Technical Note: Testing the Connection Between Hillslope Scale Runoff Fluctuations and Streamflow Hydrographs at the Outlet of Large River Basins

The mapped rivers and streams of the contiguous United States are available in a geographic information system (GIS) dataset called National Hydrography Dataset Plus (NHDPlus). This hydrographic dataset has about 3 million river and water body reaches along with information on how they are connected into networks. The U.S. Geological Survey (USGS) National Water Information System (NWIS) provides streamflow observations at about 20 thousand gauges located on the NHDPlus river network. A river network model called Routing Application for Parallel Computation of Discharge (RAPID) is developed for the NHDPlus river network whose lateral inflow to the river network is calculated by a land surface model. A matrix-based version of the Muskingum method is developed herein, which RAPID uses to calculate flow and volume of water in all reaches of a river network with many thousands of reaches, including at ungauged locations. Gauges situated across river basins (not only at basin outlets) are used to automatically optimize the Muskingum parameters and to assess river flow computations, hence allowing the diagnosis of runoff computations provided by land surface models. RAPID is applied to the Guadalupe and San Antonio River basins in Texas, where flow wave celerities are estimated at multiple locations using 15-min data and can be reproduced reasonably with RAPID. This river model can be adapted for parallel computing and although the matrix method initially adds a large overhead, river flow results can be obtained faster than with the traditional Muskingum method when using a few processing cores, as demonstrated in a synthetic study using the upper Mississippi River basin.

The purpose of this paper is to identify simple connections between observations of hydrological processes at the hillslope scale and observations of the response of watersheds following rainfall, with a view to building a parsimonious model of catchment processes. The focus is on the well‐studied Panola Mountain Research Watershed (PMRW), Georgia, USA. Recession analysis of discharge Q shows that while the relationship between dQ/dt and Q is approximately consistent with a linear reservoir for the hillslope, there is a deviation from linearity that becomes progressively larger with increasing spatial scale. To account for these scale differences conceptual models of streamflow recession are defined at both the hillslope scale and the watershed scale, and an assessment made as to whether models at the hillslope scale can be aggregated to be consistent with models at the watershed scale.Results from this study show that a model with parallel linear reservoirs provides the most plausible explanation (of those tested) for both the linear hillslope response to rainfall and non‐linear recession behaviour observed at the watershed outlet. In this model each linear reservoir is associated with a landscape type. The parallel reservoir model is consistent with both geochemical analyses of hydrological flow paths and water balance estimates of bedrock recharge. Overall, this study demonstrates that standard approaches of using recession analysis to identify the functional form of storage–discharge relationships identify model structures that are inconsistent with field evidence, and that recession analysis at multiple spatial scales can provide useful insights into catchment behaviour. Copyright © 2008 John Wiley & Sons, Ltd.

Impact of climate change on river flooding assessed with different spatial model resolutions

Conventional calibration methods adopted in hydrological modelling are based on streamflow data measured at certain river sections. However, streamflow measurements are usually sparse and, in such instances, remote-sensing-based products may be used as an additional dataset(s) in hydrological model calibration. This study compares two main calibration approaches: (a) single variable calibration with streamflow and evapotranspiration separately, and (b) multi-variable calibration with both variables together. Here, we used remote sensing-based evapotranspiration data from Global Land Evaporation: the Amsterdam Model (GLEAM ET), and measured streamflow at four stations to calibrate a Soil and Water Assessment Tool (SWAT) and evaluate the performances for Chindwin Basin, Myanmar. Our results showed that when one variable (either streamflow or evapotranspiration) is used for calibration, it led to good performance with respect to the calibration variable but resulted in reduced performance in the other variable. In the multi-variable calibration using both streamflow and evapotranspiration, reasonable results were obtained for both variables. For example, at the basin outlet, the best NSEs (Nash-Sutcliffe Efficiencies) of streamflow and evapotranspiration on monthly time series are, respectively, 0.98 and 0.59 in the calibration with streamflow alone, and 0.69 and 0.73 in the calibration with evapotranspiration alone. Whereas, in the multi-variable calibration, the NSEs at the basin outlet are 0.97 and 0.64 for streamflow and evapotranspiration, respectively. The results suggest that the GLEAM ET data, together with streamflow data, can be used for model calibration in the study region as the simulation results show reasonable performance for streamflow with an NSE > 0.85. Results also show that many different sets of parameter values (‘good parameter sets’) can produce results comparable to the best parameter set.

