An evaluation of a methodology for seasonal soil water forecasting for Australian dry land cropping systems

Western, Andrew W.; Dassanayake, K. B.; Perera, Kushan C.; Argent, Robert M.; Alves, Oscar; Young, Griffith; Ryu, Dongryeol

doi:10.1016/j.agrformet.2018.02.012

Cited by 14 publications

(3 citation statements)

References 46 publications

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“…Instead, it relies on the intervariable correlations in the GCM being approximately correct, and, therefore, it isn't a full calibration method (Maraun 2013;Zhao et al 2017). However, it is a method currently supported by the Australian Bureau of Meteorology and being investigated in agricultural applications of seasonal forecasts (e.g., Brown et al 2018;Western et al 2018) and, therefore, it is a useful method for comparison purposes.…”

Section: E Transformed Quantile Mapping (Tqm)mentioning

confidence: 99%

On the Joint Calibration of Multivariate Seasonal Climate Forecasts from GCMs

Schepen

Everingham

Wang

2020

Monthly Weather Review

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Multivariate seasonal climate forecasts are increasingly required for quantitative modeling in support of natural resources management and agriculture. GCM forecasts typically require postprocessing to reduce biases and improve reliability; however, current seasonal postprocessing methods often ignore multivariate dependence. In low-dimensional settings, fully parametric methods may sufficiently model intervariable covariance. On the other hand, empirical ensemble reordering techniques can inject desired multivariate dependence in ensembles from template data after univariate postprocessing. To investigate the best approach for seasonal forecasting, this study develops and tests several strategies for calibrating seasonal GCM forecasts of rainfall, minimum temperature, and maximum temperature with intervariable dependence: 1) simultaneous calibration of multiple climate variables using the Bayesian joint probability modeling approach; 2) univariate BJP calibration coupled with an ensemble reordering method (the Schaake shuffle); and 3) transformation-based quantile mapping, which borrows intervariable dependence from the raw forecasts. Applied to Australian seasonal forecasts from the ECMWF System4 model, univariate calibration paired with empirical ensemble reordering performs best in terms of univariate and multivariate forecast verification metrics, including the energy and variogram scores. However, the performance of empirical ensemble reordering using the Schaake shuffle is influenced by the selection of historical data in constructing a dependence template. Direct multivariate calibration is the second-best method, with its far superior performance in in-sample testing vanishing in cross validation, likely because of insufficient data relative to the number of parameters. The continued development of multivariate forecast calibration methods will support the uptake of seasonal climate forecasts in complex application domains such as agriculture and hydrology.

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Section: E Transformed Quantile Mapping (Tqm)mentioning

confidence: 99%

On the Joint Calibration of Multivariate Seasonal Climate Forecasts from GCMs

Schepen

Everingham

Wang

2020

Monthly Weather Review

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“…Mengingat banyaknya kegiatan usaha tani yang bergantung pada kondisi iklim, maka prakiraan musim adalah salah satu perangkat penting yang diperlukan untuk mengambil keputusan. Pemanfaatan informasi prakiraan musim juga telah banyak dilakukan diantaranya untuk pengelolaan air (Ramírez-Rodrigues et al 2016), prediksi kekeringan (Martins et al 2018;Surmaini 2016), pemantauan dinamika kelembaban tanah (Wang et al 2009;Western et al 2018), pemantauan dinamika nutrisi tanah (Asseng et al 2012) dan untuk mengestimasi produksi (Challinor 2009;Koide et al 2012;Ogutu et al 2018). Prediksi produksi sangat penting untuk mengetahui stok dan cadangan pangan yang dimiliki oleh pemerintah.…”

Section: Pemanfaatan Prakiraan Musim Dalam Kegiatan Usahataniunclassified

Penggunaan Prakiraan Musim untuk Pertanian di Indonesia: Status Terkini dan Tantangan Kedepan

