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Elizabeth I. Okoyeh, Anthony E. Akpan, B. C. E. Egboka, and H. I. Okeke

Abstract

Gully erosion–induced problems have been challenging the people and government of Anambra State in southeastern Nigeria for a long time. In spite of the numerous geoscientific and engineering studies so far conducted in the area, the underlying causes of these problems still remain poorly understood. In an attempt to contribute to the understanding of the underlying processes responsible for the persistent gully erosion problems in Anambra State, an integrated study utilizing hydrological, geomorphological, and geophysical data was undertaken. Results of the analyses show that bulk density, pH, and organic matter content of the soil range from 1610 to 1740 kg m−3, 5.10 to 5.30, and 0.32% to 0.46%, respectively. Particle size analyses results show that the soils are dominated by coarse sand materials (50%–68%). Variations in the Atterberg limit parameters (liquid limit, plastic limit, and plasticity index) also point to the dominance of coarse materials in the shallow subsurface. Vertical electrical sounding results capture the shallow surface as being dominated by resistive sandy materials that are underlain by lowly resistive clayey materials. Thus, the area is dominated by porous, friable, and poorly cemented coarse materials that are located on a long and steeply sloping terrain of the tectonically elevated Awka–Orlu cuesta. Both overland and subsurface flow processes are responsible for the gully erosion problems confronting the area. Human activities (e.g., deforestation, uncontrolled urbanization, and absence of requisite legislation to protect the environment) and the high elevation of the Awka–Orlu cuesta have aggravated the severity of the problems. An aggressive reforestation program particularly with native trees, promulgation of necessary legislation to protect the environment, and setting up and empowering an enforcement agency should be vigorously pursued. Also, necessary enlightenment campaigns on best agricultural practices that can reduce surface runoff in soil and water conservation may also be helpful in changing the mindset of people.

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Kirk Zmijewski and Richard Becker

Abstract

The decrease in size the Aral Sea in central Asia, seen as both lower water levels and reduction in areal extent, has been one of the greatest examples of anthropogenic modification of a natural system in recent history. Many studies have monitored the extent and rate of this water loss and provided estimates on the expected life span of the remaining water. However, with little data for groundwater monitoring in the post-Soviet era, it is unclear what the water balance currently is in the remainder of the watershed. Redistribution of water upstream in the watershed including damming to create reservoirs and groundwater recharge from irrigation has not only deprived the sea of water but also increased evapotranspiration and altered local climate patterns. Using Tropical Rainfall Measurement Mission (TRMM) and Global Precipitation Climatology Centre (GPCC) data, rainfall trends for the Aral Sea watershed were analyzed over 10- and 30-yr periods and only minimal changes in rainfall were detected. Using Gravity Recovery and Climate Experiment (GRACE) gravity data from 2003 to 2012, trends in equivalent water mass were determined for the entire watershed. Estimates show up to 14 km3 of equivalent water mass has been lost from the watershed annually from 2002 to 2013. The mass loss throughout the basin is most likely attributable to increased evapotranspiration due to the inefficient irrigation systems and other human modification increasing the need for international cooperation and conservation programs to minimize negative impacts throughout the region.

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P. Grady Dixon, Andrew E. Mercer, Katarzyna Grala, and William H. Cooke

Abstract

The fundamental purpose of this research is to highlight the spatial seasonality of tornado risk. This requires the use of objective methods to determine the appropriate spatial extent of the bandwidth used to calculate tornado density values (i.e., smoothing the raw tornado data). With the understanding that a smoothing radius depends partially upon the period of study, the next step is to identify objectively ideal periods of tornado analysis. To avoid decisions about spatial or temporal boundaries, this project makes use of storm speed and tornado pathlength data, along with statistical cluster analysis, to establish tornado seasons that display significantly different temporal and spatial patterns. This method yields four seasons with unique characteristics of storm speed and tornado pathlength.

The results show that the ideal bandwidth depends partially upon the temporal analysis period and the lengths of the tornadoes studied. Hence, there is not a “one size fits all,” but the bandwidth can be quantitatively chosen for a given dataset. Results from this research, based upon tornado data for 1950–2011, yield ideal bandwidths ranging from 55 to 180 km. The ideal smoothing radii are then applied via a kernel density analysis of each new tornado season.

