Precipitation-Runoff Modeling Research Articles

LSTM-FKAN coupled with feature extraction technique for Precipitation–Runoff modeling

LSTM-FKAN coupled with feature extraction technique for Precipitation–Runoff modeling

Review and Comparative Study of Hydrological Models for Rainfall-Runoff Modelling

Water is considered as an important resources for human existence on the earth. In order to simulate or optimized hydrological data for various water resources management, several hydrological models are very useful to attain this aim for water resources management and as a decision support tools. A rainfall-runoff model is a quantitative prototype explaining the rainfall-runoff interactions at basin scale. The hydrological models have peculiarities in terms of capabilities for various water resources management. This paper tends to reviewed over fifty (50) papers that are peculiar to hydrological models as applicable to rainfall-runoff modeling. It involved evaluating and comparing different hydrological models used in simulating rainfall process converting into surface runoff for water use efficiency. Several runoff models such as Hydrologic Engineering Center - Hydrologic Modeling System (HEC-HMS), Soil and Water Assessment Tool (SWAT), Precipitation-Runoff Modeling System (PRMS), Variable Infiltration Capacity model (VIC), LISt-based Erosion Model (LISEM), MIKE Surface Water - Groundwater Hydrology (MIKE SHE) and Runoff Prophet were critically assessed. Rainfall-runoff models are globally utilized for different applications to enhance water use efficiency across different sectors. However, types of hydrological models by examining various hydrological models, model accuracy by evaluating the accuracy and reliability of each model in predicting runoff from rainfall data, scope of applications by determining the adequacy of the models for numerous geographical regions and climatic circumstances, complexity and usability by assessing the complexity of the models, their data requirements, ease of use and computational efficiency, also the models advantages and limitations in capturing the dynamics of the rainfall-runoff process were critically assessed. This was to aid modeling objectives. It was inferred that HEC-HMS is widely applied for modelling precipitation-runoff processes in watersheds of various sizes, aiding in flood forecasting, reservoir operation, and water management for agricultural and urban water use efficiency. SWAT is used for assessing the impact of land management practices (e.g., crop rotation, irrigation, land use changes) on water resources, including runoff generation and water quality, thus optimizing water use efficiency in agriculture. PRMS is applied to model the transport of water via complex hydrological systems, aiding in watershed management and water use efficiency assessments. In conclusion, this comparative review seeks to guide water scientists, the users of hydrological models and hydrological engineers in selecting the most suitable models for their specific modelling needs for sustainable water resources management.

Large scale water yield assessment for sparsely monitored river basins: A case study for Afghanistan

This paper presents results from a study on water yield assessment across five major river basins of Afghanistan. The study was conducted using GR4J and GR4JSG precipitation-runoff models. The river basins were divided into 207 subcatchments and each subcatchment was divided into multiple functional units. The model was calibrated using observed streamflow data from 2008 to 2015 and validated over the 2016 to 2020 period. Model parameters were calibrated for an unregulated subcatchment in each basin and calibrated parameters from the best-performing subcatchment were transferred to other subcatchments. Results show that modelled water yield across the five basins varies from 0.3 mm in the Helmand basin to 248 mm in the Panj-Amu basin, with an average of 72.1 mm for the entire country. In the period of 2008 to 2020, area averaged water yield in the five basins varies from 36 to 174 mm. For the same period, mean annual precipitation for the entire country is 234.0 mm, indicating a water yield of 30.8%. The nation-wide average water yield of 72.1 mm is equivalent to 46.3 billion cubic meters (BCM) of surface water for the country. In addition, about 28.9 BCM generates annually in the neighbouring Tajikistan and Pakistan from snow and glaciers of the Hindu-Kush mountains. The elevated northern parts of Afghanistan, including parts of neighbouring Tajikistan are the primary water source. Water yield across the country varies between years but there is no consistent increasing or decreasing trends. About 60 to 70% of flow occurs between March to June. The study identified the high water yield areas and investigated variability at monthly, seasonal, and annual time scales. An importance finding is the large spatial and temporal variability of water yield across the basins. This information is crucial for long-term water resources planning and management for agricultural development.

