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He Sun, Tandong Yao, Fengge Su, Zhihua He, Guoqiang Tang, Ning Li, Bowen Zheng, Jingheng Huang, Fanchong Meng, Tinghai Ou, and Deliang Chen

Abstract

Precipitation is one of the most important atmospheric inputs to hydrological models. However, existing precipitation datasets for the Third Pole (TP) basins show large discrepancies in precipitation magnitudes and spatiotemporal patterns, which poses a great challenge to hydrological simulations in the TP basins. In this study, a gridded (10 km×10 km) daily precipitation dataset is constructed through a random forest-based machine learning algorithm (RF algorithm) correction of the ERA5 precipitation estimates based on 940 gauges in 11 upper basins of TP for 1951–2020. The data set is evaluated by gauge observations at point scale, and is inversely evaluated by the Variable Infiltration Capacity hydrological model linked with a glacier melt algorithm (VIC-Glacier). The corrected ERA5 (ERA5_cor) agrees well with gauge observations after eliminating the severe overestimation in the original ERA5 precipitation. The corrections greatly reduce the original ERA5 precipitation estimates by 10%–50% in 11 basins of the TP, and present more details on precipitation spatial variability. The inverse hydrological model evaluation demonstrates the accuracy and rationality, and we provide an updated estimate of runoff components contribution to total runoff in seven upper basins in the TP based on the VIC-Glacier model simulations with the ERA5_cor precipitation. This study provides good precipitation estimates with high spatiotemporal resolution for 11 upper basins in the TP, which are expected to facilitate the hydrological modeling and prediction studies in this high mountainous region.

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Parthkumar A. Modi, Eric E. Small, Joseph Kasprzyk, and Ben Livneh

Abstract

Snowpack provides the majority of predictive information for water supply forecasts (WSFs) in snow-dominated basins across the western US. Drought conditions typically accompany decreased snowpack and lowered runoff efficiency, negatively impacting WSFs. Here, we investigate the relationship between snow water equivalent (SWE) and April-July streamflow volume (AMJJ-V) during drought in small headwater catchments, using observations from 31 USGS streamflow gages and 54 SNOTEL stations. A linear regression approach is used to evaluate forecast skill under different historical climatologies used for model fitting, as well as with different forecast dates. Experiments are constructed in which extreme hydrological drought years are withheld from model training, i.e., years with AMJJ-V below the 15th percentile. Subsets of the remaining years are used for model fitting to understand how the climatology of different training subsets impacts forecasts of extreme drought years. We generally report overprediction in drought years. However, training the forecast model on drier years, i.e., below-median years (P15, P57.5]), minimizes residuals by an average of 10% in drought year forecasts, relative to a baseline case, with the highest median skill obtained in mid to late April for colder regions. We report similar findings using a modified NRCS procedure in nine large UCRB basins, highlighting the importance of the snowpack-streamflow relationship in streamflow predictability. We propose an ‘adaptive sampling’ approach of dynamically selecting training years based on antecedent SWE conditions, showing error reductions of upto 20% in historical drought years relative to the period of record. These alternate training protocols provide opportunities for addressing the challenges of future drought risk to water supply planning.

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Zhixia Wang, Shengzhi Huang, Qiang Huang, Weili Duan, Guoyong Leng, Yi Guo, Xudong Zheng, Mingqiu Nie, Zhiming Han, Haixia Dong, and Jian Peng

Abstract

In the propagation from meteorological to hydrological drought, there are time-lag and step-abrupt effects, quantified in terms of propagation time and threshold, which play an important role in hydrological drought early warning. However, seasonal drought propagation time and threshold and their dynamics as well as the corresponding driving mechanism remain unknown in a changing environment. To this end, Standardized precipitation index (SPI) and Standardized Runoff Index (SRI) were used respectively to characterize meteorological and hydrological droughts and to determine the optimal propagation time. Then, a seasonal drought propagation framework based on Bayesian network was proposed for calculating the drought propagation threshold with SPI. Finally, the seasonal dynamics and preliminary attribution of propagation characteristics were investigated based on the random forest model and correlation analysis. The results show that: (1) relatively short propagation time (less than 9 months) and large propagation threshold (−3.18∼−1.19) can be observed in the Toxkan River basins (sub-basin II), especially for spring, showing low drought resistance; (2) drought propagation time shows an extended trend in most seasons, while the drought propagation threshold displays an increasing trend in autumn and winter in the Aksu River basin (sub-basins I-II), and the opposite characteristics in the Hotan and Yarkant River basins (sub-basins III-V); (3) the impacts of precipitation, temperature, potential evapotranspiration and soil moisture on drought propagation dynamics are inconsistent across sub-basins and seasons, noting that reservoir serve as a buffer to regulate the propagation from meteorological to hydrological droughts. The findings of this study can provide scientific guidelines for watershed hydrological drought early warning and risk management.

