Some recent land surface models can explicitly represent land surface process and focus more on sub-grid terrestrial features. Many studies have involved the analysis of how hillslope water dynamics determine vegetation patterns and shape ecologically and hydrologically important landscapes, such as desert riparian and waterlogged areas. However, the global locations and abundance of hillslope-dominated landscapes remain unclear. To address this knowledge gap, we propose a globally applicable method that employs high-resolution elevation, hydrography, and land cover data to neatly resolve explicit land cover heterogeneity for the mapping of hillslope-dominated landscapes. First, we aggregate pixels into unit catchments to represent topography-based hydrological units, and then vertically discretize them into height bands to approximate the hillslope profile. The dominant land cover type in each height band is determined, and the uphill land cover transition is analyzed to identify hillslope-dominated landscapes. The results indicate that hillslope-dominated landscapes are distributed extensively worldwide in diverse climate zones. Notably, some landscapes, including gallery forests in northeastern Russia and desert riparian in the Horn of Africa, are newly revealed. Furthermore, the proposed strategy enables more accurate representation of explicit land cover heterogeneity than does the simple downscaling of a rectangular grid from larger to smaller units, revealing its capability to neatly resolve land cover heterogeneity in land surface modeling with relatively high accuracy. Overall, we present the extensive global distribution of landscapes shaped by hillslope water dynamics, underscoring the importance of the explicit resolution of heterogeneity in land surface modeling.

A document by Yuki Kimura. Click on the document to view its contents.

Flooding leads to disastrous impacts on human society and activities worldwide, including damage to physical assets and interruptions to daily activities. However, evaluation for such impacts remains challenging, particularly beyond inundation zones, due to the difficulties in monitoring human activities on a global scale. Nighttime light (NTL) remote sensing data provides a unique perspective for human activities on a large scale, reflecting variations in light intensity caused by flood impact. Here we show the possibility of using a high-quality NTL dataset to assess flood impact on human society and activities. Indices providing impact severity and duration were generated with NTL as proxies for flood impact on pixel scale. Results show the consistency of NTL-derived and reported impact duration for five selected cases, which confirms the reliability of NTL flood impact. A large portion (> 96%) of NTL-based affected areas did not overlap with the satellite-based inundation area for 99 cases in 2013, indicating the unique value of NTL in assessing flood impact beyond inundation. The NTL flood impact indices were mapped at 15 arc-second spatial resolution for 876 events on a global scale from 2013 to 2021. Then, administrative-level characteristics of NTL flood impact were compared at a global scale. It was found that lower developed regions exhibit higher vulnerability and challenge in recovery, and are more likely to experience extremely serious and long-lasting impacts compared to higher developed areasverall, using NTL data, in addition to conventional inundation-based methods, offers an innovative perspective on flood impact evaluation.

Global River Models (GRMs), which simulate river flow and flood processes, have rapidly developed in recent decades. However, these advancements necessitate meaningful and standardized quality assessments and comparisons against a suitable set of observational variables using appropriate metrics, a requirement currently lacking within GRM communities. This study proposes the implementation of a benchmark system designed to facilitate the assessment of river models and enables comparisons against established benchmarks. The benchmark system incorporates satellite remote sensing data, including water surface elevation and inundation extent information, with necessary preprocessing. Consequently, this evaluation system encompasses a larger geographical area compared to traditional methods relying solely on in-situ river discharge measurements for GRMs. A set of evaluation and comparison metrics has been developed, including a quantile-based comparison metric that allows for a comprehensive analysis of multiple simulation outputs. The test application of this benchmark system to a global river model (CaMa-Flood), utilizing diverse runoff inputs, illustrates that the incorporation of bias-corrected runoff data leads to improved model performance across various observational variables and performance metrics. The current iteration of the benchmark system is suitable for global-scale assessments and can effectively evaluate the impact of model development as well as facilitate intercomparisons among different models. The source codes are accessiable from https://doi.org/10.5281/zenodo.10903211.