Abstract. Spatially distributed hydrological models are commonly employed to optimize the locations of engineering control measures across a watershed. Yet, parameter screening exercises that aim to reduce the dimensionality of the calibration search space are typically completed only for gauged locations, like the watershed outlet, and use screening metrics that are relevant to calibration instead of explicitly describing the engineering decision objectives. Identifying parameters that describe physical processes in ungauged locations that affect decision objectives should lead to a better understanding of control measure effectiveness. This paper provides guidance on evaluating model parameter uncertainty at the spatial scales and flow magnitudes of interest for such decision-making problems. We use global sensitivity analysis to screen parameters for model calibration, and to subsequently evaluate the appropriateness of using multipliers to adjust the values of spatially distributed parameters to further reduce dimensionality. We evaluate six sensitivity metrics, four of which align with decision objectives and two of which consider model residual error that would be considered in spatial optimizations of engineering designs. We compare the resulting parameter selection for the basin outlet and each hillslope. We also compare basin outlet results for four calibration-relevant metrics. These methods were applied to a RHESSys ecohydrological model of an exurban forested watershed near Baltimore, MD, USA. Results show that (1) the set of parameters selected by calibration-relevant metrics does not include parameters that control decision-relevant high and low streamflows, (2) evaluating sensitivity metrics at the basin outlet misses many parameters that control streamflows in hillslopes, and (3) for some multipliers, calibrating all parameters in the set being adjusted may be preferable to using the multiplier if parameter sensitivities are significantly different, while for others, calibrating a subset of the parameters may be preferable if they are not all influential. Thus, we recommend that parameter screening exercises use decision-relevant metrics that are evaluated at the spatial scales appropriate to decision making. While including more parameters in calibration will exacerbate equifinality, the resulting parametric uncertainty should be important to consider in discovering control measures that are robust to it.

The resolution of digital elevation model (DEM) used for watershed delineation and data extracted from the delineated watershed relevant to hydrography plays an important role in the calibration of hydrological models. The effect of DEM resolution on the drainage length, watershed area and average slope are studied in many earlier works. DEM resolution can have impact on hydrologic modeling parameters, water quality simulation results. The impacts of DEM resolution on results of the watershed response and water quality modeling are studied using HSPF (Hydrologic Simulation Program Fortran) model applied to a watershed in Kentucky in this study. Three major streams in this watershed are impaired due to pathogen pollution according to the current state of Kentucky water quality standard set for recreation. The effects of DEM resolution on water quality modeling and calibration for fecal coliform bacteria are studied using a HSPF model. Results from this study indicate that the DEM resolution affects hydrography parameters of the delineated watershed, influence the calibration process, and the non-point and point source loadings to the streams in the watershed.

An artificial neural network is well known as a flexible mathematical tool that has the ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. The radial basis function (RBF) method is applied to model the relationship between rainfall and runoff for Sungai Bekok Catchment (Johor, Malaysia) and Sungai Ketil catchment (Kedah, Malaysia). The RBF is used to predict the streamflow hydrograph based on storm events. Evaluation on the performance of RBF is demonstrated based on errors (between predicted and actual) and comparison with the results of the Hydrologic Engineering Center hydrologic modeling system model. It is obvious that the RBF method offers an accurate modeling of streamflow hydrograph.

