Rainfall-runoff Model Research Articles

Subgrid Model of Water Storage in Paddy Fields for a Grid-Based Distributed Rainfall–Runoff Model and Assessment of Paddy Field Dam Effects on Flood Control

Paddy field dams are basin-level flood control measures that promote rainwater storage; however, a general runoff model cannot adequately describe the water balance in paddy fields. This study develops a subgrid model for evaluating paddy water balance considering land use on a computational grid. Subgrid models can account for the storage effect of paddy field dams without disregarding the general grid-based distributed rainfall–runoff model framework. To investigate the effect of current paddy field storage and the introduction of paddy field dams on reducing peak flood discharge, rainfall–runoff analysis was conducted using the proposed model in the Kashima River basin, which flows into Lake Inba-numa in Chiba Prefecture, Japan. The computational results indicated that the rainwater storage effect of current paddy fields reduces the peak river discharge, suggesting that the drainage process of the paddy field should be incorporated into runoff models. Furthermore, the storage effect of paddy fields became more pronounced as the height of the drainage pipe in the paddy field dam increased. The calculated results quantitatively show the flood control effect of paddy field storage over the entire basin; thus, the proposed subgrid model may be a useful tool for promoting basin-level flood control measures.

A novel fast and efficient adaptive shuffled complex evolution algorithm for model parameter calibration

The research and optimization of hydrological forecasting models are among the most crucial components in the scope of water management and flood protection. Optimizing the calibration of hydrological forecasting models is crucial for forecasting performance. A rapid adaptive Shuffled Complex Evolution (SCE) method called Fast Adaptive SCE (FASCE) is proposed for calibrating model parameters. It builds upon the previously established SCE-UA, known for its effectiveness and robustness in the same calibration context. The robustness of the original SCE-UA is expanded upon, introducing a revised adaptive simplex search to bolster efficiency. Additionally, a new strategy for setting up the initial population base enhances explorative capacities. FASCE’s performance has been assessed alongside numerous methods from prior studies, demonstrating its effectiveness. Initial tests were conducted on a set of functions to assess FASCE’s efficacy. Findings revealed that FASCE could curtail the failure rate by a minimum of 80%, whereas the requirement for function evaluations fell between 30% and 60%. Two hydrological models - Support Vector Machine (SVM) and Xinanjiang rainfall-runoff model were employed to estimate the new algorithm’s performance. No failures were reported, and there was a reduction of at least 30% in function evaluations using FASCE. The outcomes from these studies affirm that FASCE can considerably reduce both the number of failures and the count of function evaluations required to reach the global maximum. Hence, FASCE emerges as a viable substitute for model parameter calibration.

Enhancing rainfall-runoff model accuracy with machine learning models by using soil water index to reflect runoff characteristics.

The advancement of data-driven models contributes to the improvement of estimating rainfall-runoff models due to their advantages in terms of data requirements and high performance. However, data-driven models that rely solely on rainfall data have limitations in responding to the impact of soil moisture changes and runoff characteristics. To address these limitations, a method was developed for selecting predictor variables that utilize the accumulation of rainfall at various time intervals to represent soil moisture, the changes in the runoff coefficient, and runoff characteristics. Furthermore, this study investigated the utility of rainfall products [such as climate hazards group infrared precipitation with station data (CHIRPS) and global precipitation measurement (GPM)] for representing rainfall data, while also using the soil water index (SWI) to enhance runoff estimation. To assess these methods, the random forest (RF) and artificial neural network (ANN) models were utilized to simulate daily runoff. Incorporating both the rainfall and SWI data led to improved outcomes. The RF demonstrated superior performance compared with the ANN and the conceptual model, without the need for baseflow separation or antecedent runoff. Furthermore, accumulated rainfall was shown to be a valuable input for the models. These findings should facilitate the estimation of runoff in locations with limited measurement data on rainfall and soil moisture by utilizing remote sensing data.

