Distributed Hydrology Soil Vegetation Model Research Articles

Changes in snow water storage and hydrologic partitioning in an alpine catchment in the Colorado Front Range

AbstractAs the climate warms, snow‐dominated regions are increasingly susceptible to precipitation phase changes and earlier snowmelt. In alpine catchments, snowmelt is the majority of runoff, governed by heterogeneous interactions among snowfall, wind, topography and vegetation. The role of the alpine snowpack in delaying the downstream release of accumulated snowfall remains understudied, despite its fundamental role in sustaining lower‐elevation ecosystems and populations. We evaluated fine‐scale changes in the magnitude and duration of alpine snow water storage and release by comparing effective precipitation seasonality to that of surface water inputs using a Snow Storage Index (SSI). We forced the Distributed Hydrology Soil Vegetation Model with historical climate conditions and an end‐of‐century warming signal down‐scaled from a 4‐km regional climate model, to simulate future changes in snow water storage and partitioning of precipitation to runoff and evapotranspiration. The hydrological modelling was conducted on a 2‐m spatial grid to resolve fine scale processes over a 0.6‐km2 headwater catchment within the Niwot Ridge Long‐Term Ecological Research site located in the Rocky Mountains, Colorado, USA. Proportionally more rainfall, reduced snowpack size and extent, and earlier snowmelt occurred in the future scenario with decreased catchment average SSI and thus snow water storing capabilities, with inordinately large decreases occurring in areas of the catchment with reconstructed snowdrifts (−57% average and −100% maximum). Using the Budyko framework to evaluate annual hydrologic partitioning, precipitation inputs to streamflow increased by a maximum 10% in large SSI regions with snowdrifts under warming conditions, signifying a potential buffer for total catchment and annual streamflow loss in a future climate. The fine‐scale analysis of hydrologic sensitivities to warming presented herein are important because climate conditions are expected to further reduce alpine snow water storage, alter hydrologic partitioning, and runoff generation for ecosystems and people living downstream.

Response of Streamflow to Future Land Use and Cover Change and Climate Change in the Source Region of the Yellow River

This study utilizes meteorological and leaf area index (LAI) data for three shared socioeconomic pathways (SSP1–2.6, SSP2–4.5, and SSP5–8.5) from four general circulation models (GCMs) of the sixth climate model intercomparison project (CMIP6) spanning from 2015 to 2099. Employing calibrated data and incorporating future land use data under three SSPs, the distributed hydrology soil vegetation model (DHSVM) is employed to simulate streamflow in the source region of the Yellow River (SRYR). The research aims to elucidate variations in streamflow across different future scenarios and to estimate extreme streamflow events and temporal distribution changes under future land use and cover change (LUCC) and climate change scenarios. The main conclusions are as follows: The grassland status in the SRYR will significantly improve from 2020 to 2099, with noticeable increases in temperature, precipitation, and longwave radiation, alongside a pronounced decrease in wind speed. The probability of flooding events increases in the future, although the magnitude of the increase diminishes over time. Both LUCC and climate change contribute to an increase in the multi-year average streamflow in the region, with respective increments of 48.8%, 24.5%, and 18.9% under SSP1–2.6, SSP2–4.5, and SSP5–8.5. Notably, the fluctuation in streamflow is most pronounced under SSP5–8.5. In SSP1–2.6, the increase in streamflow during the near future (2020–2059) exceeds that of the distant future (2059–2099). Seasonal variations in streamflow intensify across most scenarios, leading to a more uneven distribution of streamflow throughout the year and an extension of the flood season.

