Modeling Of Hydrological Processes Research Articles

Development of a distributed hydrological model combined with sediment transport calculations and its application to a medium-scale basin in northeast China

Abstract An accurate assessment of sediment transport can assist land and resource managers in any region. The primary objective of this study was to develop a distributed hydrological model for calculating rainfall-runoff coupled with sediment transport to provide a practical method for estimating sediment transport in western Heilongjiang Province, China. The Alun River Basin, which has typical meteorological and hydrological characteristics of western Heilongjiang Province, was chosen as the study area. A sediment transport calculation module was developed based on physically based formulas for the suspended load and bedload, which was coupled with a numerical model of the physical distributed rainfall-runoff process. The model’s ability was validated by comparing the sediment sampling survey results of the particle size group distribution (PSGD) in the flow and the corresponding value of the model calculation results for a control case. The root mean square error (RMSE) and the mean absolute error (MAE) were mostly <10%. The model was used to calculate the rainfall-runoff combined with sediment transport in 2016, and the results indicate that the primary form of sediment transport was as suspended load. The suspended load accounted for over 90% of the total sediment transport caused by the maximum flood peak, and the suspended load accounted for over 80% during the period of normal water level across the selected sections. The sediment transport process was dominated by the runoff process. The contribution of the monthly sediment transport in winter to the annual total value was mostly <1%; while in the rainy season, the contribution of the monthly sediment transport to the annual total value was >15%. The results provide a basis for the assessment of sediment transport and the development of a numerical platform for modeling hydrological processes coupled with land surface processes in western Heilongjiang Province, China.

A coupled regional-scale numerical model for hydrological processes and interactions between groundwater and surface water in a controlled drainage district

Controlled drainage (CD) has emerged as an effective strategy to prevent field waterlogging and mitigate drought during crop growth by altering the hydrological process, while few studies have quantified its effect on groundwater-surface water interactions and groundwater recharge at a regional scale. In this study, a new model is developed for the coupled numerical simulation of water flow in ditches, soil, and underground aquifers under CD conditions. The domain is partitioned into horizontal sub-areas and two vertical layers, with ditches discretized into segments based on groundwater flow grids. The Saint-Venant equations, Richards equation, and MODFLOW are applied to describe water movement processes in ditches, soil, and underground aquifers, respectively. The proposed model is calibrated and validated using five years of observations of ditch water levels (DWL) and groundwater levels, demonstrating excellent agreement with observed data. Subsequently, water balance components of the shallow aquifer below 1 m depth (GW), 1 m of root layer soil profile (SW), and ditch water (DW) under seven scenarios with different control schemes during flood and dry seasons are analyzed to quantify variations in hydrological processes caused by CD. The results show that CD significantly impacts water within SW, GW, and DW, soil evaporation, infiltration, and net recharge from GW to SW (GtoS) by altering regional groundwater table depth (GWT) through the exchange between DW and GW (DtoG and GtoD). GWT and DW show moderate correlation with GtoD, while their correlation with DtoG varies significantly under different rainfall conditions. Smaller precipitation results that precipitation and GWT have a higher linear correlation with water balance components during the dry season compared to the flood season. The correlation values (r) between transpiration (T) and GWT range from −0.99 to 0.96 and −1 to −0.85 during the flood and dry seasons, respectively, indicating a pronounced influence of CD on crop growth. On average, a 1 m increase in DWL control scheme leads to a 0.51 m increase in DWL, and regional GWT decreases by 0.35 m and 0.24 m during flood and dry seasons, respectively. Furthermore, for every 1 m decrease in GWT, net GtoS increases by 36.1 mm and 28.1 mm, respectively, resulting in an increase in SW by 13.9 mm and 11.8 mm, respectively. Considering the varying main crop water stress and the impact of CD under different rainfall conditions, an appropriate CD scheme needs to be adjusted accordingly. These findings provide a quantitative reference for the effect of CD on regional hydrological process and serve as a typical case for similar areas aiming to implement CD strategies for waterlogging and drought control.

