Selection of an appropriately simple storm runoff model

An appropriately simple event runoff model for catchment hydrological studies was derived. The model was selected from several variants as having the optimum balance between simplicity and the ability to explain daily observations of streamflow from 260 Australian catchments (23–1902 km<sup>2</sup>). Event rainfall and runoff were estimated from the observations through a combination of baseflow separation and storm flow recession analysis, producing a storm flow recession coefficient (<i>k</i><sub>QF</sub>). Various model structures with up to six free parameters were investigated, covering most of the equations applied in existing lumped catchment models. The performance of alternative structures and free parameters were expressed in Aikake's Final Prediction Error Criterion (FPEC) and corresponding Nash-Sutcliffe model efficiencies (NSME) for event runoff totals. For each model variant, the number of free parameters was reduced in steps based on calculated parameter sensitivity. The resulting optimal model structure had two or three free parameters; the first describing the non-linear relationship between event rainfall and runoff (<i>S</i><sub>max</sub>), the second relating runoff to antecedent groundwater storage (<i>C</i><sub>Sg</sub>), and a third that described initial rainfall losses (<i>L</i><sub>i</sub>), but which could be set at 8 mm without affecting model performance too much. The best three parameter model produced a median NSME of 0.64 and outperformed, for example, the Soil Conservation Service Curve Number technique (median NSME 0.30–0.41). Parameter estimation in ungauged catchments is likely to be challenging: 64% of the variance in <i>k</i><sub>QF</sub> among stations could be explained by catchment climate indicators and spatial correlation, but corresponding numbers were a modest 45% for <i>C</i><sub>Sg</sub>, 21% for <i>S</i><sub>max</sub> and none for <i>L</i><sub>i</sub>, respectively. In gauged catchments, better estimates of event rainfall depth and intensity are likely prerequisites to further improve model performance

Spatiotemporal variation in soil moisture and hydraulic properties and their impacts on rainfall -runoff and infiltration processes

In arid and semi-arid regions such as in the southwestern United States, soil moisture is an essential component of desert ecosystems. Gaining better knowledge of moisture dynamics through appropriate numerical modeling will help us understand physical mechanisms that influence soil hydrologic processes in these regions. Moreover, numerical modeling of these processes is often emphasized because most desert watersheds are ungauged, and thus field observations are either not readily available or difficult to simulate. In this dissertation, three modeling studies were conducted to investigate the temporal and spatial soil moisture variation and hydraulic properties, and their effect on rainfall-runoff and infiltration processes; The goal of the first study was to simulate the long-term (18,000 yrs) multi-phase (liquid and vapor) water fluxes and associated chloride fluxes in the northern Mojave Desert by applying different reconstructed boundary conditions in the simulation. The results showed that the observed near-surface chloride peak reflected the combined boundary conditions of precipitation, root-water uptake, and soil evaporation. The results showed that climate shift alone (with normal precipitation patterns) was not the major driving force that initiated the observed near-surface chloride accumulation. Rather, the results showed that root water uptake and extreme storm events, embedded within the normal precipitation patterns, were the major driving forces that controlled the paleo-water fluxes and chloride profile distributions. Also, the results showed that chloride accumulations were highest at the zone of maximum root zone distribution of Mojave Desert shrubs, not the depth of the roots. Thus, observed chloride accumulations deeper than the active root zones still cannot been fully explained; The second study was to assess three different methods used to generate spatially distributed hydraulic properties, by simulating surface runoff on a semi-arid rangeland at the Walnut Gulch Experimental Watershed, outside of Tombstone, AZ. By collecting 66 soil samples (2 samples at each of 33 sites) and using pedotransfer functions, soil hydraulic properties were derived. Then three methods were used to generate the parameter fields of a two-dimensional diffusion wave model to simulate a total of eight storm events with measured runoff. The results showed that co-kriging was the best approach to represent the spatial variability of soil hydraulic properties. The results also showed that the need to calibrate plant interception models based on historical records of shrub versus grassland coverage; The goal of the third study was to understand the influence of desert pavement on infiltration and surface runoff, and to calibrate relevant Green-Ampt infiltration parameters. To achieve the goal, twelve rainfall simulator tests were conducted in the Mojave National Preserve, CA and the in-situ infiltration and surface runoff were measured. The results showed no statistical difference between the infiltration characteristics between plots with and without desert pavement (i.e., clast) surfaces. The results indicated that variability of soil texture exerted a larger effect on infiltration than the effects introduced by the surface clasts only. However, an optimization method was necessary to calibrate the Green-Ampt parameters. In these cases, the optimized parameters underestimated and overestimated hydraulic conductivity values, compared to pedotransfer functions and tension infiltrometer tests, respectively; The modeling results in this dissertation showed how numerical simulations can be used to assess soil moisture dynamics and model parameter variations in arid and semi-arid regions. The studies quantitatively modeled several hydrologic processes in the northern Mojave Desert such as vertical water fluxes, and helped determine effective hydraulic conductivities on alluvial fans with desert pavement. These results provide fundamental knowledge of infiltration and deep percolation in this desert region of the United States, and show how features of these sites were well preserved during the physically-based modeling processes. We showed also that the modeling approaches can more effective than empirical correlations for predicting water flux as environmental (i.e., climate) conditions change. But it is noted that appropriate boundary conditions and model parameters were found to be most important aspects of producing reliable modeling results

