This study focuses on two main broad areas of active research on climate: climate variability and climate change and their implications on regional precipitation characteristics. All the analysis is carried out for a climate change-sensitive region, the state of Florida, USA. The focus of the climate variability analysis is to evaluate the influence of individual and coupled phases (cool and warm) of Atlantic multidecadal oscillation (AMO) and El Niäno southern oscillation (ENSO) on regional precipitation characteristics. The two oscillations in cool and warm phases modulate each other which have implications on flood control and water supply in the region. Extreme precipitation indices, temporal distribution of rainfall within extreme storm events, dry and wet spell transitions and antecedent conditions preceding extremes are evaluated. Kernel density estimates using Gaussian kernel for distribution-free comparative analysis and bootstrap sampling-based confidence intervals are used to compare warm and cool phases of different lengths. Depth-duration-frequency (DDF) curves are also developed using generalized extreme value (GEV) distributions characterizing the extremes. ... This study also introduces new approaches to optimally select the predictor variables which help in modeling regional precipitation and further provides a mechanism to select an optimum spatial resolution to downscale the precipitation projections. New methods for correcting the biases in monthly downscaled precipitation projections are proposed, developed and evaluated in this study. The methods include bias corrections in an optimization framework using various objective functions, hybrid methods based on universal function approximation and new variants. / by Aneesh Goly. / Thesis (Ph.D.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

The thesis is concerned with development of efficient regional frequency analysis (RFA) approaches to estimate quantiles of hydrometeorological events. The estimates are necessary for various applications in water resources engineering. The classical approach to estimate quantiles involves fitting frequency distribution to at-site data. However, this approach cannot be used when data at target site are inadequate or unavailable to compute parameters of the frequency distribution. This impediment can be overcome through RFA, in which sites having similar attributes are identified to form a region, and information is pooled from all the sites in the region to estimate the quantiles at target site. The thesis proposes new approaches to RFA of precipitation, meteorological droughts and floods, and demonstrates their effectiveness. The approach proposed for RFA of precipitation overcomes shortcomings of conventional approaches with regard to delineation and validation of homogeneous precipitation regions, and estimation of precipitation quantiles in ungauged and data sparse areas. For the first time in literature, distinction is made between attributes/variables useful to form homogeneous rainfall regions and to validate the regions. Another important issue is that some of the attributes considered for regionalization vary dynamically with time. In conventional approaches, there is no provision to consider dynamic aspects of time varying attributes. This may lead to delineation of ineffective regions. To address this issue, a dynamic fuzzy clustering model (DFCM) is developed. The results obtained from application to Indian summer monsoon and annual rainfall indicated that RFA based on DFCM is more effective than that based on hard and fuzzy clustering models in arriving at rainfall quantile estimates. Errors in quantile estimates for the hard, fuzzy and dynamic fuzzy models based on the proposed approach are shown to be significantly less than those computed for Indian summer monsoon rainfall regions delineated in three previous studies. Overall, RFA based on DFCM and large scale atmospheric variables appeared promising. The performance of DFCM is followed by that of fuzzy and hard clustering models. Next, a new approach is proposed for RFA of meteorological droughts. It is suggested that homogeneous precipitation regions have to be delineated before proceeding to develop drought severity - areal extent - frequency (SAF) curves. Drought SAF curves are constructed at annual and summer monsoon time scales for each of the homogeneous rainfall regions that are newly delineated in India based on the proposed approach. They find use in assessing spatial characteristics and frequency of meteorological droughts. It overcomes shortcomings associated with classical approaches that construct SAF curves for political (e.g., state, country) and physiographic regions (e.g., river basin), based on spatial patterns of at-site values of drought indices in the study area, without testing homogeneity in rainfall. Advantage of the new approach can be noted especially in areas that have significant variations in temporal and spatial distribution of precipitation (possibly due to variations in topography, landscape and climate). The DFCM is extended to RFA of floods, and its effectiveness in prediction of flood quantiles is demonstrated by application to Godavari basin in India, considering precipitation as time varying attribute. Six new homogeneous regions are formed in Godavari basin and errors in quantile estimates based on those regions are shown to be significantly less than those computed based on sub-zones delineated in Godavari basin by Central Water Commission in a previous study.

