwhile, there was large power in the 2‐ to 7‐year short‐period and over the 34‐ to 35‐ and 40‐ to 43‐year long‐periods. We suggest the precipitation variability may be associated with large‐scale oscillations in the climate system. The reconstruction sheds new light on precipitation variability and the changes in a region where the climate history over the past several centuries is poorly understood.

Atmospheric Predictors for Annual Maximum Precipitation in North Africa

AbstractThe high precipitation variability over North Africa presents a major challenge for the population and the infrastructure in the region. The last decades have seen many flood events caused by extreme precipitation in this area. There is a strong need to identify the most relevant atmospheric predictors to model these extreme events. In the present work, the effect of 14 different predictors calculated from NCEP–NCAR reanalysis, with daily to seasonal time steps, on the maximum annual precipitation (MAP) is evaluated at six coastal stations located in North Africa (Larache, Tangier, Melilla, Algiers, Tunis, and Gabès). The generalized extreme value (GEV) B-spline model was used to detect this influence. This model considers all continuous dependence forms (linear, quadratic, etc.) between the covariates and the variable of interest, thus providing a very flexible framework to evaluate the covariate effects on the GEV model parameters. Results show that no single set of covariates is valid for all stations. Overall, a strong dependence between the NCEP–NCAR predictors and MAP is detected, particularly with predictors describing large-scale circulation (geopotential height) or moisture (humidity). This study can therefore provide insights for developing extreme precipitation downscaling models that are tailored for North African conditions.

Impacts of uncertainties in European gridded precipitation observations on regional climate analysis.

ABSTRACTGridded precipitation data sets are frequently used to evaluate climate models or to remove model output biases. Although precipitation data are error prone due to the high spatio‐temporal variability of precipitation and due to considerable measurement errors, relatively few attempts have been made to account for observational uncertainty in model evaluation or in bias correction studies. In this study, we compare three types of European daily data sets featuring two Pan‐European data sets and a set that combines eight very high‐resolution station‐based regional data sets. Furthermore, we investigate seven widely used, larger scale global data sets. Our results demonstrate that the differences between these data sets have the same magnitude as precipitation errors found in regional climate models. Therefore, including observational uncertainties is essential for climate studies, climate model evaluation, and statistical post‐processing. Following our results, we suggest the following guidelines for regional precipitation assessments. (1) Include multiple observational data sets from different sources (e.g. station, satellite, reanalysis based) to estimate observational uncertainties. (2) Use data sets with high station densities to minimize the effect of precipitation undersampling (may induce about 60% error in data sparse regions). The information content of a gridded data set is mainly related to its underlying station density and not to its grid spacing. (3) Consider undercatch errors of up to 80% in high latitudes and mountainous regions. (4) Analyses of small‐scale features and extremes are especially uncertain in gridded data sets. For higher confidence, use climate‐mean and larger scale statistics. In conclusion, neglecting observational uncertainties potentially misguides climate model development and can severely affect the results of climate change impact assessments.

Temporal Variability in Microclimatic Conditions for Grass Germination and Emergence in the Sagebrush Steppe

Sagebrush steppe ecosystems in the western United States are characterized by harsh environmental conditions with high annual and seasonal variability in both precipitation and temperature. Environmental variability contributes to widespread failure in establishing stands of desired species on degraded and invaded landscapes. To characterize seasonal microclimatic patterns and planting date effects on restoration outcomes, we evaluated long-term simulations of seed germination response of cheatgrass (Bromus tectorum L.), bottlebrush squirreltail (Elymus elymoides [Raf] Swezey), and Idaho fescue (Festuca idahoensis Elmer) to annual patterns of soil temperature and moisture. Extremely high annual variability in both the conditions favorable for germination and patterns of post-germination drought and thermal stress make it difficult to justify general inferences about seedbed treatment and planting date effects from individual, short-term field studies. We discuss the interpretation of individual-year and seasonal plant establishment factors and offer a mechanistic model for interpreting planting date and year effects on initial seedling establishment. Historical ranking and mechanistic descriptions of individual-year seedbed conditions may allow for expanded inferences through meta-analysis of limited-term field experiments.

Stalagmite Inferred High Resolution Climatic Changes through Pleistocene-Holocene Transition in Northwest Indian Himalaya

Investigated for d18O and d13C isotopes, mineralogy and growth rate, a 20 cm long and 230Th-dated calcite stalagmite from Kalakot (Jammu and Kashmir Himalaya), has recorded high resolution precipitation variability during the Pleistocene-Holocene transition. At present, the study area is influenced by both the Indian Summer Monsoon (ISM) and Westerlies. The StalAge model indicates that the stalagmite grew between 16.3 ka to 9.5 ka BP under the ideal isotopic equilibrium conditions as revealed by the Hendy test results. The d18O and d13C values range from -5.41 to -8.82% and -7.09 to -10.84% respectively. Although the U/Th chronology is poor due to low Uranium content in the samples resulting in relatively large errors, the first stalagmite inferred precipitation variability reconstructed from NW Indian Himalaya makes this study significant. The near footprints of three global events, e.g., Older Dryas (OD), Allerod period and Younger Drays (YD) can be noticed at ~14.3-13.9, 13.9-12.7 and 12.7-12.2 ka BP. The precipitation strength was weaker during the OD and YD, but was stronger during the Allerod interstadial. By the termination of YD interval, the climate seems fluctuating in the NW Himalaya. There seems variation in commencement, duration and termination of the above mentioned events in different parts of the globe due to latitude location and response time.

Impacts of precipitation variability on plant species and community water stress in a temperate deciduous forest in the central US

Variations in precipitation regimes can shift ecosystem structure and function by altering frequency, severity and timing of plant water stress. There is a need for predictively understanding impacts of precipitation regimes on plant water stress in relation to species water use strategies. Here we first formulated two complementary, physiologically-linked measures of precipitation variability (PV)—Precipitation Variability Index (PVI) and Average Recurrence Interval of Effective Precipitation (ARIEP). We then used nine-year continuous measurements of Predawn Leaf Water Potential Integral (PLWPI) in a central US forest to relate PVI and ARIEP to actual plant water availability and comparative water stress responses of six species with different capacities to regulate their internal water status. We found that PVI and ARIEP explained nearly all inter-annual variations in PLWPI for all species as well as for the community scaled from species measurements. The six species investigated showed differential sensitivities to variations in precipitation regimes. Their sensitivities were reflected more in the responses to PVI and ARIEP than to the mean precipitation rate. Further, they exhibited tradeoffs between responses to low and high PV. Finally, PVI and ARIEP were closely correlated with temporal integrals of positive temperature anomalies and vapor pressure deficit. We suggest that the comparative responses of plant species to PV are part of species-specific water use strategies in a plant community facing the uncertainty of fluctuating precipitation regimes. PVI and ARIEP should be adopted as key indices to quantify physiological drought and the ecological impacts of precipitation regimes in a changing climate.