Technology has greatly promoted ecohydrological model development, but runoff generation and confluence simulations have fallen behind in ecohydrological model development due to limited innovations. To fully understand ecohydrological processes and accurately describe the coupling between ecological and hydrological processes, a distributed ecohydrological model was constructed by integrating multisource information into MYEH. We mainly describe runoff generation and convergence modules. Based on the improved HBV model and degree-3 hour factor method, runoff generation and snow routines were constructed for semiarid grassland basins. In view of meandering and variable steppe river channels and steep hydrological relief characteristics, a confluence module was constructed; the 1-km bend radius equivalent concept was innovatively proposed to unify river channel bend degrees. The daily runoff simulation validation results obtained using two datasets were R2=0.947 and 0.932, NSE=0.945 and 0.905, and KGE=0.029 and 0.261. In the 3-hour flood simulations, the MYEH model could better restore small long-distance water flows than the confluence method that did not consider actual river lengths or bend energy losses; the MYEH model more accurately simulated the flood peak arrival time than the confluence method that did not consider overflow. The simulated mainstream overflow frequency increased by 0.84/10 years, and significant interaction periods of 10 to 13 years occurred with local precipitation, ecological status and global climate change. An approximately 2-year lag occurred in the global climate change response. This study helps us further understand and reveal the ecohydrological processes of steppe rivers in semiarid regions.

Key Points: • Ecological and evapotranspiration characteristics of ten typical vegetation communities in semi-arid steppe were refined and decomposed. • Sensitive parameters of dynamic evapotranspiration improve the regional simulation effect. • Deep learning was used to downscale regional evapotranspiration at the 3-hour scale. Abstract Reports on ecohydrological models for semi-arid steppe basins with scarce historical data are rare. To fully understand the ecohydrological processes in such areas and accurately describe the coupling and mutual feedback between ecological and hydrological processes, a distributed ecohydrological model was constructed , which integrates multi-source information into the MY Ecohydrology (MYEH) model. This paper mainly describes the evapotranspiration module (Eva module) based on sensitive parameters and deep learning. Based on multi-source meteorological, soil, vegetation, and remote sensing data, the historical dynamic characteristics of ten typical vegetation communities in the semi-arid steppe are refined in this study and seven evaporation (ET) components in the Xilin River Basin (XRB) from 1980 to 2018 are simulated. The results show that the Naive Bayesian model constructed based on the temperature and three types of surface reflectance can clearly distinguish between snow-covered or-free conditions. Based on the refinement of typical vegetation communities, the ET process characteristics of different vegetation communities in response to climate change can be determined. Dynamic sensitive parameters significantly improve the regional ET simulation. Based on the validation with the Global Land Evaporation Amsterdam Model product and multiple models in multiple time scales (year, quarter, day, 3 h), a relatively consistent and reliable ET process 1 was obtained for the XRB at the 3-hour scale. The uncertainties of adding and dynamizing more ET process parameters and adjusting the algorithm structure must be further studied.

The Prairie Pothole Region of North America is characterized by millions of depressional wetlands, which provide critical habitats for globally significant populations of migratory waterfowl and other wildlife species. Due to their relatively small size and shallow depth, these wetlands are highly sensitive to climate variability and anthropogenic changes, exhibiting inter- and intra-annual inundation dynamics. Moderate-resolution satellite imagery (e.g., Landsat, Sentinel) alone cannot be used to effectively delineate these small depressional wetlands. By integrating multi-temporal (2009-2018) NAIP aerial imagery and ancillary geospatial datasets, a fully automated approach was developed to delineate wetland inundation extent at watershed scales using Google Earth Engine. Machine learning algorithms were used to classify aerial imagery with additional spectral indices to extract potential wetland inundation areas, which were further refined using ancillary geospatial datasets. The wetland delineation results were then compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and existing global-scale surface water products to evaluate the performance of the proposed method. The results showed that the proposed method can not only delineate the most up-to-date wetland inundation status, but also demonstrate wetland hydrological dynamics, such as wetland coalescence through fill-spill hydrological processes. The proposed automated algorithm provides a practical, reproducible, and scalable framework, which can be easily adapted to delineate wetland inundation dynamics at broad geographic scales.

Despite human-induced changes in floodplains over the past century, comprehensive data of long-term land use change within floodplains of large river basins are limited. Data of long-term and large-scale floodplain land use are required to effectively quantify floodplain functions and development trajectories. They also provide a holistic perspective on the future of floodplain management and restoration – and concomitantly flood-risk mitigation. Here, we present the first available dataset that provides spatially explicit estimates of land use change along the floodplains of the Mississippi River Basin (MRB) covering 60 years (1941-2000) at a 250-m resolution. We derived this MRB floodplain land use change dataset from two input data sources: (i) the high-resolution global floodplain extent dataset GFPLAIN250m, and (ii) the annual FOREcasting SCEnarios of Land-use Change (FORE-SCE) dataset for the continental United States. Our results suggest that MRB floodplains have transitioned irreversibly from natural ecosystems to predominantly agricultural land use (e.g., more than 10,000 km2 of wetlands have been lost due to agricultural expansion). Developed land use within the floodplain has also steadily increased. The dataset is publicly available through HydroShare: https://gishub.org/mrb-data as well as an interactive online map interface: https://gishub.org/mrb-floodplain. These products will support MRB resilience and sustainability goals by advancing data-driven decision making on floodplain restoration, buyout, and conservation scenarios.

The increasing availability of surface water inundation data has encouraged modelers and managers to include small yet abundant surface water storage systems (e.g., wetlands and other landscape depressions) in process-based models. Yet, these model applications have been largely limited to small- to meso- watershed scales, with drainage areas ranging from a few hectares to several thousand square kilometers. The conventional practice of overlooking these surface water storage systems in basin-scale (e.g., >10,000 m2) hydrologic modeling may be missing the total picture of flood and drought hazards. To fill this gap, we developed a 30-m resolution topography-based wetland and depression storage (maximum surface area and storage volume) database for the Upper Mississippi, Ohio, and Missouri River Basins ⎼ encompassing the 2.35 million km2 upstream domain of the Mississippi River system. Further, we integrated this depression dataset into a process-based model to simulate sub-catchment and river reach-scale hydrologic fluxes (surface runoff, soil wetness, evapotranspiration) and flows (streamflow). Compared with a “no depression” conventional model constructed for the Missouri and Upper Mississippi River Basins, our exploratory analyses demonstrate that a depression-integrated model (i) significantly alters the spatial patterns and magnitudes of water yields, (ii) improves streamflow simulation accuracy, and (iii) provides realistic spatial distributions of landscape wetness conditions. These emerging findings provide us with new insights into the effects of small surface water storage and stimulates a reassessment of current practices for basin-scale hydrologic modeling and water management.