Seasonal and Monthly Climate Variability in South Korea’s River Basins: Insights from a Multi-Model Ensemble Approach

This study conducts a comprehensive analysis of the impacts of climate change on South Korea’s climate and hydrology, utilizing a Multi-Model Ensemble (MME) approach with thirteen Climate Model Intercomparison Project Phase 5 (CMIP5) models under two Representative Concentration Pathways, RCP4.5 and RCP8.5. We observed an average temperature increase of up to 3.5 °C under RCP8.5 and around 2.0 °C under RCP4.5. Precipitation patterns showed an overall increase, particularly during the summer months, with increases up to 20% under RCP8.5 and 15% under RCP4.5, characterized by more intense and frequent rainfall events. Evapotranspiration rates are projected to rise by approximately 5–10% under RCP8.5 and 3–7% under RCP4.5. Runoff is expected to increase significantly, particularly in the summer and autumn months, with increases up to 25% under RCP8.5 and 18% under RCP4.5. This research focuses on employing the Precipitation Runoff Modeling System (PRMS) to project future streamflow across South Korea, with an emphasis on both monthly and seasonal scales to understand the varying impacts of climate change on different river basins. These climatic changes have profound implications for agriculture, urban water management, and ecosystem sustainability, stressing the need for dynamic and region-specific adaptation measures. This study emphasizes the critical role of localized factors, such as topography, land use, and basin-specific characteristics, in influencing the hydrological cycle under changing climatic conditions.

Groundwater Hydraulics in Increased Spring Discharge following Earthquakes: Some Applications and Considerations

Earthquakes often entail alterations in the groundwater flow regime, in the phreatic level, surges and losses of springs, and the discharge in brooks. A variety of theoretical approaches attempt to elucidate the post-earthquake effects on spring discharge. This study adopts a conceptual approach, primarily presenting diverse methods to estimate water released by earthquakes involving calculations of discharge surpluses in springs. This study delves into refined techniques rooted in groundwater hydraulics, displaying applications of analytical and simulation methodologies to quantify earthquake-induced groundwater discharge in springs. This research investigates springs as natural indicators and applies mathematical precipitation–runoff models, particularly the CREC model, to simulate hydrographs in post-earthquake scenarios. We apply analytical procedures or mathematical simulation techniques employed in groundwater hydraulics for natural aquifer recharge calculations. Firstly, we briefly describe the methods based on the analysis of depletion curves of hydrographs in spring discharge. Additionally, specific mathematical rainfall–runoff models used to simulate hydrographs of karstic springs, along with derived analytical approximations, are adapted for this scenario. These hydraulic calculations involve the depletion coefficient and hydrodynamic volumes of aquifers, parameters that reveal certain aspects of the relation between groundwater and earthquakes. Three main features are: (a) Acknowledging faults as the primary geological structures in transmitting pore pressures due to earthquakes. Thus, for large and deep faults, which connect the ground surface with the Earth’s crust bottom—where earthquakes trigger—the depletion coefficient, α, usually reaches high values (α = 0.1 days−1). Therefore, these faults become more sensitive to pore pressure than other lithologies. (b) Elucidating the mechanisms of permeability enhancement caused by earthquakes. (c) Highlighting the substantial volumes in motion within the Earth’s interior, which, for instance, could constitute a significant source for the origin of mineral deposits. Mathematical calculations enable the determination of the volume of mobilized water that can be discharged by gravity in each earthquake. This, along with its recurrence, justifies the substantial mineralization volumes.

