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During July and August of 2022, the Yangtze River Basin (YRB) experienced its most extreme high temperature (EHT) event since 1979, resulting in large numbers of human casualties and severe economic losses. This paper reveals the spatial and temporal features of the EHT over the YRB (YRB-EHT) in 2022 and disentangles its extreme nature from a historical perspective. Results showed the following: 1) The record-breaking YRB-EHT was directly caused by the adiabatic heating associated with an anomalous barotropic high pressure (or heat dome) and descending motion in the region. The intensified and westward-shifted western North Pacific subtropical high and eastward-extended South Asian high played critical roles in the formation of the heat dome and descending motion anomaly. 2) Convection anomalies over the tropical Atlantic and Pacific induced by the reintensified La Niña–like Pacific sea surface temperature anomaly pattern, along with the strong positive North Atlantic Oscillation (NAO), were the key contributing factors to the formation of the barotropic high pressure anomaly and YRB-EHT. 3) A physics-based empirical simulation model constructed using the factors of the NAO and tropical convection successfully reproduced the historical year-to-year variation of YRB temperatures, as well as the extreme in 2022, implying that the unprecedented 2022 YRB-EHT had universal dynamic origins. This study highlights the importance of the combined impacts of tropical and extratropical forcings in the record-breaking YRB-EHT in 2022, and thus may provide useful clues for seasonal predictions of summer mean or extreme temperatures in the YRB. Significance Statement: Extreme high temperatures hit the Yangtze River Basin (YRB) in summer 2022, causing large numbers of human casualties and severe economic losses. Here, from a historical perspective, we disentangle the dynamic origins of these high temperatures over the YRB. Based on evidence from both observations and model simulations, the present study highlights the importance of the combined impacts of tropical and extratropical routes of influence on these extremely high temperatures in the YRB, and thus may provide useful clues for seasonal predictions of YRB summer mean or extreme temperatures. [ABSTRACT FROM AUTHOR]

Harmful algae and cyanobacteria blooms are increasing in frequency and intensity in freshwater systems due to anthropogenic impacts such as nutrient loading in watersheds and engineered alterations of natural waterways. There are multiple physical factors that affect the conditions in a freshwater system that contribute to optimal habitats for harmful algae and toxin-producing cyanobacteria. A growing body of research shows that climate change stressors also are impacting water-body conditions that favor harmful algae and cyanobacteria species over other phytoplankton. The overgrowth of these organisms, or a "bloom," increases the opportunity for exposure to toxins by humans, companion animals, livestock, and wildlife. As waters warm and precipitation patterns change over time, exposure to these blooms is projected to increase. Hence, it is important that states and tribes develop monitoring and reporting strategies as well as align governmental policies to protect their citizens and ecosystems within their jurisdiction. Currently, the policies and approaches taken to monitor and report on harmful algae and cyanobacteria blooms vary widely among states, and it is undetermined if any tribes have specific policies on harmful algae blooms. This paper synthesizes research on algal blooms in inland freshwater systems of the United States. This review examines how climate change contributes to trends in bloom frequency or severity and outlines approaches that states and tribes may use to monitor, report, and respond to harmful algae and cyanobacteria blooms. Significance Statement: Inland bodies of freshwater supply drinking water for humans and animals, water for irrigating crops, habitats for aquatic species, places of cultural significance for Indigenous peoples, and other important functions. Many of these bodies of water have been polluted with runoff from industry, including agriculture, and already support harmful algal blooms during warm conditions. Hot extremes associated with climate change are expected to increase the occurrence and duration of harmful algal blooms, and in some places, initiate blooms where none have been recorded previously. These toxic blooms are harmful to people, companion animals, livestock, and wildlife. It is important to review the interconnections among biological, climate, and water systems to monitor blooms and alert the public about their toxin production. [ABSTRACT FROM AUTHOR]

This paper examines the controls on supercooled liquid water content (SLWC) and drop number concentrations (Nt,CDP) over the Payette River basin during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE) campaign. During SNOWIE, 27.4% of 1-Hz in situ cloud droplet probe samples were in an environment containing supercooled liquid water (SLW). The interquartile range of SLWC, when present, was found to be 0.02-0.18 g m23 and 13.3-37.2 cm23 for Nt,CDP, with the most extreme values reaching 0.40-1.75 g m23 and 150-320 cm23 in isolated regions of convection and strong shear-induced turbulence. SLWC and Nt,CDP distributions are shown to be directly related to cloud-top temperature and ice particle concentrations, consistent with past research over other mountain ranges. Two classes of vertical motions were analyzed as potential controls on SLWC and Nt,CDP, the first forced by the orography and fixed in space relative to the topography (stationary waves) and the second transient, triggered by vertical shear and instability within passing synoptic-scale cyclones. SLWC occurrence and magnitudes, and Nt,CDP associated with fixed updrafts were found to be normally distributed about ridgelines when SLW was present. SLW was more likely to form at low altitudes near the terrain slope associated with fixed waves due to higher mixing ratios and larger vertical air parcel displacements at low altitudes. When considering transient updrafts, SLWC and Nt,CDP appear more uniformly distributed over the flight track with little discernable terrain dependence as a result of time and spatially varying updrafts associated with passing weather systems. The implications for cloud seeding over the basin are discussed. [ABSTRACT FROM AUTHOR]

Precipitation (P) gauge undercatch (PUC) is an important source of error when using observed meteorological datasets for hydrologic modeling studies in regions with cold and windy winters. Preliminary simulations using the Variable Infiltration Capacity (VIC) hydrological model forced with different meteorological datasets showed significant underprediction of simulated streamflow throughout the domain. A new hybrid gridded meteorological dataset at 1/16° resolution based on observed station data was assembled over the U.S. Midwest and Great Lakes region from 1915 to 2021 at a daily time step. Correction of primary station data using existing techniques is generally difficult or infeasible in the United States due to missing station metadata and lack of local wind speed (WS) measurements. We developed and tested several different postprocessing adjustment techniques using regridded WS obtained from the NCEP–NCAR reanalysis. The most effective approach corrected rain or mixed P using WS alone, and P as snow using a regressed snow-to-P ratio from a group of wind-shielded reference stations (to account for different and generally unknown snow measurement techniques). The PUC-corrected gridded products were validated against high-quality shielded stations and corrected Global Historical Climatology Network stations with in situ WS, showing good overall agreement. Observed monthly streamflow at 40 river basins was also compared to hydrologic model simulations forced by datasets with and without PUC corrections. The best PUC-corrected dataset produced improvements in streamflow simulations in at least 80% of the streamflow locations for three validation metrics (r2, Nash–Sutcliff efficiency, bias in the mean), demonstrating its value for hydrometeorological studies in the greater Midwest region. Significance Statement: Many applications in hydrology require in situ precipitation (P) measurements, which are known to have a systematic low bias due to the effects of wind, also known as precipitation undercatch (PUC). Addressing PUC is problematic in the United States due to limited access to detailed station metadata (SMD) and local wind speed (WS) measurements. In this paper we develop a set of procedures to create gridded precipitation datasets for the U.S. Midwest region that incorporate corrections for PUC without needing either (i) detailed SMD or (ii) local WS measurements. Among other tests, results in 40 test basins throughout the Midwest show substantial improvements in simulated streamflow in 32 out of 40 basins when PUC corrections are included in meteorological driving datasets. [ABSTRACT FROM AUTHOR]

The performance of first-moment and full-distribution bias-correction methods of monthly temperature distributions for seasonal prediction is analyzed by comparing two approaches: the standard all-in-data procedure and the 6-hourly stratification of data. Five models are applied to remove the systematic errors of the CFSv2 forecasts of temperature for the rainy season in the Ethiopian Blue Nile River basin domain. Using deterministic evaluation measures, it is found that the stratification marginally increases the forecast skill especially in regions where the data distribution of temperature is prominently multimodal. The improvement may be attributed to a split of the mixed distribution into a set of unimodal distributions. A necessary condition for this splitting into unimodal distributions is that the amplitude of the diurnal cycle be larger than the interannual variability in the sample. The maximum improvement of stratification is achieved by the first-moment correction model. Significance Statement: This paper evaluates bias-correction methods of monthly forecast distributions of temperature to improve seasonal forecast skill. It is found that marginal skill is gained when bias correction of the diurnal cycle is performed. This paper contributes to the discussion on the value of subdaily model output data. [ABSTRACT FROM AUTHOR]

