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Hydrological models serve as essential tools in hydrological research, allowing us to address practical hydrological issues. This study focuses on the Xunhe Watershed in Shandong Province, China, constructing a distributed Xin'anjiang hydrological model. Furthermore, traditional manual calibration and automatic calibration using the Particle Swarm Optimization (PSO) algorithm were employed to determine model parameters, followed by hydrological simulations, with the aim of investigating the applicability of the distributed Xin'anjiang model in this watershed. The research findings indicate that the distributed Xin'anjiang model accurately simulates the hydrological processes in the Xunhe Watershed. There is a high level of agreement between the observed data and the simulated results, including key indicators such as peak discharge, runoff volume, and peak time. After optimizing the model parameters using the PSO algorithm, the distributed Xin'anjiang model demonstrates improved simulation performance in the Xunhe Watershed. During the calibration period, the mean relative peak discharge error (RPE) is 4.1%, the mean relative runoff error (RRE) is 4.34%, and the average Nash–Sutcliffe efficiency (NSE) for simulating the flood events is 0.89. During the validation period, the mean RPE is 3.82%, the mean RRE is 6.1%, and the average NSE for the process is 0.83. This indicates that the distributed Xin'anjiang model has good applicability in this watershed, providing a reliable reference for flood control and disaster reduction in the Xunhe Watershed. [ABSTRACT FROM AUTHOR]

The understanding and identification of peak streamflow events are important for flood risk and water resources management. Precipitation products based on the radar and satellite are an alternative or supplementary product to the areas where ground-based rain gauge station is unavailable, limited in number or sparsely located. The objectives of this study are to (1) assess the performance of TRMM_3B42 product for selected seven flood events by use of statistical and categorical matrices, and identification of elevation-related trends in them and (2) drive the HEC-HMS model by use of bias-corrected TRMM_3B42 data to reproduce the flood hydrograph in Banas River Basin, semi-arid region, Rajasthan, India. The bias-corrected TRMM_3B42 data have improved the overall accuracy of hydrologic simulation as compared to ground-based rain gauge station data. The HEC-HMS results revealed that bias-corrected TRMM_3B42 data can simulate the streamflow with less than 30% deviation in the prediction of peak discharge and flood volume and show an NSE and R2 value of 0.90 and 0.95, respectively. The bias-corrected TRMM_3B42 data are found to be excellent to reproduce the flood hydrograph at a daily temporal scale. [ABSTRACT FROM AUTHOR]

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Fine-temporal resolution rainfall is a prerequisite for flood risk-related studies. In recent years, satellite precipitation products (SPPs) have gained some attention in the hydro-meteorology field, but the application of these products for hydrological studies is still uncertain. This study discusses the methodology for temporal disaggregation of daily rain gauge (DRG) data using SPP for improved accuracy in hydrologic simulation. The objectives of this research article are (i) to temporally dis-aggregate the DRG data using 3-h SPP TRMM_3B42RT; (ii) to suggest the best approach in terms of hydrologic simulation capabilities using the compound factor (CF) technique. The hydrologic simulation in HEC-HMS was performed for various temporally disaggregated rain gauge (TDRG) data and results were analyzed using 10 efficiency criteria. The best approach was selected using the CF technique. The lowest CF of 2.4 reveals that peak discharge can be computed with less than 8% deviation using the TDRG data, which utilizes the least error tile/grid/pixel from rain gauge rainfall value out of four nearby tile/grid/pixel. This study concluded that flood volume can be computed with less than 5% deviation using DRG data but it failed to reproduce the flood peak. This approach has overcome the insufficiency of sub-daily rainfall observation. HIGHLIGHTS Evaluation of SPP TRMM_3B42RT for a major flood event using statistical measurements like RB (%), CC, and RMSE (mm/day).; Temporal disaggregation of the DRG data using 3-h SPP TRMM_3B42RT.; Identification of the best approach in terms of hydrologic simulation by using 10 efficiency criteria and the use of the CF technique.;

Study region: The Tonghui River–a treated effluent-affected urban river located in Beijing, China. Study focus: Inspired by the signal processing theory, this study presented a simulation scheme for the treated effluent-affected river based on hydrologic monitoring, pattern recognization, pattern extraction, and hydrologic/hydraulic modelling. It aimed to precisely depict the river flow patterns when detailed wastewater treatment plant effluent data was absent and to fill in the gap of the application of signal-based hydrological time series processing methods in physically based hydraulic simulation. New hydrological insights for the region: Diurnal and semidiurnal patterns caused by the wastewater treatment plant (WWTP) effluent were recognized from the water level series using the continuous wavelet transform. Due to their small amplitudes, they were masked during flood events but dominated the flow regime in dry seasons. Based on the discrete wavelet decomposition and Fourier series fitting, these periodical patterns were extracted and fitted. With a preliminarily calibrated hydraulic model and a linear signal amplifier, a simulated WWTP effluent was retrieved. Dry seasons simulation utilizing the simulated effluent obtained significantly better performance than using the average effluent data from the aspects of conventional evaluation metrics, cross-wavelet transform, and wavelet coherence.

The hydrological process in the dry–warm valley of the mountainous area of southwest China has unique characteristics and has attracted scientific attention worldwide. Given that this is an area with fragile ecosystems and intensive water resource conflicts in the upper reaches of the Yangtze River, a systematic identification of its hydrological responses to climate and land use variations needs to be performed. In this study, MIKE SHE was employed and calibrated for the Anning River Basin in the dry–warm valley. Subsequently, a deep learning neural network model of the long short-term memory (LSTM) and a traditional multi-model ensemble mean (MMEM) method were used for an ensemble of 31 global climate models (GCMs) for climate projection. The cellular automata–Markov model was implemented to project the spatial pattern of land use considering climatic, social, and economic conditions. Four sets of climate projections and three sets of land use projections were generated and fed into the MIKE SHE to project hydrologic responses from 2021 to 2050. For the calibration and first validation periods of the daily simulation, the coefficients of determination (R) were 0.85 and 0.87 and the Nash–Sutcliffe efficiency values were 0.72 and 0.73, respectively. The advanced LSTM performed better than the traditional MMEM method for daily temperature and monthly precipitation. The average monthly temperature projection under representative concentration pathway 8.5 (RCP8.5) was expected to be slightly higher than that under RCP4.5; this is contrary to the average monthly precipitation from June to October. The variations in streamflow and actual evapotranspiration (ET) were both more sensitive to climate change than to land use change. There was no significant relationship between the variations in streamflow and the ET in the study area. This work could provide general variation conditions and a range of hydrologic responses to complex and changing environments, thereby assisting with stochastic uncertainty and optimizing water resource management in critical regions.

