Tushar Sinha

Reclaiming freshwater sustainability in the Cadillac Desert

Increasing human appropriation of freshwater resources presents a tangible limit to the sustainability of cities, agriculture, and ecosystems in the western United States. Marc Reisner tackles this theme in his 1986 classic Cadillac Desert: The American West and Its Disappearing Water. Reisners analysis paints a portrait of region-wide hydrologic dysfunction in the western United States, suggesting that the storage capacity of reservoirs will be impaired by sediment infilling, croplands will be rendered infertile by salt, and water scarcity will pit growing desert cities against agribusiness in the face of dwindling water resources. Here we evaluate these claims using the best available data and scientific tools. Our analysis provides strong scientific support for many of Reisners claims, except the notion that reservoir storage is imminently threatened by sediment. More broadly, we estimate that the equivalent of nearly 76% of streamflow in the Cadillac Desert region is currently appropriated by humans, and this figure could rise to nearly 86% under a doubling of the regions population. Thus, Reisners incisive journalism led him to the same conclusions as those rendered by copious data, modern scientific tools, and the application of a more genuine scientific method. We close with a prospectus for reclaiming freshwater sustainability in the Cadillac Desert, including a suite of recommendations for reducing region-wide human appropriation of streamflow to a target level of 60%.

Hydrologic Impacts of Projected Future Climate Change in the Lake Michigan Region

ABSTRACT The Great Lakes are an important source of fresh water, recreation resource and transportation corridor for the Midwestern United States and Canada. The timing and quantity of fresh water inputs and how those may change under projections of future climate change are important for understanding how conditions, including river flows, and lake levels, within the region may be affected. Water quality and the density and diversity of in-stream habitats are responsive to changes in the distribution of daily streamflow, something not typically included in studies of climate change impacts. Projections of precipitation and air temperature changes in the four states surrounding Lake Michigan from the IPCC AR4 were downscaled and biascorrected before being used to drive a large-scale hydrology model and produce maps of surface runoff and baseflow. These were then routed along drainage networks for regional rivers, and hydrologic metrics describing aspects of the distribution of daily flows important for hydrology and in-stream ecology were computed. The impact of regional climate change projections on early- (water years 2010–2039) and midcentury (water years 2040–2069) streamflow was highly variable; however, by the late-century period (water years 2070–2099) annual streamflow was found to have increased in all rivers. Seasonally, winter and spring flows increased significantly by the late-century period, but summer flows become more variable with a decrease in low-flows and an increase in peak-flows. The number of days with flows above the annual mean-flow (TQmean) decreased in summer, but flashiness (R-B Index) increased.

Time Series Analysis of Soil Freeze and Thaw Processes in Indiana

Abstract Seasonal cycles of freezing and thawing influence surface energy and water cycle fluxes. Specifically, soil frost can lead to the reduction in infiltration and an increase in runoff response, resulting in a greater potential for soil erosion. An increase in the number of soil freeze–thaw cycles may reduce soil compaction, which could affect various hydrologic processes. In this study, the authors test for the presence of significant trends in soil freeze–thaw cycles and soil temperatures at several depths and compare these with other climatic variables including air temperature, snowfall, snow cover, and precipitation. Data for the study were obtained for three research stations located in northern, central, and southern Indiana that have collected soil temperature observations since 1966. After screening for significant autocorrelations, testing for trends is conducted at a significance level of 5% using Mann–Kendall’s test. Observations from 1967 to 2006 indicate that air temperatures during th...

Impacts of Historic Climate Variability on Seasonal Soil Frost in the Midwestern United States

Abstract The present study examines the effects of historic climate variability on cold-season processes, including soil temperature, frost depth, and the number of frost days and freeze–thaw cycles. Considering the importance of spatial and temporal variability in cold-season processes, the study was conducted in the midwestern United States using both observations and model simulations. Model simulations used the Variable Infiltration Capacity (VIC) land surface model (LSM) to reconstruct and to analyze changes in the long-term (i.e., 1917–2006) means of soil frost variables. The VIC model was calibrated using observed streamflow records and near-surface soil temperatures and then evaluated for streamflow, soil temperature, frost depth, and soil moisture before its application at the regional scale. Soil frost indicators—such as the number of frost days and freeze–thaw cycles—were determined from observed records and were tested for the presence of significant trends. Overall trends in extreme and mean ...

Dams in the Cadillac Desert: downstream effects in a geomorphic context

This paper was motivated by the 25th anniversary of the publication of Marc Reisners book, Cadillac Desert: The American West and its Disappearing Water. Dams are ubiquitous on rivers in the United States, and large dams and storage reservoirs are the hallmark of western U.S. riverscapes. The effects of dams on downstream river ecosystems have attracted much attention and are encapsulated in the serial discontinuity concept (SDC). In the SDC, dams create abrupt shifts in continua of downstream changes in physical and biotic properties. In this paper, we develop a framework for understanding how channel geometry and network structure influence how the physical components of habitat and the biota rebound from discontinuities set up by large dams. We apply this framework to data describing the flow regime, temperature, sediment flux, and fish community composition below Garrison Dam on the Missouri River, Glen Canyon Dam on the Colorado River, and Flaming Gorge Dam on the Green River. Sediment flux in dam tailwaters is under strong control by channel geometry. By contrast, dam‐related changes in temperature and flow variation are not significantly modulated by channel geometry or tributary inputs if flow volumes are small (Missouri and Colorado River tributaries). Instead, small tributaries provide near‐native conditions (flow and temperature variation) and, as such, provide key refuges for biota from novel habitats in mainstem rivers below large dams. Unregulated tributaries that are large relative to their respective mainstem (e.g., Yampa River) provide refuges as well as significant amelioration of flow and temperature effects from upstream dams. Finally, the proportion of native fish increases with distance from dam and exhibits sharp increases near tributary junctions. These results suggest that tributaries—even minor ones in terms of relative discharge—act as key refugia for native species in regulated river networks. Moreover, large, unregulated tributaries are key to restoring continuity in physical habitat and the biota in large regulated rivers.

