Sub-basin Scale Research Articles

Spatiotemporal responses of runoff to climate change in the southern Tibetan Plateau

Abstract. A comprehensive understanding of spatiotemporal runoff changes in the Yarlung Zangbo (YZ) basin in the southern Tibetan Plateau (TP) at a sub-basin scale, amidst varying climatic and cryospheric conditions, is imperative for effective water resources management. However, spatiotemporal differences of runoff composition and change and their attribution within the YZ basin have not been extensively explored, primarily due to the lack of hydrometeorological observations, especially in the downstream region. In this study, we investigated historical and future evolution of annual and seasonal total water availability, as well as glacier runoff and snowmelt contributions across six sub-basins of the YZ, with a particular focus on the comparison between the upstream Nuxia (NX) basin and the downstream Nuxia–Pasighat (NX-BXK) basin, based on a newly generated precipitation dataset and a well-validated model with streamflow, glacier mass, and snow cover observations. Our findings revealed that large spatiotemporal differences in changes exist within the YZ basin for 1971–2020. Firstly, runoff generation was dominated by rainfall runoff throughout the YZ basin, with glacier runoff playing a more important role in the annual total runoff (19 %) in the NX-BXK sub-basin compared to other sub-basins. Notably, glacier runoff contributed 52 % of the total runoff at the Pasighat outlet of the YZ basin. Secondly, annual runoff exhibited an increasing trend in the NX basin but a decreasing trend in the NX-BXK, primarily attributed to rainfall runoff changes influenced by atmospheric moisture. Glacier runoff enhanced water supply by offsetting the decreasing contribution from rainfall. Total runoff will consistently increase (27–100 mm (10 yr)−1) across the sub-basins through the 21st century, resulting from increased rainfall runoff and a minor effect of increased snowmelt and glacier runoff.

Wetland-mediated nitrate reductions attenuate downstream: Insights from a modeling study

Connections between agricultural runoff and excess nitrogen in the Upper Mississippi River Basin are well-documented, as is the potential role of constructed wetlands in mitigating this surplus nitrogen. However, limited knowledge exists about the “best” placement of these wetlands for downstream nitrogen reductions within a whole watershed context as well as how far downstream these benefits are realized. In this study, we simulate the cumulative impacts of diverse wetland restoration scenarios on downstream nitrate reductions in different subbasins of the Raccoon River Watershed, Iowa, USA, and spatially trace their relative effects downstream. Our simulated results underscore previous work demonstrating that the total area of wetlands and the wetland-catchment-to-wetland area ratio are both significant factors for determining the nitrate load reduction benefits of wetlands at subbasin scales. Simulated wetland conservation scenarios resulted in nitrate load decreases ranging from 7.5 to 43.2% of our baseline model loads. However, we found these wetland-mediated nitrate reduction benefits are quickly attenuated downstream: load reductions were <1% at the watershed outlet across all model scenarios, despite the magnitude of the subbasin-scale nitrate decreases. The relatively rapid attenuation of wetland effects is largely due to downstream nitrate load contributions from untreated subbasins. However, higher subbasin-scale nitrate reductions from wetland-based conservation practices resulted in longer downstream distances prior to attenuation. This study highlights the importance of considering the spatial location of constructed or restored wetlands relative to the area within the watershed where nitrogen reductions are most needed.

A multi-variable calibration framework at the grid scale for integrating streamflow with evapotranspiration data to improve the simulation of distributed hydrological model

Study regionThe Ganjiang River Basin, China Study focusParameter calibration is crucial for the accurate and reliable operation of hydrological models. Traditional methods face challenges in calibrating spatially heterogeneous parameters of distributed hydrological models, and existing multi-variable calibration strategies often fall short in comprehensively improving model performance, particularly in streamflow simulations. To address these challenges, this study proposes a multi-objective calibration framework that integrates observed streamflow data and satellite-based evapotranspiration (ET) data. The spatiotemporal information of the merged ET is utilized to calibrate six hydrological parameters of the Variable Infiltration Capacity (VIC) model at the grid scale, enhancing hydrological simulations for the Ganjiang River basin. New hydrological insightsCompared to the benchmark scheme based solely on streamflow, the proposed calibration framework improves simulations of area-average ET at the sub-basin scale and soil moisture content in the Ganjiang River basin, without compromising the accuracy of daily streamflow simulations. Additionally, notable enhancements are observed in monthly streamflow simulations. This study provides a promising and comprehensive calibration framework using satellite-based data to constrain parameters and enhance the performance of distributed hydrological models.