Reduction of uncertainty in hydrologic predictions is important for water resources development and management, especially in ungauged basins. The objective of this study was to predict rainfall and runoff in an ungauged basin located in a mountainous region of northern Thailand by using a weather radar to estimate rainfall and a modeling approach to simulate rainfall-runoff processes. The calibrated Ze-R relationship (Ze=18R1.45) was used to estimate rainfall over a gauged basin and an assumed ungauged basin. The runoff simulations in the gauged and ungauged basins were conducted using the quasi-distributed hydrological model HEC-HMS. The simulation results in the gauged basin, for both calibration and verification, were in agreement with the observed data. Model parameters in the assumed ungauged basin were estimated using transposition and regionalization techniques. The predicted direct runoff hydrograph in the assumed ungauged basin using radar measured rainfall and the regionalization technique matches well with the measurements at the basin outlet. Based on the study results, the radar rainfall estimates and regionalization of the hydrologic model parameters are promising alternatives for improved hydrologic predictions in ungauged basins.

Land use and land cover changes, mostly driven by anthropogenic activities, affect the processes of the water cycle. The impacts of land use (LU) and land cover (LC) changes between 1995 and 2015 on the surface runoff of the Hadejia River System (HRS) were investigated. The LULC changes obtained through re-classifications of selected Landsat satellite images and their effects on runoff peak discharges and volumes were assessed using selected hydrologic models for runoff generation and routing available within the HEC-HMS. Physically-based parameters of the models were estimated from the LULC change maps together with a digital elevation model and soil datasets of the basin. The simulated flows from the 90 sub-catchments were routed to the basin outlet afterwards to obtain the accrued effects in the entire river basin. Model results obtained generally revealed significant and varying increases in the runoff peak discharges and volumes within some sub-basins in the whole catchment, though the change was not significant at the basin outlet. In the sub-catchments within Kano and Jigawa states, increase between 15-20% and 10-15% were observed in the peak discharge respectively. These are the areas with the highest increase in agricultural activities and urbanization within the whole catchment. In the entire basin, however, the flood peak discharges and volumes increased by at least 3.57% and 8.18% respectively. From these results, the study concludes that changes were more pronounced in Kano and Jigawa states due to the increase of urbanization and farming activities in those areas, leading to reduction of infiltration and hence, increase in surface runoff. The study successfully outlined the hydrological consequences of land cover changes, emphasizing the importance of sustainable land use and catchment management strategies. Hence, integration of remote sensing, GIS, and the hydrological model (HEC-HMS) can be used to solve hydrological problems in a river basin.

The reliability of a procedure for investigation of flooding into an ungauged river reach close to an urban area is investigated. The approach is based on the application of a semi‐distributed rainfall–runoff model for a gauged basin, including the flood‐prone area, and that furnishes the inlet flow conditions for a two‐dimensional hydraulic model, whose computational domain is the urban area. The flood event, which occurred in October 1998 in the Upper Tiber river basin and caused significant damage in the town of Pieve S. Stefano, was used to test the approach. The built‐up area, often inundated, is included in the gauged basin of the Montedoglio dam (275 km2), for which the rainfall–runoff model was adapted and calibrated through three flood events without over‐bank flow. With the selected set of parameters, the hydrological model was found reasonably accurate in simulating the discharge hydrograph of the three events, whereas the flood event of October 1998 was simulated poorly, with an error in peak discharge and time to peak of −58% and 20%, respectively. This discrepancy was ascribed to the combined effect of the rainfall spatial variability and a partial obstruction of the bridge located in Pieve S. Stefano. In fact, taking account of the last hypothesis, the hydraulic model reproduced with a fair accuracy the observed flooded urban area. Moreover, incorporating into the hydrological model the flow resulting from a sudden cleaning of the obstruction, which was simulated by a ‘shock‐capturing’ one‐dimensional hydraulic model, the discharge hydrograph at the basin outlet was well represented if the rainfall was supposed to have occurred in the region near the main channel. This was simulated by reducing considerably the dynamic parameter, the lag time, of the instantaneous unit hydrograph for each homogeneous element into which the basin is divided. The error in peak discharge and time to peak decreased by a few percent. A sensitivity analysis of both the flooding volume involved in the shock wave and the lag time showed that this latter parameter requires a careful evaluation. Moreover, the analysis of the hydrograph peak prediction due to error in rainfall input showed that the error in peak discharge was lower than that of the same input error quantity. Therefore, the obtained results allowed us to support the hypothesis on the causes which triggered the complex event occurring in October 1998, and pointed out that the proposed procedure can be conveniently adopted for flood risk evaluation in ungauged river basins where a built‐up area is located. The need for a more detailed analysis regarding the processes of runoff generation and flood routing is also highlighted. Copyright © 2005 John Wiley & Sons, Ltd.

