Donor Catchments Research Articles

Application of hydrological models to streamflow estimation at ungauged transboundary Himalayan River basin, Nepal

ABSTRACT Continuous hydrological records at the desired site with spatial and temporal coverage are essential to water resources management and flood prevention. Hydrological gauging stations and observations are limited at transboundary Himalayan River basins in developing countries. This study is carried out to estimate discharge at ungauged sites from donor catchments based on calibrated discharge. In the Tamakoshi River basin, Spatial Process in HYdrology (SPHY), Hydrologiska Byråns Vattenbalansavdelning (HBV)-light, and Hydrologic Engineering Centre Hydrologic Modelling System (HEC HMS) models were used for hydrological simulation. The ungauged discharge estimated at Benighat is based on daily, monthly, and annual bases from upstream gauged stations. Statistical indices were obtained as NSE > 0.62, R2 > 0.77, and PBias <26% in the simulation of these models. The low flow prediction is more reliable than the high flow prediction. The deviation in predicted flow mostly appeared in high flow periods. All the hydrological models simulated ungauged streamflow were similar in the flow pattern, and the estimated discharge at the ungauged receiver site was greater than at the donor site. Comparative simulations are better alternatives to estimate ungauged discharge than individual simulations. This research will support future reference to generate streamflow data at ungauged sites and can help fill the data recording gap at similar mountain river basins.

Parameter Regionalization With Donor Catchment Clustering Improves Urban Flood Modeling in Ungauged Urban Catchments

AbstractThe lack of discharge observations and reliable drainage information is a pervasive problem in urban catchments, resulting in difficulties in parameterizing urban hydrological models. Current parameterization methods for ungauged urban catchments mostly rely on subjective experiences or simplified models, resulting in inadequate accuracy for urban flood prediction. Parameter regionalization has been widely used to tackle model parameterization issues, but has rarely been employed for urban hydrological models. How to conduct effective parameter regionalization for urban hydrological models remains to be investigated. Here we propose a parameter regionalization framework (PRF) that integrates donor catchment clustering and the optimal regression‐based methods in each cluster. The PRF is applied to an urban hydrological model, the Time Variant Gain Model in urban areas (TVGM_Urban), in 37 urban catchments in Shenzhen City, China. We first show satisfactory flood simulation performance of TVGM_Urban for all urban catchments. Subsequently, we employ the PRF for parameter regionalization of TVGM_Urban. PRF classifies 37 urban catchments into three groups, and the partial least‐squares regression is identified as optimal regression‐based method for Groups 1 and 2, while the random forest model is found to be best for Group 3. To evaluate the simulation performance of PRF, we compare it with eight single regionalization methods. The results indicate better simulation performance and lower uncertainty of PRF, and donor catchment clustering can effectively enhance the simulation performance of linear regression‐based methods. Lastly, we identify curve number, land cover area ratios, and slope as critical factors for most TVGM_Urban parameters based on PRF results.

Simulation of Gauged and Ungauged Streamflow of Coastal Catchments across Australia

Australia is a unique continent, surrounded by the ocean, and the majority of its catchments flow to the coast. Some of these catchments are gauged and others are ungauged. There are 405 gauged catchments covering 2,549,000 km2 across the coastal regions of 12 drainage divisions in Australia, whereas there are 771 catchments conceptualised as ungauged covering additional 835,000 km2. The spatial and temporal distribution of mean annual rainfall and potential evaporation (PET) vary significantly from one drainage division to another. We developed a continuous daily streamflow time series of all gauged and ungauged catchments from 1993 onwards. We applied the daily GR4J lumped conceptual model to these catchments. The performance of gauged catchments was analysed through (i) visual inspection of daily hydrographs, flow duration curves, and daily scatter plots; and (ii) performance metrics, including NSE and PBias. Based on the NSE and PBias, performance ratings of 80% and 96% of the models, respectively, were found to be ‘good’. There was no relationship found between the catchment area and the model performance. The ungauged catchments were divided into four categories based on distance from potential donor catchments, where observed data are available for GR4J model calibration, and Köppen climate zone. The total ungauged catchments represent 24.7% of the total drainage division areas. The streamflow from ungauged catchments was estimated using the GR4J model based on the parameters of their donor catchments. Overall, runoff ratios from ungauged catchments were found to be higher compared to their donor-gauged catchments, likely driven by their higher rainfall and less PET. This tendency was particularly evident in two drainage divisions—the Carpentaria Coast (CC) and the Tanami–Timor Sea Coast (TTS)—where ungauged areas comprised 51% and 43%, respectively. The mean gauged annual streamflow varied significantly across drainage divisions—230 gigalitres (GL) from the South Australian Gulf (SAG) to 146,150 GL in TTS. The streamflow from all ungauged catchments was estimated at 232,200 GL per year. Overall, the average streamflow from all drainage divisions, including gauged and ungauged areas, across the coastal regions of Australia was estimated at 419,950 GL per year. This nationwide estimate of streamflow dataset could potentially enhance our understanding of coastal processes and lead to improvements in marine modelling systems and tools.

