Data-scarce Catchment Research Articles

Estimating impacts of land cover change on erosion in a data-scarce catchment: Bot River, South Africa

ABSTRACT Rainfall intensity and land use changes and from natural vegetation to cultivated fields both impact on river streamflows and sediment dynamics. Assessing the magnitude of these flow and sediment changes is important for water management. However, many rivers are still under-studied/monitored and data sparse. Fully distributed rainfall–runoff models, such as the Jena Adaptable Modelling System (JAMS/J2000), allow for simulation of streamflow, its flow components and potential sediment yield given land use and hydrological changes. This study demonstrates the use of this method in the Bot River, a data-scarce catchment in South Africa. A hydrological model was implemented, and sediment yield was estimated under three land use and two hydrological flow component scenarios. The model reported Nash-Sutcliffe efficiencies (E2) of 0.64 and 0.32 (for streamflow) for model calibration and validation periods, respectively. Modelled mean annual sediment yield increased from 423 t/km2/year in 1990 to 490 t/km2/year in 2018, attributable to land use changes.

Hybridization of deep learning, nonlinear system identification and ensemble tree intelligence algorithms for pan evaporation estimation

A reliable pan evaporation (Epan) estimation over a daily scale is vital for sustainable water and agriculture management, especially for designing water use allocations, irrigation system management, and demand and utilization assessments. However, designing a reliable computational methodology is a challenging task for Epan estimation due to its stochastic and random characteristics. In this study, the potential of five machine learning (ML) models namely Hammerstein-Weiner (HW), Random Forest (RF), Boosted Regression Tree (BRT), Long Short-Term Memory (LSTM), and Stepwise Linear Regression (SWLR) models were evaluated for Epan modeling in Lake Hawassa catchment, Ethiopia. Afterward, hybrid SWLR-LSTM, SWLR-RF, SWLR-BRT, and SWLR-HW models were developed to benefit from the strength of the non-linear and linear characteristics of the models. The estimation results were evaluated using determination coefficient (R2), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE) and Root Mean Square Error (RMSE), Index of Agreement (IA) and Kling-Gupta efficiency (KGE). The performance analysis result of individual models showed that the RF model led to more accurate estimation with NSE = 0.938, IA = 0.956, R2 = 0.94, KGE = 0.852, RMSE = 0.483 mm/day and Pbias = -1.193 mm/day during the validation phase. Among the hybrid models, SWLR-RF provides the best predictive performance improving SWLR, HW, LSTM, BRT and RF by 26.563 %, 21.348 %, 15.577 %, 9.091 % and 3.514 %, respectively based on the validation phase NSE value. Generally, the results of the current study demonstrated the capability of deep learning, ensemble tree and hybrid linear-nonlinear models for Epan estimation in the data-scarce catchment.

Applicability of CHIRPS-based Pitman model for simulation of climate change flows

Hydrological modelling and continuous monitoring are important for the management of water resources, especially with intensified climate variability. Effective water management is reliant on the availability of climate data and simulations of future projections of the available water resources. The increased likelihood of climatic extremes has made effective water management challenging, especially for vulnerable data-scarce catchments that are highly reliant on natural water resources and have low adaptive capacities. However, advancements in remote sensing have availed alternative sources of climate data for hydrological studies of such catchments. This research evaluated the applicability of Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) in simulating climate change flows. Climate change data of 11 Global Circulatory Models (GCMs), enforced by Representative Concentration Pathways (RCP) 4.5 & 8.5 were downloaded and downscaled to represent near (2021–2060) and far (2061–2099) future scenarios. Climate change signals were derived by comparing future scenarios with the present-day period (1981–2019). Climate change signals were then used to adjust input climate data for the Pitman model. The Pitman model was calibrated and projected future flows were simulated for study catchments in drainage regions B, V and G of South Africa. Climate change simulation results demonstrated projected decreases of between 1.49% and 29.96% for the near future scenarios & between 0.45% and 40.31% for the far future scenarios in all the study catchments. Simulated flows for drainage region B recorded contrasting increasing projections of between 17.18% and 24.88% (RCP 4.5) for the near future scenarios while drainage region G also illustrated contrasting increasing projections of between 6.23% and 7.67% (RCP 4.5) for the far future scenarios. Generally, the results of simulated climate change flows based on observed rainfall and CHIRPS data demonstrated decreasing future flows for the South African case study catchments, corroborating with previous studies in projecting a drier future. This research hence recommends the application of CHIRPS estimates as a suitable substitute to dwindling observed rainfall data in the simulation of climate change flows for data-scarce catchments using the Pitman model.