Dominant hydrological processes of a catchment could shift due to a changing climate. This climate-induced hydrological nonstationarity could affect the reliability of future runoff projection developed using a hydrological model calibrated for the historical period as the model or parameters may no longer be suitable under a different future hydroclimate. This paper explores whether competing parameterization approaches proposed to account for hydrological nonstationarity could improve the robustness of future runoff projection compared to the traditional approach where the model is calibrated targeting overall model performance over the entire historical period. The modeling experiments are carried out using climate and streamflow datasets from southeastern Australia, which has experienced a long drought and exhibited noticeable hydrological nonstationarity. The results show that robust multicriteria calibration based on the Pareto front can provide a more consistent model performance over contrasting hydroclimate conditions, but at a slight expense of increased bias over the entire historical period compared to the traditional approach. However, the robust calibration does not necessarily result in a more reliable projection of future runoff. This is because the systematic bias in any parameterization approach would propagate from the historical period to the future period and would largely be cancelled out when estimating the relative runoff change. Ensemble simulations combining results from different parameterization considerations could produce a more inclusive range of future runoff projection as it covers the uncertainties due to model parameterization. [ABSTRACT FROM AUTHOR]

In Part II, two classes of vertical motions, fixed (associated with vertically propagating gravity waves tied to flow over topography) and transient (associated primarily with vertical wind shear and conditional instability within passing weather systems), were diagnosed over the Payette River basin of Idaho during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE). This paper compares vertical motions retrieved from airborne Doppler radial velocity measurements with those from a 900-m-resolution model simulation to determine the impact of transient vertical motions on trajectories of ice particles initiated by airborne cloud seeding. An orographic forcing index, developed to compare vertical motion fields retrieved from the radar with the model, showed that fixed vertical motions were well resolved by the model while transient vertical motions were not. Particle trajectories were calculated for 75 cross-sectional pairs, each differing only by the observed and modeled vertical motion field. Wind fields and particle terminal velocities were otherwise identical in both trajectories so that the impact of transient vertical circulations on particle trajectories could be isolated. In 66.7% of flight-leg pairs, the distance traveled by particles in the model and observations differed by less than 5 km with transient features having minimal impact. In 9.3% of the pairs, model and observation trajectories landed within the ideal target seeding elevation range (>2000 m), whereas, in 77.3% of the pairs, both trajectories landed below the ideal target elevation. Particles in the observations and model descended into valleys on the mountains' lee sides in 94.2% of cases in which particles traveled less than 37 km. [ABSTRACT FROM AUTHOR]

An adequate representation of the interaction between the land surface and the atmosphere is critical for both numerical weather prediction and climate models. The surface energy and mass balances are tightly coupled to the terrestrial water cycle, mainly through the state of soil moisture. An inadequate representation of the terrestrial water cycle will deteriorate the state of the land surface model and introduce biases to the atmospheric model. The validation of land surface models is challenging, as there are very few observations and the soil is highly heterogeneous. In this paper, a validation framework for land surface schemes based on catchment mass balances is presented. The main focus of our development lies in the application to kilometer-resolution numerical weather prediction and climate models, although the approach is scalable in both space and time. The methodology combines information from multiple observation-based datasets. Observational uncertainties are estimated by using independent sets of observations. It is shown that the combination of observation-based datasets and river discharge measurements close the water balance fairly well for the chosen catchments. As a showcase application, the framework is then applied to compare and validate four different versions of TERRA ML, the land surface scheme of the COSMO numerical weather prediction and climate model over five mesoscale catchments in Switzerland ranging from 105 to 1713 km². Despite large observational uncertainties, validation results clearly suggest that errors in terrestrial storage changes are closely linked to errors in runoff generation and emphasize the crucial role of infiltration processes. [ABSTRACT FROM AUTHOR]

Model calibration has always been one major challenge in the hydrological community. Flood scaling properties (FS) are often used to estimate the flood quantiles for data-scarce catchments based on the statistical relationship between flood peak and contributing areas. This paper investigates the potential of applying FS and multivariate flood scaling properties [multiple linear regression (MLR)] as constraints in model calibration. Based on the assumption that the scaling property of flood exists in four study catchments in northern China, eight calibration scenarios are designed with adopting different combinations of traditional indicators and FS or MLR as objective functions. The performance of the proposed method is verified by employing a distributed hydrological model, namely, the Soil and Water Assessment Tool (SWAT) model. The results indicate that reasonable performance could be obtained in FS with fewer requirements of observed streamflow data, exhibiting better simulation of flood peaks than the Nash-Sutcliffe efficiency coefficient calibration scenario. The observed streamflow data or regional flood information are required in the MLR calibration scenario to identify the dominant catchment descriptors, and MLR achieves better performance on catchment interior points, especially for the events with uneven distribution of rainfall. On account of the improved performance on hydrographs and flood frequency curve at the watershed outlet, adopting the statistical indicators and flood scaling property simultaneously as model constraints is suggested. The proposed methodology enhances the physical connection of flood peak among sub-basins and considers watershed actual conditions and climatic characteristics for each flood event, facilitating a new calibration approach for both gauged catchments and data-scarce catchments. [ABSTRACT FROM AUTHOR]

Obtaining high-quality quantitative precipitation forecasts is a key precondition for hydrological forecast systems. Due to multisource uncertainties (e.g., initial conditions, model structures, and parameters), raw forecasts are subject to systematic biases; hence, statistical postprocessing is often required to reduce these errors before the forecasts can proceed to hydrological applications. Machine learning (ML) algorithms are canonical statistical models, and they are diverse in type and variation. It is important to verify and compare their performance in the same scenario (e.g., precipitation postprocessing). In this paper, we conduct a large-scale comparison study for the major ML models with diverse model structures and regularization strategies as postprocessors for improving the quality of precipitation forecasts. Specifically, we compare the efficiency and effectiveness of 21 ML algorithms on solving this task. Daily reforecast precipitation with lead times up to 8 days from the Global Ensemble Forecast System and corresponding observations are employed to determine the usability of different models in the Yalong River basin in China. The performance of each model is validated by a group of carefully designed experiments and statistical metrics. The results reveal that improvements in model structures are more effective than regularization strategies. Among these algorithms, the optimized extra-trees regressor exhibits the best performance, effectively reducing overestimation and achieving the best skill in forecasting precipitation. Eleven ensemble members and a 3-day forward-rolling time window can be used as predictors to obtain the best model performance. The systematic experiments and findings also offer useful guidelines for other related studies. [ABSTRACT FROM AUTHOR]

Cascade reservoirs were designed for the Yalong River basin, which ranks as the third-largest hydropower base in China. Reservoir impoundment has certain impacts on local climate, but few researches focus on this field. This paper integrates spatiotemporal analysis techniques and geostatistical methods to identify the reservoir projected meteorological indices (MIs) variations as well as the impact scope under different circumstances. Results show that the reservoir projected variations of moisture indexes were much more significant than temperatures. Anthropogenic disturbance has led to a dramatic decrease of relative humidity in the past decade, far beyond its periodic amplitude range. Moreover, dry valleys faced more serious drought risk, but reservoir impoundment of this region alleviated the local drought risk. The daily minimum temperature, relative to the maximum temperature, was more sensitive to catchment changes with an earlier appearance of temperature zone variation. Since impounding, the MIs' internal relationships in nondry valleys varied significantly more than that in dry valleys, with a positive correlation of 0.7 between the relative humidity and precipitation weakening greatly. The severe warming hotspots are distributed in the upper reach and have a probability of 0.95-1 to exceed the 1.5°C IPCC control target. This study provides references for the disturbance of reservoir impoundment to local climate at multiple temporal--spatial scales under different circumstances and contributes to the MIs variation pattern identification and quantitative risk assessment. [ABSTRACT FROM AUTHOR]