The use of cross-correlation analysis on spring discharge and precipitation data in karst aquifer basins has been used for many years to develop a conceptual understanding of an aquifer and estimate aquifer properties. However, to this point, the application of these processes has relied on gaged precipitation at discrete locations. The use of spatially varying precipitation data and cross-correlation analysis provides a means of spatially characterizing recharge locations on a karst aquifer. NEXRAD provides a spatial estimate of precipitation based by combining reflectivity measurements from radar stations and traditional precipitation gages. This study combines NEXRAD precipitation data with spring discharge data to develop maps of contributing areas for two karst springs in Central Texas. By calculating the cross-correlation of each NEXRAD measurement to spring flow data for the same period of time a map showing the locations hydraulically connected to the spring can be developed. Both numerical experiments and field applications were conducted as part of the study. The numerical experiments conducted by Padilla and Pulido-Bosch are revisited using the numerical groundwater model MODFLOW. This allowed the introduction of spatially varying parameters into the model. The results show that spatially varying parameters can be inferred based on the results cross-correlation of spatially varying precipitation with respect to a single spring discharge location. Also, contributing area maps are prepared for both Barton Springs and Jacob’s Well. Barton Springs has a precise estimate of the recharge area. The current map of the recharge area and the NEXRAD derived map show good agreement with the cross-correlation results. Conversely, Jacob’s Well has not been sufficiently studied to delineate a contributing area map. This study provides an preliminary estimate of the area contributing to flow at Jacob’s Well. Finally, the development of these maps can also be applied to the construction of regional groundwater models. An application of this methodology with the groundwater availability model for the Barton Springs portion of the Edward’s aquifer is introduced. The application of spatial cross-correlation analysis to constrain recharge in the model showed a reduction in the objective function with respect to discharge at Barton Springs of 15%. / text

The West Pacific Warm Pool (WPWP) plays an important role in the global heat budget and global hydrologic cycle, so knowledge about its past variability would improve our understanding of global climate. Variations in WPWP precipitation are most notable during El Niño-Southern Oscillation events, when climate changes in the tropical Pacific impact rainfall not only in the WPWP, but around the globe. The stalagmite records presented in this dissertation provide centennial-to-millennial-scale constraints of WPWP precipitation during three distinct climatic periods: the Last Glacial Maximum (LGM), the last deglaciation, and the Holocene. In Chapter 2, the methodologies associated with the generation of U/Th-based absolute ages for the stalagmites are presented. In the final age models for the stalagmites, dates younger than 11,000 years have absolute errors of ±400 years or less, and dates older than 11,000 years have a relative error of ±2%. Stalagmite-specific 230Th/232Th ratios, calculated using isochrons, are used to correct for the presence of unsupported 230Th in a stalagmite at the time of formation. Hiatuses in the record are identified using a combination of optical properties, high 232Th concentrations, and extrapolation from adjacent U/Th dates. In Chapter 3, stalagmite oxygen isotopic composition (d18O) records from N. Borneo are presented which reveal millennial-scale rainfall changes that occurred in response to changes in global climate boundary conditions, radiative forcing, and abrupt climate changes. The stalagmite d18O records detect little change in inferred precipitation between the LGM and the present, although significant uncertainties are associated with the impact of the Sunda Shelf on rainfall d18O during the LGM. A millennial-scale drying in N. Borneo, inferred from an increase in stalagmite d18O, peaks at ~16.5ka coeval with timing of Heinrich event 1, possibly related to a southward movement of the Intertropical Convergence Zone (ITCZ). An inferred precipitation maximum (stalagmite d18O minimum) during the mid-Holocene in N. Borneo supports La Niña-like conditions and/or a southward migration of the ITCZ over the course of the Holocene as likely mechanisms for the observed millennial-scale trends. In Chapter 4, stalagmite Mg/Ca, Sr/Ca, and d13C records reflect hydrologic changes in the overlying karst system that are linked to a combination of rainfall variability and cave micro-environmental effects. Dripwater and stalagmite geochemistry suggest that prior calcite precipitation is a mechanism which alters dripwater geochemistry in slow, stalagmite-forming drips in N. Borneo. Stalagmite Mg/Ca ratios and d13C records suggest that the LGM climate in N. Borneo was drier and that ecosystem carbon cycling may have responded to the drier conditions. Large amplitude decadal- to centennial-scale variability in stalagmite Mg/Ca, Sr/Ca and d13C during the deglaciation may be linked to deglacial abrupt climate change events.

