Basin Outlet Research Articles

The Surface Water Potentiality in Arid and Semi-Arid Basins Using GIS and HEC-HMS Modeling, Case Study: Gebel El Sibai Watershed, Red Sea

The Red Sea region is considered one of the regions that suffer most from water scarcity among the Egyptian areas. This situation reinforces the importance of maximizing the utilization of available water sources. Rainwater and flood harvesting may form a good water source if good harvesting practices are applied. Natural pastures, Bedouin communities, and wild plants may be affected by severe droughts expected due to climate change. Additional water resources are very important to enhance the resilience of the Bedouin communities to probable droughts. Five main hydrographic basins are issued from Gebel El Sibai (+1435 m), including Wadi Esel, Wadi Sharm El Bahari, Wadi Sharm El Qibli, Wadi Wizr, and Wadi Umm Gheig. Detailed investigation of morphometric parameters, runoff/rainfall relationship, and flood volume using GIS and HEC-HMS model of each basin were estimated as well as natural vegetation. This study reveals that rainfall ranges from 84 mm to 0 mm, and a storm of 84 mm (highest event) is expected to occur every 42 years with a probability of 2.4%. Quantitative morphometric analysis implies that the area has good potential for flooding, especially Wadi Sharm El Qibli and Wadi Umm Gheig, where Wadi Sharm El Bahri represents the lowest priority for flooding. The flood volume of Umm Gheig basin is the greatest: 12 million m3 at the basin outlet with a rainfall event of 15 mm. Wadi Esel is expected to collect 8.7 million m3 due to the ratio of the impervious soil and rainfall quantity, Wadi Sharm El Bahari 2.1 million m3, Wadi Sharm El Qibli 1.6 million m3, and Wadi Wizer 1.04 million m3. Seven storage dams (SD1-SD7) were proposed to enhance the utilization of the surface water potentialities of this study area.

Response time of fast flowing hydrologic pathways controls sediment hysteresis in a low-gradient watershed, as evidenced from tracer results and machine learning models

Hydrologic controls on the timing of sediment transport and sediment hysteresis patterns remain an open area of investigation in hydrology, especially for low-gradient watersheds with substantial instream sediment deposition. Sediment hysteresis, which describes the mismatch between hydrograph peak and sedigraph peak, aids with elucidation of the mechanisms of sediment transport in watersheds. Most frequently, the controls of hysteresis are attributed to the proximity of sediment sources to monitoring locations in a watershed. However, this assumption, while widely applied, is infrequently verified. We investigated the controls of sediment hysteresis in a low gradient system located in the Bluegrass Region of central Kentucky, USA. Turbidity and conductivity sensors installed at the basin outlet provided data to quantify sediment hysteresis and separate hydrologic flow pathways (i.e., by describing the source of water delivered to the watershed’s outlet) using a tracer-based approach. Predictive hydrologic parameters, including hydrologic pathways, event magnitude, and antecedent conditions, were estimated and grouped based on hydrologic similitude. Thereafter, we identified parameters required to predict sediment hysteresis using a tailored ensemble feature selection approach coupled with three machine learning algorithms—Random Forest, K-Nearest Neighbors, and Gradient Boosted Trees. Results from the analysis of 68 storm events occurring over a two-year period showed that clockwise events accounted for 85 % of the total sediment yield despite comprising only 53 % of the events. The hysteresis index (HI) can be predicted (r = 0.8, RMSE = 0.12) using three, out of the thirty-nine hydrologic parameters considered. The most important predictors of HI reflect the volume of event rainfall and the relative proportions of new water (i.e., water derived from precipitation during the storm event) and old water (i.e., water previously stored in the watershed) comprising the hydrograph. Further analyses reveal that new water timing—which changes with the rainfall volume—and sediment timing are closely linked, suggesting that variations in the hysteresis patterns are controlled by changes in the response time of fast flowing water pathways. This implies that hydrologic pathways, as opposed to sediment proximity to the watershed outlet, control sediment hysteresis in this watershed. These results have important implications for better understanding the mechanisms controlling sediment transport at the watershed scale.

