Aldo V. Vecchia

Global pattern of trends in streamflow and water availability in a changing climate

Water availability on the continents is important for human health, economic activity, ecosystem function and geophysical processes. Because the saturation vapour pressure of water in air is highly sensitive to temperature, perturbations in the global water cycle are expected to accompany climate warming. Regional patterns of warming-induced changes in surface hydroclimate are complex and less certain than those in temperature, however, with both regional increases and decreases expected in precipitation and runoff. Here we show that an ensemble of 12 climate models exhibits qualitative and statistically significant skill in simulating observed regional patterns of twentieth-century multidecadal changes in streamflow. These models project 10–40% increases in runoff in eastern equatorial Africa, the La Plata basin and high-latitude North America and Eurasia, and 10–30% decreases in runoff in southern Africa, southern Europe, the Middle East and mid-latitude western North America by the year 2050. Such changes in sustainable water availability would have considerable regional-scale consequences for economies as well as ecosystems.

Monitoring and Understanding Changes in Heat Waves, Cold Waves, Floods, and Droughts in the United States: State of Knowledge

Weather and climate extremes have been varying and changing on many different time scales. In recent decades, heat waves have generally become more frequent across the United States, while cold waves have been decreasing. While this is in keeping with expectations in a warming climate, it turns out that decadal variations in the number of U.S. heat and cold waves do not correlate well with the observed U.S. warming during the last century. Annual peak flow data reveal that river flooding trends on the century scale do not show uniform changes across the country. While flood magnitudes in the Southwest have been decreasing, flood magnitudes in the Northeast and north-central United States have been increasing. Confounding the analysis of trends in river flooding is multiyear and even multidecadal variability likely caused by both large-scale atmospheric circulation changes and basin-scale “memory” in the form of soil moisture. Droughts also have long-term trends as well as multiyear and decadal variability...

How many Stakes are Required to Measure the Mass Balance of a Glacier

Glacier mass balance is estimated for South Cascade Glacier and Maclure Glacier using a one-dimensional regression of mass balance with altitude as an alternative to the traditional approach of contouring mass balance values. One attractive feature of regression is that it can be applied to sparse data sets where contouring is not possible and can provide an objective error of the resulting estimate. Regression methods yielded mass balance values equivalent to contouring methods. The effect of the number of mass balance measurements on the final value for the glacier showed that sample sizes as small as five stakes provided reasonable estimates, although the error estimates were greater than for larger sample sizes. Different spatial patterns of measurement locations showed no appreciable influence on the final value as long as different surface altitudes were intermittently sampled over the altitude range of the glacier. Two different regression equations were examined, a quadratic, and a piecewise linear spline, and comparison of results showed little sensitivity to the type of equation. These results point to the dominant effect of the gradient of mass balance with altitude of alpine glaciers compared to transverse variations. The number of mass balance measurements required to determine the glacier balance appears to be scale invariant for small glaciers and five to ten stakes are sufficient.

Fitting Continuous ARMA Models to Unequally Spaced Spatial Data

Abstract Methods for fitting continuous spatial autoregressive moving average (ARMA) models to unequally spaced observations in two dimensions are reviewed and extended. These are models with rational two-dimensional spectra. Assuming Gaussian input noise and observational errors, maximum likelihood methods are used to estimate the ARMA parameters and the regression coefficients of the deterministic trend. When the number of observations is too large for exact maximum likelihood estimation, approximate maximum likelihood estimation is used based on nearest neighbors. Comparisons of nearest-neighbor methods with exact likelihood methods are presented. Predictions of the height of the field at unobserved points can be calculated with confidence intervals.

Impact of Climate Variability on Runoff in the North-Central United States

AbstractLarge changes in runoff in the north-central United States have occurred during the past century, with larger floods and increases in runoff tending to occur from the 1970s to the present. The attribution of these changes is a subject of much interest. Long-term precipitation, temperature, and streamflow records were used to compare changes in precipitation and potential evapotranspiration (PET) to changes in runoff within 25 stream basins. The basins studied were organized into four groups, each one representing basins similar in topography, climate, and historic patterns of runoff. Precipitation, PET, and runoff data were adjusted for near-decadal scale variability to examine longer-term changes. A nonlinear water-balance analysis shows that changes in precipitation and PET explain the majority of multidecadal spatial/temporal variability of runoff and flood magnitudes, with precipitation being the dominant driver. Historical changes in climate and runoff in the region appear to be more consiste...

Assessment of water quality trends in the Minnesota River using non-parametric and parametric methods.

Excessive loading of sediment and nutrients to rivers is a major problem in many parts of the United States. In this study, we tested the non-parametric Seasonal Kendall (SEAKEN) trend model and the parametric USGS Quality of Water trend program (QWTREND) to quantify trends in water quality of the Minnesota River at Fort Snelling from 1976 to 2003. Both methods indicated decreasing trends in flow-adjusted concentrations of total suspended solids (TSS), total phosphorus (TP), and orthophosphorus (OP) and a generally increasing trend in flow-adjusted nitrate plus nitrite-nitrogen (NO(3)-N) concentration. The SEAKEN results were strongly influenced by the length of the record as well as extreme years (dry or wet) earlier in the record. The QWTREND results, though influenced somewhat by the same factors, were more stable. The magnitudes of trends between the two methods were somewhat different and appeared to be associated with conceptual differences between the flow-adjustment processes used and with data processing methods. The decreasing trends in TSS, TP, and OP concentrations are likely related to conservation measures implemented in the basin. However, dilution effects from wet climate or additional tile drainage cannot be ruled out. The increasing trend in NO(3)-N concentrations was likely due to increased drainage in the basin. Since the Minnesota River is the main source of sediments to the Mississippi River, this study also addressed the rapid filling of Lake Pepin on the Mississippi River and found the likely cause to be increased flow due to recent wet climate in the region.

