Hydrological Parameters Research Articles

Evolution of Air-Sea CO2 Flux during ARAMIS, EGEE and ROAM

The aim of this study is to compare evolution of the CO2 in two different areas of the tropical Atlantic Ocean. We investigate the spatial and temporal variability of CO2 fluxes, Total Alkalinity (TA), Dissolved Inorganic Carbon (DIC) and hydrological parameters (Salinity and Temperature) in the western and eastern tropical Atlantic for three years (2005-2006-2007), using the data collected during the ARAMIS 7, 8 and 10, EGEE and ROAM cruises (2019-2020). Our results showed that Sea Surface Salinity (SSS), Sea Surface Temperature (SST), TA and DIC are higher on average in the western area than in the eastern tropical Atlantic, except the oceanic CO2 fugacity (fCO2sw). A north-south gradient is observed with high values in the south of the Equator and low values in the north. This gradient is due to the Equatorial upwelling, which upwelled CO2-rich deep water to the surface; in addition, the Guinea current (GC) transports low salinity and parameters of carbon in the eastern area and Amazon outflow at West decreases the concentration of these parameters. On average, the western area was a sink of 0.34 mmol.m-2.d-1 and the eastern basin a source of 1.15 mmol.m-2.d-1. During ROAM cruises, air-sea CO2 flux was ten times higher than during all the EGEE cruises and more for all ARAMIS cruises.

Quantifying Uncertainty in Hydrological Drought Index Using Calibrated SWAT Model

The hydrological model requires precise parameter calibration to reflect the basin characteristicsproperly. The optimum parameter values depend highly on the runoff characteristics; thus, the simulated runoff data affects the uncertainty in hydrological drought assessment using hydrological models. This study quantified the hydrological drought uncertainty based on all available calibrated parameters for 12 different observation periods. The Soil and water assessment Tool was used for hydrological modelling, and the uncertainty was analyzed using Bayesian model averaging (BMA). The hydrological drought index using the simulated runoff was analyzed to vary significantly with the optimized hydrological parameter cases. The drought index calculation using the estimated runoff showed that the number of droughts occurred frequently as the duration increased, But, it was observed that severe droughts of 21-month duration occur most frequently. The uncertainty analysis conducted with BMA revealed that for durations shorter than 18 months, the uncertainties in drought index calculations were higher than those in runoff simulations. As the duration increased, uncertainties decreased, with values of 57%, 54%, 50%, 23%, 23%, 19%, 16% and 16% for 3, 6, 9, 12, 15, 18, 21 and 24-month durations, respectively. The average uncertainty was larger for runoff estimation than for drought calculation. Still, the uncertainty variation by each PC was larger for drought calculation, ranging from 0 to 94% as the duration increased to 24 months. Therefore,the results of this study confirm that hydrological drought analysis using hydrological models contains uncertainties depending on the hydrological model parameter cases.

Neglecting hydrological errors can severely impact predictions of water resource system performance

Risk-based decision making for water resource systems often relies on streamflow predictions from hydrological models. These predictions are integral for estimating the frequency of high consequence extreme events, such as floods and droughts. However, streamflow predictions are known to have errors due to various factors such as incomplete hydrological understanding, parameter misspecification, and uncertain data. Despite these errors being well known, they are frequently neglected when undertaking risk-based decision-making. This paper demonstrates that neglecting hydrological errors can impact on drought risk estimation for high stakes decisions with potentially severe consequences for water resource system performance. A generic framework is introduced to evaluate the impact of hydrological errors for a wide range of water resource system properties. This framework is applied in two Australian case study catchments, where we use a stochastic rainfall model, the GR4J hydrological model, a residual error model, and a simplified reservoir storage model to estimate water resource performance metrics (risk and yield). The results underscore the impact of neglecting hydrological errors on decision-making. In one case study catchment, the yield was over-estimated by ∼15%–55%, resulting in the (actual) risk of running out of water being ∼2–30 times larger than reservoir design. The magnitude of these errors in water resource performance metrics is striking, especially considering that the streamflow predictions appear reasonable based on typical performance metrics (e.g., NSE of ∼0.7). The errors in performance metrics stem from the complex propagation of hydrological errors through the water resource system modelling chain. By accounting for critically important hydrological errors we can mitigate highly erroneous risk estimates and improve decision-making related to water resource management.