Reliability-weighted approach for streamflow prediction at ungauged catchments

Understanding the hydrological processes and the determination of the frequencies and magnitudes of streamflow are crucial for better oversight of available water resources. A significant obstacle to accurately assessing the water resources of a basin is the accessibility of data for hydrologic modeling. For example, nearly 70% of Ethiopia's Omo-Gibe River basin is ungauged, despite the fact that it is currently undergoing major socioeconomic activity, including the development of cascade hydropower generation. As a result, regionalization remains a critical difficulty in creating solid projections in these ungauged catchments. Thus, this study aimed to provide a dependable technique for accurately determining surface water resources in ungauged catchments. To accomplish this, a new approach called Reliability-weighted (RB) has been introduced to predict runoff in regions with unreliable data. This method combines the advantages of three commonly used parameter transfer methods: global mean, physical similarity, and spatial proximity through weighted averaging. The weights are computed using the reliability-weighted values of each parameter transfer strategy, which are also computed using the donor catchment's hydrological model's reliability value during the calibration and validation periods. The suitability of the proposed scheme was checked in the Omo Gibe and Upper Blue Nile River basins, in Ethiopia. After analyzing parameter transfer techniques using the jackknife algorithm at several target-gauging stations, it was found that the reliability-weighted method outperforms all three regionalization approaches by about 30% for the test catchments. In addition, approximately 85% of the time, the proposed strategy's regionalization performance has a metric Nash-Sutcliffe efficiency greater than 0.50. Alternatively, according to the median Efficiency of the Nash-Sutcliffe model, the newly proposed strategy reduces the spatial and temporal losses by around 0.01 and 0.02, respectively, when compared to the second-best performing option. Furthermore, the performance of the presented approach's runoff prediction at the test gauged catchments was very comparable to the performance of the on-site calibrated model. The newly proposed approach therefore proved to be a useful tool for assessing the potential of available water resources in ungauged catchments. Finally, the uncertainty in runoff prediction at ungauged basins was reduced by combining the strengths of parameters transfer strategies relying on their reliability at test gauging stations.

Exploring Signature‐Based Model Calibration for Streamflow Prediction in Ungauged Basins

AbstractCalibration of precipitation‐streamflow models to streamflow signatures is a promising approach for streamflow prediction in ungauged basins (PUB). The estimation of parameter and prediction uncertainty in this case is not trivial because: (a) calibration takes place in the signature domain, while predictions are required in the time domain, and (b) streamflow signatures are estimated (e.g., from donor catchments) rather than “observed” (computed from observed streamflow in the target catchment), and therefore particularly uncertain. This study investigates model calibration using estimated signatures in an Approximate Bayesian Computation framework. First, we construct a stochastic signature transfer model, based on seasonal flow duration curves. Second, we calibrate a precipitation‐streamflow model to the estimated signatures, accounting for their uncertainty. The proposed method is tested in six catchments of the Thur basin, Switzerland. Three data availability scenarios are considered: (a) concomitant scenario, where signatures are “observed,” (b) non‐concomitant scenario, where signatures are transferred from a different time period, and (c) regionalization scenario, where signatures are transferred from (neighboring) donor catchments. In this study, the switch from observed to regionalized signatures increases predictive streamflow uncertainty by 38% and worsens the (deterministic) fit to observations by 17% (in terms of Nash‐Sutcliffe efficiency). Despite this deterioration, posterior predictive uncertainty remains lower than prior predictive uncertainty generated using uniform priors over representative parameter ranges (“uncalibrated” model), which demonstrates the effectiveness of the proposed signature‐based calibration. More importantly, uncertainty is reliably estimated at the ungauged catchments, which represents a key advance in stochastic streamflow PUB.

