Civil and Environmental Engineering

Hydrograph separation is considered as the first step to catchment-scale water balance analysis. A wide variety of hydrograph separation methods exists ranging from empirical to analytical and physical. This study discusses a physically-based approach that combines baseflow separation and event identification with minimal data requirement. The input datasets are basin-average rainfall and discharge time series. Outputs are baseflow time series, the timing of the runoff events, differentiated as single- or multi-peak, and the associated rainfall event time series. To assess the method’s feasibility, hydrograph properties are evaluated for both long-term (annual and monthly) and event-scale time series. Results show that the long-term derived baseflow indices are positive (negative) correlated with basin area (runoff coefficient). The event scale analysis shows that the timing-related parameters (i.e. durations of rainfall and flow events and time lag between rainfall to flow events) increase with basin area in terms of magnitude and variability. Similar dependence on basin scale is shown for the water balance-related parameters determined from this analysis, namely event rainfall and baseflow volumes and baseflow index. Water balance parameters are shown to be characterized with less degree of variability for single-peak events relative to multi-peak events.

Catchment flood response consists of multiple components of flow generated by the heterogeneity of catchment surface. This study proposes an analytical framework built upon the Viglione et al. (2010a) to assess the dependence of catchment flood response on different flow components. The analytical framework is compared to simulations from a distributed hydrologic model. A large number of rainfall-runoff events from three catchments 5 of Tar River basin in North Carolina are used to illustrate the analytical framework. Specifically, the framework is used to estimate three flood events characteristics (cumulative runoff volume, centroid and spreadness of hydrograph) through three corresponding framework parameters: the rainfall excess and the mean and variance of catchment response time. Results show that under the smooth topographic setups of the study area, the spatial and/or temporal correlation between rainfall and runoff generation are insignificant to flood response; delay in 10 flood response due to runoff generation and routing are of equal importance; the shape of flood is mainly controlled by the variability in runoff generation stage but with non-negligible contribution from the runoff routing stage. Sensitivity tests show that the framework's main error source is the systematic underestimation of flood event's centroid and spreadness, while the random error is relatively low.

Basin morphometry is vital information for relating storms to hydrologic hazards, such as landslides and floods. In this paper we present the first comprehensive global dataset of distributed basin morphometry at 30 arc seconds resolution. The dataset includes nine prime morphometric variables; in addition we present formulas for generating twenty-one additional morphometric variables based on combination of the prime variables. The dataset can aid different applications including studies of land-atmosphere interaction, and modelling of floods and droughts for sustainable water management. The validity of the dataset has been consolidated by successfully repeating the Hack's law.

Hydrograph separation method reported in Mei & Anagnostou (2015) includes two aspects: baseflow separation and event identification. The baseflow separation method, namely filtered revised constant k (FRCK) method, is a hybrid-method of the revised constant k (RCK) and recursive digital filter (RDF); it splits the streamflow record into baseflow and event flow component. The event identification method is called characteristic point method (CPM); it extracts rainfall-runoff events from long records of streamflow and basin- average rainfall time series, based on the time series characteristics. More information about this method can be found in the "CPM User Manual_4.3.2017.pdf" document.
The above hydrograph separation methods are contained in six Matlab codes: two for the baseflow separation section and four codes for the event identification. The manuscript provides detailed explanation on the uses, inputs and outputs of these Matlab codes. It is accompanied by a demo Matlab code, illustrating an example basin.
Matlab code can be downloaded from http://ucwater.engr.uconn.edu/models-data/ or https://www.researchgate.net/profile/Yiwen_Mei/publication/316148287_User_Manual_of_the_Characteristic_Point_Method_for_Automatic_Hydrograph_Separation/data/5936ba83458515969b900991/CPM-662017.zip.

This study evaluates the feasibility of using satellite precipitation datasets in flood frequency analysis based on the accuracy of different return period flows derived using a hydrological model driven with satellite and ground-based reference rainfall fields over the Connecticut River Basin. Four quasi-global satellite products (TRMM-3B42V7, TRMM-3B42RT, CMORPH, and PERSIANN) at 3-h/0.25 resolution and the National Weather Service (Stage IV) gauge-adjusted radar rainfall dataset (representing the reference rainfall) are integrated in this study, with the Coupled Routing and Excess Storage distributed hydrological model to simulate annual peak flows during warm season (May–November) months. The log-Pearson type III frequency distribution applied to an 11-year record of annual peak flow data is used to derive different return period flows. Evaluation against the Stage IV-driven simulations shows that the TRMM-3B42V7 product has the highest correlation and lowest bias in terms of the derived annual maxima flows compared to the other satellite products. In terms of the different return period flood frequency curves, the various satellite product-based results well-represent the variability across the different basins depicted in the reference precipitation-driven simulations. With the increasing record length of high-resolution satellite products, results from this paper can motivate future studies over basins lacking adequate ground-based records to support flood frequency analyses.