Multiyear climate predictions provide climate outlooks from years to a decade in advance. As multiyear temperature predictions become more mainstream and skillful, guidance is needed to assist practitioners who wish to explore this maturing field. This paper demonstrates the process and considerations of incorporating multiyear temperature predictions into water resources planning. Multiyear temperature predictions from the Community Earth System Model Decadal Prediction Large Ensemble are presented as discrete and probabilistic products and are used to force two common hydrologic modeling approaches: conceptual and empirical. The approaches are demonstrated to simulate streamflow in the upper Colorado River basin watershed in Colorado, where diagnostics show that increasing temperatures are associated with decreasing streamflows. Using temperature information for lead years 2-6, two analyses are performed: (i) a retrospective hindcast for the climatological period (1981-2010) and (ii) a blind forecast for 2011-15. For the retrospective hindcast, including temperature information improved the percent error as compared with climatology. For the blind forecast, the multiyear temperature prediction for warming was skillful, but the corresponding multiyear average streamflow predictions from both approaches were counterintuitive: with the predicted warming, the multiyear average streamflow was predicted to be lower than the climatological mean; however, the observed multiyear average streamflow was higher than the climatological mean. This was due to above-average precipitation during the prediction time frame, particularly for one of the years. With that year removed, the multiyear streamflow average became lower than the climatological mean. Temperature provides a marginal source of streamflow predictability, but there will be substantial uncertainty until prediction skill for year-to-year climate variability, especially for precipitation, increases. [ABSTRACT FROM AUTHOR]

This paper introduces a new, two-part scheme for postprocessing single-valued precipitation forecast to create probabilistic quantitative precipitation forecast (PQPF). This scheme, herein referred to as the mixed-type nonhomogeneous regression (MNHR), combines the use of logistic regression for estimating rainfall intermittency and nonhomogeneous regression for estimation of additional parameters of the conditional distribution. The performance of MNHR is evaluated relative to operational mixed-type meta-Gaussian distribution (MMGD) and the censored, shifted gamma distribution (CSGD) in postprocessing Global Ensemble Forecast System (GEFS) reforecasts averaged over 25 watersheds in the American River basin in California. The results point to superior performance of MNHR relative to MMGD and CSGD in terms of the skill of postprocessed PQPFs at 24- and 96-h accumulation windows. In addition, it is observed that the performance of CSGD tends to trail behindMNHRandMMGDat least for the 24-h window, though the performance differences tend to narrow at higher forecast amounts and longer lead times. Our analyses suggest that CSGD's underperformance arises partly from its tendency to inflate the shift parameter estimates, which is pronounced over the study site possibly because of infrequent rainfall occurrence. By contrast,MNHR's use of logistic regression helps avoid such bias, and its formulation of conditional distribution addresses the lack of skewness of MMGD for higher forecast amounts. Moreover, MHNR-based PQPF exhibits both superior calibration and relatively high sharpness at short lead times and on an unconditional sense, whereas it features lower sharpness relative to the other two suites when conditioned on higher forecast amount. This trade-off between calibration and conditional sharpness warrants further research. [ABSTRACT FROM AUTHOR]

Initial abstraction (Ia) is a sensitive parameter in hydrological models, and its value directly determines the amount of runoff. Ia, which is influenced by many factors related to antecedent watershed condition (AWC), is difficult to estimate due to lack of observed data. In the Soil Conservation Service curve number (SCS-CN) method, it is often assumed that Ia is 0.2 times the potential maximum retention S. Yet this assumption has frequently been questioned. In this paper, Ia/S and factors potentially influencing Ia were collected from rainfall–runoff events. Soil moisture and evaporation data were extracted from GLDAS-Noah datasets to represent AWC. Based on the driving factors of Ia, identified using the Pearson correlation coefficient and maximal information coefficient, artificial neural network (ANN)-estimated Ia was applied to simulate the selected flood events in the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The results indicated that Ia/S varies over different events and different watersheds. Over 75% of the Ia/S values are less than 0.2 in the two study areas. The driving factors affecting Ia vary over different watersheds, and the antecedent precipitation index appears to be the most influential factor. Flood simulation by the HEC-HMS model using statistical Ia gives the best fitness, whereas applying ANN-estimated Ia outperforms the simulation with median Ia/S. For over 60% of the flood events, ANN-estimated Ia provided better fitness in flood peak and depth, with an average Nash–Sutcliffe efficiency coefficient of 0.76 compared to 0.71 for median Ia/S. The proposed ANN-estimated Ia is physically based and can be applied without calibration, saving time in constructing hydrological models. [ABSTRACT FROM AUTHOR]

The time at which climate change signal can be clearly distinguished from noise is known as time of emergence (ToE) and is typically detected by a general circulation model (GCM) signal-to-noise ratio exceeding a certain threshold. ToE is commonly estimated at large scales from GCMs, although management decisions and adaptation strategies are implemented locally. This paper proposes a methodology to estimate ToE for both precipitation and temperature at local scales (i.e., river basin). The methodology considers local climatic conditions and unbiased GCM projections to estimate ToE by using the statistical power to find when the climate significantly differs from the historical one. The method suggests that ToE for temperature already occurred in three Chilean basins (Limarí, Maipo, and Maule). However, in terms of precipitation, an earlier ToE is clearly identified for the Maule basin, indicating that risk assessment and adaptation measures should be implemented first in this basin. [ABSTRACT FROM AUTHOR]

Forecasting the timing and magnitude of snowmelt and runoff is critical to managing mountain water resources. Warming temperatures are increasing the rain–snow transition elevation and are limiting the forecasting skill of statistical models relating historical snow water equivalent to streamflow. While physically based methods are available, they require accurate estimations of the spatial and temporal distribution of meteorological variables in complex terrain. Across many mountainous areas, measurements of precipitation and other meteorological variables are limited to a few reference stations and are not adequate to resolve the complex interactions between topography and atmospheric flow. In this paper, we evaluate the ability of the Weather Research and Forecasting (WRF) Model to approximate the inputs required for a physics-based snow model, iSnobal, instead of using meteorological measurements, for the Boise River Basin (BRB) in Idaho, United States. An iSnobal simulation using station data from 40 locations in and around the BRB resulted in an average root-mean-square error (RMSE) of 4.5 mm compared with 12 SNOTEL measurements. Applying WRF forcings alone was associated with an RMSE of 10.5 mm, while including a simple bias correction to the WRF outputs of temperature and precipitation reduced the RMSE to 6.5 mm. The results highlight the utility of using WRF outputs as input to snowmelt models, as all required input variables are spatiotemporally complete. This will have important benefits in areas with sparse measurement networks and will aid snowmelt and runoff forecasting in mountainous basins. [ABSTRACT FROM AUTHOR]

This article presents the data collected and analyzed using the University of Iowa's X-band polarimetric (XPOL) radars that were part of the spring 2013 hydrology-oriented Iowa Flood Studies (IFloodS) field campaign, sponsored by NASA's Global Precipitation Measurement (GPM) Ground Validation (GV) program. The four mobile radars have full scanning capabilities that provide quantitative estimation of the rainfall at high temporal and spatial resolutions over experimental watersheds. IFloodS was the first extensive test of the XPOL radars, and the XPOL radars demonstrated their field worthiness during this campaign with 46 days of nearly uninterrupted, remotely monitored, and controlled operations. This paper presents detailed postcampaign analyses of the high-resolution, research-quality data that the XPOL radars collected. The XPOL dual-polarimetric products and rainfall are compared with data from other instruments for selected diverse meteorological events at high spatiotemporal resolutions from unprecedentedly unique and vast data generated during IFloodS operations. The XPOL data exhibit a detailed, complex structure of precipitation viewed at multiple range resolutions (75 and 30 m). The inter-XPOL comparisons within an overlapping scanned domain demonstrate consistency across different XPOL units. The XPOLs employed a series of heterogeneous scans and obtained estimates of the meteorological echoes up to a range oversampling of 7.5 m. A finer-resolution (30 m) algorithm is described to correct the polarimetric estimates for attenuation at the X band and obtain agreement of attenuation-corrected products with disdrometers and NASA S-band polarimetric (NPOL) radar. The paper includes hardware characterization of Iowa XPOL radars conducted prior to the deployment in IFloodS following the GPM calibration protocol. [ABSTRACT FROM AUTHOR]