PhDRDV / Institute for Rural Development / Currently, there is limited understanding, appreciation and dissemination of indigenous raining making practices. Yet indigenous rain making is part of the rich African heritage. The current study was premised on the view that indigenous rain making practices can help combat the negative effects of climate change if properly integrated with western science. A mixture of exploratory and survey designs was adopted in this study, which sought to examine the common indigenous rainmaking practices in Chimanimani District of Zimbabwe prior to developing strategies for reducing the negative impacts of climate change on the livelihoods of rural households. Various studies with the following specific objectives were carried out: to analyze the general community perceptions on the potential of indigenous rain making practices in combating the negative effects of climate change; to examine the components of indigenous rainmaking practices; analyse the means of disseminating knowledge on indigenous rainmaking; to identify the negative effects of climate change on the livelihoods of rural households; to assess the effectiveness of existing strategies used by households to cope with the negative effects of climate change; and to propose strategies for utilizing indigenous rainmaking practices to counter the negative effects of climate change on the livelihoods of rural households. Semi-structured interview guides and a questionnaire requiring responses on a Likert-type scale were used to collect data. Key informants and ordinary community members were selected using judgmental, convenient and snowballing sampling techniques. The Thematic Content Analysis technique was used to draw meaning out of the qualitative data. Chi-Square tests for Goodness of Fit were conducted using the Statistical Package for Social Sciences (SPSS) to establish if there were significant relationships among perceptions. It was indicated that the shift in seasons as exemplified by the Nyamavhuvhu wind which now swept Chimanimani in September or October instead of end of July to August was evidence of climate change. Responses with respect to the negative effects of climate change included food insecurity, and drying up of streams and rivers. Availability of water for domestic, agricultural and animal use was becoming increasingly unreliable. The respondents argued that they believed in the effectiveness of indigenous rain making if it is conducted following local customs and traditions. Significant differences in the following perceptions were observed for “Besides makoto and Christian prayers there are other common rainmaking practices practiced in Chimanimani District” (p < 0.05). Similar results were observed with regard to “I believe indigenous and western knowledge of rainmaking can complement each other” (P < 0.001), and “There is increase in pests and plant diseases than before” (P < 0.01). Components of indigenous rain making v identified in the current study included rain making ceremonies (makoto), which entailed use of beer, sacrificial bird (normally a cock) and natural resources conservation such as keeping places for local rain making rituals sacred (zvitenguro), not destroying very big trees for example fig tree (muonde: Ficus capensis), mukute (Syzygium cordatum) and others, and treating forests as sacred. With respect to the negative effects of climate change, a highly significant difference was observed for duration of stay in relation to, “There is now a high risk in planting winter wheat due to changes in climate” (P < 0.01); “Wetlands are disappearing in our area” (P < 0.01); “There is general reduction in yields due to climate change” (P < 0.001) and “We are experiencing scarcity of water for domestic animals and for household use” (P < 0.05). Lastly, highly significant relationships between “Rivers are drying up in our area” and education (P < 0.01) and duration of stay (P < 0.001). Methods used to disseminate indigenous knowledge of rain making were said to be