Quantifying Streamflow Regime Behavior and its Sensitivity to Demand
The metrics developed here to quantify drought and flood episodes are based on an extended sequent peak algorithm that tracks the dynamic shifts in hydrological behavior of streamflow by accounting for water supply and demand with respect to streamflow. When applied to streamflow reconstructions in the Upper Missouri River Basin (UMRB), we find that the duration of dry periods increase conspicuously as a function of increasing demand levels, the duration of pluvial events decrease as a function of increasing demand levels, and that the general tendency is for streamflow gauges on or near the main stem of the river to have shorter dry spell durations and typically lower drought severity. On the other hand, being on or near the main stem tends to result in longer-duration pluvial events, though these pluvials are typically less severe than those off the main stem. It was also found that there is a stochastic dependence between the length of a drought and the time to recovery from that drought, and this dependence is used to create simple conditional probability curves to help water managers prepare for future extreme events.
This study gave rise to a new way of characterizing, quantifying and identifying hydrological regime behavior using novel, original metrics that are jointly dependent on the supply-side as well as demand-side of streamflow activity. Additionally, this study outlined the importance of partitioning the dry phase of a regime cycle into two distinct sub-phases of drought duration and recovery, investigated their regime behavior and joint dependency, and exploited this joint dependency to develop a concept of conditional probability plots that water managers can use to help plan and prepare for drought/flood events, which are societally costly and traumatic events.
We developed seven metrics to quantitatively define a particular aspect of regime behavior that can then be used as an analytical tool on streamflow data to understand the patterns of regime behavior in the given data record. These metrics build on our ongoing efforts to advance our understanding, quantification, and prediction of hydrologic extremes and regimes. Understanding the past relative to current and future demand is the only way to know the risks to the water systems and what to expect in the future, making this a crucial element of water resources management. The results of the methodology developed and employed in this paper can be useful to a next step in which we use the results about past hydrological behavior concerning drought and pluvial events to simulate ensembles of future streamflow scenarios that can then be used directly in a basin management process model. These models are in turn used to develop water policies for now as well as the future.