Dynamic probabilistic analytical modeling for estimating rainfall–runoff transformation rates in drylands

Abstract Precipitation-runoff changes used in the development of precipitation-runoff models depict general runoff mechanisms in physical precipitation-runoff processes. For this purpose, it must be able to adequately express the characteristics of the physical system. Similar to other water resources management models, analytical-probabilistic models may be developed with different levels of complexity according to different types of rainfall–runoff developments. In this research, a log-normal probabilistic model was used to estimate the return period of rainfall and Soil and Water Assessment Tool (SWAT) software was used to convert rainfall values into discharge and runoff height in agricultural lands. The slope and the level of plant shade cover were considered the two basic factors in the estimation of the subsurface flow used in agriculture. Slopes of less than 0.015 and land cover level of more than 70% of extreme points were obtained to ensure subsurface water sources and prevent erosion.

Subsurface mixing of different soil water pools in a permafrost-underlain headwater catchment, Qilian Mountains, Northeastern Tibetan Plateau

Recently studies postulate that there are two distinct soil water pools, i.e., mobile and immobile soil water (MSW and IMSW), and different soil waters have different mobility to generate runoff. Evaluating the influences of soil thawing on the subsurface mixing between different soil waters is important for understanding subsurface hydrological processes. In this study, the isotopic differences between different soil waters were evaluated in a permafrost-underlain headwater catchment by comparing the variations of stable isotopes and seasonal origin of MSW and bulk soil water (BSW), including both IMSW and MSW. BSW was sampled using soil augering and cryogenic vacuum distillation in thawed and frozen soil layers, and MSW was collected using a zero-tension lysimeter in thawed soil layers. The results show the following: (1) The average δ2H values of MSW, thawed BSW, and frozen BSW were similar during the spring snowmelt period (SSP), this can be attributed to weak evaporation and their similar origin, which is supported by the high line-conditioned excess values and their similar seasonal origin index values. (2) The enriched MSW collected during the SRP compared to the spring MSW and was associated with enriched summer precipitation, and the IMSW experienced strong evaporation during the period from the SSP to the SRP, both leading to enriched summer BSW compared to the spring BSW. (3) The frozen and thawed BSW had very similar isotopic compositions and seasonal origin, indicating the close hydrological connection between them and the important recharge of the melting frozen soil to soil water or runoff. The different freeze–thaw stages and soil moisture conditions controlled the stable isotope variations and mixing of different soil waters, this results in the different mobility of the mobile/immobile soil waters involved in the runoff generation processes during the SRP and the SSP, and therefore these features are useful to verify the isotopes-aided precipitation–runoff models applied in such catchments.

Improving Runoff Prediction Accuracy in a Mountainous Watershed Using a Remote Sensing-Based Approach

Due to the limited number and sparse distribution of meteorological and hydrometric stations in most watersheds, the runoff estimation based on these stations may not be accurate. However, the accurate determination of the Antecedent Soil Moisture (ASM) in watersheds can improve the accuracy of runoff forecasting. The objective of this study is to utilize the ASM derived from satellite imagery to enhance the accuracy of runoff estimation in a mountainous watershed. In this study, a range of Remote Sensing (RS) data, including surface biophysical and topographic features, climate data, hydrometric station flow data, and a ground-based measured SM database for the Balikhli-Chay watershed in Iran, were utilized. The Soil Conservation Service Curve Number (SCS-CN) method was employed to estimate runoff. Two approaches were used for estimating the ASM: (1) using the precipitation data recorded in ground stations, and (2) using the SM data obtained from satellite images. The accuracy of runoff estimation was then calculated for these two scenarios and compared. The mean Nash–Sutcliffe statistic was found to be 0.63 in the first scenario and 0.74 in the second scenario. The inclusion of ASM derived from the satellite imagery in the precipitation–runoff model resulted in a 51% increase in the accuracy of runoff estimation compared to using precipitation data recorded in ground stations. These findings have significant implications for improving the accuracy of ASM and runoff modeling in various applications.