An integrated climate and water resource climate service prototype for long term water allocation in the Upper Yellow River region of China

Water Resourcing in China has historically been a complex issue requiring the ability to deal with regular floods, droughts and diverse water needs. Climate change represents another challenge to this sector, albeit one that is not traditionally considered by water managers. In this sector in China water management is predominantly based on historic, seasonal and annual forecast data while multi-annual and (multi-)decadal data are seldom used. In this paper, we present the co-development of a climate service prototype designed to provide water managers with insights into the impacts of climate change on the Upper Yellow River region for the next century. The paper is an outcome from our project that encouraged water resource planners and water resource managers to utilise long-term climate information to understand the uncertainties and the challenges our changing climate is likely to have in the region. Using an interdisciplinary team and adopting a user-centred, co-production approach, a prototype web-based data visualisation tool was developed. The development of the prototype was based on a design specification constructed from the findings of detailed interviews that allowed it to be developed and tested under SARS-CoV-2 pandemic restrictions that prevented the typical development process to be undertaken. The developed prototype presents climate information and communicates uncertainties regarding climate change in the remainder of the century through data sets that are typically used by the water sector in China in a simple, easy to understand style. Models that estimate river levels under different extraction scenarios and results about estimated river level and flow, and flood risk are also presented. The prototype was shown to be successful, as key messages relating to the impact of climate change and the challenges for water resource management could be effectively communicated through the tool interface. Practical implicationsUnderstanding the impacts of climate change on water resourcing is complicated and multifaceted. There is a need for better data about what water there is and how it is moving around between and within catchments. Estimates of past, present and future climate variables along with historical measurements of river flow can be used to help visualise some of the uncertainties and changes that may happen in the next 50 years. In addition, there is a need to understand changing water demands and water resource management practices. Current water resource management practices are based on historical conditions and assumptions that are less likely to hold true in a more variable and warmer climate. Communicating how future changes will impact future water resourcing is critical to water resources in a changing climate (Belcher et al. 2018).This research outlines the construction of a tool to visualise the impacts of climate change on water availability in part of China that is typically water scarce, using models developed using the Soil Water Assessment Toolkit (SWAT). A model of the Upper Yellow River (UYR) was developed to demonstrate the impact of climate change on river levels in the catchment based on climate variables. The rainfall-runoff model was based on climate predictions from the CMIP5 assessment HadGEM3-GC3.05 climate model and incorporated information about water resource allocations for different administrative regions of the catchment The general climate trend for the region is that it is expected to become significantly warmer. The total amount of precipitation is likely to be about the same, and yet it is expected that overall, the catchment will become significantly drier over time as winter shortens and summer lengthens. The outputs from the model reflect the changes in climate variables. The uncertainties were communicated via a Web based tool. Water resource managers in China helped to coproduce the tool by participating in workshops and providing feedback on prototypes. The workshops helped scientists and water resource managers to communicate about climate change impacts on water resources and water resource management.

Predicting Flood Water Level Using Combined Hybrid Model of Rainfall-Runoff and AI-Based Models

Vietnam faces on significant human and property losses from floods almost every year. Therefore, the aim of this study is to provide timely and highly accurate flood prediction information using a developed hybrid model by combination of the rainfall-runoff model and AI-based model. We used Tank model as a rainfall-runoff model for peak flood discharge and parameter calibration was conducted by GA (genetic algorithm) and PS (pattern search). The sum of squared residuals (SSR) and weighted sum of squared residuals (WSSR) as objective functions were used for peak flood discharge evaluation. The simulated flood discharge was converted as flood water level by rating curve and Monte Carlo simulation was applied to estimate the confidence bounds for 95% confidence level. These estimates were then utilized as input data for the AI-based models to identify the strengths and weaknesses of each model and develop an optimal flood water level prediction model. Two AI-based models, deep neural network (DNN) and long short-term memory (LSTM), were used for flood water level prediction. The LSTM model demonstrated the best performance with a correlation coefficient (CC) of 0.98, normalized root mean square error (NRMSE) of 0.04, and Nash-Sutcliffe efficiency (NSE) of 0.98.

