Space–time variability in soil moisture  droughts in the Himalayan region

Nepal, Santosh; Pradhananga, Saurav; Shrestha, Narayan Kumar; Kralisch, Sven; Shrestha, J; Fink, Manfred

doi:10.5194/hess-25-1761-2021

Cited by 16 publications

(7 citation statements)

References 41 publications

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“…Bias correction can be difficult in environments where extreme isotope signatures are the most important and drive the hydrological response, such as for the Western Cape (van Rooyen et al, 2021;Watson, Eilers, & Miller, 2020), but various quantile mapping approaches (Gudmundsson et al, 2012) are available and can be used under different climatic conditions. As the JAMS/J2000 has a modular component structure these new developments could support a wider use of isotope-enabled models, given that the JAMS/J2000 has already been applied across many environments and climates (Fink et al, 2007(Fink et al, , 2017Künne et al, 2019;Meinhardt et al, 2018;Nepal et al, 2021;Watson et al, 2021).…”

Section: Isotope Mixing Considering Data Scarcitymentioning

confidence: 99%

Towards the development of an isotope‐enabled rainfall‐runoff model: Improving the ability to capture hydrological and anthropogenic change

Watson

Vystavna

Kralisch

et al. 2023

Hydrological Processes

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Global hydrological alterations are being driven by climate change. However, while modelling tools have been instrumental in identifying these changes, their calibration is often dependent solely on streamflow data, which limits the ability to simulate important hydrological processes with the required level of certainty. Constraining hydrological process simulations is required to identify robust model parameters, reducing model uncertainty and providing a baseline model for climate‐related risk assessments. In this study, stable isotope measurements of rainwater, groundwater and stream water (δ2H and δ18O), together with an end member mixing analysis (EMMA) (as a post‐processing step) were integrated with the Jena adaptative modelling system (JAMS)/J2000 rainfall‐runoff model, named J2000iso. The J2000iso models of δ2H and δ18O were able to achieve a streamflow Nash–Sutcliffe efficiency (NSE) of 0.75 and 0.72 and an isotope mixing Kling–Gupta efficiency (KGE) of 0.51 and 0.60, respectively. Compared with the base version, the J2000iso had a much smaller variability with 56% less ensemble variance in the NSE, 13% more simulated interflow which resulted in a better soil‐moisture simulation at an observation point, and lower simulated flow component uncertainty. The J2000iso δ2H model was more robust than the base version during a subsequent validation in terms of KGE and Bias with 0.60 and 0.002 compared with 0.51 and −0.14 for the base version. The EMMA provided a means to improve the hydrograph separation ability of the JAMS/J2000. As many catchments around the world are still ungauged and are affected by anthropogenic activities, the development and enhancement of isotope‐enabled modelling provides a means to improve the simulation of key hydrological processes.

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Section: Isotope Mixing Considering Data Scarcitymentioning

confidence: 99%

Towards the development of an isotope‐enabled rainfall‐runoff model: Improving the ability to capture hydrological and anthropogenic change

Watson

Vystavna

Kralisch

et al. 2023

Hydrological Processes

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“…Background illustration credit: Nouri Atchabao/Vecteezy. Hock et al, 2019;Gusain et al, 2020;Azam et al, 2021;Nepal et al, 2021). Furthermore, recent studies indicate that human activities, both agricultural and land use change, that are missing from models have significantly impacted the regional water budget (Budakoti et al, 2021;.…”

Section: Water In the Himalaya Along Different Dimensionsmentioning

confidence: 99%

Challenges in Understanding the Variability of the Cryosphere in the Himalaya and Its Impact on Regional Water Resources

et al. 2022

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The Himalaya plays a vital role in regulating the freshwater availability for nearly a billion people living in the Indus, Ganga, and Brahmaputra River basins. Due to climate change and constantly evolving human-hydrosphere interactions, including land use/cover changes, groundwater extraction, reservoir or dam construction, water availability has undergone significant change, and is expected to change further in the future. Therefore, understanding the spatiotemporal evolution of the hydrological cycle over the Himalaya and its river basins has been one of the most critical exercises toward ensuring regional water security. However, due to the lack of extensive in-situ measurements, complex hydro-climatic environment, and limited collaborative efforts, large gaps in our understanding exist. Moreover, there are several significant issues with available studies, such as lack of consistent hydro-meteorological datasets, very few attempts at integrating different data types, limited spatiotemporal sampling of hydro-meteorological measurements, lack of open access to in-situ datasets, poorly accounted anthropogenic climate feedbacks, and limited understanding of the hydro-meteorological drivers over the region. These factors result in large uncertainties in our estimates of current and future water availability over the Himalaya, which constraints the development of sustainable water management strategies for its river catchments hampering our preparedness for the current and future changes in hydro-climate. To address these issues, a partnership development workshop entitled “Water sEcurity assessment in rIvers oriGinating from Himalaya (WEIGH),” was conducted between the 07th and 11th September 2020. Based on the intense discussions and deliberations among the participants, the most important and urgent research questions were identified. This white paper synthesizes the current understanding, highlights, and the most significant research gaps and research priorities for studying water availability in the Himalaya.

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“…The AMSR-E, AMSR2 and WindSat global daily soil moisture products are utilised from 2002 to 2022. These three sensors are onboarded at the Aqua satellite, GCOM-W1 and Coriolis satellite, respectively (Nepal et al, 2021). The AMSR-E, AMSR2 and WindSat products are all passive sensors for soil moisture retrieving.…”

Section: Satellite-based Soil Moisture Productsmentioning

confidence: 99%

SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022

Zhang

Yuan²,

Jin³

et al. 2022

Earth Syst. Sci. Data

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Abstract. The drawbacks of low-coverage rate in global land inevitably exist in satellite-based daily soil moisture products because of the satellite orbit covering scopes and the limitations of soil moisture retrieving models. To solve this issue, Zhang et al. (2021a) generated seamless global daily soil moisture (SGD-SM 1.0) products for the years 2013–2019. Nevertheless, there are still several shortages in SGD-SM 1.0 products, especially in temporal range, sudden extreme weather conditions and sequential time-series information. In this work, we develop an improved seamless global daily soil moisture (SGD-SM 2.0) dataset for the years 2002–2022, to overcome the above-mentioned shortages. The SGD-SM 2.0 dataset uses three sensors, i.e. AMSR-E, AMSR2 and WindSat. Global daily precipitation products are fused into the proposed reconstructing model. We propose an integrated long short-term memory convolutional neural network (LSTM-CNN) to fill the gaps and missing regions in daily soil moisture products. In situ validation and time-series validation testify to the reconstructing accuracy and availability of SGD-SM 2.0 (R: 0.672, RMSE: 0.096, MAE: 0.078). The time-series curves of the improved SGD-SM 2.0 are consistent with the original daily time-series soil moisture and precipitation distribution. Compared with SGD-SM 1.0, the improved SGD-SM 2.0 outperforms on reconstructing accuracy and time-series consistency. The SGD-SM 2.0 products are recorded in https://doi.org/10.5281/zenodo.6041561 (Zhang et al., 2022).

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Space–time variability in soil moisture droughts in the Himalayan region

Cited by 16 publications

References 41 publications

Towards the development of an isotope‐enabled rainfall‐runoff model: Improving the ability to capture hydrological and anthropogenic change

Towards the development of an isotope‐enabled rainfall‐runoff model: Improving the ability to capture hydrological and anthropogenic change

Challenges in Understanding the Variability of the Cryosphere in the Himalaya and Its Impact on Regional Water Resources

SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022

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