Hydrological Monitoring Stations Research Articles

Rhodoferax potami sp. nov. and Rhodoferax mekongensis sp. nov., isolated from the Mekong River in Thailand.

Four newly discovered Gram-stain-negative bacteria, designated as BL00010T, BL00058, D8-11T and BL00200, were isolated from water samples collected at three hydrological monitoring stations (namely Chiang Saen, Chiang Khan and Nong Khai) located along the Mekong River in Thailand. An investigation encompassing phenotypic, chemotaxonomic and genomic traits was conducted. The results of phylogenetic analysis based on 16S rRNA gene sequences indicated that all four isolates represented members of the genus Rhodoferax. These isolates were closely related to Rhodoferax bucti KCTC 62564T with a similarity of 99.59%. The major fatty acids of the four novel isolates included C16:0 and C16:1ω7c and/or C16 : 1ω6c, whereas the major respiratory quinone was identified as ubiquinone Q-8. In addition, phosphatidylethanolamine was identified as a major polar lipid in these bacteria. The genomes of BL00010T, BL00058, D8-11T and BL00200 were similar in size (3.88-4.01 Mbp) and DNA G+C contents (59.5, 59.3, 59.5 and 59.3 mol%, respectively). In contrast to R. bucti KCTC 62564T and Rhodoferax aquaticus KCTC 32394T, the newly discovered species possessed several genes involved in nitrite and nitrile metabolism, which may be related to their unique adaptation to nitrile-rich environments. From the results of the pairwise analysis of average nucleotide identity of the whole genome and digital DNA-DNA hybridisation, it was evident that BL00010T and D8-11T represented two novel species, for which we propose the nomenclature Rhodoferax potami sp. nov., with the type strain BL00010T (TBRC 17198T = NBRC 116413T), and Rhodoferax mekongensis sp. nov., with the type strain D8-11T (TBRC 17307T = NBRC 116415T).

2001–2020年中国东北区域土壤水蚀数据集

The black soil area in Northeast China plays an important role in food production and ecological security in China. However, over-cultivation practices have led to severe soil erosion, which seriously threatens food security and ecological environment in Northeast China. Accurate simulations of water erosion with high spatio-temporal resolution are important for advancing sustainable development goals, such as promoting sustainable agriculture and monitoring land degradation in Northeast China. This dataset is based on Google Earth Engine (GEE) cloud platform, integrating multi-source remote sensing data, deconstructing RUSLE model, and optimizing algorithm combinations for each factor of the model. We compared and verified the annual sand transport volume and sand transport modulus data from the main hydrological monitoring stations in Northeast China with the soil erosion modulus estimated by the RUSLE model. Then, we selected the optimal factor algorithm combination based on three accuracy metrics (time series correlation, root mean square error and mean absolute error), and obtained the estimation results of soil water erosion modulus with the resolution of 250 m. This dataset can better depict the spatial distribution and temporal changes of soil erosion modulus in Northeast China from 2001 to 2020. It can serve as an effective reference for soil erosion control and assessments in Northeast China.

Identifying Potential Locations of Hydrologic Monitoring Stations Based on Topographical and Hydrological Information

Identifying Potential Locations of Hydrologic Monitoring Stations Based on Topographical and Hydrological Information

Water Area Extraction and Water Level Prediction of Dongting Lake Based on Sentinel-1 Dual-Polarization Data Decomposition

The Sentinel-1 imaging radar mission provides a short revisit-time, continuous all-weather, and day-and-night imagery at the C-band, which brings opportunities for the dynamic extraction of lake water areas. For wetland-type lakes, it is difficult to distinguish between the water, submerged plants, and mudflats at the edge of a lake, which leads to difficulty in recognizing the water edge of a lake and affects the accuracy of lake water area extraction. In this paper, a water area extraction and water level prediction algorithm based on Sentinel-1 dual-polarization data decomposition is proposed to solve the problem. We can accurately extract lake water through generalized Stokes polarization decomposition. At the same time, we can verify the accuracy of water area extraction by establishing the water area and in situ water level elevation (A–E) relationship, and predicting the water level according to the calculated water area. In this study, dual-polarization Sentinel-1 time series SAR data covering the Dongting Lake wetland from 2018 to 2022 are used to verify the proposed water area extraction algorithm and establish the A–E relationship of the East Dongting Lake basin. The results show that the generalized Stokes decomposition parameters are very sensitive to the water boundary, and the R2 of the water area and the water level can reach 0.98 by using the piecewise linear function. It confirms the accuracy of the water area inversion, which is of extremely important significance for the high-precision monitoring of the water area of Dongting Lake with long-term Sentinel-1 data. Meanwhile, the predicted lake water level acquired using the A–E relationship established in this paper is compared with the field water level, with an RMSE of 0.4857 m and R2 of 0.9930. This means that the water level derived using the method in this study is quite compatible with the field observations, which provides a good idea for the water level monitoring of lakes lacking hydrological monitoring stations.