Analysis of hydrological responses using semi-distributed conceptual models in a mountainous catchment in the Hindu Kush Himalayan region

ABSTRACT The Hindu Kush Karakoram Himalaya region, an important global water unit, is the origin of 10 major river basins. The Upper Indus basin, a part of this region, is a primary water source for the downstream population and is primarily fed by snow and glacier melt. Four semi-distributed conceptual hydrological models were used to understand this basin’s hydrological responses. As the observed data was unavailable, global gridded precipitation and discharge data from the Global Flood Awareness System (GloFAS) were used to set up the model at the catchment outlet. Degree-day factor (DDF)-based snow and glacier modules are incorporated into the model structure to measure melt contribution. The results showed very good performance of all four models with Nash-Sutcliffe efficiency > 0.75, with the GR4J model exhibiting superior performance. The dominance of snow and glacier melt during summer monsoon affects the peak discharge and offers insights for investigations at the sub-basin scale.

Unraveling the Impacts of River Network Connectivity on Ecological Quality Dynamics at a Basin Scale

The ecological quality of river basins is significantly influenced by the complex network of river structures and their connectivity. This study measured the temporal and spatial variability of ecological quality, as reflected by remote sensing ecological indices (RSEI), and examined their responses to river network connectivity (RNC). In total, 8 RNC indices, including river structure of river density (Dr), water surface ratio (Wr), edge-node ratio (β), and network connectivity (γ), and node importance indices of betweenness centrality (BC), PageRank (PG_R), out_degree centrality (Out_D), and in_closeness centrality (In_C), were generated at the subbasin scale. Our results highlighted the significance of RNC in influencing both the values and variability of RSEI, and the extent of this influence varied across different time periods. Specifically, three distinct clusters can be extracted from the temporal variability of RSEI, representing wet, near-normal, and dry years. The river structure index of γ significantly influenced the spatial patterns of subbasin RSEIs, particularly in wet years (R2 = 0.554), whereas β displayed a pronounced U-shape correlation with subbasin RSEIs in dry years (R2 = 0.512). Although node importance indices did not correlate directly with subbasin RSEI levels, as the river structure indices did, they significantly positively affected temporal variability of subbasin RSEIs (EI_SD_t). Higher values of PG_R, Out_D, and In_C were associated with increased subbasin RSEI variability. Based on these correlations, we developed RNC-based RSEI and EI_SD_t models with high adjusted coefficients of determination to facilitate the assessment of ecosystem quality. This study provides essential insights into ecosystem dynamics related to river connectivity within a basin and offers valuable guidance for effective watershed management and conservation efforts aimed at enhancing ecological resilience and sustainability.

Spatiotemporal interactions between soil moisture and water availability across the Yellow River Basin, China

Study regionYellow River Basin (YRB) in China. Study focusThis study attempts to shed new light on regional land-atmosphere coupling and relevant impacts on basin-scale water resource management. Specifically, the objectives are to investigate the driving factors and physical mechanisms of SM changes via land-atmosphere coupling. New hydrological insights for the regionEcological conservation and the high-quality development in the YRB stand as a pivotal national strategy. The equilibrium of water availability (PME，precipitation minus evapotranspiration) poses a significant challenge to the sustainability of the basin's ecosystem. Unfortunately, a comprehensive examination of the regional response, spatiotemporal heterogeneity, and the mechanism governing soil moisture (SM) in response to PME remains limited. An enhanced multiple linear regression method is implemented to quantify monthly sensitivity coefficients, revealing a notable correlation: reduced SM in arid regions correlates with heightened PME. This decline in SM triggers a decrease in evapotranspiration, attenuates the cooling effect of evapotranspiration, and amplifies temperature disparities. Consequently, this process results in an intensified boundary layer and tropospheric ascending motion, thereby increasing water vapor transport. This feedback loop is most pronounced during drought conditions, particularly in summer arid areas (sensitivity coefficient =-0.27). The findings underscore the intricate interplay between land and atmosphere, elucidating the discernible impact of climate change on water resources at a sub-basin scale.

