CCAM: China Catchment Attributes and Meteorology dataset

The absence of a compiled large-scale catchment characteristics dataset is a key obstacle limiting the development of large-sample hydrology research in China. We introduce the first large-scale catchment attribute dataset in China. We compiled diverse data sources, including soil, land cover, climate, topography, and geology, to develop the dataset. The dataset also includes catchment-scale 31-year meteorological time series from 1990 to 2020 for each basin. Potential evapotranspiration time series based on Penman's equation are derived for each basin. The 4911 catchments included in the dataset cover all of China. We introduced several new indicators that describe the catchment geography and the underlying surface differently from previously proposed datasets. The resulting dataset has a total of 125 catchment attributes and includes a separate HydroMLYR (hydrology dataset for machine learning in the Yellow River Basin) dataset containing standardized weekly averaged streamflow for 102 basins in the Yellow River Basin. The standardized streamflow data should be able to support machine learning hydrology research in the Yellow River Basin. The dataset is freely available at https://doi.org/10.5281/zenodo.5729444 (Zhen et al., 2021). In addition, the accompanying code used to generate the dataset is freely available at https://github.com/haozhen315/CCAM-China-Catchment-Attributes-and-Meteorology-dataset (last access: 26 November 2021) and supports the generation of catchment characteristics for any custom basin boundaries. Compiled data for the 4911 basins covering all of China and the open-source code should be able to support the study of any selected basins rather than being limited to only a few basins.

Automation of hydrological drought typology to study drought propagation in a tropical catchment

Understanding how droughts propagate through the hydrological cycle from precipitation to streamflow and groundwater is important for improving water and risk management policies. At the catchment scale, the analysis of drought propagation and classification into drought types is usually done manually, which can be time consuming and difficult to replicate. Here, we developed an automated, objective procedure for classification of different drought types with the aim to study drought propagation in the tropics. The method was applied to the Savegre catchment in Costa Rica as a proof-of-concept. We first confirmed that drought events in the catchment could be classified into the process-based typology from the literature: classical rainfall deficit drought, wet-to-dry season drought, and composite drought. The automation algorithm was able to replicate the classification obtained with the manual typology with the exception of two events, and thus it is a development towards objective and time efficient hydrological drought analysis in tropical catchments. Most of the detected hydrological droughts (80% and 76% of all river discharge and baseflow droughts, respectively) were classical rainfall deficit droughts, which suggests that climate plays a more important role in drought development than catchment characteristics in this catchment. However, the importance of catchment characteristics was revealed by the presence of severe composite drought events and by the attenuation of significant precipitation droughts.

Uncertainties and their interaction in flood hazard assessment with climate change

The assessment of future impacts of climate change is associated with a cascade of uncertainty linked to the modelling chain employed in assessing local-scale changes. Understanding and quantifying this cascade is essential for developing effective adaptation actions. We evaluate and quantify uncertainties in future flood quantiles associated with climate change for four catchments, incorporating within our modelling chain uncertainties associated with 12 global climate models contained in the Coupled Model Intercomparison Project Phase 6, five different bias correction approaches, hydrological model parameter uncertainty and the use of three different extreme value distributions for flood frequency analysis. Results indicate increased flood hazard in all catchments for different Shared Socioeconomic Pathways (SSPs), with changes in flooding consistent with changes in annual maximum precipitation. We use additive chains and analysis of variance (ANOVA) to quantify and decompose uncertainties and their interactions in estimating selected flood quantiles for each catchment. We find that not only do the contributions of different sources of uncertainty vary by catchment, but that the dominant sources of uncertainty can be very different on a catchment-by-catchment basis. While uncertainties in future projections are widely assumed to be dominated by the ensemble of climate models used, we find that in one of our catchments uncertainties associated with bias correction methods dominate, while in another the uncertainty associated with the use of different extreme value distributions outweighs the uncertainty associated with the ensemble of climate models. These findings highlight the inability to generalise a priori about the importance of different components of the cascade of uncertainty in future flood hazard at the catchment scale. Moreover, we find that the interaction of components of the modelling chain employed are substantial (> 20 % of overall uncertainty in two catchments). While our sample is small, there is evidence that the dominant components of the cascade of uncertainty may be linked to catchment characteristics and rainfall–runoff processes. Future work that seeks to further explore the characteristics of the uncertainty cascade as they relate to catchment characteristics may provide insight into a priori identifying the key components of modelling chains to be targeted in climate change impact assessments.

Exploring an Approach to Estimate Runoff in an Ungauged Mixed Urban Micro Catchment - A Case Study, Pune, India

Sustainable and integrated water resource management needs an hour, and achieving accurate estimation of runoff is key. The decision-making on urban landscaping planning for low-impact development techniques depends largely on the accuracy of rainfall. The haphazardly developed cities in India are encountering flooding crises due to the unexpected expansion. These mixed urban catchments comprise a muddle of residential, commercial, urban-rural, and industrial zones in any combination. Due to this change in urban catchments, the hydrological cycle gets affected and results in elevated runoff volume. The solutions to these are therefore necessary to be planned at a micro catchment level. This paper aims to explore an approach to calculate the runoff of such a micro mixed urban catchment. The geographical scope of this study is the fringe boundary of Pune city. For this ungauged basin, the basic mass balance equation was used to estimate runoff values compared with the runoff values calculated from empirical equations previously developed. From this comparison, it is observed that runoff values obtained from empirical equations were underestimated, which may be due to rapid land-use caused by urbanization. Hence, a need was felt to re-evaluate the coefficients of these empirical models, which take into cognizance the current scenario and its allied changes over the years. An attempt is made to modify the coefficients of empirical equations considering precipitation as the primary parameter. These modified coefficients fetched better runoff results than the runoff results obtained from the coefficients of previously established empirical equations. However, even with these modified coefficients, the runoff results were underestimated, which may be because of not considering the physical characteristics of the catchment in these equations. Therefore, to increase the accuracy of these results, a numerical model that considers these catchment characteristics was chosen. In the present study, a dynamic rainfall-runoff model - stormwater management models (SWMM) is used and compared to assess runoff for an ungauged micro-catchment. The runoff results achieved from these SWMM models better reproduced the hydrologic and hydraulic behavior of the study area (with RMSE of 2.51) by considering detailed catchment characteristics compared to those obtained from all the other empirical models.

Influence of the Landscape Template on Chemical and Physical Habitat for Brown Trout Within a Boreal Stream Network

We used the distribution of stream-dwelling brown trout (Salmo trutta) in a 67 km2 boreal catchment to explore the importance of environmental organizing factors at a range of spatial scales, including whole-catchment characteristics derived from map data, and stream reach chemical and physical characteristics. Brown trout were not observed at any sites characterized by pH < 5.0 during the spring snowmelt episode, matching published toxicity thresholds. Brown trout distributions were patchy even in less acidic regions of the stream network, positively associated with glaciofluvial substrate and negatively associated with fine sand/silty sediments. A multivariate model including only whole-catchment characteristics explained 43% of the variation in brown trout densities, while models with local site physical habitat characteristics or local stream chemistry explained 33 and 25%, respectively. At the stream reach scale, physical habitat apparently played a primary role in organizing brown trout distributions in this stream network, with acidity placing an additional restriction by excluding brown trout from acidic headwater streams. Much of the strength of the catchment characteristics-fish association could be explained by the correlation of catchment-scale landscape characteristics with local stream chemistry and site physical characteristics. These results, consistent with the concept of multiple hierarchical environmental filters regulating the distribution of this fish species, underline the importance of considering a range of spatial scales and both physical and chemical environments when attempting to manage or restore streams for brown trout.