The 2021 floods in the Netherlands from a river engineering perspective

In July of 2021, large areas in the catchment of the Meuse River in Belgium, the Netherlands and Germany were affected by extreme rainfall and floods. In the Netherlands, measured peak discharges in the upstream part of the Meuse and regional tributaries were the highest ever recorded. This resulted in extreme water levels in the upstream reaches, but downstream of Roermond (about 80 km downstream of the border with Belgium) water levels were significantly lower than during previous flood events with similar discharge levels near the border with Belgium. The lower water levels resulted from the implementation of the large-scale room for the river program, in combination with very strong peak attenuation of the flood wave. Peak attenuation was especially high in the so-called Lake Meuse in the Netherlands, due to large storage areas in the floodplains. The floods in the Netherlands came in certain parts of the Meuse basin rather unexpected. The flood forecasts in the upstream part of the Meuse in the Netherlands depended heavily on rainfall forecasts and rainfall-runoff modelling and underestimated the peak water levels up to 36 hours before the flood actually peaked. Further downstream, the lead time increases and forecasts are based on discharge levels that are measured in upstream parts of the catchments. This results in more accurate estimates. The estimated probability of occurrence of the measured peak water level is around 1:100 per year for the Meuse at Borgharen (rkm 16) and decreases to 1:15 per year downstream near Gennep (rkm155). In the tributaries in Limburg the probabilities of occurrence of the measured water levels vary widely: at many locations along the rivers Geul, Geleenbeek and Roer, exceedance probabilities are estimated to be between 1:100 and 1:1000 per year. The floods have also resulted in unprecedented morphological changes. The armour layer in the riverbed of the ‘Common Meuse’, consisting of very coarse gravel, was mobilized and layers of fine sand quickly eroded. This resulted in multiple scour holes with depths of 3 to 15m. Riverbank erosion was observed and large quantities of sand were deposited on the river banks.

Ensemble rainfall–runoff modeling of physically based semi-distributed models using multi-source rainfall data fusion

Abstract This study was aimed at ensemble rainfall–runoff modeling by Soil and Water Analysis Tool (SWAT), the Hydrologic Engineering Center's Hydraulic Modeling System, and Hydrologiska Byråns Vattenbalansavdelning of Gilgel-Abay watershed, Blue Nile basin, Ethiopia. For modeling, daily rainfall datasets of five gauges and three satellites, streamflow, and spatial data were used. In the modeling stage, first, the runoff was simulated separately using the rainfall data of gauges, satellites, and their fusion. Second, ensemble rainfall–runoff simulation of the rainfall dataset fusion-based runoff result was conducted via the proposed weighted average, simple average, and neural network (NNE) ensemble techniques. The results exhibited that all models are good in capturing the rainfall–runoff relationship; however, SWAT showed slight superiority by Nash–Sutcliffe efficiency of 0.807 and 0.821 for gauge and fusion data, respectively. The rainfall fusion ensemble model revealed significant improvement over modeling by satellite rainfall owing to the bias-correcting gauge rainfall over satellite rainfall. The NNE technique enhanced the efficiency of the low-performing satellite-rainfall-based model by 17.5% and the rainfall fusion-based model by 13.3% at the validation stage. In general, the result of this study points out that the rainfall datasets’ fusion from multi-sources would be worthy for the rainfall–runoff simulation of ungauged basins.

Ground-Runoff Harvesting to Increase Water Availability in Isolated Households on Hilly Mediterranean Islands: A Case Study in a Micro-Catchment of Ibiza (Spain)

Mediterranean islands suffer from a lack of freshwater due to persistent and recursive droughts, limited groundwater availability and mass tourism. In Ibiza (Spain), private estates disconnected from the water distribution network consume about 21% of the total freshwater demand on the island. We conducted a study to evaluate the potential of ground-runoff harvesting (GRH) as a sustainable and inexpensive solution to increase freshwater availability in isolated households in Ibiza. The study involved an innovative modular tank of 40 m3 buried in the garden of a private property. The tank intercepted runoff forming in a 12,300 m2 hilly micro-catchment. We found that an extreme rainfall event with an intensity of 65 mm/h was able to create sufficient runoff to fill up the tank in one hour. A curve-number-based rainfall-runoff model was used to simulate the experimental results and to obtain a first-cut estimation of the potential of GRH at the scale of the island. The analysis indicates that, if installed in all forest areas in Ibiza with a similar slope to the study area, a volume of 1.31×106 m3 of freshwater could be harvested per year on the island just from extreme precipitation events. Such a volume of water is equivalent to about 5% of the island’s total freshwater budget. The study concludes that GRH is a highly valuable, yet still unexploited opportunity to save large freshwater volumes in dry-climate areas like Ibiza. GRH should be promoted across Mediterranean islands, and it can be easily incorporated within local water regulations.