Flood risk management studies require reliable estimates of extreme precipitation at high spatial–temporal distribution to force hydrologic models. Recently, Remote Sensing Rainfall Products (RRPs) have gained significant importance in the field of hydrometeorology, but their applicability in urban hydrologic predictions remains uncertain. The current study evaluates the accuracy of RRPs in comparison with observed rainfall and the significance of space–time representation of rain in simulating single and bimodal flood hydrographs. The study is conducted for the Adyar river basin, a rapidly developing urban area in Chennai experiencing frequent floods. Sub-daily rainfall retrievals from Integrated Multi-satellite Retrievals for Global Precipitation Measurement version 6 final run product (IMERG GPM), the near-real-time Global Satellite Mapping of Precipitation (GSMaP_NRT) version 6, GSMaP gauge adjusted (GSMaP_Gauge), Precipitation Dynamic Infrared Rain Rate near real-time (PDIR-Now) and Doppler Weather Radar (DWR) are the Remote sensing Rainfall products (RRPs) selected in the present study. Continuous and categorical statistical indices are selected to evaluate the performance of satellite rainfall estimates for the period from 2001 to 2015. Then the hydrologic utility of RRPs is conducted using the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model for five extreme precipitation events. The RRPs simulated the rising and recession portion of flood hydrographs accurately with a bias in peak discharge. Then, two approaches are selected to further improve the flood hydrograph simulations; 1) Hydrologic model simulations after disaggregating the daily station data to sub-daily scale using time characteristics of RRPs, 2) Hydrologic simulations after bias adjusting the RRPs with station data. The study finds substantial improvements in model results in the two approaches. The disaggregation approach using satellite rainfall estimates has overcome the insufficiency of sub-daily rainfall observations. The bias adjusted radar rainfall data is found as best performing for the flood hydrograph simulations. [ABSTRACT FROM AUTHOR]

Assessments of water and energy security over historical and future periods require hydrologic models that can accurately simulate reservoir operations. However, scare reservoir operation data limits the accuracy of current reservoir representations in simulating reservoir behaviors. Furthermore, the reliability of these representations under changing inflow regimes remains unclear, which makes their application for long future planning horizons questionable. To this end, we propose a synergistic framework to predict the release, storage, and hydropower production of ungauged reservoirs (i.e., reservoirs without in‐situ inflow, release, storage, and operating rules) by combining remotely sensed reservoir operating patterns and model‐simulated reservoir inflow with conceptual reservoir operation schemes within a land surface‐hydrologic model. A previously developed reservoir operation scheme is extended with a storage anomaly based calibration approach to accommodate the relatively short time series and large time intervals of remotely sensed data. By setting up controlled experiments in the Yalong River Basin in China, we show that remote sensing can improve the parameter estimation and simulations of ungauged reservoirs for all selected reservoir operation schemes, thereby improving the downstream flood and streamflow simulations. However, most of these schemes show degraded accuracies of reservoir operation simulations under a changing inflow regime, which could lead to unreliable assessments of future water resources and hydropower production. In comparison, our newly extended reservoir operation scheme can be more adaptable to flow regime variations. Our study provides a practical framework for reservoir impact assessments and predictions with the ongoing satellite altimetry projects such as Surface Water and Ocean Topography. Key Points: Satellite remote sensing can improve the representation of ungauged reservoirs and streamflow simulations in hydrologic modelsA reservoir operation scheme for ungauged reservoirs is extended and tailored to the use of remotely sensed reservoir operation dataReservoir operation schemes with storage‐based model structures can be more reliable in reservoir simulations under a changing flow regime [ABSTRACT FROM AUTHOR]

This study developed an ecological-water-demand-based interval chance–constrained system eco-compensation model (EW-ICCEC) for eco-compensation planning and ecological restoration. Interval chance–constrained programming, the Soil and Water Assessment Tool, ensurance of ecological water demand, and inexact modeling are integrated in a general framework for eco-compensation. The EW-ICCEC model was applied to a case of China's Daguhe watershed. Results show that the ecological-water-demand-based system eco-compensation mechanism can achieve maximum system benefits and minimum system loss in medium- and long-term eco-compensation planning. The contributions of the study include that (1) a systematic watershed ecological compensation model is established; and (2) the optimal mechanism and a scheme of the eco-compensation bill and land-use transformation are obtained for the planning of eco-compensation. [ABSTRACT FROM AUTHOR]

Post-disaster flood risk assessment is extremely difficult owing to the great uncertainties involved in all parts of the assessment exercise, e.g., the uncertainty of hydrologic–hydraulic models and depth–damage curves. In the present study, a robust and fast data-driven tool for residential flood risk assessment is introduced. The proposed tool can be used by scientists, practitioners and/or stakeholders as a first step for better understanding and quantifying flood risk in monetary terms. Another contribution of the present study is the fitting of an equation through depth–damage points provided by the Joint Research Center (JRC). The approach is based on hydrologic simulations for different return periods, employing a free and widely used software, HEC-HMS. Moreover, flood depths for the study area are estimated based on hydrodynamic simulations employing the HEC-RAS software and the Inverse Distance Weighting (IDW) interpolation method. Finally, flood risk, in monetary terms, is determined based on the flood depths derived by the coupling of hydrodynamic simulations and the IDW method, depth–damage curves reported in the literature, vulnerability of residential areas and the residential exposure derived by employing GIS tools. The proposed tool is applied in a highly urbanized and flood-prone area, Mandra city, in the Attica region of Greece. The results are maps of flood depths and flood risk maps for specific return periods. Overall, the results derived from the application of the proposed approach reveal that the tool can be highly effective for post-disaster flood risk management. However, it must be noted that additional information and post-disaster data are needed for the verification of the damages from floods. Additional information can result in better calibration, validation and overall performance of the proposed flood risk assessment tool. [ABSTRACT FROM AUTHOR]

Satellite remote sensing precipitation products with high temporal–spatial resolution and large area coverage have great potential in hydrometeorological research. This paper analyzes the performance of four satellite products from 2000 to 2008 in the Yarlung Zangbo River Basin, namely the Tropical Rainfall Measuring Mission (TRMM), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Climate Prediction Center morphing method (CMORPH). The four products are evaluated from three aspects: spatial distribution, temporal characteristics, and hydrological simulation. The results show that: (1) the four products exhibit similar annual and daily precipitation patterns, with the highest daily precipitation accuracy concentrated in the center, followed by the east and west; (2) TRMM, CHIRPS, and CMORPH exhibit the largest positive bias for monthly precipitation estimation in December, while PERSIANN shows the largest positive bias in July. All products overestimate the precipitation of 0.1–5 mm/d, and underestimate the precipitation above 5 mm/d, especially for PERSIANN; (3) certain Products tend to perform better than others at elevations of 3000–4000 m and in relatively humid zones. TRMM shows relatively stable performance for various elevation and climate zones; (4) for hydrological model validation, TRMM has the best performance during the calibration period, although it is inferior to CHIRPS during the validation period. Overall, TRMM has the highest applicability in the Yarlung Zangbo River Basin; however, its impact on the uncertainty of hydrological modeling needs to be further studied. [ABSTRACT FROM AUTHOR]