The Role of Snowmelt Temporal Pattern in Flood Estimation for a Small Snow‐Dominated Basin in the Sierra Nevada

AbstractPrior research confirmed the substantial bias from using precipitation‐based intensity‐duration‐frequency curves (PREC‐IDF) in design flood estimates and proposed next‐generation IDF curves (NG‐IDF) that represent both rainfall and snow processes in runoff generation. This study improves the NG‐IDF technology for a snow‐dominated test basin in the Sierra Nevada. A well‐validated physics‐based hydrologic model, the Distributed Hydrology Soil Vegetation Model (DHSVM), is used to continuously simulate snowmelt and streamflow that are used as benchmark data sets to systematically assess the NG‐IDF technology. We find that, for the studied small snow‐dominated basin, the use of standard rainfall hyetographs in the NG‐IDF technology leads to substantial underestimation of design floods. Thus, we propose probabilistic hyetographs that can represent unique patterns of events with different underlying mechanisms. For the test basin where flooding events are generated entirely by snowmelt, we develop a hyetograph that characterizes snowmelt temporal patterns, which greatly improves the performance of NG‐IDF technology in design flood estimates. In contrast to the standard rainfall hyetographs characterized by a symmetrically peaked, bell‐shaped curve, the snowmelt hyetograph displays a more rapid rise (i.e., greater intensity) and a distinct diurnal pattern influenced by solar energy input. The results also show that the uncertainty of hyetography plays an important role in design flood estimation and can have important implications for future flood projections.

Estimating Future Surface Water Availability Through an Integrated Climate‐Hydrology‐Management Modeling Framework at a Basin Scale Under CMIP6 Scenarios

AbstractClimate change and increasing water demand due to population growth pose serious threats to surface water availability. The biggest challenge in addressing these threats is the gap between climate science and water management practices. Local water planning often lacks the integration of climate change information, especially with regard to its impacts on surface water storage and evaporation as well as the associated uncertainties. Using Texas as an example, state and regional water planning relies on the use of reservoir “Firm Yield” (FY)—an important metric that quantifies surface water availability. However, this existing planning methodology does not account for the impacts of climate change on future inflows and on reservoir evaporation. To bridge this knowledge gap, an integrated climate‐hydrology‐management (CHM) modeling framework was developed, which is generally applicable to river basins with geographical, hydrological, and water right settings similar to those in Texas. The framework leverages the advantages of two modeling approaches—the Distributed Hydrology Soil Vegetation Model (DHSVM) and Water Availability Modeling (WAM). Additionally, the Double Bias Correction Constructed Analogues method is utilized to downscale and incorporate Coupled Model Intercomparison Project Phase 6 GCMs. Finally, the DHSVM simulated naturalized streamflow and reservoir evaporation rate are input to WAM to simulate reservoir FY. A new term—“Ratio of Firm Yield” (RFY)—is created to compare how much FY changes under different climate scenarios. The results indicate that climate change has a significant impact on surface water availability by increasing reservoir evaporation, altering the seasonal pattern of naturalized streamflow, and reducing FY.

Tracking snowmelt during hydrological surface processes using a distributed hydrological model in a mesoscale basin on the Tibetan Plateau

Mountain snowpack is an important water resource in high altitude and latitude regions where terrain is typically complex. However, only limited information has been published on snowmelt pathways in such regions and their de facto contributions to streamflow and soil moisture. To fill this knowledge gap, this study integrated a snowmelt pathway tracking algorithm to the high-resolution physically based Distributed Hydrology Soil Vegetation Model (DHSVM) to track snowmelt movement and to quantify snowmelt contributions during surface hydrologic processes. A simple reservoir operation scheme was also integrated into the model. The modified model was applied to a dammed mesoscale watershed in the northeastern region of the Tibetan Plateau, China, to explore relevant snow and reservoir effects. Results show that both the annual snow contribution to soil moisture (SC-SM) and the snow contribution to streamflow (SC-S) significantly decreased between 1965 and 2019. On a monthly scale, SC-SM amplitudes were highest in the upper soil layer, while peaks in deeper layers lagged behind those in upper layers. Moreover, mean monthly SC-S from all stations revealed bimodal distributions that corresponded to the snowfall season. Finally, reservoir regulation measures only exerted minimal impacts (≤2.0 %) on SC-S. If current climate change rates continue on the same trajectory, mountain snowpack reductions will be the primary cause for decreases in monthly and annual streamflow at the outlet of this basin. To mitigate climate change impacts, better water resource management is needed in this watershed.