Enhancing Understanding of Hydrologic Processes in the Shafe Watershed, Ethiopia

Research on the combined impact of watershed attributes on streamflow is crucial in water resources planning and management, particularly due to the strong link between landscape modification and watershed attributes. However, identifying critical watershed attributes remains a challenge. This study focuses on investigating the impact of watershed attributes on streamflow in the Hare catchment of Ethiopia’s rift valley lakes basin. The Soil and Water Assessment Tool (SWAT) rainfall–runoff model was used, calibrated, and validated against observed discharge data to identify remedial measures for streamflow generation. The model’s performance was evaluated using criteria such as R2, NSE, PBIAS, and RSR, which yielded satisfactory values. The study found significant changes in land cover, with forest and shrub land declining and agricultural land expanding. Comparing mean annual streamflow between 1998, 2009, and 2021, streamflow at 2021 land use and land cover increased by 13.03% compared to 2009, which had already increased by 16.05% compared to 1998. The study also examined the impact of climate variations by manipulating meteorological data length and average slope. Overall, this study provides valuable insights into the relationship between watershed attributes and streamflow, emphasizing the importance of considering land cover changes and climate variations for effective water resource management in the Hare catchment.

Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling

Abstract. Dynamical (i.e., model-based) methods are widely used by forecasting centers to generate seasonal streamflow forecasts, building upon process-based hydrological models that require parameter specification (i.e., calibration). Here, we investigate the extent to which the choice of calibration objective function affects the quality of seasonal (spring–summer) streamflow hindcasts produced with the traditional ensemble streamflow prediction (ESP) method and explore connections between hindcast skill and hydrological consistency – measured in terms of biases in hydrological signatures – obtained from the model parameter sets. To this end, we calibrate three popular conceptual rainfall-runoff models (GR4J, TUW, and Sacramento) using 12 different objective functions, including seasonal metrics that emphasize errors during the snowmelt period, and produce hindcasts for five initialization times over a 33-year period (April 1987–March 2020) in 22 mountain catchments that span diverse hydroclimatic conditions along the semiarid Andes Cordillera (28–37∘ S). The results show that the choice of calibration metric becomes relevant as the winter (snow accumulation) season begins (i.e., 1 July), enhancing inter-basin differences in hindcast skill as initializations approach the beginning of the snowmelt season (i.e., 1 September). The comparison of seasonal hindcasts shows that the hydrological consistency – quantified here through biases in streamflow signatures – obtained with some calibration metrics (e.g., Split KGE (Kling–Gupta efficiency), which gives equal weight to each water year in the calibration time series) does not ensure satisfactory seasonal ESP forecasts and that the metrics that provide skillful ESP forecasts (e.g., VE-Sep, which quantifies seasonal volume errors) do not necessarily yield hydrologically consistent model simulations. Among the options explored here, an objective function that combines the Kling–Gupta efficiency (KGE) and the Nash–Sutcliffe efficiency (NSE) with flows in log space provides the best compromise between hydrologically consistent simulations and hindcast performance. Finally, the choice of calibration metric generally affects the magnitude, rather than the sign, of correlations between hindcast quality attributes and catchment descriptors, the baseflow index and interannual runoff variability being the best predictors of forecast skill. Overall, this study highlights the need for careful parameter estimation strategies in the forecasting production chain to generate skillful forecasts from hydrologically consistent simulations and draw robust conclusions on streamflow predictability.

Artificial hummingbird algorithm-optimized boosted tree for improved rainfall-runoff modelling

Abstract Rainfall-runoff modelling is a critical component of hydrological studies, and its accuracy is essential for water resource management. Recent advances in machine learning have led to the development of more sophisticated rainfall-runoff models, but there is still room for improvement. This study proposes a novel approach to streamflow modelling that uses the artificial hummingbird algorithm (AHA) to optimize the boosted tree algorithm. the AHA-boosted tree algorithm model was compared against two established methods, the support vector machine (SVM) and the Gaussian process regression (GPR), using a variety of statistical and graphical performance measures. The results showed that the AHA-boosted tree algorithm model significantly outperformed the SVM and GPR models, with an R2 of 0.932, RMSE of 5.358 m3/s, MAE of 2.365 m3/s, and MSE of 28.705 m3/s. The SVM model followed while the GPR model had the least accurate performance. However, all models underperformed in capturing the peak flow of the hydrograph. Evaluations using both statistical and graphical performance measures, including time series plots, scatter plots, and Taylor diagrams, were critical in this assessment. The results suggest that the AHA-boosted tree algorithm could potentially be a superior alternative for enhancing the precision of rainfall-runoff modelling, despite certain challenges in predicting peak flow events.