ineffective. Information was being passed on through oral means. It was indicated that better use of modern technology and social media, in particular radio, television, Twitter, WhatsApp and Facebook might enhance people’s knowledge on indigenous rain making. By so doing, the perception that indigenous rain making was merely history and not knowledge that can be used in people’s daily lives would be eliminated. Furthermore, current strategies utilized to combat the negative effects of climate change were reported to be unsustainable. Among these were reliance on harvesting wild fruits for sale and hunting. Human activities such as veld fires, deforestation and over harvesting of wildlife were viewed in negative light with respect to combating negative effects of climate change. It was proposed that communities should revert to respecting traditional beliefs of conserving forests. This said to be key in normalizing climate, attracting back the birds and animals that used to be key in weather forecasting. Replanting and indiscriminate cutting of trees along rivers as effective prevention of stream bank cultivation were proposed. Re-introduction of heavy fines by traditional leadership was suggested as a tried and tested strategy that was no longer being applied when implementing conservation initiatives. The observation made in this study that western science and indigenous rain making practices were similar in many respects, suggested that these were opportunities that could be used to anchor strategies for integrating them. In addition to this, the need for establishing collective deliberation or interface platforms coupled with continuous communication and careful management of intellectual property was obvious.

Hydro-climate extreme analysis helps understanding the process of spatio-temporal variation of extreme events due to climate change, and it is an important aspect in designing hydrological structures, forecasting floods and an effective decision making in the field of water resources design and management. The study evaluates extreme precipitation events over the Columbia River Basin (CRB), the fourth largest basin in the U.S., by simulating four CMIP5 global climate models (GCMs) for the historical period (1970-1999) and future period (2041-2070) under RCP85 GHG scenario. We estimated the intensity of extreme and average precipitation for both winter (DJF) and summer (JJA) seasons by using the GEV distribution and multi-model ensemble average over the domain of the Columbia River Basin. The four CMIP5 models performed very well at simulating precipitation extremes in the winter season. The CMIP5 climate models showed heterogeneous spatial pattern of summer extreme precipitation over the CRB for the future period. It was noticed that multi-model ensemble mean outperformed compared to the individual performance of climate models for both seasons. We have found that the multi-model ensemble shows a consistent and significant increase in the extreme precipitation events in the west of the Cascades Range, Coastal Ranges of Oregon and Washington State, the Canadian portion of the basin and over the Rocky Mountains. However, the mean precipitation is projected to decrease in both winter and summer seasons in the future period. The Columbia River is dominated by the glacial snowmelt, so the increase in the intensity of extreme precipitation and decrease in mean precipitation in the future period, as simulated by four CMIP5 models, is expected to aggravate the earlier snowmelt and contribute to the flooding in the low lying areas especially in the west of the Cascades Range. In addition, the climate change shift could have serious implications on transboundary water issues in between the United States and Canada. Therefore, adaptation strategies should be devised to cope the possible adverse effects of the changing the future climate so that it could have minimal influence on hydrology, agriculture, aquatic species, hydro-power generation, human health and other water related infrastructure.