Impacts of climate change on future hurricane induced rainfall and flooding in a coastal watershed: A case study on Hurricane Harvey

The warming climate is likely to increase hurricane-associated extreme rainfall and lead to sea-level rise (SLR). Thus, how the floods induced by intense hurricanes respond to these potential changes is of great concern. This study investigates the future warmer climate impacts on hurricane-induced extreme rainfall, and—more importantly—the subsequent compound flooding at the watershed scale (from an event-based analysis perspective). To this goal, a modeling framework is designed based on the Distributed Hydrology Soil Vegetation Model (DHSVM), the Two-Dimensional fvand the Regional Community Earth System Model (R-CESM). The framework was applied to Hurricane Harvey (2017) at the Clear Creek watershed (a coastal watershed in the southern Houston) as a case study. The results show that the projected maximum rainfall totals over the watershed would be exacerbated by 17.7 % and 49.7 % in the 2050s and 2090s (respectively) under Representative Concentration Pathway 8.5 (RCP 8.5). This means a 16.1 % increase in Harvey rainfall over the watershed per degree Celsius increase in Mean Surface Temperature over the Gulf of Mexico region (18°∼31° N, 77°∼98° W). Meanwhile, the increases in maximum inundation extent would be 11.0 % (2050s) and 19.5 % (2090s). Furthermore, considerable increases in maximum inundation depth and duration in regions along the middle and downstream of Clear Creek (and also those around Clear Lake) are expected. The projected SLR will have little effect on the maximum inundation depth and extent if storm surge changes are not taken into account; meanwhile, it will influence the inundation duration at downstream locations. This modeling framework can be also applied at other coastal watersheds to evaluate the projected climate change impacts on the compound flooding induced by extreme climate events.

Evaluation of Remote Sensing-Based Evapotranspiration Datasets for Improving Hydrological Model Simulation in Humid Region of East China

Conventional calibration methods used in hydrological modelling are based on runoff observations at the basin outlet. However, calibration with only runoff often produces reasonable runoff but poor results for other hydrological variables. Multi-variable calibration with both runoff and remote sensing-based evapotranspiration (ET) is developed naturally, due to the importance of ET and its data availability. This study compares two main calibration schemes: (1) calibration with only runoff (Scheme I) and (2) multi-variable calibration with both runoff and remote sensing-based ET (Scheme II). ET data are obtained from three remote sensing-based ET datasets, namely Penman–Monteith–Leuning (PML), FLUXCOM, and the Global Land Evaporation Amsterdam Model (GLEAM). The aforementioned calibration schemes are applied to calibrate the parameters of the Distributed Hydrology Soil Vegetation Model (DHSVM) through ε-dominance non-dominated sorted genetic algorithm II (ε-NSGAII). The results show that all three ET datasets have good performance for areal ET in the study area. The DHSVM model calibrated based on Scheme I produces acceptable performance in runoff simulation (Kling–Gupta Efficiency, KGE = 0.87), but not for ET simulation (KGE < 0.7). However, reasonable simulations can be achieved for both variables based on Scheme II. The KGE value of runoff simulation can reach 0.87(0.91), 0.72(0.85), and 0.75(0.86) in the calibration (validation) period based on Scheme II (PML), Scheme II (FLUXCOM), and Scheme II (GLEAM), respectively. Simultaneously, ET simulations are greatly improved both in the calibration and validation periods. Furthermore, incorporating ET data into all three Scheme II variants is able to improve the performance of extreme flow simulations (including extreme low flow and high flow). Based on the improvement of the three datasets in extreme flow simulations, PML can be utilized for multi-variable calibration in drought forecasting, and FLUXCOM and GLEAM are good choices for flood forecasting.