Factors influencing residual air saturation during consecutive imbibition processes in an air-water two-phase fine sandy medium – A laboratory-scale experimental study

The residual air saturation plays a crucial role in modeling hydrological processes of groundwater and the migration and distribution of contaminants in subsurface environments. However, the influence of factors such as media properties, displacement history, and hydrodynamic conditions on the residual air saturation is not consistent across different displacement scenarios. We conducted consecutive drainage-imbibition cycles in sand-packed columns under hydraulic conditions resembling natural subsurface environments, to investigate the impact of wetting flow rate, initial fluid state, and number of imbibition rounds (NIR) on residual air saturation. The results indicate that residual air saturation changes throughout the imbibition process, with variations separated into three distinct stages, namely, unstable residual air saturation (Sgr-u), momentary residual air saturation (Sgr-m), and stable residual air saturation (Sgr). The results also suggest that the transition from Sgr-u to Sgr is driven by changes in hydraulic pressure and gradient; the calculated values followed the following trend: Sgr > Sgr-u > Sgr-m. An increase in capillary number, which ranged from 1.46 × 10−7 to 3.07 × 10−6, increased Sgr-u and Sgr-m in some columns. The increase in Sgr ranged from 0.034 to 0.117 across all the experimental columns; this consistent increase can be explained by water film expansion at the primary wetting front along with a strengthening of the hydraulic gradient during water injection. Both the pre-covered water film on the sand grain surface and a pore-to-throat aspect ratio of up to 4.42 were identified as important factors for the increased residual air saturation observed during the imbibition process. Initial air saturation (Sai) positively influenced all three types of residual air saturation, while initial capillary pressure (Pci) exhibited a more pronounced inhibitory effect on residual air saturation, as it can partly characterized the initial connectivity of the air phase generated under different drying flow rates. Under identical wetting flow rate conditions, Sgr was higher during the second imbibition than during the first imbibition due to variations in initial fluid state, involving both fluid distribution and the concentration of dissolved air in the pore water. In contrast, NIR did not have an obvious effect on the three types of residual air saturation. This work aims to provide empirical evidences and offer further insights into the capture of non-wetting phases in groundwater environments, as well as to put forward some potential suggestion for future investigations on the retention and migration of contaminants that involves multiphase interface interactions in subsurface environments.

Uncertainty of canopy interception modeling in high-altitude Picea crassifolia forests of Semi-arid regions

The study of physically-based rainfall interception is crucial for comprehending the water balance within forest ecosystems and the contribution of vegetation to the hydrological cycle, particularly in arid/semi-arid ecosystems. Despite its importance, there is a lack of comprehensive sensitivity analysis and parameter optimization, resulting in uncertain or suboptimal predictive accuracy. To mitigate these shortcomings, this research involved the establishment and assessment of three quintessential forest canopy interception models namely, the power Návar model, the reformulated Gash model, and the Liu model, within semi-arid forest environments at two different elevations. A global sensitivity analysis conducted on the three physical models indicated that the canopy saturation point and the mean rainfall intensity required for canopy saturation were the parameters to which the reformulated Gash and Liu models were most sensitive when applied to high-altitude settings. Conversely, for the Návar model, the most sensitive parameters were the interception coefficient of the linear equation, and the parameters of the power equation k and c. The quantification indices of model sensitivity exert a certain influence on the ranking of parameter sensitivities. However, for models with a limited number of parameters, the impact of these results is constrained. Conversely, the identification and utilization of characteristics specific to the parameter tuning process can significantly enhance the efficiency of model calibration. The three models employed by the research institute have all demonstrated commendable performance in modeling the canopy interception process of subalpine P. crassifolia in arid, high-altitude regions, achieving a "good" rating with Nash-Sutcliffe Efficiency values exceeding 0.7. In practical applications, we recommend giving priority to the use of the Liu model. The findings of this study provide a reference for model selection, sensitivity analysis, parameter calibration, and model evaluation in the context of extensive canopy interception modeling in arid areas with significant altitudinal variation. This constitutes an important theoretical support for the refined modeling of hydrological processes in high-altitude forests within arid zones.