ABSTRACT The upper Teles Pires River basin is located in the Brazilian agriculture frontier in the north of Mato Grosso state and has experienced significant changes in land use and cover, which can cause major changes in its hydrological dynamics. Climatic and hydrologic data are scarce in the region, which poses uncertainties in the decision-making process aiming at the sustainable management of water resources in this strategic area. The aim of this study was to evaluate the performance of the Large-Scale Distributed Hydrological Model (MGB-IPH) to assess water availability of the upper Teles Pires basin and support water resource management in the Amazon-Cerrado ecotone. The MGB-IPH model was calibrated and validated using data from three streamflow stations available in the basin. In order to verify the model performance, the Nash-Sutcliffe (NS) and the PBIAS statistical parameters were applied. Our results show that, by using the MGB-IPH model with generally available data, the maximum and minimum flow regimes can be successfully assessed in the upper Teles Pires basin. The continuity curves of daily flow simulated by the model showed a good fit with the observed flow. Overall, the results demonstrated the applicability of the MGB-IPH model for water resource assessment and management in the basin.

Post-disaster flood risk assessment is extremely difficult owing to the great uncertainties involved in all parts of the assessment exercise, e.g., the uncertainty of hydrologic–hydraulic models and depth–damage curves. In the present study, a robust and fast data-driven tool for residential flood risk assessment is introduced. The proposed tool can be used by scientists, practitioners and/or stakeholders as a first step for better understanding and quantifying flood risk in monetary terms. Another contribution of the present study is the fitting of an equation through depth–damage points provided by the Joint Research Center (JRC). The approach is based on hydrologic simulations for different return periods, employing a free and widely used software, HEC-HMS. Moreover, flood depths for the study area are estimated based on hydrodynamic simulations employing the HEC-RAS software and the Inverse Distance Weighting (IDW) interpolation method. Finally, flood risk, in monetary terms, is determined based on the flood depths derived by the coupling of hydrodynamic simulations and the IDW method, depth–damage curves reported in the literature, vulnerability of residential areas and the residential exposure derived by employing GIS tools. The proposed tool is applied in a highly urbanized and flood-prone area, Mandra city, in the Attica region of Greece. The results are maps of flood depths and flood risk maps for specific return periods. Overall, the results derived from the application of the proposed approach reveal that the tool can be highly effective for post-disaster flood risk management. However, it must be noted that additional information and post-disaster data are needed for the verification of the damages from floods. Additional information can result in better calibration, validation and overall performance of the proposed flood risk assessment tool.

Satellite remote sensing precipitation products with high temporal–spatial resolution and large area coverage have great potential in hydrometeorological research. This paper analyzes the performance of four satellite products from 2000 to 2008 in the Yarlung Zangbo River Basin, namely the Tropical Rainfall Measuring Mission (TRMM), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Climate Prediction Center morphing method (CMORPH). The four products are evaluated from three aspects: spatial distribution, temporal characteristics, and hydrological simulation. The results show that: (1) the four products exhibit similar annual and daily precipitation patterns, with the highest daily precipitation accuracy concentrated in the center, followed by the east and west; (2) TRMM, CHIRPS, and CMORPH exhibit the largest positive bias for monthly precipitation estimation in December, while PERSIANN shows the largest positive bias in July. All products overestimate the precipitation of 0.1–5 mm/d, and underestimate the precipitation above 5 mm/d, especially for PERSIANN; (3) certain Products tend to perform better than others at elevations of 3000–4000 m and in relatively humid zones. TRMM shows relatively stable performance for various elevation and climate zones; (4) for hydrological model validation, TRMM has the best performance during the calibration period, although it is inferior to CHIRPS during the validation period. Overall, TRMM has the highest applicability in the Yarlung Zangbo River Basin; however, its impact on the uncertainty of hydrological modeling needs to be further studied.

The challenge of estimating design flood magnitude under climate change has led to the development of multiple approaches to long‐term flood projection: stationarity, informed‐parameter (composed of both trend informed and climate informed), and hydrologic simulation. This is the first study to compare these approaches across a large set of hydro‐climatologically diverse basins located throughout the contiguous United States, grouped into distinct clusters based on catchment and flood characteristics. The comparison is achieved using a split‐sample test conducted over the historic period; the climate (specifically, precipitation) informed and hydrologic simulation (specifically, Sacramento Soil Moisture Accounting model) approaches are forced with observations and downscaled general circulation model (GCM) simulations. The results provide a quantitative perspective on key long‐term flood projection issues. The precipitation informed approach can be informative for projections of regional change; the hydrologic simulation approach can be informative for direction of change for a single basin. When forced with GCM outputs, precipitation informed is both more accurate and more precise than hydrologic simulation, after accounting for model error. For hydrologic simulation, forcing with GCMs will add positive bias for basins in the Rocky Mountains and Southwest and will add negative bias with large uncertainty across GCMs for basins in the lower Midwest, the South, and the mid‐Atlantic seaboard. The results illustrate the need for continued improvement in long‐term flood projection approaches and for design paradigms incorporating uncertainty. Key Points: Long‐term flood projection approaches are compared across 233 U.S. river basins using split‐sample tests over the historic periodPrecipitation informed is useful for regional change projections; hydrologic simulation is useful for direction of change in a single basinGeneral circulation model outputs as forcing introduces less additional bias for precipitation informed compared to hydrologic simulation [ABSTRACT FROM AUTHOR]

ABSTRACT Hydrological models (HMs) can be applied for different purposes, and a key step is model calibration using objective functions (OF) to quantify the agreement between observed and calculated discharges. Fully understanding the OF is important to properly take advantage of model calibration and interpret the results. This study evaluates 36 OF proposed in the literature, considering two watersheds of different hydrological regimes. Daily simulated streamflow time-series, using a distributed hydrological model (MGB-IPH), and ten daily streamflow synthetic time-series, generated from the observed and calculated streamflows, were used in the analysis of each watershed. These synthetic data were used to evaluate how does each metric evaluate hypothetical cases that present isolated very well known error behaviors. Despite of all NSE-derived (Nash-Sutcliffe efficiency) metrics that use the square of the residuals in their formulation have shown higher sensitivity to errors in high flows, the ones that use daily and monthly averages of flow rates in absolute terms were more stringent than the others to assess HMs performance. Low flow errors were better evaluated by metrics that use the flow logarithm. The constant presence of zero flow rates deteriorate them significantly, with the exception of the metrics TRMSE (Transformed root mean square error) did not demonstrate this problem. An observed limitation of the formulations of some metrics was that the errors of overestimation or underestimation are compensated. Our results reassert that each metric should be interpreted specifically thinking about the aspects it has been proposed for, and simultaneously taking into account a set of metrics would lead to a broader evaluation of HM ability (e.g. multiobjective model evaluation). We recommend that the use of synthetic time series as those proposed in this work could be useful as an auxiliary step towards better understanding the evaluation of a calibrated hydrological model for each study case, taking into account model capabilities and observed hydrologic regime characteristics.