Modeling Riparian Restoration Impacts on the Hydrologic Cycle at the Babacomari Ranch, SE Arizona, USA

This paper describes coupling field experiments with surface and groundwater modeling to investigate rangelands of SE Arizona, USA using erosion-control structures to augment shallow and deep aquifer recharge. We collected field data to describe the physical and hydrological properties before and after gabions (caged riprap) were installed in an ephemeral channel. The modular finite-difference flow model is applied to simulate the amount of increase needed to raise groundwater levels. We used the average increase in infiltration measured in the field and projected on site, assuming all infiltration becomes recharge, to estimate how many gabions would be needed to increase recharge in the larger watershed. A watershed model was then applied and calibrated with discharge and 3D terrain measurements, to simulate flow volumes. Findings were coupled to extrapolate simulations and quantify long-term impacts of riparian restoration. Projected scenarios demonstrate how erosion-control structures could impact all components of the annual water budget. Results support the potential of watershed-wide gabion installation to increase total aquifer recharge, with models portraying increased subsurface connectivity and accentuated lateral flow contributions.Walton Family Foundation; Land Change Science (LCS) Program, under the Land Resources Mission Area of the US Geological Survey (USGS); NSF [DBI-0735191, DBI-1265383]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Thermal and Hydrologic Performance of an Extensive Green Roof in Syracuse, New York

As the climate continues to change, humanity is increasingly under threat of the associated environmental consequences. Sustainable cities, resilient in the face of changing climate, are needed as a habitat for humanity. Cities are complex and there are many factors that influence their resiliency and health. Some recent efforts have focused on increasing urban green spaces and their associated ecosystem services as a way to address multiple urban issues synergistically. One green infrastructure technology, green roofs, provide the opportunity to add green spaces to cities without losing valuable real estate at street level, while providing multiple ecosystem services not provided by a traditional roof. Despite increasing construction of green roofs within urban centers, the performance of green roofs has not been adequately measured quantitatively. My work aims to enhance our understanding of the thermal and hydrologic performance of a large extensive green roof. Further, to bridge the gap between research and the practice of engineering, this study also considers the performance of commonly applied models in predicting green roof performance. Empirical data are collected at Syracuse, New York’s OnCenter green roof over the course of eight years, including rainfall, runoff, soil moisture, thermal roof properties, and meteorological parameters. As we collectively work to adapt to a changing climate and to design more resilient cities, green roofs are being investigated for their role in the overall energy balance of buildings. In the first part of this work, I determine the thermal properties of the OnCenter green roof using temperature sensors installed during roof construction. Temperature sensors were installed at five stations across the roof to measure temperature at four depths within the roof layers. Heat fluxes range from −5.76 W m−2 to 9.46 W m−2. Negative (downward) heat flux is found during summer and early fall, and positive (upward) heat flux dominates during the heating season. Solar radiation can heat the upper layers of the roof significantly above ambient air temperatures during the summer. Accumulated snow acts as an insulator during the winter months. Thermal resistance, R, is determined during a two-week period with significant snow accumulation, during which time heat flow through the roof reached a quasi-steady state. Thermal resistance for the overall roof is found to average 3.1 m2 K W−1. The largest individual thermal resistance is from the extruded polystyrene insulation layer (R = 2.6 m2 K W−1). Overall, the green roof dampens the temperature or heat flux responses often observed on urban roofs. Vegetation and substrate layers may be used in addition to insulation but are not recommended in lieu of insulation for a Central New York climate. Success in creating resilient cities also relies on the ability of urban areas to function with the hydrologic changes brought on by climate change. Green roof hydrologic performance reported in the literature varies widely – the result of differences in green roof design and climate, as well as limitations to study design and duration. In the second part of this work, I quantify the hydrologic performance of the extensive green roof on the OnCenter over a period of 21 months. Over the monitoring period, the roof retains 56% of the 1062 mm of rainfall recorded. Peak runoff is reduced by an average of 65%. Eleven events exceed 20 mm and are responsible for 38% of the rainfall and 24% of the annual retention. Retention in the summer is lower than that in the fall or spring, as a result of greater rainfall intensity during the period sampled. Soil moisture during winter months remains high, reducing the ability of the roof to retain rainfall volume from new events. Comparison of seasonal data demonstrates the strong influence of rainfall intensity on runoff and the effect of initial soil moisture on event retention. Green roofs are being applied as a modern stormwater management tool at an increasing rate across the globe. To apply this technology, however, practitioners must conform with regulatory requirements, for which two methods dominate: the SCS Curve Number method and the Rational method. Universally accepted model inputs (CN and Cv respectively) do not exist for green roofs, and likely vary based on roof composition and region. In this study, I calibrate CN and Cv using nearly seven years of rainfall-runoff data from the OnCenter green roof. Median event CN and a least-squares estimate both result in a CN of 96. When season is included in the analysis, calculated CN in the winter (CN = 99) exceeds that generally used to model an impervious surface (CN = 98), while the summer has the lowest CN (95). Event Cv ranges from 0 to 0.99 with a median of 0.06, however Cv increases with depth of rainfall. Overall, the values skew towards the higher side of what is reported in the literature and closer to impervious surfaces than natural vegetated surfaces. This pattern may indicate the inappropriateness of the currently accepted methods to fully capture the performance of green roofs and their contribution to urban stormwater management. The results of this study suggest overestimating the hydrologic performance of a green roof via lower or inaccurate curve numbers may have negative consequences in practice. This research makes a valuable contribution to our understanding of green roofs and their performance within the context of the Central New York climate. The contribution of this work, however, extends beyond the region by highlighting future areas of research and capturing the inability of commonly-used hydrologic models to accurately account for the performance of green roofs. The results of this work inform the design and adoption of green roofs by practitioners, and the regulations enacted by policymakers that influence our built environment

