Thorough characterization of the response of finite water resources to climatic factors is essential for water monitoring and management. In this study, groundwater level data from U.S. Geological Survey Ground-Water Climate Response Network wells were used to analyze the relationship between selected drought indices and groundwater level fluctuation. The drought episodes included in this study were selected using climate division level drought indices. Indices included the Palmer Drought Severity Index, Palmer Hydrological Drought Index, and Standardized Precipitation Index (SPI-6, 9, 12, 24). Precipitation and the average temperature were also used. SPI-24 was found to correlate best with groundwater levels during drought. For 17 out of 32 wells, SPI-24 showed the best correlation amongst all of the indices. For 12 out of 32 wells, SPI-24 showed correlation coefficients of −0.6 or stronger; and for other wells, reasonably good correlation was demonstrated. The statistical significance of SPI-24 in predicting groundwater level was also tested. The correlation of average monthly groundwater levels with SPI-24 does not change much throughout the timeframe, for all of the studied wells. The duration of drought also had a significant correlation with the decline of groundwater levels. This study illustrates how drought indices can be used for a rapid assessment of drought impact on groundwater level.

An understanding of drought occurrences and their characteristics such as intensity, duration, frequency, and areal coverage, and their variations on different spatial scales, is crucial to plan for droughts in different regions and in different sized areas. This study investigated the variations of spatio-temporal characteristics of droughts under selected spatial scales: National (Contiguous U.S.), regional (High Plains), state (North Dakota, ND), climatic division (South Central, ND), and county (Grant, ND). Weekly drought area coverage data for the period spanning the years 2000–2014 from the U.S. Drought Monitor of the National Drought Mitigation Center were used. The study captured the areal coverages and occurrence frequency of droughts with different intensity levels for the years 2000 to 2014 for the contiguous U.S. Year to year variability in spatial distribution of the areal coverages of droughts with different intensity levels were also analysed. The study further investigated how the weekly percentage area under different intensity categories varied along time, and extracted the spatio-temporal characteristics of different drought intensity categories at different spatial scales. The study identified areas that are frequently affected by droughts of different intensity categories in the U.S. at the national scale, and reported the spatial scale dependence of drought characteristics.

The USEPA’s Total Maximum Daily Load (TMDL) program under the Clean Water Act requires taking into account the critical conditions for stream flow, loading, and water quality parameters to develop TMDLs. However, properly defining a waterway’s critical conditions is one of the key challenges in developing a TMDL. Consequently, many current TMDLs address this aspect only in a cursory manner. This paper presents a review of critical condition modeling and analysis approaches and illustrates the strengths and weaknesses of available methodologies to define critical conditions with a view to provide guidance for TMDL development and implementation. Four major approaches for critical condition modeling and analysis are typically employed in TMDL studies: low-flow analysis using steady-state models, continuous simulation using dynamic models, statistically based load-duration curves, and the event-based critical flow-storm approach. By understanding better how to define critical conditions, water quality professionals can develop more-meaningful and improved estimates of TMDLs and formulate appropriate strategies to control pollution from point and nonpoint sources at a watershed level.

This study demonstrates how geospatial analysis tools can be effectively used to improve drought monitoring and management. Understanding spatial variation of drought is vital in drought monitoring and management. Drought indices, generally derived from station-wide meteorological and hydrological data are commonly used to monitor and assess droughts. These indices are generally reported for specific spatial scales such as climate division, county, hydrological unit etc. This study involves spatial interpolation of drought data and quantification of the uncertainty in drought reporting at different spatial scales. The standardized precipitation index (SPI) with one month time step, a metrological drought index, from 57 stations across the State of North Dakota (ND), U.S., was used. Two interpolation methods, inverse distance weighting (IDW) and ordinary Kriging (OK) were evaluated for their performance using selected cross-validation parameters. The variability within each of the three spatial scales: county, climate division, and state were quantified based on interpolated values. This study found that (a) IDW with power one, and (b) OK with circular semivariogram model can be used for interpolation of the selected data obtained from the current distribution of stations. Significant variation of drought was found within each spatial scale. Predicted values of SPI were compared with reported values from commonly used drought reporting sources. A discussion of uncertainty in drought reporting at spatial units of different size is also included.