Hydrological Alteration Index as an Indicator of the Calibration Complexity of Water Quantity and Quality Modeling in the Context of Global Change

Modeling is a useful way to understand human and climate change impacts on the water resources of agricultural watersheds. Calibration and validation methodologies are crucial in forecasting assessments. This study explores the best calibration methodology depending on the level of hydrological alteration due to human-derived stressors. The Soil and Water Assessment Tool (SWAT) model is used to evaluate hydrology in South-West Europe in a context of intensive agriculture and water scarcity. The Index of Hydrological Alteration (IHA) is calculated using discharge observation data. A comparison of two SWAT calibration methodologies are done; a conventional calibration (CC) based on recorded in-stream water quality and quantity and an additional calibration (AC) adding crop managements practices. Even if the water quality and quantity trends are similar between CC and AC, water balance, irrigation and crop yields are different. In the context of rainfall decrease, water yield decreases in both CC and AC, while crop productions present opposite trends (+33% in CC and -31% in AC). Hydrological performance between CC and AC is correlated to IHA: When the level of IHA is under 80%, AC methodology is necessary. The combination of both calibrations appears essential to better constrain the model and to forecast the impact of climate change or anthropogenic influences on water resources

Parameter estimation of a land surface scheme using multicriteria methods

Attempts to create models of surface-atmosphere interactions with greater physical realism have resulted in land surface schemes (LSS) with large numbers of parameters. The hope has been that these parameters can be assigned typical values by inspecting the literature. The potential for using the various observational data sets that are now available to extract plot-scale estimates for the parameters of a complex LSS via advanced parameter estimation methods developed for hydrological models is explored in this paper. Results are reported for two case studies using data sets of typical quality but very different location and climatological regime (ARM-CART and Tucson). The traditional single-criterion methods were found to be of limited value. However, a multicriteria approach was found to be effective in constraining the parameter estimates into physically plausible ranges when observations on at least one appropriate heat flux and one properly selected state variable are available. Copyright 1999 by the American Geophysical Union

Integrated surface-subsurface model to investigate the role of groundwater in headwater catchment runoff generation : a minimalist approach to parameterisation

This work was funded by NERC/JPI SIWA project (NE/M019896/1) and the European Research Council ERC (project GA 335910 VeWa). Numerical simulations were performed using the Maxwell High Performance Computing Cluster of the University of Aberdeen IT Service, provided by Dell Inc. and supported by Alces Software. Aquanty Inc. is acknowledged for support in providing HGS simulation software compatible with the Maxwell High Performance Computing Cluster. We would also like to thank the anonymous reviewers for their constructive comments that improved the manuscript.Peer reviewedPublisher PD

Deterministic-statistical model coupling in a DSS for river-basin management

This paper presents a method for appropriate coupling of deterministic and statistical models. In the decision-support system for the Elbe river, a conceptual rainfall-runoff model is used to obtain the discharge statistics and corresponding average number of flood days, which is a key input variable for a rule-based model for floodplain vegetation. The required quality of the discharge time series cannot be determined by a sensitivity analysis because a deterministic model is linked to a statistical model. To solve the problem, artificial discharge time series are generated that mimic the hypothetical output of rainfall-runoff models of different accuracy. The results indicate that a feasible calibration of the rainfall-runoff model is sufficient to obtain consistency with the vegetation model in view of its sensitivity to changes in the number of flood days in the floodplains

Impact of Precipitation Pre-Processing Methods on Hydrological Model Performance using High-Resolution Gridded Dataset

Effective representation of precipitation inputs is one of the essential components in hydrological model structures, especially when gauge measurements for the modelled catchment are sparse. Assessment of the impact of precipitation pre-processing is often nontrivial as precipitation data are very limited in the first place. In this paper, we demonstrate a study using a semi-distributed hydrological model, the Soil and Water Assessment Tool (SWAT) to examine the impact of different precipitation pre-processing methods on model calibration and the overall model performance with regards to the operational use. A river catchment in the UK is modelled to test against the three pre-processing methods: the Centroid Point Estimation Method (CPEM), the Grid Area Method (GAM) and the Grid Point Method (GPM). Cross-calibration and validation are then carried out by using the high-resolution Centre for Ecology & Hydrology–Gridded Estimate Areal Rainfall (CEH-GEAR) dataset. The results show that the proposed methods GAM and GPM can improve the model calibration significantly against the one calibrated with the existing CPEM method used by the model; the performance differences in the validation among the calibrated models, however, remain small and become irrelevant. The findings indicate that it is preferable to always make use of high-quality rainfall data, when available, with a better pre-processing method, even with models that are previously calibrated with low-quality rainfall inputs. It is also shown that such improvements are affected by the size of catchment and become less significant for smaller catchments

Comparison of daily and sub-daily SWAT models for daily streamflow simulation in the Upper Huai River Basin of China