This study uses data from the Tar-River Basin in North Carolina to explore how space-time rainfall variability influences the hydrologic response from observational and modeling perspectives. For understanding the basin scale effect, the Tar-River Basin is divided into four cascade sub-basins ranging from 1106 km2 up to 5654 km2. The study evaluates the catchments’ response to rainfall for a large number of storm events by computing the event runoff coefficient based on streamflow observations and through simulations from a semi-distributed hydrological model. Comparison of observed to simulated hydrographs from the hydrological model shows that distributed rainfall forcing gives improved performance evaluation metrics relative to basin-average rainfall forcing data. We employ the concepts of “Spatial Moments of Catchment Rainfall (defined as Δ1 and Δ2)” and “Catchment Scale Storm Velocity (defined as Vs)” reported in Zoccatelli et al. (2011) to quantify the effect of spatial rainfall organization and basin geomorphology on modeling the flood response. Our analysis using the above conceptual framework shows that the rainfall spatiotemporal variation plays a significant role on the timing and dispersion of the simulated hydrographs. Specifically, Δ1 increases linearly with the difference in timing between lumped and distributed rainfall forcing. Δ2 and the product between Vs and the variance of hydrograph arrival time exhibit an increasing trend with the difference in dispersion of simulated
hydrographs between lumped and distributed rainfall forcing.

The overarching goal of the research described in this dissertation is to understand the hydrologic implications of error propagation from satellite precipitation products to hydrologic simulations. The complex interaction between precipitation error and corresponding hydrologic response is examined following a numerical- and an analytical-based approach. The application of a hydrologic model forced by various satellite precipitation products is adopted as the numerical-based framework that is used to identify the properties of error propagation with respect to a number of factors (e.g. basin scale, seasonality, severity of rainfall and flow). The investigation is conducted in complex terrain basins of the Eastern Italian Alps. Results show better consistency between gauges for events occurred over larger scale basins during warm season months that are associated with moderate intensity of rain and flow rate. Furthermore, an event -based error analysis is conducted focusing on the evaluation of satellite-simulated flood event characteristics for different flood types. Results revealed that on average systematic rainfall error is reduced in simulated runoff, highlighting the dampening effect on error during the rainfall-runoff transformation. The dampening effect on random error was decreasing with increasing runoff coefficient. In addition to the numerical investigation, an analytical framework is developed to decompose the error propagation into space and time components. This essentially allows to assess the relative contribution of the different processes of catchment flood response on error propagation. Demonstration of the analytical framework is conducted based on 180 rainfall-runoff events that occurred over the Tar River basin in North Carolina. It is shown that error in timing of flood event is attributed equally to error in runoff generation and routing time. Error in hydrograph shape is mainly controlled by the error in the variability of runoff generation time while error in flood volume is predominantly controlled by the error in rainfall volume. Overall, these investigations provide important information for the hydrologic modelers to choose the appropriate precipitation products for the hydrologic-related practice. It also serves as guidance for the satellite precipitation-product developers on the designs of more advance retrieval algorithms.

This study uses an analytical hydrological framework to investigate the error propagation from satellite precipitation products to hydrological simulations. Specifically, the analytical formulation of the framework allows linking the error in hydrograph properties (i.e., cumulative volume, centroid, and dispersion) to the space-time characteristics of error in satellite-precipitation, runoff generation, and routing. Main finding from this study are that (i) the error in spatial and temporal covariance between rainfall and runoff generation is not contributing significantly to the error in cumulative volume of flood events; (ii) errors in runoff generation and routing time are of equal importance in terms of the overall error in the arrival of flood event centroid; and (iii) errors in the variability of runoff generation time is the main contributor to the error in dispersion of flood event hydrograph. Furthermore, sensitivity tests show that errors in hydrograph properties are strongly correlated with errors in the space-time characteristics of precipitation, runoff generation and routing parameters estimated by the analytical framework.

