Monte Carlo sensitivity analysis of land surface parameters using the Variable Infiltration Capacity model

Terrestrial hydrological features of the Pearl River basin in South China

Understanding the hydrological response of the larger catchment/river basin is a challenge under the changing environment/climate change. In the present study, the Variable Infiltration Capacity (VIC) model, a macroscale hydrological model, is applied over the Tekra catchment (with area of 108780 km2), a part of Godavari river basin in India. The characteristic of the VIC model are: consideration of variability of soil moisture storage within the grid, nonlinear representation of baseflow and explicit representation of vegetation class. All these characteristics strengthen its suitability to simulate the hydrological changes. In this paper, the performance assessment of the VIC model to simulate the hydrology of the considered large catchment is done. Result shows reasonable agreement between the observed and simulated daily stream flows for the monsoon season of 2009. The gridwise computation of the VIC model allows the meteorological data directly from gridded dataset or General Circulation Models (GCMs) for climate change assessment.

Model parameter calibration is a fundamentally important stage that must be completed before applying a model to address practical problems. In this study, we describe an automatic calibration framework that combines sensitivity analysis (SA) and an adaptive surrogate modeling‐based optimization (ASMO) algorithm. We use this framework to calibrate catchment‐specific sensitive parameters for streamflow simulation in the variable infiltration capacity (VIC) model with a 0.25° spatial resolution over 10 major river basins of China from 1960 to 1979. We found that three parameters—the infiltration parameter (B) and two of the soil depth parameters (D1, D2)—are highly sensitive in most basins, while other parameter sensitivities are strongly related to the dynamic environment of the basin. Compared with directly calibrating the seven parameters recommended for the default calibration procedure, our framework not only reduced the computing time by two thirds through opting out of insensitive parameters (type I error) but also improved the Nash‐Sutcliffe model efficiency coefficient (NSE) for optimized results when it identified a missing sensitive parameter (type II error) in the case study river basins. Results show that the SA‐based ASMO framework is an effective and efficient model‐optimization technique for matching simulated streamflow with observations across China. The NSE for monthly streamflow ranged from 0.75 to 0.97 and from 0.71 to 0.97 during the validation and calibration periods, respectively. The calibrated parameters can be applied directly in streamflow simulations across China, and the proposed calibration framework holds important implications for relevant simulation studies in other regions.

In this study, the authors applied version 4 of the Community Land Model (CLM4) integrated with an uncertainty quantification (UQ) framework to 20 selected watersheds from the Model Parameter Estimation Experiment (MOPEX) spanning a wide range of climate and site conditions to investigate the sensitivity of runoff simulations to major hydrologic parameters and to assess the fidelity of CLM4, as the land component of the Community Earth System Model (CESM), in capturing realistic hydrological responses. They found that for runoff simulations, the most significant parameters are those related to the subsurface runoff parameterizations. Soil texture–related parameters and surface runoff parameters are of secondary significance. Moreover, climate and soil conditions play important roles in the parameter sensitivity. In general, water-limited hydrologic regime and finer soil texture result in stronger sensitivity of output variables, such as runoff and its surface and subsurface components, to the input parameters in CLM4. This study evaluated the parameter identifiability of hydrological parameters from streamflow observations at selected MOPEX basins and demonstrated the feasibility of parameter inversion/calibration for CLM4 to improve runoff simulations. The results suggest that in order to calibrate CLM4 hydrologic parameters, model reduction is needed to include only the identifiable parameters in the unknowns. With the reduced parameter set dimensionality, the inverse problem is less ill posed.

Surface and subsurface runoff generation processes and their influencing factors on a hillslope in northern China