Snow Depth Estimation and Spatial and Temporal Variation Analysis in Tuha Region Based on Multi-Source Data

In the modelling of hydrological processes on a regional scale, remote-sensing snow depth products with a high spatial and temporal resolution are essential for climate change studies and for scientific decision-making by management. The existing snow depth products have low spatial resolution and are mostly applicable to large-scale studies; however, they are insufficiently accurate for the estimation of snow depth on a regional scale, especially in shallow snow areas and mountainous regions. In this study, we coupled SSM/I, SSMIS, and AMSR2 passive microwave brightness temperature data and MODIS, TM, and Landsat 8 OLI fractional snow cover area (fSCA) data, based on Python, with 30 m spatially resolved fractional snow cover area (fSCA) data obtained by the spatio-temporal dynamic warping algorithm to invert the low-resolution passive microwave snow depths, and we developed a spatially downscaled snow depth inversion method suitable for the Turpan–Hami region. However, due to the long data-processing time and the insufficient arithmetical power of the hardware, this study had to set the spatial resolution of the result output to 250 m. As a result, a day-by-day 250 m spatial resolution snow depth dataset for 20 hydrological years (1 August 2000–31 July 2020) was generated, and the accuracy was evaluated using the measured snow depth data from the meteorological stations, with the results of r = 0.836 (p ≤ 0.01), MAE = 1.496 cm, and RMSE = 2.597 cm, which are relatively reliable and more applicable to the Turpan–Hami area. Based on the spatially downscaled snow depth data produced, this study found that the snow in the Turpan–Hami area is mainly distributed in the northern part of Turpan (Bogda Mountain), the northwestern part of Hami (Barkun Autonomous Prefecture), and the central part of the area (North Tianshan Mountain, Barkun Mountain, and Harlik Mountain). The average annual snow depth in the Turpan–Hami area is only 0.89 cm, and the average annual snow depth increases with elevation, in line with the obvious law of vertical progression. The annual mean snow depth in the Turpan–Hami area showed a “fluctuating decreasing” trend with a rate of 0.01 cm·a−1 over the 20 hydrological years in the Turpan–Hami area. Overall, the spatially downscaled snow depth inversion algorithm developed in this study not only solves the problem of coarse spatial resolution of microwave brightness temperature data and the difficulty of obtaining accurate shallow snow depth but also solves the problem of estimating the shallow snow depth on a regional scale, which is of great significance for gaining a further understanding of the snow accumulation information in the Tuha region and for promoting the investigation and management of water resources in arid zones.

Visualizing sustainable rainwater harvesting: A case study of Karbala Province

Abstract The management of rainwater collection in a practical way is a fundamental need for the management of water resources in a manner that is sustainable. The goal of this research is to determine whether or not remote sensing technology is effective in providing data on precipitation for the purpose of locating rainwater collection tank locations in the province of Karbala. Rainfall patterns fluctuate considerably. Remote sensing may not capture variability enough to estimate the rainfall period and location. Sustainable rainfall harvesting requires accurate rainfall timing and distribution. This information is applied in the modeling of hydrological processes, the management of disasters, and environmental research. Following the completion of a geographical study, it has been established that the city of Karbala may be divided into two basic sections. Through the use of estimation, it is possible to more easily identify the region that is ideal for the location of rainwater-harvesting reservoirs and lakes. On the contrary, it is crucial to keep in mind that a location that was chosen based on average rainfall over a period of two years could not be suitable for other time periods. This is an idea that should be kept in mind several times. To put this into perspective, when choosing a location, it is vital to take into consideration the severity of the rainfall as well as the geographical location of the area. Particularly in locations such as Karbala, the implementation of data visualization systems into water management practices has the potential to improve both the efficiency and sustainability of water management methods. The findings of this study show the significance of implementing precise site selection techniques to enhance rainwater collection systems and encourage activities that are environmentally responsible for water management.

The Role of Parameterizations and Model Coupling on Simulations of Energy and Water Balances – Investigations With the Atmospheric Model WRF and the Hydrologic Model WRF‐Hydro