Hydrologic modeling of semi-arid watersheds is imperative for the development of appropriate water and soil conservation plans. In the current study, the efficiency of Kinematic Runoff and Erosion model-version 2 (K2) model was used to evaluate water discharge and sediment load simulation of Bar watershed, located in the north-eastern part of Iran. The K2 model relies on the kinematic wave approach to route surface flow. The drainage network and planes are discretized to represent the watershed. In order to evaluate the model, 3 and 2 reported rainfall incidents in various dates were selected for K2 calibration and validation, respectively. The multiplier approach was employed for model calibration. The results of sensitivity investigation revealed that the soil parameters Ks-CH, n and G had the highest impact on flow discharge. Through the calibration process, the Nash-Sutcliff Efficiency and the coefficient of determination as fitting metrics for water discharge simulation (based on event #2, dated 16 March 1992) were estimated to be 0.78 and 0.88, respectively. According to the aggregated measure, the highest K2 efficiency was obtained during the calibration process based on event #2. Other storm events were resulted in a good simulation, as well. During the validation process, K2 simulation (based on event #4, dated 07 March 1991) led to the Nash-Sutcliffe Efficiency and R2 of 0.77 and 0.71, respectively. The K2 calibration for sediment load simulation was performed through the alterations of the Pave and Rainsplash parameters. The bias percentages between simulated and observed total sediment loads based on events #2 and #4 were 5% and 16%, respectively. Conclusively, the K2 model showed an acceptable robustness in the hydrological simulation of Bar watershed as a representative semi-arid watershed in northeast of Iran.

The research features how parallel computing can advance hydrological performances associated with different calibration schemes (SCOs). The result shows that parallel computing can save up to 90% execution time, while achieving 81% simulation improvement. Basic statistics, including (1) index of agreement (D), (2) coefficient of determination (R2 ), (3) root mean square error (RMSE), and (4) percentage of bias (PBIAS) are used to evaluate simulation performances after model calibration in computer parallelism. Once the best calibration scheme is selected, additional efforts are made to improve model performances at the selected calibration target points, while the Rescaled Adjusted Partial Sums (RAPS) is used to evaluate the trend in annual streamflow. The qualitative result of reducing execution time by 86% on average indicates that parallel computing is another avenue to advance hydrologic simulations in the urban-rural interface, such as the Boise River Watershed, Idaho. Therefore, this research will provide useful insights for hydrologists to design and set up their own hydrological modeling exercises using the cost-effective parallel computing described in this case study. [ABSTRACT FROM AUTHOR]

Highlights • Precipitation (P)-Temperature (T) cross-correlation structure impacts hydrologic simulations. • Simulation of surface/sub-surface variables is improved by reproducing P-T cross-correlation. • Multivariate pre-processing approaches are essential for climate change impact studies. Abstract Uncertainty in climate forcings causes significant uncertainty in estimating streamflow and other land-surface fluxes in hydrologic model simulations. Earlier studies primarily analyzed the importance of reproducing cross-correlation between precipitation and temperature (P-T cross-correlation) using various downscaling and weather generator schemes, leaving out how such biased estimates of P-T cross-correlation impact streamflow simulation and other hydrologic variables. The current study investigates the impacts of biased P-T cross-correlation on hydrologic variables using a fully coupled hydrologic model (Penn-state Integrated Hydrologic Model, PIHM). For this purpose, a synthetic weather generator was developed to generate multiple realizations of daily climate forcings for a specified P-T cross-correlation. Then, we analyzed how reproducing/neglecting P-T cross-correlation in climate forcings affect the accuracy of a hydrologic simulation. A total of 50 synthetic data sets of daily climate forcings with different P-T cross-correlation were forced into to estimate streamflow, soil moisture, and groundwater level under humid (Haw River basin in NC, USA) and arid (Lower Verde River basin in AZ, USA) hydroclimate settings. Results show that climate forcings reproducing the P-T cross-correlation yield lesser root mean square errors in simulated hydrologic variables (primarily on the sub-surface variables) as compared to climate forcings that neglect the P-T cross-correlation. Impacts of P-T cross-correlation on hydrologic simulations were remarkable to low flow and sub-surface variables whereas less significant to flow variables that exhibit higher variability. We found that hydrologic variables with lower internal variability (for example: groundwater and soil-moisture depth) are susceptible to the bias in P-T cross-correlation. These findings have potential implications in using univariate linear downscaling techniques to bias-correct GCM forcings, since univariate linear bias-correction techniques reproduce the GCM estimated P-T cross-correlation without correcting the bias in P-T cross-correlation. [ABSTRACT FROM AUTHOR]

To evaluate the accuracy and applicability of the TMPA 3B42-V7 precipitation product for the Lancang River basin, we used different statistical indices to explore the performance of the product in comparison to gauge data. Then, we performed a hydrologic simulation using the Variable Infiltration Capacity (VIC) hydrological model with two scenarios (Scenario I: streamflow simulation using gaugecalibrated parameters; Scenario II: streamflow simulation using 3B42-V7-recalibrated parameters) to verify the applicability of the product. The results of the precipitation analysis show good accuracy of the V7 precipitation data. The accuracy increases with the increase of both space and time scales, while time scale increases cause a stronger effect. The satellite can accurately measure most of the precipitation but tends to misidentify non-precipitation events as light precipitation events (<1 mm/day). The results of the hydrologic simulation show that the VIC hydrological model has good applicability for the Lancang River basin. However, 3B42-V7 data did not perform as well under Scenario I with the lowest Nash-Sutcliffe coefficient of efficiency (NSCE) of 0.42; Scenario II suggests that the error drops significantly and the NSCE increases to 0.70 or beyond. In addition, the simulation accuracy increases with increased temporal scale. [ABSTRACT FROM AUTHOR]