Benchmarking high-resolution hydrologic model performance of long-term retrospective streamflow simulations in the contiguous United States

Abstract. Because use of high-resolution hydrologic models is becoming more widespread and estimates are made over large domains, there is a pressing need for systematic evaluation of their performance. Most evaluation efforts to date have focused on smaller basins that have been relatively undisturbed by human activity, but there is also a need to benchmark model performance more comprehensively, including basins impacted by human activities. This study benchmarks the long-term performance of two process-oriented, high-resolution, continental-scale hydrologic models that have been developed to assess water availability and risks in the United States (US): the National Water Model v2.1 application of WRF-Hydro (NWMv2.1) and the National Hydrologic Model v1.0 application of the Precipitation–Runoff Modeling System (NHMv1.0). The evaluation is performed on 5390 streamflow gages from 1983 to 2016 (∼ 33 years) at a daily time step, including both natural and human-impacted catchments, representing one of the most comprehensive evaluations over the contiguous US. Using the Kling–Gupta efficiency as the main evaluation metric, the models are compared against a climatological benchmark that accounts for seasonality. Overall, the model applications show similar performance, with better performance in minimally disturbed basins than in those impacted by human activities. Relative regional differences are also similar: the best performance is found in the Northeast, followed by the Southeast, and generally worse performance is found in the Central and West areas. For both models, about 80 % of the sites exceed the seasonal climatological benchmark. Basins that do not exceed the climatological benchmark are further scrutinized to provide model diagnostics for each application. Using the underperforming subset, both models tend to overestimate streamflow volumes in the West, which could be attributed to not accounting for human activities, such as active management. Both models underestimate flow variability, especially the highest flows; this was more pronounced for NHMv1.0. Low flows tended to be overestimated by NWMv2.1, whereas there were both over and underestimations for NHMv1.0, but they were less severe. Although this study focused on model diagnostics for underperforming sites based on the seasonal climatological benchmark, metrics for all sites for both model applications are openly available online.

Improving LSTM hydrological modeling with spatiotemporal deep learning and multi-task learning: A case study of three mountainous areas on the Tibetan Plateau

Long short-term memory (LSTM) networks have demonstrated their excellent capability in processing long-length temporal dynamics and have proven to be effective in precipitation-runoff modeling. However, the current LSTM hydrological models lack the incorporation of multi-task learning and spatial information, which limits their ability to make full use of meteorological and hydrological data. To address this issue, this study proposes a spatiotemporal deep-learning (DL)-based hydrological model that couples the 2-Dimension convolutional neural network (CNN) and LSTM and introduces actual evaporation (Ea) as an additional training target. The proposed CNN-LSTM model is tested on three large mountainous basins on the Tibetan Plateau, and the results are compared to those obtained from the LSTM-only model. Additionally, a probe method is used to decipher the internal embedding layers of the proposed DL models. The results indicate that both LSTM and CNN-LSTM hydrological models perform well in simulating runoff (Q) and Ea, with Nash-Sutcliffe efficiency coefficients (NSEs) higher than 0.82 and 0.95, respectively. The higher NSEs suggest that introducing spatial information into LSTM-only models can improve the overall and peak model performance. Moreover, multi-task simulation with LSTM-only models shows better accuracy in the estimation of Q volume and performance, with NSEs increasing by approximately 0.02. The probe method also reveals that CNN can capture the basin-averaged meteorological values in CNN-LSTM models, while LSTM Q (Ea) models contain the information about the known Ea (Q) process. Overall, this study demonstrates the value of spatial information and multi-task learning in LSTM hydrological modeling and provides a perspective for interpreting the internal embedding layers of DL models.