Assessment of the eutrophication status at Mediterranean sub-basin scale, within the European Marine Strategy Framework Directive

The aim of this work is to define harmonized reference conditions and assessment thresholds for selected criteria elements of the Marine Strategy Framework Directive (MSFD) Descriptor 5 (Eutrophication) in the Eastern Mediterranean Sea and to test if a tool for integrated assessment of the status of marine systems can be used as a common methodological approach. In this frame, we tested two statistical approaches in order to set threshold values for four criteria of Descriptor 5: nutrients, chlorophyll a, transparency and dissolved oxygen in the bottom waters. It is noteworthy that this work revealed the need to apply common procedures in data treatment and assessment evaluation. This is the first attempt to set common methods for the assessment of eutrophication in the Eastern Mediterranean, which is essential in marine environments, especially those shared by several countries. To this end, we have applied common criteria and metrics and established thresholds “Good” and “Moderate” for nutrients, chlorophyll a, transparency and dissolved oxygen in the bottom waters for the different Water Types of the Adriatic and Aegean Seas (I, II, IIIW, IIIE), based on datasets provided by Italy, Slovenia, Croatia and Greece. The selected criteria elements were common for all countries, providing a unified approach to GES assessment of two case study areas: the Adriatic Sea and the Saronikos Gulf. Dissolved Inorganic Nitrogen (DIN) threshold values of 15.6, 6.85, 1.61 and 2.11 μmol L−1 were set for the Water Types I, II, IIIW and IIIE, respectively.We also tested if an aggregation tool for GES assessment, such as Nested Environmental status Assessment Tool (NEAT), could be used as a common methodological approach. The comparison of NEAT with TRIX showed good comparability. In this end, NEAT can be used as a useful and much needed assessment tool for assessing eutrophication status of the marine.

An optimized crop-livestock system can achieve a safe and just planetary boundary for phosphorus at the sub-basin level in China.

The contribution of crop and livestock production to the exceedance of the planetary boundary for phosphorus (P) in China is still unclear, despite the country's well-known issues with P fertilizer overuse and P-related water pollution. Using coupled models at sub-basin scales we estimate that livestock production increased the consumption of P fertilizer fivefold and exacerbated P losses twofold from 1980 to 2017. At present, China's crop-livestock system is responsible for exceeding what is considered a 'just' threshold for fertilizer P use by 30% (ranging from 17% to 68%) and a 'safe' water quality threshold by 45% (ranging from 31% to 74%) in 25 sub-basins in China. Improving the crop-livestock system will keep all sub-basins within safe water quality and just multigenerational limits for P in 2050.

Multi-Facet analysis of analytical and numerical models to resolve sustainable artificial recharge rates in unconfined aquifers

Managed aquifer recharge (MAR) has emerged as a potential solution to resolve water insecurity, globally. However, integrated studies quantifying the surplus source water, suitable recharge sites and safe recharge capacity is limited. In this study, a novel methodology is presented to quantify transient injection rates in unconfined aquifers and generate MAR suitability maps based on estimated surplus water and permissible aquifer recharge capacity (PARC). Subbasin scale monthly surplus surface runoff was estimated at 75% dependability using a SWAT model. A linear regression model based on numerical solution was used to capture the aquifer response to injection and to calculate PARC values at subbasin level. The available surplus runoff and PARC values was then used to determine the suitable site and recharge rate during MAR operation. The developed methodology was applied in the semi-arid region of Lower Betwa River Basin (LBRB), India. The estimated surplus runoff was generally confined to the monsoon months of June to September and exhibited spatial heterogeneity with an average runoff rate of 5000 m3/d in 85% of the LBRB. Analysis of the PARC results revealed that thick alluvial aquifers had large permissible storage capacity and about 50% of the LBRB was capable of storing over 3500 m3/d of water. This study revealed that sufficient surplus runoff was generated in the LBRB, but it lacked the adequate safe aquifer storage capacity to conserve it. A total 65 subbasins was identified as the best suited sites for MAR which had enough surplus water and storage capacity to suffice 20% of the total water demand in the LBRB. The developed methodology was computationally efficient, could augment the field problem of determining scheduled recharge rates and could be used as a decision-making tool in artificial recharge projects.

Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach

Abstract. Deep learning (DL) algorithms have previously demonstrated their effectiveness in streamflow prediction. However, in hydrological time series modelling, the performance of existing DL methods is often bound by limited spatial information, as these data-driven models are typically trained with lumped (spatially aggregated) input data. In this study, we propose a hybrid approach, namely the Spatially Recursive (SR) model, that integrates a lumped long short-term memory (LSTM) network seamlessly with a physics-based hydrological routing simulation for enhanced streamflow prediction. The lumped LSTM was trained on the basin-averaged meteorological and hydrological variables derived from 141 gauged basins located in the Great Lakes region of North America. The SR model involves applying the trained LSTM at the subbasin scale for local streamflow predictions which are then translated to the basin outlet by the hydrological routing model. We evaluated the efficacy of the SR model with respect to predicting streamflow at 224 gauged stations across the Great Lakes region and compared its performance to that of the standalone lumped LSTM model. The results indicate that the SR model achieved performance levels on par with the lumped LSTM in basins used for training the LSTM. Additionally, the SR model was able to predict streamflow more accurately on large basins (e.g., drainage area greater than 2000 km2), underscoring the substantial information loss associated with basin-wise feature aggregation. Furthermore, the SR model outperformed the lumped LSTM when applied to basins that were not part of the LSTM training (i.e., pseudo-ungauged basins). The implication of this study is that the lumped LSTM predictions, especially in large basins and ungauged basins, can be reliably improved by considering spatial heterogeneity at finer resolution via the SR model.

Nonlinear hydrological time series modeling to forecast river level dynamics in the Rio Negro Uruguay basin.

Floods significantly impact the well-being and development of communities. Hence, understanding their causes and establishing methodologies for risk prevention is a critical challenge for effective warning systems. Complex systems such as hydrological basins are modeled through hydrological models that have been utilized to understand water recharge of aquifers, available volume of dams, and floods in diverse regions. Acquiring real-time hydrometeorological data from basins and rivers is vital for establishing data-driven-based models as tools for the prediction of river-level dynamics and for understanding its nonlinear behavior. This paper introduces a hydrological model based on a multilayer perceptron neural network as a useful tool for time series modeling and forecasting river levels in three stations of the Rio Negro basin in Uruguay. Daily time series of river levels and rainfall serve as the input data for the model. The assessment of the models is based on metrics such as the Nash-Sutcliffe coefficient, the root mean square error, percent bias, and volumetric efficiency. The outputs exhibit varying model performance and accuracy during the prediction period across different sub-basin scales, revealing the neural network's ability to learn river dynamics. Lagged time series analysis demonstrates the potential for chaos in river-level time series over extended time periods, mainly when predicting dam-related scenarios, which shows physical connections between the dynamical system and the data-based model such as the evolution of the system over time.

A new perspective on water quality: Exploring spatial and temporal patterns of impaired waters

Managing surface water quality is a global challenge, and understanding spatial and temporal patterns of water quality is a key component to effective management. However, analysis of spatiotemporal patterns of impaired waters over broad areas is sparse due to disparate water quality data and variable water quality standards. Thus, here we leverage the Alabama 303(d) List of impaired waters to present a new perspective for investigating spatiotemporal water quality patterns. Every two years, each state in the United States is required to assess its surface water quality and compile a list of impaired waterbodies, meaning waters that do not meet water quality standards for their designated usage – referred to as the 303(d) List. The purpose of the 303(d) List is to identify impaired waters so that corrective action can be taken to reduce pollutant loads and, ultimately, improve water quality. Using GIS, a space time cube was created to analyze and visualize spatiotemporal patterns of the impaired rivers added to the Alabama 303(d) Lists from 1996 to 2022. For this analysis, the percentage of river length impaired out of the total river length, and number of times each impairment cause was listed, were summarized within Alabama sub-basins (Hydrologic Unit Code 8) (n = 51). Trend and hot spot analyses were conducted on the river impairment and causes. There was an up trend in river impairment for eight sub-basins across the state and a downtrend in one sub-basin. Over half of the sub-basins with an up trend in impairment also had an up trend in the number of times pathogens was listed as a cause of impairment. Additionally, coastal sub-basins were found to be a hot spot for river impairment. Interestingly, there was a down trend in the number of times nutrients, ammonia, and siltation were listed as a cause of impairment at the state and sub-basin scales of analysis. Altogether, these findings show the use of spatiotemporal pattern analysis of impaired waters and can indicate where, both spatially and by pollutant, management should prioritize water quality improvement efforts.