Review of Machine Learning Methods for River Flood Routing

River flood routing computes changes in the shape of a flood wave over time as it travels downstream along a river. Conventional flood routing models, especially hydrodynamic models, require a high quality and quantity of input data, such as measured hydrologic time series, geometric data, hydraulic structures, and hydrological parameters. Unlike physically based models, machine learning algorithms, which are data-driven models, do not require much knowledge about underlying physical processes and can identify complex nonlinearity between inputs and outputs. Due to their higher performance, lower complexity, and low computation cost, researchers introduced novel machine learning methods as a single application or hybrid application to achieve more accurate and efficient flood routing. This paper reviews the recent application of machine learning methods in river flood routing.

Analysis of gap filling techniques for GRACE/GRACE-FO terrestrial water storage anomalies in Canada

Terrestrial water storage anomaly (TWSA) data derived from the Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) have become indispensable for hydrological monitoring since the first mission launched in 2002. Evaluation of basin dynamics is enabled through TWSA integration with independent hydrological and climate datasets such as precipitation, evapotranspiration, and surface runoff. With over 20 years of observations, long-term statistical analysis reveals water storage trends, provided the 11-month gap between the two missions is filled. This study compares four methods for reconstructing GRACE-like TWSA over Canada, namely: extreme gradient boosting, artificial neural networks, automated machine learning, and projection onto convex sets. The GRACE mascon product released by the Jet Propulsion Laboratory is used over the study region. Nine sets of hydrological and climate parameters are used as predictors for the machine learning models, namely GRACE average seasonal signals, TWSA from the GLDAS Catchment Land Surface Model, precipitation, air temperature, glacier surface mass balance, ocean tides, the North Atlantic Oscillation, the Multivariate ENSO Index, and sea surface temperature. For each of the four methods, 20 % of the GRACE TWSA data (the ‘testing data’) is omitted from the model training, and the model is used to fill the artificial gap. Root mean square error (RMSE) is computed using the difference between testing data and model predictions. Normalized RMSE expresses the RMSE as a proportion of the range of the GRACE TWSA data. The automated machine learning algorithm performs best in terms of mean normalized root mean square error, with 6 % (2.4 cm equivalent water height RMSE), followed by projection onto convex sets, extreme gradient boosting, and artificial neural networks. Inclusion of glacier surface mass balance models derived from the GMAO Modern-Era Retrospective Analysis for Research and Applications Version 2 reanalysis and the Randolph Glacier Inventory improved the automated machine learning average root mean square error by 70 %. Results indicate that filling the gap between missions allows for more comprehensive analysis of terrestrial water storage changes, including basin-by-basin analysis, which is ongoing.

Spatiotemporal distribution of groundwater drought using GRACE-based satellite estimates: a case study of Lower Gangetic Basin, India.

Droughts frequently occurring in India have significant societal, economic, and environmental effects. The lack of direct measurements of groundwater in location and time hinders quantitative methods to analyse the intricate nature of groundwater drought. This work used the datasets derived from the Gravity and Climate Experiment (GRACE and GRACE-FO) and Global Land Data Assimilation System (GLDAS) to extensively analyse Groundwater Storage changes in the Lower Gangetic Basin (LGB) using unique hydrological parameters between the years 2003 and 2022. The analysis highlights that the GRACE-derived terrestrial water storage anomaly in the LGB decreased significantly (-12.12 mm/yr), and the amount of Groundwater Storage Anomaly (GWSA) decreased similarly (-10.80 mm/yr), while in the GRACE-FO period, a positive trend has been noticed in TWSA (33.96 mm/yr) and GWSA (64.8 mm/yr) respectively. A drought indicator called the GRACE-derived groundwater drought index (GGDI) has been computed for the entire LGB region. A traditional drought study viz. Standardised Precipitation Index (SPI) was performed over LGB to justify the results of the GGDI. The results from GGDI study effectively matched the periods of significant drought occurrences with the 12-month SPI time series. From the GGDI, this study examined groundwater drought's spatial distribution, temporal evolution, and trend (Modified Mann Kendall trend) aspects. According to research findings, the LGB experienced three major drought periods between 2009-2010, 2019 (moderate), and 2015-2016 (severe). The study offers reliable quantitative data on the evolution of GRACE-derived groundwater drought, which may add a new perspective to additional drought research in the densely populated study area, which depends majorly on agriculture, livestock and less skilled water-intensive industries such as leather and textile industries in a sub-tropical climate. This paradigm incorporates changes in groundwater resources caused by human activities and climate change, paving the way for measuring progress towards sustainable use and water security.