Evaluating Basin-Scale Forest Adaptation Scenarios: Wildfire, Streamflow, Biomass, and Economic Recovery Synergies and Trade-Offs

Active forest management is applied in many parts of the western United States to reduce wildfire severity, mitigate vulnerability to drought and bark beetle mortality, and more recently, to increase snow retention and late-season streamflow. A rapidly warming climate accelerates the need for these restorative treatments, but the treatment priority among forest patches varies considerably. We simulated four treatment scenarios across the 3,450 km2 Wenatchee River basin in eastern Washington, United States. We used a decision support tool (DST) to assess trade-offs and synergies within and among treatments on wildfire risk and smoke emissions, water yield and snow retention, biomass production, and economic return. Treatment scenarios emphasized prescribed burning (BurnOnly), biomass production (MaxBiomass), gap-based thinning to optimize water yield (IdealWater), and a principle-based restoration scenario (RA1). Fire hazard, smoke emissions, and biomass production metrics were evaluated across scenarios using the Forest Vegetation Simulator, and water yields were modeled using the Distributed Hydrology Soil Vegetation Model. Simulations were summarized to both patch- (101–102 ha) and subwatershed- (103–104 ha) scales, and treatment effects were evaluated against an untreated baseline landscape. We used logic models to rank effect sizes by scenario across metrics along a continuum between −1 (no or weak effect) to +1 (large effect). All treatments produced benefits across one or more ecosystem services and led to synergistic benefits to water yield and wildfire hazard reduction. Tradeoffs among resource benefits were clear in wilderness where reliance on prescribed burning without mechanical treatment increased costs and eliminated the potential for biomass recovery. The BurnOnly scenario improved fire risk metrics and streamflow, but effect sizes were lower compared to other treatments. IdealWater showed the strongest benefits overall, demonstrating the ability to capture multiple resource benefits through spatially explicit thinning. Our study provides a framework for integrating strategic and tactical models that evaluate tradeoffs and synergies gained through varied management approaches. We demonstrate the utility of decision support modeling to enhance management synergies across large landscapes.

Soil Moisture Estimation for Winter-Wheat Waterlogging Monitoring by Assimilating Remote Sensing Inversion Data into the Distributed Hydrology Soil Vegetation Model

Waterlogging crop disasters are caused by continuous and excessive soil water in the upper layer of soil. In order to enable waterlogging monitoring, it is important to collect continuous and accurate soil moisture data. The distributed hydrology soil vegetation model (DHSVM) is selected as the basic hydrological model for soil moisture estimation and winter-wheat waterlogging monitoring. To handle the error accumulation of the DHSVM and the poor continuity of remote sensing (RS) inversion data, an agro-hydrological model that assimilates RS inversion data into the DHSVM is used for winter-wheat waterlogging monitoring. The soil moisture content maps retrieved from satellite images are assimilated into the DHSVM by the successive correction method. Moreover, in order to reduce the modeling error accumulation, monthly and real-time RS inversion maps that truly reflect local soil moisture distributions are regularly assimilated into the agro-hydrological modeling process each month. The results show that the root mean square errors (RMSEs) of the simulated soil moisture value at two in situ experiment points were 0.02077 and 0.02383, respectively, which were 9.96% and 12.02% of the measured value. From the accurate and continuous soil moisture results based on the agro-hydrological assimilation model, the waterlogging-damaged ratio and grade distribution information for winter-wheat waterlogging were extracted. The results indicate that there were almost no high-damaged-ratio and severe waterlogging damage areas in Lixin County, which was consistent with the local field investigation.

Modeling streamflow sensitivity to climate warming and surface water inputs in a montane catchment