Can geomorphic flood descriptors coupled with machine learning models enhance in quantifying flood risks over data-scarce catchments? Development of a hybrid framework for Ganga basin (India).

Quantifying flood risks through a cascade of hydraulic-cum-hydrodynamic modelling is data-intensive and computationally demanding- a major constraint for economically struggling and data-scarce low and middle-income nations. Under such circumstances, geomorphic flood descriptors (GFDs), that encompass the hidden characteristics of flood propensity may assist in developing a nuanced understanding of flood risk management. In line with this, the present study proposes a novel framework for estimating flood hazard and population exposure by leveraging GFDs and Machine Learning (ML) models over severely flood-prone Ganga basin. The study incorporates SHapley Additive exPlanations (SHAP) values in flood hazard modeling to justify the degree of influence of each GFD on the simulated floodplain maps. A set of 15 relevant GFDs derived from high-resolution CartoDEM are forced to five state-of-the-art ML models; AdaBoost, Random Forest, GBDT, XGBoost, and CatBoost, for predicting flood extents and depths. To enumerate the performance of ML models, a set of twelve statistical metrics are considered. Our result indicates a superior performance of XGBoost (κ = 0.72 and KGE = 82%) over other ML models in flood extent and flood depth prediction, resulting in about 47% of the population exposure to high-flood risks. The SHAP summary plots reveal a pre-dominance of Height Above Nearest Drainage during flood depth prediction. The study contributes significantly in comprehending our understanding of catchment characteristics and its influence in the process of sustainable disaster risk reduction. The results obtained from the study provide valuable recommendations for efficient flood management and mitigation strategies, especially over global data-scarce flood-prone basins.

Hydrologic interpretation of machine learning models for 10-daily streamflow simulation in climate sensitive upper Indus catchments

Machine learning for hydrologic modeling has seen significant recent development and has been suggested as a valuable augmentation to physical hydrological modeling, especially in data-scarce catchments. In Pakistan, surface water flows predominantly originate from the transboundary Upper Indus sub-catchments of Chenab, Jhelum, Indus, and Kabul rivers. These catchments have large drainage areas, climate-driven streamflows, high variations in elevation, and limited streamflow gauge coverage. Hence, using machine learning models for data-driven river flow modeling may be well-suited for these catchments. However, hydrologic interpretability of machine learning models is important for the practical use of such models for these catchments. Thus, the current study besides evaluating the potential of three machine learning models (XGBOOST, Classification and Regression Trees(CART), and RandomForest) for streamflow simulation also focused on the hydrologic interpretation of machine learning models using SHapley Additive exPlananations (SHAP). All of these models performed well and the range of R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{R}^2$$\\end{document} and Nasche-Efficiency for all three models lies between 0.61 to 0.90. Moreover, SHAP correctly identified minimum temperature as the most critical feature in glacier-fed Indus and Chenab catchments. It also provides logical insights into interactions between minimum temperature and precipitation for the indus and Chenab catchment. The findings of this study strongly illustrate the usefulness of SHAP analysis in interpreting the behavior of data-scarce high-elevation climate-sensitive catchments using tree-based machine learning models.

Developing a model to assess the impact of farm dams and irrigation for data-scarce catchments

ABSTRACT Productive agricultural supply chains require the support of functional ecosystems, but intense agricultural practices change local hydrological systems (e.g. river diversion). In this study, the impact of farm dams was assessed for the Verlorenvlei catchment, a sensitive ecosystem currently under a state of hydrological change in South Africa. We developed a new module for the Jena Adaptable Modelling System (JAMS)/J2000 rainfall–runoff model to assess the streamflow impact from the points of abstraction, losses during storage and irrigation. The model achieved a satisfactory streamflow calibration with efficiencies Nash Sutcliffe Efficiency (NSE, logNSE) of 0.52 and 0.51. The irrigated area reduced simulated streamflow by 12 to 19%. The results from the study agree with remote sensed evapotranspiration, measured lake surface water levels and streamflow, but uncertainty remains in the total simulated am evaporation. While many catchments lack the data required for a detailed irrigation impact assessment, this approach considers total water use, dam storage to area relationships and general farming practices.

