SWMM-Based Assessment of Urban Mountain Stormwater Management Effects under Different LID Scenarios

The types of urban mountains are diverse, and the surrounding environment is complex. The conditions of runoff generation and convergence in different regions of the same mountain vary. Using the Lijia Mountain in China’s Nanjing City as a case study, this study investigates the effects of such mountain-region-based LID (Low Impact Development) systems. Based on the hydrological analysis of this mountain region, SWMM (Storm Water Management Model) software is used to model and compare the runoff control effects of two LID systems schemes, namely segmental detention and retention and terminal detention and retention. The study’s findings demonstrate that the terminal detention and retention scheme can effectively delay the time of peak flooding and partly reduce peak discharge. In contrast, the segmental detention and retention scheme has a limited delay effect on flood peaks but significantly reduces the peak discharge. This research breaks through the limitations of the previous construction of a single LID scheme for mountainous regions in built-up urban areas. It serves as a theoretical model and technical reference for selecting LID scenarios in response to different mountain conditions.

Risk Assessment of Runoff Generation Using Artificial Neural Network and Field Plots in The Area of Roads and Forest Lands

Runoff generation potential (RGP) on hillslopes is an important issue in the forest roads network monitoring process. In this study, the artificial neural network (ANN) was used to predict RGP in forest road hillslopes. We trained, optimized, and tested the ANN by using field plot data from the Shirghalaye watershed located in the southern part of the Caspian Sea (Iran). 45 plots were installed to measure actual runoff volume (RFP) in different environmental conditions including land cover, slope gradient, soil texture, and soil moisture. A multi-layer perceptron (MLP) network was implemented. The runoff volume was the output variable and the ground cover, slope gradient, initial moisture of soil, soil texture (clay, silt, and sand percentage) were the network inputs. The results showed that ANN can predict runoff volume within the values of an appropriate level in the training (R2=0.95, MSE= 0.009) and test stages (R2=0.80, MSE= 0.01). Moreover, the tested network was used to predict the runoff volume on the forest road hillslopes in the study area. Finally, an RGP map was generated based on the results of the prediction of the ANNs and the GIS capabilities. The results showed that using both an ANN and a GIS is a good tool to predict the RGP in the forest road hillslopes.

Examining the Effectiveness of Catch Crops as a Nature-Based Solution to Mitigate Surface Soil and Water Losses as an Environmental Regional Concern

The main goal of this research was to conduct a biophysical, economic, social, and perception-based approach to foresee the solutions that could be used to mitigate the soil loss problem cost-effectively in “La Ribera del Xúquer” district (Valencia Region, Spain). To achieve these goals, a farmer perception survey was carried out, and an assessment of the biophysical impact of catch crops on soil organic matter, bulk density, steady-state infiltration rate (double-ring infiltrometer) and runoff generation, and soil erosion (rainfall simulation experiments) was carried out in 2016. For the biophysical approach, two paired plots, i.e., catch crops vs. glyphosate herbicide treatment (in advance, control plot), were selected under clementine citrus production. The results show that soil organic matter increased from 1.14 to 1.63%, and bulk density decreased from 1.47 to 1.27 g cm−3 after 10 years of treatments using catch crops. They also facilitated higher infiltration rates from 16.7 to 171 mm h−1 and a delay in runoff generation from 149 to 654 s for control and catch crop plots. Both runoff rates (from 50.6 to 3.1%) and soil erosion (from 3.9 to 0.04 Mg ha−1 h−1) were reduced once the catch crops were deployed in the field. After surveying (2018–2019), farmers stated the use of catch crops as a speck of dirt and a cause of possible loss of reputation when used. Moreover, farmers (N = 73) would accept the catch crops as an effective nature-based alternative only if a subsidy of 131.17€ ha−1 would be paid. The survey results also demonstrated that the farmers' community would see catch crop more as a benefit for the planet's health and society. Few constraints, such as ageing of the farmers’ population, lack of education and negative perception for other management factors, are the critical detrimental factors for adopting catch crops as a nature-based solution to reduce soil and water losses. There is a need for an effective agrarian extension service to change the fate of the current agriculture and achieve sustainability by adopting new management strategies in contemporary agricultural practices.

Comparing the runoff decompositions of small testbed catchments: end-member mixing analysis against hydrological modelling

This study is focused on the comparison of catchment streamflow composition simulated with three well-known rainfall-runoff (RR) models (ECOMAG, HBV, SWAT) against hydrograph decomposition onto the principal constituents evaluated from End-Member Mixing Analysis (EMMA). There used the data provided by the short-term in-situ observations at two small mountain-taiga experimental catchments located in the south of Pacific Russia. All used RR models demonstrate that two neighboring small catchments disagree significantly in mutual dynamics of the runoff fractions due to geological and landscape structure differences. The geochemical analysis confirmed the differences in runoff generation processes at both studied catchments. The assessment of proximity of the runoff constituents to the hydrograph decomposition with the EMMA that makes a basis for the RR models benchmark analysis. We applied three data aggregation intervals (season, month and pentad) to find a reasonable data generalization period ensuring results clarity. In terms of runoff composition, the most conformable RR model to EMMA is found to be ECOMAG, HBV gets close to reflect specific runoff events well enough, SWAT gives distinctive behavior against other models. The study shows that along with using the standard efficiency criteria reflected proximity of simulated and modelling values of runoff, compliance with the EMMA results might give useful auxiliary information for hydrological modelling results validation.