Study regionGordon Gulch, an upper-montane forest watershed in the Colorado Front Range. Study focusAs the climate warms, the fraction of precipitation falling as snow is expected to decrease and the timing of snowmelt is expected to shift earlier in spring. In snow-dominated regions, these changes in snow accumulation and melt prompt us to examine downstream changes in streamflow. The objective of this study is to understand how changes in precipitation phase and snowmelt timing alter the timing of surface water inputs (i.e. rainfall and snowmelt) and the partitioning of these inputs between evapotranspiration and streamflow. We used the Distributed Hydrology Soil Vegetation Model and Weather Research and Forecasting Model-based projections of future climatic conditions to simulate streamflow. New hydrological insights for the regionModeled annual streamflow decreased by 22% for the period 2071–2100. Surface water inputs increased during winter when atmospheric water demand was relatively low. Subsequently, the winter-period partitioning of water (as rain or snowmelt) to streamflow (as opposed to evapotranspiration) increased, by 15%, while partitioning to evapotranspiration decreased, effectively buffering what would have otherwise been a larger net streamflow decline associated with warming. Seasonal streamflow buffering is unique to snow-influenced systems, as the magnitude and timing of water released from snowpacks is sensitive to warming. This effect may diminish as warming drives snow-influenced systems toward rain-dominance, with implications for hydrological and ecological processes and water-resource management.

Hydrologic and suspended sediment effects of forest roads using field and DHSVM modelling studies

Forest road hydrologic and surface erosion effects were evaluated using a two-step approach in South Fork Caspar Creek: (1) road runoff and suspended sediment measurements and (2) use of the Distributed Hydrology Soil Vegetation Model (DHSVM) to examine road scenarios at the watershed scale. Statistical evaluation showed the best multiple linear regression (MLR) model to predict Log10 suspended sediment load (SSL) was from road runoff event peak flow and cube root of maximum turbidity combined with road characteristics of either cutslope cover or road surface type. MLR using road dimensions and event precipitation, without runoff or turbidity, suggested the best model to predict Log10 SSL used road length times slope squared (LS2), road surface type, and cutslope area. DHSVM simulations for three different road drainage scenarios for two road networks were used to evaluate hydrologic and suspended sediment effects. The 2018 road network represented road networks placed high on slopes away from watercourses and a pre-1974 road network lower on the hillslopes with high numbers of watercourse crossings and streamside roads. Forest harvest of South Fork Caspar Creek (SFC) was predicted by DHSVM to increase storm peak flows by 1.5% to 11% relative to pre-harvest, no-road peak flows. The 2018 road network, with 13% of road length adjacent to watercourses, was predicted to make < 2% increase in peak flows. In contrast, the pre-1974 road network scenario, with 58% of road length adjacent to watercourses, was predicted to increase peak flows as much as 46%. There were low levels of suspended sediment contributions, 23–86 kg/ha/yr, predicted from the 2018 road network scenario with few road watercourse crossings. The pre-1974 road network was predicted to contribute 347 – 2158 kg/ha/yr, depending on road drainage scenario.

Sensitivity and Performance Analyses of the Distributed Hydrology–Soil–Vegetation Model Using Geomorphons for Landform Mapping

Landform classification is important for representing soil physical properties varying continuously across the landscape and for understanding many hydrological processes in watersheds. Considering it, this study aims to use a geomorphology map (Geomorphons) as an input to a physically based hydrological model (Distributed Hydrology Soil Vegetation Model (DHSVM)) in a mountainous headwater watershed. A sensitivity analysis of five soil parameters was evaluated for streamflow simulation in each Geomorphons feature. As infiltration and saturation excess overland flow are important mechanisms for streamflow generation in complex terrain watersheds, the model’s input soil parameters were most sensitive in the “slope”, “hollow”, and “valley” features. Thus, the simulated streamflow was compared with observed data for calibration and validation. The model performance was satisfactory and equivalent to previous simulations in the same watershed using pedological survey and moisture zone maps. Therefore, the results from this study indicate that a geomorphologically based map is applicable and representative for spatially distributing hydrological parameters in the DHSVM.