Application, interpretability and prediction of machine learning method combined with LSTM and LightGBM-a case study for runoff simulation in an arid area

The runoff prediction can provide scientific basis for flood control, disaster reduction and water resources planning. Due to a large number of uncertainties in runoff prediction, it is difficult to make precise predictions. To improve the accuracy of runoff prediction, this study combines techniques of Long Short-Term Memory (LSTM) and Light Gradient Boosting Machine (LightGBM) in machine learning with reciprocal error method to develop an integrated data-driven model (i.e., LSTM-LightGBM) for runoff prediction. To demonstrate its applicability, the model is applied to the annual runoff prediction of the Caiqi hydrological monitoring station in the Shiyang River in an arid area. Indicators include Error of Peak (EP), Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) are adopted to evaluate the prediction performance of the LSTM, LightGBM, and LSTM-LightGBM methods under the same hyperparameter combinations. Then, the interpretability of LSTM and LightGBM models is also explored based on the permutation importance method and Shapley Additive exPlanations (SHAP) values, respectively. Finally, future annual runoff at the Caiqi for the next 50 years (2025–2075) is predicted based on LSTM-LightGBM model under 12 climate scenarios. Therefore, results show that: 1. the integrated model (LSTM-LightGBM) has good performance than two single models in NSE (0.92), RMSE (0.075 million m3) and MAE (0.046 million m3) and EP value (i.e., for bridging the peak-valley runoff). 2. In this case, it is found that four feature variables have the greatest influence on the target variables through the interpretable analysis. 3. The 12 combined climate scenarios used in this investigation produced generally steady predictions. The scenarios with the highest and lowest mean values are GFDL RCP 6.0 (3.12 × 108m3) and IPSL RCP 2.6 (3.04 × 108m3), respectively, with a decrease of 24.09 % and 26.03 % compared to the mean annual runoff of 4.11 × 108m3 in the baseline period (1955–2017). These findings can provide scientific bases for future water resources planning in the downstream of the Shiyang River Basin.

Intelligent monitoring of water quality based on image analytics

Owing to the limitations of the spatial arrangement of monitoring stations and time acquisition of satellite remote sensing images, the water quality monitoring of rivers, especially small- and medium-sized rivers, cannot be satisfied in terms of time and space continuity. In this study, we propose a standardization method for the camera images derived at different locations on different time considering the influences of light, shadows, reflections, ripples, leaves and so on. After this algorithm is established, an estimation algorithm based on the comprehensive appearance of water body is proposed, which has the potential of realising real-time, mobile, and continuous monitoring of water quality with low costs. The test results showed that the accuracy of the model was quite high compared to the results of the hydrological monitoring stations. Compared with the single-point detection of water quality monitoring stations, this method has advantages in terms of dynamic detection and small- andmedium-sized water body detection, which can serve as a supplement to traditional detection.

Revealing spatial variability of groundwater level in typical ecosystems of the Tarim Basin through ensemble algorithms and limited observations

