Abstract The authors examine five recent reanalysis products [NCEP Climate Forecast System Reanalysis (CFSR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Japanese 25-year Reanalysis Project (JRA-25), Interim ECMWF Re-Analysis (ERA-Interim), and Arctic System Reanalysis (ASR)] for 1) trends in near-surface radiation fluxes, air temperature, and humidity, which are important indicators of changes within the Arctic Ocean and also influence sea ice and ocean conditions, and 2) fidelity of these atmospheric fields and effects for an extreme event: namely, the 2007 ice retreat. An analysis of trends over the Arctic for the past decade (2000–09) shows that reanalysis solutions have large spreads, particularly for downwelling shortwave radiation. In many cases, the differences in significant trends between the five reanalysis products are comparable to the estimated trend within a particular product. These discrepancies make it difficult to establish a consensus on likely changes occurring in the Arctic solely based on results from reanalyses fields. Regarding the 2007 ice retreat event, comparisons with remotely sensed estimates of downwelling radiation observations against these reanalysis products present an ambiguity. Remotely sensed observations from a study cited herewith suggest a large increase in downwelling summertime shortwave radiation and decrease in downwelling summertime longwave radiation from 2006 and 2007. On the contrary, the reanalysis products show only small gains in summertime shortwave radiation, if any; however, all the products show increases in downwelling longwave radiation. Thus, agreement within reanalysis fields needs to be further checked against observations to assess possible biases common to all products.

Abstract Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001–10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.

Abstract The Ganges–Brahmaputra–Meghna (GBM) river basins exhibit extremes in surface water availability at seasonal to annual time scales. However, because of a lack of basinwide hydrological data from in situ platforms, whether they are real time or historical, water management has been quite challenging for the 630 million inhabitants. Under such circumstances, a large-scale and spatially distributed hydrological model, forced with more widely available satellite meteorological data, can be useful for generating high resolution basinwide hydrological state variable data [streamflow, runoff, and evapotranspiration (ET)] and for decision making on water management. The Variable Infiltration Capacity (VIC) hydrological model was therefore set up for the entire GBM basin at spatial scales ranging from 12.5 to 25 km to generate daily fluxes of surface water availability (runoff and streamflow). Results indicate that, with the selection of representative gridcell size and application of correction factors to evapotranspiration calculation, it is possible to significantly improve streamflow simulation and overcome some of the insufficient sampling and data quality issues in the ungauged basins. Assessment of skill of satellite precipitation forcing datasets revealed that the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product of 3B42RT fared comparatively better than the Climate Prediction Center (CPC) morphing technique (CMORPH) product for simulation of streamflow. The general conclusion that emerges from this study is that spatially distributed hydrologic modeling for water management is feasible for the GBM basins under the scenario of inadequate in situ data availability. Satellite precipitation forcing datasets provide the necessary skill for water balance studies at interannual and interseasonal scales. However, further improvement in skill may be required if these datasets are to be used for flood management at daily to weekly time scales and within a data assimilation framework.

Abstract Karst topography links rainfall to groundwater recharge; therefore, possible changes in the hydrology can play an important role in ecosystem function especially in tropical dry forests where water is the most limiting resource. This study investigates the temporal variation in isotopic composition (δ18O and δD values) of rainwater and groundwater in the Guánica Dry Forest of southwestern Puerto Rico. The study not only establishes a dataset of oxygen and hydrogen isotopic composition of rainwater to assist in local ecohydrological studies but also establishes the origin of rainfall in the semiarid region of the island. The geographical position of Puerto Rico in the northeastern Caribbean causes the study site to receive marine air masses from the North Atlantic Ocean and Caribbean Sea. This research documents the monthly to annual variability in stable isotopic composition of rainwater and estimates the source of groundwater recharge in the Guánica Dry Forest.To calculate the local meteoric water line (LMWL), the authors analyzed the isotopic signatures of rainwater, collected at near-monthly intervals from January 2008 to December 2011. The LMWL (δD = 7.79δ18O + 10.85) is close to the global meteoric water line (δD = 8.17δ18O + 11.27). Isotopic signatures of rainwater for the Guánica Dry Forest are consistent with southeastern Caribbean, where rainfall is of marine origin with an annual cycle contributed by sea surface temperature (SST) and significant intermonthly fluctuations due to rainfall and winds during tropical weather events. The d-excess values in the period of data collection (2008–11) respond to the rainfall–evaporation balance, with little seasonal cycle and strong pulsing events. Comparison of rain and groundwater isotopic compositions in the United Nations Educational, Scientific and Cultural Organization (UNESCO) Man and the Biosphere Programme (MAB) Guánica Dry Forest indicates that groundwater recharge is confined to rainfall events of more than 90 mm. Imbalances between rainfall and drought place cumulative stresses on ecosystems where plants and animals synchronize their growth phenology and reproduction to climatic patterns, especially in areas with variable annual cycles. Therefore, it is useful in ecohydrological studies to determine the origins and temporal dynamics of rainfall and groundwater recharge in the Caribbean, where predictions of climate models indicate drying trends.

Abstract Studies seeking to understand the impacts of climate variability and change on the hydrology of a region need to take into account the dynamics of vegetation and its interaction with the hydrologic and energy cycles. Yet, most of the hydrologic models used for these kinds of studies assume that vegetation is static. This paper presents a dynamic, spatially explicit model that couples a vertical energy balance scheme (surface and canopy layer) to a hydrologic model and a forest growth component to capture the dynamic interactions between energy, vegetation, and hydrology at hourly to daily time scales. The model is designed to be forced with outputs from regional climate models. Lateral water transfers are simulated using a 1D kinematic wave model. Infiltration is simulated using the Green and Ampt approximation to Richard's equation. The dynamics of soil moisture and energy drives carbon assimilation and forest growth, which in turn affect the distribution of energy and water through leaf dynamics by altering light interception, shading, and enhanced transpiration. The model is demonstrated in two case studies simulating energy, water, and vegetation dynamics at two different spatial and temporal scales.