Effects of Streamside Buffers on Stream Temperatures Associated With Forest Management and Harvesting Using DHSVM-RBM; South Fork Caspar Creek, California

Forest harvesting has been shown to effect water quantity and water quality parameters, highlighting the need for comprehensive forest practice rules. Being able to understand and predict these impacts on stream temperature is especially critical where federally threatened or endangered fish species are located. The goal of this research was to predict responses in stream temperature to potential riparian and forest harvest treatments in a maritime, mountainous environment. The Distributed Hydrology Soil Vegetation Model (DHSVM) and River Basin Model (RBM) were calibrated to measured streamflow and stream temperatures in the South Fork of the Caspar Creek Experimental Watersheds during critical summer periods when temperatures are highest and flows are low for hydrologic years 2010–2016. The modeling scenarios evaluated were (1) varying percentages of stream buffer canopy cover, (2) a harvest plan involving incrementally reduced stand densities in gauged sub-watersheds, and (3) an experimental design converting dominant riparian vegetation along set reaches. The model predicted a noticeable rise in stream temperatures beginning when stream buffer canopy cover was reduced to 25 and 0% retention levels. Larger increases in Maximum Weekly Maximum Temperatures (MWMT), compared to Maximum Weekly Average Temperatures (MWAT), occurred across all scenarios. There was essentially no difference in MWAT or MWMT between altering buffers along only fish bearing (Class I) watercourses and altering buffers along all watercourses. For the scenario with stream buffers at 0% retention, MWMTs consistently rose above recommended thermal limits for coho salmon(Oncorhynchus kisutch). Predictions when clearcutting the entire watershed showed less of an effect than simulations with 0% buffer retention, suggesting groundwater inflows mitigate stream temperature rises in the South Fork. The harvest simulation showed a small but consistent increase in MWATs (avg. 0.11°C), and more varied increases in MWMTs (avg. 0.32°C). Sensitivity analyses suggest potentially unrealistic tracking of downstream temperatures, making the vegetation conversion simulations inconclusive. Additional sensitivity analyses suggest tree height and monthly extinction coefficient (a function of leaf area index) were most influential on temperatures in the South Fork, which was consistent with other modeling studies suggesting management focus on tall, dense buffers compared to wider buffer widths.

Assessing climate-change-induced flood risk in the Conasauga River watershed: an application of ensemble hydrodynamic inundation modeling

Abstract. This study evaluates the impact of potential future climate change on flood regimes, floodplain protection, and electricity infrastructures across the Conasauga River watershed in the southeastern United States through ensemble hydrodynamic inundation modeling. The ensemble streamflow scenarios were simulated by the Distributed Hydrology Soil Vegetation Model (DHSVM) driven by (1) 1981–2012 Daymet meteorological observations and (2) 11 sets of downscaled global climate models (GCMs) during the 1966–2005 historical and 2011–2050 future periods. Surface inundation was simulated using a GPU-accelerated Two-dimensional Runoff Inundation Toolkit for Operational Needs (TRITON) hydrodynamic model. A total of 9 out of the 11 GCMs exhibit an increase in the mean ensemble flood inundation areas. Moreover, at the 1 % annual exceedance probability level, the flood inundation frequency curves indicate a ∼ 16 km2 increase in floodplain area. The assessment also shows that even after flood-proofing, four of the substations could still be affected in the projected future period. The increase in floodplain area and substation vulnerability highlights the need to account for climate change in floodplain management. Overall, this study provides a proof-of-concept demonstration of how the computationally intensive hydrodynamic inundation modeling can be used to enhance flood frequency maps and vulnerability assessment under the changing climatic conditions.

Evaluation of the subseasonal forecast skill of floods associated with atmospheric rivers in coastal Western U.S. watersheds