The predictability of hydrometeorological flood events is investigated through the combined use of radar nowcasting and distributed hydrologic modeling. Nowcasting of radar-derived rainfall fields can extend the lead time for issuing flood and flash flood forecasts based on a physically based hydrologic model that explicitly accounts for spatial variations in topography, surface characteristics, and meteorological forcing. Through comparisons to discharge observations at multiple gauges (at the basin outlet and interior points), flood predictability is assessed as a function of forecast lead time, catchment scale, and rainfall spatial variability in a simulated real-time operation. The forecast experiments are carried out at temporal and spatial scales relevant for operational hydrologic forecasting. Two modes for temporal coupling of the radar nowcasting and distributed hydrologic models (interpolation and extended-lead forecasting) are proposed and evaluated for flood events within a set of nested basins in Oklahoma. Comparisons of the radar-based forecasts to persistence show the advantages of utilizing radar nowcasting for predicting near-future rainfall during flood event evolution.

Abstract. Globally, there are over 24 000 storage structures (e.g. dams and reservoirs) that contribute over 7000 km3 of storage, yet most reservoir data is not openly accessible. As a result, many studies rely on generalized assumptions about reservoir storage dynamics to create generalized operational policies. With the creation of remotely sensed reservoir storage datasets such as RealSat and GloLakes and localized datasets such as ResOpsUS for the contiguous United States, and the Mekong Data Monitor for the Mekong River basin, the inference of reservoir operations using data derived techniques has become much more ubiquitous for regional studies. Yet to our knowledge, there has been no global application of data-derived methods due to data limitations and model complexities. Our analysis aims to fills this gap by providing a workflow for implementing data derived reservoir operations in large scale hydrologic models. This methodology uses global satellite altimetry data from GloLakes, a parameterization methodology developed by Turner et al. (2021), and a random forest model to extrapolate operational bounds. Our results demonstrate that our random forest algorithm can capture storage dynamics and that the associated errors are propagated from the type of data used. Additionally, we observe that deriving operational bounds from historical reservoir time series only directly impacts streamflow directly downstream of dams and has minimal impacts at the basin outlets. We do, however, observe that the data-derived methodology increases the accuracy of simulated global reservoir storage when compared to remotely sensed and observed storage observations. These derived storages are much lower than in generic operation schemes which suggests that current operational schemes are overestimating the amount of reservoir storage and potentially overestimating water availability. We also evaluated the sensitivity of our modelling framework to different downstream operating areas (i.e. 0 to 1100 km) and found that there were slight improvements when including downstream demands. Ultimately, our workflow allows global hydrologic models to capitalize on recent data acquisition by remote sensing to provide more accurate reservoir storage and global water security.