Regionalization methods for PUB: a comprehensive review of progress after the PUB decade

Abstract This paper presents an updated review of model-dependent regionalization methods in hydrology since the PUB decade, incorporating new regions and methodological advancements. Two categories of regionalization methods are discussed: distance-based and regression-based, with various modification approaches. Several factors affecting the accuracy of regionalization performance are identified, including hydrological models, climate characteristics, data availability, and regionalization techniques. The review concludes that distance-based regionalization methods with an output averaging option from multiple donor catchments are the most statistically reliable, and a threshold of 0.5–0.8 for donor selection is optimal for performance. The parsimonious hydrological model is also recommended, particularly in data-limited contexts. Other insights include the effectiveness of the ensemble concept and limited impact of prior classification. Additionally, it is found that the general performance difference between climatic classes is larger than between methods, and regression-based methods may have large uncertainties in tropical regions. Overall, this study provides practical guidance for improving regionalization studies and advancing the field of hydrology.

Regional streamflow prediction in northwest Spain: A comparative analysis of regionalisation schemes

Study Region: The present study was conducted in 24 watersheds located in the region of Galicia, in the northwest of Spain, covering an extension of approximately 13,000 km2.Study focus: This study is focused on the application and evaluation of different schemes for streamflow Prediction in Ungauged Basins (PUB). The MHIA model (Spanish acronym for Modelo HIdrológico Agregado), is first used to reproduce the observed time series of discharge in several gauged basins. Then, six different regionalisation schemes are applied to transfer the hydrological model parameters to ungauged catchments. For that purpose, we explore and compare two physical similarity, two spatial proximity and two regression-based regionalisation schemes. Output averaging (also known as ensemble modelling) as well as parameter averaging implementations of the physical similarity and spatial proximity methods are analysed.New hydrological insights: The most efficient methods are those based on output averaging, with acceptable success rates (SR) in 88% of the cases. On the other hand, the parameter averaging-based methods have the lowest SR. The methods based on spatial proximity output averaging provide the best performance when the receptor basin has a sufficient number of nearby donor basins. On the other hand, the methods based on physical similarity output averaging show a better performance in areas where there is a low density of donor catchments. The regression-based methods showed the lowest performance in all cases. The existence of correlations between the performance of the regionalisation schemes and the area of the receptor catchments was observed, with higher performances in large basins than in small basins.

Evaluation of using local information from donor catchments to improve the performance of selected deterministic and empirical design flood estimation methods in South Africa

There are a number of design flood estimation methods routinely used in South Africa. Flood Frequency Analysis (FFA) remains the preferred technique in instances where adequate records of observed data are available. However, in many parts of South Africa, rivers are not gauged for continuous streamflow monitoring. In the case of ungauged catchments, hydrologists and engineers generally make use of methods based on deterministic and empirical approaches for design flood estimation. Due to the limitations and performance of these methods, improved approaches need to be developed for design flood estimation in ungauged catchments. International practice has shown that making use of local information transfer from nearby gauged catchments, also referred to as donor catchments, can improve flood estimation in ungauged catchments. The main considerations for implementing such methods are the type of information transferred and selection of suitable donor catchments. Approaches for donor catchment selection include physical similarity, spatial proximity and integrated similarity. Some studies have also shown that the use of multiple donor catchments can offer further improvements. Thus, a methodology is proposed and evaluated in this paper for using local information transfer in the form of a method and location-specific adjustment factor from gauged donor catchments to improve the performance of selected deterministic and empirical flood estimation methods widely used in South Africa. The pilot study was undertaken at 48 catchments located in the north-eastern part of South Africa, with river flow data provided by the Department of Water and Sanitation. The results show that the degree of improvement for each of the methods is largely dependent on the approach used for donor catchment selection. Even though the best approach for donor catchment selection varied for the different design flood estimation methods, the integrated similarity approach performed consistently well for all methods considered. Similar to the use of single donor catchments, the results obtained from the use of multiple nearest donor catchments also varied for each of the methods used. Due to the general improvements, and the promising results and success of the simple approach used in this study, it is recommended that further refinements of the proposed methodology and approaches to donor catchment selection be considered for future research projects in order for the method to be used by engineers and hydrologists in practice.