Owing to the lack of hydrological monitoring stations and groundwater observations, particularly for natural habitats, the distribution of groundwater along the Mountainous (alluvial fan)-Oasis-Desert System (MODS) in the middle reaches of the Tarim River has not yet been systematically investigated. The performance of ensemble algorithms with multi-source/multi-scale (30 m, 90 m, 250 m, 500 m, and 1000 m) remote sensing data to predict the groundwater levels of the MODS of the Tarim Basin should be investigated. To improve the current knowledge on the spatial distribution of groundwater in this region, we conducted a study on the applicability of ensemble learning algorithms for groundwater assessments in typical regions of dryland, namely the Oasis-Taklimakan Desert (OTD), Nature land in Oasis system (NLOS), Irrigation area (IA), and Oasis system (OS). The results showed that the R2 values of the groundwater level prediction accuracy for the four ecosystems were 0.92, 0.96, 0.86, and 0.50, respectively, and their corresponding optimal ensemble algorithms were AR_GRBFN, AR_RF, RotationForest_MLP, and AR_ GRBFN. The scales corresponding to the optimal prediction accuracy of OTD, NLOS, IA, and OS were 250 m, 30 m, 250 m, and 90 m, respectively, and the effect of the scale on their respective groundwater level prediction accuracies (maximum value compared to minimum value) were 11.48%, 21.63%, 26.73%, and 10.59%. Random Subspace, Rotation Forest, and Additive Regression improved the prediction accuracy of the base learning method by 76%, 72%, and 64%, respectively, followed by Bagging, whereas Dagging greatly increased prediction errors. Rotation Forest and Random Subspace showed stable performances and guaranteed relatively low prediction errors. Among all ensemble algorithms, Additive Regression helped the base learners obtain relatively optimal prediction accuracies with high probabilities. Except for the OS system, groundwater level could be predicted with much greater accuracy at depths of >8 m than at <8 m. The contribution analysis showed that topography and land-use patterns controlled the spatial distribution of groundwater across MODS. The ensemble learning algorithm showed good performance using multi-source and multi-scale data.

Dynamics of the water environment in a water quantity-quality-soil model of China's Yellow River basin: imbalance and driving factors.

Dynamic assessment of the water environment reflects variations in water resources in a basin under the combined influence of nature and humans and is a prerequisite for rational water management. This study provides an integrated assessment of the water environment in a water quantity-quality-soil model. Using the long-term monthly data from hydrological monitoring stations, the water environment of the Yellow River basin is assessed from the year 2006 to 2019. The kernel density estimation and the Dagum Gini coefficient are used to analyze the spatial and temporal imbalances of the water environment. Geographic detectors are used to extract external driving factors of the unbalanced evolution. The study results reveal that (1) the water environment in the basin shows a fluctuating downward trend, which mainly depends on the organic pollution control indicators, with a contribution of 22.85%. Scores of the water environment in the midstream are lower than those in the upstream and downstream due to the heavy pollutant discharges. (2) The spatial imbalance shows a fluctuating downward trend. Inter-regional variation is the primary source of regional variation in the water environment, with an average contribution of 56.02%. (3) The temporal imbalance of the water environment is on the rise, with a degree of multipolarity. The significant left trailing feature of the kernel density curve suggests that there are areas within the basin where the water environment is extremely poor. (4) For the overall basin and upstream, economic development and technological innovation are the main external driving factors influencing the spatial and temporal imbalances of the water environment. For the midstream and downstream, population density and environmental regulations are the main drivers. The interaction of any two factors has a greater impact than the single one.

A hybrid decomposition and Machine learning model for forecasting Chlorophyll-a and total nitrogen concentration in coastal waters

Information regarding Chlorophyll-a (ChlA) and total nitrogen (TN) is critical for early warning of algal blooms. However, reliable models for accurate forecasting of the ChlA and TN are still lacking due to the optical complexity of coastal waters. To address this issue, we proposed a novel hybrid model named the CEEMDAN-VMD-XGBOOST-SARIMA (CVXS) model to forecast ChlA and TN concentrations. The model performance was validated at three hydrological monitoring stations in Hong Kong, China. Four independent models including extreme gradient boosting (XGBoost), support vector regression (SVR), deep learning (DL), and Seasonal autoregressive integrated moving average (SARIMA), and three hybrid models including complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-XGBoost, CEEMDAN-SVR, and CEEMDAN-DL were developed to compare their performance with the CVXS model. In addition, the physical mechanisms of the CVXS model were further explored through correlation analysis between the decomposed time series of water quality parameters. The result indicated that (1) the CVXS model had the best accuracy among all models for forecasting ChlA and TN, and all the NSEs remained above 0.97 at three hydrological monitoring stations. For forecasting ChlA, the performance of the eight models is ranked as CVXS > CEEMDAN-XGBoost > CEEMDAN-DL > CEEMDAN-SVR > XGBoost > DL > SARIMA > SVR. For forecasting TN, the performance of the eight models is ranked as CVXS > CEEMDAN-XGBoost > CEEMDAN-DL > CEEMDAN-SVR > XGBoost > SVR > SARIMA > DL; (2) the optimal forecasting time horizons of the CVXS model were one to two months; and (3) the variability of ChlA and TN concentrations induced by hydrologic factors has been inherently embedded in the decomposed time series data, thus providing the theoretical basis for the CVXS model forecasting water quality parameters. The results of this study are promising with respect to forecasting algal blooms and coastal water resource management.