Optimizing parameter learning and calibration in an integrated hydrological model: Impact of observation length and information

Integrated hydrological modeling is gaining popularity due to its mechanistic representation of the surface and subsurface processes. However, estimating the parameters of such process-based models can be computationally expensive if careful consideration is not given to the length of streamflow observations used during model calibration. Here we evaluate the influence of the calibration period, the role of streamflow information content, and the gage location in parameter learning and calibration of a fully integrated hydrological model, the Advanced Terrestrial Simulator (ATS). We conducted the study at the Upper Neversink River Watershed within the Delaware River Basin, where streamflow observations are available at 11 gages with varying record lengths. We leveraged a recently proposed knowledge-informed deep learning technique for parameter estimation. To assess the impact of observation period and gage location, model parameters were learned on scenarios using different chunks of streamflow observations, including (1) using only one year or consecutive multiple years of streamflow observations at the watershed outlet and (2) using one shared year of observations at each of the sub-catchment gages, with the period from 1991-10-01 through 1999-09-30 as the overall calibration period. Using the estimated parameters, ATS was rerun for each scenario and evaluated on a subsequent period from 1999-10-01 through 2002-09-30. Results show that the basin outlet discharge prediction is mostly improved when using at least four years of observations for parameter estimation. Further, the performance of the calibrated ATS run correlates with the information content of the observed streamflows, suggesting that the information-theoretic metrics could be indicators for selecting the observation period for parameter estimation. Finally, we find that observations from an informative gage can be used in learning parameters to predict the streamflow at a nearby gage, which would potentially lower the computational expense by reducing the watershed domain used in calibration. Our success underscores the potential of using information theory to achieve robust model parameter estimation on a reduced computational budget.

Assessment of climate change on river streamflow under different representative concentration pathways

Climate change and excessive greenhouse gas emissions profoundly impact hydrological cycles, particularly in arid and semi-arid regions, necessitating assessments of their effects on water resource management, agriculture, soil fertility, nutrient transport, hydropower generation, and flood risk. This study investigates climate change repercussions on streamflow in the Zarrineh River Basin, Iran, across three decadal intervals (2020–2029, 2055–2064, and 2090–2099) aiming to develop effective adaptation and mitigation strategies. Four General Circulation Models (GCMs), chosen based on distinct Representative Concentration Pathways (RCPs) determined by the annual mean temperature gradient, are employed. These models generate daily maximum (Tmax) and minimum (Tmin) temperatures along with precipitation data. Subsequently, these variables are integrated into the Soil and Water Assessment Tool (SWAT) model to analyze river flow alterations for each decadal timeframe. Comparison between future projections and observed climate data reveals a gradual decline in precipitation and Tmax, coupled with a substantial increase in Tmin. The average precipitation diminishes from 0.77 mm in the period 1985–1994 to a range of 0.42–0.28 mm in 2090–2099. The simulated flow at the basin outlet highlights that the GCM with the highest annual mean temperature gradient yields the lowest streamflow, while conversely, the model with the lowest gradient generates the highest. Consequently, streamflow experiences a decline from 52 m3/s in 1985–1994 to a range of 41–20 m3/s in 2090–2099.

Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations

Suspended sediment concentration (SSC) is a crucial indicator for aquatic ecosystems and reservoir management but is challenging to predict at large scales. It is unclear whether SSC is predictable using macroscopic environmental attributes and forcings. This study tested the feasibility of deep-network-based models to predict daily SSC at basin outlets given only basin-averaged forcings, readily-available physiographic attributes, and streamflow (from observation or model). We trained long short-term memory (LSTM) deep networks both separately for each of the 377 sites across the conterminous United States (CONUS) (termed “local models”), and on all the sites collectively (Whole-CONUS). The Whole-CONUS and local models presented median coefficient of determination (R2) values of 0.63 and 0.52, respectively. This comparison agrees with previously acknowledged “data synergy” effects for LSTM models, where more data from more sites can help improve predictions overall. Furthermore, the continental-scale analysis provided a wealth of insights about SSC patterns. Both local and Whole-CONUS models tended to be more successful where SSC-streamflow correlations (Rs-q) were high − typically in the humid Eastern US − and with lower SSC. Low Rs-q basins were often found in the arid Southwest with higher SSC. The highly-nonlinear SSC-streamflow relationships seem related to seasonality, basin size, and heterogeneity of land cover and rainfall within the basins, suggesting these basins need to be simulated at higher spatial resolution and may require additional inputs related to SSC induced by seasonality. The Whole-CONUS model also performed well for spatial extrapolation (basins not included in the training dataset, median R2 = 0.55), supporting large-scale modeling efforts. These state-of-the-art results using only minimal inputs suggest data-driven approaches can exploit the natural coevolution of sediment processes and the environment to support sediment modeling.