The hydrologic response of different climate-change emission scenarios for the twenty-first century were evaluated in 14 basins from different hydroclimatic regions across the United States using the Precipitation-Runoff Modeling System (PRMS), a process-based, distributed-parameter watershed model. This study involves four major steps: 1) setup and calibration of the PRMS model in 14 basins across the United States by local U.S. Geological Survey personnel; 2) statistical downscaling of the World Climate Research Programme''s Coupled Model Intercomparison Project phase 3 climate-change emission scenarios to create PRMS input files that reflect these emission scenarios; 3) run PRMS for the climate-change emission scenarios for the 14 basins; and 4) evaluation of the PRMS output. This paper presents an overview of this project, details of the methodology, results from the 14 basin simulations, and interpretation of these results. A key finding is that the hydrological response of the different geographical regions of the United States to potential climate change may be very different, depending on the dominant physical processes of that particular region. Also considered is the tremendous amount of uncertainty present in the climate emission scenarios and how this uncertainty propagates through the hydrologic simulations. This paper concludes with a discussion of the lessons learned and potential for future work. [ABSTRACT FROM AUTHOR]

The large-scale equatorial circulation known as the Madden–Julian oscillation (MJO) has been shown to impact tropical cyclone activity in several basins around the globe. In this paper, the author utilizes an MJO index created by Wheeler and Hendon to examine its impacts on tropical genesis and intensification in the Atlantic. Large differences in frequency and intensity of tropical cyclone activity are seen, both in the tropical Atlantic as well as in the northwest Caribbean and Gulf of Mexico depending on the MJO phase. Coherent changes in upper- and lower-level winds and relative humidity are likely responsible for these differences. Since the MJO shows potential predictability out to about two weeks, the relationships discussed in this paper may be useful for short-term predictions of the probability of tropical cyclone activity in the Atlantic as a complement to the already available longer-term seasonal predictions. [ABSTRACT FROM AUTHOR]

The objective of the North American Land Data Assimilation System (NLDAS) is to provide best-available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across the majority of the eastern United States and on the simulation of surface and root zone soil moisture across the country. Smaller improvements are seen in the simulation of snow depth, and the impact of GRACE DA on simulated river discharge and evapotranspiration is regionally variable. The use of GRACE scaling factors during assimilation improved DA results in the western United States but led to small degradations in the eastern United States. The study also found comparable performance between the use of gridded and basin-averaged GRACE observations in assimilation. Finally, the evaluations presented in the paper indicate that GRACE DA can be helpful in improving the representation of droughts. [ABSTRACT FROM AUTHOR]

This paper presents a detailed analysis of how future climate change may affect water availability in a typical arid endorheic river basin, the Heihe River basin (HRB), in northwest China. The analysis is based on the improved Soil Water Assessment Tool (SWAT), which is calibrated and validated with historical streamflow data from the upper HRB and is used to predict future hydrological responses. Six general circulation models (GCMs), under two emission scenarios (RCP4.5 and RCP8.5), are downscaled to construct future climate change scenarios. The results suggest that the climate of the upper HRB will likely become warmer and wetter in the near future (2021-50), with the largest increase in precipitation occurring in the summer. Correspondingly, the basinwide evapotranspiration, snowmelt, and runoff are projected to increase over the same period. The mean temperature in the near future is projected to rise, relative to the recent 30 years (1981-2010), by 1.2°-1.7°C under scenario RCP4.5 and by 1.4°-2.1°C under scenario RCP8.5. The mean precipitation is projected to increase by 10.0%-16.6% under scenario RCP4.5, and by 10.5%-22.0% under scenario RCP8.5. The mean values of evapotranspiration, snowmelt, and runoff are expected to increase by 14.2%, 4.3%, and 11.4%, respectively, under scenario RCP4.5 and to increase by 18.7%, 5.8%, and 12.8%, respectively, under scenario RCP8.5. Though the model simulations forecast an increase in streamflows in the headwater region of the HRB, future water availability varies significantly over space and time. The findings of this study will help to frame more effective water management strategies for the HRB under changing climatic conditions. [ABSTRACT FROM AUTHOR]

A common term in the continental and oceanic components of the global water cycle is freshwater discharge to the oceans. Many estimates of the annual average global discharge have been made over the past 100 yr with a surprisingly wide range. As more observations have become available and continental-scale land surface model simulations of runoff have improved, these past estimates are cast in a somewhat different light. In this paper, a combination of observations from 839 river gauging stations near the outlets of large river basins is used in combination with simulated runoff fields from two implementations of the Variable Infiltration Capacity land surface model to estimate continental runoff into the world's oceans from 1950 to 2008. The gauges used account for ~58% of continental areas draining to the ocean worldwide, excluding Greenland and Antarctica. This study estimates that flows to the world's oceans globally are 44 200 (±2660) km3 yr−1 (9% from Africa, 37% from Eurasia, 30% from South America, 16% from North America, and 8% from Australia-Oceania). These estimates are generally higher than previous estimates, with the largest differences in South America and Australia-Oceania. Given that roughly 42% of ocean-draining continental areas are ungauged, it is not surprising that estimates are sensitive to the land surface and hydrologic model (LSM) used, even with a correction applied to adjust for model bias. The results show that more and better in situ streamflow measurements would be most useful in reducing uncertainties, in particular in the southern tip of South America, the islands of Oceania, and central Africa. [ABSTRACT FROM AUTHOR]

The South Asian monsoon is critically important for agricultural production in the region that includes the vast, fertile Indus and Ganges basins. However, the behavior of the South Asian monsoon is not well understood because of its complex nature, and existing instrumental climate records are insufficient for investigating the risks of the low-frequency but high-impact megadroughts that have historically occurred. This paper develops an inverse Palmer drought severity index (PDSI) model to retrieve paleoprecipitation for the region during the time period of 1300-1899, using available data of the water-holding capacity of soil, temperature, and reconstructed PDSI based on the tree-ring analysis of Cook et al.. Temperature data are reconstructed by a regression analysis utilizing an existing temperature reconstruction in an adjacent region and the Pacific decadal oscillation. Based on the retrieved paleoprecipitation, several megadroughts are identified during the reconstruction period. The drought frequency in the Indus basin is higher than that in the Ganges basin. The intensity, frequency, and spatial extent of severe droughts increased from 1300-1899 to 1900-2010. As a signal of climate change, increasing intensity and frequency of severe drought in the Indus and Ganges River basins needs adaptation strategies and drought preparedness measures to secure the food production in this area. [ABSTRACT FROM AUTHOR]

In this paper, monthly, maximum seasonal, and maximum annual hydrometeorological (i.e., evaporation, lake water levels, and rainfall) data series from the Kariba catchment area of the Zambezi River basin, Zimbabwe, have been analyzed in order to determine appropriate probability distribution models of the underlying climatology from which the data were generated. In total, 16 probability distributions were considered and the Kolmogorov-Sminorv (KS), Anderson-Darling (AD), and chi-square (χ2) goodness-of-fit (GoF) tests were used to evaluate the best-fit probability distribution model for each hydrometeorological data series. A ranking metric that uses the test statistic from the three GoF tests was formulated and used to select the most appropriate probability distribution model capable of reproducing the statistics of the hydrometeorological data series. Results showed that, for each hydrometeorological data series, the best-fit probability distribution models were different for the different time scales, corroborating those reported in the literature. The evaporation data series was best fit by the Pearson system, the Lake Kariba water levels series was best fit by the Weibull family of probability distributions, and the rainfall series was best fit by the Weibull and the generalized Pareto probability distributions. This contribution has potential applications in such areas as simulation of precipitation concentration and distribution and water resources management, particularly in the Kariba catchment area and the larger Zambezi River basin, which is characterized by (i) nonuniform distribution of a network of hydrometeorological stations, (ii) significant data gaps in the existing observations, and (iii) apparent inherent impacts caused by climatic extreme events and their corresponding variability. [ABSTRACT FROM AUTHOR]