AbstractThe distributed hydrologic model WRF‐Hydro can operate in a fully‐coupled mode with the atmospheric Weather, Research and Forecasting (WRF) model. WRF‐Hydro enhances the modeling of terrestrial hydrologic processes in coupled WRF/WRF‐Hydro by simulating lateral surface and subsurface water flows. The objectives of this study are (a) to examine the effect of WRF‐Hydro on the surface energy and water balance in fully‐coupled WRF/WRF‐Hydro simulations and (b) to examine the impact of five WRF physics parameterizations on WRF‐Hydro streamflow. The study area is the Mediterranean island of Cyprus and 31 mountainous watersheds. The domain‐average soil moisture was 20% higher in the coupled WRF/WRF‐Hydro, relative to the standalone WRF model, during a 1‐year simulation. The higher soil moisture could explain the increase in latent heat (36%) and evapotranspiration (33%). The increase in these fluxes was less with a modification in the model transpiration parameterization to represent nocturnal transpiration and the use of remote sensing leaf area index data. The simulated precipitation of the coupled model increased up to 3%, relative to WRF. Two‐year long WRF‐Hydro simulations gave a median Nash‐Sutcliffe Efficiency for daily streamflow of the 31 watersheds of 0.5 for observed precipitation forcing and between −1.9 and 0.2 for the forcing of the five WRF parameterizations. This study showed that the enhancement of the standalone WRF model with lateral water flow processes in the coupled mode with WRF‐Hydro modifies the terrestrial energy and water balance. The improved terrestrial process representation should be considered for future hydrological cycle studies with WRF.

Integrated modeling of snow-covered areas and hydrological processes in the Larji Sub-Basin, Himachal Pradesh, India, using SRM and SWAT models

Abstract Alpine snow is crucial for the water cycle, as the runoff and environment of arid and semi-arid regions rely entirely on these glaciers. Mountain-fed rivers provides the water for domestic, agricultural irrigation, hydroelectric power, and other uses. Due to climate variability, river catchment flows may shift, causing floods and droughts that will aggravate the economy. This study estimated the SCA using Google Earth Engine (GEE) for the Larji River basin, situated in Himachal Pradesh, from the year 2001 to 2021. The snow cover area (SCA) varies from 0.7–22% in the basin. The present study highlights the effectiveness of cloud computing for assessing trends in snow-cover regions in the basin. Moderate Resolution Imaging Spectroradiometer (MODIS) snow product (MOD10A1 V6 Snow Cover Daily Global 500m product) is utilized for SCA calculation. This study simulates runoff from the Larji river, using Snow-melt Runoff Model (SRM) and Soil Water Assessment Tool (SWAT) for years (2019-2021). Furthermore, the Coefficient of determination (R2) Coefficient of corelation (r), Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) were used to evaluate the efficacy of models. This study will help the policymakers and stakeholders to carry out the water resource management practices in the study area.

Mountain Streambed Roughness and Flood Extent Estimation from Imagery Using the Segment Anything Model (SAM)

Machine learning models facilitate the search for non-linear relationships when modeling hydrological processes, but they are equally effective for automation at the data preparation stage. The tasks for which automation was analyzed consisted of estimating changes in the roughness coefficient of a mountain streambed and the extent of floods from images. The Segment Anything Model (SAM) developed in 2023 by Meta was used for this purpose. Images from many years from the Wielka Puszcza mountain stream located in the Polish Carpathians were used as the only input data. The model was not additionally trained for the described tasks. The SAM can be run in several modes, but the two most appropriate were used in this study. The first one is available in the form of a web application, while the second one is available in the form of a Jupyter notebook run in the Google Colab environment. Both methods do not require specialized knowledge and can be used by virtually any hydrologist. In the roughness estimation task, the average Intersection over Union (IoU) ranges from 0.55 for grass to 0.82 for shrubs/trees. Ultimately, it was possible to estimate the roughness coefficient of the mountain streambed between 0.027 and 0.059 based solely on image data. In the task of estimation of the flood extent, when selecting appropriate images, one can expect IoU at the level of at least 0.94, which seems to be an excellent result considering that the SAM is a general-purpose segmentation model. It can therefore be concluded that the SAM can be a useful tool for a hydrologist.

A New Benchmark on Machine Learning Methodologies for Hydrological Processes Modelling: A Comprehensive Review for Limitations and Future Research Directions

The best practice of watershed management is through the understanding of the hydrological processes. As a matter of fact, hydrological processes are highly associated with stochastic, non-linear, and non-stationary phenomena. Hydrological processes simulation and modeling are challenging issues in the domains of hydrology, climate and environment. Hence, the development of machine learning (ML) models for solving those complex hydrological problems took essential place over the past couple decades. It can be observed, hydrological data availability has increased remarkably, and thus computational resources has led to a resurgence in ML models’ development. It has been witnessed huge efforts on the hydrological processes modeling using the facility of ML models and several review researches have been conducted. Literature studies approved the capacity of ML models in the field of hydrology over the classical “traditional models” based on their forecastability, flexibility, precision, generalization, and modeling execution convergence speed. However, although several potential merits were observed in ML model’s development, several limitations are allied such as the interpretability of those black-box models, the practicality of the ML models in watershed management, and difficulty to explain the physical hydrological processes. In this survey, an exhibition for all the published review articles on the development of ML models for hydrological processes and recognize all the research gaps and potential research direction. The ultimate aim of the current survey is to establish a new milestone for the interested hydrology, environment and climate researchers on the applications of ML models.