Comparing conceptual and super ensemble deep learning models for streamflow simulation in data-scarce catchments

Study RegionSore and Masha river catchments in Baro Akobo river basin: Ethiopia. Study FocusThis research addresses the challenges associated with conventional data-driven streamflow modelling, which often exhibits inconsistent performance across different variability states. To bridge this gap, we explore the efficiency of model ensembles, a popular hydrological approach that harnesses the strengths of multiple models while preserving the fundamental characteristics of the data. Specifically, we compare three modified super ensemble meta-learners with eight single and hybrid machine learning base learners. The study also incorporates a semi-distributed HBV-light conceptual hydrological model and utilizes a decade of remote sensing and ground hydro-meteorological daily time series data. Different remote sensing-based vegetation index data products are employed to simulate single-step daily streamflow. The Recursive Feature Elimination (RFE) algorithm is applied to extract influential input parameters. New Hydrological Insights for the RegionOur findings consistently demonstrate that the three super ensemble learners outperform both the eight base models and the HBV-light model. The top-ranked Extra Tree Regression Super Ensemble (ETRSE) model exhibits superior performance, surpassing the HBV-light model by 24% according to the R2 performance measure. This study highlights the positive impact of selecting influential input parameters on the overall performance of machine learning models, providing valuable insights for hydrological modelling in the region.

Machine learning method is an alternative for the hydrological model in an alpine catchment in the Tianshan region, Central Asia

Study regionKaidu River catchment in the Tianshan Mountain, northwestern China. Study focusThis paper compared the applicability and accuracy of four machine learning models and two hydrological ones to simulate the daily streamflow and extreme streamflow of the Kaidu River catchment. The machine learning models are Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), Random Forests (RF), and Long Short-Term Memory (LSTM), while the hydrological models are the Soil and Water Assessment Tool (SWAT) and the extended SWAT with a glacier dynamic module (SWAT-Glacier). New hydrological insights for the regionLSTM achieved better model performance in simulating daily streamflow than SWAT and SWAT-Glacier, with Kling-Gupta efficiency of 0.92, 0.82, and 0.80, respectively. Meanwhile, SVR, XGBoost, and RF showed satisfactory performance, with KGE of 0.67, 0.71, and 0.70, respectively. LSTM, SWAT and SWAT-Glacier could well simulate the annual peak flow (i.e., annual maximum 1-day streamflow and 5-day average streamflow) but failed to mimic the annual minimum 7-day average streamflow, with PBIAS exceeding 28%. Furthermore, all the models failed to reproduce the dates of hydrological extremes. Nevertheless, using the quantile loss function in the LSTM model resulted in significantly improved model performance in the low streamflow indices, compared to that using mean squared error as the loss function. Overall, LSTM could be a good alternative for simulating daily streamflow and extreme streamflow in data-scarce catchments.

The Potential Use of the SCS-CN Method to Estimate Extreme Floods in the West Bank Data-scarce Catchments

In the West Bank, extreme floods are becoming more frequent. This can be attributed to the expansion of urban areas together with the climate change impacts (the increasing rainfall intensity). This situation worsens given the lack of proper hydraulic infrastructures required to safely dispose of the accumulated floods. Thus, it becomes es-sential to estimate extreme flood values so as help decision-makers in formulating proper corrective and preventive strategies. This study utilizes the SCS-CN approach to estimate extreme flood values at the different West Bank data-scarce catchments. Maximum annual daily rainfall (MADR) values were used to obtain extreme rainfall daily grids for different selected return periods (10, 25, 50, and 100 years). The GIS was heavily used to apply the SCS-CN method for the entire West Bank catchments. Results show that extreme daily rainfall values are increasing with the increase of the return period. Further, catchments draining into the west (into the Mediterranean Sea) have more extreme daily rainfall values than those draining to the east (into the Dead Sea and the Jordan river) and as such, the expected extreme floods in these catchments are high for the selected return periods. Moreover, based on the average expected extreme flood values, a flood hazard map was developed that classifies West Bank catchments into 5 flood hazard zones. By area, about 42% of the total West Bank catchments are under high to very high flood hazard zone. The results of this research can be adapted to realize any administrative strategy changes for cohe-sive catchment-based extreme flood hazard management and to prioritize flood remedial plans at the different West Bank catchments

Development of a new agro-meteorological drought index (SPAEI-Agro) in a data-scarce region

ABSTRACT Drought complexity may not be accurately characterized by univariate meteorological or hydrological drought indices under the intensification of hydrological cycle due to climate change and human activities. In particular, such drought indices require long series of hydro-meteorological data, which are unavailable over ungauged and data-scarce catchments. In this study, a multivariate drought index, Standardized Precipitation Actual Evapotranspiration Index-Agro (SPAEI-Agro), is proposed, which combines meteorological and hydrological variables as precipitation (P), actual evapotranspiration (AET), runoff and groundwater (GW) of ungauged catchments and sub-catchment scales with diverse climatology. SPAEI-Agro was able to characterize severe drought events more accurately in humid and dry sub-humid sub-catchments compared to Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Streamflow Drought Index (SSDI) for Tunga-Bhadra River, India. SPEI (based upon potential evapotranspiration) and SSDI (based on streamflow) showed intensified drought characteristics compared to the new P, AET and GW-based drought indicator SPAEI-Agro in semi-arid and dry sub-humid climates.