Identification of dominant source of errors in developing streamflow and groundwater projections under near‐term climate change

Uncertainties in projecting the changes in hydroclimatic variables (i.e., temperature and precipitation) under climate change partly arises from the inability of global circulation models (GCMs) in explaining the observed changes in hydrologic variables. Apart from the unexplained changes by GCMs, the process of customizing GCM projections to watershed scale through a model chain—spatial downscaling, temporal disaggregation, and hydrologic model—also introduces errors, thereby limiting the ability to explain the observed changes in hydrologic variability. Toward this, we first propose metrics for quantifying the errors arising from different steps in the model chain in explaining the observed changes in hydrologic variables (streamflow and groundwater). The proposed metrics are then evaluated using a detailed retrospective analyses in projecting the changes in streamflow and groundwater attributes in four target basins that span across a diverse hydroclimatic regimes over the U.S. Sunbelt. Our analyses focused on quantifying the dominant sources of errors in projecting the changes in eight hydrologic variables—mean and variability of seasonal streamflow, mean and variability of 3 day peak seasonal streamflow, mean and variability of 7 day low seasonal streamflow, and mean and standard deviation of groundwater depth—over four target basins using an Penn state Integrated Hydrologic Model (PIHM) between the period 1956–1980 and 1981–2005. Retrospective analyses show that small/humid (large/arid) basins show increased (reduced) uncertainty in projecting the changes in hydrologic attributes. Further, changes in error due to GCMs primarily account for the unexplained changes in mean and variability of seasonal streamflow. On the other hand, the changes in error due to temporal disaggregation and hydrologic model account for the inability to explain the observed changes in mean and variability of seasonal extremes. Thus, the proposed metrics provide insights on how the error in explaining the observed changes being propagated through the model under different hydroclimatic regimes.

Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

Decomposition of Sources of Errors in Monthly to Seasonal Streamflow Forecasts in a Rainfall-Runoff Regime

AbstractDespite considerable progress in developing real-time climate forecasts, most studies have evaluated the potential in seasonal streamflow forecasting based on ensemble streamflow prediction (ESP) methods, utilizing only climatological forcings while ignoring general circulation model (GCM)-based climate forecasts. The primary limitation in using GCM forecasts is their coarse resolution, which requires spatiotemporal downscaling to implement land surface models. Consequently, multiple sources of errors are introduced in developing real-time streamflow forecasts utilizing GCM forecasts. A set of error decomposition metrics is provided to address the following questions: 1) How are errors in monthly streamflow forecasts attributed to various sources such as temporal disaggregation, spatial downscaling, imprecise initial hydrologic conditions (IHCs), climatological forcings, and imprecise forecasts? and 2) How do these errors propagate with lead time over different seasons? A calibrated Variable Infil...

The Role of Multimodel Climate Forecasts in Improving Water and Energy Management over the Tana River Basin, Kenya

The Masinga Reservoir located in the upper Tana River basin, Kenya, is extremely important in supplying the country’s hydropower and protecting downstream ecology. The dam serves as the primary storage reservoir, controlling streamflow through a series of downstream hydroelectric reservoirs. The Masinga dam’s operation is crucial in meeting power demands and thus contributing significantly to the country’s economy. La Ni~ prolonged droughts of 1999‐2001 resulted in severe power shortages in Kenya. Therefore, seasonal streamflow forecasts contingent on climate information are essential to estimate preseason water allocation. Here, the authors utilize reservoir inflow forecasts downscaled from monthly updated precipitation forecasts from ECHAM4.5 forced with constructed analog SSTs and multimodel precipitation forecastsdevelopedfromtheEnsemble-BasedPredictionsofClimateChangesandtheirImpacts(ENSEMBLES) project to improve water allocation during the April‐June and October‐December seasons for the Masinga Reservoir. Three-month-ahead inflow forecasts developed from ECHAM4.5, multiple GCMs, and climatological ensembles are used in a reservoir model to allocate water for power generation by ensuring climatological probability of meeting the end-of-season target storage required to meet seasonal water demands. Retrospective reservoir analysis shows that inflow forecasts developed from single GCM and multiple GCMs perform better than use of climatological values by reducing the spill and increasing the allocation for hydropower during above-normal inflow years. Similarly, during below-normal inflow years, both of these forecasts could be effectively utilized to meet the end-of-season target storage by restricting releases for power generation. The multimodel forecasts preserve the end-of-season target storage better than the singlemodel inflow forecasts by reducing uncertainty and the overconfidence of individual model forecasts.

Multivariate Downscaling Approach Preserving Cross Correlations across Climate Variables for Projecting Hydrologic Fluxes

AbstractMost of the currently employed procedures for bias correction and statistical downscaling primarily consider a univariate approach by developing a statistical relationship between large-scale precipitation/temperature with the local-scale precipitation/temperature, ignoring the interdependency between the two variables. In this study, a multivariate approach, asynchronous canonical correlation analysis (ACCA), is proposed and applied to global climate model (GCM) historic simulations and hindcasts from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to downscale monthly precipitation and temperature over the conterminous United States. ACCA is first applied to the CNRM-CM5 GCM historical simulations for the period 1950–99 and compared with the bias-corrected dataset based on quantile mapping from the Bureau of Reclamation. ACCA is also applied to CNRM-CM5 hindcasts and compared with univariate asynchronous regression (ASR), which applies regular regression to sorted GCM and observed v...