Error characteristics associated with satellite-derived precipitation products are important for atmospheric and hydrological model data assimilation, forecasting, and climate diagnostic applications. This information also aids in the refinement of physical assumptions within algorithms by identifying geographical regions and seasons where existing algorithm physics may be incorrect or incomplete. Examination of relative errors between independent estimates derived from satellite microwave data is particularly important over regions with limited surface-based equipments for measuring rain rate such as the global oceans and tropical continents. In this context, analysis of microwave based satellite datasets from the Tropical Rainfall Measuring Mission (TRMM) enables to not only provide information regarding the inherent uncertainty within the current TRMM products, but also serves as an opportunity to prototype error characterization methodologies for the TRMM follow-on program, the Global Precipitation Measurement (GPM) . Most of the TRMM uncertainty evaluation studies focus on the accuracy of rainfall accumulated over time (e.g., season/year). Evaluation of instantaneous rainfall intensities from TRMM orbital data products is relatively rare. These instantaneous products are known to potentially cause large uncertainties during real time flood forecasting studies at the watershed scale. This is more so over land regions, where the highly varying land surface emissivity offers a myriad of complications, hindering accurate rainfall estimation. The error components of orbital data products also tend to interact nonlinearly with hydrologic modeling uncertainty. Keeping these in mind, the present thesis fosters the development of uncertainty analysis using instantaneous satellite orbital data products (latest version 7 of 1B11, 2A25, 2A23, 2B31, 2A12) derived from the passive and active microwave sensors onboard TRMM satellite, namely TRMM Microwave Imager (TMI) and precipitation radar (PR). The study utilizes 11 years of orbital data from 2002 to 2012 over the Indian subcontinent and examines the influence of various error sources on the convective and stratiform precipitation types. Two approaches are taken up to examine uncertainty. While the first approach analyses independent contribution of error from these orbital data products, the second approach examines their combined effect. Based on the first approach, analysis conducted over the land regions of Mahanadi basin, India investigates three sources of uncertainty in detail. These include 1) errors due to improper delineation of rainfall signature within microwave footprint (rain/no rain classification), 2) uncertainty offered by the transfer function linking rainfall with TMI low frequency channels and 3) sampling errors owing to the narrow swath and infrequent visits of TRMM sensors. The second approach is hinged on evaluating the performance of rainfall estimates from each of these orbital data products by accumulating them within a spatial domain and using error decomposition methodologies. Microwave radiometers have taken unprecedented satellite images of earth’s weather, proving to be a valuable tool for quantitative estimation of precipitation from space. However, as mentioned earlier, with the widespread acceptance of microwave based precipitation products, it has also been recognized that they contain large uncertainties. One such source of uncertainty is contributed by improper detection of rainfall signature within radiometer footprints. To date, the most-advanced passive microwave retrieval algorithms make use of databases constructed by cloud or numerical weather model simulations that associate calculated microwave brightness temperature to physically plausible sample rain events. Delineation of rainfall signature from microwave footprints, also known as rain/norain classification (RNC) is an essential step without which the succeeding retrieval technique (using the database) gets corrupted easily. Although tremendous advances have been made to catapult RNC algorithms from simple empirical relations formulated for computational expedience to elaborate computer intensive schemes which effectively discriminate rainfall, a number of challenges remain to be addressed. Most of the algorithms that are globally developed for land, ocean and coastal regions may not perform well for regional catchments of small areal extent. Motivated by this fact, the present work develops a regional rainfall detection algorithm based on scattering index methodology for the land regions of study area. Performance evaluation of this algorithm, developed using low frequency channels (of 19 GHz, 22 GHz), are statistically tested for individual case study events during 2011 and 2012 Indian summer monsoonal months. Contingency table statistics and performance diagram show superior performance of the algorithm for land regions of the study region with accurate rain detection observed in 95% of the case studies. However, an important limitation of this approach is comparatively poor detection of low intensity stratiform rainfall. The second source of uncertainty which is addressed by the present thesis, involves prediction of overland rainfall using TMI low frequency channels. Land, being a radiometrically warm and highly variable background, offers a myriad of complications for overland rain retrieval using microwave radiometer (like TMI). Hence, land rainfall algorithms of TRMM TMI have traditionally incorporated empirical relations of microwave brightness temperature (Tb) with rain rate, rather than relying on physically based radiative transfer modeling of rainfall (as implemented in TMI ocean algorithm). In the present study, sensitivity analysis is conducted using spearman rank correlation coefficient as the indicator, to estimate the best combination