Abstract India is the greatest groundwater consumer in the world, with estimated annual withdrawals exceeding 230 km3. More than 60% of irrigated agriculture, 85% of drinking water supplies, and 50% of urban and industrial water needs are dependent on sustainable groundwater management. Regardless, groundwater overextraction is a growing problem in many regions. Predictions of groundwater resource availability in India are problematic in part because of a limited number of monitoring sites and insufficient data quality and quantity. Regional groundwater assessments are further complicated because of sporadic and low-frequency data. To help overcome these issues and more accurately quantify groundwater resource availability, scientists have begun using satellite-derived remote sensing data. In this study, the authors used seasonal and annual hydrologic signals obtained by NASA Gravity Recovery and Climate Experiment (GRACE) satellites and simulated soil moisture variations from land data assimilation systems to show groundwater depletion trends in the northwest state of Gujarat (surface area of 196 030 km2), India. Results were evaluated using direct measurement data from 935 wells. Remote sensing generated results compared favorably with well data (e.g., r2 = 0.89 for Gandhinagar, a representative highly urbanized district in Gujarat: confidence interval (CI) = 0.05 and P = 0.002). Results show that remote sensing is an effective tool to compliment and interpolate observed regional groundwater well data and improve groundwater storage estimations in Gujarat, India. Properly implemented, the method will supply reliable science-based information to enhance management of groundwater resources in India and other geographic locations.

Abstract Because of their stochastic nature, droughts vary in space and time, and therefore quantifying droughts at different time units is important for water resources planning. The authors investigated the relationship between meteorological variables and hydrological drought properties using the Palmer hydrological drought index (PHDI). Twenty different spatial units were chosen from the unit of a climatic division to a regional unit across the United States. The relationship between meteorological variables and PHDI was investigated using a wavelet–Bayesian regression model, which enhances the modeling strength of a simple Bayesian regression model. Further, the wavelet–Bayesian regression model was tested for the predictability of global climate models (GCMs) to simulate PHDI, which will also help understand their role for downscaling purposes.

Abstract Physical characteristics of forests and other ecosystems control land–atmosphere exchanges of water and energy and partly dictate local and regional meteorology. Insect infestation and resulting forest dieback can alter these characteristics and, further, modify land–atmosphere exchanges. In the past decade, insect infestation has led to large-scale forest mortality in western North America. This study uses a high-resolution mesoscale meteorological model coupled with a detailed land surface model to investigate the sensitivity of near-surface variables to insect-related forest mortality. The inclusion of this land surface disturbance in the model increased in simulated skin temperature by as much as 2.1 K. The modeled 2-m temperature increased an average of 1 K relative to the default simulations. A latent to sensible heat flux shift with a magnitude of 10%–15% of the available energy in the forested ecosystem was predicted after the inclusion of insect infestation and forest dieback. Although results were consistent across multiple model configurations, the characteristics of forests affected by insect infestations must be better constrained to more accurately predict their impacts. Despite the limited duration of the simulations (one week), these initial results suggest the importance of including large-scale forest mortality due to insect infestation in meteorological models and highlight the need for better observations of the characteristics and exchanges of these disturbed landscapes.

Abstract The dynamic vegetation and carbon cycling component, LM3V, of the Geophysical Fluid Dynamics Laboratory (GFDL) prototype Earth system model (ESM2.1), has been designed to simulate the effects of land use on terrestrial carbon pools, including secondary vegetation regrowth. Because of the long time scales associated with the carbon adjustment, special consideration is required when initializing the ESM when historical simulations are conducted. Starting from an equilibrated, preindustrial climate and potential vegetation state in an offline land-only model (LM3V), estimates of historical land use are instantaneously applied in five experiments beginning in the following calendar years: 1500, 1600, 1700, 1750, and 1800. This application results in the land carbon pools experiencing an abrupt change—a carbon shock—and the secondary vegetation needs time to regrow into consistency with the harvesting history. The authors find that it takes approximately 100 years for the vegetation to recover from the carbon shock, whereas soils take at least 150 years to recover. The vegetation carbon response is driven primarily by land-use history, whereas the soil carbon response is affected by both land-use history and the geographic pattern of soil respiration rates. Based on these results, the authors recommend the application of historical land-use scenarios in 1700 to provide sufficient time for the land carbon in ESMs with secondary vegetation to equilibrate to adequately simulate carbon stores at the start of the historical integrations (i.e., 1860) in a computationally efficient manner.

Abstract The hydrologic response of different climate-change emission scenarios for the twenty-first century were evaluated in 14 basins from different hydroclimatic regions across the United States using the Precipitation-Runoff Modeling System (PRMS), a process-based, distributed-parameter watershed model. This study involves four major steps: 1) setup and calibration of the PRMS model in 14 basins across the United States by local U.S. Geological Survey personnel; 2) statistical downscaling of the World Climate Research Programme’s Coupled Model Intercomparison Project phase 3 climate-change emission scenarios to create PRMS input files that reflect these emission scenarios; 3) run PRMS for the climate-change emission scenarios for the 14 basins; and 4) evaluation of the PRMS output.This paper presents an overview of this project, details of the methodology, results from the 14 basin simulations, and interpretation of these results. A key finding is that the hydrological response of the different geographical regions of the United States to potential climate change may be very different, depending on the dominant physical processes of that particular region. Also considered is the tremendous amount of uncertainty present in the climate emission scenarios and how this uncertainty propagates through the hydrologic simulations. This paper concludes with a discussion of the lessons learned and potential for future work.