Variation in quantities such as precipitation and temperature is often assessed by detecting and characterizing trends in available meteorological data. The objective of this study was to determine the long-term trends in annual precipitation and mean annual air temperature for the state of Kentucky. Non-parametric statistical tests were applied to homogenized and (as needed) pre-whitened annual series of precipitation and mean air temperature during 1950-2010. Significant trends in annual precipitation were detected (both positive, averaging 4.1 mm/year) for only two of the 60 precipitation-homogenous weather stations (Calloway and Carlisle counties in rural western Kentucky). Only three of the 42 temperature-homogenous stations demonstrated trends (all positive, averaging 0.01˝C/year) in mean annual temperature: Calloway County, Allen County in southern-central Kentucky, and urbanized Jefferson County in northern-central Kentucky. In view of the locations of the stations demonstrating positive trends, similar work in adjacent states will be required to better understand the processes responsible for those trends and to properly place them in their larger context, if any.
Connectivity of groundwater flow within crystalline-rock aquifers controls the sustainability of abstraction and baseflow to rivers, yet is often poorly constrained at a catchment scale. Here groundwater connectivity in a sheared gneiss aquifer is investigated by studying the intensively abstracted Berambadi catchment (84 km 2) in the Cauvery River Basin, southern India, with geological characterisation, aquifer properties testing, hydrograph analysis, hydrochemical tracers and a numerical groundwater flow model. The study indicates a well-connected system, both laterally and vertically, that has evolved with high abstraction from a laterally to a vertically dominated flow system. Likely as a result of shearing, a high degree of lateral connectivity remains at low groundwater levels. Because of their low storage and logarithmic reduction in hydraulic conductivity with depth, crystalline-rock aquifers in environments such as this, with high abstraction and variable seasonal recharge, constitute a highly variable water resource, meaning farmers must adapt to varying water availability. Importantly, this study indicates that abstraction is reducing baseflow to the river, which, if also occurring in other similar catchments, will have implications downstream in the Cauvery River Basin.
Global climate variations are expected to cause serious challenges to water resources planning and management, including an increase in sea level, abrupt changes in rainfall patterns and changes in ecosystems. This study evaluates impacts of mid-century climate variability as projected by climate models in the Haw River watershed, which contributes significantly to Jordan Lake, a major source of drinking water supply in central North Carolina, USA. The watershed-based hydrological model, Soil and Water Assessment Tool (SWAT), was successfully calibrated with very good to excellent performance. Projected precipitation and temperature information for 2040-2069 from four dynamically downscaled regional climate models (RCMs) was used to force the SWAT modeling set-up of the watershed. On a long-term basis, a 38% decrease in the precipitation in early fall is expected while spring months are expected to receive 30% higher precipitation compared to the baseline condition . Water yield was found to increase in spring months, with a maximum of 74% increase on average. Summer months are expected to have on average 8% higher evapotranspiration (ET) than the baseline. Analysis of the change in average monthly streamflow at the watershed outlet (which leads to Lake Jordan) shows that there might be, on average, an 80% increase in streamflow in spring months (February, March, April and May), with the greatest increase (107%) in May. In general, simulation results indicated that the hydrological response of the watershed is very sensitive to the potential variation in climate (precipitation and temperature), with precipitation being one of the decisive factors in water yield increase.
Abstract. G2DC-PL+, a gridded 2 km daily climate dataset for the union of the Polish territory and the Vistula and Odra basins, is an update and extension of the CHASE-PL Forcing Data – Gridded Daily Precipitation and Temperature Dataset – 5 km (CPLFD-GDPT5). The latter was the first publicly available, high-resolution climate forcing dataset in Poland, used for a range of purposes including hydrological modelling and bias correction of climate projections. While the spatial coverage of the new dataset remained the same, it has undergone several major changes: (1) the time coverage was increased from 1951–2013 to 1951–2019; (2) its spatial resolution increased from 5 to 2 km; (3) the number of stations used for interpolation of temperature and precipitation approximately doubled; and (4) in addition to precipitation and temperature, the dataset consists of relative humidity and wind speed data. The main purpose for developing this product was the need for long-term areal climate data for earth-system modelling, and particularly hydrological modelling. Geostatistical methods (kriging) were used for interpolation of the studied climate variables. The kriging cross-validation revealed improved performance for precipitation compared to the original dataset expressed by the median of the root mean squared errors standardized by standard deviation of observations (0.59 vs. 0.79). Kriging errors were negatively correlated with station density only for the period 1951–1970. Values of the root mean squared error normalized to the standard deviation (RMSEsd) were equal to 0.52 and 0.4 for minimum and maximum temperature, respectively, suggesting a small to moderate improvement over the original dataset. Relative humidity and wind speed exhibited lower performance, with median RMSEsd equal to 0.82 and 0.87, respectively. The dataset is openly available from the 4TU Centre for Research Data at https://doi.org/10.4121/uuid:a3bed3b8-e22a-4b68-8d75-7b87109c9feb (Piniewski et al., 2020).
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