Satellite-based drought assessment in the endorheic basin of Lake Balkhash

Introduction: This study investigates into the drought-prone region of the Lake Balkhash basin, conducting a thorough analysis spanning 2 decades, emphasizing its significant impact on agriculture and water challenges in Central Asia. Beyond the specific focus on this region, the research aims to contribute valuable insights that extend our understanding of the broader implications of drought in the area.Methods: Utilizing MODIS satellite imagery, the study employs the Vegetation Health Index a comprehensive indicator combining the Vegetation Condition Index and Temperature Condition Index ranging from 0 (extreme aridity) to 100 (optimal moisture). The assessment of drought conditions from 2000 to 2020 includes probabilistic mapping, trend analysis, and Pearson’s correlation coefficients. Connections with hydrological factors, such as river water levels and the Balkhash Lake area, are explored, along with in-depth analyses of land use data and the GRACE dataset on water equivalent thickness, enhancing the study’s depth and reliability.Results: Drought affected approximately 44% of the Balkhash Lake Basin during the study period, impacting both Kazakhstan and China. Severe drought episodes occurred in 2000, 2008, 2014, and 2015, highlighting the region’s vulnerability. Analysis of drought trends revealed diverse patterns: 23% exhibited an increase, 17% showed a decrease, and 60% remained stable. Correlations between drought and hydrological parameters varied among stations, with positive correlations at Kapshagay and Shelek Stations, a weak correlation at Ayagoz Station, and a significant positive correlation at Lepsy despite the elevation.Discussion: This research underscores the intricate link between drought and hydrological factors in the Balkhash Lake Basin, emphasizing the need for precise water resource management and climate adaptation. Crucial strategies include proactive monitoring, tailored interventions, and the application of probabilistic drought mapping to enhance water supply management, contributing actionable insights for sustainable practices in the region.Conclusion: This study significantly advances our understanding of drought dynamics in the Balkhash Lake Basin, recommending adaptive strategies, site-specific interventions, and sustainable water management. The findings provide a crucial foundation for informed water resource decisions in Central Asia, emphasizing the importance of region-specific approaches to address diverse challenges posed by drought.

Assessing Recession Constant Sensitivity and Its Interaction with Data Adjustment Parameters in Continuous Hydrological Modeling in Data-Scarce Basins: A Case Study Using the Xinanjiang Model

Data scarcity plays the crucial role in hydrological modeling, causing the uncertainties in hydrological model calibration and parameterization. Therefore, while considering the sensitivity of the parameter optimization, it is essential to determine which parameters have the most significant implications on model performance, especially when there is limited hydro-climatological information. Previous studies have underscored the significance of data adjustment parameter sensitivity and its consequential influence on both Xinanjiang (XAJ) model performance and the determination of the acceptable minimum data length, particularly in data-scarce regions. Nevertheless, it is essential to consider the recession constant sensitivity as it has been identified as the most sensitive parameter on an annual scale while keeping the data adjustment parameters constant during a period of data scarcity. Hence, the objective of this study is to extend the previous research by examining the relationship between recession constant sensitivity and data adjustment parameters in shorter datasets leading to more reliable parameter estimation for data-scarce basins. Five U.S. river basins were analyzed using the 28-year dataset and shorter subsets to highlight the impacts of recession constant sensitivities with different data lengths. This study explores the impact of recession constant sensitivities over the hydrological parameter estimation using two approaches (cg): (i) assessing the relationship between the recession constant (cg) and the data adjustment parameter (Cep), for the 28-year dataset, and (ii) investigating the significant impacts of the sensitivity of cg over Cep in shorter datasets, which can affect the estimation of the acceptable minimum data length in the data-scarce basins. The polynomial regression analysis was applied to compare and evaluate the model results, varying over the recession constant with different data lengths. The findings indicated that the influence of the recession constant over the data adjustment parameters in the 28-year dataset is limited in the annual scale. However, there is a significant impact of recession constant sensitivity over the model performance while calibrating the model with subsets, particularly in the worst-case scenario. This study underscores the importance of the recession constant sensitivity for reliable continuous hydrological model predictions, especially in data-scarce areas.