This paper develops a multivariate mosaic subgrid approach to represent subgrid variability in land surface models (LSMs). The k-means clustering is used to take an arbitrary number of input descriptors and objectively determine areas of similarity within a catchment or mesoscale model grid box. Two different classifications of hydrologic similarity are compared: an a priori classification, where clusters are based solely on known physiographic information, and an a posteriori classification, where clusters are defined based on high-resolution LSM simulations. Simulations from these clustering approaches are compared to high-resolution gridded simulations, as well as to three common mosaic approaches used in LSMs: the 'lumped' approach (no subgrid variability), disaggregation by elevation bands, and disaggregation by vegetation types in two subcatchments. All watershed disaggregation methods are incorporated in the Noah Multi-Physics (Noah-MP) LSM and applied to snowmelt-dominated subcatchments within the Reynolds Creek watershed in Idaho. Results demonstrate that the a priori clustering method is able to capture the aggregate impact of finescale spatial variability with O(10) simulation points, which is practical for implementation into an LSM scheme for coupled predictions on continental-global scales. The multivariate a priori approach better represents snow cover and depth variability than the univariate mosaic approaches, critical in snowmelt-dominated areas. Catchment-averaged energy fluxes are generally within 10%-15% for the high-resolution and a priori simulations, while displaying more subgrid variability than the univariate mosaic methods. Examination of observed and simulated streamflow time series shows that the a priori method generally reproduces hydrograph characteristics better than the simple disaggregation approaches. [ABSTRACT FROM AUTHOR]

This paper presents an application of the Variable Infiltration Capacity (VIC) model to the Fraser River basin (FRB) of British Columbia (BC), Canada, over the latter half of the twentieth century. The Fraser River is the longest waterway in BC and supports the world's most abundant Pacific Ocean salmon populations. Previous modeling and observational studies have demonstrated that the FRB is a snow-dominated system, but with climate change, it may evolve to a pluvial regime. Thus, the goal of this study is to evaluate the changing contribution of snow to the hydrology of the FRB over the latter half of the twentieth century. To this end, a 0.25° atmospheric forcing dataset is used to drive the VIC model from 1949 to 2006 (water years) at a daily time step over a domain covering the entire FRB. A model evaluation is first conducted over 11 major subwatersheds of the FRB to quantitatively assess the spatial variations of snow water equivalent (SWE) and runoff ( R). The ratio of the spatially averaged maximum SWE to R ( RSR) is used to quantify the contribution of snow to the runoff in the 11 subwatersheds of interest. From 1949 to 2006, RSR exhibits a significant decline in 9 of the 11 subwatersheds (with p < 0.05 according to the Mann-Kendall test statistics). To determine the sensitivity of RSR, the air temperature and precipitation in the forcing dataset are then perturbed. The ratio RSR decreases more significantly, especially during the 1990s and 2000s, when air temperatures have warmed considerably compared to the 1950s. On the other hand, increasing precipitation by a multiplicative factor of 1.1 causes RSR to decrease. As the climate continues to warm, ecological processes and human usage of natural resources in the FRB may be substantially affected by its transition from a snow to a hybrid (nival/pluvial) and even a rain-dominated system. [ABSTRACT FROM AUTHOR]

Long-term changes in the water budget of lakes in the Tibetan Plateau due to climate change are of great interest not only for the importance of water management, but also for the critical challenge due to the lack of observations. In this paper, the water budget of Nam Co Lake during 1980-2010 is simulated using a dynamical monthly water balance model. The simulated lake level is in good agreement with field investigations and the remotely sensed lake level. The long-term hydrological simulation shows that from 1980 to 2010, lake level rose from 4718.34 to 4724.93 m, accompanied by an increase of lake water storage volume from 77.33 × 109 to 83.66 × 109 m3. For the net lake level rise (5.93 m) during the period 1980-2010, the proportional contributions of rainfall-runoff, glacier melt, precipitation on the lake, lake percolation, and evaporation are 104.7%, 56.6%, 41.7%, −22.2%, and −80.9%, respectively. A positive but diminishing annual water surplus is found in Nam Co Lake, implying a continuous but slowing rise in lake level as a hydrological consequence of climate change. [ABSTRACT FROM AUTHOR]

Assessing climate change risk to municipal water supplies is often conducted by hydrologic modeling specific to local watersheds and infrastructure to ensure that outputs are compatible with existing planning frameworks and processes. This study leverages the modeling capacity of an operational National Weather Service River Forecast Center to explore the potential impacts of future climate-driven hydrologic changes on factors important to planning at the Salt Lake City Department of Public Utilities (SLC). Hydrologic modeling results for the study area align with prior research in showing that temperature changes alone will lead to earlier runoff and reduced runoff volume. The sensitivity of average annual flow to temperature varies significantly between watersheds, averaging -3.8% °F-1 and ranging from 21.8% to 26.5% flow reduction per degree Fahrenheit of warming. The largest flow reductions occur during the high water demand months of May-September. Precipitation drives hydrologic response more strongly than temperature, with each 1% precipitation change producing an average 1.9% runoff change of the same sign. This paper explores the consequences of climate change for the reliability of SLC's water supply system using scenarios that include hydrologic changes in average conditions, severe drought scenarios, and future water demand test cases. The most significant water management impacts will be earlier and reduced runoff volume, which threaten the system's ability to maintain adequate streamflow and storage to meet late-summer water demands. [ABSTRACT FROM AUTHOR]

Global warming will have direct impacts on regional water resources by accelerating the hydrological cycle. Hydrological simulation is an important approach to studying climate change impacts. In this paper, a snowmelt-based water balance model (SWBM) was used to simulate the effect of climate change on runoff in the Kuye River catchment of the Loess Plateau, China. Results indicated that the SWBM is suitable for simulating monthly discharge into arid catchments. The response of runoff in the Kuye River catchment to climate change is nonlinear, and runoff is more sensitive to changes in precipitation than to changes in temperature. The projections indicated that the Kuye River catchment would undergo more flooding in the 2020s, and global warming would probably shorten the main flood season in the catchment, with greater discharge occurring in August. Although projected changes in annual runoff are uncertain, the possibilities of regional water shortages and regional flooding are essential issues that need to be fully considered. [ABSTRACT FROM AUTHOR]

A major research plan entitled "Integrated research on the eco-hydrological process of the Heihe River Basin" was launched by the National Natural Science Foundation of China in 2010. One of the key aims of this research plan is to establish a research platform that integrates observation, data management, and model simulation to foster twenty-first-century watershed science in China. Based on the diverse needs of inter-disciplinary studies within this research plan, a program called the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) was implemented. The overall objective of HiWATER is to improve the observability of hydrological and ecological processes, to build a world-class watershed observing system, and to enhance the applicability of remote sensing in integrated eco-hydrological studies and water resource management at the basin scale. This paper introduces the background, scientific objectives, and experimental design of HiWATER. The instrumental setting and airborne mission plans are also outlined. The highlights are the use of a flux observing matrix and an eco-hydrological wireless sensor network to capture multiscale heterogeneities and to address complex problems, such as heterogeneity, scaling, uncertainty, and closing water cycle at the watershed scale. HiWATER was formally initialized in May 2012 and will last four years until 2015. Data will be made available to the scientific community via the Environmental and Ecological Science Data Center for West China. International scientists are welcome to participate in the field campaign and use the data in their analyses. [ABSTRACT FROM AUTHOR]