Temporal–Spatial Characteristics and Trade-off–Synergy Relationships of Water-Related Ecosystem Services in the Yangtze River Basin from 2001 to 2021

The Yangtze River Basin serves as an essential ecological shelter in China, yet it has encountered escalating aquatic ecological challenges. Exploring the spatial–temporal changes and the trade-off–synergy relationships of water-related ecosystem services (WESs) is necessary for formulating management and planning policies targeting the sustainable development of watersheds. In this study, the InVEST model is utilized to evaluate the spatial–temporal variations in water yield (WY), water purification (WP), and soil conservation (SC) in the Yangtze River Basin using remote-sensed data from 2001 to 2021. The spatial overlay method and a correlation analysis were adopted to reveal the trade-off–synergy relationship among the three WESs. Additionally, we performed a comparative analysis across the grid and sub-basin scales. The results showed that the multi-year average WY, WP, and SC were 536.10 mm, 1.32 kg/ha, and 250.08 t/ha, representing increasing rates of 4.74 mm/a, −0.001 kg/ha/a, and 1.88 t/ha/a, respectively. Moreover, the trade-off–synergy relationships of WESs exhibited spatial variability; specifically, the WY-WP, WP-SC, and WY-SC pairs demonstrated reduced synergy magnitude over time. The WES interactions were stable across the scales of interest, while synergy strength showed noticeable variability. The findings may contribute to the sustainable development of the Yangtze River Basin and enhance the comprehensive management of WESs.

Spatial-temporal change of river thermal environment and anthropogenic impact in China

River water temperature is important and closely related to river ecosystem, concerning fishery industry, human health, and the land-sea exchange of nutrients, especially for great powers with a good deal of heat emission from once-through cooling systems of thermal power plants. However, the changes in river water temperature under the joint action of climate change and human activity such as the heat emission have not been well investigated for rising powers, hampering environmental policy making for sustainable development. Therefore, we have taken advantage of a recently-developed land surface model including river water temperature calculation with anthropogenic thermal discharge and zonal statistics to quantitatively make out the river water temperature variation and the man-made influence over the past thirty years (1981–2010) in China for the first time. Results show that the estimated water temperature in major rivers is generally close to the observation with the r2 of 0.83, though the underestimation exists in some rivers. The river water in the Pearl River Basin was the warmest with the mean temperature of 19.2 °C and the others in order were in the Southeast Basin, the Huaihe River Basin, the Yangtze River Basin, the Haihe River Basin, the Yellow River Basin, the Southwest Basin, the Song-Liao River Basin, and the Continental Basin, ranging from 16.7 °C to 6.3 °C. The Huaihe River Basin had the fastest mean increase rate of ca. 0.27 °C decade−1, while the slowest mean increase rate of ca. 0.13 °C decade−1 existed in the Pearl River Basin. At the subbasin scale, a meridionally-distributed hot spot zone (along the 115°E) of the increasing water temperature has been identified, where the trends ranged from 0.2 °C decade−1 to 0.5 °C decade−1. Air temperature exerted a major control on the spatial pattern of climatological water temperature, while both air temperature and downwelling solar flux played a leading role in the distribution of water temperature change trends. Although anthropogenic thermal emission heated the rivers locally, the impacts in the Song-Liao River, the Haihe River, the Huaihe River, and the middle and lower reaches of Yellow River and Yangtze River had been raised up to ca. 4.5 °C, when comparing with those controlled by climate change only. In general, these results show an acceptable level of river water temperature simulation in the land surface model, and could provide a scientific reference for the assessment on riverine thermal environment under the climate change and social impact in China.

A multi-spatial scale assessment of land-use stress on water quality in headwater streams in the Platinum Belt, South Africa