Identifying most relevant controls on catchment hydrological similarity using model transferability – A comprehensive study in Iran

A key goal of hydrological science is to understand the climatic controls on catchment hydrological response behavior. Progress toward this goal would result in improved model transferability from gauged to gauged catchments. In this sense, the effectiveness of the model's transferability is contingent on the proper selection of donor and target catchment pairs. Thus, using a rainfall-runoff model, we evaluate two distinct types of hydrological similarity in this study: (i) the apparent similarity measured by similarity distance based on observable catchments descriptors (CDs) and a Euclidean distance based on physical similarity (PS) method and (ii) behavioral similarity, which is determined by highest-performance of transferred model parameters between gauged donor catchment and ungauged target catchment (best-donor case (BD)). It is believed that catchments that apparently to be similar in terms of CDs, have a similar hydrological behavior. We wish to see if that assumption is valid in this paper. Spatial proximity (SP) is also implemented to see if it might be used as an alternative for PS where there is no apparent physical similarity between catchments. To test the study's assumptions, the HBV conceptual rainfall-runoff model is used in 576 catchments across four climate regions in Iran. The results indicate that: (1) as expected, the best-donor (BD) case performs the best, and the more than 75 % of our physically similar catchments have a hydrological similarity (the overlap was ≥ 70 %), (2) the superiority of PS over SP demonstrates that the CDs exert a great influence on transferability within each climate region than geographical distance. However, we demonstrated that the SP is superior, when spatial distance between donor and target catchments is reduced (nearest neighbor ≤ 20 km), (3) consistent with CDs, when utilizing SP method, geographical distance has a varying effect on model transferability within wetter and drier regions, such that SP performs better in wetter regions than it does in dry interior regions, (4) throughout Iran, the dominant controls on model transferability differ by region. Thus, the climatic (aridity index or PET/P), topographic (mean elevation), and physiographic (catchment area) properties exert a greater influence on parameter transfer to ungauged catchments than do other CDs, and (5) the runoff ratio (streamflow signature) confirmed the superiority of the wetter regions over the drier regions in terms of control on the parameters transfer.

Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology

Model parameters estimation is a pivotal issue for runoff modeling in ungauged catchments. The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization, which is the most widely used approach. Runoff modeling was studied in 38 catchments located in the Yellow–Huai–Hai River Basin (YHHRB). The values of the Nash–Sutcliffe efficiency coefficient (NSE), coefficient of determination (R2), and percent bias (PBIAS) indicated the acceptable performance of the soil and water assessment tool (SWAT) model in the YHHRB. Nine descriptors belonging to the categories of climate, soil, vegetation, and topography were used to express the catchment characteristics related to the hydrological processes. The quantitative relationships between the parameters of the SWAT model and the catchment descriptors were analyzed by six regression-based models, including linear regression (LR) equations, support vector regression (SVR), random forest (RF), k-nearest neighbor (kNN), decision tree (DT), and radial basis function (RBF). Each of the 38 catchments was assumed to be an ungauged catchment in turn. Then, the parameters in each target catchment were estimated by the constructed regression models based on the remaining 37 donor catchments. Furthermore, the similarity-based regionalization scheme was used for comparison with the regression-based approach. The results indicated that the runoff with the highest accuracy was modeled by the SVR-based scheme in ungauged catchments. Compared with the traditional LR-based approach, the accuracy of the runoff modeling in ungauged catchments was improved by the machine learning algorithms because of the outstanding capability to deal with nonlinear relationships. The performances of different approaches were similar in humid regions, while the advantages of the machine learning techniques were more evident in arid regions. When the study area contained nested catchments, the best result was calculated with the similarity-based parameter regionalization scheme because of the high catchment density and short spatial distance. The new findings could improve flood forecasting and water resources planning in regions that lack observed data.