Probabilistic interval estimation of design floods under non-stationary conditions by an integrated approach

Abstract Quantifying the uncertainty of non-stationary flood frequency analysis is very crucial and beneficial for planning and design of water engineering projects, which is fundamentally challenging especially in the presence of high climate variability and reservoir regulation. This study proposed an integrated approach that combined the Generalized Additive Model for Location, Scale and Shape parameters (GAMLSS) method, the Copula function and the Bayesian Uncertainty Processor (BUP) technique to make reliable probabilistic interval estimations of design floods. The reliability and applicability of the proposed approach were assessed by flood datasets collected from two hydrological monitoring stations located in the Hanjiang River of China. The precipitation and the reservoir index were selected as the explanatory variables for modeling the time-varying parameters of marginal and joint distributions using long-term (1954–2018) observed datasets. First, the GAMLSS method was employed to model and fit the time-varying characteristics of parameters in marginal and joint distributions. Second, the Copula function was employed to execute the point estimations of non-stationary design floods. Finally, the BUP technique was employed to perform the interval estimations of design floods based on the point estimations obtained from the Copula function. The results demonstrated that the proposed approach can provide reliable probabilistic interval estimations of design floods meanwhile reducing the uncertainty of non-stationary flood frequency analysis. Consequently, the integrated approach is a promising way to offer an indication on how design values can be estimated in a high-dimensional problem.

The impact of runoff flux and reclamation on the spatiotemporal evolution of the Yellow River estuarine wetlands

The Yellow River Delta is located in the intertwining zone of marine, terrestrial and river ecosystems, and its evolution is not only restricted by hydrological conditions, but also affected by human activities, which leads to serious degradation of estuarine wetlands. Here, long-term data from a hydrological monitoring station and remotely sensed satellite images were used to explore the effects from runoff, sediment load, and human activities on the evolution of wetlands in the Yellow River Delta from 1976 to 2018. Our results showed that the delta area fluctuated between 233.25 × 103 and 260.40 × 103 ha during the period of 1976–2018. The evolution of the old estuary was mainly affected by erosion, while the current estuary was mainly regulated by sedimentation during 1976–1995 and erosion during 1995–2018. Specifically, during the past four decades, natural wetlands, such as tidal flats and marshes, decreased dramatically by 55.0%, and these wetlands were mainly transformed into aquacultural ponds, salt pans, oil wells and dry land. In addition, the evolution of natural wetlands in different regions in the YRD was influenced to different degrees by natural factors and human activities. In the estuarine zone, the reduction in natural wetlands was mainly driven by natural factors such as coastal erosion, runoff and sediment load. In the old estuary, coastal erosion explained 91.2% of the changes in natural wetlands. The evolution of natural wetlands in the current estuary was mainly controlled by the sediment flux of the Yellow River and human activities, which accounted for 62.7% and 19.0% of the variation in the current estuary, respectively. In the central zone, human activities, such as reclamation and urban construction, have converted 82.7% of natural wetlands into human-made wetlands and non-wetlands. Our study indicates that quantitative quantification of the impact of the runoff and sediment, ocean dynamics and human activities on wetland evolution is conducive to the restoration of estuarine wetlands and the healthy and sustainable development of ecosystems.

Streamflow Variations across Nepal during 1986-2015

This paper presents a picture of the streamflow variation in major hydrologic stations across Nepal during 1986-2015 in two periods; before and after 2000. Our study selected 27 high-quality hydrologic monitoring stations maintained by the Department of Hydrology and Meteorology (DHM), Government of Nepal distributed across the country, representing a wide range of basin size, from ~308 to ~54100 km2. We compare the flow duration curves (FDCs) for two periods, T1: 1986-1999 and T2: 2000-2015. The study quantifies the changes of different streamflow indices including minimum, maximum, average daily streamflow, different percentiles and top 10, 20 and 50 maximum daily streamflow. Many studies reported that the climate (such as temperature and precipitation) showed an abrupt variation during the late 90s in the central Himalayas. This study explores whether similar effects are observed in the streamflow of the Himalayan catchments. The results of the study show a mixed pattern of positive and negative changes for different streamflow indices. However, about 60% of analyzed station (i.e. 17 out of 27) showed the positive deviation of the maximum daily streamflow meaning more extremes were observed in the latter period compared to the former period. Seven stations revealed &gt; +20% shift in the maximum daily streamflow from period T1 to T2. In particular, East Rapti at Rajaiya showed more than 80% increase with respect to T1 which deviated from an average of 304 (ranging from 123 to 682) m3/s to an average of 555 (ranging from 171 to 1260) m3/s. Overall, about 10% of increment could be found on time-sliced averaged maximum daily discharge between two periods. Most importantly, the inter- and intra- annual variation of extreme streamflow show a clear tendency of the elevated peak streamflow recurrently over time.