Development of pedotransfer functions for predicting hydraulic parameters of van Genuchten model by incorporating environmental variables on the Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) region lacks large database of soil hydraulic parameters (SHPs) due to difficulty in soil sampling, and time- and labor-consuming measurement, which prevents accurate modeling of hydrological and biogeochemical processes. Pedotransfer functions (PTFs) using easily measurable soil properties may provide an alternative way to indirectly estimate SHPs. In this study, soil properties (bulk density (BD), clay, silt, sand, soil organic carbon, porosity, fitting parameter related to inverse of air entry pressure (α) and soil pore distribution (n), residual and saturated soil water content (θr and θs), and environmental parameters (altitude, slope gradient, annual average precipitation, air temperature, and evaporation) were determined from a north-south transect (ca. 500 km) in Qinghai Province and an east-west transect (ca. 1800 km) in Tibet Autonomous Region for establishing PTFs for SHPs on the QTP. The coefficient of variation of α was the largest, followed by θr, θs, and n, suggesting that α had the greatest spatial variability at the regional scale. There were significant differences in BD among 0–10, 10–20, and 20–30 cm soil layers (p < 0.05), whereas there were no significant differences in α, n, θr, and θs among these soil layers (p > 0.05). The five most important variables from the random forest analysis were selected to establish the PTFs of the SHPs and BD for the 0–30 cm soil layer on the QTP. The established PTFs for predicting SHPs and BD using multiple linear regression analysis were jointly affected by relief, climate, and soil properties. The accuracy of our newly established PTFs with the consideration of gravel content (< 10% or 10–30%) and mattic epipedon (existence or non-existence) was higher than that of the existing PTFs. We developed the first PTFs of SHPs on the QTP, providing a foundation to construct SHPs prediction models for other alpine regions in the world.

Modeling hydrological processes under Multi-Model projections of climate change in a cold region of Hokkaido, Japan

Local hydrological systems are highly sensitive to global warming, especially in cold-climate-dominated basins. The integrated climate-hydrological model is a powerful tool to analyze and predict such effects. However, modeling hydrological processes contains considerable uncertainty in cold areas due to the complex dynamics of freeze–thaw cycles and requirements of lot inputs data. Here, we developed a fast and simple approach to modify the soil temperature module in soil and water assessment tool (SWAT) model in a cold region of the Ishikari River basin (IRB), Hokkaido, Japan. Water balance components and sediment yield simulations were explored by the modified SWAT model, outperforming the original SWAT model by PBIAS less than 15 % and NSE higher than 0.60 in monthly streamflow and sediment yield during both calibration (2011–2015) and validation phases (2016–2019). The future prediction was integrated with future climate datasets under four Shared Socioeconomic Pathways (SSP) scenarios (SSP126, SSP245, SSP370, and SSP585) derived from two Global Circulation Models (GCMs), in two future stages (2045–2069 and 2075–2099) with relative to the baseline stage (1990–2014). The varying climate with warmer temperatures (+3.05℃) and less precipitation (-7%) in the future would lead to that evapotranspiration (ET, 67 %) increased. Water yield (WY, −9%), surface runoff (SURQ, −18 %), groundwater flow (GWQ, −10 %), lateral flow (LATQ, −7%), percolation (PERC, −10 %), streamflow (-7%) and sediment yield (-14 %) decreased across the whole basin. However, due to climatic spatial variation, at the subbasin scale, hydrological components (e.g., WY, GWQ, LATQ, and PERC) would increase in the northern region, at the same time, SURQ and ET rise significantly in the central and southern areas. Our findings could offer evidence for future water and soil protection policymaking across the IRB.