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Anthony R. Buda, Seann M. Reed, Gordon J. Folmar, Casey D. Kennedy, David J. Millar, Peter J. A. Kleinman, Douglas A. Miller, and Patrick J. Drohan

Abstract

Accurate and reliable forecasts of quickflow, including interflow and overland flow, are essential for predicting rainfall–runoff events that can wash off recently applied agricultural nutrients. In this study, we examined whether a gridded version of the Sacramento Soil Moisture Accounting model with Heat Transfer (SAC-HT) could simulate and forecast quickflow in two agricultural watersheds in east-central Pennsylvania. Specifically, we used the Hydrology Laboratory–Research Distributed Hydrologic Model (HL-RDHM) software, which incorporates SAC-HT, to conduct a 15-yr (2003–17) simulation of quickflow in the 420-km2 Mahantango Creek watershed and in WE-38, a 7.3-km2 headwater interior basin. We directly calibrated HL-RDHM using hydrologic observations at the Mahantango Creek outlet, while all grid cells within Mahantango Creek, including WE-38, were calibrated indirectly using scalar multipliers derived from the basin outlet calibration. Using the calibrated model, we then assessed the quality of short-range (24–72 h) deterministic forecasts of daily quickflow in both watersheds over a 2-yr period (July 2017–October 2019). At the basin outlet, HL-RDHM quickflow simulations showed low biases (PBIAS = 10.5%) and strong agreement (KGE″ = 0.81) with observations. At the headwater scale, HL-RDHM overestimated quickflow (PBIAS = 69.0%) to a greater degree, but quickflow simulations remained satisfactory (KGE″ = 0.65). When applied to quickflow forecasting, HL-RDHM produced skillful forecasts (>90% of Peirce and Gerrity skill scores above 0.5) at all lead times and significantly outperformed persistence forecasts, although skill gains in Mahantango Creek were slightly lower. Accordingly, short-range quickflow forecasts by HL-RDHM show promise for informing operational decision-making in agriculture.

Significance Statement

Daily runoff forecasts can alert farmers to rainfall–runoff events that have the potential to wash off recently applied fertilizers and manures. To gauge whether daily runoff forecasts are accurate and reliable, we used runoff monitoring data from a large agricultural watershed and one of its headwater tributaries to evaluate the quality of short-term runoff forecasts (1–3 days ahead) that were generated by a National Weather Service watershed model. Results showed that the accuracy and reliability of daily runoff forecasts generally improved in both watersheds as lead times increased from 1 to 3 days. Study findings highlight the potential for National Weather Service models to provide useful short-term runoff forecasts that can inform operational decision-making in agriculture.

Open access
Shibo Guo, Fushan Wang, Dejun Zhu, Guangheng Ni, and Yongcan Chen

Abstract

The WRF-lake, as a one-dimensional (1D) lake model popularly used for coupling with the Weather Research and Forecasting (WRF) system and modeling lake–atmosphere interactions, does not consider the heat exchange caused by inflow–outflow, which is an important characteristic of large reservoirs and can affect the energy budget and reservoir–atmosphere interactions. We evaluated the WRF-lake model by applying it at a large dimictic reservoir, Miyun Reservoir, in northern China. The results show that the WRF-lake model, though ignoring inflow–outflow, yields good surface water temperature simulation through reasonable parameterization. The Minlake model, as a better physically based model in reservoirs, was used to test the effect of inflow–outflow, including heat carried by inflow–outflow water exchange and water level change on the 1D model’s performance. The effect of heat carried by inflow–outflow is mainly in summer, negatively correlated with hydraulic residence time and positively correlated with temperature difference between inflow and outflow. For a reservoir with hydraulic residence time of 3 years and temperature difference between inflow and outflow about 10°C in summer, the heat carried by inflow–outflow is far less than the heat exchange through the surface (<2%) and therefore has little influence on total energy balance. The effect of water level change is mainly on latent heat and sensible heat in unit area, rather than outgoing longwave radiation. Though influencing the temperature in deep layers, the water level change does not have a significant impact on the surface temperature.