AbstractAtmospheric rivers (ARs) are responsible for up to 90% of major flood events along the U.S. West Coast. The timescale of subseasonal forecasting (two weeks to one month) is a critical lead time for proactive mitigation of flood disasters. The NOAA/Climate Testbed Subseasonal Experiment (SubX) is a research-to-operations project with almost immediate availability of forecasts. It has produced a reforecast database that facilitates evaluation of flood forecasts at these subseasonal lead times. Here, we examine the SubX driven forecast skill of AR-related flooding out to 4-week lead using the Distributed Hydrology Soil Vegetation Model (DHSVM), with particular attention to the role of antecedent soil moisture (ASM), which modulates the relationship between meteorological and hydrological forecast skill. We study three watersheds along a transect of the U.S. West Coast: the Chehalis River basin in Washington, the Russian River basin in Northern California, and the Santa Margarita River basin in Southern California. We find that the SubX driven flood forecast skill drops quickly after week 1, during which there is relatively high deterministic forecast skill. We find some probabilistic forecast skill relative to climatology as well as ensemble streamflow prediction (ESP) in week 2, but minimal skill in weeks 3-4, especially for annual maximum floods, notwithstanding some probabilistic skill for smaller floods in week 3. Using ESP and reverse-ESP experiments to consider the relative influence of ASM and SubX reforecast skill, we find that ASM dominates probabilistic forecast skill only for small flood events at week 1, while SubX reforecast skill dominates for large flood events at all lead times.

Greater vulnerability of snowmelt-fed river thermal regimes to a warming climate

While the advective flux from cool melt runoff can be a significant source of thermal energy to mountainous rivers, it has been a much less addressed process in river temperature modeling and thus our understanding is limited with respect to the spatiotemporal effect of melt on river temperatures at the watershed scale. In particular, the extent and magnitude of the melt cooling effect in the context of a warming climate are not yet well understood. To address this knowledge gap, we improved a coupled hydrology and stream temperature modeling system, distributed hydrology soil vegetation model and river basin model (DHSVM-RBM), to account for the thermal effect of cool snowmelt runoff on river temperatures. The model was applied to a snow-fed river basin in the Pacific Northwest to evaluate the responses of snow, hydrology, stream temperatures, and fish growth potential to future climates. Historical simulations suggest that snowmelt can notably reduce the basin-wide peak summer temperatures particularly at high-elevation tributaries, while the thermal impacts of melt water can persist through the summer along the mainstem. Ensemble climate projections suggested that a warming climate will decrease basin mean peak snow and summer streamflow by 92% and 60% by the end of the century. Due to the compounded influences of warmer temperatures, lower flows and diminished cooling from melt, river reaches in high elevation snow-dominated areas were projected to be most vulnerable to future climate change, showing the largest increases in summer peak temperatures. As a result, thermal habitat used by anadromous Pacific salmon was projected to exhibit substantially lower growth potential during summer in the future. These results have demonstrated the necessity of accounting for snowmelt influence on stream temperature modeling in mountainous watersheds.

A Calibration Framework for High‐Resolution Hydrological Models Using a Multiresolution and Heterogeneous Strategy

AbstractIncreasing spatial and temporal resolution of numerical models continues to propel progress in hydrological sciences, but, at the same time, it has strained the ability of modern automatic calibration methods to produce realistic model parameter combinations for these models. This paper presents a new reliable and fast automatic calibration framework to address this issue. In essence, the proposed framework, adopting a divide and conquer strategy, first partitions the parameters into groups of different resolutions based on their sensitivity or importance, in which the most sensitive parameters are prioritized with the highest resolution in the parameter search space, while the least sensitive ones are initially explored with the coarsest resolution. This is followed by an optimization‐based iterative calibration procedure consisting of a series of subtasks or runs. Between consecutive runs, the setup configuration is heterogeneous with parameter search ranges and resolutions varying among groups. At the completion of each subtask, the parameter ranges within each group are systematically refined from their previously estimated ranges, which are initially based on a priori information. Parameters attain stable convergence progressively with each run. A comparison of this new calibration framework with a traditional optimization‐based approach was performed using a quasi‐synthetic double‐model setup experiment to calibrate 134 parameters and two well‐known distributed hydrological models: the Variable Infiltration Capacity (VIC) model and the Distributed Hydrology Soil Vegetation Model (DHSVM). The results demonstrate statistically that the proposed framework can better mitigate equifinality problem, yields more realistic model parameter estimates, and is computationally more efficient.