Trends in concentrations and use of agricultural herbicides for Corn Belt rivers, 1996-2006.

Trends in the concentrations and agricultural use of four herbicides (atrazine, acetochlor, metolachlor, and alachlor) were evaluated for major rivers of the Corn Belt for two partially overlapping time periods: 1996-2002 and 2000-2006. Trends were analyzed for 11 sites on the mainstems and selected tributaries in the Ohio, Upper Mississippi, and Missouri River Basins. Concentration trends were determined using a parametric regression model designed for analyzing seasonal variability, flow-related variability, and trends in pesticide concentrations (SEAWAVE-Q). The SEAWAVE-Q model accounts for the effect of changing flow conditions in order to separate changes caused by hydrologic conditions from changes caused by other factors, such as pesticide use. Most of the trends in atrazine and acetochlor concentrations for both time periods were relatively small and nonsignificant, but metolachlor and alachlor were dominated by varying magnitudes of concentration downtrends. Overall, with trends expressed as a percent change per year, trends in herbicide concentrations were consistent with trends in agricultural use; 84 of 88 comparisons for different sites, herbicides, and time periods showed no significant difference between concentration trends and agricultural use trends. Results indicate that decreasing use appears to have been the primary cause for the concentration downtrends during 1996-2006 and that, while there is some evidence that nonuse management factors may have reduced concentrations in some rivers, reliably evaluating the influence of these factors on pesticides in large streams and rivers will require improved, basin-specific information on both management practices and use over time.

Innovations algorithm for periodically stationary time series

Periodic ARMA, or PARMA, time series are used to model periodically stationary time series. In this paper we develop the innovations algorithm for periodically stationary processes. We then show how the algorithm can be used to obtain parameter estimates for the PARMA model. These estimates are proven to be weakly consistent for PARMA processes whose underlying noise sequence has either finite or infinite fourth moment. Since many time series from the fields of economics and hydrology exhibit heavy tails, the results regarding the infinite fourth moment case are of particular interest.

Eleven-year trend in acetanilide pesticide degradates in the Iowa River, Iowa.

Trends in concentration and loads of acetochlor, alachlor, and metolachlor and their ethanasulfonic (ESA) and oxanilic (OXA) acid degradates were studied from 1996 through 2006 in the main stem of the Iowa River, Iowa and in the South Fork Iowa River, a small tributary near the headwaters of the Iowa River. Concentration trends were determined using the parametric regression model SEAWAVE-Q, which accounts for seasonal and flow-related variability. Daily estimated concentrations generated from the model were used with daily streamflow to calculate daily and yearly loads. Acetochlor, alachlor, metolachlor, and their ESA and OXA degradates were generally present in >50% of the samples collected from both sites throughout the study. Their concentrations generally decreased from 1996 through 2006, although the rate of decrease was slower after 2001. Concentrations of the ESA and OXA degradates decreased from 3 to about 23% yr. The concentration trend was related to the decreasing use of these compounds during the study period. Decreasing concentrations and constant runoff resulted in an average reduction of 10 to >3000 kg per year of alachlor and metolachlor ESA and OXA degradates being transported out of the Iowa River watershed. Transport of acetochlor and metolachlor parent compounds and their degradates from the Iowa River watershed ranged from <1% to about 6% of the annual application. These trends were related to the decreasing use of these compounds during the study period, but the year-to-year variability cannot explain changes in loads based on herbicide use alone. The trends were also affected by the timing and amount of precipitation. As expected, increased amounts of water moving through the watershed moved a greater percentage of the applied herbicides, especially the relatively soluble degradates, from the soils into the rivers through surface runoff, shallow groundwater inflow, and subsurface drainage.

Tree-ring-based estimates of long-term seasonal precipitation in the Souris River Region of Saskatchewan, North Dakota and Manitoba

Historically unprecedented flooding occurred in the Souris River Basin of Saskatchewan, North Dakota and Manitoba in 2011, during a longer term period of wet conditions in the basin. In order to develop a model of future flows, there is a need to evaluate effects of past multidecadal climate variability and/or possible climate change on precipitation. In this study, tree-ring chronologies and historical precipitation data in a four-degree buffer around the Souris River Basin were analyzed to develop regression models that can be used for predicting long-term variations of precipitation. To focus on longer term variability, 12-year moving average precipitation was modeled in five subregions (determined through cluster analysis of measures of precipitation) of the study area over three seasons (November–February, March–June and July–October). The models used multiresolution decomposition (an additive decomposition based on powers of two using a discrete wavelet transform) of tree-ring chronologies from Canada and the US and seasonal 12-year moving average precipitation based on Adjusted and Homogenized Canadian Climate Data and US Historical Climatology Network data. Results show that precipitation varies on long-term (multidecadal) time scales of 16, 32 and 64 years. Past extended pluvial and drought events, which can vary greatly with season and subregion, were highlighted by the models. Results suggest that the recent wet period may be a part of natural variability on a very long time scale.