Assessment of the Impact of Land Use and Land Cover Change on the Surface Runoff of Hadejia River System, Kano, Nigeria

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.

Physically based vs. data-driven models for streamflow and reservoir volume prediction at a data-scarce semi-arid basin

Physically based or data-driven models can be used for understanding basinwide hydrological processes and creating predictions for future conditions. Physically based models use physical laws and principles to represent hydrological processes. In contrast, data-driven models focus on input–output relationships. Although both approaches have found applications in hydrology, studies that compare these approaches are still limited for data-scarce, semi-arid basins with altered hydrological regimes. This study aims to compare the performances of a physically based model (Soil and Water Assessment Tool (SWAT)) and a data-driven model (Nonlinear AutoRegressive eXogenous model (NARX)) for reservoir volume and streamflow prediction in a data-scarce semi-arid region. The study was conducted in the Tersakan Basin, a semi-arid agricultural basin in Türkiye, where the basin hydrology was significantly altered due to reservoirs (Ladik and Yedikir Reservoir) constructed for irrigation purposes. The models were calibrated and validated for streamflow and reservoir volumes. The results show that (1) NARX performed better in the prediction of water volumes of Ladik and Yedikir Reservoirs and streamflow at the basin outlet than SWAT (2). The SWAT and NARX models both provided the best performance when predicting water volumes at the Ladik reservoir. Both models provided the second best performance during the prediction of water volumes at the Yedikir reservoir. The model performances were the lowest for prediction of streamflow at the basin outlet (3). Comparison of physically based and data-driven models is challenging due to their different characteristics and input data requirements. In this study, the data-driven model provided higher performance than the physically based model. However, input data used for establishing the physically based model had several uncertainties, which may be responsible for the lower performance. Data-driven models can provide alternatives to physically-based models under data-scarce conditions.

Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach

Abstract. Deep learning (DL) algorithms have previously demonstrated their effectiveness in streamflow prediction. However, in hydrological time series modelling, the performance of existing DL methods is often bound by limited spatial information, as these data-driven models are typically trained with lumped (spatially aggregated) input data. In this study, we propose a hybrid approach, namely the Spatially Recursive (SR) model, that integrates a lumped long short-term memory (LSTM) network seamlessly with a physics-based hydrological routing simulation for enhanced streamflow prediction. The lumped LSTM was trained on the basin-averaged meteorological and hydrological variables derived from 141 gauged basins located in the Great Lakes region of North America. The SR model involves applying the trained LSTM at the subbasin scale for local streamflow predictions which are then translated to the basin outlet by the hydrological routing model. We evaluated the efficacy of the SR model with respect to predicting streamflow at 224 gauged stations across the Great Lakes region and compared its performance to that of the standalone lumped LSTM model. The results indicate that the SR model achieved performance levels on par with the lumped LSTM in basins used for training the LSTM. Additionally, the SR model was able to predict streamflow more accurately on large basins (e.g., drainage area greater than 2000 km2), underscoring the substantial information loss associated with basin-wise feature aggregation. Furthermore, the SR model outperformed the lumped LSTM when applied to basins that were not part of the LSTM training (i.e., pseudo-ungauged basins). The implication of this study is that the lumped LSTM predictions, especially in large basins and ungauged basins, can be reliably improved by considering spatial heterogeneity at finer resolution via the SR model.

Time-varying drainage basin development and erosion on volcanic edifices

Abstract. The erosional state of a landscape is often assessed through a series of metrics that quantify the morphology of drainage basins and divides. Such metrics have been well explored in tectonically active environments to evaluate the role of different processes in sculpting topography, yet relatively few works have applied these analyses to radial landforms such as volcanoes. We quantify drainage basin geometries on volcanic edifices of varying ages using common metrics (e.g., Hack's law, drainage density, and number of basins that reach the edifice summit, as well as basin hypsometry integral, length, width, relief, and average topographic slope). Relating these measurements to the log-mean age of activity for each edifice, we find that drainage density, basin hypsometry, basin length, and basin width quantify the degree of erosional maturity for these landforms. We also explore edifice drainage basin growth and competition by conducting a divide mobility analysis on the volcanoes, finding that young volcanoes are characterized by nearly uniform fluvial basins within unstable configurations that are more prone to divide migration. As basins on young volcanoes erode, they become less uniform but adapt to a more stable configuration with less divide migration. Finally, we analyze basin spatial geometries and outlet spacing on edifices, discovering an evolution in radial basin configurations that differ from typical linear mountain ranges. From these, we present a novel conceptual model for edifice degradation that allows new interpretations of composite volcano histories and provides predictive quantities for edifice morphologic evolution.