<p>The land use and land cover (LULC) change induces hydrologic variability in a catchment and studying this variability is central to efficient water management practice in a catchment. The assessment of the alteration in hydrological processes due to LULC change and its influence on overall river ecosystem functioning is even more pertinent to developing nations that face the issue of water scarcity and pollution. In this work, we investigate the influence of the LULC change over a period of ~40 years (1970-2013) on the variability of natural or virgin flow in the Ramganga river, a major tributary of the Ganga river, India. For LULC change data, object-based image classification was performed on high-resolution satellite imageries acquired for the Ramganga river basin – CORONA (1970) and LISS IV (2013) images. The natural or virgin flows (i.e., the flow in the river without regulation practices such as construction of dams or barrages) were estimated by performing hydrological modeling using the Soil and Water Assessment Tool (SWAT). Initially, the SWAT model was set up, calibrated, and validated for the present flow scenario (i.e. with all management practices present) using LULC data of the year 2013. Natural flows were derived by removing all interventions and keeping agricultural practices only rain-fed. Next, keeping all parameters unchanged, the LULC data of the year 2013 was replaced by the LULC data of the year 1970. This enabled us to study the effects of LULC change on river hydrology between the period 1970-2013. The model showed good agreement between the observed and simulated flows with R<sup>2</sup> values of 0.82 for the calibration period (2002-2014) and 0.68 for the validation period (1990-1999). The Nash-Sutcliffe efficiency values were 0.81 and 0.66 for calibration and validation periods respectively. The comparison of LULC data between the study period (1970 and 2013) reveals that land cover classes of agriculture, built-up, mixed forest, barren land, shrubs and bushes, and water areas were altered by nearly 6%, 102%, -7%, -59%, -75%, and -2% respectively (‘-’ sign indicates decrement in the land cover area). The influence of this LULC change was evident in the results from the hydrological model. For the years 2002-2013 (calibration period), the natural flows estimated using the LULC map of 2013 at the basin outlet were observed to be higher by 3-12% compared to flows estimated using the LULC input of 1970. The estimates of mean monthly flows for the years 2002-2013 at the basin outlet reveal that while the natural flows estimated using the LULC map of 2013 were higher compared to flow estimates using the LULC map of 1970 for most of the months, the flows during the dry months (May-July) were observed to be lower for the former compared to the latter. Our work provides valuable insights into hydrological variability in a major sub-basin of the Ganga river induced due to LULC changes and we advocate that alterations associated with LULC must be incorporated into water management strategies.</p>

One of the challenges in rainfall-runoff modeling is the identification of an appropriate model spatial resolution that allows streamflow estimation at customized locations of the river basin. In lumped modeling, spatial resolution is not an issue as spatial variability is not accounted for, whereas in distributed modeling grid or cell resolution can be related to spatial resolution but its application is limited because of its large data requirements. Streamflow estimation at the data-poor customized locations is not possible in lumped modeling, whereas it is challenging in distributed modeling. In this context, semi-distributed modeling offers a solution including model resolution and estimation of streamflow at customized locations of a river basins with less data requirements. In this study, the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model is employed in semi-distribution mode on river basins of six different spatial resolutions. The model was calibrated and validated for fifteen and three selected flood events, respectively, of three types, i.e., single peak (SP), double peak (DP)- and multiple peaks (MP) at six different spatial resolution of the Sabari River Basin (SRB), a sub-basin of the Godavari basin, India. Calibrated parameters were analyzed to understand hydrologic parameter variability in the context of spatial resolution and flood event aspects. Streamflow hydrographs were developed, and various verification metrics and model scores were calculated for reference- and calibration- scenarios. During the calibration phase, the median of correlation coefficient and NSE for all 15 events of all six configurations was 0.90 and 0.69, respectively. The estimated streamflow hydrographs from six configurations suggest the model’s ability to simulate the processes efficiently. Parameters obtained from the calibration phase were used to generate an ensemble of streamflow at multiple locations including basin outlet as part of the validation. The estimated ensemble of streamflows appeared to be realistic, and both single-valued and ensemble verification metrics indicated the model’s good performance. The results suggested better performance of lumped modeling followed by the semi-distributed modeling with a finer spatial resolution. Thus, the study demonstrates a method that can be applied for real-time streamflow forecast at interior locations of a basin, which are not necessarily data rich.