Heavy rainfall hit the Yangtze-Huai Rivers basin (YHRB) of east China several times during the pro-longed 2007 mei-yu season, causing the worst flood since 1954. There has been an urgent need for attaining and processing high-quality, kilometer-scale, hourly rainfall data in order to understand the mei-yu pre-cipitation processes, especially at the mesoß and smaller scales. In this paper, the authors describe the construction of the 0.07°-resolution gridded hourly rainfall analysis over the YHRB region during the 2007 mei-yu season that is based on surface reports at 555 national and 6572 regional automated weather sta-tions with an average resolution of about 7 km. The gridded hourly analysis is obtained using a modified Cressman-type objective analysis after applying strict quality control, including not only the commonly used internal temporal and spatial consistency and extreme value checks, but also verifications against mosaic radar reflectivity data. This analysis reveals many convectively generated finescale precipitation structures that could not be seen from the national station reports. A comprehensive quantitative as-sessment ensures the quality of the gridded hourly precipitation data. A comparison of this dataset with the U.S. Climate Prediction Center morphing technique (CMORPH) dataset on the same resolution suggests the dependence of the latter's performance on different rainfall intensity categories, with sub-stantial underestimation of the magnitude and width of the mei-yu rainband as well as the nocturnal and morning peak rainfall amounts, due mainly to its underestimating the occurrences of heavy rainfall (i.e., >10mmh-1). [ABSTRACT FROM AUTHOR]

The majority of the world's population growth to 2050 is projected to occur in the tropics. Hence, there is a serious need for robust methods for undertaking water resource assessments to underpin the sustainable management of water in tropical regions. This paper describes the largest and most comprehensive assessment of the future impacts of runoff undertaken in a tropical region using conceptual rainfall-runoff models (RRMs). Five conceptual RRMs were calibrated using data from 115 streamflow gauging stations, and model parameters were regionalized using a combination of spatial proximity and catchment similarity. Future rainfall and evapotranspiration projections (denoted here as GCMES) were transformed to catchment-scale variables by empirically scaling (ES) the historical climate series, informed by 15 global climate models (GCMs), to reflect a 1°C increase in global average surface air temperature. Using the best-performing RRM ensemble, approximately half the GCMES used resulted in a spatially averaged increase in mean annual runoff (by up to 29%%) and half resulted in a decrease (by up to 26%%). However, ~70%% of the GCMES resulted in a difference of within ±5%% of the historical rainfall (1930-2007). The range in modeled impact on runoff, as estimated by five RRMs (for individual GCMES), was compared to the range in modeled runoff using 15 GCMES (for individual RRMs). For mid- to high runoff metrics, better predictions will come from improved GCMES projections. A new finding of this study is that in the wet-dry tropics, for extremely large runoff events and low flows, improvements are needed in both GCMES and rainfall-runoff modeling. [ABSTRACT FROM AUTHOR]

The paper aims to quantitatively estimate the role of snowmelt in the spring flood flow and the redistribution of river runoff for the northern (Arctic) part of the western Siberian Plain (the rivers Poluy, Nadym, Pur, and Taz). In this region, the presence of wetlands and thermokarst lakes significantly influences the seasonal redistribution of river discharge. First the study region is described, and the snow regime from in situ observations at the Tarko-Sale meteorological station is analyzed. As Special Sensor Microwave Imager (SSM/I) estimates of snow depth for this region are lower than in situ observations, a correction of the SSM/I snow depth estimates is done using snow parameters measured on the snow transect near the meteorological station Tarko-Sale for 1991-96. This reestimated snow depth is then used to assess the volume of water stored every winter on the watersheds for 1989-2006. This snow product is compared with the spring flood streamflow estimated from in situ observations, and the regional relationship between the snow water storage and flood flow is constructed. The proportion of meltwater that does not reach the main rivers and is thus evaporated or stored by the wetlands is estimated to be on average 30%% (varying from 0%% to 74%%). We observe an increasing trend of this value from 20%%-30%% in the early 1990s to 50%%-60%% in the mid-2000s. This increase could be attributed to several factors such as increased air temperature (leading to increased evaporation, changes in vegetation cover, and active layer depth) and also to human activity. [ABSTRACT FROM AUTHOR]

Different methods have been used to obtain the daily rainfall time series required to drive conceptual rainfall-runoff models, depending on data availability, time constraints, and modeling objectives. This paper investigates the implications of different rainfall inputs on the calibration and simulation of 4 rainfall-runoff models using data from 240 catchments across southeast Australia. The first modeling experiment compares results from using a single lumped daily rainfall series for each catchment obtained from three methods: single rainfall station, Thiessen average, and average of interpolated rainfall surface. The results indicate considerable improvements in the modeled daily runoff and mean annual runoff in the model calibration and model simulation over an independent test period with better spatial representation of rainfall. The second experiment compares modeling using a single lumped daily rainfall series and modeling in all grid cells within a catchment using different rainfall inputs for each grid cell. The results show only marginal improvement in the 'distributed' application compared to the single rainfall series, and only in two of the four models for the larger catchments. Where a single lumped catchment-average daily rainfall series is used, care should be taken to obtain a rainfall series that best represents the spatial rainfall distribution across the catchment. However, there is little advantage in driving a conceptual rainfall-runoff model with different rainfall inputs from different parts of the catchment compared to using a single lumped rainfall series, where only estimates of runoff at the catchment outlet is required. [ABSTRACT FROM AUTHOR]

This paper attempts to project possible changes in the frequency of daily rainfall events late in this century for four selected river basins (i.e., Grand, Humber, Rideau, and Upper Thames) in Ontario, Canada. To achieve this goal, automated synoptic weather typing as well as cumulative logit and nonlinear regression methods was employed to develop within-weather-type daily rainfall simulation models. In addition, regression-based downscaling was applied to downscale four general circulation model (GCM) simulations to three meteorological stations (i.e., London, Ottawa, and Toronto) within the river basins for all meteorological variables (except rainfall) used in the study. Using downscaled GCM hourly climate data, discriminant function analysis was employed to allocate each future day for two windows of time (2046--65, 2081--2100) into one of the weather types. Future daily rainfall and its extremes were projected by applying within-weather-type rainfall simulation models together with downscaled future GCM climate data. A verification process of model results has been built into the whole exercise (i.e., statistical downscaling, synoptic weather typing, and daily rainfall simulation modeling) to ascertain whether the methods are stable for projection of changes in frequency of future daily rainfall events. Two independent approaches were used to project changes in frequency of daily rainfall events: method I--comparing future and historical frequencies of rainfall-related weather types, and method II--applying daily rainfall simulation models with downscaled future climate information. The increases of future daily rainfall event frequencies and seasonal rainfall totals (April--November) projected by method II are usually greater than those derived by method I. The increase in frequency of future daily heavy rainfall events greater than or equal to 25 mm, derived from both methods, is likely to be greater than that of future daily rainfall events greater than or equal to 0.2 mm: 35%%--50%% versus 10%%--25%% over the period 2081--2100 derived from method II. In addition, the return values of annual maximum 3-day accumulated rainfall totals are projected to increase by 20%%--50%%, 30%%--55%%, and 25%%--60%% for the periods 2001--50, 2026--75, and 2051--2100, respectively. Inter-GCM and interscenario uncertainties of future rainfall projections were quantitatively assessed. The intermodel uncertainties are similar to the interscenario uncertainties, for both method I and method II. However, the uncertainties are generally much smaller than the projection of percentage increases in the frequency of future seasonal rain days and future seasonal rainfall totals. The overall mean projected percentage increases are about 2.6 times greater than overall mean intermodel and interscenario uncertainties from method I; the corresponding projected increases from method II are 2.2--3.7 times greater than overall mean uncertainties. [ABSTRACT FROM AUTHOR]

Based on the changes in the size of closed basin lakes, the author shows that water availability has undergone large fluctuations in response to conditions experienced during full glacial time and during the period of deglaciation. Based on these observations, the author concludes that as the planet warms in response to the buildup of fossil fuel CO2, the distribution of available water will shift. The resulting changes will be particularly noticeable in the world''s drylands. In this paper, the author attempts to invert the response to cold glacial conditions and to interhemispheric temperature differences, which occurred during the interval of deglaciation, to predict how the hydrologic conditions in the western United States might evolve over the next half-century. The tentative conclusion on both scores is that this region will likely undergo a progressive drying. However, because both the glacial and deglacial hydrologic shifts were certainly influenced by the presence of excess ice, as well as by colder temperatures, the author admits that the validity of this inversion is subject to challenge. [ABSTRACT FROM AUTHOR]