Assessment of hydrological, geological, and biological parameters of a river basin impacted by old Hg mining in NW Spain

Mercury (Hg) is a toxic metal that can cause adverse effects for the health of ecosystems. The Caudal River is one of the main rivers in the Asturias region (NW Spain), whose basin is highly anthropized, hosting several Hg mines, closed in the last century. Arsenic (As) is also found in the mineral paragenesis of the Hg deposits, posing a greater environmental risk. In the mining sites, remaining old facilities and tailings continue to release these elements into the environment. In this work, samples of fluvial sediments and water were taken, both in areas affected by anthropic activity and in pristine areas, in order to establish the background levels for the critical elements. The mineralogical study of the sediments, combined with EDS microanalysis, is useful to identify mineralogical traps such as Fe oxides or clays to retain the As. The As content in all sampled sediments is above the threshold effect levels (TEL), the possible effect range within which adverse effects occasionally occur, according to the Canadian Sediment Quality Guidelines for the Protection of Aquatic Life. This fact is related to a naturally high geochemical background in the basin, due to the existing mineral deposits. The PEL threshold (the probable effect range within which adverse effects frequently occur) is exceeded by more than an order of magnitude in the sediments downstream of the Hg mines. In these points, the As content in the water, exceeding 700 μg L−1 As, is also above the quality standard established in Spanish legislation. As a result, the Caudal River tributaries in the lower part of the basin do not reach a good ecological state, according to the Hydrological Planning Office, and in some cases their state is deficient, showing low richness and high dominance of macroinvertebrates. Although the concentrations decrease with distance from the source, these findings justify the low ecological quality of the affected watercourses.

Stratification of dissolved organic matter in the upper 5000 m water column in the western Pacific Ocean

Marine dissolved organic matter (DOM) is a principal reservoir involved in biogeochemical cycles and exerts a pivotal influence on global carbon flux dynamics. In this study, excitation-emission matrix fluorescence spectroscopy combined with parallel factor analysis (EEM-PARAFAC) was conducted on 230 DOM samples collected from 21 sites between February and April 2022 in the Western Pacific Ocean (WPO). We identified five distinct fluorescence peaks (peaks B, T, A, C, and M), predominantly protein-like and humic-like components. These findings, marked by significant differences (p < 0.01) in fluorescence intensities and spectral indices, characterized the transformation of DOM with ocean depth, illustrating a transition from active to recalcitrant forms. Additionally, random forest analysis (RFA) identified depth as a key factor influencing marine dissolved organic carbon (DOC), with a 32.59% importance value. Correlations between hydrological and fluorescent parameters underscored the complexity of DOM sources and influencing processes. Overall, this work broadens our understanding of DOM variability in the upper 5000 m of the WPO, enhancing our knowledge of the marine environment's role in the global carbon cycle.