<strong class="journal-contentHeaderColor">Abstract.</strong> Global-scale river routing models (RRMs) are commonly used in a variety of studies, including studies on the impact of climate change on extreme flows (floods and droughts), water resources monitoring or large-scale flood forecasting. Over the last two decades, the increasing number of observational datasets, mainly from satellite missions, and increasing computing capacities have allowed better performance by RRMs, namely by increasing their spatial resolution. The spatial resolution of a RRM corresponds to the spatial resolution of its river network, which provides the flow directions of all grid cells. River networks may be derived at various spatial resolutions by upscaling high-resolution hydrography data. This paper presents a new global-scale river network at <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M4" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">1</mn><mo>/</mo><mn mathvariant="normal">12</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="27pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="07fed5d74f2aeb2b5f3d0c2f39a20333"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-14-2239-2022-ie00003.svg" width="27pt" height="14pt" src="essd-14-2239-2022-ie00003.png"/></svg:svg></span></span><span class="inline-formula">^∘</span> derived from the MERIT-Hydro dataset. The river network is generated automatically using an adaptation of the hierarchical dominant river tracing (DRT) algorithm, and its quality is assessed over the 70 largest basins of the world. Although this new river network may be used for a variety of hydrology-related studies, it is provided here with a set of hydro-geomorphological parameters at the same spatial resolution. These parameters are derived during the generation of the river network and are based on the same high-resolution dataset, so that the consistency between the river network and the parameters is ensured. The set of parameters includes a description of river stretches (length, slope, width, roughness, bankfull depth), floodplains (roughness, sub-grid topography) and aquifers (transmissivity, porosity, sub-grid topography). The new river network and parameters are assessed by comparing the performances of two global-scale simulations with the CTRIP model, one with the current spatial resolution (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M6" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">1</mn><mo>/</mo><mn mathvariant="normal">2</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="20pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="30983957d36bd426045bd46d18dc5543"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-14-2239-2022-ie00004.svg" width="20pt" height="14pt" src="essd-14-2239-2022-ie00004.png"/></svg:svg></span></span><span class="inline-formula">^∘</span>) and the other with the new spatial resolution (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M8" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">1</mn><mo>/</mo><mn mathvariant="normal">12</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="27pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="502245f6084c839702b86a6bba5944ce"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-14-2239-2022-ie00005.svg" width="27pt" height="14pt" src="essd-14-2239-2022-ie00005.png"/></svg:svg></span></span><span class="inline-formula">^∘</span>). It is shown that, overall, CTRIP at <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M10" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">1</mn><mo>/</mo><mn mathvariant="normal">12</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="27pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="f673665b14aa6db3bde6a89b20964070"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-14-2239-2022-ie00006.svg" width="27pt" height="14pt" src="essd-14-2239-2022-ie00006.png"/></svg:svg></span></span><span class="inline-formula">^∘</span> outperforms CTRIP at <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M12" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">1</mn><mo>/</mo><mn mathvariant="normal">2</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="20pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="874df865b7bfab9765ae76544de4be2d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-14-2239-2022-ie00007.svg" width="20pt" height="14pt" src="essd-14-2239-2022-ie00007.png"/></svg:svg></span></span><span class="inline-formula">^∘</span>, demonstrating the added value of the spatial resolution increase. The new river network and the consistent hydro-geomorphology parameters, freely available for download from Zenodo (<a href="https://doi.org/10.5281/zenodo.6482906">https://doi.org/10.5281/zenodo.6482906</a>, <span class="cit" id="xref_altparen.1"><a href="#bib1.bibx42">Munier and Decharme</a>, <a href="#bib1.bibx42">2022</a></span>), may be useful for the scientific community, especially for hydrology and hydro-geology modelling, water resources monitoring or climate studies.