The objective of this study was to calibrate and validate the IPH II model for the estimation of daily streamflows in a subbasin of the Pomba river and check your accuracy in simulating extreme events, in order to obtain information for planning and management of water resources, in addition to the prediction and mitigation of flood events in basin. The Sub-basin selected had the measuring station of streamflow "Guarani" as control section, which drains an area of 1,650 km2, located in the State of Minas Gerais. Precipitation and reference evapotranspiration data, required as entry data on IPH II model, were obtained through Thiessen and Hargreaves-Samani methods, respectively. The calibration of model was performed automatically using the Shuffled Complex Evolution (SCE-UA) algorithm which made possible the estimation of model parameters quickly and efficiently. The results obtained with the use of the IPH II model showed that the estimates of daily streamflows were appropriate and good, including the maximum and minimum annual daily and also the minimum for grants, which leads to the conclusion that the model has the potential to be used in the management of water resources, in forecast of flood events and mitigation of their effects as well as for consistency analysis and filling in gaps in the data flow. [ABSTRACT FROM AUTHOR]

Extreme flood estimates for dam safety are routinely obtained from hydrologic simulations driven by selected design storms. The temporal structure of such design storms can be obtained from Rainfall Mass Curves (RMCs), which are adimensionalized curves of the cumulative precipitation depth as a function of event duration. This paper assesses for the first time the spatialand temporal variability of observed RMCs for Switzerland, an Alpine region with complex topography. The relevance of the detected RMC variability for extreme flood estimation is illustrated based on an application to a high elevation catchment, the Mattmark dam catchment in the Swiss Alps. The obtained results underline that quantile RCMs represent a simple yet powerful tool to construct design storms for dam safety verification and that regional, seasonal and event-duration effects on RMCs are small enough to justify the use of a unique set of Swiss-wide quantile RMCs. The presented analysis could be refined in the future by explicitly accounting for orographic, convective or frontal precipitation events. [ABSTRACT FROM AUTHOR]

In recent years, global reanalysis weather data has been widely used in hydrological modeling around the world, but the results of simulations vary greatly. To consider the applicability of Climate Forecast System Reanalysis (CFSR) data in the hydrologic simulation of watersheds, the Bahe River Basin was used as a case study. Two types of weather data (conventional weather data and CFSR weather data) were considered to establish a Soil and Water Assessment Tool (SWAT) model, which was used to simulate runoff from 2001 to 2012 in the basin at annual and monthly scales. The effect of both datasets on the simulation was assessed using regression analysis, Nash-Sutcliffe Efficiency ( NSE), and Percent Bias ( PBIAS). A CFSR weather data correction method was proposed. The main results were as follows. (1) The CFSR climate data was applicable for hydrologic simulation in the Bahe River Basin ( R of the simulated results above 0.50, NSE above 0.33, and | PBIAS| below 14.8. Although the quality of the CFSR weather data is not perfect, it achieved a satisfactory hydrological simulation after rainfall data correction. (2) The simulated streamflow using the CFSR data was higher than the observed streamflow, which was likely because the estimation of daily rainfall data by CFSR weather data resulted in more rainy days and stronger rainfall intensity than was actually observed. Therefore, the data simulated a higher base flow and flood peak discharge in terms of the water balance, except for some individual years. (3) The relation between the CFSR rainfall data ( x) and the observed rainfall data ( y) could be represented by a power exponent equation: y=1.4789 x ( R =0.98, P<0.001). There was a slight variation between the fitted equations for each station. The equation provides a theoretical basis for the correction of CFSR rainfall data. [ABSTRACT FROM AUTHOR]

Macropore flow carries water from the soil surface to deeper profile or groundwater, bypassing the intermediate soil profile. The phenomenon is ubiquitous and not rare. A theoretical framework of this flow has not been perfected so far, but ignoring this process may lead to incomplete conceptualization of soil-water flow. The macropore flow has been modelled based on observed data on morphometry, macropore size distribution and fractal dimensions of soil voids and stain patterns, and incorporated in the Watershed Processes Simulation (WAPROS) model. The performance of WAPROS model was evaluated to be good (NSE – hourly; daily = 0.8578; 0.9020), when applied to a real watershed. The sensitivity of macropore flow submodel showed that the adjustment factor was linearly related to macropore flow. Simulations were performed for five types of soil, namely sandy loam, sandy clay loam, sandy clay, clay loam and silty clay loam (A, B, C, D and E respectively). The values of macroporosity factors and fractal dimensions generated for the five types of soil have been presented. The model generated data for A, B, C, D and E soil types were: the number of macropores: 379, 3074, 3412, 153 and 0; the macropore flow (mm): 1.5121, 9.3667, 15.1728, 4.4055 and 0; the average pore flow (mm/pore): 0.0040, 0.0030, 0.0044, 0.0287 and 0; and the macropore flow to base flow ratio: 0.0055, 0.0474, 0.1908, 0.2759 and 0. The modelling methodology gives encouraging results. The model can be updated as and when better equations are made available. [ABSTRACT FROM AUTHOR]

To better understand the Agricultural Policy Environmental Extender (APEX) model's strengths and limitations in simulating streamflow and water quality, we evaluated its performance under a diverse range of climatic, topographic, soil, cover, and land management conditions using three parameter settings: best professional judgment (BPJ), partially calibrated, and fully calibrated. A total of 18 calibration parameters governing streamflow, crop yield, and sediment, N, and P were adjusted. Hydrologic and water quality responses were simulated on ten USDA-ARS watersheds with heterogeneous forested, pasture/range, and/or corn-soybean cropping systems. Based on percent bias and Nash-Sutcliffe coefficient of efficiency test statistics, 0%, 0%, 15%, and 9% of the data sets were considered satisfactory or better in simulating monthly streamflow, sediment, N, and P, respectively, in the BPJ mode. However, when used with the fully calibrated settings, 73%, 43%, 38%, and 27% of the data sets were satisfactory or better. Findings indicate that when fully calibrated, APEX estimates streamflow very well, sediment and nitrogen moderately well, and phosphorus marginally well at a monthly time scale. However, when using the BPJ approach, APEX does not estimate watershed-level streamflow, sediment, or nutrients accurately at a monthly time step. Additionally, APEX lacks an element of robustness in simulating streamflow and water quality constituents when applied to the same watershed with a different period of record or to a nearby watershed with a similar period of record. Based on these findings, it is recommended that users exercise caution when employing APEX with the BPJ approach or in validation mode at watershed scales. [ABSTRACT FROM AUTHOR]

Este artigo apresenta uma ferramenta para análise de sistemas complexos de recursos hídricos denominada AcquaNetXL, que foi desenvolvida no formato de planilha eletrônica, na qual foram incorporados dois modelos de otimização, um linear e outro não linear. Essa ferramenta mantém os conceitos e os atributos de um sistema de suporte a decisão, ou seja, possui as características de estreitar a comunicação entre o usuário e o computador, de facilitar o entendimento, a formulação do problema, a interpretação dos resultados de análise e auxiliar no processo de tomada de decisão, tornando-o mais ordenado, objetivo e transparente. O desempenho do algoritmo utilizado na solução dos problemas de alocação de água foi satisfatório, especialmente para o modelo linear.This article presents a tool for the allocation analysis of complex systems of water resources, called AcquaNetXL, developed in the form of spreadsheet in which a model of linear optimization and another nonlinear were incorporated. The AcquaNetXL keeps the concepts and attributes of a decision support system. In other words, it straightens out the communication between the user and the computer, facilitates the understanding and the formulation of the problem, the interpretation of the results and it also gives a support in the process of decision making, turning it into a clear and organized process. The performance of the algorithms used for solving the problems of water allocation was satisfactory especially for the linear model.