Regionalization for Ungauged Catchments — Lessons Learned From a Comparative Large‐Sample Study

AbstractModel parameter values for ungauged catchments can be regionalized from hydrologically similar gauged catchments. Achieving reliable and robust predictions in ungauged catchments by regionalization, however, is still a major challenge. Here, we conduct a comparative assessment of 19 regionalization approaches based on previously published literature to contribute new insights into their performance in different geographic regions. The approaches use geographical information, physical catchment attributes, hydrological signatures, or a combination thereof to select donor catchments and to subsequently transfer their entire parameter sets to the ungauged receiver catchment. Each regionalization approach was tested in a leave‐one‐out cross‐validation with a bucket‐type catchment model (the HBV model) using 671 gauged catchments in the United States with a diverse hydroclimatology. We then evaluated regionalization performance for several hydrograph aspects, compared it against calibration and regionalization benchmarks, and linked it to catchment descriptors. The results of this large‐sample regionalization study can be summarized in three major lessons: (a) Catchments can benefit from a well‐chosen regionalization approach independent of their geographic region and independent of how well they can be modeled or regionalized at best. (b) Almost perfect donors exist for most catchments and an excellent relative model performance can be reached for most catchments with current regionalization approaches. This implies that there is considerable potential for improvement in the prediction in ungauged catchments. (c) The ranking of regionalization approaches depends on how the predicted hydrographs are evaluated. These findings indicate that a multi‐criteria evaluation is essential for a robust assessment of regionalization performance.

Impact of the number of donor catchments and the efficiency threshold on regionalization performance of hydrological models

Over recent decades, hydrologists have proposed a variety of methods to predict discharge in ungauged catchments, and significant progress has been made in the field of hydrological model parameter regionalization. However, uncertainties from both hydrological models and regionalization methods make it a challenge to draw clear conclusions for some questions in regionalization (e.g., the best performing regionalization method, the optimal number of donor catchments, and the optimal efficiency threshold of donor catchments). In this study, for the first time, we made an attempt to address such questions in one paper through a comprehensive evaluation of model performance by using five regionalization methods with two weighting schemes, two averaging options, five efficiency thresholds, and four lumped hydrological models over a broad set of 3444 catchments under varying hydroclimatic conditions in North America. The results show that: (1) Spatial Proximity with the Inverse Distance Weighting method and the output average option (SPI-out) generally performs better than or comparable to other regionalization methods for different climate regions and hydrological models, while the global mean method performs the worst. (2) The rank of different regionalization methods is similar among different climatic regions and hydrological models. Compared to catchments with other climates, regionalization methods perform worst in the arid regions. (3) The selection of five donors is relatively efficient for distance/attributes-based regionalization approaches with the output averaging option disregarding the efficiency threshold. (4) The differences of median Kling-Gupta efficiency values among thresholds of “all”, 0.6 and 0.7 are no more than 0.05 for each regionalization method. However, the regionalization performance significantly deteriorates from using efficiency thresholds of 0.7 to 0.9 due to the significant reduction of available donor catchments. Thus, the poorly calibrated catchments may need to be included in the regionalization process, especially when the number of catchments is insufficient.

Uncertainty analysis in parameter regionalization for streamflow prediction in ungauged semi-arid catchments

ABSTRACT Predicting in ungauged basins (PUB) depends on the modelling uncertainties in the donor catchments (DCs). However, PUB is normally limited to a unique outcome, which may be quite uncertain, mainly in semi-arid basins where streamflow variability is high. Our goal is to assess the uncertainty in the parameter regionalization for streamflow prediction in semi-arid ungauged basins (UB). We used Differential Evolution Adaptive Metropolis (DREAM) for a parameter calibration that considers its intrinsic uncertainty. A basin similarity regionalization was performed to transfer the parameters to UB, considering one, three and five DCs. Leave-one-out cross-validation was applied for 28 gauged catchments. Regionalization performance had average Nash-Sutcliffe efficiency above 0.50. The approach considering one DC performed better than the others. The model of a poorly monitored catchment performed better using the transferred parameters from long-recorded, similar catchments than using those calibrated in the catchment itself. The developed regionalization may be a relevant tool for water management in drylands.