River water quality is affected by various stressors (land-uses) operating at different hydrological spatial scales. Few studies have employed a multi-scaled analyses to differentiate effects of natural grasslands and woodlands, agriculture, impoundments, urban and mining stressors on headwater streams. Using a multi-scaled modeling approach, this study disentangled the distinct spatial signatures and mechanistic effects of specific stressors and topographic drivers on individual water quality parameters in tributaries of the Gwathle River Catchment in the Platinum Belt of South Africa. Water samples were collected on six occasions from 15 sites on three rivers over 12-months. Physio-chemical parameters as well as major anions, cations and metals were measured. Five key water quality parameters were identified using principal components analysis: sulfate, ammonium, copper, turbidity, and pH to characterise catchment water quality conditions. Using class-level composition (PLAND) and connectedness (COHESION) metrics together with topographic data, generalized linear mixed models were developed at multiple scales (sub-basin, cumulative catchment, riparian buffers) to identify the most parsimonious model with the dominant drivers of each water quality parameter. Ammonium concentrations were best explained by urban stress, Cu increased with mining and agriculture, turbidity increased with elevation heterogeneity, agriculture, urbanisation and fallow lands all at the sub-basin scale. River pH was positively predicted by slope heterogeneity, mining cover and impoundment connectivity at the catchment scale. Sulfate increased with mining and agriculture composition in the 100 m riparian buffer. Hierarchical cluster analysis of water quality and scale-dependent parsimonious drivers separated the river sites into three distinct groups distinguishing pristine, moderately impacted, and heavily mined sites. By demonstrating stressor- and scale-dependent water quality responses, this multi-scale nested modeling approach reveals the importance of developing adaptive, targeted management plans at hydrologically meaningful scales to sustain water quality amid intensifying land use.

Hydro-sediment responses to management options under current and future climate change scenarios in Maybar watershed, South Wollo Zone, Ethiopia

Climate change coupled with inappropriate land use management is a serious environmental challenge in the highland part of Ethiopia. The aim of this study was to investigate hydro-sediment responses to management options under current and future climate change scenarios in Maybar watershed, Northern Ethiopia. We employed soil and water assessment tool (ArcSWAT 2012.10_7.24) for hydrologic modeling and CORDEX-climate data under RCP 4.5 and RCP 8.5 emission scenarios for climate projections. Soil map was prepared by digitizing soil characterization map of the watershed generated by Weigel in 1986. DEM data with 2 m spatial resolution was obtained from water and land resource center and land use classification was done through digitization of google earth images. Better agreement between calibrated SWAT simulation and observed variables was achieved. Delta change bias correction method was employed to improve the climate simulation in reproducing the observed climate variables. We found a considerable increase in annual precipitation by 18.71 % and 22.33 % for RCP 4.5 and RCP 8.5, respectively compared with the current climate conditions. Climate change induced hydro-sediment results were observed to be consistent with predicted precipitation. Under the current climatic conditions, average sediment yield at the sub-basin scale varies from negligible (under terrace complementation) to approximately more than 50 t ha−1 yr−1 with a basin average of 32 t ha−1 yr−1 (under no-terrace conditions). The implementation of management alternatives gets the dry season's hydrological behavior of the catchment improved and climate change induced sediment yield reduction. Generally, the information could possibly support decision makers and planners to implement best-fitted management options and thereby reduce current and expected aggressive sediment loss situations.

Calibration using R-programming and parallel processing at the HUC12 subbasin scale in the Mid-Atlantic region: Development of national SWAT hydrologic calibration

The first phase of a national scale Soil and Water Assessment Tool (SWAT) model calibration effort at the HUC12 (Hydrologic Unit Code 12) watershed scale was demonstrated over the Mid-Atlantic Region (R02), consisting of 3036 HUC12 subbasins. An R-programming based tool was developed for streamflow calibration including parallel processing for SWAT-CUP (SWAT- Calibration and Uncertainty Programs) to streamline the computational burden of calibration. Successful calibration of streamflow for 415 gages (KGE ≥0.5, Kling-Gupta efficiency; PBIAS ≤15%, Percent Bias) out of 553 selected monitoring gages was achieved in this study, yielding calibration parameter values for 2106 HUC12 subbasins. Additionally, 67 more gages were calibrated with relaxed PBIAS criteria of 25%, yielding calibration parameter values for an additional 150 HUC12 subbasins. This first phase of calibration across R02 increases the reliability, uniformity, and replicability of SWAT-related hydrological studies. Moreover, the study presents a comprehensive approach for efficiently optimizing large-scale multi-site calibration.

A novel approach to predictor selection among large-scale climate indices for seasonal rainfall forecasting in small catchments

ABSTRACT Utilizing identical climate indices as predictors for all climate divisions within large basins may result in unreliable rainfall forecasts at the sub-basin scale. This study aimed to develop a new approach to identify the most effective predictors among large-scale climate indices for seasonal rainfall forecasting in small areas. The proposed approach combines a selective singular value decomposition method (SSVD) with a non-linear sequential forward selection method (NLSFS). Applying the new algorithm for seasonal rainfall forecasting within two climate divisions in Karkheh basin, Iran, indicated that the climate indices identified by the SSVD differed between the study areas. The combination of these indices exhibited a correlation with seasonal rainfall approximately 11% higher than those derived from the SVD method. Moreover, NLSFS significantly enhanced the forecast accuracy compared to the frequently employed linear sequential forward selection (SFS) method, and the optimal predictors chosen by the two methods differed across all seasons.