Flash Flood Early Warning System in Colima, Mexico.

This paper presents a system of sensors used in flash flood prediction that offers critical real-time information used to provide early warnings that can provide the minutes needed for persons to evacuate before imminent events. Flooding is one of the most serious natural disasters humans confront in terms of loss of life and results in long-term effects, which often have severely adverse social consequences. However, flash floods are potentially more dangerous to life because there is often little or no forewarning of the impending disaster. The Emergency Water Information Network (EWIN) offers a solution that integrates an early warning system, notifications, and real-time monitoring of flash flood risks. The platform has been implemented in Colima, Mexico covering the Colima and Villa de Alvarez metropolitan area. This platform consists of eight fixed riverside hydrological monitoring stations, eight meteorological stations, nomadic mobile monitoring stations called “drifters” used in the flow, and a sniffer with data muling capability. The results show that this platform effectively compiles and forwards information to decision-makers, government officials, and the general public, potentially providing valuable minutes for people to evacuate dangerous areas.

Emission estimation and fate modelling of three typical pesticides in Dongjiang River basin, China

Pesticides are widely and intensively used in the world for crops protection. High pesticide loadings can potentially pollute the water resource. However, little is known about the usage, environmental emission and fate of pesticides in river basins. Here, we firstly established a pesticide emission estimation method, and investigated the environmental fate of three commonly used pesticides (chlorpyrifos, triazophos, and isoprothiolane) in Dongjiang River basin, southern China using mathematical modelling approach in combination with field monitoring. The distributed hydrological model SWAT (Soil and Water Assessment Tool) was applied to model the emission of the target pesticides from farmland to stream water, and their fate in the basin. A satisfactory model calibration for flow and suspended sediment was obtained based on eight-year observation data of four hydrological monitoring stations in Dongjiang River basin. The differences between the simulation and observation of pesticides were almost within an order of magnitude, including more than 53% differences within 0.5 order of magnitude. In the river basin, 78860 kg of chlorpyrifos, 54990 kg of triazophos and 35320 kg of isoprothiolane were sprayed onto the crops, the estimated annual emissions of the basin come up to 1801 kg, 3779 kg, and 2330 kg under the conditions of rainfall, surface runoff and percolation. After a series of environmental processes including settlement and degradation within the channels, the predicted export masses for chlorpyrifos, triazophos and isoprothiolane were reduced to 266 kg, 1858 kg, 1350 kg, respectively. Successful prediction suggests that the reliable estimation method combing the SWAT modelling can help us understand the source, concentration levels and fate of pesticides in river basin in different scales. Combing the method of emission and fate modelling method we proposed, countries and regions lacking pesticide-application database can facilitate better management of pesticides.

Relationship between salinity and sonic fish advertisement behavior in a managed sub-tropical estuary: Making the case for an acoustic indicator species