of TMI low frequency channels that are highly sensitive to near surface rainfall rate (NSR) from PR. Results indicate that, the TMI channel combinations not only contain information about rainfall wherein liquid water drops are the dominant hydrometeors, but also aids in surface noise reduction over a predominantly vegetative land surface background. Further, the variations of rainfall signature in these channel combinations were seldom assessed properly due to their inherent uncertainties and highly non linear relationship with rainfall. Copula theory is a powerful tool to characterize dependency between complex hydrological variables as well as aid in uncertainty modeling by ensemble generation. Hence, this work proposes a regional model using Archimedean copulas, to study dependency of TMI channel combinations with respect to precipitation, over the land regions of Mahanadi basin, India, using version 7 orbital data from TMI and PR. Studies conducted for different rainfall regimes over the study area show suitability of Clayton and Gumbel copula for modeling convective and stratiform rainfall types for majority of the intraseasonal months. Further, large ensembles of TMI Tb (from the highly sensitive TMI channel combination) were generated conditional on various quantiles (25th, 50th, 75th, 95th) of both convective and stratiform rainfall types. Comparatively greater ambiguity was observed in modeling extreme values of convective rain type. Finally, the efficiency of the proposed model was tested by comparing the results with traditionally employed linear and quadratic models. Results reveal superior performance of the proposed copula based technique. Another persistent source of uncertainty inherent in low earth orbiting satellites like TRMM arise due to sampling errors of non negligible proportions owing to the narrow swath of satellite sensors coupled with a lack of continuous coverage due to infrequent satellite visits. This study investigates sampling uncertainty of seasonal rainfall estimates from PR, based on 11 years of PR 2A25 data product over the Indian subcontinent. A statistical bootstrap technique is employed to estimate the relative sampling errors using the PR data themselves. Results verify power law scaling characteristics of relative sampling errors with respect to space time scale of measurement. Sampling uncertainty estimates for mean seasonal rainfall was found to exhibit seasonal variations. To give a practical demonstration of the implications of bootstrap technique, PR relative sampling errors over the sub tropical river basin of Mahanadi, India were examined. Results revealed that bootstrap technique incurred relative sampling errors of <30% (for 20 grid), <35% (for 10 grid), <40% (for 0.50 grid) and <50% (for 0.250 grid). With respect to rainfall type, overall sampling uncertainty was found to be dominated by sampling uncertainty due to stratiform rainfall over the basin. In order to study the effect of sampling type on relative sampling uncertainty, the study compares the resulting error estimates with those obtained from latin hypercube sampling. Based on this study, it may be concluded that bootstrap approach can be successfully used for ascertaining relative sampling errors offered by TRMM-like satellites over gauged or ungauged basins lacking in in-situ validation data. One of the important goals of TRMM Ground Validation Program has been to estimate the random and systematic uncertainty associated with TRMM rainfall estimates. Disentangling uncertainty in seasonal rainfall offered by independent observations of TMI and PR enables to identify errors and inconsistencies in the measurements by these instruments. Motivated by this thought, the present work examines the spatial error structure of daily precipitation derived from the version 7 TRMM instantaneous orbital data products through comparison with the APHRODITE data over a subtropical region namely Mahanadi river basin of the Indian subcontinent for the seasonal rainfall of 6 years from June 2002 to September 2007. The instantaneous products examined include TMI and PR data products of 2A12, 2A25 and 2B31 (combined data from PR and TMI). The spatial distribution of uncertainty from these data products was quantified based on the performance metrics derived from the contingency table. For the seasonal daily precipitation over 10x10 grids, the data product of 2A12 showed greater skill in detecting and quantifying the volume of rainfall when compared with 2A25 and 2B31 data products. Error characterization using various error models revealed that random errors from multiplicative error models were homoscedastic and that they better represented rainfall estimates from 2A12 algorithm. Error decomposition technique, performed to disentangle systematic and random errors, testified that the multiplicative error model representing rainfall from 2A12 algorithm, successfully estimated a greater percentage of systematic error than 2A25 or 2B31 algorithms. Results indicate that even though the radiometer derived 2A12 is known to suffer from many sources of uncertainties, spatial and temporal analysis over the case study region testifies that the 2A12 rainfall estimates are in a very good agreement with the reference estimates for the data period considered. These findings clearly document that proper characterization of error structure offered by TMI and PR has wider implications in decision making, prior to incorporating the resulting orbital products for basin scale hydrologic modeling. The current missions of GPM envision a constellation of microwave sensors that can provide instantaneous products with a relatively negligible sampling error at daily or higher time scales. This study due to its simplicity and physical approach offers the ideal basis for future improvements in uncertainty modeling in precipitation.

