On the dialog between experimentalist and modeler in catchment hydrology

The dialog between experimentalist and modeler in catchment hydrology has been minimal to date. The experimentalist often has a highly detailed yet highly qualitative understanding of dominant runoff processes—thus there is often much more information content on the catchment than we use for calibration of a model. While modelers often appreciate the need for 'hard data' for the model calibration process, there has been little thought given to how modelers might access this 'soft' or process knowledge. We present a new method where soft data (i.e., qualitative knowledge from the experimentalist that cannot be used directly as exact numbers) are made useful through fuzzy measures of model-simulation and parameter-value acceptability. We developed a three-box lumped conceptual model for the Maimai catchment in New Zealand, a particularly well-studied process-hydrological research catchment. The boxes represent the key hydrological reservoirs that are known to have distinct groundwater dynamics, isotopic composition and solute chemistry. The model was calibrated against hard data (runoff and groundwater-levels) as well as a number of criteria derived from the soft data (e.g. percent new water, reservoir volume, etc). We achieved very good fits for the three-box model when optimizing the parameter values with only runoff (Reff=0.93). However, parameter sets obtained in this way showed in general a poor goodness-of-fit for other criteria such as the simulated new-water contributions to peak runoff. Inclusion of soft-data criteria in the model calibration process resulted in lower Reff-values (around 0.84 when including all criteria) but led to better overall performance, as interpreted by the experimentalist’s view of catchment runoff dynamics. The model performance with respect to soft data (like, for instance, the new water ratio) increased significantly and parameter uncertainty was reduced by 60% on average with the introduction of the soft data multi-criteria calibration. We argue that accepting lower model efficiencies for runoff is 'worth it' if one can develop a more 'real' model of catchment behavior. The use of soft data is an approach to formalize this exchange between experimentalist and modeler and to more fully utilize the information content from experimental catchments

Framework for Event-based Semidistributed Modeling that Unifies the SCS-CN Method, VIC, PDM, and TOPMODEL

Hydrologists and engineers may choose from a range of semidistributed rainfall-runoff models such as VIC, PDM, and TOPMODEL, all of which predict runoff from a distribution of watershed properties. However, these models are not easily compared to event-based data and are missing ready-to-use analytical expressions that are analogous to the SCS-CN method. The SCS-CN method is an event-based model that describes the runoff response with a rainfall-runoff curve that is a function of the cumulative storm rainfall and antecedent wetness condition. Here we develop an event-based probabilistic storage framework and distill semidistributed models into analytical, event-based expressions for describing the rainfall-runoff response. The event-based versions called VICx, PDMx, and TOPMODELx also are extended with a spatial description of the runoff concept of ‘‘prethreshold’’ and ‘‘threshold-excess’’ runoff, which occur, respectively, before and after infiltration exceeds a storage capacity threshold. For total storm rainfall and antecedent wetness conditions, the resulting ready-to-use analytical expressions define the source areas (fraction of the watershed) that produce runoff by each mechanism. They also define the probability density function (PDF) representing the spatial variability of runoff depths that are cumulative values for the storm duration, and the average unit area runoff, which describes the so-called runoff curve. These new event-based semidistributed models and the traditional SCS-CN method are unified by the same general expression for the runoff curve. Since the general runoff curve may incorporate different model distributions, it may ease the way for relating such distributions to land use, climate, topography, ecology, geology, and other characteristics

Reply to Comment by Fred L. Ogden et al. on Beyond the SCS-CN Method: A Theoretical Framework for Spatially Lumped Rainfall-Runoff Response

Though Ogden et al. list several shortcomings of the original SCS-CN method, fit for purpose is a key consideration in hydrological modelling, as shown by the adoption of SCS-CN method in many design standards. The theoretical framework of Bartlett et al. [2016a] reveals a family of semidistributed models, of which the SCS-CN method is just one member. Other members include event-based versions of the Variable Infiltration Capacity (VIC) model and TOPMODEL. This general model allows us to move beyond the limitations of the original SCS-CN method under different rainfall-runoff mechanisms and distributions for soil and rainfall variability. Future research should link this general model approach to different hydrogeographic settings, in line with the call for action proposed by Ogden et al

Stochastic Rainfall-runoff Model with Explicit Soil Moisture Dynamics

Stream runoff is perhaps the most poorly represented process in ecohydrological stochastic soil moisture models. Here we present a rainfall-runoff model with a new stochastic description of runoff linked to soil moisture dynamics. We describe the rainfall-runoff system as the joint probability density function (PDF) of rainfall, soil moisture and runoff forced by random, instantaneous jumps of rainfall. We develop a master equation for the soil moisture PDF that accounts explicitly for a general state-dependent rainfall-runoff transformation. This framework is then used to derive the joint rainfall-runoff and soil moisture-runoff PDFs. Runoff is initiated by a soil moisture threshold and a linear progressive partitioning of rainfall based on the soil moisture status. We explore the dependence of the PDFs on the rainfall occurrence PDF (homogeneous or state-dependent Poisson process) and the rainfall magnitude PDF (exponential or mixed-exponential distribution). We calibrate the model to 63 years of rainfall and runoff data from the Upper Little Tennessee watershed (USA) and show how the new model can reproduce the measured runoff PDF