This paper analyzes the ability of the North American Regional Climate Change Assessment Program (NARCCAP) ensemble of regional climate models to simulate extreme monthly precipitation and its supporting circulation for regions of North America, comparing 18 years of simulations driven by the National Centers for Environmental Prediction (NCEP)--Department of Energy (DOE) reanalysis with observations. The analysis focuses on the wettest 10%% of months during the cold half of the year (October--March), when it is assumed that resolved synoptic circulation governs precipitation. For a coastal California region where the precipitation is largely topographic, the models individually and collectively replicate well the monthly frequency of extremes, the amount of extreme precipitation, and the 500-hPa circulation anomaly associated with the extremes. The models also replicate very well the statistics of the interannual variability of occurrences of extremes. For an interior region containing the upper Mississippi River basin, where precipitation is more dependent on internally generated storms, the models agree with observations in both monthly frequency and magnitude, although not as closely as for coastal California. In addition, simulated circulation anomalies for extreme months are similar to those in observations. Each region has important seasonally varying precipitation processes that govern the occurrence of extremes in the observations, and the models appear to replicate well those variations. [ABSTRACT FROM AUTHOR]

This paper describes a fully automated scheme that has provided calibrated 1–10-day ensemble river discharge forecasts and predictions of severe flooding of the Brahmaputra and Ganges Rivers as they flow into Bangladesh; it has been operational since 2003. The Bangladesh forecasting problem poses unique challenges because of the frequent life-threatening flooding of the country and because of the absence of upstream flow data from India means that the Ganges and Brahmaputra basins must be treated as if they are ungauged. The meteorological–hydrological forecast model is a hydrologic multimodel initialized by NASA and NOAA precipitation products, whose states and fluxes are forecasted forward using calibrated European Centre for Medium-Range Weather Forecasts ensemble prediction system products, and conditionally postprocessed to produce calibrated probabilistic forecasts of river discharge at the entrance points of the Ganges and Brahmaputra into Bangladesh. Forecasts with 1–10-day horizons are presented for the summers of 2003–07. Objective verification shows that the forecast system significantly outperforms both a climatological and persistence forecast at all lead times. All severe flooding events were operationally forecast with significant probability at the 10-day horizon, including the extensive flooding of the Brahmaputra in 2004 and 2007, with the latter providing advanced lead-time warnings for the evacuation of vulnerable residents. [ABSTRACT FROM AUTHOR]

The analysis of rainstorms is of prime importance for the planning and design of water resources of any region. In the absence of actual records of streamflow data, analysis of rainstorms is useful for assessing flood potentialities as well as the runoff of river catchments. This study provides pertinent information on the spatial distribution characteristics of severe rainstorm events that occurred over the Godavari and its subbasins on the basis of 110 yr (1891–2000) of available rainfall data. The analysis showed that the August 1986 rainstorm had its areal extension over more than a 150 000 km2 area. It is also seen that the August 1913 rainstorm was the most efficient rainstorm over the basin. The rain depth data of severe rainstorms given in this paper can provide a reliable set of information to assess water resource planning on a probability basis. The relationship between the point to areal rainfall and area of the rainstorms from 1- to 3-day durations is also determined. [ABSTRACT FROM AUTHOR]

Lakes, ponds, and wetlands are common features in many low-gradient arctic watersheds. Storage of snowmelt runoff in lakes and wetlands exerts a strong influence on both the interannual and interseasonal variability of northern rivers. This influence is often not well represented in hydrology models and the land surface schemes used in climate models. In this paper, an algorithm to represent the evaporation and storage effects of lakes and wetlands within the Variable Infiltration Capacity (VIC) macroscale hydrology model is described. The model is evaluated with respect to its ability to represent water temperatures, net radiation, ice freeze–thaw, and runoff production for a variety of high-latitude locations. It is then used to investigate the influence of surface storage on the spatial and temporal distribution of water and energy fluxes for the Kuparuk and Putuligayuk Rivers, on the Alaskan arctic coastal plain. Inclusion of the lake and wetland algorithm results in a substantial improvement of the simulated streamflow hydrographs, as measured using the monthly Nash–Sutcliffe efficiency. Simulations of runoff from the Putuligayuk watershed indicate that up to 80% of snow meltwater goes into storage each year and does not contribute to streamflow. Approximately 46% of the variance in the volume of snowmelt entering storage can be explained by the year-to-year variation in maximum snow water equivalent and the lake storage deficit from the previous summer. The simulated summer lake storage deficit is much lower than the cumulative precipitation minus lake evaporation (−47 mm, on average) as a result of simulated recharge from the surrounding uplands. [ABSTRACT FROM AUTHOR]

This paper explores the relationship between vegetation in the Liangzhou Oasis in the Upper Shiyang River watershed (USRW) of west-central Gansu, China, and within-watershed precipitation, soil water storage, and oasis self-support. Oases along the base of the Qilian Mountains receive a significant portion of their water supply (over 90%) from surface and subsurface flow originating from the Qilian Mountains. Investigation of vegetation control on oasis water conditions in the USRW is based on an application of a process model of soil water hydrology. The model is used to simulate long-term soil water content (SWC) in the Liangzhou Oasis as a function of (i) monthly composites of Moderate Resolution Imaging Spectroradiometer (MODIS) images of land surface and mean air temperature, (ii) spatiotemporal calculations of monthly precipitation and relative humidity generated with the assistance of genetic algorithms (GAs), and (iii) a 80-m-resolution digital elevation model (DEM) of the area. Modeled removal of vegetation is shown to affect within-watershed precipitation and soil water storage by reducing the exchange of water vapor from the land surface to the air, increasing the air’s lifting condensation level by promoting drier air conditions, and causing the high-intensity precipitation band in the Qilian Mountains to weaken and to be displaced upward, leading to an overall reduction of water to the Liangzhou Oasis. [ABSTRACT FROM AUTHOR]

Landscape-driven microclimates in mountainous terrain pose significant obstacles to predicting the response of organisms to atmospheric warming, but few if any studies have documented the extent of such finescale variation over large regions. This paper demonstrates that ground-level temperature regimes in Great Smoky Mountains National Park (Tennessee and North Carolina) vary considerably over fine spatial scales and are only partially linked to synoptic weather patterns and environmental lapse rates. A 120-sensor network deployed across two watersheds in 2005–06 exhibited finescale (<1000-m extent) temperature differences of over 2°C for daily minima and over 4°C for daily maxima. Landscape controls over minimum temperatures were associated with finescale patterns of soil moisture content, and maximum temperatures were associated with finescale insolation differences caused by topographic exposure and vegetation cover. By linking the sensor array data to 10 regional weather stations and topographic variables describing site radiation load and moisture content, multilevel spatial models of 30-m resolution were constructed to map daily temperatures across the 2090-km2 park, validated with an independent 50-sensor network. Maps reveal that different landscape positions do not maintain relative differences in temperature regimes across seasons. Near-stream locations are warmer in the winter and cooler in the summer, and sites of low elevation more closely track synoptic weather patterns than do wetter high-elevation sites. This study suggests a strong interplay between near-ground heat and water balances and indicates that the influence of past and future shifts in regional temperatures on the park’s biota may be buffered by soil moisture surfeits from high regional rainfall. [ABSTRACT FROM AUTHOR]

Traditionally, little attention has been focused on the systematic verification of operational hydrologic forecasts. This paper summarizes the results of forecast verification from 15 river basins in the United States. The verification scores for these forecast locations do not show improvement over the periods of record despite a number of forecast process improvements. In considering a root cause for these results, the authors note that the current paradigm for designing hydrologic forecast process improvements is driven by expert opinion and not by objective verification measures. The authors suggest that this paradigm should be modified and objective verification metrics should become the primary driver for hydrologic forecast process improvements. [ABSTRACT FROM AUTHOR]