Stochastic Watershed Model Ensembles for Long‐Range Planning: Verification and Validation

AbstractDeterministic watershed models (DWMs) are used in nearly all hydrologic planning, design, and management activities, yet they cannot generate streamflow ensembles needed for hydrologic risk management (HRM). The stochastic component of DWMs is often ignored in practice, leading to a systematic bias in extreme events. Since traditional stochastic streamflow models used in HRM struggle to account for anthropogenic change, there is a need to convert DWMs into stochastic watershed models (SWMs) to generate ensembles for use in HRM. A DWM can be converted to an SWM using a post‐processing (pp) approach to add error to the DWM predictions. Many pp methods advanced in the area of flood forecasting are useful in HRM and for correcting extreme event biases. Selecting a suitable error model for pp is challenging due to nonnormality, skewness, heteroscedasticity, and autocorrelation. We develop a parsimonious pp method based on an autoregressive (AR) model of the logarithm of the ratio of the observations and simulations, which leads to AR model residuals that are approximately symmetric and independent. We document the value of pp for improving flood and low flow frequency analysis and we reintroduce the concepts of verification and validation of stochastic streamflow ensembles to ensure that the SWM can reproduce both statistics it was and was not designed to reproduce, respectively. These concepts are illustrated on a Massachusetts basin using the USGS Precipitation Runoff Modeling System, with an additional analysis indicating the approach may be applicable to 1,225 other sites across the United States.

Simulating the Hydrologic Response to Climate Change in Three New Zealand Headwater Basins Using CMIP6 Datasets

Abstract Air temperature and precipitation outputs from 10 CMIP6 GCMs were input to the Precipitation-Runoff Modeling System hydrologic model to evaluate water and energy responses in three headwater basins to projected climate change over the twenty-first century. The headwater basins (398–801 km2) are located within the Mataura River basin in the South Island of New Zealand. CMIP6 datasets included two emission scenarios [shared socioeconomic pathways (SSPs) SSP2-4.5 and SSP5-8.5]. Half of the 10 GCMs selected in the study have equilibrium climate sensitivity (ECS) values above 4.5°C, which has been considered the upper end of equilibrium climate sensitivity. Modeling results included increased annual air temperature, evapotranspiration, and precipitation by the end of the twenty-first century for both SSP emissions scenarios, both high- and low-ECS GCMs, and all three headwater basins. Monthly precipitation and evapotranspiration totals also increased for all or most months. Monthly streamflow changes generally corresponded with monthly precipitation changes. Snowpack decreased significantly in depth and seasonal duration in all basins. However, streamflow increased for all SSP and ECS groups and basins because increased precipitation was consistently greater than increased evapotranspiration losses. Sources of uncertainty include the GCMs, climate sensitivity, downscaling, bias adjustment, emission scenarios, and the hydrologic model. Simulated hydrologic responses based on climate data from GCMs with ECS values of greater than 4.5°C could be less plausible since previous studies have suggested true ECS ranges from 1.5° to 4.5°C.

Mekânsal otokorelasyon ve kümeleme analizi yaklaşımı ile Göksu Çayı Havzası’nın (Sakarya Nehri Havzası) bütünleşik ve sürdürülebilir havza yönetim modeli

Doğal ve beşeri ortam koşullarının yoğun etkileşim halinde olduğu havzalarda birçok kapsamdaçeşitli modellerle yönetim çalışmaları uygulanmaktadır. Bu araştırmanın amacı, coğrafi çeşitliliği,etkileşimleri ve potansiyel riskleri barındıran Göksu Çayı Havzası’nın farklı değişkenler üzerindenmekânsal otokorelasyon ve kümeleme analizine dayalı havza yönetim modelinin oluşturulmasıdır.Coğrafi Bilgi Sistemlerinin (CBS) etkin kullanıldığı çalışmada, deterministik, kantitatif,korelasyon ve dağılış analizi yöntemleriyle çok basamaklı sistematik oluşturulmuştur. Havzanınbütün coğrafi unsurlarını, etkileşimleri, doğal dinamik işleyiş yapısını ortaya koymak ve ilişkiselolarak kümelenme dağılışını oluşturmak için birçok parametrenin analizleri ile dört ana değişken(alt model) üretilmiştir. Ana değişkenler, jeomorfolojik uygunluk-elverişlilik, yağış akış, çoklu-riskve arazi kullanım modellerinden oluşur. Her bir model karşılıklı olarak mekansal korelasyona tabitutulmuş ve havzanın kümeleme analizi dağılış verisi üretilmiştir. Beş farklı kümenin tespit edildiğiveri, sorun-risk potansiyeli ve sürdürülebilir-uygun kullanım potansiyeli açısından da analizedilmiştir. Daha sonra dağılış verisi, Lokal Moran’s I-Anselin testi ve Getis-Ord Gİ istatistiği ile anlamlılıkve kümelenme açısından test edilmiştir. Analizlerden, havzanın yüksek çerçevesini oluşturansahaların sürdürülebilir-uygun kullanım potansiyeline sahip kümelenme gösterdiği, İnegölOvası, Yenişehir kuzeyi ve Göksu Vadisi’nde sorun-risk potansiyeli yüksek kümelenmenin olduğutespit edilmiştir. Havzada sürdürülebilirliğin sağlanması için, ekolojik sahaların korunması, sel,taşkın, erozyon, heyelan tedbirlerin arttırılması, akarsulardaki su kalitesinin kontrol edilmesi veantropojenik baskı yoğunlaşmasının daha uygun alanlara yönlendirilmesi gerekmektedir.