Rainfall-induced changes in aquatic microbial communities and stability of dissolved organic matter: Insight from a Fen river analysis

Microbial communities are pivotal in aquatic ecosystems, as they affect water quality, energy dynamics, nutrient cycling, and hydrological stability. This study explored the effects of rainfall on hydrological and photosynthetic parameters, microbial composition, and functional gene profiles in the Fen River. Our results demonstrated that rainfall-induced decreases in stream temperature, dissolved oxygen, pH, total phosphorus, chemical oxygen demand, and dissolved organic carbon concentrations. In contrast, rainfall increased total dissolved solids, salinity, and ammonia-nitrogen concentrations. A detailed microbial community structure analysis revealed that Cyanobacteria was the dominant microbial taxon in the Fen River, accounting for approximately 75% and 25% of the microalgal and bacterial communities, respectively. The abundance of Chlorophyta and Bacillariophyta increased by 47.66% and 29.92%, respectively, whereas the relative abundance of Bacteroidetes decreased by 37.55% under rainfall conditions. Stochastic processes predominantly affected the assembly of the bacterial community on rainy days. Functional gene analysis revealed variations in bacterial functions between sunny (Sun) and rainy (Rain) conditions, particularly in genes associated with the carbon cycle. The 3-oxoacyl-[acyl-carrier-protein] reductase gene was more abundant in the Fen River bacterial community. Particular genes involved in metabolism and environmental information processing, including the acetyl-CoA C-acetyltransferase (atoB), enoyl-CoA hydratase (paaF), and branched-chain amino acid transport system gene (livK), which are integral to environmental information processing, were more abundant in Sun than the Rain conditions. In contrast, the phosphate transport system gene, the galactose metabolic gene, and the pyruvate metabolic gene were more abundant in Rain. The excitation-emission matrix analysis with parallel factor analysis identified four fluorescence components (C1–C4) in the river, which were predominantly protein- (C1) and humic-like (C2–C4) substances. Rainfall affected organic matter production and transport, leading to changes in the degradation and stability of dissolved organic matter. Overall, this study offers insight into how rainfall affects aquatic ecosystems.

The Prediction of Debris Flow Based on Eruption and Rainfall Event for River Infrastructure Mitigation: Study Case Opak River, Sleman Regency

Mount Merapi is a volcano active in Indonesia. The eruption that occurred in 2010 was a major eruption with a return period of 100 years. Dominant debris to the Opak – Gendol Watershed; the biggest debris flood occurred in 2010 – 2011. One of the disaster mitigations on the Opak River is the Sabo Dam infrastructure. Currently, in the upper reaches of the Opak River, there are 5 Sabo Dams. In 2022, 2 additional Sabo Dams were built, namely OP RRC4 and OP RRC3a. This study will be used modelling 2D HEC-RAS Non-Newtonian before the construction of 2 Sabo Dams and after the construction of 2 Sabo Dams with river geometry measured in 2020 and flood discharge as measured from rainfall triggering debris flow on 03 January 2011, from this rainfall a 6-hour rain distribution was carried out using the PSA coefficient to be input in the HEC-HMS, the hydrological parameters used on 03 January 2011, were used to calculate the Q100 flood discharge. The results of modelling at the observation point using the Q100 showed that the elevation of the debris flow after the presence of the new Sabo Dam series could be reduced by up to 47.21 %, Sabo Dam effectively reduces the rate of debris flow to reduce the potential debris flood.

Impact of land surface model schemes in snow-dominated arid and semiarid watersheds using the WRF-hydro modeling systems

&lt;abstract&gt; &lt;p&gt;In the past century, water demand increased extensively due to the rapid growth of the human population. Ground observations can reveal hydrological dynamics but are expensive in the long term. Alternatively, hydrological models could be utilized for assessing streamflow with historical observations as the control point. Despite the advancements in hydrological modeling systems, watershed modeling over mountainous regions with complex terrain remains challenging. This study utilized the multi-physical Weather Research and Forecasting Hydrological enhancement model (WRF-Hydro), fully distributed over the Amu River Basin (ARB) in Afghanistan. The calibration process focused on land surface model (LSM) physics options and hydrological parameters within the model. The findings emphasize the importance of LSM for accurate simulation of snowmelt–runoff processes over mountainous regions. Correlation coefficient (R), coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;), Nash-Sutcliff efficiency (NSE), and Kling-Gupta efficiency (KGE) were adopted for accuracy assessment over five discharge observation stations at a daily time scale; overall performance results were as follows: R was 0.85–0.42, R&lt;sup&gt;2&lt;/sup&gt; was 0.73–0.17, NSE was 0.52 to −8.64, and KGE was 0.74 to −0.56. The findings of the current study can support snowmelt process simulation within the WRF-Hydro model.&lt;/p&gt; &lt;/abstract&gt;