Taking a Closer Look at Distance: Does Increasing the Maximal Donor Distance Range Shorten Waitlist Times in Pediatric Heart Transplant Candidates?

<h3>Purpose</h3> Data suggest that pediatric heart transplant (HT) centers can reduce waitlist time by expanding donor catchment vis expanded parameters including the maximal donor weight range. Contrary to donor weight ranges, little is known about the effect of increasing the donor distance (DD) range. We sought to determine whether increasing the DD range can reduce waitlist time and how well actual DD predicts ischemic time (IT). <h3>Methods</h3> All US children <18 years listed for HT at centers with ≥20 HT since 2007 were identified using OPTN data. Center variation in maximal DD range, defined as the median value of a center's maximal DD range, was analyzed and compared to actual DD. Pearson correlation was used to examine the correlation between actual DD and IT. Cox proportional hazards analysis was used to determine whether maximal DD range is associated with waitlist time. <h3>Results</h3> Among 4,240 children who met study criteria, the median age was 2 (IQR 0, 11) years, weight 12 (6, 35) kg, 53% had CHD. Centers varied considerably in their maximal DD range (1000-10,000 miles); median 1500 miles (Figure A). Overall, 86% of donors originated from ≤500 miles; 97% ≤1,000 miles (Figures A & B) with sharp drop-offs at 500 and 1000 miles (Figure B). After adjusting for patient factors, maximal DD range was associated with a shorter waitlist time. The correlation between actual DD and IT is fair (R=0.69) but explains only half (R²=0.49) of the variability in IT. <h3>Conclusion</h3> Pediatric HT centers vary considerably in their maximal donor distance ranges, though most donors still fall within 500-1000 miles of recipients. Increasing the maximal DD range appears is associated with a shorter waitlist time. Actual DD explains only half of the variability in IT. Further research is needed to understand the optimal regional sharing distance for organ allocation and whether contemporary navigational software could better predict IT than the linear distance to the donor hospital.

Improving the flood forecasting capability of the Xinanjiang model for small- and medium-sized ungauged catchments in South China

Prediction in ungauged basins (PUB) is as crucial as it is challenging. Thus far, there have been abundant regionalization studies on PUB, whereas "regionalization" is the main research method. In order to estimate Xinanjiang model parameters in ungauged areas and improve the accuracy of flood simulation for small and medium-sized ungauged catchments, the Xinanjiang model was applied on 33 mountainous small- and medium-sized catchments in south China. This study investigated the relative benefits of traditional regionalization methods (physical similarity and parameter regression) and physically consistent parameter estimation method. The latter can directly estimate three sensitive parameters of the Xinanjiang model without the need of regionalization. In addition, the effect of the number of donor catchments was addressed. The results show that the prediction accuracy of the traditional regionalization methods did not obtain satisfactory prediction results. However, the integrated schemes, which combine the regionalization methods with physically consistent methods, performed considerably better than the traditional regionalization methods, indicating that directly performing a parameter estimation from underlying surface data of ungauged catchments can improve the transferability of the Xinanjiang model in these catchments. On the other hand, the best accuracy was obtained when the number of donor catchments was equal to five in the integrated schemes. The integrated parameter estimation schemes proposed in this study support more effective hourly flood event simulation for small- and medium-sized ungauged catchments in southern China.