Hydrometeorological analysis of the 12 and 13 September 2019 widespread flash flooding in eastern Spain

On 12 and 13 September 2019, a long-lasting heavy precipitation episode (HPE) affected the València, Murcia and Almería regions in eastern Spain. Observed rainfall amounts were close to 500 mm in 48 h, being the highest cumulative precipitation registered in some rain-gauges for the last century. Subsequent widespread flash flooding caused seven fatalities and estimated economical losses above 425 million EUR. High-resolution precipitation estimates from weather radar observations and flood response from stream-gauges are used in combination with a fully-distributed hydrological model to examine the main hydrometeorological processes within the HyMeX program. This HPE was characterized by successive, well-organized convective structures that impacted a spatial extent of 7500 km2, with rainfall amounts equal or larger than 200 mm. The main factors driving the flood response were quasi-stationarity of heavy precipitation, very dry initial soil moisture conditions and large storage capacities. Most of the examined catchments exhibited a dampened and delayed hydrological response to cumulative precipitation: Until runoff thresholds were exceeded, infiltration-excess runoff generation did not start. This threshold-based hydrological behaviour may impact the shape of flood peak distributions, hindering strict flood frequency statistical analysis due to the generally limited lengths of data records in arid and semi-arid catchments. As an alternative, simple scaling theory between flood magnitude and total rainfall amount is explored.

Using an Uncertainty Quantification Framework to Calibrate the Runoff Generation Scheme in E3SM Land Model V1

Runoff is a critical component of the terrestrial water cycle and Earth System Models (ESMs) are essential tools to study its spatio-temporal variability. Runoff schemes in ESMs typically include many parameters so model calibration is necessary to improve the accuracy of simulated runoff. However, runoff calibration at global scale is challenging because of the high computational cost and the lack of reliable observational datasets. In this study, we calibrated 11 runoff relevant parameters in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) using an uncertainty quantification framework. First, the Polynomial Chaos Expansion machinery with Bayesian Compressed Sensing is used to construct computationally inexpensive surrogate models for ELM-simulated runoff at 0.5° × 0.5° for 1991–2010. The main methodological advance in this work is the construction of surrogates for the error metric between ELM and the benchmark data, facilitating efficient calibration and avoiding the more conventional, but challenging, construction of high-dimensional surrogates for ELM itself. Second, the Sobol index sensitivity analysis is performed using the surrogate models to identify the most sensitive parameters, and our results show that in most regions ELM-simulated runoff is strongly sensitive to 3 of the 11 uncertain parameters. Third, a Bayesian method is used to infer the optimal values of the most sensitive parameters using an observation-based global runoff dataset as the benchmark. Our results show that model performance is significantly improved with the inferred parameter values. Although the parametric uncertainty of simulated runoff is reduced after the parameter inference, it remains comparable to the multi-model ensemble uncertainty represented by the global hydrological models in ISMIP2a. Additionally, the annual global runoff trend during the simulation period is not well constrained by the inferred parameter values, suggesting the importance of including parametric uncertainty in future runoff projections.

Longer Growing Seasons Cause Hydrological Regime Shifts in Central European Forests

In this study, we present evidence for a hydrological regime shift in upland central European forests. Using a combination of long-term data, detailed field measurements and modelling, we show that there is a prolonged and persistent decline in annual runoff: precipitation ratios that is most likely linked to longer growing seasons. We performed a long term (1950–2018) water balance simulation for a Czech upland forest headwater catchment calibrated against measured streamflow and transpiration from deciduous and coniferous stands. Simulations were corroborated by long-term (1965–2018) borehole measurements and historical drought reports. A regime shift from positive to negative catchment water balances likely occurred in the early part of this century. Since 2007, annual runoff: precipitation ratios have been below the long-term average. Annual average temperatures have increased, but there have been no notable long term trends in precipitation. Since 1980, there has been a pronounced April warming, likely leading to earlier leaf out and higher annual transpiration, making water unavailable for runoff generation and/or soil moisture recharge. Our results suggest a regime shift due to second order effects of climate change where increased transpiration associated with a longer growing season leads to a shift from light to water limitation in central European forests. This will require new approaches to managing forests where water limitation has previously not been a problem.