Neste estudo, a experimentação e a modelagem hidrológica de eventos de precipitação transformada em escoamento superficial em sistemas naturais de floresta são investigadas, contemplando os seguintes tratamentos: construção de algoritmo computacional iterativo de otimização para a identificação do hidrograma unitário médio (função de transferência chuva-descarga) da bacia e para o cálculo das precipitações efetivas adequadas à cada evento chuva-escoamento; aplicação da metodologia à bacia hidrográfica do igarapé Bolívia, situada no interior da Reserva Florestal Adolpho Ducke; análise e interpretação do algoritmo criado, enfocando a sua convergência e o significado dos resultados desse processo de otimização; e comparação com outros estudos em pequenas bacias da Amazônia Central. O período de medidas de chuva e de vazões estende-se de julho de 2004 a setembro de 2006, incluindo a fase de preparação da curva-chave do igarapé Bolívia e a formação de um conjunto amplo e diversificado de eventos de chuva e de escoamento no curso d'água.In this study, experimentation and hydrologic modeling of rainfall events transformed into runoff in natural forest systems, are investigated, including the following treatments: construction of an iterative optimization computational algorithm to identify the average unity hydrograph (rain-discharge transfer function) of the watershed, and to compute the effective rainfalls adequate to each discharge-rainfall event; applying the methodology to the Igarapé Bolívia watershed, which is located inside the Adolpho Ducke Forest Reserve; analysis and interpretation of the established algorithm, focusing on its convergence and on the significance of the results of this optimization process; and comparison with other studies in small watersheds of Central Amazonia. Rainfall and runoff measurements were made from July 2004 to September 2006, including the preparation phase of the level-discharge curve of the Igarapé Bolívia and the making of a large and diversified set of rainfall and runoff events.

Summary Satellite–gauge quantitative precipitation estimate (QPE) products may reduce the errors in near real-time satellite precipitation estimates by combining rain gauge data, which provides great potential to hydrometeorological applications. This study aims to comprehensively evaluate four of the latest satellite–gauge QPEs, including NASA’s Tropical Rainfall Measuring Mission (TRMM) 3B42V7 product, NOAA’s Climate Prediction Center (CPC) MORPHing technique (CMORPH) bias-corrected product (CMORPH CRT), CMORPH satellite–gauge merged product (CMORPH BLD) and CMORPH satellite–gauge merged product developed at the National Meteorological Information Center (NMIC) of the China Meteorological Administration (CMA) (CMORPH CMA). These four satellite–gauge QPEs are statistically evaluated over the Huaihe River basin during 2003–2012 and applied into the distributed Variable Infiltration Capacity (VIC) model to assess hydrologic utilities. Compared to the China Gauge-based Daily Precipitation Analysis (CGDPA) newly developed at CMA/NMIC, the four satellite–gauge QPEs generally depict the spatial distribution well, with the underestimation in the southern mountains and overestimation in the northern plain of the Huaihe River basin. Specifically, both TRMM and CMORPH CRT adopt simple gauge adjustment algorithms and exhibit relatively poor performance, with evidently deteriorated quality in winter. In contrast, the probability density function-optimal interpolation (PDF-OI) gauge adjustment procedure has been applied in CMORPH BLD and CMORPH CMA, resulting in higher quality and more stable performance. CMORPH CMA further benefits from a merged dense gauge observation network and outperforms the other QPEs with significant improvements in rainfall amount and spatial/temporal distributions. Due to the insufficient gauge observations in the merging process, CMORPH BLD features the similar error characteristics of CMORPH CRT with a positive bias of light precipitation and a negative bias of heavy precipitation, in contrast to the overall large overestimation by TRMM. The quality of QPEs directly impacts streamflow simulations, as the precipitation biases are propagated into simulated streamflow through interaction with hydrologic processes. The general streamflow pattern is well captured at multiple time scales by the simulations using the four satellite–gauge QPEs as the input forcing. CMORPH CRT shows the worst simulations in both long-term streamflow and extreme flood events, while CMORPH CMA forced streamflow simulations even outperform that forced by CGDPA. CMORPH CMA is able to reproduce the July 2003 flood event, while the other three QPEs fail to generate such extreme flood. Overall, CMORPH CMA shows great potential to improve the precipitation distribution and hydrometeorological simulations, and can serve as an alternative high quality QPE in China. [ABSTRACT FROM AUTHOR]

This paper presents the design and integration of a GIS-based data model for the regional hydrologic simulation in the Meijiang watershed, China. Hydrologic systems (HS) require integration of data and models simulating different processes. Here, an object-oriented approach using Unified Modeling Language (UML) is introduced, which supports the development of GIS-based Geodatabase model-GeoHydro/DataBase (GH/DB). Spatial data, such as basins, stream network, and observation stations are stored in the feature classes. The time series and their attributes are included in the tables. Relationship classes are used to link associated objects. The new development within the scientific program OpenGeoSys (OGS) is the integration of GH/DB into the numerical simulations. The graphical user interface is implemented for the pre- and post-processing of the simulation. As for the case study, a regional hydrologic model is developed in the Meijiang watershed area for the understanding of water infiltration from surface into groundwater via soil layer with various time scales. The integration of databases and modeling tool represents the comprehensive hydrosystems and thus it is a useful tool to understand the different processes and interactions between the related hydrological compartments. [ABSTRACT FROM AUTHOR]

Suitable selection of hydrological modeling tools and techniques for specific hydrological study is an essential step. Currently, hydrological simulation studies are relied on various physically based, conceptual and data driven models. Though data driven model such as Adoptive Neuro Fuzzy Inference System (ANFIS) has been successfully applied for hydrologic modeling ranging from small watershed scale to large river basin scale, its performance against physically based model has yet to be evaluated to ensure that ANFIS are as capable as any physically based model for simulation study. This study was conducted in Chickasaw Creek watershed, which is located in Mobile County of South Alabama. Since adequate rain gauge stations were not available near the watershed proximity, and also the study area was affected with the El Niño Southern Oscillation (ENSO), the sea surface temperature (SST) and sea level pressure (SLP) were additionally incorporated in the ANFIS model. The research concluded that ANFIS model performance was equally comparable to a physically based watershed model, Loading Simulation Program C ++ (LSPC), especially when rain gauge stations were not adequate. Additionally, the research concludes that ANFIS model performance was equally comparable to that of LSPC no matter whether SST and SLP in ANFIS input vector was included or not. [ABSTRACT FROM AUTHOR]

