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Sankarasubramanian, A.

Publications and source records attributed to Sankarasubramanian, A..

How Does Flow Alteration Propagate Across a Large, Highly Regulated Basin? Dam Attributes, Network Context, and Implications for Biodiversity

Abstract Large dams are a leading cause of river ecosystem degradation. Although dams have cumulative effects as water flows downstream in a river network, most flow alteration research has focused on local impacts of single dams. Here we examined the highly regulated Colorado River Basin (CRB) to understand how flow alteration propagates in river networks, as influenced by the location and characteristics of dams as well as the structure of the river network—including the presence of tributaries. We used a spatial Markov network model informed by 117 upstream‐downstream pairs of monthly flow series (2003–2017) to estimate flow alteration from 84 intermediate‐to‐large dams representing >83% of the total storage in the CRB. Using Least Absolute Shrinkage and Selection Operator regression, we then investigated how flow alteration was influenced by local dam properties (e.g., purpose, storage capacity) and network‐level attributes (e.g., position, upstream cumulative storage). Flow alteration was highly variable across the network, but tended to accumulate downstream and remained high in the main stem. Dam impacts were explained by network‐level attributes (63%) more than by local dam properties (37%), underscoring the need to consider network context when assessing dam impacts. High‐impact dams were often located in sub‐watersheds with high levels of native fish biodiversity, fish imperilment, or species requiring seasonal flows that are no longer present. These three biodiversity dimensions, as well as the amount of dam‐free downstream habitat, indicate potential to restore river ecosystems via controlled flow releases. Our methods are transferrable and could guide screening for dam reoperation in other highly regulated basins.

54 ENVIRONMENTAL SCIENCES↗

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↗