Improvement and further development of SSM/I overland parameter algorithms using the WetNet workstation

Since the launch of the DMSP Special Sensor Microwave/Imager (SSM/I), several algorithms have been developed to retrieve overland parameters. These include the present operational algorithms resulting from the Navy calibration/validation effort such as land surface type (Neale et al. 1990), land surface temperature (McFarland et al. 1990), surface moisture (McFarland and Neale, 1991) and snow parameters (McFarland and Neale, 1991). In addition, other work has been done including the classification of snow cover and precipitation using the SSM/I (Grody, 1991). Due to the empirical nature of most of the above mentioned algorithms, further research is warranted and improvements can probably be obtained through a combination of radiative transfer modelling to study the physical processes governing the microwave emissions at the SSM/I frequencies, and the incorporation of additional ground truth data and special cases into the regression data sets. We have proposed specifically to improve the retrieval of surface moisture and snow parameters using the WetNet SSM/I data sets along with ground truth information namely climatic variables from the NOAA cooperative network of weather stations as well as imagery from other satellite sensors such as the AVHRR and Thematic Mapper. In the case of surface moisture retrievals the characterization of vegetation density is of primary concern. The higher spatial resolution satellite imagery collected at concurrent periods will be used to characterize vegetation types and amounts which, along with radiative transfer modelling should lead to more physically based retrievals. Snow parameter retrieval algorithm improvement will initially concentrate on the classification of snowpacks (dry snow, wet snow, refrozen snow) and later on specific products such as snow water equivalent. Significant accomplishments in the past year are presented

Surface and subsurface water contributions during snowmelt in a small Precambrian Shield watershed

ABSTRACT The rates and pathways of snowmelt runoff on Precambrian Shield watersheds are not fully understood. The results of a detailed field study and numerical analysis of these processes are presented for a small watershed located in Muskoka , Ontario, that has been gauged both chemically and physically since 1976 by the Ontario Ministry of the Environmentas a part of its Acid Precipitation in Ontario Study. Field data show that in many areas soils with very high hydraulic conductivities contributed signifcant amounts of subsurface flow to the hydrograph during and shortly after melt. Quickflow in the first phase of snowmelt was generated by a combination of saturation overland flow and saturated-unsaturated subsurface storm flow from shallow side slopes; however, most of the total water volume was delivered via slower ground-water flow. In the final phase of the melt period, the streamflow was mainly due to ground-water flow recharged on aforested area and delivered over limited distances through shallow, highly permeable overburden materials. Quickjow peaks superimposed on this general trend could be accounted for mainly as surface runoff over groundwater effuent areas mapped throughout the watershed. In support of ongoing hydrochemical studies by the Ontario Ministry of the Environment, these data and interpretations wil aid in modifing and improving hydrologic submodels by adding to our understanding of water movement in acidifed watersheds. RESUME Les taux ainsi que les parcours des ruissellements de la neige fondue dans un bassin hydrographique situe dans Ie bouclier precambrien ne sont pas tres bien compris. Le

Prevalence and magnitude of groundwater use by vegetation:A global stable isotope meta-analysis

The role of groundwater as a resource in sustaining terrestrial vegetation is widely recognized. But the global prevalence and magnitude of groundwater use by vegetation is unknown. Here we perform a meta-analysis of plant xylem water stable isotope (δ(2)H and δ(18)O, n = 7367) information from 138 published papers – representing 251 genera, and 414 species of angiosperms (n = 376) and gymnosperms (n = 38). We show that the prevalence of groundwater use by vegetation (defined as the number of samples out of a universe of plant samples reported to have groundwater contribution to xylem water) is 37% (95% confidence interval, 28–46%). This is across 162 sites and 12 terrestrial biomes (89% of heterogeneity explained; Q-value = 1235; P < 0.0001). However, the magnitude of groundwater source contribution to the xylem water mixture (defined as the proportion of groundwater contribution in xylem water) is limited to 23% (95% CI, 20–26%; 95% prediction interval, 3–77%). Spatial analysis shows that the magnitude of groundwater source contribution increases with aridity. Our results suggest that while groundwater influence is globally prevalent, its proportional contribution to the total terrestrial transpiration is limited