Significance Statement

The purpose of this study is to evaluate the applicability of WRF-lake, an important submodule of the Weather Research and Forecasting (WRF) system, in the large dimictic reservoir. This is important because WRF-lake does not consider the effect of inflow–outflow and water level change, which are important characteristics of large reservoirs and can affect the heat budget and reservoir–atmosphere interactions. The applicability of WRF-lake in large reservoirs with frequent inflow–outflow and water level change is widely concerned but has never been discussed in previous studies. Our research explored the applicability of WRF-lake in the large dimictic reservoir and discussed the effect of inflow–outflow and water level change quantitively.

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Molly Margaret Chaney, James A Smith, and Mary Lynn Baeck

Abstract

We examine polarimetric rainfall estimates of extreme rainfall through intercomparisons of radar rainfall estimates with rainfall observations from a dense network of rain gauges in Kansas City. The setting provides unique capabilities for examining range dependence in polarimetric rainfall estimates due to the overlapping coverage of the Kansas City, Missouri, and Topeka, Kansas, WSR-88D radars. We focus on polarimetric measurements of specific differential phase shift, K DP, for estimating extreme rainfall. Gauge–radar intercomparisons from the “close-range” Kansas City radar and from the “far-range” Topeka radar show that K DP can provide major improvements in estimating extreme rainfall, but the advantages of K DP rainfall estimates diminish with range. Storm-to-storm variability of multiplicative bias remains an important issue for polarimetric rainfall estimates; variability in bias is comparable at both close and far range from the radar. “Conditional bias,” in which peak radar rainfall estimates are lower than rain gauge observations, is a systematic feature of polarimetric rainfall estimates, but is more severe at far range. The Kansas City region has experienced record flooding in urban watersheds since the polarimetric upgrade of the Kansas City and Topeka radars in 2012. Polarimetric rainfall estimates from the far-range Topeka radar provide useful quantitative information on basin-average rainfall, but the ability to resolve spatial variation of the most extreme rain rates diminishes significantly with range from the radar.

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Danlu Cai, Lijun Yu, Jianfeng Zhu, Klaus Fraedrich, Yanning Guan, Frank Sielmann, Chunyan Zhang, and Min Yu

Abstract

A comprehensive ecohydrological analysis is designed to understand the formation and evolution of lake Lop Nur and the environmental change over the Tarim River basin. Three temporal scales from century-based climatological mean to decade-based quasi-steady state change and to annual-scale-based abrupt change test are included. Combining the Budyko and Tomer–Schilling framework, this research first analyzes hydroclimatic and ecohydrological resistance/resilience conditions, then attributes observed changes to external/climate impact or to internal/anthropogenic activities, and finally diagnoses the possible tipping point on ecohydrological dynamics. (i) The arid regions reveal less sensitivity in terms of low variabilities of excess water W and energy U and show high resilience, which will more likely stay the same pattern in the future. (ii) Present towns situated in the semiarid regions with a natural hydraulic linkage with the mainstream of the Tarim River show a higher sensitivity and likelihood to be affected if drier scenarios occurred in the future. (iii) The attribution from two subsequent quasi-steady states indicates increasing effects of the anthropogenic activities increase (1961–80 versus 1981–2000) and provides climatical evidence that the central Tarim River basin was getting wetter before 1960 and then kept drying afterward. (iv) In the Kongqi subcatchment, the excess water reveals a significant decrease-then-increase evolution, from which the years with abrupt changes are observed in the 1960s. Generally, both points iii and iv are in agreement with that the closed-basin lake Lop Nur desiccation until the 1970s and its connection with the eastern part of the Taklamakan Sand Sea.