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Lei Meng and Yanjun Shen

Abstract

Soil moisture conditions affect energy partitioning between sensible and latent heat fluxes, resulting in a change in surface temperatures. In this study, the relationships between antecedent soil moisture conditions [as indicated by the 6-month standardized precipitation index (SPI)] and several temperature indices are statistically quantified using the quantile regression analysis across East China to investigate the influence of soil moisture on summer surface temperatures. These temperature indices include percentage of hot days (%HD), heat-wave duration (HWD), daily temperature range (DTR), and daily minimum temperature (Tmin). It was demonstrated that soil moisture had a significant impact on %HD and HWD at higher quantiles in all regions but the east, suggesting that drier soil moisture conditions tend to intensity summer hot extremes. It was also found that hot extremes (%HD and HWD at higher quantiles) had increased substantially from 1958 to 2010. Soil moisture also significantly affected the DTR in all regions but tended to have more impacts on the DTR in soil moisture-limited regimes than in energy-limited regimes. This study provides observational evidence of soil moisture influences on hot extremes in East China.

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Tristan Ballard, Richard Seager, Jason E. Smerdon, Benjamin I. Cook, Andrea J. Ray, Balaji Rajagopalan, Yochanan Kushnir, Jennifer Nakamura, and Naomi Henderson

Abstract

The Prairie Pothole Region (PPR) of the northern Great Plains is a vital ecosystem responsible each year for producing 50%–80% of new recruits to the North American duck population. Climate variability and change can impact the hydrology and ecology of the region with implications for waterfowl populations. The historical relationship between PPR wetlands, duck populations, and seasonal hydroclimate are explored. Model experiments from phase 5 of the Coupled Model Intercomparison Project are used to determine whether a recent wetting trend is due to natural variability or changing climate and how PPR hydroclimate will change into the future. Year-to-year variations in May duck populations, pond numbers, and the Palmer drought severity index are well correlated over past decades. Pond and duck numbers tend to increase in spring following La Niña events, but the correlation is not strong. Model simulations suggest that the strengthening of the precipitation gradient across the PPR over the past century is predominantly due to natural variability and therefore could reverse. Model projections of future climate indicate precipitation will increase across the PPR in all seasons except summer, but this gain for surface moisture is largely offset by increased evapotranspiration because of higher temperatures and increased atmospheric evaporative demand. In summer, the combined effects of warming and precipitation changes indicate seasonal surface drying in the future. The presented hydroclimate analyses produce potential inputs to ecological and hydrological simulations of PPR wetlands to inform risk analysis of how this North American waterfowl habitat will evolve in the future, providing guidance to land managers facing conservation decisions.

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Bappaditya Nag, V. Misra, and S. Bastola

Abstract

In this study, the authors contrast four century-long meteorological datasets comprising of two sets of observations [Climate Research Unit (CRU) and Parameter–Elevation Regressions on Independent Slopes Model (PRISM)] and two atmospheric reanalyses [Twentieth Century Reanalysis (20CR) and Florida Climate Institute–Florida State University Land–Atmosphere Regional Reanalysis version 1.0 (FLAReS1.0)] to diagnose the El Niño–Southern Oscillation (ENSO) forced variations on the streamflow in 28 watersheds spread across the southeastern United States (SEUS). The datasets are used to force three different lumped (calibrated) hydrological models with precipitation from these four sources of century-long datasets separately to obtain the median prediction from 1800 (=3 models × 600 simulations per model per watershed per season) multimodel estimates of seasonal mean streamflow across the 28 watersheds in the SEUS for each winter season from 1906 to 2005. The authors then compare and contrast the mean streamflow and its variability estimates from all three of the century-long climate forcings. The multimodel strategy of simulating the seasonal mean streamflow is to reduce the hydrological model uncertainty. The authors focus on the boreal winter season when ENSO influence on the SEUS climate variations is well known.

The authors find that the atmospheric reanalysis over the SEUS is able to reasonably capture the ENSO teleconnections as depicted in the CRU and PRISM precipitation datasets. Even the observed decadal modulation of this teleconnection by Atlantic multidecadal oscillation (AMO) is broadly captured. The streamflow in the 28 watersheds also show similar consistency across the four datasets in that the positive correlations of the boreal winter Niño-3.4 SST anomalies with corresponding anomalies of streamflow, the associated shift in the probability density function of the streamflow with the change in phase of ENSO, and the decadal modulation of the ENSO teleconnection by the AMO are sustained in the streamflow simulations forced by all four climate datasets (CRU, PRISM, 20CR, and FLAReS1.0). However, the ENSO signal in the streamflow is consistently much stronger in the southern watersheds (over Florida) of the SEUS across all four climate datasets. During the negative phase of the AMO, however, there is a clear shift of the ENSO teleconnections with streamflow, with winter streamflows in northern watersheds (over the Carolinas) exhibiting much stronger correlations with the ENSO Niño-3.4 index relative to the southern watersheds of the SEUS. This study clearly indicates that the proposed methodology using FLAReS1.0 serves as a viable alternative to reconstruct twentieth-century SEUS seasonal winter hydrology that captures the interannual variations of ENSO and associated decadal variations forced by the AMO. However, it is found that the FLAReS1.0 forced streamflow is far from adequate in simulating the streamflow dynamics of the watershed over the SEUS at a daily time scale.

