Comparing and Advancing Approaches to Long-Term Flood Projection

<p>How should design flood magnitudes be estimated under climate change? Apart from assuming stationarity, the two main approaches are hydrologic simulation and informed-parameter, which is generally based on either trend or climate covariates. Here, we compare these approaches across a large set of hydro-climatologically diverse basins located throughout the contiguous United States, splitting the historic record into a calibration and validation time period. We evaluate performance when the approaches are forced with observed climate as well as simulated climate from general circulation models. We also investigate how the strengths of the climate informed and hydrologic simulation approaches can be combined to improve projections; here, we use the outputs of hydrologic simulation as covariates in the climate informed approach. The results provide a quantitative perspective on key long-term flood projection issues and provide a route forward to improving projections given the identified strengths and weaknesses of each approach.</p>

On the Performance of Satellite-Based Precipitation Products in Simulating Streamflow and Water Quality During Hydrometeorological Extremes

This study provides a comprehensive evaluation of streamflow and water quality simulated by a hydrological model using three different Satellite Precipitation Products (SPPs) with respect to observations from a dense rain gauge network over the Occoquan Watershed, located in Northern Virginia, suburbs to Washington, D.C., U.S. Eight extreme hydrometeorological events within a 5-year period between 2008 and 2012 are evaluated using SPPs, TMPA 3B42-V7, CMORPH V1. 0, and PERSIANN-CCS, which are based on different retrieval algorithms with varying native spatial and temporal resolutions. A Hydrologic Simulation Program FORTRAN (HSPF) hydrology and water quality model was forced with the three SPPs to simulate output of streamflow (Q), stream temperature (TW), and concentrations of total suspended solids (TSS), orthophosphate phosphorus (OP), total phosphorus (TP), ammonium-nitrate (NH4-N), nitrate-nitrogen (NO3-N), dissolved oxygen (DO), and biochemical oxygen demand (BOD) at six evaluation points within the watershed. Results indicate fairly good agreement between gauge- and SPP-simulated Q for TMPA and CMORPH, however, PERSIANN-simulated Q is lowest among SPPs, due to its inability to accurately measure stratiform precipitation between intense periods of precipitation during an extreme event. Correlations of water quality indicators vary considerably, however, TW has the strongest positive linear relationship compared to other indicators evaluated in this study. SPP-simulated TSS, a flow-dependent variable, has the weakest relationship to gauge-simulated TSS among all water quality indicators, with CMORPH performing slightly better than TMPA and PERSIANN. This study demonstrated that the spatiotemporal variability of SPPs, along with their algorithms to estimate precipitation, have an influence on water quality simulations during extreme hydrometeorological events.

Performance evaluation of multiple satellite rainfall products for Dhidhessa River Basin (DRB), Ethiopia

Precipitation is a crucial driver of hydrological processes. Ironically, reliable characterization of its spatiotemporal variability is challenging. Ground-based rainfall measurements using rain gauges can be more accurate. However, installing a dense gauging network to capture rainfall variability can be impractical. Satellite-based rainfall estimates (SREs) can be good alternatives, especially for data-scarce basins like in Ethiopia. However, SREs rainfall is plagued with uncertainties arising from many sources. The objective of this study was to evaluate the performance of the latest versions of several SREs products (i.e., CHIRPS2, IMERG6, TAMSAT3, and 3B42/3) for the Dhidhessa River Basin (DRB). Both statistical and hydrologic modelling approaches were used for performance evaluation. The Soil and Water Analysis Tool (SWAT) was used for hydrological simulations. The results showed that whereas all four SREs products are promising to estimate and detect rainfall for the DRB, the CHIRPS2 dataset performed the best at annual, seasonal, and monthly timescales. The hydrologic simulation-based evaluation showed that SWAT's calibration results are sensitive to the rainfall dataset. The hydrologic response of the basin is found to be dominated by the subsurface processes, primarily by the groundwater flux. Overall, the study showed that both CHIRPS2 and IMERG6 products can be reliable rainfall data sources for hydrologic analysis of the Dhidhessa River Basin.

Importance of Detailed Soil Information for Hydrological Modelling in an Urbanized Environment

Soil information is critical in watershed-scale hydrological modelling; however, it is still debated which level of complexity the soil data should contain. In the present study, we have compared the effect of two levels of soil data on the hydrologic simulation of a mesoscale, urbanised watershed (630 km2) in central South Africa. The first level of soil data, land type (LT) data, is currently the best, readily available soil information that covers the whole of South Africa. In the LT database, the entire study area is covered by only two soil types. The second level of soil data (DSM) was created by means of digital soil mapping based on hydropedological principles. It resulted in six different soil types with different hydrological behaviour (e.g., interflow, recharge, responsive). The two levels of soil data were each included in the revised version of the Soil and Water Assessment Tool (SWAT+). To compare the effects of different complexity of soil information on the simulated water balance, the outputs of the uncalibrated models were compared to the three nested gauging stations of the watershed. For the LT scenario, the simulation efficiencies calculated with the Kling–Gupta efficiency (KGE) for the three nested gauging stations (640 km2, 550 km2, 54 km2) of 0, 0.33 and −0.23 were achieved, respectively. Under the DSM scenario, KGE increased to 0.28, 0.44 and 0.43 indicating an immediate improvement of the simulation by integrating soil data with detailed information on hydrological behaviour. In the LT scenario, actual evapotranspiration (aET) was clearly underestimated compared to MODIS-derived aET, while surface runoff was overestimated. The DSM scenario resulted in higher simulated aET compared to LT and lower surface runoff. The higher simulation efficiency of DSM in the smaller headwater catchments can be attributed to the inclusion of the interflow soil type, which covers the governing runoff generation process better than the LT scenario. Our results indicate that simulations benefit from more detailed soil information, especially in smaller areas where fewer runoff generation processes dominate.