Indiana University-Purdue University Indianapolis (IUPUI) / Heat is one of the most important factors in our atmosphere for precipitation (thunderstorm) formation. Thermal energy from local urban land-cover is also a common source of heat in the lower atmosphere. This phenomenon is known as the urban heat island effect (UHI) and is identified as a substantial cause to a changing climate in surface weather modification. The proceeding study investigates this connection between the UHI and surface weather using remote sensing platforms A ten-year analysis of the Indianapolis UHI and thunderstorms were studied from the summer months of May, June, July, August and September (MJJAS) from 2002 until 2011. LANDSAT space borne satellite technology and land-surface based weather radar technology was used in this analysis for thunderstorm investigation. Precipitation areas identified from land-based NEXRAD WSR-88D technology were used to identify convection from non-synoptic forcing and non-normal surface diurnal heating scenarios. Only convection appearing from the UHI were studied and analyzed. Results showed twenty-one events over eighteen days with the year 2005 and 2011 having the largest frequency of events. The month of August had the largest concentration with seven events during the late afternoon hours. UHI results showed July had the largest heat island magnitude with April and September having the lowest magnitude in UHI temperatures. Three events of the twenty-one storm paths did not had a significant mean temperature difference in the heat island below the storm reflectivity. The nineteen storm paths that were significant had a warmer area underneath storm path development by an average 6.2°C than surrounding areas. UHI initiation points showed twelve of the twenty-one events having statistically significant differences between 2 km initiation areas and the rest of the study areas. Land-cover results showed low intensity developed areas had the most land-cover type (48%) in the 2km initiation buffer regions.

Indiana University-Purdue University Indianapolis (IUPUI) / Precipitation trends and their driving mechanisms are examined over a variety of spatial and temporal scales using a multi-proxy, decadally-resolved sediment record from Martin Lake that spans the last 2300 years. This unique archive from a northern Indiana kettle lake documents significant climate variability during the last 2 millennia and shows that the Midwest has experienced a wide range of precipitation regimes in the late Holocene. Three independent proxies (i.e., oxygen and carbon isotopes of authigenic carbonate and %lithics) record variations in synoptic, in-lake and watershed processes related to hydroclimate forcing, respectively. Together, these proxies reveal enhanced summer conditions, with a long period of water column stratification and enhanced summer rainfall from 450 to 1200 CE, a period of time that includes the so-called Medieval Climate Anomaly (950-1300 CE). During the Little Ice Age, from 1260 to 1800 CE, the three proxy records all indicate drought, with decreased summer rainfall and storm events along with decreased lake stratification. The Martin Lake multi-proxy record tracks other Midwest climate records that record water table levels and is out-of-phase with hydroclimate records of warm season precipitation from the High Plains and western United States. This reveals a potential warm season precipitation dipole between the Midwest and western United States that accounts for the spatial pattern of late Holocene drought variability (i.e., when the Midwest is dry, the High Plains and the western United States are wet, and vice versa). The spatiotemporal patterns of late Holocene North American droughts are consistent with hydroclimate anomalies associated with mean state changes in the Pacific North American teleconnection (PNA). Close associations between late Holocene North American hydroclimate and records of Northern Hemisphere temperatures and the Pacific Ocean-atmosphere system suggests a mechanistic linkage between these components of the global climate system that is in line with observational data and climate models. Based on our results, predominantly –PNA conditions and enhanced Midwestern summer precipitation events are likely to result from continued warming of the climate system. In the western United States, current drought conditions could represent the new mean hydroclimate state.