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Satish Bastola, Vasubandhu Misra, and Haiqin Li

Abstract

The authors evaluate the skill of a suite of seasonal hydrological prediction experiments over 28 watersheds throughout the southeastern United States (SEUS), including Florida, Georgia, Alabama, South Carolina, and North Carolina. The seasonal climate retrospective forecasts [the Florida Climate Institute–Florida State University Seasonal Hindcasts at 50-km resolution (FISH50)] is initialized in June and integrated through November of each year from 1982 through 2001. Each seasonal climate forecast has six ensemble members. An earlier study showed that FISH50 represents state-of-the-art seasonal climate prediction skill for the summer and fall seasons, especially in the subtropical and higher latitudes. The retrospective prediction of streamflow is based on multiple calibrated rainfall–runoff models. The hydrological models are forced with rainfall from FISH50, (quantile based) bias-corrected FISH50 rainfall (FISH50_BC), and resampled historical rainfall observations based on matching observed analogs of forecasted quartile seasonal rainfall anomalies (FISH50_Resamp).

The results show that direct use of output from the climate model (FISH50) results in huge biases in predicted streamflow, which is significantly reduced with bias correction (FISH50_BC) or by FISH50_Resamp. On a discouraging note, the authors find that the deterministic skill of retrospective streamflow prediction as measured by the normalized root-mean-square error is poor compared to the climatological forecast irrespective of how FISH50 (e.g., FISH50_BC, FISH50_Resamp) is used to force the hydrological models. However, our analysis of probabilistic skill from the same suite of retrospective prediction experiments reveals that, over the majority of the 28 watersheds in the SEUS, significantly higher probabilistic skill than climatological forecast of streamflow can be harvested for the wet/dry seasonal anomalies (i.e., extreme quartiles) using FISH50_Resamp as the forcing. The authors contend that, given the nature of the relatively low climate predictability over the SEUS, high deterministic hydrological prediction skills will be elusive. Therefore, probabilistic hydrological prediction for the SEUS watersheds is very appealing, especially with the current capability of generating a comparatively huge ensemble of seasonal hydrological predictions for each watershed and for each season, which offers a robust estimate of associated forecast uncertainty.

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Y. Govender, E. Cuevas, L. D. S. Sternberg, and M. R. Jury

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.

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Gregory J. McCabe, Julio L. Betancourt, Gregory T. Pederson, and Mark D. Schwartz

Abstract

Singular value decomposition is used to identify the common variability in first leaf dates (FLDs) and 1 April snow water equivalent (SWE) for the western United States during the period 1900–2012. Results indicate two modes of joint variability that explain 57% of the variability in FLD and 69% of the variability in SWE. The first mode of joint variability is related to widespread late winter–spring warming or cooling across the entire west. The second mode can be described as a north–south dipole in temperature for FLD, as well as in cool season temperature and precipitation for SWE, that is closely correlated to the El Niño–Southern Oscillation. Additionally, both modes of variability indicate a relation with the Pacific–North American atmospheric pattern. These results indicate that there is a substantial amount of common variance in FLD and SWE that is related to large-scale modes of climate variability.

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Kelsey Scheitlin

Abstract

This study analyzes the influence of the Atlantic Ocean and Chesapeake Bay on the diurnal temperature range (DTR) reported by nearby weather stations. Coastal locations reported the smallest DTRs and DTR fluctuations, and DTR increased with distance from the ocean. Month of the year and airmass type also proved to be significant predictors of DTR. All locations showed a bimodal annual DTR pattern with peaks during the transitional seasons and experienced the greatest DTR during dry and/or warm air masses. Proximity to the ocean had the largest (smallest) influence on DTR during dry (moist) air masses with extreme (moderate) temperatures. Seasonally, the proximity to the ocean had the strongest impact on DTR during early–middle spring. A multiple regression model using distance from water, month, and airmass type explains over 30% of DTR variability in the area (p < 0.01). Airmass type has the largest influence on DTR, and changes in both air mass and month impacted the DTR of continental locations more than coastal locations. Land use, cloud cover, and wind speed/direction are additional variables that could account for differences not explained by the model.

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