TDG prediction model improvement by analysis and validation of experiments on a dam model.

The combination of aerated flows and a high-pressure environment in a stilling basin can result in the supersaturation of total dissolved gas (TDG) downstream of hydraulic projects, posing an ecological risk to aquatic populations by inducing gas bubble disease (GBD) or other negative effects. There is limited literature reporting TDG mass transfer experiments on a complete physical dam model; most existing research is based on measurements in prototype tailwaters. In this study, TDG mass transfer experiments were conducted on a physical model of an under-constructed dam, with TDG-supersaturated water as the inflow, and TDG concentrations were meticulously monitored within the stilling basin. The measurements indicate that the TDG saturation at the outlet of the stilling basin decreased by 13.7% and 10.6% compared to the inlet for the two cases, respectively. Subsequently, an improved TDG prediction model was developed by incorporating a sub-grid air entrainment model and a phase-constrained scalar model. The numerical simulation results were compared with experimental data, indicating a maximum error in TDG saturation at all measured points of less than ± 3%. Moreover, the TDG saturation showed an error of only ± 0.3% at the outlet of the stilling basin. This model has broad applicability to various flow types for obtaining TDG mass transfer results and evaluating mitigation measures of TDG supersaturation to reduce the harmful effects on aquatic ecosystems.

Technical note: Testing the connection between hillslope-scale runoff fluctuations and streamflow hydrographs at the outlet of large river basins

Abstract. A series of numerical experiments were conducted to test the connection between streamflow hydrographs at the outlet of large watersheds and the time series of hillslope-scale runoff yield. We used a distributed hydrological routing model that discretizes a large watershed (∼ 17 000 km2) into small hillslope units (∼ 0.1 km2) and applied distinct surface runoff time series to each unit that deliver the same volume of water into the river network. The numerical simulations show that distinct runoff delivery time series at the hillslope scale result in indistinguishable streamflow hydrographs at large scales. This limitation is imposed by space-time averaging of input flows into the river network that are draining the landscape. The results of the simulations presented in this paper show that, under very general conditions of streamflow routing (i.e., nonlinear variable velocities in space and time), the streamflow hydrographs at the outlet of basins with Horton–Strahler (H–S) order 5 or above (larger than 100 km2 in our setup) contain very little information about the temporal variability of runoff production at the hillslope scale and therefore the processes from which they originate. In addition, our results indicate that the rate of convergence to a common hydrograph shape at larger scales (above H–S order 5) is directly proportional to how different the input signals are to each other at the hillslope scale. We conclude that the ability of a hydrological model to replicate outlet hydrographs does not imply that a correct and meaningful description of small-scale rainfall–runoff processes has been provided. Furthermore, our results provide context for other studies that demonstrate how the physics of runoff generation cannot be inferred from output signals in commonly used hydrological models.

Research on the distributed Clark confluence model based on basic watershed units

Abstract Based on the confluence principle, the confluence should represent the aggregate of net rainfall confluences within each distinct basic watershed unit (BWU) of a basin. The BWUs are categorized as V-type and Horseshoe-type based on their shape characteristics and two types of time–area curves of slope convergence have been derived separately. The cascade river convergence is modeled using a lagged linear reservoir, resulting in the development of a distributed CLARK convergence model based on the BWUs of a basin (BWU-DCLARK). The key findings are as follows: (1) The BWU-DCLARK model effectively captures the runoff convergence process and has been successfully applied in the Yanduhe River basin. Modeling results demonstrate high simulation accuracy. (2) The time of slope convergence indicates that the regulatory and storage effects on runoff of BWUs cannot be overlooked. (3) The BWU-DCLARK confluence model not only enables the calculation of flow at the basin outlet but also facilitates the computation of flow at any node along the river chain which is of great significance for hydrological forecasting in un-gauged basins but the application effect will need further verification.