HYDROLOGICAL MODELING AND WATERSHED ANALYSIS OF SWAT RIVER BASIN BY USING HBV LIGHT MODEL AND ARC GIS

Climate change is a pivotal global phenomenon, particularly impactful in nations like Pakistan. This study delves into the myriad factors driving climate change, specifically focusing on its profound effects on economically vulnerable countries heavily reliant on natural resources for water. Utilizing historical climatic and discharge data from the Swat River, located within the Khyber Pakhtunkhwa province, the research employs temperature, precipitation, and discharge data to scrutinize changes in the KALAM basin at a regional level. Situated between 34° and 36° N latitude and 71° to 72° E longitude, the Swat River Basin relies heavily on glacier snowmelt. The HBV model and GIS techniques are harnessed to dissect climate variations within the KALAM Basin. GIS aids in mapping the study area and visualizing temperature fluctuations in the basin. The basic HBV rainfall-runoff model undergoes automatic calibration using a straightforward yet effective method, providing satisfactory results during the 1981-2000 calibration and 2001-2010 validation periods. Noteworthy findings include the HBV-light model's adeptness at simulating stream flow and snowmelt in the snow-fed basin. However, the study emphasizes the substantial impact of parameter set values on the model's performance, highlighting the challenge of selecting an ideal parameter configuration. Sensitivity tests underscore the significant influence of climate change, specifically temperature increases, on stream flow across yearly, seasonal, and snowmelt contributions. Employing geographic information systems (GIS) alongside a digital elevation model proves instrumental in managing and analyzing water resources, aiding in watershed delineation—a crucial step in hydrological modeling. GIS, particularly the Arc Hydro tool, is valuable for determining grid directions in catchment areas, drainage lines, and flow accumulation. The research underscores the need for comprehensive scientific exploration to address knowledge gaps and enhance understanding of the applications of the HBV light model in the context of climate variations, particularly concerning water resources.

Fractional Linear Reservoir Model as Elementary Hydrologic Response Function

This paper presents a fractional linear reservoir model as the elementary response function of hydrologic systems corresponding to the classical linear reservoir model and tests its applicability to rainfall–runoff modeling. To this end, we formulate a fractional linear reservoir model in terms of fractional calculus following the same procedure as the classical linear reservoir model and, at the simplest level, compare its performance of rainfall–runoff modeling with the linear and nonlinear reservoir models. The impulse response function of a fractional linear reservoir model, a probability density function (PDF) following the Mittag–Leffler distribution, shows nonlinearity due to its time-variant behavior compared to that of a linear reservoir model. In traditional linear hydrologic system theory, the lag and route version of a fractional linear reservoir model produces the fast-rising and slow-recession of runoff hydrographs, implying the mixed response of linear and nonlinear reservoir models to rainfall. So, a fractional linear reservoir model could be considered a fundamental tool to effectively reflect the nonlinearity of rainfall–runoff phenomena within the framework of the linear hydrologic system theory. In this respect, the fractional order of the storage relationship specifying a fractional linear reservoir model can be viewed as a kind of parameter to quantify the heterogeneity of runoff generation within a river basin.