ANALISIS HUBUNGAN TUTUPAN LAHAN DAN INDIKATOR KESEHATAN HIDROLOGI DAS REJOSO, JAWA TIMUR

With increasing concerns regarding water resource management and environmental sustainability, understanding land use change and the hydrological health of watersheds is critical for informed decision-making. This study aimed to explore the effect of land use changes on hydrological health resilience in the Rejoso watershed, East Java, using remote sensing techniques and geographic information system tools to characterize the various land cover types. Landsat ETM 7+ satellite imagery was used to describe land cover classes. Hydrological health indicators such as water transmission, water availability during the dry season, and peak rainfall buffer events were systematically analyzed in relation to land cover change conditions. Regression statistical methods were used to measure the impact of land cover changes on hydrological parameters. The study results showed that the area of forest land in the Rejoso watershed tended to increase from 2011 to 2021; on the other hand, the area of agroforestry land tended to decrease along with the increase in upland land. In general, the hydrological health of the Rejoso watershed is decreasing from year to year. The increase in forest area, agroforestry, settlements, and ponds provides a positive response to the hydrological health of the watershed. On the other hand, the increase in moorland and rice fields has a negative impact on the hydrological health of the watershed.

Comparison of soft-computing techniques: Data-driven models for flood forecasting

&lt;p&gt;Accurate flood forecasting is a crucial process for predicting the timing, occurrence, duration, and magnitude of floods in specific zones. This prediction often involves analyzing various hydrological, meteorological, and environmental parameters. In recent years, several soft computing techniques have been widely used for flood forecasting. In this study, flood forecasting for the Narmada River at the Hoshangabad gauging site in Madhya Pradesh, India, was conducted using an Artificial Neural Network (ANN) model, a Fuzzy Logic (FL) model, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. To assess their capacity to handle different levels of information, three separate input data sets were used. Our objective was to compare the performance and evaluate the suitability of soft computing data-driven models for flood forecasting. For the development of these models, monthly discharge data spanning 33 years from six gauging sites were selected. Various performance measures, such as regression, root mean square error (RMSE), and percentage deviation, were used to compare and evaluate the performances of the different models. The results indicated that the ANN and ANFIS models performed similarly in some cases. However, the ANFIS model generally predicted much better than the ANN model in most cases. The ANFIS model, developed using the hybrid method, delivered the best performance with an RMSE of 211.97 and a coefficient of regression of 0.96, demonstrating the potential of using these models for flood forecasting. This research highlighted the effectiveness of soft computing techniques in flood forecasting and established useful suitability criteria that can be employed by flood control departments in various countries, regions, and states for accurate flood prognosis.&lt;/p&gt;

Tradeoffs Between Temporal and Spatial Pattern Calibration and Their Impacts on Robustness and Transferability of Hydrologic Model Parameters to Ungauged Basins

AbstractOptimization of spatially consistent parameter fields is believed to increase the robustness of parameter estimation and its transferability to ungauged basins. The current paper extends previous multi‐objective and transferability studies by exploring the value of both multi‐basin and spatial pattern calibration of distributed hydrologic models as compared to single‐basin and single‐objective model calibrations, with respect to tradeoffs, performance and transferability. The mesoscale Hydrological Model (mHM) is used across six large central European basins. Model simulations are evaluated against streamflow observations at the basin outlets and remotely sensed evapotranspiration patterns. Several model validation experiments are performed through combinations of single‐ (temporal evaluation through discharge) and multi‐objective (temporal and spatial evaluation through discharge and spatial evapotranspiration patterns) calibrations with holdout experiments saving alternating basins for model evaluation. The study shows that there are very minimal tradeoffs between spatial and temporal performance objectives and that a joint calibration of multiple basins using multiple objective functions provides the most robust estimations of parameter fields that perform better when transferred to ungauged basins. The study indicates that particularly the multi‐basin calibration approach is key for robust parametrizations, and that the addition of an objective function tailored for matching spatial patterns of ET fields alters the spatial parameter fields while significantly improving the spatial pattern performance without any tradeoffs with discharge performance. In light of model equifinality, the minimal tradeoff between spatial and temporal performance shows that adding spatial pattern evaluation to the traditional temporal evaluation of hydrological models can assist in identifying optimal parameter sets.