Understanding spatial and temporal variations in terrestrial waters is key to assessing the global hydrological cycle. The future Surface Water and Ocean Topography (SWOT) satellite mission will observe the elevation and slope of surface waters at <100 m resolution. Methods for incorporating SWOT measurements into river hydrodynamic models have been developed to generate spatially and temporally continuous discharge estimates. However, most of SWOT data assimilation studies have been performed on a local scale. We developed a novel framework for estimating river discharge on a global scale by incorporating SWOT observations into the CaMa-Flood hydrodynamic model. The local ensemble transform Kalman filter with adaptive local patches was used to assimilate SWOT observations. We tested the framework using multi-model runoff forcing and/or inaccurate model parameters represented by corrupted Manning’s coefficient. Assimilation of virtual SWOT observations considerably improved river discharge estimates for continental-scale rivers at high latitudes (>50°) and also downstream river reaches at low latitudes. High assimilation efficiency in downstream river reaches was due to both local state correction and the propagation of corrected hydrodynamic states from upstream river reaches. Accurate global river discharge estimates were obtained (Kling–Gupta efficiency [KGE] > 0.90) in river reaches with > 270 accumulated overpasses per SWOT cycle when no model error was assumed. Introducing model errors decreased this accuracy (KGE ≈ 0.85). Therefore, improved hydrodynamic models are essential for maximizing SWOT information. These synthetic experiments showed where discharge estimates can be improved using SWOT observations. Further advances are needed for data assimilation on global-scale.

The Mekong river is one of the most complex river systems in the world that is shared by six nations in Southeast Asia. The river still remains relatively undammed (most existing dams are in the tributaries and are small), and its hydrology today is dominated by large natural flow variations that support the highly productive agricultural and riverine ecological systems; however, this is changing due to the alterations in land use and construction of new dams both in the tributaries the mainstream. Understanding the changes in surface water dynamics is therefore crucial to provide realistic future predictions of changes in downstream floodplain and riverine ecology due to the construction of dams in the upstream. While the existing dams have caused little impact on mainstream flows, those under construction and planned are likely to cause severe and potentially permanent damage to downstream hydro-agro-ecological systems, and adversely impact the livelihood of millions. Here, using hydrodynamic model simulations (CaMa-Flood), we show that the effects of flow regulation on downstream river-floodplain dynamics are relatively predictable along the mainstream Mekong, but flow regulations could potentially disrupt the flood dynamics in the Tonle Sap River (TSR) and small distributaries in the Mekong Delta. Results suggest that TSR flow reversal could cease if the Mekong flood pulse is dampened by 50% and delayed by one-month. While flood occurrence in the vicinity of the Tonle Sap Lake and middle reach of the delta could increase due to enhanced low flow, it could decrease by up to five months in other areas due to dampened high flow, particularly during dry years. Further, areas flooded for less than five months and over six months are likely to be impacted significantly by flow regulations, but those flooded for 5-6 months could be impacted the least.

River channels store large volumes of water globally, critically impacting ecological and biogeochemical processes. Despite the importance of river channel storage, there is not yet an observational constraint on this quantity. We introduce a 26-year record of entirely remotely sensed volumetric channel water storage anomaly (VCWS) on 26 major world rivers. We find mainstem VCWS climatology amplitude (VCWSCA) represents an appreciable amount of basin-wide terrestrial water storage variability (median 2.2%, range 0.05-13.8% across world rivers), despite the fact that mainstem rivers themselves represent an average of just 0.2% of basin area. We find that two global river routing schemes coupled with land surface models reasonably approximate VCWSCA (within {plus minus}50%) in only 19.2 % and 23.1 % of rivers considered (by model). These findings demonstrate VCWS is a useful measurement for assessing global hydrological model performance, and for advancing understanding of spatial patterns in global hydrology.

Continental-scale river hydrodynamic modeling is useful for understanding the global hydrological cycle, and model evaluation is essential for robust calibration and assessing model performance. Although many models have been robustly evaluated using several variables separately, methods for the integrated multivariable evaluation of models have yet to be established. Here, we propose an evaluation method using the overall basin skill score (OSK), based on considering the spatial distribution of different variables via a sub-basin approach. The OSK approach integrates multiple variables to overcome observation-related limitations, such as the distinct temporal and spatial dimensions and unit of measurement unique to each variable, thus judging model performance objectively at the sub-basin and basin scales. As a case study, the global river model, CaMa-Flood, was evaluated using three variables¾discharge, water surface elevation, and flooded area¾for the Amazon Basin, focusing on the impact of using different types of baseline topography data (SRTM and MERIT digital elevation models [DEMs]). CaMa-Flood with the MERIT DEM performed robustly well over a wide range of river depth parameters with a maximum OSK of 0.51 against 0.46 for the SRTM DEM. Single-variable evaluation for all three variables proved inadequate due to low sensitivity for river bathymetry, with good performance outcomes potentially arising for the wrong reasons. This study confirmed that model evaluation using this method enables a balanced evaluation of different variables and a robust estimation of the best parameter set. The proposed method proved useful for flexible, integrated multivariable model evaluation, with modifications allowed per the user’s requirements.