Application of Xin'anjiang Model in the flow prediction of ungauged small- and medium-sized catchments in the middle and lower reaches of the Yangtze River Basin

水文资料匮乏流域的洪水预报（PUBs）是水文科学与工程中一个尚未解决的重大挑战.中国湿润山区中小流域大多是水文资料匮乏的流域，在此地区进行洪水预报的重要手段之一就是水文模型参数的估计.对基于参数物理意义的估算方法（以下简称物理估算法）及两种区域化方法进行了研究，将其用于新安江模型参数的估算及移植.皖南山区的29个中小流域被选作水文资料丰富的测量流域，鄂西山区的3个中小流域被视为水文资料匮乏的目标流域，目的是研究目标流域与测量流域空间位置较远但物理条件相似时，区域化等方法是否可以有效估计模型参数.结果表明，即使目标流域与测量流域空间距离较远，区域化及物理估算法也能一定程度上减少参数估计导致的模型效率损失，且在研究区的最优参数估计方案为单流域物理相似法结合回归法及物理估算法.为长江中下游资料匮乏的山区中小流域提出了可行的新安江模型参数估计方案，为该地区的洪水预报提供指导.;Flow prediction in ungauged basins (PUB) is as important as it is difficult. PUB is a major unsolved challenge in scientific and engineering hydrology. So far, empirical and stochastic methods have mainly been used for this purpose. Many small catchments in humid mountainous areas of China are with little or no hydrological data. The principal method of flood forecasting in this area is the parameter estimation of the hydrological model. Xin'anjiang Model was used to predict flow in this study, and Xin'anjiang Model parameters were estimated based on the physical meaning of them and regionalization. Regression and physical similarity approach were included in regionalization methods in this study. The 29 small basins in mountainous areas in southern Anhui Province were selected for gauged catchments, and three small basins in mountainous regions in western Hubei Province were treated as ungauged catchments. The main goal of this approach was to research whether regionalization and other methods could effectively estimate model parameters when the spatial location of the gauged catchments and the ungauged catchments were far away, but the physical conditions were similar. It is shown that:(1) Xin'anjiang Model has high accuracy in flow prediction of study areas. (2) Even if the space distance of gauged catchments and ungauged catchments is far, regionalization and physical-based estimation method can also reduce the efficiency loss caused by parameter estimation to some extent. (3) With the increase in the number of donor catchments, the efficiency of the physical similarity approach is reduced. (4) The optimal parameter estimation scheme in the study area is the ingenious combination of physical similarity approach (only one donor catchment), regression approach and physical-based estimation method. A feasible parameter estimation scheme of the Xin'anjiang Model is proposed in this study, which provides guidance for flow forecasting in small ungauged catchments in humid mountainous areas among the middle and lower reaches of the Yangtze River.

An end-user-friendly hydrological Web Service for hydrograph prediction in ungauged basins

ABSTRACT A consolidated geomorphology-based approach for discharge prediction in ungauged basins (PUB) through deconvolution of discharge signals from gauged donor catchments, their transposition and their convolution towards target outlets, has been made available online to end users through a Web Processing Service (WPS) for the synoptic peninsular region of Brittany, France. In the spirit of hydrological services, the SIMFEN WPS allows anyone to execute the hydrological modelling package transfR online through: (1) Open Geospatial Consortium (OGC®) interoperability standards; (2) collection and use of public hydrometric data; (3) connection to the pre-existing MNTSurf WPS for geomorphometric analysis; (4) visualization using a collaboratively developed Mviewer; and (5) innovative WPS workflows. The ability to model discharge series at any ungauged outlet of the synoptic region is offered to specialists of other disciplines, non-modeller water practitioners and interested citizens to support interdisciplinarity, water monitoring and management, and related science-society-policy debates and actions.

Importance of the informative content in the study area when regionalising rainfall-runoff model parameters: the role of nested catchments and gauging station density

Abstract. The setup of a rainfall-runoff model in a river section where no streamflow measurements are available for its calibration is one of the key research activities for the Prediction in Ungauged Basins (PUB): in order to do so it is possible to estimate the model parameters based on the hydrometric information available in the region. The informative content of the dataset (i.e. which and how many gauged river stations are available) plays an essential role in the assessment of the best regionalisation method. This study analyses how the performances of regionalisation approaches are influenced by the “information richness” of the available regional dataset, i.e. the availability of potential donors, and in particular by the gauging density and by the presence of nested donor catchments, which are expected to be hydrologically very similar to the target section. The research is carried out over a densely gauged dataset covering the Austrian country, applying two rainfall-runoff models and different regionalisation approaches. The regionalisation techniques are first implemented using all the gauged basins in the dataset as potential donors and then re-applied, decreasing the informative content of the dataset. The effect of excluding nested basins and the status of “nestedness” is identified based on the position of the closing section along the river or the percentage of shared drainage area. Moreover, the impact of reducing station density on regionalisation performance is analysed. The results show that the predictive accuracy of parameter regionalisation techniques strongly depends on the informative content of the dataset of available donor catchments. The “output-averaging” approaches, which exploit the information of more than one donor basin and preserve the correlation structure of the parameter, seem to be preferable for regionalisation purposes in both data-poor and data-rich regions. Moreover, with the use of an optimised set of catchment descriptors as a similarity measure, rather than the simple geographical distance, results are more robust to the deterioration of the informative content of the set of donors.