Effectiveness and resilience of BMPs to watershed climate adaptation considering the uncertainty of hydrological model and GCMs

Climate change significantly impacts catchment hydrologic and water quality processes. Best management practices (BMPs) can serve as adaptation strategies to negate the impact of climate change on sediment and total nitrogen (TN) loads. One major controversial issue in climate change adaptation studies is the highly uncertain nature of such changes. Previous studies have rarely focused on the combined impact of the uncertainty in climate change and watershed model parameters, which could be the main sources of uncertainty in climate change adaptation research. In this study, the effectiveness and resilience (defined as continued effectiveness under a changing climate) of BMPs in reducing sediment and TN loads were explored under future climate change in the Shanmei Reservoir watershed (SMW) of Southeast China. Climate change projections provided by 10 general circulation models (GCMs) under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios from 2031 to 2060 were applied in the Annualized Agricultural Nonpoint Source (AnnAGNPS) model to evaluate the effectiveness and resilience of 4 BMPs (riparian buffers (RB), no-tillage (NT), fertilization reduction (FR), and parallel terraces (PT)). The ensemble average of 10 GCMs and 10 behavior parameter groups were adopted to reduce the uncertainty resulting from the hydrological model parameters and GCMs. The results indicated that the average annual temperature and precipitation in the SMW will increase in the future. On a seasonal scale, the average temperature during all seasons will increase, and precipitation will decrease in summer and autumn but increase in spring and winter. The annual sediment and TN loads will decrease, but the loads in spring and winter will increase. BMPs could be effective as climate adaptation strategies for reducing sediment and TN loads under future climate conditions, with PT as the most effective option. Structural BMPs were more effective in reducing sediment and TN loads in spring and winter, whereas nonstructural BMPs were more effective in reducing loads in summer and autumn. BMPs were more resilient when future watershed runoff changes were slight or climate sensitivity was reduced, with higher BMP resilience in spring and winter than in summer and autumn. This study aimed to provide systematic references for watersheds through the implementation of BMPs for mitigating the effects of climate change and extending the boundaries of the AnnAGNPS model application.

Enhancing Model Calibration and Uncertainty Estimation through Multi-Objective Optimization with SUFI-2 Algorithm

The research aimed to quantify the uncertainty in hydrological modeling results, especially in data-scarce areas like the Teesta River. The developed grid-based model from Texas Agricultural and Management University (TAMU) with the help of simulation plugin Quantum Soil and Water Assessment Tools (QSWAT) and calibration (CAL) &amp; validation (VAL) using SWAT-Calibration Uncertainty Program (CUP). Multiple algorithms Minimize &amp; Maximize (MIN &amp; MAX), specifically with Sequential Uncertainty Fitting Version-2 (SUFI-2) algorithm (Best fitted simulation), were used as the popular for the many ideal simulations as possible that are within 95% of the true predictions. The research investigated how eleven different objective functions (OBJF) influenced the CAL results, parameterization, and surface water estimation for the stream flow of the Teesta River. While several OBJF performed well, eight of them, namely Chi-square (Chi2), Multiplicative (Multi), Modified Nash-Sutcliffe efficiency factor (MNSE), Nash-Sutcliffe efficiency (NSE), summation (SUM), Coefficient of determination (R2), Modified coefficient of determination (bR2), and Relative Squared Error (RSR), consistently yielded very similar results because of mostly used MIN methods. For instance, NSE resulted in a CAL &amp; VAL score of 0.85 and 0.88, while R2, MNSE, and bR2 had scores of 0.88 and 0.89 during both CAL &amp; VAL periods. All results from NSE, R2 and bR2 show a correlation between 0.84 to 0.90. However, other OBJF provided slightly different sensitivity values. The Percentage Bias (PBIAS) scored 0.76 for CAL and 0.77 for VAL. The Kling-Gupta efficiency (KGE) scored 0.79 during both the CAL &amp; VAL periods. Additionally, the sum of squares (SSQR) obtained shallow values for both CAL &amp; VAL processes. The aforementioned research shows that varied OBJF can affect CAL findings and hydrological modelling parameter values, emphasizing the need to use several OBJF in uncertainty analysis for more accurate water resource estimation the calibrated model and its outputs aid hydrologists and water resource administrators in agricultural and water assessment. They also support research on climate change's effects on water supply and quality, droughts, and food. The unique idea and flexible techniques make them suitable for most countries.