Changes in the hydro-climatic regime of the Hunza Basin in the Upper Indus under CMIP6 climate change projections

The Upper Indus Basin (UIB) heavily depends on its frozen water resources, and an accelerated melt due to the projected climate change may significantly alter future water availability. The future hydro-climatic regime and water availability of the Hunza basin (a sub-basin of UIB) were analysed using the newly released Coupled Model Intercomparison Project Phase 6 (CMIP6) climate projections. A data and parameter parsimonious precipitation-runoff model, the Distance Distribution Dynamics (DDD) model, was used with energy balance-based subroutines for snowmelt, glacier melt and evapotranspiration. The DDD model was set up for baseline (1991–2010), mid-century (2041–2060) and end-century (2081–2100) climates projections from two global circulation models (GCM), namely EC-Earth3 and MPI-ESM. The projections indicate a substantial increase in temperature (1.1–8.6 °C) and precipitation (12–32%) throughout the twenty-first century. The simulations show the future flow increase between 23–126% and the future glacier melt increase between 30–265%, depending on the scenarios and GCMs used. Moreover, the simulations suggest an increasing glacier melt contribution from all elevations with a significant increase from the higher elevations. The findings provide a basis for planning and modifying reservoir operation strategies with respect to hydropower generation, irrigation withdrawals, flood control, and drought management.

Insight into Hurricane Maria Peak Daily Streamflows from the Development and Application of the Precipitation-Runoff Modeling System (PRMS): Including Río Grande de Arecibo, Puerto Rico, 1981–2017

The Precipitation-Runoff Modeling System (PRMS) was used to develop a simulation of watershed hydrology on the island of Puerto Rico for the period 1981–2017, concentrating on the Río Grande de Arecibo, a river with some of the highest streamflows on the island. This development is part of the U.S. Geological Survey’s (USGS) National Hydrologic Model (NHM) infrastructure which supports coordinated, comprehensive, and consistent hydrologic modeling at the watershed scale for the coterminous United States (CONUS). A goal of the NHM program is to expand the domain outside of CONUS, leading to a PRMS application in Puerto Rico. This model was used to simulate the effects of Hurricane Maria on daily streamflow and provide information at locations where streamgages were damaged by the hurricane. Comparisons with streamflow estimates made by indirect methods in the field, up to ten times higher than simulated values, lends insight into the uncertainties in both the indirect methods and model simulated values and helps to identify potential error in the daily streamflow estimates. The PRMS can be applied to look at the effects of changes in climate and land use, water management, industrial and public water usage, and many other factors that affect hydrology on the island of Puerto Rico. The model is also designed as a support tool for the USGS National Water Census which provides comprehensive reporting of national information on withdrawal, conveyance, consumptive use, and return flow by water-use category.

HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool

Evaluating whether hydrological models are right for the right reasons demands reproducible model benchmarking and diagnostics that evaluate not just statistical predictive model performance but also internal processes. Such model benchmarking and diagnostic efforts will benefit from standardized methods and ready-to-use toolkits. Using the Jupyter platform, this work presents HydroBench, a model-agnostic benchmarking tool consisting of three sets of metrics: 1) common statistical predictive measures, 2) hydrological signature-based process metrics, including a new time-linked flow duration curve and 3) information-theoretic diagnostics that measure the flow of information among model variables. As a test case, HydroBench was applied to compare two model products (calibrated and uncalibrated) of the National Hydrologic Model - Precipitation Runoff Modeling System (NHM-PRMS) at the Cedar River watershed, WA, United States. Although the uncalibrated model has the highest predictive performance, particularly for high flows, the signature-based diagnostics showed that the model overestimates low flows and poorly represents the recession processes. Elucidating why low flows may have been overestimated, the information-theoretic diagnostics indicated a higher flow of information from precipitation to snowmelt to streamflow in the uncalibrated model compared to the calibrated model, where information flowed more directly from precipitation to streamflow. This test case demonstrated the capability of HydroBench in process diagnostics and model predictive and functional performance evaluations, along with their tradeoffs. Having such a model benchmarking tool not only provides modelers with a comprehensive model evaluation system but also provides an open-source tool that can further be developed by the hydrological community.

Evaluation of cloud seeding on the securement of additional water resources in the Boryeong Dam Basin, Korea

This study evaluates the effect of cloud seeding on the increase of runoff or securing of additional water resources. Unlike previous studies, this study concentrates more on the change of various components of the hydrologic processes. Both the event-based analysis and the annual analysis are conducted to evaluate the change of these components. The Boryeong Dam Basin in Korea is considered as a study basin. The WRF (Weather Research and Forecasting) model is used in the cloud seeding simulation, while the PRMS (Precipitation Runoff Modeling System) model is used in the rainfall–runoff analysis. The target years of this study are 2018 and 2019. As results, the event-based analysis shows that the infiltration, effective rainfall, interflow, and direct runoff are increased relatively more among the eight rainfall-runoff components considered in this study. In particular, the increase of interflow is noticeable. More than 60 % of the increased rainfall is found to contribute to the runoff increase. In the annual analysis, this ratio is found to become even higher. The ratio is estimated to be more than 90 %, which is mostly due to the contribution of baseflow. As important factors controlling the effectiveness of cloud seeding, the effects of antecedent soil moisture and threshold daily rainfall depth on the increase of total runoff are also evaluated. The results show that wet soil condition is more favorable to the successful cloud seeding experiment to secure additional water resources. Also, it is confirmed that the cloud seeding experiment can be more effective when the daily rainfall is more than 20 mm. Finally, the results of this study imply the efficiency of cloud seeding and its economic values are potentially high.

Assessment of Termination Criteria at Each Drought Response Stage on Climate Change in a Multi-Purpose Dam

In this study, the termination criteria at each drought response stage were shown to increase storage volume while maintaining the stability of the water supply service during drought in multi-purpose dams. For 52 of the Representative Concentration Pathway (RCP) 4.5/8.5 scenarios of the IPCC (Intergovernmental Panel on Climate Change) fifth assessment report (AR5), dam inflows were calculated through the precipitation-runoff modeling system (PRMS), and time reliability and supply reliability were estimated. Moreover, given the application of termination criteria at each drought response stage, the volume of additional water supply was calculated, while the number of days for additional supply availability of water for residential and industrial use and water for residential, industrial, and agricultural use and river maintenance was quantified as well. The CMCC-CMS(RCP4.5) GCM exhibited the largest volume of additional water supply at 74.15 million m3, which corresponds to 52.0 days of water for residential and industrial use and to 47.7 days of water for residential, industrial, and agricultural use and river maintenance. The analysis revealed that the volume and the number of days of additional water supply also increased for other GCMs due to the application of termination criteria at each drought response stage.