Bayesian model averaging (BMA) is a popular approach to combine hydrologic forecasts from individual models and characterize the uncertainty induced by model structure. In the original form of BMA, the conditional probability density function (PDF) of each model is assumed to be a particular probability distribution (e.g., Gaussian, gamma, etc.). If the predictions of any hydrologic model do not follow certain distribution, a data transformation procedure is required prior to model averaging. Moreover, it is strongly recommended to apply BMA on unbiased forecasts, whereas it is sometimes difficult to effectively remove bias from the predictions of complex hydrologic models. To overcome these limitations, we develop an approach to integrate a group of multivariate functions, the so‐called copula functions, into BMA. Here we introduce a copula‐embedded BMA (Cop‐BMA) method that relaxes any assumption on the shape of conditional PDFs. Copula functions have a flexible structure and do not restrict the shape of posterior distributions. Furthermore, copulas are effective tools in removing bias from hydrologic forecasts. To compare the performance of BMA with Cop‐BMA, they are applied to hydrologic forecasts from different rainfall‐runoff and land‐surface models. We consider the streamflow observation and simulations for 10 river basins provided by the Model Parameter Estimation Experiment (MOPEX) project. Results demonstrate that the predictive distributions are more accurate and reliable, less biased, and more confident with small uncertainty after Cop‐BMA application. It is also shown that the postprocessed forecasts have better correlation with observation after Cop‐BMA application.

Hypsometric analysis describes the distribution of horizontal cross-sectional area of watershed morphology with respect to elevation (areaaltitude analysis). In geologic time scale youth, mature and old geologic stage represents peculiar hypsometric forms of watershed. The hypsometric analysis give picture about pertinent condition of transition from youth to old stage of watershed. Hypsometric characteristics of a watershed plays important role in the dynamics of surface and subsurface runoff generation and resulting soil erosion and denudation processes. Hypsometry is essential aid to measure and represent the form of a watershed and its evolution. Aim of the paper is to carry out the hypsometric analysis of Naula watershed, situated in mid Himalayan region of Uttarakhand using remote sensing and GIS technologies. Hypsometric data were derived and analyzed for Naula watershed from the 1 arc-second Cartosat-1 DEM. In the present study hypsometric integral was found as 0.357, which is indicative of existence of 35.7% of original rock mass of Naula watershed.

Successful streamflow forecasts depend on an adequate performance evaluation of the hydrological model. In this study, the hydrological responses were compared using two hydrological models, physic-based and semi-distributed, Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC), using input data from the Verde River Watershed, located in the Minas Gerais state in southern Brazil. This is a study of one of the most important headwater watershed regions of Brazil (Mantiqueira Range). Both models were suitable for streamflow simulation, with values of R2 (determination coefficient) and NSE (Nash-Sutcliffe) higher than 0.8, NSELog higher than 0.35 (Nash–Sutcliffe Efficiency of the logarithmic values of discharge) and PBIAS (percentage deviation) less than 25%. The integration of SWAT and VIC models can be useful in different water-resource assessment studies. Therefore, based upon this study further investigations should be conducted using various hydrological models and climate, land-use and land-cover changes scenarios in the region.

The problems of identifying the most appropriate model structure for a given problem and quantifying the uncertainty in model structure remain outstanding research challenges for the discipline of hydrology. Progress on these problems requires understanding of the nature of differences between models. This paper presents a methodology to diagnose differences in hydrological model structures: the Framework for Understanding Structural Errors (FUSE). FUSE was used to construct 79 unique model structures by combining components of 4 existing hydrological models. These new models were used to simulate streamflow in two of the basins used in the Model Parameter Estimation Experiment (MOPEX): the Guadalupe River (Texas) and the French Broad River (North Carolina). Results show that the new models produced simulations of streamflow that were at least as good as the simulations produced by the models that participated in the MOPEX experiment. Our initial application of the FUSE method for the Guadalupe River exposed relationships between model structure and model performance, suggesting that the choice of model structure is just as important as the choice of model parameters. However, further work is needed to evaluate model simulations using multiple criteria to diagnose the relative importance of model structural differences in various climate regimes and to assess the amount of independent information in each of the models. This work will be crucial to both identifying the most appropriate model structure for a given problem and quantifying the uncertainty in model structure. To facilitate research on these problems, the FORTRAN‐90 source code for FUSE is available upon request from the lead author.