Consistent Modeling of Transport Processes and Travel Times—Coupling Soil Hydrologic Processes With StorAge Selection Functions

AbstractUnderstanding the transport processes and travel times of pollutants in the subsurface is crucial for an effective management of drinking water resources. Transport processes and soil hydrologic processes are inherently linked to each other. In order to account for this link, we couple the process‐based hydrologic model RoGeR with StorAge Selection (SAS) functions. We assign to each hydrological process a specific SAS function (e.g., power law distribution function). To represent different transport mechanisms, we combined a specific set of SAS functions into four transport model structures: complete‐mixing, piston flow, advection‐dispersion and advection‐dispersion with time‐variant parameters. In this study, we conduct modeling experiments at the Rietholzbach lysimeter, Switzerland. All modeling experiments are benchmarked with HYDRUS‐1D. We compare our simulations to the measured hydrologic variables (percolation and evapotranspiration fluxes and soil water storage dynamics) and the measured water stable isotope signal (18O) in the lysimeter seepage for a period of ten years (1997–2007). An additional virtual bromide tracer experiment was used to benchmark the models. Additionally, we carried out a sensitivity analysis and provided Sobol indices for hydrologic model parameters and SAS parameters. Our results indicate that the advection‐dispersion transport model produces the best results. And thus, advective‐dispersive transport processes play a dominant role at Rietholzbach lysimeter. Our modeling approach provides the capability to test hypotheses of different transport mechanisms and to improve process understanding and predictions of transport processes. Overall, the combined model allows a very effective simulation of combined flux and transport processes at various temporal and spatial scales.

Assessment of habitat and hydrological parameters of the lotic and lentic wetlands of the Sonitpur district, Assam, India

This paper represents the riparian zone and water parameters in twelve selected sampling sites of both lotic and lentic wetlands of Sonitpur district. The study has been carried out from January 2021 to February 2023. Data were collected representing all the four seasons of the year. Physical Characteristics viz; soil colour, soil moisture, soil texture, vegetation coverage, canopy coverage and litter depth were assessed during the survey period. Vegetation and canopy coverage of the riparian zone adjacent to the lotic wetlands were found to be the highest. Litter depth (3.1cm) was found to be recorded at a single sampling site S4 . Water quality assessment recorded that Water temperature was found to be one of the major factors having the most intense direct and indirect effect on the riverine system ranged from 4.85±0.02 to 27.60±0.10 in the lotic wetlands and between 11.15±0.02 to 27.44±0.05 in the lentic wetlands. The overall pH value ranged from 6.09±0.05 to 7.24±0.32 in the lotic wetlands and between 6.44±0.02 to 7.52±0.08 in the lentic wetlands which is a convenient zone. DO values ranged from 6.01±0.32 to 7.37±0.22 in the lotic wetlands and between 6.39±0.30 to 7.63±0.30 in the lentic wetlands. Maximum DO value was recorded in the monsoon period. Water velocity was observed to vary from 0.20±0.03 to 1.30±0.28 in the lotic wetlands. Highest water velocity was recorded during the monsoon period and lowest in the winter period.