Conceptualization of groundwater-surface water interaction with evidence from environmental isotopes and hydrogeochemistry in lake Babati Basin in Northern Tanzania

The holistic management of water resources is attainable with evidence-based planning constructed on reliable hydrological information. However, data paucity, especially in sub-Saharan Africa, leads to a significant knowledge gap regarding surface water and groundwater interactions, leaving considerable uncertainty to support relevant decisions. This study combined hydrogeochemistry and environmental isotopes to explore the connections between the surface water and groundwater in the data scarce catchment of Lake Babati Basin in northern Tanzania. The results showed that: i) Lake Babati receives groundwater influx from recently recharged aquifers endowed in the pyroclastic formation in the lake basin; ii) the lake is not well mixed as presented by the heterogenous hydrogeochemistry and confirmed by the differences in the isotopic composition of deuterium and oxygen – 18 at different locations within it; iii) the deep wells located near the lake abstract a mixture of recently recharged water, deep aquifer waters and lake water with enriched isotopic compositions; and iv) the hydrogeochemistry and isotopic data reveal the presence of a hydraulic connection between the lake and deep wells. The findings of this study demonstrate that the catchment, the groundwater system, and Lake Babati are interlinked and, therefore, require the implementation of integrated water resources management to enhance sustainable development.

A multi-criteria approach for improving streamflow prediction in a rapidly urbanizing data scarce catchment

ABSTRACT This study advocates a multi-criteria approach to improve the streamflow predictions in a data-scarce catchment of Chennai metropolitan city of India using the Soil Water and Assessment Tool (SWAT). The remotely sensed evapotranspiration (ET) data, groundwater recharge estimation, and parameter regionalization were used to improve model prediction. Dynamic change of Land Use and Land Cover (LULC) was accounted for along with multi-parameter calibration for reducing the uncertainty in model parameters. The results revealed an improved streamflow prediction accuracy by 10%, especially in the prediction of medium and high flows with the Nash-Sutcliffe efficiency of 0.60. The enhanced parameters were regionalized to ungauged sub-basins and validated using a measured flow event downstream of regionalization with 15% prediction uncertainty. This semi-arid catchment is dominated by ET (58%) and runoff (27%) in the region's hydrology. The finding of this study can be applied to improve the hydrological modelling and predictions in data-scarce regions.

Estimating degree-day factors of snow based on energy flux components

Abstract. Meltwater from mountainous catchments dominated by snow and ice is a valuable source of fresh water in many regions. At mid-latitudes, seasonal snow cover and glaciers act like a natural reservoir by storing precipitation during winter and releasing it in spring and summer. Snowmelt is usually modelled either by energy balance or by temperature-index approaches. The energy balance approach is process-based and more sophisticated but requires extensive input data, while the temperature-index approach uses the degree-day factor (DDF) as a key parameter to estimate melt of snow and ice merely from air temperature. Despite its simplicity, the temperature-index approach has proved to be a powerful tool for simulating the melt process especially in large and data-scarce catchments. The present study attempts to quantify the effects of spatial, temporal, and climatic conditions on the DDF of snow in order to gain a better understanding of which influencing factors are decisive under which conditions. The analysis is based on the individual energy flux components; however, formulas for estimating the DDF are presented to account for situations where observed data are limited. A detailed comparison between field-derived and estimated DDF values yields a fair agreement with bias = 0.14 mm ∘C−1 d−1 and root mean square error (RMSE) = 1.12 mm ∘C−1 d−1. The analysis of the energy balance processes controlling snowmelt indicates that cloud cover and snow albedo under clear sky are the most decisive factors for estimating the DDF of snow. The results of this study further underline that the DDF changes as the melt season progresses and thus also with altitude, since melting conditions arrive later at higher elevations. A brief analysis of the DDF under the influence of climate change shows that the DDFs are expected to decrease when comparing periods of similar degree days, as melt will occur earlier in the year when solar radiation is lower, and albedo is then likely to be higher. Therefore, the DDF cannot be treated as a constant parameter especially when using temperature-index models for forecasting present or predicting future water availability.