In this paper we present a variable bucket capacity (VBC) representation of TOPMODEL (Beven and Kirkby, 1979), generalizing the Manabe ‘simple bucket’ scheme widely used in climate models. In doing this, we have been motivated by the variable infiltration capacity (VIC) model presented by Wood et al. (1992). For catchments, or for parts of the land surface, whose hydrology fits the TOPMODEL assumptions, this lumped version offers an alternative representation that can be used as the building block for land surface hydrology models at large spatial scales. The model is first applied, with satisfactory results, to a small catchment near Canberra in Australia. Next, using the same model, the effects of random spatial variability of rainfall are studied, but in the context of land surface hydrological parameterizations for global climate models. Sub-grid rainfall variability, especially the partial coverage that characterizes rainfall events at such large scales, is found to cause significant biases in the estimation of land surface fluxes. Other manifestations of the effects of the spatial variability of rainfall are also investigated, also by means of model simulations, but with a view to gaining insights for developing simple parameterizations of land surface fluxes at large scales. This includes the investigation of the dynamics, i.e. spatial and temporal variability, of the soil moisture state variable during and after a rainfall event. In addition, empirical relationships linking the variable contributing area, subsurface runoff and evaporation, to a soil moisture state variable (e.g. soil moisture storage), are derived based on model simulations, and the effects of the sub-grid rainfall variability on these relationships are examined. Because of the strong non-linearity inherent in the process descriptions within the model, the above ‘constitutive’ relationships are strongly affected by rainfall heterogeneity. © 1997 John Wiley & Sons, Ltd.

<p>Catchments are complex self-organizing environmental systems for which the form, drainage network, channel geometries, soil and vegetation, are all an outcome of co-evolution and adaptation to the ecological, geomorphologic and land-forming processes. Quantification of hydrological signatures provides vital information about the complex system properties and the functional behaviour of catchments. This work aims at evaluating catchment similarity with respect to geomorphology and hydrological signatures such as runoff ratio, flow duration curves and peak flows for calibrating and upscaling model parameters. The study is carried out on the sub-catchments of Cauvery river basin which is a major river basin in Peninsular India. The basin is characterized by extensive regional variability in surface and groundwater availability and large-scale shift in land use patterns in recent decades. With a significant number of anthropogenic interventions such as check dams and reservoirs, the basin faces water management challenges at the local, regional and basin scales. Hydrological signatures derived from elevation, streamflow and meteorological data are used to evaluate geomorphologic and hydrological similarity between the sub-catchments. We employ the physically based macroscale Variable Infiltration Capacity (VIC) model coupled with a routing model to simulate the streamflow. Streamflow simulations are carried out for various sub-catchments delineated based on discharge gauging stations. Model parameters are estimated and hydrological signatures are assessed for effective model calibration. Impact of interventions on flow signatures at the catchment scale is also assessed. This work can significantly improve the scientific understanding of variability of hydrological processes at various scales and provide useful insights for development of scaling relationships. It can also aid in examining the model parameter transferability across scales.</p>

Plant phenology was assessed as one of the most key Essential Biodiversity Variables on species functional traits, which underpins a variety of aspects of ecosystem function and is a vital determinant of species distribution. Flowering phenology is one of the most sensitive indicators of the effects of recent climatic change. Understanding of intraspecific variation in phenological responses to climate change can contribute to the accurate prediction of phenology and the assessment of potential impacts of climate change on organisms. However, this intraspecific variation in phenological sensitivity in natural systems is still unclear, especially at large spatial scales. Using the widespread perennial herb Spiranthes sinensis, we quantified intraspecific variation in phenological responses to warming among groups located along climatic gradients. Phenological data were derived from 1,681 collections of herbarium specimens and photographs across eastern Asia and southeastern Australia over the period 1902–2017 and spans eight humid and eight non‐humid regions. We found that peak flowering dates of Spiranthe sinensis (FLss) showed substantial intraspecific variation in temperature sensitivity, ranging from −5.16 ± 1.13 to 4.16 ± 0.90 days/°C. Across all 16 regions, FLss exhibited the strongest delayed response to warming in southeastern Australia. Temperature sensitivity of the FLss was significantly correlated with annual precipitation and aridity index of a region, suggesting a possible role of declined water availability in reducing temperature sensitivity. Notably, contrasting phenological responses to warming were found between collections with different humidity regimes: humid regions generally flowered earlier (M: −3.57 days/°C) and non‐humid regions exhibited slightly delayed flowering (M: 1.25 days/°C). These findings provide empirical evidence of substantial intraspecific variation in phenological sensitivity across large spatial scales, and highlights that soil water availability may play a key role in a warming planet, especially in non‐humid regions.