The Florida Everglades have been subjected to decades of human disturbance directly and indirectly related to freshwater management. As a result, intensive efforts directed at restoring water quality in the region have been congressionally mandated. Long-term hydrological monitoring of the Florida Bay Estuary has significantly improved scientific understanding of physical environmental processes within the southern portion of Everglades National Park. Linking environmental processes to ecosystem dynamics, however, requires a relevant metric of biological activity that 1) is representative of ecosystem interactions, 2) can be measured at the same temporal and spatial scales of hydrological data collection, and 3) is at sufficient resolution to provide an understanding of organismal, population, and community-level changes. Through changes in physiology, behavior, or ecology, fishes may respond to acute and chronic changes in their habitats. Passive acoustic detection of sonic fishes may provide an opportunity to link physical and ecosystem processes in estuarine systems. Gulf toadfish (Opsanus beta, family Batrachoididae) are a mid-trophic-level species with a widespread distribution throughout Florida Bay and the broader Gulf of Mexico. Advertising males call in both reproductive and agonistic contexts and exhibit high degrees of nest fidelity. As a result, their calls can be reliably used as an indication of animal presence, as well as behavioral state and reproductive condition. By investigating toadfish call occurrence across three hydrological monitoring stations in Florida Bay, we determined that Gulf toadfish have highest probability of acoustic occurrence at sites with lower salinity (Dunn’s test, p < 0.01), and that Gulf toadfish calling significantly changed with variation in salinity regimes at their nesting locations. For every one unit increase in salinity the probability of toadfish acoustic occurrence decreases by 16% (95% C.I. 7%–23%). This study indicated significant behavioral and acoustic heterogeneity over relatively short distances (7–30 km) within Florida Bay, and demonstrates that local environmental conditions are related to calling behavior in addition to seasonal or broader climatic conditions. Given that a great deal is known about the behavior, physiology, and life history of toadfishes, evidence that Gulf toadfish consistently responded to salinity between and within sites lends strong initial support for the use of Gulf toadfish calling behavior as an acoustic indicator of estuary conditions and environmental change in estuaries targeted for ecological restoration.

Real-time observation of an active debris flow watershed in the Wenchuan Earthquake area

Debris flows occur frequently and cause considerable damage in the Wenchuan Earthquake area; however, there has been no systematic real-time monitoring of such events. This study used a monitoring system to consider the spatial variation of meteorological and flow processes in a 39.4 km2 watershed. The system comprised three rainfall gauges at different locations, and three hydrological monitoring stations, located on the mainstream and tributaries, with instruments to measure the velocity, height, and density of debris flows. Based on the monitoring data, the debris flow events were categorized and the general runoff velocity, discharge, and density were analysed. We proposed empirical methods for estimating the velocity and the total volume of debris flows based on other easily obtainable parameters, e.g., maximum flow depth and duration. Comparison of the results derived using the monitoring data and empirical formulas with those obtained in other small watersheds worldwide revealed the debris flow characteristics in this relatively large watershed. Rainfall threshold for the debris flows was proposed, which is higher than those proposed for the same region for periods shortly after the Wenchuan Earthquake (2008–2013), and the temporal evolution of the rainfall conditions necessary for debris flows was addressed.

Trend analysis of rainfall and runoff in the Jhelum basin of Kashmir Valley

Trend analysis of rainfall and runoff at selected locations of the basin can be good indicator of the flood prediction. In this study, Modified Man Kendall Test (MMKT) was undertaken to observe the existence of significant trends in the rainfall depth of both annual and monsoon periods for a period of 30 years (1980-2009), for six stations in the Jhelum River Basin (JRB) in India (viz. Srinagar, Quazigund, Pahalgam, Gulmarg, Kupwara and Kokernag). Besides this, the maximum and minimum stream flow measured at six hydrologic monitoring stations in JRB (viz. Sangam, Awantipora, Padshibagh, Munshibagh and Baramulla) for a period of 30 years (1980 to 2009) were also subjected to MMKT using a developed interface in MATLAB. It was observed that only Kupawara, Gulmarg and Kokernag stations exhibited significant decreasing trend of annual rainfall depth at 0.05 significance level corroborated by Sen’s slope -12.51, -18.14 and -15.1 respectively. Whereas, the monsoon rainfall depth for only Kupwara showed significant decreasing trend at 0.05 level of significance but the magnitude of decrease was less as indicated by Sen’s slope of -1.94 as compared to the annual rainfall depth. It was also observed that the decreasing trend of annual rainfall is more than that of the monsoon rainfall which indicated low probability of occurrence of flood due to rainfall in the JRB. Moreover, the Awantipora and Munshibagh runoff monitoring stations exhibited decreasing trend in the peak discharge and Baramulla, Awantipora, and Asham showed decreasing trend of minimum discharge at P=0.05. Nonetheless, it was observed that long term trend analysis of rainfall and surface runoff of JRB would serve as a good indicator of flood occurrences under changing climate.