Hydrologic modeling requires the handling of a wide range of highly nonlinear processes from the scale of a hill slope to the continental scale, and thus the computational efficiency of the model becomes a critical issue for water resource management. This work is aimed at implementing and evaluating a flexible parallel computing framework for hydrologic simulations by applying OpenMP in the HydroGeoSphere (HGS) model. HGS is a 3D control-volume finite element model that solves the nonlinear coupled equations describing surface–subsurface water flow, solute migration and energy transport. The computing efficiency of HGS is improved by three parallel computing schemes: 1) parallelization of Jacobian matrix assembly, 2) multi-block node reordering for performing LU solve efficiently, and 3) parameter privatization for reducing memory access latency. Regarding to the accuracy and consistency of the simulation solutions obtained with parallel computing, differences in the solutions are entirely due to use of a finite linear solver iteration tolerance, which produces slightly different solutions which satisfy the convergence tolerance. The maximum difference in the head solution between the serial and parallel simulations is less than 10 −3 m, using typical convergence tolerances. Using the parallel schemes developed in this work, three key achievements can be summarized: (1) parallelization of a physically-based hydrologic simulator can be performed in a manner that allows the same code to be executed on various shared memory platforms with minimal maintenance; (2) a general, flexible and robust parallel iterative sparse-matrix solver can be implemented in a wide range of numerical models employing either structured or unstructured mesh; and (3) the methodology is flexible, especially for the efficient construction of the coefficient and Jacobian matrices, compared to other parallelized hydrologic models which use parallel library packages. [ABSTRACT FROM AUTHOR]

In this study, a robust simulation-optimization modeling system (RSOMS) is developed for supporting agricultural nonpoint source (NPS) effluent trading planning. The RSOMS can enhance effluent trading through incorporation of a distributed simulation model and an optimization model within its framework. The modeling system not only can handle uncertainties expressed as probability density functions and interval values but also deal with the variability of the second-stage costs that are above the expected level as well as capture the notion of risk under high-variability situations. A case study is conducted for mitigating agricultural NPS pollution with an effluent trading program in Xiangxi watershed. Compared with non-trading policy, trading scheme can successfully mitigate agricultural NPS pollution with an increased system benefit. Through trading scheme, [213.7, 288.8] × 10 kg of TN and [11.8, 30.2] × 10 kg of TP emissions from cropped area can be cut down during the planning horizon. The results can help identify desired effluent trading schemes for water quality management with the tradeoff between the system benefit and reliability being balanced and risk aversion being considered. [ABSTRACT FROM AUTHOR]

A model for the design of detention basin systems is presented that interfaces a simulated annealing (SA) optimization procedure with the U.S. Army Corps of Engineer's Hydrologic Engineering Center - Hydrologic Modeling System (HEC-HMS). The optimization model is based upon the simulated annealing method to optimize the size and location of detention basin system including the outlet structures subject to design constraints. The program is implemented in Visual Basic for Applications (VBA) interfacing the simulated annealing model with the HEC-HMS model using an MS Excel environment. The respective result files are created by using a VBA executed Python script to extract the appropriate data from the HEC-HMS project DSS file after each simulation performed for the SA procedure. Example applications include a single detention basin system and multiple detention basin systems considering two scenarios. Though the implementation requires considerable computational effort with respect to the number of hydrologic simulations, simulated annealing proves to be an effective tool in the optimal design of detention basin systems as compared to traditional standards of practice. [ABSTRACT FROM AUTHOR]

Abstract: The assessment of hydrologic responses to climate change is required in watershed management and planning to protect water resources and environmental quality. This study is designed to evaluate and enhance watershed modeling approach in characterizing climate change impacts on water supply and ecosystem stressors. Soil and Water Assessment Tool (SWAT) was selected as a base model, and improved for the CO2 dependence of potential evapotranspiration and stream temperature prediction. The updated model was applied to quantify the impacts of projected 21st century climate change in the northern Coastal Ranges and western Sierra Nevada, which are important water source areas and aquatic habitats of California. Evapotranspiration response to CO2 concentration varied with vegetation type. For the forest-dominated watersheds in this study, only moderate (1–3%) reductions on evapotranspiration were predicted by solely elevating CO2 concentration under emission scenarios A2 and B1. Modeling results suggested increases in annual average stream temperature proportional to the projected increases in air temperature. Although no temporal trend was confirmed for annual precipitation in California, increases of precipitation and streamflow during winter months and decreases in summers were predicted. Decreased streamflow during summertime, together with the higher projected air temperature in summer than in winter, would increase stream temperature during those months and result in unfavorable conditions for cold-water species. Compared to the present-day conditions, 30–60 more days per year were predicted with average stream temperature >20°C during 2090s. Overall, the hydrologic cycle and water quality of headwater drainage basins of California, especially their seasonality, are very sensitive to projected climate change. [Copyright &y& Elsevier]

Summary: This study presents a comprehensive investigation of the State Soil Geographic Database (STATSGO) and the Soil Survey Geographic Database (SSURGO) soil databases for their applications in hydrologic modeling practices, and provides detailed instructions on soil data aggregation. Two types of soil data aggregations are developed and improved for the preparation of soil input data: (1) spatially-based aggregation for hydrologic models which require one representative soil profile for each spatial units of modeling simulations; and (2) taxonomy-based aggregation to handle the potential edge-matching issues in SSURGO, i.e. the artificial split of a soil type by the boundary of soil survey areas. The developed approach and program were applied at the spatial scales of soil survey area and watershed in California, U.S. representing hydrologic simulation domains with areas at the magnitudes of 1000km2 and 10,000km2, respectively. Edge-matching issues were identified for more than 20% of the involved soil map-units for both cases, about 90% among which were handled by the proposed approach in this study. The naming inconsistency in soil taxonomy was recognized as one of the causes for remaining issues. The reductions of soil map-units and components numbers before and after the aggregation are less than 10%, indicating that the proposed procedure has the capability to effectively handle the edge-matching issues while maintaining spatial resolution of the soil data. [ABSTRACT FROM AUTHOR]