Coupling the modified SCS-CN and RUSLE models to simulate hydrological effects of restoring vegetation in the Loess Plateau of China

Predicting event runoff and soil loss under different land covers is essential to quantitatively evaluate the hydrological responses of vegetation restoration in the Loess Plateau of China. The Soil Conservation Service curve number (SCS-CN) and Revised Universal Soil Loss Equation (RUSLE) models are widely used in this region to this end. This study incorporated antecedent moisture condition (AMC) in runoff production and initial abstraction of the SCS-CN model, and considered the direct effect of runoff on event soil loss by adopting a rainfall-runoff erosivity factor in the RUSLE model. The modified SCS-CN and RUSLE models were coupled to link rainfall-runoff-erosion modeling. The effects of AMC, slope gradient and initial abstraction ratio on curve number of SCS-CN, as well as those of vegetation cover on cover-management factor of RUSLE, were also considered. Three runoff plot groups covered by sparse young trees, native shrubs and dense tussock, respectively, were established in the Yangjuangou catchment of Loess Plateau. Rainfall, runoff and soil loss were monitored during the rainy season in 2008–2011 to test the applicability of the proposed approach. The original SCS-CN model significantly underestimated the event runoff, especially for the rainfall events that have large 5-day antecedent precipitation, whereas the modified SCS-CN model was accurate in predicting event runoff with Nash-Sutcliffe model efficiency (EF) over 0.85. The original RUSLE model overestimated low values of measured soil loss and underpredicted the high values with EF values only about 0.30. In contrast, the prediction accuracy of the modified RUSLE model improved with EF values being over 0.70. Our results indicated that the AMC should be explicitly incorporated in runoff production, and direct consideration of runoff should be included when predicting event soil loss. Coupling the modified SCS-CN and RUSLE models appeared to be appropriate for evaluating hydrological effects of restoring vegetation in the Loess Plateau. The main advantages, limitations and future study scopes of the proposed models were also discussed

Turbidity-Based Sediment Monitoring in Northern Thailand: Hysteresis, Variability, and Uncertainty

Annual total suspended solid (TSS) loads in the Mae Sa River in northern Thailand, determined with an automated, turbidity-based monitoring approach, were approximately 62,000, 33,000, and 14,000 Mg during the three years of observation. These loads were equivalent to basin yields of 839 (603-1170), 445 (217-462), and 192 (108-222) Mg km-2 for the 74.16-km2 catchment during 2006, 2007, and 2008, respectively. The yearly uncertainty ranges indicate our loads may be underestimated by 38-43% or overestimated by 28-33%. In determining the annual loads, discharge (Q) and turbidity (T) values were compared against 333 hand-sampled total suspended solid concentrations (TSS) measured during 18 runoff events and other flow conditions across the three-year period. Annual rainfall varied from 1632 to 1934 mm; and catchment runoff coefficients (annual runoff/annual rainfall) ranged from 0.25 to 0.41. Measured TSS ranged from 8-15,900 mg l-1; the low value was associated with dry-season base flow; the latter, a wet-season storm. Storm size and location played an important role in producing clockwise, anticlockwise, and complex hysteresis effects in the Q-TSS relationship. Turbidity alone was a good estimator for turbidity ranges of roughly 10-2800 NTU (or concentrations approximately 25-4000 mg l-1). However, owing to hysteresis and high sediment concentrations that surpass the detection limits of the turbidity sensor during many annual storms, TSS was estimated best using a complex multiple regression equation based on high/low ranges of turbidity and Q as independent variables. Turbidity was not a good predictor of TSS fractions \u3e 2000 μm. Hysteresis in the monthly Q-TSS relationship was generally clockwise over the course of the monsoon season, but infrequent large dry-season storms disrupted the pattern in some years. The large decrease in annual loads during the study was believed to be related to depletion of fine sediment delivered to the stream by several landslides occurring the year prior to the study. The study indicated the importance of monitoring Q and turbidity at fine resolutions (e.g., sub-hourly) to capture the TSS dynamics and to make accurate load estimations in this flashy headwater stream where hysteresis in the Q-TSS signature varied at several time scales