Assessing the impacts of dams and levees on the hydrologic record of the Middle and Lower Mississippi River, USA

The impacts of dams and levees on the long-term (>130 years) discharge record was assessed along a ~1200 km segment of the Mississippi River between St. Louis, Missouri, and Vicksburg, Mississippi. To aid in our evaluation of dam impacts, we used data from the U.S. National Inventory of Dams to calculate the rate of reservoir expansion at five long-term hydrologic monitoring stations along the study segment. We divided the hydrologic record at each station into three periods: (1) a pre-rapid reservoir expansion period; (2) a rapid reservoir expansion period; and (3) a post-rapid reservoir expansion period. We then used three approaches to assess changes in the hydrologic record at each station. Indicators of hydrologic alteration (IHA) and flow duration hydrographs were used to quantify changes in flow conditions between the pre- and post-rapid reservoir expansion periods. Auto-regressive interrupted time series analysis (ARITS) was used to assess trends in maximum annual discharge, mean annual discharge, minimum annual discharge, and standard deviation of daily discharges within a given water year. A one-dimensional HEC-RAS hydraulic model was used to assess the impact of levees on flood flows. Our results revealed that minimum annual discharges and low-flow IHA parameters showed the most significant changes. Additionally, increasing trends in minimum annual discharge during the rapid reservoir expansion period were found at three out of the five hydrologic monitoring stations. These IHA and ARITS results support previous findings consistent with the observation that reservoirs generally have the greatest impacts on low-flow conditions. River segment scale hydraulic modeling revealed levees can modestly increase peak flood discharges, while basin-scale hydrologic modeling assessments by the U.S. Army Corps of Engineers showed that tributary reservoirs reduced peak discharges by a similar magnitude (2 to 30%). This finding suggests that the effects of dams and levees on peak flood discharges are in part offsetting one another along the modeled river segments and likely other substantially leveed segments of the Mississippi River.

Predictive Modeling of Hourly Water-Level Fluctuations Based on the DCT Least-Squares Extended Model

Water-level is one of the critical parameters for a river. It has a close relation to human living & production and socio-economic sustainability development. WLF (water-level fluctuation) evaluation and forecasting for a river is then becoming increasingly important. For water resource planning and management, traditionally, mathematical models are separately developed and designed for sectorial applications. As predictions utilizing more different forecast variables require additional efforts and costs to acquire and predict the variables, advantages of time-series-based or data-driven modeling lie on its conciseness and good performance even higher accuracy. The Fourier-based analysis technology is a classical tool widely used for time-series analysis. However, the Fourier-related approach in its conventional form is not directly applicable to prediction. Addressing hourly WLF prediction from the viewpoint of time-series analysis, a called DCT-LS-extended (“discrete cosine transform (DCT)-based least-squares-extended”) forecast model is presented in this study. The DCT coefficients for the proposed DCT-based forecast modeling are determined in the least-squares sense on the basis of previous hourly WLF observations. Experiments at hydrological monitoring stations in the XiangJiang River of China yield stultifying results. Potentiality of the proposed method is demonstrated by further analysis. The proposed DCT-LS-extended model forecasts hourly WLFs best fitting with less than 12-term DCT coefficients. The proposed method may benefit other applications.

Hydrology of the North Klondike River: carbon export, water balance and inter-annual climate influences within a sub-alpine permafrost catchment

ABSTRACT Arctic and sub-arctic watersheds are undergoing significant changes due to recent climate warming and degrading permafrost, engendering enhanced monitoring of arctic rivers. Smaller catchments provide understanding of discharge, solute flux and groundwater recharge at the process level that contributes to an understanding of how larger arctic watersheds are responding to climate change. The North Klondike River, located in west central Yukon, is a sub-alpine permafrost catchment, which maintains an active hydrological monitoring station with a record of >40 years. In addition to being able to monitor intra-annual variability, this data set allows for more complex analysis of streamflow records. Streamflow data, geochemistry and stable isotope data for 2014 show a groundwater-dominated system, predominantly recharged during periods of snowmelt. Radiocarbon is shown to be a valuable tracer of soil zone recharge processes and carbon sources. Winter groundwater baseflow contributes 20 % of total annual discharge, and accounts for up to 50 % of total river discharge during the spring and summer months. Although total stream discharge remains unchanged, mean annual groundwater baseflow has increased over the 40-year monitoring period. Wavelet analysis reveals a catchment that responds to El Niño and longer solar cycles, as well as climatic shifts such as the Pacific Decadal Oscillation. Dedicated to Professor Peter Fritz on the occasion of his 80th birthday