Assessment of spectral vegetation indices' change along a climatic gradient as surrogates of climate change effect on desert fringe ecosystems is conducted in the southeastern Mediterranean utilizing Hyperspectral strip acquired by SPECIM FENIX sensor. This study presents the strip' center-line profile of 6 spectral indices (out of 27 indices): Normalized Difference Vegetation Index (NDVI), Crude Protein (CP), Visible Atmospherically Resistant Index (VARI), Red Edge Ratio Normalized Difference Vegetation Index (RERNDVI), Neutral detergent fiber (NDF), and Landsat TM2 and TM4 ratio. These profiles were smoothed using Savitzki-Gulay filter and their general trend along the profile was determined by using linear regression technique. R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , the coefficient of determination, represents implicitly the sensitivity of each of the spectral indices for changes in vegetation and soil properties to spatial variation in rainfall and Potential Evapotranspiration (PET). Three indices showed significant north to south trend despite effects of human disturbance on the natural ecosystems: NDVI $\left(0.45 R^{2}\right)$; Red-Edge index $\left(0.528 \mathrm{R}^{2}\right)$ and VARI $\left(0.689 \mathrm{R}^{2}\right)$. These indices may serve mapping and monitoring desert fringe ecosystems response to global warming.

Remotely sensed evapotranspiration (ETRS) shows promise for enhancing hydrological models, especially in regions lacking in situ streamflow observations. However, model calibration studies showed conflicting results regarding the ability of ETRS products to improve streamflow simulation. Rather than relying on model calibration, here we produce the first randomized experiment that explores the full streamflow–ET skill distribution, and also the first probabilistic assessment of the value of different global ETRS products for streamflow simulation. Using 280,000 randomized SWAT (Soil and Water Assessment Tool) model runs across seven catchments and four ETRS products, we show that the relationship between ET and streamflow skills is complex, and simultaneous improvement in both skills is only possible in a limited range. Parameter sensitivity analysis indicates that the most sensitive parameters can have opposite contributions to ET and streamflow skills, leading to skill trade-offs. Conditional probability assessment reveals that models with good ET skills are likely to produce good streamflow skills, but not vice versa. We suggest that randomized experiments such as ours should be performed before model calibration to determine whether using ETRS is worthwhile, and to help in interpreting the calibration results.

Vegetation parameters for the Variable Infiltration Capacity (VIC) hydrologic model were recently updated using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Previous work showed that these MODIS-based parameters improved VIC evapotranspiration simulations when compared to eddy covariance observations. Due to the importance of evapotranspiration within the Colorado River basin, this study provided a basin-by-basin calibration of VIC soil parameters with updated MODIS-based vegetation parameters to improve streamflow simulations. Interestingly, while both configurations had similar historic streamflow performance, end-of-century hydrologic projections, driven by 29 downscaled global climate models under the RCP8.5 emissions scenario, differed between the two configurations. The calibrated MODIS-based configuration had an ensemble mean that simulated little change in end-of-century annual streamflow volume (+0.4%) at Lees Ferry, Arizona, relative to the historical period (1960–2005). In contrast, the previous VIC configuration, which is used to inform decisions about future water resources in the Colorado River basin, projected an 11.7% decrease in annual streamflow. Both VIC configurations simulated similar amounts of evapotranspiration in the historical period. However, the MODIS-based VIC configuration did not show as much of an increase in evapotranspiration by the end of the century, primarily within the upper basin’s forested areas. Differences in evapotranspiration projections were the result of the MODIS-based vegetation parameters having lower leaf area index values and less forested area compared to previous vegetation estimates used in recent Colorado River basin hydrologic projections. These results highlight the need to accurately characterize vegetation and better constrain climate sensitivities in hydrologic models. Significance Statement Understanding systemic changes in annual Colorado River basin flows is critical for managing long-term reservoir levels. Single-digit percentage decreases have the potential to degrade the regions’ water supply, hydropower generation, and environmental concerns. Hydrology projections under climate change have largely been based on simulations from the Variable Infiltration Capacity model. Updating the model’s vegetation representation based on updated satellite information highlighted the sensitivity of the hydrologic projections to the models’ vegetation representation primarily within forested areas. This updated model did not increase in evapotranspiration by the end of the century as much as previous simulations. This increased the mean and ensemble spread of the projected streamflow changes, emphasizing the need to properly characterize the hydrologic model’s vegetation parameters and better constrain model climate sensitivity.