Rainfall–runoff process and sediment yield in response to different types of terraces and their characteristics: A case study of runoff plots in Zhangjiachong watershed, China

AbstractA field experiment was conducted in the Zhangjiachong watershed to analyze the characteristics of rainfall–runoff process and sediment yield under different types of terraces. Various runoff plots were utilized, and a two serial tank model was employed to simulate runoff generation and concentration processes, taking into account vegetation interception. A soil erosion model incorporating raindrop erosion and sheet erosion was established to replicate sediment yield and concentration process. The parameters of the rainfall–runoff model and soil erosion model were calibrated and validated using observed runoff and sediment yield data. The controlling factors on runoff and sediment yield were explored and compared. The results indicate that both calibration and validation outcomes were acceptable. The earth‐banked terraces–citrus demonstrated the largest coefficients of upper and lower surface runoff, while its upper runoff threshold of the upper tank and the runoff threshold of the lower tank were the lowest, suggesting that it was more likely to produce surface and subsurface runoff. On the other hand, the stone dike terraces–crops exhibited the smallest coefficients of upper and lower surface runoff, while its upper runoff threshold of the upper tank was the highest. The lower runoff threshold of the upper tank was the same across different types of terraces. Compared to the earth‐banked terraces, the stone dike terraces have a larger infiltration coefficient in the upper tank and a smaller infiltration coefficient in the lower tank. Sensitivity analysis indicates that the most sensitive factors affecting runoff simulation results are the runoff threshold and the infiltration coefficient of the lower tank. Terraces, whether constructed with earth banks or stone dikes, are effective in reducing soil erosion. However, during storm rainfall events, these terraces may be partially destroyed, resulting in a higher sediment yield than that of the same vegetation cover on slope land. Terraces with hedgerows have a more significant effect on reducing runoff and sediment than those without hedgerows.

Sustainable Management of Aquaculture and Water Footprint Analysis in Sunderban

India is the world’s second-largest producer of fish, where the state of West Bengal is leading in fish production. The Sundarbans, located in the southern part of India’s West Bengal state, is a UNESCO-designated world heritage site. The Indian Sundarbans is a tide-dominated region in the southern part of deltaic West Bengal, and is home to 4.43 million people. Even though it is traversed by numerous creeks and rivulets and receives a significant amount of precipitation during the monsoon season, freshwater is a scarce resource in the Sundarbans. During the dry season, there is a lack of fresh water above and below the ground, increasing siltation results in shallower channels, high salinity of the water and soil, and congestion in drainage making it difficult for people to make a living. During the dry season, most blocks experience water scarcity as a result of the ever-increasing population’s demand for water. According to this study, Sundarbans’ current annual domestic and drinking water demands are 105.1 mcm and 8.08 mcm, respectively. By combining the area under various crops and their lifecycle water requirements, the water demand for agriculture has been calculated to be 2782.83 mcm. The rainfall-runoff modelling aims to get a general idea of how much fresh water is available each year through surface runoff. It has also been estimated how much water is available from different sources in each block. It is estimated that deep and shallow bore wells contribute approximately 400 mcm, whereas surface water sources like tanks and canals contribute approximately 50 mcm. The communities that live in the Sundarbans eco-region benefit greatly from aquaculture’s contribution to their socio-economic development. For the sustainable development of aquaculture in the Sundarbans Delta, strong technical, financial and extension services from government organisations and research institutions are urgently required to meet these obstacles. Additionally, this study emphasises that roof-top rainwater harvesting in this region has the potential to supply 45 mcm more water, which could be used to partially satisfy the region’s domestic water demand. Future major policy options for meeting the Sundarbans ecoregion’s water demand include large-scale rainwater harvesting, rejuvenation and reconnection of disconnected river channels, artificial recharge of shallow aquifers to lower their salinity, and de-salination of shallow groundwater.

Assimilation of remotely sensed evapotranspiration products for streamflow simulation based on the CAMELS data sets

Data assimilation (DA) techniques that integrate remotely sensed observations and in-situ measurements into hydrological models have been extensively implemented, but few studies have applied multisource actual evapotranspiration (ETa) products to evaluate its impacts on the DA performances through a large number of basins with different catchment conditions. This study performs a DA experiment to assess the influence of remotely sensed ETa products on streamflow simulations over 149 Catchment Attributes and Meteorology for Large-sample Studies basins across the contiguous United States, and the relationships between the DA results and multiple catchment characteristics are further analyzed. In this experiment, three satellite ETa products from the GLEAM, REA-ET, and GLDAS are assimilated into a conceptual rainfall-runoff model (i.e., HYMOD) using the ensemble Kalman filter. The results show that assimilating the three ETa products provides relatively small improvements compared to the open loop (OL) scenario in approximately 48%-58% of the study basins, and around 14%-19% of these basins exhibit an increase in Nash-Sutcliffe Efficiency over 0.01. Specifically, the GLEAM ETa shows the greatest improvements in runoff, followed by the GLDAS and REA-ET products. The OL performance plays a significant role in runoff improvements, greater improvements are achieved for basins with poor OL runoff simulations. The assimilation efficiency is revealed to be relevant to the accuracy of the satellite ETa products. While the catchment conditions such as the aridity index, mean slope, the fraction of forest, and catchment area are found to have a limited effect on the spatial pattern of ET assimilation performances.

