No abstract

Reservoir storage-area-depth relationships are the most important factors controlling thermal stratification in reservoirs and, more broadly, the water, energy, and biogeochemical dynamics in the reservoirs and subsequently their impacts on downstream rivers. However, most land surface or Earth system models do not account for the gradual changes of reservoir surface area and storage with the changing depth, inhibiting a consistent and accurate representation of mass, energy, and biogeochemical balances in reservoirs. Here we present a physically coherent parameterization of reservoir storage-area-depth data set at the global scale. For each reservoir, the storage-area-depth relationships were derived from an optimal geometric shape selected iteratively from five possible regular geometric shapes that minimize the error of total storage and surface area estimation. We applied this algorithm to over 6,800 reservoirs included in the Global Reservoir and Dam database. The relative error between the estimated and observed total storage is no more than 5% and 50% for 66% and 99% of all Global Reservoir and Dam reservoirs, respectively. More importantly, the storage-depth profiles derived from the approximated reservoir geometry compared well with remote sensing based estimation at 40 major reservoirs from previous studies and ground-truth measurements for 34 reservoirs in the United States and China. The new global reservoir storage-area-depth data set is critical for advancing future modeling and understanding of reservoir processes and subsequent effects on the terrestrial hydrological, ecological, and biogeochemical cycles at the regional and global scales.Previous land surface modeling studies used simplified storage-area-depth relationships for reservoir modeling. For example, Neumann (1959) and Lehman (1975) used depth ratio (mean depth to maximum YIGZAW ET AL.10,372

A recent U.S. Department of Energy study estimated that more than one billion tons of biofuel feedstock could be produced by 2030 in the United States from increased corn yield, and changes in agricultural and forest residue management and land uses. To understand the implications of such increased production on water resources and stream quality at regional and local scales, we have applied a watershed model for the Upper Mississippi River Basin, where most of the current and future crop/residue-based biofuel production is expected. The model simulates changes in water quality (soil erosion, nitrogen and phosphorus loadings in streams) and resources (soil-water content, evapotranspiration, and runoff) under projected biofuel production versus the 2006 baseline year and a business-as-usual scenario. The basin average results suggest that the projected feedstock production could change the rate of evapotranspiration in the UMRB by approximately +2%, soil-water content by about -2%, and discharge to streams by -5% from the baseline scenario. However, unlike the impacts on regional water availability, the projected feedstock production has a mixed effect on water quality, resulting in 12% and 45% increases in annual suspended sediment and total phosphorus loadings, respectively, but a 3% decrease in total nitrogen loading. These differences in water quantity and quality are statistically significant (p < 0.05). The basin responses are further analyzed at monthly time steps and finer spatial scales to evaluate underlying physical processes, which would be essential for future optimization of environmentally sustainable biofuel productions.

Understanding the causes of flood seasonality is critical for better flood management. This study examines the seasonality of annual maximum floods (AMF) and its changes before and after 1980 at over 250 natural catchments across the contiguous United States. Using circular statistics to define a seasonality index, the analysis focuses on the variability of the flood occurrence date. Generally, catchments with more synchronized seasonal water and energy cycles largely inherit their seasonality of AMF from that of annual maximum rainfall (AMR). In contrast, the seasonality of AMF in catchments with loosely synchronized water and energy cycles are more influenced by high antecedent storage, which is responsible for the amplification of the seasonality of AMF over that of AMR. This understanding then effectively explains a statistically significant shift of flood seasonality detected in some catchments in the recent decades. Catchments where the antecedent soil water storage has increased since 1980 exhibit increasing flood seasonality while catchments that have experienced increases in storm rainfall before the floods have shifted toward floods occurring more variably across the seasons. In the eastern catchments, a concurrent widespread increase in event rainfall magnitude and reduced soil water storage have led to a more variable timing of floods. The findings of the role of antecedent storage and event rainfall on the flood seasonality provide useful insights for understanding future changes in flood seasonality as climate models projected changes in extreme precipitation and aridity over land.

Despite progresses in representing different processes, hydrological models remain uncertain. Their uncertainty stems from input and calibration data, model structure, and parameters. In characterizing these sources, their causes, interactions and different uncertainty analysis (UA) methods are reviewed. The commonly used UA methods are categorized into six broad classes: (i) Monte Carlo analysis, (ii) Bayesian statistics, (iii) multi-objective analysis, (iv) least-squares-based inverse modeling, (v) response-surface-based techniques, and (vi) multi-modeling analysis. For each source of uncertainty, the status-quo and applications of these methods are critiqued in gauged catchments where UA is common and in ungauged catchments where both UA and its review are lacking. Compared to parameter uncertainty, UA application for structural uncertainty is limited while input and calibration data uncertainties are mostly unaccounted. Further research is needed to improve the computational efficiency of UA, disentangle and propagate the different sources of uncertainty, improve UA applications to environmental changes and coupled human–natural-hydrologic systems, and ease UA’s applications for practitioners.