Development and evaluation of temperature-induced variable source area runoff generation model

In permafrost regions, temperature and precipitation play decisive roles in hydrological processes. Soil freeze–thaw cycles in the active layer cause the runoff generation process to show multiple mechanisms and seasonal alternating patterns. In this study, based on the precipitation-runoff modeling system implemented in the Java modeling framework Object Modeling System (PRMS-OMS), we developed a runoff generation model with temperature-induced variable source area (TVSA) for permafrost regions by introducing an active layer parameterization scheme, an active layer freeze–thaw module, a glacier module, and the sub-permafrost groundwater module. The TVSA model was calibrated and validated using field observations in Fenghuoshan (FHS), a typical permafrost watershed, and Tuotuohe (TTH), a typical permafrost-glacier catchment, on the Qinghai-Tibet Plateau (QTP). During the calibration period, parameters related to the thermal conditions of the active layer and runoff processes were calibrated using the observed freeze and thaw depth data and the discharge data. For the validation period, the model successfully reproduced the freeze–thaw processes (average root mean squared error = 0.205 m) and discharges (FHS, Nash–Sutcliffe efficiency coefficient (NSE) = 0.99; TTH, NSE = 0.85). The TVSA model is a powerful tool for the system identification of variable source runoff generation processes and the associated physical mechanism under temperature control in permafrost basins; additionally, the model accurately reproduces hydrological processes and the associated response to climate change.

Hydrologic Effects of Fire in a Sub-Alpine Watershed: AgES Outperforms Previous PRMS Simulations

HighlightsAgES and PRMS accurately simulated the dramatic change in streamflow response to precipitation after fire.Measured annual streamflow increased by 170% in 4-year postfire period with similar precipitation.Postfire AgES parameters were drastically reduced for snow and rain interception, canopy density, and ET.Decreased soil depression storage and field capacity also contributed to increased streamflow in AgES.AgES shows promise for simulating hydrologic impacts of sub-alpine forest resource management and fire response.Abstract. Forest fires alter hydrologic responses to precipitation, and streamflow changes can be challenging to simulate. Streamflow data before and after wildfire in a 14 km2 mixed-conifer, sub-alpine watershed in south-central New Mexico were used to calibrate a watershed model (Agricultural Ecosystems Services, AgES) using four years of prefire and postfire conditions. Streamflow results and parameter values from AgES and a prior Precipitation Runoff Modeling System (PRMS) modeling study in the same watershed were compared. Both AgES and PRMS simulated the dramatic change in streamflow response to precipitation after a fire, including a smaller precipitation threshold to induce streamflow. Still, AgES had substantially less bias and better performance when comparing monthly metrics. Only AgES was assessed at the daily scale. AgES simulated daily streamflow accurately throughout the study period, with slight overestimation in the prefire period (Oct 2007 to Oct 2011, 1.2% bias, 0.90 Nash-Sutcliffe efficiency [NSE]) and in the postfire period (Oct 2013 to Oct 2017, 4.4% bias, 0.54 NSE). Although each 4-year model period had nearly identical cumulative precipitation, annual streamflow increased by 170% in the postfire period compared to the prefire period. Appropriate soil and vegetation parameters were modified by the stepwise calibration process to represent the effects of fire in AgES: interception storage was decreased for rain (-99.8%) and snow (-26%), two factors influencing ET were decreased, soil depression storage was reduced (-97%), and soil field capacity was reduced (-50%). AgES demonstrated an improved streamflow simulation compared with PRMS, calibrated parameter shifts that were more consistent to interpret, and particular skill in simulating daily streamflow response to fire. Knowledge gained from this study will guide future simulations of hydrologic responses to fires in the western USA using AgES. Keywords: Agricultural Ecosystems Services model, Forest, Landscape change, Precipitation-Runoff Modeling System, Streamflow change, Surface hydrology, Wildfire.