Despite the significant role of precipitation in the hydrological cycle, few studies have been conducted to evaluate the impacts of the temporal resolution of rainfall inputs on the performance of SWAT (soil and water assessment tool) models in large-sized river basins. In this study, both daily and hourly rainfall observations at 28 rainfall stations were used as inputs to SWAT for daily streamflow simulation in the Upper Huai River Basin. Study results have demonstrated that the SWAT model with hourly rainfall inputs performed better than the model with daily rainfall inputs in daily streamflow simulation, primarily due to its better capability of simulating peak flows during the flood season. The sub-daily SWAT model estimated that 58% of streamflow was contributed by baseflow compared to 34 % estimated by the daily model. Using the future daily and three-hour precipitation projections under the RCP (Representative Concentration Pathways) 4.5 scenario as inputs, the sub-daily SWAT model predicted a larger amount of monthly maximum daily flow during the wet years than the daily model. The differences between the daily and sub-daily SWAT model simulation results indicated that temporal rainfall resolution could have much impact on the simulation of hydrological process, streamflow, and consequently pollutant transport by SWAT models. There is an imperative need for more studies to examine the effects of temporal rainfall resolution on the simulation of hydrological and water pollutant transport processes by SWAT in river basins of different environmental conditions

Model Performance Sensitivity to Objective Function during Automated Calibrations

Previous studies have reported limitations of the efficiency criteria commonly used in hydrology to describe goodness of model simulations. This study examined sensitivity of model performance to the objective function used during automated calibrations. Nine widely used efficiency criteria were evaluated for their effectiveness as objective function, and goodness of the model predictions were examined using 13 criteria. Two cases (Case I: Using observed streamflow data and Case II: Using simulated streamflow) were considered to accomplish objectives of the study using a widely used watershed model (SWAT) and good-quality field data from a well-monitored experimental watershed. Major findings of the study include (1) automated calibration results are sensitive to the objective function group—group that work based on minimization of the absolute deviations (Group I), group that work based on minimization of square of the residuals (Group II), and groups that use log of the observed and simulated streamflow values (Group III)—but not to objective functions within the group; (2) efficiency criteria that belong to Group I were the most effective when used as objective function for accurate simulation of both low flows and high flows; (3) Group I and Group II objective functions complement each other’s performance; (4) with regard to the capability to describe goodness of model simulations, efficiency criteria that belong to Group I showed superior robustness; (5) for the study watershed, use of the long-term interannual calendar day mean as baseline model did not improve capability of an efficiency criterion to describe model performance; and (6) even for ideal conditions where uncertainty in input data and model structure are fully accounted for, identifying the so-called global parameters values through calibration could be daunting as parameter values that were significantly divergent from predetermined values produced model simulations that can be considered near perfect even when judged using multiple efficiency criteria

Variation in Green Roof Storage Capacity, Associated Drivers, and Implications for Stormwater Management in Portland, Oregon

Greens roofs, also known as ecoroofs or living roofs, provide numerous ecosystem services and are becoming widely integrated into urban stormwater management systems. Despite a long and robust history of green roof projects in Portland, Oregon, there is a distinct lack of research on in situ performance. This thesis addressed this gap by investigating the hydrologic performance of five green roofs in Portland for a time period spanning November through February. Despite difficulty with the sensor calibrations, our analysis of moisture content data from these roofs revealed variation in: (1) total evapotranspiration, which serves as a proxy for total stormwater retention, and (2) storage capacity, which was defined as the difference between average maximum and minimum substrate volumetric water content measured under field conditions. Evapotranspiration was predicted by storage capacity following first order growth using non-linear regression analysis, suggesting that increasing storage capacity should correspond with increases in evapotranspiration and retention up until an inflection point of possibly 8-9mm beyond which the effect diminishes. Storage capacity was further shown to be correlated with substrate properties such as organic matter content and coefficient of curvature. Finally, retention values of 13.4% - 32.8% for Portland’s rainy season in 2015-2016 were found to be substantially lower than optimal annual values reported in other studies in Portland as well as cities around the world. This suggests that Portland’s green roofs may not perform as well as expected during months of high precipitation, which are the times when hydrologic performance is most critical. Further research should consider seasonal green roof performance in the Pacific Northwest and also revisit storage capacity as a parameter to include in future design standards

Regional calibration of the Pitman model for the Okavango River

This paper reports on the application of a monthly rainfall-runoff model for the Okavango River Basin. Streamflow is mainly generated in Angola where the Cuito and Cubango rivers arise. They then join and cross the Namibia/Angola border, flowing into the Okavango wetland in Botswana. The model is a modified version of the Pitman model, including more explicit ground and surface water interactions. Significant limitations in access to climatological data, and lack of sufficiently long records of observed flow for the eastern sub-basins represent great challenges to model calibration. The majority of the runoff is generated in the wetter headwater tributaries, while the lower sub-basins are dominated by channel loss processes with very little incremental flow contributions, even during wet years. The western tributaries show significantly higher seasonal variation in flow, compared to the baseflow dominated eastern tributaries: observations that are consistent with their geological differences. The basin was sub-divided into 24 sub-basins, of which 18 have gauging stations at their outlet. Satisfactory simulations were achieved with sub-basin parameter value differences that correspond to the spatial variability in basin physiographic characteristics. The limited length of historical rainfall and river discharge data over Angola precluded the use of a split sample calibration/validation test. However, satellite generated rainfall data, revised to reflect the same frequency characteristics as the historical rainfall data, were used to validate the model against the available downstream flow data during the 1990s. The overall conclusion is that the model, in spite of the limited data access, adequately represents the hydrological response of the basin and that it can be used to assess the impact of future development scenarios