Sarvina¹,

Surmaini²

2020

Jurnal Lahan

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Prakiraan musim sangat penting dalam pengambilan keputusan usaha tani karena variabilitas iklim yang semakin meningkat sebagai dampak dari perubahan iklim. Prakiraan musim memiliki potensi untuk bisa membantu pengambil kebijakan dan keputusan pertanian. Kajian empirik dan literatur serta kuantifikasi nilai ekonomi pemanfaatan prakiraan iklim di Indonesia untuk pertanian masih sangat terbatas. Tulisan ini merupakan tinjauan mengenai pemanfaatan prakiraan musim dalam sistem usahatani, perkembangan terkini prakiraan musim, nilai ekonomi prakiraan musim, kendala dan tantangan ke depan. Kajian nilai ekonomi prakiraan musim untuk pertanian masih belum banyak dilakukan, kuantifikasi manfaat prakiraan musim sangat penting untuk meyakinkan pengambil kebijakan atau petani bahwa prakiraan musim memberikan manfaat bagi pertanian. Prakiraan musim masih sulit dipahami oleh petani bahkan penyuluh. Beberapa user interface telah dikembangkan untuk memudahkan memanfaatkan prakiraan musim untuk pertanian. Peningkatan akurasi prakiraan musim ke depan harus menjadi prioritas pembangunan pertanian dan merupakan salah satu investasi penting dalam adaptasi variabilitas dan perubahan iklim. Peningkatan akurasi prakiraan musim sangat tergantung pada kapasitas sumber daya manusia serta sarana pendukung seperti sistem pemantauan dan pengamatan data iklim, pengembangan model prakiraan musim .Pengembangan berbagai user interface yang lebih mudah dipahami dan diaplikasikan oleh petani harus dilakukan

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“…Forecasting, however, is quite a different problem with high uncertainty and the added dimension of lead time. Indeed, studies making use of quantile‐mapped GCM forecasts in Australia have found that they perform poorly in agricultural applications (Brown et al ., ; Western et al ., ), presumably due to misspecification of temporal, spatial and inter‐variable relationships, echoing earlier findings of Ines and Hansen ().…”

Section: Introductionmentioning

confidence: 99%

Coupling forecast calibration and data‐driven downscaling for generating reliable, high‐resolution, multivariate seasonal climate forecast ensembles at multiple sites

Schepen

Everingham

Wang

2019

Intl Journal of Climatology

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Calibration and downscaling of ensemble GCM forecasts is becoming increasingly important for hydrological and agricultural modelling in support of the management and protection of valuable natural resources. Moreover, skilful and reliable daily forecast sequences are required to drive decision support models that operate on a daily time step. While downscaling of daily GCM outputs has been developed extensively in climate impacts studies, much less attention has been paid to the downscaling of ensemble GCM forecasts, which has the confronting aspect of low and diminishing skill with increasing lead time. Evidence is building that simple bias‐correction methods that do not model correlation between forecasts and observations, nor attempt to correct spatial, temporal and inter‐variable correlations, produce downscaled forecasts that perform poorly in applications models. Thus downscaling GCM forecasts requires inclusion of a full calibration component to reduce bias, improve reliability, capture skill where it is available and remove negative skill. In this study, we propose a new methodology for coupling a full GCM forecast calibration with empirical methods to (a) instil correct temporal, spatial and inter‐variable correlations in ensembles and (b) perform a multivariate downscaling of ensembles to daily sequences at multiple sites. Through a case study application, the proposed methodology is shown to produce skilful monthly–seasonal forecasts of rainfall, temperature and solar radiation at regional spatial scales. It also produces realistic and coherent multivariate daily sequences at multiple sub‐grid locations. The new methodology can be applied to more effectively integrate climate forecasts into hydrological and crop models and to support proactive decision‐making in agriculture and natural resources management.

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An evaluation of a methodology for seasonal soil water forecasting for Australian dry land cropping systems

Cited by 14 publications

References 46 publications

On the Joint Calibration of Multivariate Seasonal Climate Forecasts from GCMs

On the Joint Calibration of Multivariate Seasonal Climate Forecasts from GCMs

Penggunaan Prakiraan Musim untuk Pertanian di Indonesia: Status Terkini dan Tantangan Kedepan

Coupling forecast calibration and data‐driven downscaling for generating reliable, high‐resolution, multivariate seasonal climate forecast ensembles at multiple sites

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