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Lu Li, Yongjiu Dai, Wei Shangguan, Zhongwang Wei, Nan Wei, and Qingliang Li

Abstract

The accurate prediction of surface soil moisture (SM) is crucial for understanding hydrological processes. Deep learning (DL) models such as Long Short-Term Memory model (LSTM) provide a powerful method and have been widely used in SM prediction. However, few studies have notably high success rates due to lacking prior knowledge in forms such as causality. Here we presented a new causality structure-based LSTM model (CLSTM), which could learn time interdependency and causality information for hydrometeorological applications. We applied and compared LSTM and CLSTM methods for estimating SM across 64 FLUXNET sites globally. The results showed that CLSTM dramatically increased the predictive performance compared with LSTM. The Nash-Sutcliffe Efficiency (NSE) suggested that more than 67% of sites witnessed an improvement of SM simulation larger than 10%. It is highlighted that CLSTM had a much better generalization ability that can adapt to extreme soil conditions, such as SM response to drought and precipitation events. By incorporating causal relations, CLSTM increased predictive ability across different lead times compared to LSTM. We also highlighted the critical role of physical information in the form of causality-structure to improve drought prediction. At the same time, CLSTM has the potential to improve predictions of other hydrometeorological variables.

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Jingheng Huang, Fengge Su, Tandong Yao, and He Sun

Abstract

The upper Syr Darya (USD) and Amu Darya (UAD) basins are the two biggest flow formation zones in Central Asia and the only water supply sources for the Aral Sea. Upstream snow and ice reserves of those two basins, important in sustaining seasonal water availability, are highly sensitive and prone to climate change, but their importance and changes are still uncertain and poorly understood due to data scarcity, inaccessibility, harsh climate, and even geopolitics. Here, an improved forcing dataset of precipitation and temperature was developed and used to drive a physically-based hydrological model, which was thoroughly calibrated and validated to quantify the contributions of different runoff components to total flow and the controlling factors for total runoff variations for 1961-2016. Our analysis reveals divergent flow regimes exist across the USD and UAD and an ongoing transition from nival-pluvial toward a volatile pluvial regime along with rising temperatures. Annual total runoff has weakly increased from 1961 to 2016 for the entire USD and UAD, while the subbasins displayed divergent flow changes. Spring runoff significantly increased in all the USD and UAD basins primarily due to increased rainfall and early snow melting, tending to shift the peak flow from June-July to April-May. In contrast, distinct runoff changes were presented in the summer months among the basins primarily due to the trade-off between the increase in rainfall and the decrease in snowmelt and glacier runoff. These findings are expected to provide essential information for policy-makers to adopt strategies and leave us better poised to project future runoff changes in ongoing climate change.

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Ved Prakash and Vimal Mishra

Abstract

An accurate streamflow forecast is vital for flood prediction and early warning systems. Notwithstanding the rising frequency and intensity of floods during the summer monsoon season in India, efforts to examine the utility of data assimilation for streamflow prediction remain limited. We examine soil moisture and streamflow data assimilation (DA) to improve streamflow simulations in the Narmada River basin, considered a testbed. Data assimilation was performed using the Variable Infiltration Capacity (VIC) model at four-gauge stations in the basin. First, we used Ensemble Kalman Filter (EnKF) to assimilate the satellite soil moisture from the European Space Agency Climate Change Initiative (ESA-CCI) to the initial state of the VIC model. We examined the usefulness of observed streamflow from the India-Water Resources Information System (India-WRIS) to improve the initial hydrological conditions of the VIC model in the streamflow DA during the summer monsoon (JJAS) season from 1980 to 2018. The assimilation of ESA-CCI soil moisture showed less improvement in per cent error reduction (PER) and efficiency index (EFF) ( less than 2%) than the streamflow DA at all the four-gauge locations in the Narmada basin. On the other hand, the streamflow DA showed a significant improvement in PER and EFF (more than 10%) at all the gauge stations for both mean and high flow conditions. Streamflow data assimilation improved errors in the magnitude and timing for the major floods in 1994 and 2013.

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