Floods due to Atmospheric Rivers along the U.S. West Coast: The Role of Antecedent Soil Moisture in a Warming Climate

AbstractPrecipitation extremes are projected to become more frequent along the U.S. West Coast due to increased atmospheric river (AR) activity, but the frequency of less intense precipitation events may decrease. Antecedent soil moisture (ASM) conditions can have a large impact on flood responses, especially if prestorm precipitation decreases. Taken together with increased antecedent evaporative demand due to warming, this would result in reduced soil moisture at the onset of extreme precipitation events. We examine the impact of ASM on AR-related floods in a warming climate in three basins that form a transect along the U.S. Pacific Coast: the Chehalis River basin in Washington, the Russian River basin in Northern California, and the Santa Margarita River basin in Southern California. We ran the Distributed Hydrology Soil Vegetation Model (DHSVM) over the three river basins using forcings downscaled from 10 global climate models (GCMs). We examined the dynamic role of ASM by comparing the changes in the largest 50, 100, and 150 extreme events in two periods, 1951–2000 and 2050–99. In the Chehalis basin, the projected fraction of AR-related extreme discharge events slightly decreases. In the Russian basin, this fraction increases, however, and more substantially so in the Santa Margarita basin. This is due to increases in AR-related extreme precipitation events, as well as the fact that the relationship of extreme precipitation to extreme discharge is strengthened by projected increases in year-to-year volatility of annual precipitation in California, which increases the likelihood of concurrent occurrence of large storms and wet ASM conditions.

Quantifying the effects of urbanization on floods in a changing environment to promote water security — A case study of two adjacent basins in Texas

The increased occurrence of flood events resulting from urbanization and global climate change is a great threat to water security. To systematically evaluate the impacts of urbanization on floods, we applied a paired catchments approach to two adjacent river basins in south-central Texas — the San Antonio River Basin (SARB), with fast urbanization; and the Guadalupe River Basin (GRB), with little land cover change. A physics-based distributed hydrological model — the Distributed Hydrology Soil Vegetation Model, embedded with a multi-purpose reservoir module (DHSVM-Res) — was used to simulate streamflow and reservoir storage. The simulations were conducted under different land cover scenarios, including a newly developed continuous land cover series (CLCS). Holistic analyses were then conducted for the paired basins using three methods: analyzing the selected flood events, detecting change points (CP) of monthly floods, and testing the elasticity of long-term flood regimes. The results suggest that: (1) urbanization may reduce lag time and elevate flood peaks significantly by 3–30% in our study area; (2) when there is little land cover change, changing climate is the major driver of variations in the monthly maximum streamflow (MMS); (3) fast urbanization can amplify streamflow variability, increase MMS significantly, and thus alter the timing of CP; and (4) the mean MMS of observed streamflow in the SARB has increased by as much as 75.7% from the pre-CP to post-CP periods. This comprehensive study fills in a gap in our current understanding of the isolated impacts of urbanization on flooding and is expected to support future explorations of anthropogenic influences on floods.

Impacts of Climate Change and Land Use/Cover Change on Streamflow in Beichuan River Basin in Qinghai Province, China

Climate change (CC) and land use/cover change (LUCC) are the main drivers of streamflow change. In this study, the effects of CC and LUCC on streamflow regime as well as their spatial variability were examined by using the Distributed Hydrology Soil Vegetation Model (DHSVM) for the Beichuan River Basin in the northeast Tibetan Plateau. The results showed that CC increased annual and maximum streamflow in the upstream but decreased them in the downstream. CC also enhanced minimum streamflow in the whole river basin and advanced the occurrence of daily minimum streamflow. Temperature change exerted greater influence on streamflow regime than wind speed change did in most situations, but the impact of wind speed on streamflow reflected the characteristics of accumulative effects, which may require more attention in future, especially in large river basins. As for LUCC, cropland expansion and reservoir operation were the primary reasons for streamflow reduction. Cropland expansion contributed more to annual mean streamflow change, whereas reservoir operation greatly altered monthly streamflow pattern and extreme streamflow. Reservoir regulation also postponed the timing of minimum streamflow and extended durations of average, high, and low streamflow. Spatially, CC and LUCC played predominant roles in the upstream and the downstream, respectively.