Improved Model Parameter Transferability Method for Hydrological Simulation with SWAT in Ungauged Mountainous Catchments

The sustainability of water resources in mountainous areas has a significant contribution to the stabilization and persistence of the ecological and agriculture systems in arid and semi-arid areas. However, the insufficient understanding of hydrological processes in ungauged mountainous catchments (UMCs) is not able to scientifically support the sustainable management of water resources. The conventional parameter transferability method (transplanting the parameters of the donor catchment model with similar distances or attributes to the target catchment model) still has great potential for improving the accuracy of the hydrological simulation in UMC. In this study, 46 river catchments, with discharge survey stations and multi-type catchment characteristics in Xinjiang, are separated into the target catchments and donor catchments to promote an improved model parameter transferability method (IMPTM). This method synthetically processes the SWAT model parameters based on the distance approximation principle (DAP) and the attribute similarity principle (ASP). The performance of this method is tested in a random gauged catchment and compared with other traditional methods (DAP and ASP). The daily runoff simulation results in the target catchment have relatively low accuracy by both the DAP method ( N S = 0.27, R 2 = 0.55) and ASP method ( N S = 0.36, R 2 = 0.65), which implies the conventional approach is not capable of processing the parameters in the target regions. However, the simulation result by IMPTM is a significant improvement ( N S = 0.69, R 2 = 0.85). Moreover, the IMPTM can accurately catch the flow peak, appearance time, and recession curve. The current study provides a compatible method to overcome the difficulties of hydrological simulation in UMCs in the world and can benefit hydrological forecasting and water resource estimation in mountainous areas.

Taking a Closer Look at Distance: Does Increasing the Maximal Donor Distance Range Shorten Waitlist Times in Pediatric Heart Transplant Candidates?

Data suggest that pediatric heart transplant (HT) centers can reduce waitlist time by expanding donor catchment via expanded parameters including the maximal donor weight range. Contrary to donor weight ranges, little is known about the effect of increasing the donor distance (DD) range. We sought to determine whether increasing the DD range can reduce waitlist time and how well actual DD predicts ischemic time (IT). All US children <18 years listed for HT at centers with ≥20 HT since 2007 were identified using OPTN data. Center variation in maximal DD range, defined as the median value of a center's maximal DD range, was analyzed and compared to actual DD. Pearson correlation was used to examine the correlation between actual DD and IT. Cox proportional hazards analysis was used to determine whether maximal DD range is associated with waitlist time. Among 4,240 children who met study criteria, the median age was 2 (IQR 0, 11) years, weight 12 (6, 35) kg, 53% had CHD. Centers varied considerably in their maximal DD range (1000-10,000 miles); median 1500 miles (Figure A). Overall, 86% of donors originated from ≤500 miles; 97% ≤1,000 miles (Figures A & B) with sharp drop-offs at 500 and 1000 miles (Figure B). After adjusting for patient factors, maximal DD range was associated with a shorter waitlist time. The correlation between actual DD and IT is fair (R=0.69) but explains only half (R2=0.49) of the variability in IT. Pediatric HT centers vary considerably in their maximal donor distance ranges, though most donors still fall within 500-1000 miles of recipients. Increasing the maximal DD range is associated with a shorter waitlist time. Actual DD explains only half of the variability in IT. Further research is needed to understand the optimal regional sharing distance for organ allocation and whether contemporary navigational software could better predict IT than the linear distance to the donor hospital.