On the visual detection of non-natural records in streamflow time series: challenges and impacts

Abstract. Large datasets of long-term streamflow measurements are widely used to infer and model hydrological processes. However, streamflow measurements may suffer from what users can consider anomalies, i.e. non-natural records that may be erroneous streamflow values or anthropogenic influences that can lead to misinterpretation of actual hydrological processes. Since identifying anomalies is time consuming for humans, no study has investigated their proportion, temporal distribution, and influence on hydrological indicators over large datasets. This study summarizes the results of a large visual inspection campaign of 674 streamflow time series in France made by 43 evaluators, who were asked to identify anomalies falling under five categories, namely, linear interpolation, drops, noise, point anomalies, and other. We examined the evaluators' individual behaviour in terms of severity and agreement with other evaluators, as well as the temporal distributions of the anomalies and their influence on commonly used hydrological indicators. We found that inter-evaluator agreement was surprisingly low, with an average of 12 % of overlapping periods reported as anomalies. These anomalies were mostly identified as linear interpolation and noise, and they were more frequently reported during the low-flow periods in summer. The impact of cleaning data from the identified anomaly values was higher on low-flow indicators than on high-flow indicators, with change rates lower than 5 % most of the time. We conclude that the identification of anomalies in streamflow time series is highly dependent on the aims and skills of each evaluator, which raises questions about the best practices to adopt for data cleaning.

Gridded Precipitation Datasets and Gauge Precipitation Products for Driving Hydrological Models in the Dead Sea Region, Jordan

The consistency of hydrological process modeling depends on reliable parameters and available long-term gauge data, which are frequently restricted within the Dead Sea/Jordan regions. This paper proposes a novel method of utilizing six satellite-based and reanalysis precipitation datasets, which are assessed, evaluated, and corrected, particularly for the cases of ungauged basins and poorly monitored regions, for the first time. Due to natural processes, catchments fluctuate dramatically annually and seasonally, making this a challenge. This variability, which is significantly impacted by topo-geomorphological and climatic variables within the basins themselves, leads to increased uncertainty in models and significant restrictions in terms of runoff forecasting. However, quality evaluations and bias corrections should be conducted before the application of satellite data. Moreover, the hydrological HEC-HMS model was utilized to predict the runoff under different loss methods. Furthermore, this loss method was used with an integrated model that might be efficiently employed when designing hydraulic structures requiring high reliability in predicting peak flows. The models’ performance was evaluated using R-squared (R2), the root mean square error (RMSE), the mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). In addition, these statistical metrics were implemented to quantitatively evaluate the data quality based on the observed data collected between 2015 and 2020. The results show that AgERA5 exhibited better agreement with the gauge precipitation data than other reanalysis precipitation and satellite-based datasets. The results demonstrate that the data quality of these products could be affected by observational bias, the spatial scale, and the retrieval method. Moreover, the SC loss method demonstrated satisfactory values for the R2, RMSE, NSE, and bias compared to the IC and GA loss, indicating its effectiveness in predicting peak flows and designing hydraulic structures that require high reliability. Overall, the study suggests that AgERA5 can provide better precipitation estimates for hydrological modeling in the Dead Sea region in Jordan. Moreover, integrating the SC, IC, and GA loss methods in hydraulic structure design can enhance prediction accuracy and reliability.

A Transformer-Based Framework for Parameter Learning of a Land Surface Hydrological Process Model

The effective representation of land surface hydrological models strongly relies on spatially varying parameters that require calibration. Well-calibrated physical models can effectively propagate observed information to unobserved variables, but traditional calibration methods often result in nonunique solutions. In this paper, we propose a hydrological parameter calibration training framework consisting of a transformer-based parameter learning model (ParaFormer) and a surrogate model based on LSTM. On the one hand, ParaFormer utilizes self-attention mechanisms to learn a global mapping from observed data to the parameters to be calibrated, which captures spatial correlations. On the other hand, the surrogate model takes the calibrated parameters as inputs and simulates the observable variables, such as soil moisture, overcoming the challenges of directly combining complex hydrological models with a deep learning (DL) platform in a hybrid training scheme. Using the variable infiltration capacity model as the reference, we test the performance of ParaFormer on datasets of different resolutions. The results demonstrate that, in predicting soil moisture and transferring calibrated parameters in the task of evapotranspiration prediction, ParaFormer learns more effective and robust parameter mapping patterns compared to traditional and state-of-the-art DL-based parameter calibration methods.