Feature importance measures for flood forecasting system design

ABSTRACT Effective proxy selection in hydrological processes is crucial in several applications. This study investigates the role of sub-basins in hydrological response, which remains unclear. Our focus is on exploring feature importance measures to identify influential sub-basins in a flood forecasting system. We use the Tiber River basin as a case study and employ a synthetic flood hydrograph dataset, consisting in approximately 20 000 simulated annual maximum hydrographs across 39 sub-basins and the basin outlet. Through this study, we present a proof of concept for ranking sub-basins based on their contribution to basin response using six feature importance measures. The results reveal eight influential sub-basins and provide guidance for strategically installing measurement instrumentation for an efficient and cost-effective flood early warning system.

Climate Change: A bibliometric study of the Great Lakes Basin

The Great Lakes region of North America is experiencing climate-driven disturbances that threaten the safety and livelihoods of coastal communities and people. Limitations to the spatial and temporal coverage of research have the potential to hamper the ability to predict site-specific conditions and responses to climatic events. In this paper, we contextualize these spatial and temporal limitations of climate change research. Our summary reveals an uneven spatial distribution of research across the basin and publication outlets, with research focused on Lakes Superior, Michigan, and Huron, and the increasing availability of biological and ecological data to forecast future conditions. The spatial and temporal limitations related to historical data, region-wide monitoring efforts, research during the winter season, and the technological and methodological developments in natural resource management that can overcome these limitations are explored. Future research and emerging data needs are discussed, including the need for increased inclusion of the social sciences and improving relationships with Indigenous and First Nation communities in terms of research, data sharing, and governance.

The Impact of Non-Point Source (NPS) Management on Non-Point Source Reduction and Water Cycle Improvement in an Urban Area

Suwon, the capital and largest city of Gyeonggi-do, South Korea, was designated as a non-point source management area in 2010. The management period ended in 2020, so follow-up measures are needed. In this study, we investigated several projects implemented in Suwon for urban water cycle improvement and non-point source pollution reduction, and the long-term management effects were analyzed to suggest policy directions such as the revision of designation notices. During the 10-year management period in Suwon, the population and lot area continued to increase, and the non-point source-based annual Biochemical Oxygen Demand (BOD) discharge loads also increased by approximately 25% at the half sub-basins Hwangguji-cheon and Woncheonri-cheon in 2020 compared to 2010. Even under these conditions, statistical analyses show that the Biochemical Oxygen Demand (BOD) and Total Phosphorus (TP) concentrations monitored at the outlet of basin were decreased due to the promotion of a large-scale sewer management project as well as non-point source pollution reduction projects. Also, the field monitoring data-based Load Duration Curve (LDC) analysis results indicate that the loads decreased in the high-flow period of 2020 compared to 2015. Also, the Normalized Difference Vegetation Index (NDVI) values calculated using satellite images since 2017 tended to increase slightly during the period when the impervious area estimated using the land registration map increased. It is assumed that using the current calculation method for impervious areas has limitations regarding its ability to reflect changes in the small-scale Low-Impact Development (LID) facility and in ecological/landscape areas. On the other hand, the annual variation in direct runoff estimated at the outlet using three hydrograph separation methods did not show any improvement with regard to storm water retention during the management period. These results reveal that the effects on urban water cycle improvement, such as peak flow reduction and base flow increase, may not be noticeable despite some progress in reducing non-point source pollution and increasing green area. Therefore, additional efforts directed towards non-point source pollution management focused on water cycle improvement are required in the city, especially in the sub-basins with higher pollution loads such as Hwangguji-cheon and Seoho-cheon.

Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance

AbstractAccuracy of the Copernicus snow water equivalent (SWE) product and the impact of SWE calibration and assimilation on modelled SWE and streamflow was evaluated. Daily snowpack measurements were made at 12 locations from 2016 to 2019 across a 4104 km2 mixed‐forest basin in the Great Lakes region of central Ontario, Canada. Sub‐basin daily SWE calculated from these sites, observed discharge, and lake levels were used to calibrate a hydrologic model developed using the Raven modelling framework. Copernicus SWE was bias corrected during the melt period using mean bias subtraction and was compared to daily basin average SWE calculated from the measured data. Bias corrected Copernicus SWE was assimilated into the models using a range of parameters and the parameterizations from the model calibration. The bias corrected Copernicus product agreed well with measured data and provided a good estimate of mean basin SWE demonstrating that the product shows promise for hydrology applications within the study region. Calibration to spatially distributed SWE substantially improved the basin scale SWE estimate while only slightly degrading the flow simulation demonstrating the value of including SWE in a multi‐objective calibration formulation. The particle filter experiments yielded the best SWE estimation but moderately degraded the flow simulation. The particle filter experiments constrained by the calibrated snow parameters produced similar results to the experiments using the upper and lower bounds indicating that, in this study, model calibration prior to assimilation was not valuable. The calibrated models exhibited varying levels of skill in estimating SWE but demonstrated similar streamflow performance. This indicates that basin outlet streamflow can be accurately estimated using a model with a poor representation of distributed SWE. This may be sufficient for applications where estimating flow is the primary water management objective. However, in applications where understanding the physical processes of snow accumulation, melt and streamflow generation are important, such as assessing the impact of climate change on water resources, accurate representations of SWE are required and can be improved via multi‐objective calibration or data assimilation, as demonstrated in this study.

Combining Hydrological Models and Remote Sensing to Characterize Snowpack Dynamics in High Mountains

Seasonal snowpacks, characterized by their snow water equivalent (SWE), can play a major role in the hydrological cycle of montane environments with months of snow accretion followed by episodes of melt controlling flood risk and water resource availability downstream. Quantifying the temporal and spatial patterns of snowpack accumulation and its subsequent melt and runoff is an internationally significant challenge, particularly within mountainous regions featuring complex terrain with limited or absent observational data. Here we report a new approach to snowpack characterization using open-source global satellite and modelled data products (precipitation and SWE) greatly enhancing the utility of the widely used Soil and Water Assessment Tool (SWAT). The paper focusses on the c. 23,000 km2 Chenab river basin (CRB) in the headwaters of the Indus Basin, globally important because of its large and growing population and increasing water insecurity due to climate change. We used five area-weighted averaged satellite, gridded and reanalysis precipitation datasets: ERA5-Land, CMORPH, TRMM, APHRODITE and CPC UPP. As well as comparison to local weather station data, these were used in SWAT to model streamflow for evaluation against observed streamflow at the basin outlet. ERA5-Land data provided the best streamflow match-ups and was used to infer snowpack (SWE) dynamics at basin and sub-basin scales. Snow reference data were derived from remote sensing and modelled SWE re-analysis products: ULCA-SWE and KRA-SWE, respectively. Beyond conventional auto-calibration and single-variable approaches we undertook multi-variable calibration using R-SWAT to manually adjust snow parameters alongside observed streamflow data. Characterization of basin-wide patterns of snowpack build-up and melt (SWE dynamics) were greatly strengthened using KRA-SWE data accompanied by improved streamflow simulation in sub-basins dominated by seasonal snow cover. UCLA-SWE data also improved SWE estimations using R-SWAT but weakened the performance of simulated streamflow due to under capture of seasonal runoff from permanent snow/ice fields in the CRB. This research highlights the utility and value of remote sensing and modelling data to drive better understanding of snowpack dynamics and their contribution to runoff in the absence of in situ snowpack data in high-altitude environments. An improved understanding of snow-bound water is vital in natural hazard risk assessment and in better managing worldwide water resources in the populous downstream regions of mountain-fed large rivers under threat from climate change.

Tradeoffs Between Temporal and Spatial Pattern Calibration and Their Impacts on Robustness and Transferability of Hydrologic Model Parameters to Ungauged Basins