Flood Predictability of One-Way and Two-Way WRF Nesting Coupled Hydrometeorological Flow Simulations in a Transboundary Chenab River Basin, Pakistan

Remote sensing precipitation or precipitation from numerical weather prediction (NWP) is considered to be the best substitute for in situ ground observations for flood simulations in transboundary, data-scarce catchments. This research was aimed to evaluate the possibility of using a combination of a satellite precipitation product and NWP precipitation for better flood forecasting in the transboundary Chenab River Basin (CRB) in Pakistan. The gauge-calibrated satellite precipitation product, i.e., Global Satellite Mapping of Precipitation (GSMaP_Gauge), was selected to calibrate the Integrated Flood Analysis System (IFAS) model for the 2016 flood event in the Chenab River at the Marala Barrage gauging site in Pakistan. Precipitation from the Global Forecast System (GFS) NWP, with nine different lead times up to 4 days, was used in the calibrated IFAS model to predict the flood hydrograph in the Chenab River. The hydrologic simulations, with global GFS forecasts, were unable to predict the flood peak for all lead times. Then, the Weather Research and Forecasting (WRF) model was used to downscale the precipitation forecasts with one-way and two-way nesting approaches. In the WRF model, the CRB was centered in two domains of 25 km and 5 km resolutions. The downscaled precipitation forecasts were subsequently supplied to the IFAS model, and the predicted simulations were compared to obtain the optimal flood peak simulation in the Chenab River. It was found in this study that the simulated hydrographs, at different lead times, from the precipitation of two-way WRF nesting exhibited superior performance with the highest R2 and Nash–Sutcliffe efficiency (NSE) and the lowest percent bias (PBIAS) compared with one-way nesting. Moreover, it was concluded that the combination of GFS forecast and two-way WRF nesting can provide high-quality precipitation prediction to simulate flood hydrographs with a remarkable lead time of 96 h when applying coupled hydrometeorological flow simulation.

Using soil erosion as an indicator for integrated water resources management: a case study of Ruiru drinking water reservoir, Kenya

Functions and services provided by soils play an important role for numerous sustainable development goals involving mainly food supply and environmental health. In many regions of the Earth, water erosion is a major threat to soil functions and is mostly related to land-use change or poor agricultural management. Selecting proper soil management practices requires site-specific indicators such as water erosion, which follow a spatio-temporal variation. The aim of this study was to develop monthly soil erosion risk maps for the data-scarce catchment of Ruiru drinking water reservoir located in Kenya. Therefore, the Revised Universal Soil Loss Equation complemented with the cubist–kriging interpolation method was applied. The erodibility map created with digital soil mapping methods (R2 = 0.63) revealed that 46% of the soils in the catchment have medium to high erodibility. The monthly erosion rates showed two distinct potential peaks of soil loss over the course of the year, which are consistent with the bimodal rainy season experienced in central Kenya. A higher soil loss of 2.24 t/ha was estimated for long rains (March–May) as compared to 1.68 t/ha for short rains (October–December). Bare land and cropland are the major contributors to soil loss. Furthermore, spatial maps reveal that areas around the indigenous forest on the western and southern parts of the catchment have the highest erosion risk. These detected erosion risks give the potential to develop efficient and timely soil management strategies, thus allowing continued multi-functional use of land within the soil–food–water nexus.

Prediction of Sediment Yield in a Data-Scarce River Catchment at the Sub-Basin Scale Using Gridded Precipitation Datasets

Water-related soil erosion is a major environmental concern for catchments with barren topography in arid and semi-arid regions. With the growing interest in irrigation infrastructure development in arid regions, the current study investigates the runoff and sediment yield for the Gomal River catchment, Pakistan. Data from a precipitation gauge and gridded products (i.e., GPCC, CFSR, and TRMM) were used as input for the SWAT model to simulate runoff and sediment yield. TRMM shows a good agreement with the data of the precipitation gauge (≈1%) during the study period, i.e., 2004–2009. However, model simulations show that the GPCC data predicts runoff better than the other gridded precipitation datasets. Similarly, sediment yield predicted with the GPCC precipitation data was in good agreement with the computed one at the gauging site (only 3% overestimated) for the study period. Moreover, GPCC overestimated the sediment yield during some years despite the underestimation of flows from the catchment. The relationship of sediment yields predicted at the sub-basin level using the gauge and GPCC precipitation datasets revealed a good correlation (R2 = 0.65) and helped identify locations for precipitation gauging sites in the catchment area. The results at the sub-basin level showed that the sub-basin located downstream of the dam site contributes three (3) times more sediment yield (i.e., 4.1%) at the barrage than its corresponding area. The findings of the study show the potential usefulness of the GPCC precipitation data for the computation of sediment yield and its spatial distribution over data-scarce catchments. The computations of sediment yield at a spatial scale provide valuable information for deciding watershed management strategies at the sub-basin level.