Abstract: The hydrologic regime of the Lake Winnipeg watershed (LWW), Canada, is dominated by spring snowmelt runoff, often occurring over frozen ground. Analyses of regional climate models (RCMs) based on future climate projections presented in a companion paper of this special issue (Dibike et al., 2012) show future increases in annual precipitation and temperature in various seasons and regions of this catchment. Such changes are expected to influence the volume of snow accumulation and melt, as well as the timing and intensity of runoff. This paper presents results of modelling climate-induced hydrologic changes in two representative sub-catchments of the Red and Assiniboine basins in the LWW. The hydrologic model, Soil and Water Assessment Tool (SWAT), was employed to simulate a 21-year baseline (1980–2000) and future (2042–2062) climate based on climate forcings derived from 3 RCMs. The effects of future changes in climatic variables, specifically precipitation and temperature, are clearly evident in the resulting snowmelt and runoff regimes. The most significant changes include higher total runoff, and earlier snowmelt and discharge peaks. Some of the results also revealed increases in peak discharge intensities. Such changes will have significant implications for water availability and nutrient transport regimes in the LWW. [Copyright &y& Elsevier]

Abstract: Phase 2 of the Distributed Model Intercomparison Project (DMIP 2) was formulated primarily as a mechanism to help guide the US National Weather Service (NWS) as it expands its use of spatially distributed watershed models for operational river, flash flood, and water resources forecasting. The overall purpose of DMIP 2 was to test many distributed models with operational quality data with a view towards meeting NWS operational forecasting needs. At the same time, DMIP 2 was formulated as an experiment that could be leveraged by the broader scientific community as a platform for testing, evaluating, and improving the science of spatially distributed models. This paper presents the key results of the DMIP 2 experiments conducted for the Oklahoma region, which included comparison of lumped and distributed model simulations generated with uncalibrated and calibrated parameters, water balance tests, routing and soil moisture tests, and simulations at interior locations. Simulations from 14 independent groups and 16 models are analyzed. As in DMIP 1, the participant simulations were evaluated against observed hourly streamflow data and compared with simulations generated by the NWS operational lumped model. A wide range of statistical measures are used to evaluate model performance on both run-period and event basis. A noteworthy improvement in DMIP 2 was the combined use of two lumped models to form the benchmark for event improvement statistics, where improvement was measured in terms of runoff volume, peak flow, and peak timing for between 20 and 40 events in each basin. Results indicate that in general, those spatially distributed models that are calibrated to perform well for basin outlet simulations also, in general, perform well at interior points whose drainage areas cover a wide range of scales. Two of the models were able to provide reasonable estimates of soil moisture versus depth over a wide geographic domain and through a period containing two severe droughts. In several parent and interior basins, a few uncalibrated spatially distributed models were able to achieve better goodness-of-fit statistics than other calibrated distributed models, highlighting the strength of those model structures combined with their a priori parameters. In general, calibration solely at basin outlets alone was not able to greatly improve relative model performance beyond that established by using uncalibrated a priori parameters. Further, results from the experiment for returning DMIP 1 participants reinforce the need for stationary data for model calibration: in some cases, the improvements gained by distributed models compared to lumped were not realized when the models were calibrated using inconsistent precipitation data from DMIP 1. Event-average improvement of distributed models over the combined lumped benchmark was measured in terms of runoff volume, peak flow, and peak timing for between 20 and 40 events. The percentage of model-basin pairs having positive distributed model improvement at basin outlets and interior points was 18%, 24%, and 28% respectively, for these quantities. These values correspond to 14%, 33%, and 22% respectively, in DMIP 1. While there may not seem to be much gain compared to DMIP 1 results, the DMIP 2 values were based on more precipitation–runoff events, more model-basin combinations (148 versus 51), more interior ungauged points (9 versus 3), and a benchmark comprised of two lumped model simulations. In addition, we propose a set of statistical measures that can be used to guide the calibration of distributed and lumped models for operational forecasting. [Copyright &y& Elsevier]

The impact of climate change on floods and droughts in Huaihe River Basin is studied using a coupled land-surface hydrology model and continuous wavelet transform technique. Observed temperature in the basin has increased by approximately 0.228 °C per decade since 1951. Observed precipitation and simulated and observed streamflows are used to grade flood and drought events. Two composite grading indices derived from the three series using different weight values are defined for reducing uncertainties caused by errors of observation and simulation and the effect of human activity on observed streamflow. The frequency of flood and drought events is quantified using the Morlet wavelet transform and compared with the trend of average temperature to test for any relationship between climate change and flood/drought frequency. This study shows that flood and drought events have occurred more frequently since the 1980s. The trend of flood and drought events is positively related to climate warming with a coefficient of determination of 0.88 in the Huaihe River Basin. [ABSTRACT FROM AUTHOR]

The potential effects of long-term urbanization and climate change on the freshwater resources of the Flint River basin were examined by using the Precipitation-Runoff Modeling System (PRMS). PRMS is a deterministic, distributed-parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land cover on streamflow and multiple intermediate hydrologic states. Precipitation and temperature output from five general circulation models (GCMs) using one current and three future climate-change scenarios were statistically downscaled for input into PRMS. Projections of urbanization through 2050 derived for the Flint River basin by the Forecasting Scenarios of Future Land-Cover (FORE-SCE) land-cover change model were also used as input to PRMS. Comparison of the central tendency of streamflow simulated based on the three climate-change scenarios showed a slight decrease in overall streamflow relative to simulations under current conditions, mostly caused by decreases in the surface-runoff and groundwater components. The addition of information about forecasted urbanization of land surfaces to the hydrologic simulation mitigated the decreases in streamflow, mainly by increasing surface runoff. [ABSTRACT FROM AUTHOR]

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Climate change is causing shifts in seasonal weather patterns and variation in seasonal time scales in India. Factors including uneven distribution of water, faulty agricultural practices and water policies, low prices of farm products, and debt are leading farmers to commit suicide in Umarkhed Taluka of the Yavatmal District. This study aimed to develop a sustainable solution to water scarcity in the surrounding watershed by introducing optimization modeling in reservoir operation. Past studies have conducted different hydrologic analyses to address the water scarcity issue in this region. However, none of the studies incorporated optimization in their models. This study developed an integrated hydrologic and optimization model that can predict the daily reservoir releases for climate change scenarios from 2020 to 2069 based upon Representative Concentration Pathway (RCP-4.5 and RCP-8.5) climate change scenarios from 2020 to 2069. The integrated simulations were able to deliver around 19% more water than the historical discharge at the most downstream station of the Wardha Watershed. The simulated approaches store less water than the actual unoptimized scenario and deliver water when there is a need at the downstream locations. Finally, because the downstream locations of the Wardha Watershed receive more water, a localized storage system can be developed and a transfer method can be utilized to deliver sufficient water to the Umarkhed Taluka. [ABSTRACT FROM AUTHOR]