A new eight‐direction algorithm (iFAD8) for the simulation of drainage directions on grid digital elevation models (DEMs) is presented. In this algorithm, a flexible triangular facet construction technique (ND∞) is developed to provide local drainage directions ranging continuously from 0° to 360°. Subsequently, a flow aggregation technique using global deviations of local drainage directions is proposed to simplify the flow paths into a nondispersive format. Another algorithm (FAD8) accompanying the iFAD8 is also presented, which uses the D∞ directions as the local drainage directions. Then FAD8, iFAD8, and three existing algorithms are compared on 10 abstract terrains including nine basic terrains and a Himmelblau terrain. The algorithms are tested to reproduce exact slope lines and specific catchment areas derived from the terrain functions. The results show that iFAD8 has better performance than FAD8. Both iFAD8 and FAD8 outperform existing algorithms in most cases. The flow aggregation technique is shown to be an excellent choice for nondispersive drainage direction simulation based on infinite directions. The ND∞ direction based on flexible triangular facets is also an improvement to the local infinite direction of D∞. We conclude that the iFAD8 algorithm can provide better definitions of drainage directions.

Flow duration curves (FDCs) that represent streamflow regime function through an empirical relationship between the FDC parameters and basin descriptors are widely adopted for hydrologic applications. However, the applications of this method are highly dependent on the availability of observation data. Hence, it is still of great significance to explore the process controls of underpinning regional patterns on streamflow regimes. In this study, we developed a new regionalization method of FDCs to solve the problem of runoff prediction for ungauged mountainous basins. Five empirical equations (power, exponential, logarithmic, quadratic, and cubic) were used to fit the observed FDCs in the 64 mountainous basins in eastern China, and the power model outperforms other models. Stepwise regression was used to explore the differentiated control of 23 basin descriptors on the 13 percentile flows of FDCs, and seven descriptors remained as independent variables for further developing the regional FDCs. Application results with different combinations of these selected descriptors showed that five indices, i.e., average annual rainfall (P), average elevation (H), average gradient (β), average topographic index (TI), and maximum 7d of annual rainfall (Max7d), were the main control factors of FDCs in these areas. Through the regional method, we found that 95.31% of all the basins have NSE values greater than 0.60 and $\epsilon$ (namely the relative mean square error) values less than 20%. In conclusion, our study can guide runoff predictions to help manage booming demands for water resources and hydropower developments in mountainous areas.

Endorheic basins are important geomorphological and ecological units on the Qinghai-Tibet Plateau (QTP), which is undergoing a rapid evolution of its lake system structure and drainage reorganization that is threatening local ecology, infrastructures and residuals owing to climate change. This dataset provides a detailed delineation and classification of endorheic basins on the QTP for understanding the complex dynamics under climate changes. A newly-developed algorithm, namely the Joint Elevation-Area Threshold (JEAT) algorithm (Liu et al, 2024), is applied for delineating endorheic basins based on digital elevation model (DEM). A total of 184 endorheic basins were divided, of which the permanent divide lines were characterized. All the endorheic basins were further categorized into five groups based on the hydraulic connectivity attributes, which have been commonly observed since 2000. The dataset also includes basic information such as drainage area, water surface area, and water storage volume of each endorheic basin. It is particularly beneficial for digital watershed analysis towards ecological restoration and water resource management on the environmentally vulnerable QTP.

Extracting drainage networks from a digital elevation model (DEM) with massive cells is time-consuming due to depressions and flats, where flow paths to the outlet cannot be extracted using downslope gradients. Algorithms based on a priority queue are an efficient solution for this task. However, the existing algorithms depend on the insertion order in a priority queue to determine flow directions for cells with equal elevation. This dependency increases the sorting time in the priority queue. Our study developed an improved algorithm (referred to as DZFlood), adopting a dual priority queue. The queue considers elevation and flow distance to outlets. Cells sharing equal elevation and flow distance can be randomly arranged to reduce time costs. A secondary correction is applied to select a more tortuous yet shortest flow path for each flat cell. The visual assessment results show that the flow paths derived by DZFlood are more accurate than five existing algorithms and consistent with the real rivers and lakes. The computation efficiency of DZFlood is 19.2% faster, on average, than that of another priority-queue-based algorithm named LCP. The relative difference between their runtimes is greater when a larger DEM is used. Over a DEM with 3 × 10⁸ cells, DZFlood performs at least 28% faster than LCP.