Characterizing the groundwater storage–discharge relationship of small catchments in China

Abstract Baseflow recession is an essential part of the hydrological cycle, as it transfers unconfined aquifer storage to runoff. This study derived the parameterization of the baseflow recession from recession curves extracted from 382 catchments in China. The recession parameters of recession curves in power-law form, which control the shape of the curves and reflect the net effects of hillslope on the runoff decline process, are estimated by the baseflow recession analysis. The results show that the ranges of recession coefficient α and recession exponent β across China are 0–0.70 and 0.57–3, respectively. Most of the α values range between 0 and 0.20, and most of the β values range between 1 and 2. Generally, the α values of relatively dry catchments are higher than that of the wet catchments, and the distribution pattern of β values is opposite to that of α values. Statistical analysis is used to construct parametric equations for the recession parameters, indicating that catchment area, field capacity, etc., are essential for predicting α and β. In addition, the transplantation results of parametric equations show that equations can be applied to the estimation of α and β in other catchments. The results provide data support for storage estimation of data-scarce catchments.

Hydrological evaluation of 14 satellite-based, gauge-based and reanalysis precipitation products in a data-scarce mountainous catchment

ABSTRACT Availability of high-quality data is a major problem for climate and hydrological studies, especially in basins with complex topography where gauge network is typically limited and unevenly distributed. This study investigates the performance of 14 precipitation products – seven satellite-based (SPPs), two gauge-based (GPPs) and five reanalysis products (RPPs) – against ground observations (1998–2007) in the transboundary Jhelum River basin (33 397 km2). Among the seven SPPs (bias corrected), five demonstrate a significantly high correlation coefficient (CC > 0.7) with observed rainfall. However, most of the products tend to underestimate the seasonal precipitation amount, particularly in winter and spring. Likewise, Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of water resources APHRODITE (GPPs) and Japanese 55-year Reanalysis JRA-55 (RPPs) are the best-performing products in daily streamflow predictions, with Nash-Sutcliffe efficiency values of 0.68 and 0.62, whilst MSWEP (Multi-Source Weighted-Ensemble Precipitation), AgMERRA (Climate Forcing Dataset for Agricultural Modeling) and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) have also good potential in flow prediction. Generally, our results indicate that APHRODITE and JRA-55 could be used as alternative sources of precipitation data in the Himalayas region.

Exploring the Application of Flood Scaling Property in Hydrological Model Calibration

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

Comparing model complexity for glacio-hydrological simulation in the data-scarce Peruvian Andes

Study regionGlaciated headwaters of the Vilcanota-Urubamba river basin, Southern Peru Study focusA pivotal question is if robust hydrological simulation of streamflow in data-scarce and glaciated catchments can be achieved using parsimonious or more complex models. Therefore, a multi-model assessment of three glacio-hydrological models of different complexity was conducted thoroughly analyzing model performance, flow signatures and runoff components. New hydrological insights for the regionIn data-scarce catchments, such as in the tropical Andes, parsimonious glacio-hydrological models can provide more robust results than complex models. While the overall performance of all models was reasonably good (R2: 0.65–0.70, Nash-Sutcliffe: 0.65–0.73, Nash-Sutcliffe-ln: 0.73–0.78), with increasing data scarcity more complex models involve higher uncertainties. Furthermore, complex models require substantial understanding of the underpinning hydrological processes and a comprehensive calibration strategy to avoid apparently high model performance driven by inadequate assumptions. Based on these insights we present a framework for robust glacio-hydrological simulation under data scarcity. This stepwise approach includes, among others, a multi-model focus with a comprehensive assessment of flow signatures and runoff components. Future modeling needs to be further supported by alternative data collection strategies to substantially improve knowledge and process understanding. Therefore, the extension of sensor and station networks combined with the integration of co-produced knowledge represents a meaningful measure to robust decision-making for climate change adaptation and water management under high uncertainty.