Remote sensing-aided large-scale rainfall-runoff modelling in the humid tropics

Streamflow simulation across the tropics is limited by the lack of data to calibrate and validate large-scale hydrological models. Here, we applied the process-based, conceptual HYPE (Hydrological Predictions for the Environment) model to quantitively assess Costa Rica’s water resources at a national scale. Data scarcity was compensated using adjusted global topography and remotely-sensed climate products to force, calibrate, and independently evaluate the model. We used a global temperature product and bias-corrected precipitation from CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) as model forcings. Daily streamflow from 13 gauges for the period 1990–2003 and monthly MODIS (Moderate Resolution Imaging Spectroradiometer) potential evapotranspiration (PET) and actual evapotranspiration (AET) for the period 2000–2014 were used to calibrate and evaluate the model applying four different model configurations. The calibration consisted in step-wise parameter constraints preserving the best parameter sets from previous simulations in an attempt to balance the variable data availability and time periods. The model configurations were independently evaluated using hydrological signatures such as the baseflow index, runoff coefficient, and aridity index, among others. Results suggested that a two-step calibration using monthly and daily streamflow was a better option instead of calibrating only with daily streamflow. Additionally, including PET and AET in the calibration improved the simulated water balance and better matched hydrological signatures. Thus, the constrained parameter uncertainty increased the confidence in the simulation results. Such a large-scale hydrological model has the potential to be used operationally across the humid tropics informing decision making at relatively high spatial and temporal resolution.

Assessing watershed hydrological response to climate change based on signature indices

Due to the fact that one of the important ways of describing the performance of basins is to use the hydrological signatures, the present study is to investigate the effects of climate change using the hydrological signatures in Azarshahr Chay basin, Iran. To this end, Canadian Earth system model (CanESM2) is first used to predict future climate change (2030–2059) under two Representative Concentration Pathways (RCP2.6 and RCP8.5). Six signature indices were extracted from flow duration curve (FDC) as follows: runoff ratio (RR), high-segment volume (FHV), low-segment volume (FLV), mid-segment slope (FMS), mid-range flow (FMM), and maximum peak discharge (DiffMaxPeak). These signature indices act as sorts of fingerprints representing differences in the hydrological behavior of the basin. The results indicate that the most significant changes in the future hydrological response are related to the FHV and FLV and FMS indices. The BiasFHV index indicates an increase in high discharge rates under RCP8.5 scenario, compared to the baseline period and the RCP2.6 scenario, as well. The mean annual discharge rate, however, is lower than the discharge rate under this scenario. Generally, for the RCP8.5 scenario, the changes in the signature indices in both high discharges and low discharges are significant.

Surface runoff connectivity across scales: revisiting three simulation studies

<p>Surface runoff (dis)connectivity manifests across scales, spawning from different spatial flow patterns, which are dominated by both topography (structural connectivity) and hydrodynamics (dynamic connectivity). How the connectivity builds and evolves throughout rainfall events is integrated into observable hydrological signatures (namely, hydrographs and water balance). </p><p>In this contribution we explore the connectivity properties of runoff generation processes across spatial scales. We revisit three case studies of runoff generation during rainfall, numerically simulated by solving shallow-water equations. This approach provides a full description of the hydrodynamic flow fields, allowing to study both the connectivity properties, as well as the domain-integrated hydrological signatures (namely, hydrographs) that build up in response to flow phenomena. </p><p>We discuss and link the runoff generation processes arising from (i) runoff generation at the plot scale (20 m2 at cm resolution) with explicit microtopographies, (ii) runoff generation at the hillslope or first-order catchment scale with overland and (ephemeral) rill flows in the Hühnerwasser experimental catchment (4000 m2 at m resolution), and (iii) runoff generation at the catchment scale in the Lower Triangle catchment (15 km2 at m resolution).</p><p>The detailed study of runoff generation dynamics highlights the needs to use time-evolving connectivity metrics, which are particularly useful to understand spatiotemporal model output. We computed the number of disconnected flooded clusters (and Euler characteristic) as the main connectivity metric.</p><p>The results of the three different systems suggest similar qualitative behaviours of connectivity across scales, from plot to catchment scales, and therefore also offer the possible use of connectivity to understand how fluxes are re-scaled across the landscape, and as a multiscale indicator of hydrological function. The relationship between the connectivity response at a given scale (e.g., plot) and the hydrological signature observed at the next larger scale (e.g., hillslope) may lead into a hierarchical relationship of connectivities and signatures, in which the time-continuous nature of the connectivity signal may give rise to non-linear and threshold behaviours in the larger scale signature. </p><p>Additionally, in the context of assessing model quality, connectivity is a feature of the natural system which models (and modellers) should strive to ensure. In this sense, we argue that model formulations, meshing (including resolution/topology and preprocessing/smoothing of the terrain model) and parameterisations should be evaluated not only using integrated signatures (e.g., water balance, hydrographs) or point data (water depth, velocities) but also using (dis)connectivity metrics. In this way, it is possible to evaluate to which extent a model and its setup can simulate natural flow paths and landscape functions.</p>

Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms

Hydrological signatures, i.e., statistical features of streamflow time series, are used to characterize the hydrology of a region. A relevant problem is the prediction of hydrological signatures in ungauged regions using the attributes obtained from remote sensing measurements at ungauged and gauged regions together with estimated hydrological signatures from gauged regions. The relevant framework is formulated as a regression problem, where the attributes are the predictor variables and the hydrological signatures are the dependent variables. Here we aim to provide probabilistic predictions of hydrological signatures using statistical boosting in a regression setting. We predict 12 hydrological signatures using 28 attributes in 667 basins in the contiguous US. We provide formal assessment of probabilistic predictions using quantile scores. We also exploit the statistical boosting properties with respect to the interpretability of derived models. It is shown that probabilistic predictions at quantile levels 2.5% and 97.5% using linear models as base learners exhibit better performance compared to more flexible boosting models that use both linear models and stumps (i.e., one-level decision trees). On the contrary, boosting models that use both linear models and stumps perform better than boosting with linear models when used for point predictions. Moreover, it is shown that climatic indices and topographic characteristics are the most important attributes for predicting hydrological signatures.

The Role of Hydrological Signatures in Calibration of Conceptual Hydrological Model

Determining an optimal calibration strategy for hydrological models is essential for a robust and accurate water balance assessment, in particular, for catchments with limited observed data. In the present study, the hydrological model Bilan was used to simulate hydrological balance for 20 catchments throughout the Czech Republic during the period 1981–2016. Calibration strategies utilizing observed runoff and estimated soil moisture time series were compared with those using only long-term statistics (signatures) of runoff and soil moisture as well as a combination of signatures and time series. Calibration strategies were evaluated considering the goodness-of-fit, the bias in flow duration curve and runoff signatures and uncertainty of the Bilan model. Results indicate that the expert calibration and calibration with observed runoff time series are, in general, preferred. On the other hand, we show that, in many cases, the extension of the calibration criteria to also include runoff or soil moisture signatures is beneficial, particularly for decreasing the uncertainty in parameters of the hydrological model. Moreover, in many cases, fitting the model with hydrological signatures only provides a comparable fit to that of the calibration strategies employing runoff time series.

Linking hydrological signatures to hydrological processes and catchment attributes: a flexible approach applied to baseflow signatures

<p>Hydrological signatures aim at extracting information about certain aspects of hydrological behaviour. They can be used to quantify hydrological similarity, to explore catchment functioning and to evaluate hydrological models. Relating hydrological signatures to hydrological processes is, however, still a challenge and many signatures remain poorly understood.</p><p>We propose a flexible approach for linking hydrological signatures to hydrological processes, which might help to improve our understanding and hence the usefulness of certain hydrological signatures. As a first step, we should build a perceptual model describing the hydrological process of interest. We should then try to find or create relevant – and ideally widely available – catchment attributes that target the process of interest, and hence have the potential to explain the signature in a process-based way. We should control for climate by either incorporating it into our perceptual model or by analysing sub-climates individually, to disentangle the influences of forcing and catchment form. Lastly, simple conceptual models might be a useful tool to systematically explore the controlling factors (parameters, forcing) of a signature. Focusing on hydrological processes and explaining hydrological signatures in a process-based way will make hydrological signatures more meaningful, useful and robust.</p><p>The proposed approach is tested on signatures related to baseflow and groundwater processes, such as the baseflow index. Baseflow generation has been studied extensively, and while many regional studies could identify landscape controls on baseflow generation (e.g. soils and geology), continental or global studies have resulted in a less clear picture, partially because of the masking influence of climate at these scales. Furthermore, the relationship between controls, such as climate and catchment form, and baseflow response has often been only described statistically (e.g. by means of regression-type approaches).  A mechanistic theory based on widely available catchment attributes (e.g. soils, geology, topography) would thus be a major step towards improved understanding and transferability.</p>

Evaluating hydrological signatures and catchment similarities to estimate model parameters in Cauvery river basin, India

<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>

Diagnostic of a regional distributed hydrological model through hydrological signatures

<p>Distributed models are useful tools for the assessment of water resources in a context of global change. However, due to the high spatial heterogeneity of the corresponding catchments, these models end up being quite complex with a high number of parameters. In particular, it is not easy to obtain good performances and physically sounded parameter values at all points in the catchment. In order to complement the traditional evaluation approach based on performance criteria, we developed a diagnostic approach based on hydrological signatures. A set of hydrological signatures based on precipitation and runoff data was defined and applied to a regional model of the Rhône basin (100 000 km<sup>2</sup>) in France. The comparison of simulated and observed signatures for 45 contrasted sub-basins of various sizes, climates, geologies and land uses, show that performance and ability to reproduce signatures are not always correlated. The analysis of signature results, combined with additional hydrogeology expertise, provided directions to improve the model parameterization, especially in the groundwater compartment. The study also provided feedback on the degree of information contained in the signatures and allows us to make recommendations for future studies.</p>