The Strategic Random Search (SRS) – A new global optimizer for calibrating hydrological models

This study introduces a novel global optimization algorithm, Strategic Random Search (SRS), tailored for efficient calibration of hydrological models. SRS outperforms 14 other optimization algorithms on 23 classical benchmark functions and 29 CEC-2017 benchmark functions, demonstrating its superiority on more than half of these tests. Additionally, when applied to rainfall-runoff models, SRS consistently, rapidly, and robustly converges to optimal solutions, surpassing five other algorithms. SRS, developed independently of existing intelligent optimization methods, offers versatility with only two adjustable parameters, making it suitable for various problem types. Through rigorous testing and comparisons, SRS emerges as a robust, widely applicable, and stable convergence algorithm.

Maximising runoff retention by vegetated landscape elements positioned through spatial optimisation

Ecosystem services provided by vegetated landscape elements (vLEs) are increasingly recognised. One of the services provided is the mitigation of downstream flood risk. Obviously, the type and spatial configuration of vLEs impact the magnitude and timing of the runoff retention. Hence policy programs focused on the conservation and restoration of vLEs would benefit from a capability to determine the optimal spatial configuration of vLEs leading to maximum impact for minimal cost.We integrated the Landlab rainfall-runoff model in an iterative spatial optimisation framework to deal with rasterised linear parcel boundaries whereby the cumulative capability to reduce discharge through the installation of vLEs is the ranking criterion. We applied the procedure to a 191 ha agricultural watershed situated in the Belgian Loess belt encompassing 34 km parcel boundaries.Our results demonstrated that discharge volume can be more effectively reduced when vLEs are implemented based on the priority ranking obtained through our approach compared to both a random positioning of vLEs of the same length and type and the existing vLE configuration. The priority parcel boundaries are mainly located along preferential flow paths, highlighting the importance of the upslope area associated with vLEs and the infiltration enhancement they provoke. The application potential of the optimisation approach is not limited to the topic of finding priority locations for vLEs to reduce discharge but can be applied in a variety of disciplines that require answering questions about the optimal spatial configuration of interventions.

Využití srážkoodtokového modelování v hydrologické praxi ČHMÚ

Rainfall-runoff modelling is widely used in the hydrological practice of the CHMI. The presented text offers an insight into work of the Surface Water Department. The use of hydrological modelling procedures is described on specific selected examples such as the derivation of theoretical flood waves, the evaluation and verification of past flood events or its use in several applications operated at the CHMI. Hydrological modelling is also an integral part of scientific and research activities with a focus on updating methodological procedures and harmonizing input data.

Performance of LSTM over SWAT in Rainfall-Runoff Modeling in a Small, Forested Watershed: A Case Study of Cork Brook, RI

The general practice of rainfall-runoff model development towards physically based and spatially explicit representations of hydrological processes is data-intensive and computationally expensive. Physically based models such as the Soil Water Assessment tool (SWAT) demand spatio-temporal data and expert knowledge. Also, the difficulty and complexity is compounded in the smaller watershed due to data constraint and models’ inability to generalize hydrologic processes. Data-driven models can bridge this gap with their mathematical formulation. Long Short-Term Memory (LSTM) is a data-driven model with Recurrent Neural Network (RNN) architecture, which is better suited to solve time series problems. Studies have shown that LSTM models have competitive performance in watershed hydrology studies. In this study, a comparative analysis of SWAT and LSTM models in the Cork Brook watershed shows that results from LSTM were competitive to SWAT in flow prediction with NSE of 0.6 against 0.63, respectively, given the limited availability of data. LSTM models do not overestimate the high flows like SWAT. However, both these models struggle with low values estimation. Although interpretability, explainability, and use of models across different datasets or events outside of the training data may be challenging, LSTM models are robust and efficient.