Role of climate forecasts and initial conditions in developing streamflow and soil moisture forecasts in a rainfall–runoff regime

Abstract

Abstract. Skillful seasonal streamflow forecasts obtained from climate and land surface conditions could significantly improve water and energy management. Since climate forecasts are updated on a monthly basis, we evaluate the potential in developing operational monthly streamflow forecasts on a continuous basis throughout the year. Further, basins in the rainfall-runoff regime critically depend on the forecasted precipitation in the upcoming months as opposed to snowmelt regimes where initial hydrological conditions (IHC) play a critical role. The goal of this study is to quantify the role of updated monthly precipitation forecasts and IHC in forecasting 6-month lead monthly streamflow and soil moisture for a rainfall-runoff mechanism dominated basin -Apalachicola River at Chattahoochee, FL. The Variable Infiltration Capacity (VIC) land surface model is implemented with two forcings: (a) updated monthly precipitation forecasts from ECHAM4.5 Atmospheric General Circulation Model (AGCM) forced with sea surface temperature forecasts and (b) daily climatological ensembles. The difference in skill between the above two quantifies the improvements that could be attainable using the AGCM forecasts. Monthly retrospective streamflow forecasts are developed from 1981 to 2010 and streamflow forecasts estimated from the VIC model are also compared with those predicted by using the principal component regression (PCR) model. The mean square error (MSE) in predicting monthly streamflows, using the VIC model, are compared with the MSE of streamflow climatology under ENSO (El Niño Southern Oscilation) conditions as well as under normal years. Results indicate that VIC forecasts obtained using ECHAM4.5 are significantly better than VIC forecasts obtained using climatological ensembles and PCR models over 2-6 month lead time during winter and spring seasons in capturing streamflow variability and reduced mean square errors. However, at 1-month lead time, streamflow utilizing the climatological forcing scheme outperformed ECHAM4.5 based streamflow forecasts during winter and spring, indicating a dominant role of IHCs up to a 1-month lead time. During ENSO years, streamflow forecasts exhibit better skill even up to a six-month lead time.Comparisons of the seasonal soil moisture forecasts, developed using ECHAM4.5 forcings, with seasonal streamflows also show significant skill, up to a 6-month lead time, in the four seasons.

Assessing the resiliency of surface water and groundwater systems under groundwater pumping

“…proposed the uncertainty framework that rigorously incorporates uncertainties in initial 5 conditions and input variables (Li et al, 2009;Sinha and Sankarasubramanian, 2013;Yossef et al, 2013). We proposed an uncertainty framework that estimates the restoration time of groundwater and surface water systems by comparing the distribution of streamflow under "nopumping" with the streamflow obtained under "pumping".…”

“…Spatial variability of initial conditions can induce uncertainties on hydrologic variables especially for short-term simulation (e.g., a year simulation or less) (Moradkhani et al, 2005) Initial conditions of land surface models also influence streamflow forecasts development (Li and Sankarasubramanian., 2012;Sinha and Sankarasubramanian, 2013;Yossef et al, 2013;Li et 20 al., 2016). Given that a small difference in initial conditions such as soil moisture and groundwater storage could substantially alter the estimated groundwater time, we propose here an uncertainty estimation framework that perturbs the initial conditions under "no-pumping" 3) Uncertainty envelope on streamflow and groundwater level were then estimated as the difference between the base simulation series and each perturbed series obtained with 10 different initial conditions.…”

“…Since estimation of restoration times and depletion volumes could be estimated only through modeling efforts, we argue that it is important to consider relevant uncertainties. Hence, we proposed the uncertainty framework that rigorously incorporates uncertainties in initial conditions and input variables (Li et al, 2009;Sinha and Sankarasubramanian, 2013;Yossef et al, 2013). Based on the proposed uncertainty 15 framework, we developed the "null distribution of simulated streamflow" under "no-pumping" and then compare the estimated streamflow under "pumping" to understand the pumping impacts on watershed variables.…”

Hydrological monitoring and seasonal forecasting: Progress and perspectives

“…This may be because the precipitation amount and variation are both low in the dry regions, leading to the weak influence of precipitation to the hydrological estimates (e.g., runoff and soil moisture) (Mo and Lettenmaier, 2014). In addition, the influence of ICs to seasonal hydrological predictability has an obvious interannual variability, e.g., with more important role in neutral years than in El Niño-Southern Oscillation (ENSO)-dominant years (Yuan et al, 2013b;Sinha and Sankarasubramanian, 2013). Through an assessment during the hydrological extremes, the role of ICs differs on the phase of hydrological extremes.…”

On the Dominant Factor Controlling Seasonal Hydrological Forecast Skill in China

“…One common approach is utilizing statistical methods to examine the linkage between indicator variables (including initial moisture states and climate precursors) and the target predictand (e.g., streamflow, soil moisture) [7,8]. Alternatively, a few studies have attempted to assess the role of climate forecasts by comparing the climate model-based hydrological forecasts with the parallel ensemble streamflow prediction (ESP), which utilizes the daily climate traces from history as model forcings [9][10][11][12][13], or to quantify the contributions (isolated and combined) of soil moisture and snowpack initialization to predictive skill by conducting a set of numerical modeling experiments [14,15]. In contrast with these model-based analyses, Wood and Lettenmaier [16] proposed a theoretical ESP/reverse-ESP (revESP) framework to partition the relative roles of ICs and CFs in streamflow forecasts over two basins in western U.S. [16], mainly by comparing two sets of artificial experiments: (1) the ESP forecasts with assumed realistic estimates of initial moisture conditions but random climate forcings; and (2) the parallel revESP with prescribed observed climate forcings but random estimates of initial moisture conditions.…”