Assessing hydrological sensitivity to future climate change over the Canadian southern boreal forest

This study develops a physically based hydrological process model using the Cold Region Hydrological Modelling (CRHM) platform to simulate the water budget storages and fluxes over the Canadian southern boreal forest (SBF). Evaluation of the CRHM-based model in a well-gauged SBF basin, White Gull Creek (WGC), Saskatchewan, Canada, indicated quite good performance in reproducing historical observations of streamflow, snow water equivalent (SWE), evapotranspiration (ET) and soil moisture without parameter calibration from streamflow. The entire SBF was then evenly divided into 2243 virtual basins, each of which was structured and parameterized with the same land cover, soil and hydrological parameters as the WGC basin, but with local latitude and topography in order to examine the sensitivity of governing hydrological processes to future climate variability and perturbation. Hydrological sensitivity in the virtual basins was assessed by examining the differences between hydrological simulations driven by 4-km gridded convection-permitting Weather Research and Forecasting (WRF) outputs in the current period (ctrl, 2001–2013) and a Pseudo Global Warming period (pgw, 2087–2099). The WRF simulation in the pgw period was forced by a perturbation of the same boundary conditions from ERA reanalysis data as for the ctrl period, and the perturbation was based on the ensemble-mean of projected changes from the CMIP5 RCP 8.5 emission scenario. Results showed that temperature would increase by 4.5℃ to 7℃ over the SBF but increases in annual precipitation of 15–24% would more than compensate for the effects of warming on runoff generation and result in greater streamflow volumes. Annual streamflow volumes would increase by 64 mm (35%) and 95 mm (16%) in the west and east, and by 48 mm (17%) in the central SBF. Annual snowfall and maximum SWE would decrease by 89–109 mm (∼29%) in the east, 3–8 mm (∼6%) in the west, and 31–50 mm (∼20%) in the central SBF. Annual mean soil moisture storage would decrease by 54–56 mm (27%) in the west and central, and by only 37 mm (14%) in the east SBF. Decreases in soil moisture would be caused by reduced soil freezing and enhanced thawing under future warming which would increase soil water loss from ET, subsurface runoff and percolation into groundwater storage. The larger sensitivity of streamflow and snow processes in the east SBF is partly due to the wetter climate and the larger increase in annual precipitation, the later also buffered the sensitivity of soil moisture to warming. These results show that the SBF would switch to a higher water yield region, dominated by rainfall-runoff fed streamflow over a longer snow-free season, and provide first-order guidance for sustainable water management of the SBF in the future.

Underground dam site selection using hydrological modelling and analytic network process

The location of Iran on the dry belt of the earth has caused the expansion of the desert in central and southern regions. One of the supply water methods in these regions is the underground water resources. Underground dam is one of hydraulic structures to storage and recharge water into the aquifer. The aim of this paper is to determine the best location to build underground dam using hydrologic and network process models. In determining the location of an underground dam, many factors can be considered. One of the most important ones is the subsurface flow in the area. In this study, hydrological modelling was done by SWAT to calculate the subsurface flow. At first, by using the Boolean logic method, seventeen suitable locations were selected. The selection process was based on criteria such as ground slope, land use, geology and hydrology. Then, the areas were modelled in SWAT and the subsurface flows and runoff volumes were calculated by SUFI2 (Sequential Uncertainty Fitting Ver. 2) algorithm. In the next stage, the locations were ranked using the Analytic Network Process (ANP). The decision criteria that were considered in ANP included reservoir volume, dam axis, and economic and social issues (drinkable water demand, Agriculture water demand, Distance from road Distance from village). Finally, 17 points were selected and prioritized for underground dam construction. The results of this study point that combination of SWAT modelling and ANP can be considered as a useful approach in the selection and ranking process in identifying optimum sites for underground dams. The results of this study made it possible to identify suitable places for the construction of underground dams by saving time and money by using the Decision Support System. Despite the complexity of criteria, possible floods can be predicted and managed better in the studied area.