AbstractOptimization of spatially consistent parameter fields is believed to increase the robustness of parameter estimation and its transferability to ungauged basins. The current paper extends previous multi‐objective and transferability studies by exploring the value of both multi‐basin and spatial pattern calibration of distributed hydrologic models as compared to single‐basin and single‐objective model calibrations, with respect to tradeoffs, performance and transferability. The mesoscale Hydrological Model (mHM) is used across six large central European basins. Model simulations are evaluated against streamflow observations at the basin outlets and remotely sensed evapotranspiration patterns. Several model validation experiments are performed through combinations of single‐ (temporal evaluation through discharge) and multi‐objective (temporal and spatial evaluation through discharge and spatial evapotranspiration patterns) calibrations with holdout experiments saving alternating basins for model evaluation. The study shows that there are very minimal tradeoffs between spatial and temporal performance objectives and that a joint calibration of multiple basins using multiple objective functions provides the most robust estimations of parameter fields that perform better when transferred to ungauged basins. The study indicates that particularly the multi‐basin calibration approach is key for robust parametrizations, and that the addition of an objective function tailored for matching spatial patterns of ET fields alters the spatial parameter fields while significantly improving the spatial pattern performance without any tradeoffs with discharge performance. In light of model equifinality, the minimal tradeoff between spatial and temporal performance shows that adding spatial pattern evaluation to the traditional temporal evaluation of hydrological models can assist in identifying optimal parameter sets.

Analysis of the correlation between land use and surface runoff in a Brazilian savanna basin

– Since the 1980s, the Brazilian savanna (Cerrado) has been suppressed to make space for agricultural production, mainly soybean, corn, and cotton. This savanna strategically contains important South American river sources – Paraná, Paraguai, São Francisco, Araguaia, Tocantins, and tributaries of the Amazon River. Land use and cover are conditioning factors in generating the hydrological cycle components, notably surface runoff. Thus, this study aimed to evaluate spatial and temporal land use changes and their correlations with surface runoff in the Grande River basin. The study area is a relevant São Francisco River sub-basin, covering part of the Urucuia Aquifer System, encompassing the dry forest and savanna biomes. The inputs included landscape ecology metrics, land use time series from 1990 to 2020, and water flow values near the basin outlet. The landscape metrics were modeled with the R software landscapemetrics package. Aside from the time series trend, the correlation between land use, land cover, and flow variations was also evaluated through statistical tests – Mann-Kendall, Sen's slope, Pittitt, and rho Spearman. Changes were observed in areas covered by forest, savanna, and grassland formations. The areas presented reduction from 22,121 km2, 27,889 km2, and 15,366 km2 to 9916 km2, 27,870 km2, and 5666 km2, respectively. On the other hand, there was an increase, especially in rainfed agriculture (from 5241 km2 to 20,856 km2) and, more recently (2005), in irrigated agriculture (from 94 km2 to 1362 km2). Results show a reduction in the fragments of native vegetation (from 2,235 km2 to 1794 km2) and an increase in the fragments of agricultural activities (from 678 km2 to 1081 km2). For most classes, the change point was identified as the year 2005. A strong direct correlation was observed between the average and minimum flows (Q90) with forest and grassland formations. In comparison, an inverse correlation was noticed between pasture, urban infrastructure, rainfed agriculture, and irrigated agriculture classes.

Constraining Flood Forecasting Uncertainties through Streamflow Data Assimilation in the Tropical Andes of Peru: Case of the Vilcanota River Basin

Flood modeling and forecasting are crucial for managing and preparing for extreme flood events, such as those in the Tropical Andes. In this context, assimilating streamflow data is essential. Data Assimilation (DA) seeks to combine errors between forecasting models and discharge measurements through the updating of model states. This study aims to assess the applicability and performance of streamflow DA in a sub-daily forecasting system of the Peruvian Tropical Andes using the Ensemble Kalman Filter (EnKF) and Particle Filter (PF) algorithms. The study was conducted in a data-sparse Andean basin during the period February–March 2022. For this purpose, the lumped GR4H rainfall–runoff model was run forward with 100 ensemble members in four different DA experiments based on IMERG-E and GSMaP-NRT precipitation sources and assimilated real-time hourly discharges at the basin outlet. Ensemble modeling with EnKF and PF displayed that perturbation introduced by GSMaP-NRT’-driven experiments reduced the model uncertainties more than IMERG-E’ ones, and the reduction in high-flow subestimation was more notable for the GSMaP-NRT’+EnKF configuration. The ensemble forecasting framework from 1 to 24 h proposed here showed that the updating of model states using DA techniques improved the accuracy of streamflow prediction at least during the first 6–8 h on average, especially for the GSMaP-NRT’+EnKF scheme. Finally, this study benchmarks the application of streamflow DA in data-sparse basins in the Tropical Andes and will support the development of more accurate climate services in Peru.