Toward qualifying hydrologic changes in the High Mountain Asia (HMA) region, this study explores the use of a hyper-resolution (1 km) land data assimilation (DA) framework developed within the NASA Land Information System using the Noah Multi-parameterization Land Surface Model (Noah-MP) forced by the meteorological boundary conditions from Modern-Era Retrospective analysis for Research and Applications, Version 2 data. Two different sets of DA experiments are conducted: (1) the assimilation of a satellite-derived snow cover map (MOD10A1) and (2) the assimilation of the NASA MEaSUREs landscape freeze/thaw product from 2007 to 2008. The performance of the snow cover assimilation is evaluated via comparisons with available remote sensing-based snow water equivalent product and ground-based snow depth measurements. For example, in the comparison against ground-based snow depth measurements, the majority of the stations (13 of 14) show slightly improved goodness-of-fit statistics as a result of the snow DA, but only four are statistically significant. In addition, comparisons to the satellite-based land surface temperature products (MOD11A1 and MYD11A1) show that freeze/thaw DA yields improvements (at certain grid cells) of up to 0.58 K in the root-mean-square error (RMSE) and 0.77 K in the absolute bias (relative to model-only simulations). In the comparison against three ground-based soil temperature measurements along the Himalayas, the bias and the RMSE in the 0–10 cm soil temperature are reduced (on average) by 10 and 7%, respectively. The improvements in the top layer of soil estimates also propagate through the deeper soil layers, where the bias and the RMSE in the 10–40 cm soil temperature are reduced (on average) by 9 and 6%, respectively. However, no statistically significant skill differences are observed for the freeze/thaw DA system in the comparisons against ground-based surface temperature measurements at mid-to-low altitude. Therefore, the two proposed DA schemes show the potential of improving the predictability of snow mass, surface temperature, and soil temperature states across HMA, but more ground-based measurements are still required, especially at high-altitudes, in order to document a more statistically significant improvement as a result of the two DA schemes.

We have used the following convention to highlight our changes in response to the referees' comments: red text indicates modification, blue text indicates addition, and margin parameters indicate responses to specific comment labeled as RxCy (Reviewer #x Comment #y) and RxMCy (Reviewer #x Minor Comment #y) With more satellite and model precipitation data becoming available, new analytical methods are needed that can take advantage of emerging data patterns to make well informed predictions in many hydrological applications. We propose a new strategy where we extract precipitation variability patterns and use correlation map to build the resulting density map that serves as an input to centroidal Voronoi tessellation construction that optimizes placement of precipitation gauges. We provide results of numerical experiments based on the data from the Alto-Adige region in Northern Italy and Oklahoma and compare them against actual gauge locations. This method provides an automated way for choosing new gauge locations and can be generalized to include physical constraints and to tackle other types of resource allocation problems.

Despite increasing evidence of intensification of extreme precipitation events associated with a warming climate, the magnitude of peak river flows is decreasing in many parts of the world. To better understand the range of relationships between precipitation extremes and floods, we analyzed annual precipitation extremes and flood events over the contiguous United States from 1980 to 2014. A low correlation (less than 0.2) between changes in precipitation extremes and changes in floods was found, attributable to a small fraction of co-occurrence. The covariation between precipitation extremes and floods is also substantially low, with a majority of catchments having a coefficient of determination of less than 0.5, even among the catchments with a relatively high fraction of annual maxima precipitation that can be linked to floods. The findings indicate a need for more investigations into causal mechanisms driving a nonlinear response of floods to intensified precipitation extremes in a warming climate.

The accurate representation of the local-scale variability of precipitation plays an important role in understanding the hydrological cycle and land-atmosphere interactions in the High Mountain Asia region. Therefore, the development of hyper-resolution precipitation data is of urgent need. In this study, we propose a statistical framework to downscale the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) precipitation product using the random forest classification and regression algorithm. A set of variables representing atmospheric, geographic, and vegetation cover information are selected as model predictors, based on a recursive feature elimination method. The downscaled precipitation product is validated in terms of magnitude and variability against a set of ground-and satellite-based observations. Results suggest improvements with respect to the original resolution MERRA-2 precipitation product and comparable performance with gauge-adjusted satellite precipitation products.