Comparisons of donor stray percentages between hatchery‐ and natural‐origin Chinook Salmon and steelhead in the upper Columbia watershed

AbstractObjectiveArtificial propagation of Pacific salmon Oncorhynchus spp. is commonly used to achieve conservation and harvest goals. However, unintended effects of artificial propagation, such as high donor stray percentages, can reduce numbers of adults that return to target areas and also contribute spawners to different populations where they are undesirable. Until recently, it was difficult to assess if hatchery‐origin fish stray rates were atypical because few estimates of stray rates of natural‐origin fish were available. We sought to fill that gap by comparing donor stray percentages of hatchery‐ and natural‐origin Chinook Salmon O. tshawytscha and steelhead O. mykiss from the same locations.MethodsWe used 29,885 last passive integrated transponder tag detections of adult fish to estimate and compare donor stray percentages of hatchery‐origin and natural‐origin Chinook Salmon and steelhead in the upper Columbia River watershed between 2002 and 2018.ResultDonor stray percentages of hatchery‐origin spring, summer, and fall Chinook Salmon and steelhead were &lt;0.3% at the upper Columbia basin scale and similar to natural‐origin donor stray percentages. Only steelhead donor stray percentages were higher for hatchery‐origin adults at the subbasin scale than natural‐origin counterparts and were &lt;22% for all taxa. The percentage of hatchery‐origin steelhead and spring Chinook Salmon released in tributaries were detected at nontarget areas at higher percentages (&lt;63%) than natural‐origin counterparts born in these tributaries. Returning hatchery‐origin and natural‐origin Chinook Salmon and steelhead generally strayed in an upstream direction in similar proportions. Juvenile spring Chinook Salmon that were moved 14–391 km from centralized hatcheries to tributaries for overwintering or final acclimation strayed at a much higher rate than those that completed their incubation, rearing, and acclimation at a single location, but this did not occur with steelhead. Other adaptive management actions that were implemented to reduce straying produced mixed results. A high proportion of stray hatchery‐origin spring Chinook Salmon initially homed to tributary weirs.ConclusionDonor stray percentages of hatchery‐origin Chinook Salmon and steelhead were generally not higher than natural‐origin donor stray percentages at larger spatial scales (e.g., upper Columbia basin and subbasin) but were higher at smaller spatial scales (e.g., tributaries). A variety of approaches can be considered to reduce undesirable production of strays, but most of them involve difficult management trade‐offs.

Amplified Extreme Floods and Shifting Flood Mechanisms in the Delaware River Basin in Future Climates

AbstractHistorical records in the Delaware River Basin reveal complex and spatially diverse flood generating mechanisms influenced by the region's mountains‐to‐plains gradients. This study focuses on predicting future flood hazards and understanding the underlying drivers of changes across the region. Using a process‐based hydrological model, we analyzed the hydrometeorological condition of each historical and future flood event. For each event, at the subbasin scale, we identified the dominant flood generating mechanism, including snowmelt, rain‐on‐snow, short‐duration rain, and long‐duration rain. The rain‐induced floods are further categorized based on the soil's Antecedent Moisture Condition (AMC) before the event, whether dry, normal, or wet. Our historical analysis suggests that rain‐on‐snow is the primary flood mechanism of the Upper Basin. Although most frequent, the magnitude of rain‐on‐snow floods is often less severe than short rain floods. In contrast, historical floods in the Lower Basin are primarily caused by short rain under normal AMC. Given the uncertainties in climate projections, we used an ensemble of future climate scenarios for flood projections. Despite variations in regional climate projections, coherent perspectives emerge: the region will shift toward a warmer, wetter climate, with a projected intensification of extreme floods. The Upper Basin is projected to experience a marked decrease in rain‐on‐snow floods, but a substantial increase in short rain floods with wet AMC. The largest increase in flood magnitude will be driven by short rains with wet AMC in the Upper Basin and by short rains with normal AMC in the Lower Basin.