Hydrodynamic response of a loam soil after wetting with different methods

Different points in a field can be wetted with different natural or artificial rainfall amounts and intensities. Therefore, a non-uniform soil wetting could occur during short-term monitoring of soil hydrodynamic parameters and its global effect on soil characterization is not easily predictable. For an initially dry loam soil, a sequence of three beerkan infiltration runs was performed at fixed sampling points in a period of two weeks. Immediately after the first beerkan run, the soil was perturbed by adding an additional water volume differing with the sampling point by the amount of water (0, 100 or 227 mm) and the application methodology (rainfall simulation, another beerkan run). As compared with the initial conditions, the soil infiltration parameters (mean, initial and final infiltration rates; constant of the Horton’s infiltration model) decreased by 2.9–4.3 times after wetting. A decrease of these parameters by 1.6–2.3 times remained detectable at the end of the sampling period, even if the soil likely returned to wetness conditions close to the initial ones. Relative variability decreased too (coefficients of variation = 52–135%, depending on the parameter, for the initial runs and 36–95% for the final ones). Nevertheless, a link was recognized between the infiltration parameters determined in the subsequent runs of the sequence. The correlation was stronger when the initial runs were compared with the last runs (coefficients of determination, R2 = 0.104–0.749, depending on the parameter; R >0 at P = 0.05 in all cases) than with those performed immediately after the intermediate wetting (R2 varying from a non-significant value of 0.059 to significant 0.196–0.211 values). The hydrodynamic response of a disturbed soil appears overall related to that of the initially undisturbed soil even if the disturbing agent is not exactly the same over the sampled area. Performing other investigations by characterizing the antecedent soil conditions in more detail could help to improve interpretation and modelling of hydrological processes by a more realistic description of temporal variability of soil infiltration parameters.

Fields of Application of SWAT Hydrological Model—A Review

Soil and Water Assessment Tool (SWAT) is a widely used model for runoff, non-point source pollution, and other complex hydrological processes under changing environments (groundwater flow, evapotranspiration, snow melting, etc.). This paper reviews the key characteristics and applications of SWAT. Since its inception in the 1990s, there has been a significant increase in the number of articles related to the SWAT model. In the last 10 years, the number of articles almost reached 4000. The range of applications varies between small and large scales; however, large watershed modelling dominates in North America and Asia. Moreover, the prevailing modelling is related to hydrological impacts in a changing environment, which is a global problem. The significant shortcoming of the SWAT model is the vast quantity of data necessary to run the model to generate accurate and reliable results, which is not accessible in some regions of the world. Apart from its accessibility, it has several advantages, including continuous development, which results in a slew of new interfaces and tools supporting the model. Additionally, it can simulate human activity and agricultural measures and adapt to new circumstances and situations. This article emphasizes weaknesses and strengths of SWAT model application on modelling of hydrological processes in changing climate and environment.

An improved non-point source pollution model for catchment-scale hydrological processes and phosphorus loads

Non-point source (NPS) pollutants may cause water quality deterioration and eutrophication, posing a significant threat to aquatic systems. Understanding and modelling surface and sub-surface hydrological processes and nutrient cycles are therefore essential for water resources management and pollution control. This paper presents a new hydrological-water quality model for application in humid and semi-humid catchments in China, considering both hydrological processes, and nutrient transport and transformation. The model divides the soil into three layers, and each layer is implemented with individual computational procedures for soil wetness and nutrient process. Nutrient transport is driven by hydrological processes and follows the same pathways as water flow in the model: surface runoff, infiltration, and outflow from individual soil layers. River channels are described separately with routines to account for the turnover of nutrients. Model parameters are selected according to literature/open-sourced data or estimated from soil texture or land use types. The model is applied to simulate the hydrological processes and phosphorus transport in the Tongyang River Basin in China and the model performance is confirmed by comparing the predicted discharge and total phosphorus concentrations with measured data at the catchment outlets of Tonghe River and Yanghe River over 3 years. Uncertainty analysis has been further carried out using the GLUE method to demonstrate sensitivity of the simulation results to the selection of model parameters. After model calibration, the predicted results are found to compare well with field observations in terms of flow discharge and total phosphorus concentration. From sensitivity analysis, it is found that the recession coefficient of channel system (CS) and the pollutant recession coefficient of surface runoff (pKS) are the most influential hydrological and water quality parameters that control the arrival time and duration of flood peak and nutrient concentration peak, respectively.