Large-scale river hydrodynamic model act as fundamental tool for many scientific applications related to water cycle, biogeochemistry, and carbon cycle. Even though process representation in the physically based hydrodynamic models has improved significantly in recent times, due to many error sources the uncertainty reduction and evaluation remains a key issue. Previously most of the research focused on the evaluation of hydrodynamic model considering single variable only i.e., discharge due limitations related to models and data availability. The recent advances in hydrodynamic modelling and remote sensing helped to overcome limitation. Some recent studies performed calibration and validation considering multiple variables but were unable to integrate them into a single evaluation score due to different spatial and temporal dimension of variables and thus make it hard to judge the overall performance. Here, we have evaluated the performance of Catchment-based Macroscale Floodplain (CaMa-Flood) hydrodynamic model over Amazon basin considering multiple variables i.e., discharge (Q), water surface elevation (WSE) and flooded area (FA) for a topography data multi-error removed improved terrain (MERIT) DEM. We proposed an evaluation method and introduced a metric “overall basin skill score” (OSK) to integrate the performances due to multiple (three here) variables considering their spatial distribution via a sub-basin approach and provide the evaluation on a scale of 0 to 1. The integrated method showed the robustness in the method and able to detect the best river channel depth parameter set with maximum OSK of 0.57, whereas the evaluation using single variable proved inadequate due to different sensitivity of variables and maximum metric score were obtained for many parameters sets. The proposed method enables a balanced evaluation of different variables and proved useful to integrated multivariable model evaluation with reducing the chances of getting the right results due to wrong reasons. Preprint related to this work: https://doi.org/10.1002/essoar.10506596.1

River bathymetry is an important parameter for hydrodynamic modeling; however, it is associated with large bias because direct large-scale measurements are impractical. Recent studies adjusted river bathymetry data based on assessment of the difference between modeled and observed water surface elevation (WSE); however, river discharge model uncertainties can lead to unintended errors in the correction. In this study, we propose a simple but robust correction method using the WSE bias, based on stage–discharge rating curves rather than time series data. The rating curve represents only the characteristics of the river section, and is not sensitive to instantaneous errors. Our results showed that the “corrected river bathymetry” converged among discharge experiments driven by noise-corrupted runoff forcing. Given the difficulty of eliminating discharge errors, a method for correcting river bathymetry that is free from discharge errors is important for improving hydrodynamic modeling.

River channels store large volumes of water globally, critically impacting ecological and biogeochemical processes. Despite the importance of river channel storage, there is not yet an observational constraint on this quantity. We introduce a 26-year record of entirely remotely sensed volumetric channel water storage (CWS) change on 26 major world rivers. We find mainstem volumetric CWS climatology amplitude (CA) represents an appreciable amount of basin-wide terrestrial water storage variability (median 2.78%, range 0.04-12.54% across world rivers), despite mainstem rivers themselves represent an average of just 0.2% of basin area. We find that two global river routing schemes coupled with land surface models reasonably approximate CA (within ±50%) in only 11.5 % (CaMa-Flood) and 30.7 % (HyMap) of rivers considered. These findings demonstrate volumetric CWS is a useful quantity for assessing global hydrological model performance, and for advancing understanding of spatial patterns in global hydrology.

Land surface water is a key component of the global water cycle. Compared to remote sensing by satellites, both temporal extension and spatial continuity is superior in modeling of water surface area. However, overall evaluation of models representing different kinds of surface waters at the global scale is lacking. We estimated land surface water area (LSWA) using the Catchment-based Macro-scale Floodplain model (CaMa-Flood), a global hydrodynamic model, and compared the estimates to Landsat with 3″ spatial resolution at the global scale. Results show that the two methodologies show agreement in the general spatial patterns of LSWA (e.g., major rivers and lakes, open-to-sky floodplains), but globally consistent mismatches were found under several land surface conditions. CaMa-Flood underestimates LSWA in high northern latitudes (e.g., the Canadian Shield) and coastal areas, as the presence of isolated lakes in local depressions or small coastal rivers is not considered by the model’s physical assumptions. In contrast, model-estimated LSWA is larger than Landsat estimates in forest-covered areas (e.g., Amazon basin) due to the opacity of vegetation for optical satellite sensing, and in cropland areas due to the lack of dynamic water processes (e.g., re-infiltration, evaporation, water consumption) and constraints of water infrastructure (e.g., canals, levees). These globally consistent differences can be reasonably explained by the model’s physical assumptions or optical satellite sensing characteristics, and applying filters (e.g., floodplain topography mask, forest and cropland mask) to the two datasets allows the remaining local-scale discrepancies to be attributed to locally varying factors (e.g., channel parameters, atmospheric forcing).