This paper examines the accuracy of high-resolution nested mesoscale model simulations of surface climate. The nesting capabilities of the atmospheric fifth-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) were used to create high-resolution, 5-yr climate simulations (from 1 October 1994 through 30 September 1999), starting with a coarse nest of 20 km for the western United States. During this 5-yr period, two finer-resolution nests (5 and 1.7 km) were run over the Yampa River basin in northwestern Colorado. Raw and bias-corrected daily precipitation and maximum and minimum temperature time series from the three MM5 nests were used as input to the U.S. Geological Survey’s distributed hydrologic model [the Precipitation Runoff Modeling System (PRMS)] and were compared with PRMS results using measured climate station data. The distributed capabilities of PRMS were provided by partitioning the Yampa River basin into hydrologic response units (HRUs). In addition to the classic polygon method of HRU definition, HRUs for PRMS were defined based on the three MM5 nests. This resulted in 16 datasets being tested using PRMS. The input datasets were derived using measured station data and raw and bias-corrected MM5 20-, 5-, and 1.7-km output distributed to 1) polygon HRUs and 2) 20-, 5-, and 1.7-km-gridded HRUs, respectively. Each dataset was calibrated independently, using a multiobjective, stepwise automated procedure. Final results showed a general increase in the accuracy of simulated runoff with an increase in HRU resolution. In all steps of the calibration procedure, the station-based simulations of runoff showed higher accuracy than the MM5-based simulations, although the accuracy of MM5 simulations was close to station data for the high-resolution nests. Further work is warranted in identifying the causes of the biases in MM5 local climate simulations and developing methods to remove them. [ABSTRACT FROM AUTHOR]

This paper presents a new diagnostic dataset of monthly variations in terrestrial water storage for 37 midlatitude river basins in Europe, Asia, North America, and Australia. Terrestrial water storage is the sum of all forms of water storage on land surfaces, and its seasonal and interannual variations are in principle determined by soil moisture, groundwater, snow cover, and surface water. The dataset is derived with the combined atmospheric and terrestrial water-balance approach using conventional streamflow measurements and atmospheric moisture convergence data from the ECMWF 40-yr Re-Analysis (ERA-40). A recent study for the Mississippi River basin (Seneviratne et al. 2004) has demonstrated the validity of this diagnostic approach and found that it agreed well with in situ observations in Illinois. The present study extends this previous analysis to other regions of the midlatitudes. A systematic analysis is presented of the slow drift that occurs with the water-balance approach. It is shown that the drift not only depends on the size of the catchment under consideration, but also on the geographical region and the underlying topography. The drift is in general not constant in time, but artificial inhomogeneities may result from changes in the global observing system used in the 44 yr of the reanalysis. To remove this time-dependent drift, a simple high-pass filter is applied. Validation of the results is conducted for several catchments with an appreciable coverage of in situ soil moisture and snow cover depth observations in the former Soviet Union, Mongolia, and China. Although the groundwater component is not accounted for in these observations, encouraging correlations are found between diagnostic and in situ estimates of terrestrial water storage, both for seasonal and interannual variations. Comparisons conducted against simulated ERA-40 terrestrial water storage variations suggest that the reanalysis substantially underestimates the amplitude of the seasonal cycle. The basin-scale water-balance (BSWB) dataset is available for download over the Internet. It constitutes a useful tool for the validation of climate models, large-scale land surface data assimilation systems, and indirect observations of terrestrial water storage variations. [ABSTRACT FROM AUTHOR]

To test the accuracy of statistically downscaled precipitation estimates from numerical weather prediction models, a set of experiments to study what space and time scales are appropriate to obtain downscaled precipitation forecasts with maximum skill have been carried out. Fourteen-day forecasts from the 1998 version of the National Centers for Environmental Prediction (NCEP) Medium-Range Forecast (MRF) model were used in this study. It has been previously found that downscaled temperature fields have significant skill even up to 5 days of forecast lead time, but there is practically no valuable skill in the downscaled precipitation forecasts. Low skill in precipitation forecasts revolves around two main issues. First, the (intermittent) precipitation variability on daily and subdaily time scales could be too noisy to derive meaningful relationships with atmospheric predictors. Second, the model parameterizations and the coarse spatial resolution of the current generation of global-scale forecast models might be unable to resolve the local-scale variability in precipitation. Both of these issues may be addressed by spatial and temporal averaging. In this paper the authors present a diagnostic study using a set of numerical experiments to understand how spatial and temporal aggregations affect the skill of downscaled precipitation forecasts in the upper Colorado River basin. The question addressed is, if the same set of predictor variables from numerical weather prediction models is used, what space (e.g., station versus regional average) and time (e.g., subdaily versus daily) scales optimize regression-based downscaling models so as to maximize forecast skill for precipitation? Results in general show that spatial and temporal averaging increased the skill of downscaled precipitation estimates. At subdaily (6 hourly) and daily time scales, the skill of downscaled estimates at spatial scales greater than 50 km was generally higher than the skill of downscaled estimates at individual stations. For the 6-hourly time scale both for stations and for mean areal precipitation estimates the maximum forecast skill was found to be approximately half that of the daily time scale. At forecast lead times of 5 days, when there is very little skill at daily and subdaily time scales, useful skill emerged when station data are aggregated to 3- and 5-day averages. [ABSTRACT FROM AUTHOR]

Long-term (20 yr) river discharge records from 30 of the world’s largest river basins have been used to characterize surface hydrologic flows in relation to net precipitation inputs, ocean climate teleconnections, and human land/water use patterns. This groundwork study is presented as a precedent to distributed simulation modeling of surface hydrologic flows in large river basins. Correlation analysis is used as a screening method to classify river basins into categories based on major controls on discharge, for example, climate, land use, and dams. Comparisons of paired station records at upstream and downstream discharge locations within each major river basin suggest that the discharge signals represented in upstream discharge records are sustained in the downstream station records for nearly two-thirds of the drainage basins selected. River basins that showed the strongest localized climate control over historical discharge records, in terms of correlations with net basinwide precipitation rates, are located mainly in the seasonally warm temperate and tropical latitude zones, as opposed to river basins located mainly in the higher latitude zones (above 45°N). Ocean climate indices such as the Niño1+2 and Niño3+4 correlate highly with historical interannual patterns in monthly river discharge for only four of the selected discharge station records, namely, on the Amazon, Congo (Zaire), Columbia, and Colorado (Arizona) Rivers. Historical patterns of cropland development and irrigated areas may explain the weak climate correlations with interannual patterns in monthly river discharge rates for at least one-third of the major river drainages selected from the historical discharge dataset. This paper is part of a special theme issue on land use and ecosystems. [ABSTRACT FROM AUTHOR]

This paper describes and evaluates an automated riverflow forecasting system for the prediction of peak flows during the cool season of 1998-99 over six watersheds in western Washington. The forecast system is based on the Pennsylvania State University-National Center for Atmospheric Research (Penn State-NCAR) fifthgeneration Mesoscale Model (MM5) and the University of Washington Distributed-Hydrology-Soil-Vegetation Model (DHSVM). The control simulation used the forecasts produced by the University of Washington's realtime MM5 forecasts system as input to the hydrologic model. A second set of simulations applied a correction scheme that reduced the long-term precipitation bias identified in the MM5 precipitation field. A third set of simulations used only those observations that are available in real time for forcing the hydrologic model. The various MM5-DHSVM forecasts are also compared with those issued by the National Weather Service Northwest River Forecast Center. Results showed that the observations-based simulation produced the most accurate peak flow forecasts, although it was susceptible to inadequate input data and the overdependence on the few available observations. The control simulation performed remarkably well, although several poor synoptic (MM5) forecasts, in addition to a model wet bias, produced a significant overprediction of peak flows over one watershed. The bias correction scheme did not prove worthwhile for peak flow forecasting, but may be useful for longer-term modeling studies where the emphasis is on long-term discharge rather than peak flow forecasting. A real-time updating procedure that incorporated meteorologic observations in the creation of initial hydrologic states showed considerable promise for forecast peak streamflow error reduction. [ABSTRACT FROM AUTHOR]